Construction process scheduling method and device based on topology loop detection

Abstract

This disclosure presents a method and apparatus for scheduling building construction procedures based on topological loop detection. One specific implementation of the method includes: acquiring process logic constraint information and spatial logic constraint information of the target building construction project; generating a two-layer directed acyclic graph (DAG) topology model based on the process logic constraints and spatial logic constraints; performing loop detection processing on the two-layer DAG topology model to generate topological deadlock detection results; performing deadlock resolution processing to generate an optimized construction network graph; performing time extrapolation processing based on the optimized construction network graph to generate forward extrapolation results and backward extrapolation results; identifying critical paths under both process logic and spatial logic constraints to generate construction scheduling instructions; and executing scheduling operations corresponding to each building construction procedure. This implementation can achieve conflict-free procedure scheduling under both process and spatial constraints, thereby saving construction costs and shortening the construction period.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and specifically to a method and apparatus for scheduling building construction procedures based on topology loop detection. Background Technology

[0002] Currently, construction schedule planning for building projects mainly relies on manual preparation or single-layer network diagrams (e.g., single-layer DAGs) for scheduling. For complex spatial constraints between processes (e.g., inter-floor flow, floor overlap) and process constraints (e.g., formwork before pouring), the usual approach is to transform the spatial constraints into fixed logical relationships that are pre-set manually and then superimposed on the process network.

[0003] However, when the above-mentioned method is used to schedule construction procedures, the following technical problems often occur: spatial constraints and process constraints conflict with each other to form a closed loop, resulting in scheduling deadlock and process reversal. Furthermore, on-site delays cannot be dynamically adjusted, leading to extended construction periods and easily causing quality and safety accidents.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure propose a method, apparatus, electronic device, and computer-readable medium for scheduling building construction procedures based on topology loop detection, in order to solve one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a construction process scheduling method based on topology loop detection, comprising: acquiring process logic constraint information and spatial logic constraint information of a target construction project; generating a two-layer directed acyclic graph topology model based on the aforementioned process logic constraint information and spatial logic constraint information, the aforementioned two-layer directed acyclic graph topology model including: a process logic layer and a spatial logic layer; performing loop detection processing on the aforementioned two-layer directed acyclic graph topology model to identify topology deadlocks in the construction process, thereby generating a topology deadlock detection result; performing deadlock resolution processing based on the aforementioned topology deadlock detection result to generate an optimized construction network graph; performing time extrapolation processing based on the aforementioned optimized construction network graph to generate forward extrapolation results and backward extrapolation results; identifying critical paths under the dual constraints of process logic and spatial logic based on the aforementioned forward extrapolation results and the aforementioned backward extrapolation results, thereby generating construction scheduling instructions; and, in response to receiving the aforementioned construction scheduling instructions, executing scheduling operations corresponding to each construction process.

[0008] Secondly, some embodiments of this disclosure provide a construction process scheduling device based on topology loop detection, comprising: an acquisition unit configured to acquire process logic constraint information and spatial logic constraint information of a target construction project; a generation unit configured to generate a two-layer directed acyclic graph topology model based on the aforementioned process logic constraint information and spatial logic constraint information, the aforementioned two-layer directed acyclic graph topology model including: a process logic layer and a spatial logic layer; and a processing unit configured to perform loop detection processing on the aforementioned two-layer directed acyclic graph topology model to identify topology deadlocks in the construction process and to generate a topology. Deadlock detection results; a first execution unit is configured to perform deadlock resolution processing based on the above topology deadlock detection results to generate an optimized construction network diagram; a deduction unit is configured to perform time deduction processing based on the above optimized construction network diagram to generate forward deduction results and backward deduction results; an identification unit is configured to identify the critical path under the dual constraints of process logic and spatial logic based on the above forward deduction results and the above backward deduction results to generate construction scheduling instructions; a second execution unit is configured to execute the scheduling operation corresponding to each building construction process in response to receiving the above construction scheduling instructions.

[0009] The above-described embodiments of this disclosure have the following beneficial effects: The construction process scheduling method based on topology loop detection in some embodiments of this disclosure achieves conflict-free process scheduling under dual constraints of process and space, thereby saving construction costs and shortening the construction period. Specifically, the reason for frequent logical deadlocks and process inversions in construction process scheduling is that existing methods typically only consider process logic constraints, neglecting spatial resource exclusivity and work surface conflicts, resulting in the generated scheduling plan being physically unenforceable or frequently experiencing process deadlocks. Based on this, the construction process scheduling method based on topology loop detection in some embodiments of this disclosure first obtains the process logic constraint information and spatial logic constraint information of the target construction project. By obtaining the process logic constraints and spatial logic constraints separately, physical dependencies and spatial rules are decoupled and extracted, providing a structured data foundation for subsequent two-layer modeling, avoiding logical confusion caused by the mixing of the two types of constraints, and improving the processability and accuracy of constraint information. Then, based on the above-mentioned process logic constraint information and spatial logic constraint information, a two-layer directed acyclic graph topology model is generated, which includes a process logic layer and a spatial logic layer. By constructing a two-layer directed acyclic graph (DAG) topology model containing both process logic and spatial logic layers, the spatiotemporal constraints are uniformly mapped into a graph structure, providing complete input for loop detection and enabling the digital representation and computable modeling of complex construction dependencies. Next, loop detection processing is performed on the aforementioned two-layer DAG topology model to identify topological deadlocks in construction processes and generate topological deadlock detection results. By performing loop detection on the two-layer DAG topology model, topological deadlocks formed by conflicts between process logic and spatial logic are automatically identified, accurately locating the conflict source and deadlock type, transforming implicit logical contradictions into explicit and manageable results, and improving fault diagnosis efficiency. Secondly, based on the aforementioned topological deadlock detection results, deadlock resolution processing is performed to generate an optimized construction network diagram. Based on the deadlock detection results, hierarchical resolution is performed, with hard logic forcibly blocked and soft logic flexibly adjusted, automatically generating a conflict-free construction network diagram, breaking logical loops, eliminating scheduling deadlocks, and improving the executability and logical rigor of the construction plan. Next, based on the optimized construction network diagram, time extrapolation is performed to generate forward and backward extrapolation results. Through forward and backward time extrapolation, the earliest and latest time parameters for each process are determined, and the time windows and fluctuation ranges for each process are quantified. This provides data support for critical path identification and schedule risk analysis, enabling a quantitative assessment of schedule constraints. Then, based on the forward and backward extrapolation results, critical paths under both technological and spatial constraints are identified to generate construction scheduling instructions. By identifying critical paths under both technological and spatial constraints, the focus is on the core set of processes affecting the overall schedule, locking the scheduling optimization focus onto the critical chain, and improving the targeting of schedule control and the accuracy of resource allocation priorities.Finally, in response to the aforementioned construction scheduling instructions, the system executes the scheduling operations for each corresponding construction process. By automatically executing process scheduling operations in response to scheduling instructions, a closed-loop process of scheduling results being implemented on-site is achieved, thereby saving construction costs and shortening the construction period. Attached Figure Description The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0010] Figure 1 This is a flowchart of some embodiments of the construction process scheduling method based on topology loop detection according to the present disclosure; Figure 2 This is a structural schematic diagram of some embodiments of the building construction process scheduling device based on topology loop detection according to the present disclosure; Detailed Implementation Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0011] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0012] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0013] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0014] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0015] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0016] refer to Figure 1 The diagram illustrates a flowchart 100 of some embodiments of a construction sequence scheduling method based on topology loop detection according to the present disclosure. This construction sequence scheduling method based on topology loop detection includes the following steps: Step 101: Obtain the process logic constraint information and spatial logic constraint information of the target building construction project; In some embodiments, the execution entity (e.g., an electronic device) of the above-described construction process scheduling method based on topology loop detection can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed in the hardware devices listed above. It can be implemented as multiple software programs or software modules to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0017] In other embodiments, the executing entity can obtain process logic constraint information and spatial logic constraint information of the target building construction project. The target building construction project can be a building project for which a construction schedule is to be scheduled; for example, the target building construction project can be a 30-story frame structure residential building. The process logic constraint information can be data describing the physical dependencies between construction tasks; for example, the process logic constraint information can be "reinforcing steel binding must be completed before formwork erection." The spatial logic constraint information can be data describing the rules governing the flow of construction tasks between floors and work surfaces; for example, the spatial logic constraint information can be "reinforcing steel binding can only begin on the N+1th floor after the Nth floor has been poured."

[0018] In some optional implementations of certain embodiments, the aforementioned execution entity may obtain the technological logic constraint information and spatial logic constraint information of the target building construction project, which may include the following steps: The first step is to extract the geometric and location information of each construction component based on the pre-acquired BIM model data of the target building construction project. The BIM model data can be a digital model containing the three-dimensional geometry and attribute information of the building components; for example, it can include the dimensions and location coordinates of floor slabs, beams, and columns on each floor. Each construction component can be a physical unit constituting the building entity; for example, it can be a shear wall, floor slab, staircase, and beam on a standard floor. The geometric information can be measurement data such as the shape, size, and volume of the component; for example, it can be a floor slab with a thickness of 120mm, a length of 5m, and a width of 4m. The location information can be the coordinates of the component in three-dimensional space and its floor assignment data; for example, it can be a column located on the 5th floor with its axis intersecting at point A-1. In practice, firstly, the IFC format data of the target building construction project is exported from the BIM software. Then, the IFC file is parsed, and all elements of the IfcBuildingElement type are traversed. Finally, the geometric parameters and spatial coordinates of each element are extracted and stored as structured data to serve as the geometric and positional information of each construction component.

[0019] The second step, based on the geometric and positional information of each construction component, is to determine the spatial interference relationships between each construction process, thus obtaining spatial logical constraint information. These spatial interference relationships can be mutual constraints arising from overlapping work surfaces of different processes. For example, the spatial interference relationship could be "the third-floor masonry and the second-floor plastering cannot be carried out simultaneously within the same vertical projection plane." In practice, firstly, the spatial bounding boxes between components are calculated based on their geometric and positional information. Then, it is identified whether the spatial bounding boxes of components corresponding to different processes overlap or are adjacent. Finally, spatial logical constraint information is generated based on the degree of overlap and construction safety requirements. For example, if a vertical overlap is found between the third-floor formwork removal work space and the fourth-floor rebar hoisting work space, a spatial interference constraint of "upper-floor hoisting is prohibited before formwork removal is completed" is generated.

[0020] The third step involves determining the dependencies between various construction tasks based on the pre-acquired construction organization design documents for the aforementioned construction projects, thereby generating process logic constraint information. These construction organization design documents can be technical documents describing construction deployment, methods, and processes. For example, the document might state, "The main structure will be constructed in segments, with each process spaced 2 days apart." The dependencies can be the required sequence of construction tasks. For instance, "Backfilling can only begin after the basement waterproofing is completed." In practice, first, the construction process section of the construction organization design document is analyzed to extract the work sequence descriptions for each sub-item of the project. Then, the prerequisite and successor tasks for each task are identified, forming task node pairs. Finally, the task node pairs are converted into logical relational expressions such as FS and SS to generate process logic constraint information. For example, reading "Concrete curing can only be completed after 7 days before formwork removal" from a project's construction organization design generates a process logic constraint of FS+7d (concrete pouring completed -> formwork removal begins).

[0021] Step 102: Based on process logic constraint information and spatial logic constraint information, generate a two-layer directed acyclic graph topology model.

[0022] In some embodiments, the executing entity can generate a two-layer directed acyclic graph (DAG) topology model based on the aforementioned process logic constraints and spatial logic constraints. This two-layer DAG topology model includes a process logic layer and a spatial logic layer. The two-layer DAG topology model can be a graph structure containing superimposed constraints from both the process and spatial layers. For example, it can include nodes corresponding to the same process in both the process and spatial layers, along with cross-layer connecting edges. The process logic layer can be a constraint level describing the physical sequence of processes. For example, it can stipulate that "reinforcing bar tying must precede formwork erection." The spatial logic layer can be a constraint level describing the rules governing the flow of processes between different floors or work surfaces. For example, it can stipulate that "reinforcing bar tying can only begin on the upper layer after the lower layer is poured." The process logic layer and the spatial logic layer establish a mapping relationship through cross-layer connecting edges: each construction task node in the process logic layer is associated with a corresponding work surface node in the spatial logic layer. A two-way constraint is formed between the two layers: the process logic layer specifies the physical sequence of tasks, and the spatial logic layer specifies the flow rules of tasks between different work surfaces. For example, the task of "tying rebar on the 3rd floor" depends on "supporting formwork on the 3rd floor" in the process logic layer, is bound to "the work surface on the 3rd floor" in the spatial logic layer, and is also subject to the spatial pre-constraint of "the completion of pouring on the 2nd floor", thus forming a dual constraint of time and space.

[0023] In some optional implementations of certain embodiments, the execution entity can generate a two-layer directed acyclic graph topology model based on the aforementioned process logic constraint information and spatial logic constraint information. The two-layer directed acyclic graph topology model includes a process logic layer and a spatial logic layer, and may include the following steps: The first step, based on the aforementioned process logic constraints, is to construct a directed acyclic graph (DAG) for the process logic layer. In this DAG, nodes represent construction tasks, and edges represent process dependencies. The spatial logic layer can be a constraint level describing the rules governing the flow of processes between different floors or work surfaces. For example, the spatial logic layer could stipulate that "reinforcement binding can only begin on the upper floor after the lower floor is poured." The DAG for the process logic layer is a unidirectional acyclic graph with construction tasks as nodes and process dependencies as edges. For example, the DAG for the process logic layer could contain nodes and directed edges for "earthwork excavation -> foundation layer construction -> bottom slab reinforcement." The construction tasks can be work units with clearly defined physical and management boundaries. For example, the construction task could be the reinforcement binding operation of the 5th floor shear wall.

[0024] The second step involves constructing a directed acyclic graph (DAG) of the spatial logic layer based on the aforementioned spatial logical constraints. Nodes in this DAG represent construction areas or work surfaces, and edges represent spatial occupancy or avoidance relationships. Specifically, the aforementioned process dependency relationships can be the order in which tasks must be followed due to structural or technical reasons; for example, "the next process can only be carried out after the concrete has initially set." The aforementioned construction areas or work surfaces can be the spatial range in which the construction task occurs; for example, the aforementioned construction area or work surface can be the entire floor slab of the third floor or a section of masonry between certain axes. The aforementioned spatial occupancy or avoidance relationships can be the mutual exclusion or compatibility relationships between different processes in space; for example, the aforementioned spatial occupancy or avoidance relationship can be "vertical overlapping operations are prohibited from simultaneous construction." In practice, firstly, each construction area in the spatial logical constraint information is obtained and mapped to spatial nodes. Then, according to rules such as intra-layer flow, inter-layer transfer, and interleaving, directed edges are added between different spatial nodes. Finally, the floor difference parameter K is configured to dynamically generate cross-floor spatial dependency edges, thus generating the DAG of the spatial logic layer. For example, set the floor difference K=1, create spatial nodes for floors 1 to 5 respectively, and add inter-floor passing edges for "floor 1 completed -> floor 2 started", forming a spatial logic layer chain.

[0025] The third step involves performing association mapping on the aforementioned directed acyclic graphs (DAGs) of the process logic layer and the spatial logic layer to establish cross-layer connection edges, thereby generating a two-layer DAG topology model. These cross-layer connection edges can be mapping edges that associate process layer nodes with spatial layer nodes. For example, they can connect the process node "binding rebar on the Nth layer" with the spatial node "working surface on the Nth layer." In practice, firstly, each construction task node in the process logic layer is traversed to identify its corresponding construction area and working surface. Then, association mappings are established between the construction task nodes and their corresponding spatial logic layer nodes. Finally, bidirectional cross-layer connection edges are added between the mapped node pairs, allowing the task nodes to simultaneously carry process dependency edges and spatial association edges, forming a unified graph structure with shared nodes across the two layers. This completes the superposition and fusion of the two-layer models, generating a two-layer DAG topology model. For example, the "3rd floor wall and column reinforcement" node and the "3rd floor working surface" node are bound together by cross-layer connection edges. This process is constrained by both the "formwork -> reinforcement" sequence in the process layer and the "2nd floor completion" prerequisite in the spatial layer.

[0026] Step 103: Perform loop detection processing on the two-layer directed acyclic graph topology model to identify topological deadlocks in the construction process and generate topological deadlock detection results.

[0027] In some embodiments, the execution entity can perform loop detection processing on the two-layer directed acyclic graph topology model to identify topological deadlocks in the construction process and generate topological deadlock detection results. The loop detection can be an algorithmic process for identifying the existence of closed loops in a directed graph; for example, the loop detection can detect circular dependencies such as "A->B->A". The topological deadlock can be a closed loop formed by the mutual constraints of process and spatial logic; for example, the topological deadlock can be a cycle formed by "the third layer pouring depends on the fourth layer reinforcement, the fourth layer reinforcement depends on the third layer formwork removal, and the third layer formwork removal depends on the third layer pouring". The topological deadlock detection result can be structured data containing deadlock location, cause, and type identifiers; for example, the topological deadlock detection result can output "a true cross-layer deadlock exists between the third and fourth layers".

[0028] In addressing the technical challenges mentioned above, the application scenarios—complex construction environments with multiple disciplines and limited workspaces, such as high-rise buildings and large industrial plants—often present the following challenges: the coupling of process logic and spatial logic leads to implicit deadlocks that traditional single-layer networks cannot identify. This results in work process blockages or resource lockouts during actual execution of the scheduling plan, ultimately extending the construction period. Considering the specific requirements of this application scenario—automatic topology traversal, accurate loop detection, deadlock classification and identification, and location of the root cause—we have decided to adopt the following solution: In some optional implementations of certain embodiments, the execution entity may perform loop detection processing on the aforementioned two-layer directed acyclic graph topology model to identify topological deadlocks in the construction process and generate topological deadlock detection results, which may include the following steps: The first step is to perform node traversal sorting on the aforementioned two-layer directed acyclic graph topology model to obtain a topology traversal sequence. This topology traversal sequence can be a list of ordered nodes accessed according to topological sorting rules; for example, it could be [subbase, slab reinforcement, slab pouring]. In practice, firstly, all nodes in the two-layer directed acyclic graph topology model are obtained, and the node with an in-degree of zero is selected as the starting point. Then, the Kahn algorithm or a depth-first traversal algorithm is used to visit the nodes sequentially and delete their outgoing edges. Finally, the node access order is recorded to generate the topology traversal sequence. For example, traversing a graph containing the link "subbase -> slab reinforcement -> slab pouring" yields a topology sequence of [subbase, slab reinforcement, slab pouring].

[0029] The second step involves identifying anomalous nodes that violate the topological order based on the aforementioned topological traversal sequence and the preset acyclic topological rules, thereby generating a set of anomalous nodes for loops. The preset acyclic topological rules can be criteria for determining whether a directed graph contains a cycle; for example, the preset acyclic topological rule could be "the topological sequence length equals the total number of nodes, therefore it is acyclic." The aforementioned topological order can be the order in which nodes should be ordered in the topological sort; for example, the topological order requires that a preceding node must appear before a following node. In practice, firstly, the generated topological traversal sequence is compared with the preset acyclic topological rules (i.e., all nodes in the sequence are visited). Then, nodes that have not been visited are identified and marked as anomalous nodes that violate the topological order. Finally, all anomalous nodes and their associated edges are collected to generate a set of anomalous nodes for loops. For example, after traversal, if nodes "3rd floor pouring" and "4th floor reinforcement" are found to have not been visited, they are determined to form a loop and added to the set of anomalous nodes.

[0030] The third step involves performing a bidirectional adjacency search on each node in the aforementioned set of anomalous loop nodes to construct a candidate closed path set. This bidirectional adjacency search can be a graph search performed simultaneously along both the forward and reverse directions of directed edges. For example, it can start from a node and traverse forward and backward simultaneously. The candidate closed path set can be a set of paths that may form loops after the bidirectional search; for example, it can contain paths like "A->B->C->A".

[0031] The fourth step involves analyzing the candidate closed path set to distinguish between pseudo-loops caused by dependency conflicts within a single logic layer and true deadlocks formed by mutual constraints between the process logic layer and the spatial logic layer, thereby generating a deadlock type determination result. The pseudo-loops can be resolvable loops formed only within a single logic layer due to dependency conflicts; for example, a pseudo-loop could be a temporary loop formed within the process layer due to configuration errors. True deadlocks can be non-resolvable deadlocks formed by mutual constraints across process and spatial layers; for example, a true deadlock could be a bidirectional constraint where "process layer depends on spatial layer, and spatial layer depends on process layer." The deadlock type determination result can be a classification result distinguishing between pseudo-loops and true deadlocks, as well as hard logic and soft logic; for example, the deadlock type determination result could be "cross-layer true deadlock -> hard logic," or "same-layer pseudo-loop -> soft logic." In practice, firstly, the nodes and edges traversed by each closed path in the candidate closed path set are obtained, and their respective logic levels (process layer or spatial layer) are identified. Then, it is analyzed whether the closed path simultaneously crosses both the process logic layer and the spatial logic layer. Finally, deadlock type determination results are generated based on the cross-layer situation: single-layer inner loops are pseudo-loops, and cross-layer loops are true deadlocks. For example, if a closed path simultaneously contains the "casting -> curing" edge of the process layer and the "Nth layer -> N+1th layer" edge of the space layer, it is determined to be a true deadlock formed by the mutual constraints between the process and space.

[0032] The fifth step involves extracting key nodes and key edges based on the deadlock type determination results to generate a topology deadlock detection result. This result includes deadlock location information, cause information, and deadlock type identifier. The key nodes and key edges can be the core nodes and directed edges that form the deadlock. For example, the key nodes and key edges could be the "3rd floor pouring" node and its directed edge pointing to the "4th floor reinforcement". In practice, firstly, based on the deadlock type determination results, the key nodes and key edges forming the deadlock are extracted from the closed path corresponding to the true deadlock. Then, the identifier information, floor, process name, and constraint type of the key nodes are recorded. Finally, this information is encapsulated into structured data to generate a topology deadlock detection result containing deadlock location information, cause information, and deadlock type identifier. For example, for a true deadlock across layers, the deadlock report is output: the location is between layers 3 and 4, the cause is that "layer 3 maintenance" depends on "layer 4 reinforcement" and "layer 4 reinforcement" depends on "layer 3 demolition", and the type is true deadlock across layers -> hard logic; for pseudo-loops within the same layer, the output type is marked as "pseudo-loop within the same layer -> soft logic", and no forced resolution is required.

[0033] The above-described operational steps, combined with step 107, constitute an inventive point of this disclosure, solving the technical problem mentioned in the background art: "Due to the coupling between process logic and spatial logic, implicit deadlocks that traditional single-layer networks cannot identify occur, causing scheduling plans to experience process blockages or resource interlocks during actual execution, thus leading to extended construction periods." The reasons for this technical problem are as follows: Traditional methods typically construct only a single process logic network, lacking a mechanism to model spatial resources as independent logical entities, failing to capture complex loops formed by the intertwining of process dependencies and space occupancy, and lacking a mechanism for automatically detecting and distinguishing between true and false deadlocks. This invention, by accurately identifying true deadlocks caused by process and spatial coupling and distinguishing false loops that do not require processing, achieves the construction of a two-layer directed acyclic graph and performs cross-layer loop detection and deadlock classification. Combined with step 107, it saves on-site rework costs and construction delay losses caused by process conflicts, and reduces the computational resource overhead of repeated trial and error after scheduling failures due to deadlocks.

[0034] Step 104: Based on the topology deadlock detection results, perform deadlock resolution processing to generate an optimized construction network diagram.

[0035] In some embodiments, the execution entity may perform deadlock resolution processing based on the topology deadlock detection results to generate an optimized construction network graph. The optimized construction network graph may be a feasible directed acyclic graph after eliminating all deadlock loops; for example, it may be a loop-free construction network after removing conflicting edges.

[0036] In some optional implementations of certain embodiments, the execution entity may perform deadlock resolution processing based on the aforementioned topology deadlock detection results to generate an optimized construction network diagram, which may include the following steps: The first step is to determine hard logic deadlocks and soft logic deadlocks based on the deadlock type identifiers in the topology deadlock detection results. Hard logic deadlocks can be caused by inviolable constraints related to process structure, technical intervals, or spatial safety. For example, a hard logic deadlock could be a closed loop caused by "masonry cannot be laid without demolding." Soft logic deadlocks can be caused by adjustable constraints related to resource continuity and management compliance. For example, a soft logic deadlock could be a closed loop caused by a conflict between the "team resource continuity" requirement and spatial flow requirements. In practice, firstly, the topology deadlock detection results are analyzed to extract the deadlock type identifier field from each deadlock record. Then, the deadlock records are categorized and filtered based on the value of this field. Finally, the deadlock records are assigned to either the hard logic deadlock set or the soft logic deadlock set. For example, analyzing the deadlock report reveals a record with the type identifier "hard logic deadlock"—"cross-layer deadlock between the 3rd floor curing and the 4th floor reinforcement"—which is assigned to the hard logic deadlock set.

[0037] The second step involves adjusting the technological dependencies or spatial occupancy order of the corresponding construction tasks to generate a deadlock resolution solution. These technological dependencies or spatial occupancy orders can be the order of construction tasks or the order of use of work surfaces. For example, they could be "formwork erection before pouring" or "lower layer completed before upper layer." The deadlock resolution solution can be a forced blocking mechanism. For instance, it could delete a directed edge that caused the deadlock and output a conflict report. In practice, first, each deadlock record in the deadlock set is retrieved, and the key nodes and edges that caused the deadlock are identified. Then, based on the principle of inviolability, at least one technological dependency edge or spatial occupancy edge is forcibly deleted or reversed. Finally, the specific details of the adjustment operation are recorded, generating a deadlock resolution solution. For example, if a deadlock involves "masonry cannot be built without formwork removal," the system forcibly deletes the misconfigured reverse edge "masonry depends on formwork removal," generates a blocking resolution solution, and outputs a conflict report.

[0038] The third step is to generate a solution to resolve the aforementioned soft logic deadlocks. This solution can be a flexible adjustment mechanism, such as adjusting the shift arrival time or increasing resource allocation. In practice, first, each deadlock record in the set of soft logic deadlocks is obtained, and the type of soft constraint that caused the deadlock (e.g., resource continuity, management compliance) is analyzed. Then, adjustable resolution suggestions are generated, such as adjusting the shift arrival time or increasing resource allocation. Finally, the resolution suggestions are encapsulated into a structured solution to generate the soft logic deadlock resolution plan. For example, if a soft logic deadlock is caused by a conflict between the "shift resource continuity" constraint and spatial flow, a suggested solution is to adjust the shift transfer interval from 0 days to 1 day to break the deadlock loop.

[0039] The fourth step involves adjusting the nodes and edges based on the aforementioned hard logic deadlock resolution schemes, soft logic deadlock resolution schemes, and the aforementioned two-layer directed acyclic graph topology model to generate an adjusted topology model. This adjusted topology model can be a temporary graph model after the node and edge adjustment operations. For example, it can be a two-layer graph structure after deleting a cross-layer connecting edge. In practice, first, the adjustment instructions from the hard logic deadlock resolution schemes and soft logic deadlock resolution schemes are obtained. Then, node deletion, edge deletion, edge addition, or parameter modification operations are performed in the original two-layer directed acyclic graph topology model according to the adjustment instructions. Finally, the modified graph structure is saved, generating the adjusted topology model. For example, according to the resolution scheme, the cross-layer edge "3rd layer curing -> 4th layer rebar" is deleted, and the delay parameter of "4th layer rebar -> 3rd layer formwork removal" is modified to generate an adjusted temporary graph model.

[0040] The fifth step involves performing loop detection on the adjusted topology model to generate a resolution verification result. This resolution verification result can be a conclusion obtained by performing loop detection on the adjusted model again; for example, the result could be "loop eliminated" or "deadlock still exists." In practice, first, the adjusted topology model undergoes the same loop detection process as in step 103 (i.e., node traversal sorting, abnormal node identification, bidirectional adjacency search, etc.). Then, it is determined whether a new closed loop exists in the detection results. Finally, a resolution verification result is generated, labeled "resolved" or "deadlock still exists." For example, if loop detection is performed on the adjusted model and no closed loop is found, the resolution verification result is "deadlock completely eliminated, verification passed."

[0041] Step 6: In response to the above resolution verification results satisfying the preset resolution conditions, an optimized construction network diagram is generated. These preset resolution conditions can be standard rules for determining successful deadlock resolution; for example, the preset resolution condition could be "no closed loops exist in the adjusted model." In practice, first, the resolution verification results are obtained and compared with the preset resolution condition (no closed loops exist in the model). Then, it is determined whether the condition is met. Finally, in response to the preset resolution condition, the adjusted topology model is saved as the final optimized construction network diagram and output. For example, if the resolution verification result is "loops have been eliminated," satisfying the preset resolution condition, the system outputs the temporary model as an optimized construction network diagram for subsequent time-series simulation processing.

[0042] Step 105: Based on the optimized construction network diagram, perform time extrapolation processing to generate forward extrapolation results and backward extrapolation results.

[0043] In some embodiments, the aforementioned executing entity can perform time extrapolation processing based on the optimized construction network diagram to generate forward extrapolation results and backward extrapolation results. The forward extrapolation result can be a set of earliest time parameters for each process calculated forward from the commencement date; for example, the forward extrapolation result could be "the earliest start time for tying the rebar on the 3rd floor is day 5". The backward extrapolation result can be a set of latest time parameters for each process calculated backward from the completion date; for example, the backward extrapolation result could be "the latest start time for tying the rebar on the 3rd floor is day 7".

[0044] In some optional implementations of certain embodiments, the execution entity may perform time extrapolation processing based on the optimized construction network diagram to generate forward extrapolation results and reverse extrapolation results, which may include the following steps: The first step involves performing forward time-series calculations based on the planned durations and logical dependencies of each construction process in the optimized construction network diagram to generate a first time parameter set. The planned duration of each construction process can be the estimated working time required for each construction task; for example, the planned duration of each construction process could be "the planned duration for tying the rebar on the 3rd floor is 2 days." The logical dependencies can be pre- and post-constraint relationships between processes; for example, "the 3rd floor can only be poured after the rebar on the 3rd floor is completed." The first time parameter set can be the earliest start and earliest finish times of each process obtained through forward calculation; for example, the first time parameter set could be an array of earliest start times [ES1, ES2, ...] . In practice, firstly, all nodes in the optimized construction network diagram are obtained, and a planned duration value is configured for each node. Then, starting from the zero point of the start time, nodes are visited sequentially according to the direction of the directed edges, accumulating the duration to calculate the earliest start and earliest finish times. Finally, the calculation results for all nodes are recorded to generate the first time parameter set. For example, if the planned construction period for a certain process, "the third layer of steel reinforcement," is 2 days, and the earliest completion time for the preceding process, "the third layer of formwork," is 5 days, then the earliest start date for "the third layer of steel reinforcement" is calculated to be 5 days, and the earliest completion date is 7 days.

[0045] The second step involves performing reverse timing calculations based on the preset project deadline and the topological relationships of the optimized construction network diagram to generate a second time parameter set. The preset project deadline can be the planned completion date of the project, for example, "day 30". The topological relationships can be the connections between nodes and directed edges in the construction network diagram, for example, "node A points to nodes B and C". The second time parameter set can be the latest start and finish times of each process obtained through reverse calculation, for example, an array of latest start times [LS1, LS2, ...]". In practice, first, the preset project deadline is obtained and used as the latest finish time of the endpoint node. Then, starting from this endpoint node, the optimized construction network diagram is traversed in reverse, calculating the latest start and finish times of each process sequentially in the opposite direction of the directed edges. Finally, the reverse calculation results for all nodes are recorded to generate the second time parameter set. For example, if the project deadline is day 30, and the latest start date for a subsequent process is day 8 with a planned duration of 2 days, then the latest completion date for that process is day 8 and the latest start date is day 6.

[0046] The third step involves generating forward and reverse derivation results based on the first and second time parameter sets mentioned above. In practice, firstly, the first time parameter set generated in the first step is integrated and aligned with the second time parameter set generated in the second step. Then, according to the correspondence between process nodes, the earliest and latest times are associated with each process. Finally, forward and reverse derivation results containing the earliest start, earliest finish, latest start, and latest finish times for each process are generated. For example, for the rebar tying on the 3rd floor: the forward derivation result is the earliest start day 5 and the earliest finish day 7; the reverse derivation result is the latest start day 6 and the latest finish day 8. The complete time parameters are then combined and output as the forward and reverse derivation results.

[0047] Step 106: Based on the results of forward and backward deduction, identify the critical path under the dual constraints of process logic and spatial logic to generate construction scheduling instructions.

[0048] In some embodiments, the executing entity can identify the critical path under the dual constraints of process logic and spatial logic based on the aforementioned forward deduction results and the aforementioned backward deduction results, in order to generate construction scheduling instructions. The critical path can be the path with the longest total time under the dual constraints of process and space; for example, the critical path can be "1st layer reinforcement -> 1st layer pouring -> 2nd layer reinforcement -> 2nd layer pouring". The construction scheduling instructions can be execution commands containing key process time information; for example, the construction scheduling instructions can be "The planned start time for 3rd layer pouring is day 10, and the planned completion time is day 12".

[0049] In some optional implementations of certain embodiments, the execution entity can identify the critical path under the dual constraints of process logic and spatial logic based on the above forward deduction results and the above reverse deduction results, so as to generate construction scheduling instructions, which may include the following steps: The first step is to determine the time difference for each construction procedure based on the earliest start time in the forward deduction results and the latest start time in the reverse deduction results, thus generating a time difference parameter set. This time difference parameter set can be a collection of the earliest and latest start time differences for each procedure; for example, it could be "3rd floor rebar tying: time difference = 2 days". In practice, first, the earliest start time of each procedure in the forward deduction results and the latest start time of each procedure in the reverse deduction results are obtained. Then, for each procedure, the difference between the latest start time and the earliest start time is calculated. Finally, the differences for all procedures are summarized to generate the time difference parameter set. For example, for 3rd floor rebar tying: earliest start day 5, latest start day 7, time difference = 2 days; for 3rd floor pouring: earliest start day 7, latest start day 7, time difference = 0 days.

[0050] The second step, based on the aforementioned time difference parameter set, is to determine the construction procedures with zero time difference to generate a zero-float procedure set. This zero-float procedure set can be a collection of procedures with zero time difference and no buffer time. For example, it could be {1st floor pouring, 2nd floor reinforcement, 2nd floor pouring}. In practice, first, each procedure record in the time difference parameter set is iterated through. Then, procedures with a time difference equal to zero are selected and marked as procedures with no float time. Finally, the identification information of these procedures is summarized to generate the zero-float procedure set. For example, if the time difference parameter set has 3rd floor pouring time difference = 0 days, 4th floor reinforcement time difference = 0 days, and 4th floor pouring time difference = 0 days, while other procedures have time differences > 0 days, then the zero-float procedure set is {3rd floor pouring, 4th floor reinforcement, 4th floor pouring}.

[0051] The third step involves performing path concatenation processing based on the zero-floating process set and the directed edges in the optimized construction network graph to generate a candidate critical path set. This candidate critical path set can consist of multiple candidate paths formed by concatenating zero-floating processes; for example, it might include two candidate paths, "Path A" and "Path B". In practice, first, the directed edge information from the zero-floating process set and the optimized construction network graph is obtained. Then, a process with an in-degree of zero is selected from the zero-floating process set as the starting point, and subsequent zero-floating processes are sequentially concatenated according to the direction of the directed edges. Finally, each concatenated path is generated to form the candidate critical path set. For example, if the zero-floating process set includes A, B, C, and D, two concatenated paths, A->B->C and A->B->D, are formed to generate a candidate critical path set containing these two candidate paths.

[0052] The fourth step involves filtering the candidate critical path set based on the dual constraints of the process logic layer and the spatial logic layer to generate the target critical path. This target critical path can be the final critical path selected from the candidate set; for example, it could be "1st layer reinforcement -> 1st layer pouring -> 2nd layer reinforcement". In practice, firstly, each candidate path in the candidate critical path set is obtained, and its total duration (the sum of the planned durations of each process along the path) is calculated. Then, the candidate path with the longest total duration is selected. Finally, combining the dual constraints of the process logic layer and the spatial logic layer, it is verified whether the path is simultaneously constrained by both types of constraints, thus generating the target critical path. For example, if two candidate paths have total durations of 12 days and 9 days respectively, the path with the 12-day duration is selected. Verification shows that it simultaneously contains process dependency edges and inter-layer transitive edges, confirming it as the target critical path.

[0053] The fifth step involves extracting the timing information of each construction process along the aforementioned critical path and generating construction scheduling instructions. These instructions include the planned start and finish times for each critical process. A critical process can be one located on the critical path whose delay will affect the overall project duration; for example, it could be "second floor pouring" or "roof capping." The planned start time can be the expected start time of the process; for example, it could be "8:00 AM on day 5." The planned finish time can be the expected completion time of the process; for example, it could be "6:00 PM on day 7." In practice, first, all process nodes along the critical path and their associated time parameters are obtained. Then, the planned start and finish times for each process are extracted (from the forward inference results). Finally, this information is encapsulated into structured instructions to generate construction scheduling instructions containing the time information for each critical process. For example, if the target critical path is "1st layer pouring -> 2nd layer reinforcement -> 2nd layer pouring", the following can be extracted: 7 days from the start of the 1st layer pouring plan and 9 days from the completion plan; 9 days from the start of the 2nd layer reinforcement plan and 11 days from the completion plan; 11 days from the start of the 2nd layer pouring plan and 13 days from the completion plan, generating a complete scheduling instruction.

[0054] Step 107: In response to receiving the construction scheduling instruction, execute the scheduling operation corresponding to each construction process.

[0055] In some embodiments, the aforementioned execution entity may, in response to receiving the aforementioned construction scheduling instruction, execute scheduling operations corresponding to each construction process.

[0056] In addressing the technical problems mentioned above by adopting technical solutions, and considering the application scenario—complex schedule planning and dynamic scheduling in high-rise building construction involving multi-story continuous construction, inter-floor overlapping operations, and vertically intersecting construction—the following technical issues often arise: after the issuance of construction scheduling instructions, there are temporal and spatial conflicts in on-site resource allocation (e.g., multiple tasks requesting the same tower crane at the same time), and it is impossible to perceive on-site progress deviations in real time and dynamically adjust the scheduling plan, leading to project delays due to schedule deviations. Given the following requirements for this application scenario: it needs to automatically match tasks and resources, detect and resolve temporal and spatial conflicts, and collect on-site feedback in real time, compare progress deviations, and trigger dynamic updates to form a closed-loop scheduling management system. Therefore, we have decided to adopt the following solution: In some optional implementations of certain embodiments, the execution entity may, in response to receiving the construction scheduling instruction, perform scheduling operations corresponding to each construction process, which may include the following steps: The first step, in response to the construction scheduling instruction, is to perform the following steps: Sub-step one involves matching the planned start and finish times in the aforementioned construction scheduling instructions with the corresponding construction task identifiers to generate a list of tasks to be executed. The construction task identifier can be a unique code or ID identifying the construction task; for example, the construction task identifier could be "TASK-05-003". The list of tasks to be executed can be a list of construction tasks to be performed according to the plan; for example, the list could be [3rd floor rebar tying, 3rd floor pouring, 4th floor rebar]. In practice, firstly, the construction scheduling instructions are parsed to extract the planned start and finish times for each process. Then, the corresponding construction task identifiers are matched according to the time window. Finally, the list of tasks to be executed is generated by summarizing them in chronological order. For example, if the scheduling instruction contains "3rd floor pouring: planned start day 7, completion day 9", matching the task identifier "TASK-07" generates the list item "TASK-07, days 7-9".

[0057] Sub-step two involves generating a resource matching candidate set based on the aforementioned list of tasks to be executed and the preset resource library. The preset resource library can be a database storing resource information such as work teams, equipment, and materials. For example, it could contain information on rebar work teams, tower cranes, and concrete pump trucks. The resource matching candidate set can be a combination of available resources that meets the resource requirements of the task. For example, it could be {Rebar Team 1, Tower Crane No. 2}. In practice, firstly, the resource type and quantity required for each task in the list of tasks to be executed are obtained. Then, the status and time period of available resources in the preset resource library are queried. Finally, resource combinations that meet the conditions are matched for each task, generating a resource matching candidate set. For example, the task "Pouring the 3rd Floor" requires a concrete work team + a pump truck; "Concrete Team 2 + Pump Truck No. 1" is matched from the resource library and added to the candidate set.

[0058] Sub-step three involves performing spatiotemporal conflict detection processing on the aforementioned resource matching candidate set to generate resource conflict detection results. This spatiotemporal conflict detection can identify whether resources are repeatedly occupied in time or space; for example, it can determine whether a tower crane is simultaneously invoked by two tasks. The resource conflict detection results can be conclusions indicating whether resource contention exists; for example, the result could be "The tower crane experienced a dual-task conflict on the morning of the 5th." In practice, first, all resource allocation records in the resource matching candidate set are traversed. Then, it is checked whether the same resource is allocated to multiple tasks within the same time period. Finally, all conflicting resources and time periods are recorded, generating resource conflict detection results. For example, if the detection finds that Pump Truck No. 1 was simultaneously allocated to "3rd Floor Pouring" and "4th Floor Reinforcement" on the morning of the 8th day, a conflict record is generated: "Pump Truck No. 1, Morning of the 8th Day, Dual-Task Conflict."

[0059] Sub-step four involves performing dynamic priority arbitration based on the resource conflict detection results to generate a reconciled scheduling scheme. This dynamic priority arbitration can be a process that automatically resolves resource conflicts based on task priority. The dynamic nature of the arbitration rules can be adaptive adjustment, rather than a fixed rule. For example, normally the arbitration rule is "first come, first served (resources)," but when a task becomes a critical path task, it automatically switches to an arbitration rule of "critical process priority." In practice, first, the resource conflict detection results are obtained, and the current priority level and critical path identifier of each conflicting task are extracted. Then, it is determined whether any task has become a critical path task due to delay. If so, the arbitration rule is automatically switched from "first come, first served" to "critical process priority." Finally, the conflicting tasks are reordered according to the switched rule, and resources are preferentially allocated to the task ranked first, generating a reconciled scheduling scheme. For example, if a tower crane is requested by two tasks, A and B, normally, according to the "first come, first served" rule, A requests first and then receives the tower crane. However, since task A is a non-critical process and task B has become a critical path task due to delay, the system automatically switches the rules, prioritizing the allocation of the tower crane to task B, and re-matching task A with other hoisting equipment.

[0060] Sub-step five: Based on the scheduling scheme after conflict resolution, generate construction work orders to obtain executable work order data. These work orders can be work documents containing task details and resource allocation; for example, a work order could be "Rebar team to perform rebar tying on the 3rd floor on the morning of day 5." The executable work order data can be structured work order information that can be pushed to the system; for example, it can be a JSON-formatted work order object. In practice, first, the scheduling scheme after conflict resolution is obtained, and the resource allocation and time arrangement information for each task is extracted. Then, the above information is formatted into structured data according to the work order template. Finally, a construction work order containing task identifier, time, resources, and operation instructions is generated to obtain executable work order data. For example, a work order might be generated as follows: Work Order No. WO-001, Task: 3rd floor pouring, Time: Days 7-9, Resources: Concrete Group 2 + Pump Truck No. 1, Construction Location: 3rd floor.

[0061] Sub-step six involves pushing the executable work order data to the target mobile terminal to trigger the construction preparation process. The target mobile terminal can be a handheld device used by on-site construction personnel receiving the work order; for example, it could be a tablet or smartphone used by the team leader. The construction preparation process can include preliminary steps such as personnel deployment, material inspection, and equipment checks. For example, the construction preparation process could be "confirming the arrival of rebar, team sign-in, and tower crane trial run." In practice, first, the executable work order data is obtained, and the target mobile terminal (such as the team leader's phone or tablet) is determined. Then, the work order data is pushed to the target terminal via a wireless network. Finally, the construction preparation process notification is triggered on the terminal.

[0062] Sub-step seven involves real-time collection of work progress feedback data and equipment status data from the construction site to generate actual construction progress information. The work progress feedback data can be the actual completion status reported on-site, for example, "80% of the third layer of rebar has been completed." The equipment status data can be the operating status information of construction machinery, for example, "Tower crane is currently idle, pump truck is operating." The actual construction progress information can be the true progress status generated after comprehensive feedback, for example, "The third layer of rebar is completed, the third layer is being poured." In practice, firstly, on-site data is received via mobile terminals, IoT sensors, or manual reporting. Then, work progress information (such as completion percentage) and equipment status information (e.g., idle / operating) are extracted. Finally, the actual construction progress information is generated by summarizing these data. For example, if the on-site report is "100% of the third layer of rebar is completed," and the equipment data shows "Tower crane is idle," the actual progress information "The third layer of rebar is completed, tower crane is ready" is generated.

[0063] Sub-step eight involves comparing the actual construction progress information with the construction scheduling instructions to generate deviation information. This deviation information can be the difference between the planned and actual progress; for example, it could be "the third floor pouring is delayed by 2 days." In practice, first, the actual construction progress information and the planned progress information in the construction scheduling instructions are obtained. Then, the actual completion time of each process is compared with the planned completion time. Finally, the deviation days or deviation percentage are calculated to generate deviation information. For example, if the planned completion date for the third floor reinforcement is day 5, and it is actually completed on day 6, the deviation information "the third floor reinforcement is delayed by 1 day" is generated.

[0064] Sub-step nine involves generating a progress compliance confirmation report in response to the aforementioned deviation information meeting preset conditions. This process continuously tracks the work progress and equipment status at the construction site to complete each construction procedure. The preset conditions can be thresholds or rules for determining progress compliance; for example, a preset condition could be "deviation days ≤ 1 day". The progress compliance confirmation report can be an acceptance document confirming that the progress meets the plan; for example, a progress compliance confirmation report could be "current progress deviation is 0 days, acceptance passed". In practice, first, deviation information is obtained, and it is determined whether the preset conditions (e.g., deviation days ≤ 1 day) are met. Then, if the conditions are met, a progress compliance confirmation report is generated, marked "progress qualified". Finally, the work progress and equipment status at the construction site are continuously tracked, and data is collected cyclically to complete each construction procedure. For example, if the deviation information is 0 days behind, meeting the preset conditions, a report "3rd floor construction progress compliant, acceptance passed" is generated, and monitoring of the next procedure continues.

[0065] The second step involves generating an updated construction scheduling instruction in response to the aforementioned deviation information failing to meet preset conditions. This updated instruction is then used as the new construction scheduling instruction to continue executing the previous steps. In practice, firstly, deviation information is acquired, and it is determined whether it does not meet preset conditions (e.g., deviation days > 1 day). Then, based on the type and magnitude of the deviation, time extrapolation processing or resource scheduling processing is automatically re-executed. Finally, an updated construction scheduling instruction is generated, and this instruction is used as the new construction scheduling instruction to continue executing the previous sub-steps. For example, if the deviation information is "the third floor pouring is delayed by 3 days," which does not meet the preset condition (≤ 1 day), the subsequent process time is re-estimated, the scheduling instruction is updated, and the updated instruction is re-dispatched for execution.

[0066] The above-described operational steps, as an inventive point of this disclosure, solve the technical problem mentioned in the background art: "After the construction scheduling instruction is issued, there are temporal and spatial conflicts in the allocation of on-site resources (for example, the same tower crane is requested by multiple tasks at the same time), and it is impossible to perceive on-site progress deviations in real time and dynamically adjust the scheduling plan, thus causing project delays due to progress deviations." The reasons for the above technical problems are as follows: Traditional scheduling methods regard the issuance of instructions as the end point, lacking an automatic detection and arbitration mechanism for resource temporal and spatial conflicts; at the same time, on-site feedback relies on manual reporting and is disconnected from the scheduling system, so progress deviations cannot trigger automatic rescheduling. The inventive point of this invention, through closed-loop processing of scheduling instructions, including resource matching, conflict detection, priority arbitration, work order push, on-site data collection, deviation comparison, and dynamic updates, realizes fully automated closed-loop management of the entire process of scheduling instructions from generation to execution to feedback updates, saving the time cost of coordinating resource conflicts and the losses caused by project delays due to untimely responses to progress deviations.

[0067] The above-described embodiments of this disclosure have the following beneficial effects: The construction process scheduling method based on topology loop detection in some embodiments of this disclosure achieves conflict-free process scheduling under dual constraints of process and space, thereby saving construction costs and shortening the construction period. Specifically, the reason for frequent logical deadlocks and process inversions in construction process scheduling is that existing methods typically only consider process logic constraints, neglecting spatial resource exclusivity and work surface conflicts, resulting in the generated scheduling plan being physically unenforceable or frequently experiencing process deadlocks. Based on this, the construction process scheduling method based on topology loop detection in some embodiments of this disclosure first obtains the process logic constraint information and spatial logic constraint information of the target construction project. By obtaining the process logic constraints and spatial logic constraints separately, physical dependencies and spatial rules are decoupled and extracted, providing a structured data foundation for subsequent two-layer modeling, avoiding logical confusion caused by the mixing of the two types of constraints, and improving the processability and accuracy of constraint information. Then, based on the above-mentioned process logic constraint information and spatial logic constraint information, a two-layer directed acyclic graph topology model is generated, which includes a process logic layer and a spatial logic layer. By constructing a two-layer directed acyclic graph (DAG) topology model containing both process logic and spatial logic layers, the spatiotemporal constraints are uniformly mapped into a graph structure, providing complete input for loop detection and enabling the digital representation and computable modeling of complex construction dependencies. Next, loop detection processing is performed on the aforementioned two-layer DAG topology model to identify topological deadlocks in construction processes and generate topological deadlock detection results. By performing loop detection on the two-layer DAG topology model, topological deadlocks formed by conflicts between process logic and spatial logic are automatically identified, accurately locating the conflict source and deadlock type, transforming implicit logical contradictions into explicit and manageable results, and improving fault diagnosis efficiency. Secondly, based on the aforementioned topological deadlock detection results, deadlock resolution processing is performed to generate an optimized construction network diagram. Based on the deadlock detection results, hierarchical resolution is performed, with hard logic forcibly blocked and soft logic flexibly adjusted, automatically generating a conflict-free construction network diagram, breaking logical loops, eliminating scheduling deadlocks, and improving the executability and logical rigor of the construction plan. Next, based on the optimized construction network diagram, time extrapolation is performed to generate forward and backward extrapolation results. Through forward and backward time extrapolation, the earliest and latest time parameters for each process are determined, and the time windows and fluctuation ranges for each process are quantified. This provides data support for critical path identification and schedule risk analysis, enabling a quantitative assessment of schedule constraints. Then, based on the forward and backward extrapolation results, critical paths under both technological and spatial constraints are identified to generate construction scheduling instructions. By identifying critical paths under both technological and spatial constraints, the focus is on the core set of processes affecting the overall schedule, locking the scheduling optimization focus onto the critical chain, and improving the targeting of schedule control and the accuracy of resource allocation priorities.Finally, in response to the aforementioned construction scheduling instructions, the system executes the scheduling operations for each corresponding construction process. By automatically executing process scheduling operations in response to scheduling instructions, a closed-loop process of scheduling results being implemented on-site is achieved, thereby saving construction costs and shortening the construction period.

[0068] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a construction process scheduling device based on topology loop detection. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this construction process scheduling device based on topology loop detection can be specifically applied to various electronic devices.

[0069] like Figure 2 As shown, a construction process scheduling device 200 based on topology loop detection includes: an acquisition unit 201, a generation unit 202, a processing unit 203, a first execution unit 204, a deduction unit 205, an identification unit 206, and a second execution unit 207. The acquisition unit 201 is configured to acquire the process logic constraint information and spatial logic constraint information of the target construction project. The generation unit 202 is configured to generate a two-layer directed acyclic graph (DAG) topology model based on the aforementioned process logic constraint information and spatial logic constraint information. The two-layer DAG topology model includes a process logic layer and a spatial logic layer. The processing unit 203 is configured to perform loop detection processing on the two-layer DAG topology model to identify topology deadlocks in the construction process and generate a topology deadlock detection result. The first execution unit 204 is configured to perform deadlock resolution processing based on the topology deadlock detection result to generate an optimized construction network diagram. The deduction unit 205 is configured to perform time-based deduction processing based on the optimized construction network diagram to generate forward and backward deduction results. The identification unit 206 is configured to identify the critical path under the dual constraints of process logic and spatial logic based on the forward and backward deduction results to generate construction scheduling instructions. The second execution unit 207 is configured to execute scheduling operations corresponding to each construction process in response to receiving the construction scheduling instructions.

[0070] It is understandable that the units described in the construction process scheduling device 200 based on topology loop detection are related to the reference. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method are also applicable to the construction process scheduling device 200 based on topology loop detection and the units contained therein, and will not be repeated here.

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0072] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit 201, a generation unit 202, a processing unit 203, a first execution unit 204, a deduction unit 205, an identification unit 206, and a second execution unit 207. The names of these units do not necessarily limit the specific unit; for example, the acquisition unit may also be described as "a unit that acquires the technological logic constraint information and spatial logic constraint information of a target building construction project."

[0073] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0074] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A construction process scheduling method based on topology loop detection, characterized in that, include: Obtain the technological and spatial logical constraints of the target building construction project; Based on the process logic constraint information and the spatial logic constraint information, a two-layer directed acyclic graph topology model is generated, which includes: a process logic layer and a spatial logic layer. Loop detection processing is performed on the two-layer directed acyclic graph topology model to identify topological deadlocks in the construction process and generate topological deadlock detection results. Based on the topology deadlock detection results, deadlock resolution processing is performed to generate an optimized construction network diagram; Based on the optimized construction network diagram, time extrapolation processing is performed to generate forward extrapolation results and backward extrapolation results; Based on the forward and reverse deduction results, the critical path under the dual constraints of process logic and spatial logic is identified to generate construction scheduling instructions. In response to receiving the construction scheduling instruction, the scheduling operation corresponding to each building construction process is executed.

2. The method according to claim 1, characterized in that, The acquisition of the technological logic constraint information and spatial logic constraint information of the target building construction project includes: Based on the pre-acquired BIM model data of the target building construction project, the geometric and positional information of each construction component is extracted; Based on the geometric and positional information of each construction component, the spatial interference relationship between each construction process is determined, and spatial logical constraint information is obtained. Based on the pre-acquired construction organization design documents of the construction project, the dependencies between various construction tasks are determined to generate process logic constraint information.

3. The method according to claim 1, characterized in that, Based on the process logic constraint information and the spatial logic constraint information, a two-layer directed acyclic graph topology model is generated. This two-layer directed acyclic graph topology model includes: a process logic layer and a spatial logic layer, comprising: Based on the process logic constraint information, a directed acyclic graph of the process logic layer is constructed. In the directed acyclic graph of the process logic layer, the nodes represent construction tasks and the edges represent process sequence dependencies. Based on the aforementioned spatial logic constraint information, a directed acyclic graph of the spatial logic layer is constructed. In the directed acyclic graph of the spatial logic layer, nodes represent construction areas or work surfaces, and edges represent spatial occupancy or avoidance relationships. The directed acyclic graphs of the process logic layer and the spatial logic layer are associated and mapped to establish cross-layer connection edges, thereby generating a two-layer directed acyclic graph topology model.

4. The method according to claim 1, characterized in that, The step of performing deadlock resolution processing based on the topology deadlock detection results to generate an optimized construction network diagram includes: Based on the deadlock type identifier in the topology deadlock detection results, hard logic deadlock and soft logic deadlock are determined. For the hard logic deadlock, adjust the process dependencies or space occupancy order of the corresponding construction tasks to generate a hard logic deadlock resolution solution; For the aforementioned soft logic deadlock, a soft logic deadlock resolution solution is generated; Based on the hard logic deadlock resolution scheme, the soft logic deadlock resolution scheme, and the two-layer directed acyclic graph topology model, the nodes and edges are adjusted to generate the adjusted topology model. The adjusted topology model is subjected to loop detection processing to generate resolution verification results; In response to the digestion verification results satisfying the preset digestion conditions, an optimized construction network diagram is generated.

5. The method according to claim 1, characterized in that, The optimized construction network diagram is used for time-based extrapolation to generate forward and backward extrapolation results, including: Based on the planned duration and logical dependencies of each construction process in the optimized construction network diagram, a forward time-series calculation is performed to generate a first time parameter set. Based on the preset project deadline and the topological relationship of the optimized construction network diagram, reverse time-series deduction processing is performed to generate a second time parameter set; Based on the first time parameter set and the second time parameter set, forward deduction results and reverse deduction results are generated.

6. The method according to claim 1, characterized in that, Based on the forward and reverse deduction results, the critical path under the dual constraints of technological and spatial logic is identified to generate construction scheduling instructions, including: Based on the earliest start time in the forward deduction results and the latest start time in the reverse deduction results, the time difference of each construction procedure is determined to generate a time difference parameter set; Based on the time difference parameter set, construction procedures with zero time difference are determined to generate a zero-float procedure set; Based on the zero-floating process set and the directed edges in the optimized construction network graph, path concatenation processing is performed to generate a candidate critical path set. Based on the dual constraints of the process logic layer and the spatial logic layer, the candidate critical path set is filtered to generate the target critical path; Based on the target critical path, the timing information of each construction procedure on the target critical path is extracted to generate a construction scheduling instruction, which includes the planned start time and planned completion time of each critical procedure.

7. A construction process scheduling device based on topology loop detection, characterized in that, include: The acquisition unit is configured to acquire the technological logic constraint information and spatial logic constraint information of the target building construction project. The generation unit is configured to generate a two-layer directed acyclic graph topology model based on the process logic constraint information and the spatial logic constraint information. The two-layer directed acyclic graph topology model includes a process logic layer and a spatial logic layer. The processing unit is configured to perform loop detection processing on the two-layer directed acyclic graph topology model to identify topological deadlocks in the construction process and generate topological deadlock detection results. The first execution unit is configured to perform deadlock resolution processing based on the topology deadlock detection results to generate an optimized construction network diagram; The extrapolation unit is configured to perform time extrapolation processing based on the optimized construction network diagram to generate forward extrapolation results and backward extrapolation results; The identification unit is configured to identify the critical path under the dual constraints of process logic and spatial logic based on the forward deduction results and the reverse deduction results, so as to generate construction scheduling instructions; The second execution unit is configured to execute scheduling operations corresponding to each construction process in response to receiving the construction scheduling instruction.

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