A method and system for data-driven dynamic scheduling of a continuing super-high-rise building construction progress

Abstract

This invention discloses a dynamic scheduling method and system for the construction progress of continued super high-rise buildings driven by data. By constructing a digital mapping base with three-level planning and four-line linkage, the system collects multi-dimensional dynamic data such as vertical transportation equipment capacity, material arrival, and personnel attendance in real time and aggregates them to generate a spatiotemporal snapshot of the project. Then, it calculates the progress deviation index to automatically identify high-risk areas. Once the deviation exceeds the threshold, the system recalculates the task time window in combination with the reallocated vertical transportation capacity and generates an optimized daily / weekly work plan. This transforms the originally lagging post-event manual adjustment into a real-time data-driven dynamic response, effectively solving the problem of the disconnect between plan and reality caused by fluctuations in vertical transportation capacity and mismatch in resource supply. This ensures the continuous progress of critical path tasks and significantly improves the ability to meet the construction schedule of continued super high-rise buildings.

Description

Technical Field

[0001] This invention relates to the field of task scheduling technology, specifically to a dynamic scheduling method and system driven by construction progress data for continued construction of super high-rise buildings. Background Technology

[0002] The continued construction of super high-rise buildings typically faces challenges such as tight schedules, large scale, and high dependence on vertical transportation. Their schedule management heavily relies on the hierarchical decomposition of the overall control plan, regional plans, and weekly / daily plans. Existing schedule scheduling methods mainly adopt a static planning management model, which involves developing a fixed work plan before construction begins. Subsequently, deviations between actual and planned progress are periodically compared using manual experience, and resource allocation is manually adjusted or the construction period is extended.

[0003] However, this model suffers from significant lag: it cannot perceive in real time the instantaneous capacity fluctuations of vertical transportation equipment (such as tower cranes and construction elevators), the transit status of the material supply chain, and the dynamic changes in the efficiency of work teams. When sudden capacity bottlenecks or material shortages occur on-site, traditional systems cannot automatically quantify their impact on the critical path, often leading to a severe disconnect between planning and reality, causing critical processes to stop due to material shortages or idle resources, making it difficult to achieve accurate dynamic correction. Summary of the Invention

[0004] This invention aims to provide a dynamic scheduling method and system driven by construction progress data for continued construction of super high-rise buildings, which effectively solves the problem of the disconnect between planning and reality caused by fluctuations in vertical transportation capacity and mismatch in resource supply.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a dynamic scheduling method driven by construction progress data for continued super high-rise buildings, comprising: A digital mapping base for three-level planning and four-line linkage is constructed. Based on the digital mapping base, the capacity status of vertical transportation equipment, the arrival status of materials, and the attendance rate of team members are collected in real time and aggregated to generate a spatiotemporal snapshot of the project at the current moment. Based on the project's spatiotemporal snapshot and the digital mapping base, the actual progress completion rate of tasks in each region and the real-time supply capacity of vertical transportation resources are calculated, and the progress deviation index is determined accordingly. When the schedule deviation index exceeds a preset threshold, the task queue in the high-risk area is extracted, and the task time window is recalculated and an optimized daily / weekly work plan is generated in combination with the reallocated vertical transportation capacity. The optimized daily / weekly work plan is converted into a resource scheduling instruction sheet and distributed to the field execution terminal. Monitor the execution process of the resource scheduling instruction sheet, collect execution feedback data, and output an execution result report.

[0006] Preferably, the digital mapping foundation for constructing a three-level plan and four-line linkage includes: Define the hierarchical decomposition relationship between the overall control plan, regional plans, and weekly / daily plans, and break down the project objectives into atomic tasks; Based on the aforementioned atomic tasks, data flow standards are established for the design line, procurement line, construction line, and acceptance line, so that the status of the four lines can be associated with the corresponding atomic tasks. Based on the data flow standard, the raw data from the multi-source system is cleaned and standardized, and then written into the central database; Based on the standardized data, an initial correlation matrix is ​​established between the atomic tasks and the required resources to form a baseline for resource requirements for task execution.

[0007] Preferably, the real-time collection of vertical transportation equipment capacity status, material arrival status, and team attendance rate includes: deploying sensors to monitor the task queues and remaining capacity of tower cranes and construction elevators; accessing supplier and logistics systems to obtain material in transit and inventory status; and collecting the actual number of team members on duty and work efficiency through facial recognition and mobile terminals.

[0008] Preferably, the aggregation to generate a spatiotemporal snapshot of the project at the current moment includes: integrating atomic task status, vertical transportation capacity, material inventory and personnel attendance data into a structured set of statuses with a unified timestamp, using regions as units.

[0009] Preferably, the calculation of the actual progress completion rate of tasks in each region and the real-time supply capacity of vertical transportation resources, and the determination of the progress deviation index accordingly, includes: Based on the completed workload in the project's spatiotemporal snapshot and the planned total workload in the digital mapping base, calculate the actual progress completion rate of tasks in each region. Based on the idle time, single load capacity and round-trip cycle of vertical transportation equipment in the spatiotemporal snapshot of the project, the real-time supply capacity of vertical transportation resources is calculated. The difference between the actual progress completion rate and the planned progress completion rate is weighted and correlated with the ratio of the real-time supply capacity to the theoretically required transportation capacity to obtain the progress deviation index.

[0010] Preferably, the generation of the optimized daily / weekly work plan includes: When the schedule deviation index exceeds a preset threshold, the corresponding high-risk area task queue is extracted from the project spatiotemporal snapshot, and the tasks affecting the path are identified. Based on the reallocated vertical transport capacity and the remaining workload of tasks in high-risk areas, the estimated duration of each task is recalculated. Based on the recalculated expected duration and process logic, a rolling mechanism is used to generate detailed work instructions for the next 24 hours; The detailed work instructions are verified for total resources and process logic. Once the verification is successful, the final execution plan is output.

[0011] Preferably, the optimized daily / weekly work plan is converted into a resource scheduling instruction sheet, including: generating instruction content containing task priority, equipment assignment, material allocation route and personnel shift, and then pushing it to the tower crane operator, manager and team terminal via wireless network.

[0012] Preferably, monitoring the execution process of the resource scheduling instruction sheet includes: Based on the equipment assignment in the resource scheduling instruction sheet, track the actual operating trajectory and load status of the vertical transportation equipment to verify the consistency of instruction execution; Based on the material allocation route in the resource scheduling instruction sheet, confirm whether the materials have arrived at the designated work site on time; Verify whether the team members are in their positions as instructed, based on the personnel shift schedule in the resource scheduling instruction sheet.

[0013] Preferably, the output execution result report includes: Collect and monitor the actual start time, completion amount, and reasons for deviations of tasks generated during the monitoring process to form execution feedback data; Based on the execution feedback data, a multi-dimensional statistical report covering time, space, resources, and task dimensions is generated. The multidimensional statistical reports, resource scheduling instruction sheets, and execution feedback data are packaged and solidified into a complete execution case for this scheduling.

[0014] On the other hand, this invention proposes a dynamic scheduling system driven by construction progress data for continued super high-rise buildings, comprising: The data foundation building module is used to construct a digital mapping foundation for three-level planning and four-line linkage; The status awareness module is used to collect real-time data on the capacity status of vertical transportation equipment, the arrival status of materials, and the attendance rate of team members, and aggregate these data to generate a spatiotemporal snapshot of the project at the current moment. The deviation calculation module is used to calculate the actual progress completion rate, the real-time supply capacity of vertical transportation resources, and the progress deviation index based on the project's spatiotemporal snapshot and the digital mapping base. The plan generation module is used to extract the task queue of high-risk areas when the schedule deviation index exceeds a preset threshold, and generate an optimized daily / weekly work plan by combining the reallocated vertical transportation capacity. The instruction distribution module is used to convert the optimized daily / weekly work plan into a resource scheduling instruction sheet and distribute it to the field execution terminal; The execution monitoring module is used to monitor the execution process of the resource scheduling instruction sheet, collect execution feedback data, and output an execution result report.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a digital mapping base with three-level planning and four-line linkage, which collects multi-dimensional dynamic data such as vertical transportation equipment capacity, material arrival, and personnel attendance in real time and aggregates them to generate a spatiotemporal snapshot of the project. Then, it calculates the progress deviation index to automatically identify high-risk areas. Once the deviation exceeds the threshold, the system recalculates the task time window and generates an optimized daily / weekly work plan based on the reallocated vertical transportation capacity. This transforms the originally lagging post-event manual adjustment into a real-time data-driven dynamic response, effectively solving the problem of the disconnect between plan and reality caused by fluctuations in vertical transportation capacity and mismatch in resource supply. It ensures the continuous progress of critical path tasks and significantly improves the ability to meet the construction schedule of continued super high-rise buildings. Attached Figure Description

[0016] Figure 1 The flowchart shows the dynamic scheduling method driven by the construction progress data of the super high-rise building of the present invention. Figure 2 This is a block diagram of the dynamic scheduling system driven by the construction progress data of the super high-rise building of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0019] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0020] Please see Figure 1 A dynamic scheduling method driven by data on the construction progress of continued super high-rise buildings is proposed. This method abandons the traditional model of adjusting plans based on manual experience, and instead constructs a standardized data foundation to collect multi-dimensional data on-site conditions in real time, calculates the progress deviation index, and generates optimized work instructions accordingly. Specifically, it includes: Construct a digital mapping foundation for a three-tiered plan and four-line linkage; specifically including: defining the hierarchical decomposition relationship of the overall control plan, regional plans, and weekly / daily plans, breaking down engineering objectives into atomic tasks; based on atomic tasks, establishing data flow standards for the design line, procurement line, construction line, and acceptance line, so that the status of the four lines can be associated with the corresponding atomic tasks; according to the data flow standards, cleaning and standardizing the raw data from multiple source systems and writing it into the central database; based on the standardized data, establishing an initial correlation matrix between atomic tasks and required resources, forming the resource requirement benchmark for task execution.

[0021] By constructing a digital mapping foundation with three-level planning and four-line linkage, the atomized decomposition of engineering objectives and the standardized integration of data throughout the entire lifecycle of design, procurement, construction, and acceptance have been achieved. This not only eliminates the silo effect between multi-source heterogeneous data, but also establishes a precise correlation benchmark between tasks and resource requirements. It provides a unified, reliable, and traceable data foundation for subsequent real-time perception of site conditions, accurate calculation of progress deviations, and dynamic generation of optimized scheduling schemes.

[0022] Based on a digital mapping platform, real-time data is collected on the capacity status of vertical transportation equipment, material arrival status, and team attendance. Specifically, this includes: deploying sensors to monitor the task queues and remaining capacity of tower cranes and construction elevators; accessing supplier and logistics systems to obtain material transit and inventory status; and collecting data on actual on-duty personnel and operational efficiency of work teams through facial recognition and mobile devices. This aggregates and generates a spatiotemporal snapshot of the project at the current moment, including: integrating atomic task status, vertical transportation capacity, material inventory, and personnel attendance data by region, forming a structured set of status data with a unified timestamp.

[0023] By using multi-dimensional sensing methods to capture the dynamic changes in vertical transportation, material supply, and personnel efficiency in real time, and aggregating multi-source heterogeneous data into a project snapshot under a unified spatiotemporal dimension, this effectively solves the pain points of lagging on-site status perception and data fragmentation in traditional management. It transforms discrete on-site elements into quantifiable and computable real-time digital twins, providing high-fidelity, multi-dimensional decision-making basis for accurately identifying progress bottlenecks and triggering dynamic scheduling mechanisms.

[0024] Based on the project's spatiotemporal snapshot and digital mapping base, the actual progress completion rate of tasks in each region and the real-time supply capacity of vertical transportation resources are calculated, and the progress deviation index is determined accordingly. Specifically, this includes: calculating the actual progress completion rate of tasks in each region based on the completed workload in the project's spatiotemporal snapshot and the planned total workload in the digital mapping base; calculating the real-time supply capacity of vertical transportation resources based on the idle time, single transport capacity, and round-trip cycle of vertical transportation equipment in the project's spatiotemporal snapshot; and weighting the difference between the actual progress completion rate and the planned progress completion rate with the ratio of real-time supply capacity to the theoretically required transport capacity to obtain the progress deviation index.

[0025] By integrating actual workload with planned baseline to calculate progress completion rate, and by measuring vertical transportation supply capacity in real time based on equipment operating parameters, a multi-dimensional weighted correlation model is constructed to quantify the dual deviation between progress and capacity. This transforms the fuzzy on-site lag problem into a quantifiable index indicator, enabling early and accurate automatic warning of critical path risks. This provides a scientific and objective decision threshold for triggering targeted resource reallocation and dynamic plan correction.

[0026] When the schedule deviation index exceeds a preset threshold, the task queue for high-risk areas is extracted. Combined with the reallocated vertical transportation capacity, the task time window is recalculated, and an optimized daily / weekly work plan is generated. Specifically, this includes: extracting the corresponding high-risk area task queue from the project's spatiotemporal snapshot when the schedule deviation index exceeds the preset threshold, and identifying tasks affecting the path; recalculating the estimated duration of each task based on the reallocated vertical transportation capacity and the remaining workload of the high-risk area tasks; generating detailed work instructions for the next 24 hours using a rolling mechanism based on the recalculated estimated duration and process logic; verifying the total resource amount and process logic of the detailed work instructions, and outputting the final execution plan after successful verification.

[0027] By utilizing a rolling mechanism and logic verification to generate a refined work plan, while ensuring rigorous process logic and controllable total resources, the bottleneck of vertical transportation on the critical path is effectively eliminated, significantly improving the resilience of super high-rise buildings in terms of schedule performance and scheduling response speed under complex working conditions.

[0028] The optimized daily / weekly work plan is converted into a resource scheduling instruction sheet and distributed to the on-site execution terminal. Specifically, this includes generating instruction content that includes task priority, equipment assignment, material allocation route and personnel shift, and then pushing it to the tower crane operator, manager and team terminal via wireless network.

[0029] By transforming the optimization plan into a standardized instruction sheet containing priorities, resource allocation, and route planning, and pushing it to the front-line terminals in real time, the time lag and distortion in information transmission are eliminated, ensuring that tower crane operators, managers, and work teams have accurate synchronization of work objectives and resource allocation, thereby achieving the immediate implementation of construction instructions and efficient collaborative on-site operations.

[0030] Monitor the execution process of resource scheduling instructions, collect execution feedback data, and output execution result reports.

[0031] The process of monitoring the execution of resource scheduling instructions includes: tracking the actual operating trajectory and load status of vertical transportation equipment based on the equipment assignment content in the resource scheduling instructions, and verifying the consistency of instruction execution; confirming whether materials arrive at the designated work site on time based on the material allocation route in the resource scheduling instructions; and verifying whether the team members are in place as instructed according to the personnel shift arrangement in the resource scheduling instructions.

[0032] The output execution result report includes: collecting the actual start time, completion amount and reasons for deviation of tasks generated during the monitoring process to form execution feedback data; based on the execution feedback data, summarizing and generating multi-dimensional statistical reports covering time, space, resources and task dimensions; and packaging the multi-dimensional statistical reports, resource scheduling instruction sheets and execution feedback data into a complete execution case of this scheduling.

[0033] By verifying the consistency of equipment, materials, and personnel execution in real time across multiple dimensions, discrete on-site operation data is transformed into a traceable structured case library. This not only enables immediate correction of scheduling deviations and traceability of responsibility, but also provides high-fidelity measured data support for the self-iterative optimization of subsequent algorithm models, continuously improving the adaptive capability and decision-making accuracy of the resource scheduling system.

[0034] On the other hand, this invention proposes a dynamic scheduling system driven by construction progress data for continued super high-rise buildings, such as... Figure 2 As shown, it includes: The data foundation building module is used to construct a digital mapping foundation for three-level planning and four-line linkage; The status awareness module is used to collect real-time data on the capacity status of vertical transportation equipment, the arrival status of materials, and the attendance rate of team members, and aggregate these data to generate a spatiotemporal snapshot of the project at the current moment. The deviation calculation module is used to calculate the actual progress completion rate, the real-time supply capacity of vertical transportation resources, and the progress deviation index based on the project's spatiotemporal snapshot and the digital mapping base. The plan generation module is used to extract the task queue of high-risk areas when the schedule deviation index exceeds a preset threshold, and generate an optimized daily / weekly work plan by combining the reallocated vertical transportation capacity. The instruction distribution module is used to convert the optimized daily / weekly work plan into a resource scheduling instruction sheet and distribute it to the field execution terminal; The execution monitoring module is used to monitor the execution process of the resource scheduling instruction sheet, collect execution feedback data, and output an execution result report.

[0035] Furthermore, the modules described above are also used to implement other steps of the aforementioned dynamic scheduling method for the continued construction progress of super high-rise buildings, as follows: Step 1: Construct a digital mapping foundation for a three-tiered plan and four-line linkage; Before implementing dynamic scheduling, the primary task is to establish a standardized data foundation capable of supporting complex engineering information. This foundation is not simply database storage, but a structured reorganization of the planning system for the entire lifecycle of ongoing super high-rise projects, transforming abstract management objectives into computer-recognizable digital objects. The core of this step lies in defining the logical mapping relationship between the "three-level plan" and the "four lines," and cleaning and storing historical data and current design documents to provide a benchmark for subsequent data collection and comparison. Specifically, this includes: Step 1.1: Define the three-level plan hierarchy and task atomization decomposition; First, it is necessary to clarify the inclusion and decomposition relationships among the overall control plan, regional plans, and weekly / daily plans. The overall control plan, as the top-level framework, defines the macro-level milestones of the project from resumption of work to completion and delivery, typically at a monthly or quarterly granularity. To support subsequent refined scheduling, the key milestones in the overall control plan must be further broken down.

[0036] Taking a continuing super high-rise project as an example, the "main structure topping-out" node set in the overall control plan is broken down into several sub-items, each corresponding to a specific floor range or construction stage. These sub-items constitute the input source for the regional plan. The regional plan focuses on the monthly or bi-weekly work objectives of specific vertical zones (such as the tower core tube, outer frame steel structure, etc.), refining the timeline of the overall control plan into specific spatial units. For example, the structural construction from the 40th to the 50th floor is defined as "Region A," and the construction from the 51st to the 60th floor is defined as "Region B." Finally, the weekly / daily plan, as the smallest unit at the execution level, further breaks down the objectives of the regional plan into specific daily work tasks. This process is not a simple linear division, but an atomized decomposition based on the logical connections of construction techniques. Each atomic task has a unique identifier and is associated with the required resource type, estimated working hours, and prerequisites. Through this progressive decomposition, the originally general engineering objectives are transformed into independently traceable micro-task units. These modular task lists form the raw dataset for subsequent data collection and status comparison, ensuring that any lag in macro progress can be quickly located to specific micro-task stages.

[0037] Step 1.2: Establish parallel data flow standards for the four lines of design, procurement, construction, and acceptance; After completing the time and space decomposition of the task, it is essential to simultaneously establish a four-line data flow standard to support task execution. These four lines are the design line, procurement line, construction line, and acceptance line, representing the operational trajectories of information flow, material flow, work flow, and quality flow, respectively. The design line focuses on the timeliness of drawing changes and technical solution output, with data characteristics including design document version number, approval status, and issuance time. The procurement line focuses on the ordering, production, transportation, and arrival status of materials and equipment, with data characteristics including material codes, specifications, supplier production schedules, and logistics locations. The construction line records the actual execution of manpower, machinery, materials, methods, and environment, with data characteristics including team attendance, machine operating time, and process completion volume. The acceptance line is responsible for the pass rate of quality inspection batches and the rectification closure status, with data characteristics including inspection batches, testing indicators, and pass / fail judgment results. To enable these four lines to work collaboratively, a unified data exchange standard must be established.

[0038] For example, it is stipulated that all order numbers generated on the procurement line must be consistent with the material requisition numbers on the construction line, and that drawing version updates on the design line must automatically trigger material parameter verification on the procurement line. Through this standardized definition, four originally independent business lines are integrated into the same data coordinate system. Any change in the status of one line will affect the data integrity verification of the other three lines through preset field mapping relationships. This cross-line data association mechanism lays a solid foundation for subsequent resource matching and avoids decision-making errors caused by information silos.

[0039] Step 1.3: Clean and standardize the multi-source heterogeneous data before storing it in the database; After establishing the task breakdown structure and four-line data standards, the next step is to tackle massive amounts of raw data from different systems and formats. This data may originate from building information modeling platforms, material management systems, labor real-name systems, and supervision and acceptance software, with vastly different data structures. The task of this step is to clean and standardize this multi-source heterogeneous data to make it conform to a unified data entry standard.

[0040] First, to address the issue of inconsistent timestamps, all data was converted to Coordinated Universal Time (UTC) or the standard time zone specified by the project, eliminating time discrepancies caused by system clock differences. Second, to address inconsistent material codes, a global material master data dictionary was established, mapping custom codes within each subsystem to standard codes, ensuring that the identification of the same type of rebar is unique and consistent across different systems.

[0041] Furthermore, for unstructured data described in text, key entities are extracted using natural language processing rules, such as standardizing colloquial descriptions like "rebar arrival" and "formwork erection" into predefined process codes.

[0042] Finally, missing or outlier values ​​are filled or marked according to preset rules. For example, if there are no construction records for a certain area on a given day, it is marked as "zero progress" instead of a null value to prevent division by zero errors in subsequent calculations. The cleaned and standardized data is written into a central database, forming the project's basic data asset library. The standardized dataset generated in this step is the only reliable source for real-time status awareness and deviation calculation in subsequent steps, ensuring the objectivity and accuracy of the analysis results.

[0043] Step 1.4: Establish an initial correlation matrix between task attributes and resource requirements; After data cleaning and storage are completed, the atomic tasks defined in step 1.1 need to be initially associated with the "four-line" resource requirements defined in step 1.2 to form an association matrix between task attributes and resource requirements. Each atomic task not only contains time and space information, but also carries a complete list of resources required for its execution.

[0044] For example, a task like "45th Floor Core Tube Reinforcement Binding" has an attribute matrix that explicitly records the required C30 concrete (XX cubic meters), HRB400 steel bars (XX tons), tower crane T-01 (number of times), carpentry team (XX people), and corresponding quality inspector numbers. These resource requirements are not fixed but dynamically configured based on the standardized process quotas in step 1.3. The matrix also records resource priority weights; for example, tasks on certain critical paths have the highest priority locking rights for their required resources. By constructing this association matrix, the system transforms abstract construction tasks into specific resource consumption requests. When subsequent steps obtain real-time resource status, they can directly query this matrix to quickly determine whether the current resources are sufficient to support the task's progress. The establishment of this association matrix creates a strong coupling between the plan and resources, providing necessary parameter support for subsequent calculations of the schedule deviation index and ensuring the feasibility of scheduling instructions.

[0045] Step 2: Real-time acquisition of multi-dimensional dynamic state data and construction of spatiotemporal snapshots; After completing the construction of the data foundation and the association of task resources, the system enters the real-time perception phase. This step aims to utilize IoT devices, mobile terminals, and automation interfaces to collect dynamic change data from the construction site at high frequency, and aggregate discrete data points into a "spatiotemporal snapshot" reflecting the overall status of the project at the current moment. This snapshot not only includes the static planned task status but also incorporates dynamic constraints such as vertical transportation, material supply, and personnel efficiency. It serves as a bridge connecting the plan and reality, specifically including: Step 2.1: Deploy a high-frequency monitoring network for the capacity status of vertical transportation equipment; Based on the correlation matrix established in step 1.4, the system first focuses on the most critical bottleneck resource restricting the construction of super high-rise buildings—vertical transportation equipment. The available transport capacity of equipment such as tower cranes and construction elevators directly determines the efficiency of material handling and personnel movement. This step requires the deployment of high-precision sensors and intelligent terminals on each piece of equipment to collect its operating status in real time.

[0046] Specifically, the monitoring content includes the equipment's current load, operating speed, current location coordinates, task queue length, and remaining effective capacity. For example, for a tower crane with a rated load of 5 tons, the system reads in real time that its current lifting weight is 3.2 tons, and the remaining available capacity is 1.8 tons. Simultaneously, by reading the logs of its PLC control system, the system obtains the current queue of lifting tasks to be executed, showing that there are 3 tasks ahead, with an estimated queuing time of 15 minutes. This data is uploaded to the central server at a rate of seconds, forming a continuous time-series data stream. Through smoothing of historical data and removing noise caused by signal interference, a true capacity curve is obtained. This high-frequency monitoring network not only records the current physical state of the equipment but also predicts resource occupancy in the near future. When a task in a certain area requires the centralized transportation of a large amount of materials, the system can immediately determine whether the vertical transportation equipment in the vicinity has sufficient instantaneous throughput capacity, thereby avoiding downtime due to insufficient capacity.

[0047] Step 2.2: Access real-time feedback of the entire material supply chain status; Following the monitoring of transportation capacity in step 2.1, the actual arrival status of materials is another crucial factor determining whether construction can proceed continuously. This step achieves real-time feedback on the entire material supply chain, from factory to site unloading, by integrating with the supplier's ERP system and the logistics GPS positioning system. For critical components, such as precast beams and columns, and large steel structure nodes, the system records their production progress, loading time, en route location, and estimated arrival time.

[0048] For bulk materials such as steel bars and concrete, the system updates inventory levels in real time using weighbridge data and entry gate records. For example, when a truck carrying 50 tons of steel bars is 5 kilometers from the construction site, the system already indicates it is "coming soon" and estimates its arrival time to be 10 minutes later. Once the vehicle arrives at the weighbridge, the system automatically deducts the in-transit inventory, increases the on-site storage inventory, and triggers the acceptance process. Furthermore, for materials with supply risks, such as imported equipment that may be delayed due to weather conditions, the system provides early warnings based on meteorological data. This end-to-end real-time feedback mechanism transforms material supply from a black-box operation into a transparent and quantifiable data flow. By matching the current inventory with the resource requirements in step 1.4, the system can accurately calculate whether the current inventory meets the construction needs for the next N hours, thus providing data support for subsequent resource allocation.

[0049] Step 2.3: Collect dynamic data on team member attendance and work efficiency; After addressing the status perception of materials and machines, the human factor becomes the final link affecting progress. This step utilizes facial recognition gates, smart safety helmets, and mobile attendance systems to collect real-time attendance and work efficiency data for each construction team. The system not only counts the number of people present each day but also calculates the overall efficiency coefficient of personnel by comparing planned working hours with actual working hours. For example, a steel reinforcement team planned to deploy 20 people, but only 18 were actually on duty, resulting in an attendance rate of 90%. Simultaneously, by monitoring the number of welding operations and the amount of work completed, it was found that their average daily output per person was 15% lower than the quota standard, resulting in an efficiency coefficient of 0.85. These data are updated hourly, reflecting the true level of labor input on site.

[0050] In addition, the system records the skill level distribution of personnel to facilitate precise allocation when high-skilled jobs are needed. Through this granular personnel data collection, the system can identify which work teams are hindering progress and which have the potential to exceed targets. This dynamic data, combined with the manpower requirements in step 1.4, can accurately assess whether current human resources are sufficient to support the established construction schedule, preventing plans from falling through due to personnel shortages or inefficiency.

[0051] Step 2.4: Aggregate multidimensional data to generate a spatiotemporal snapshot of the project at the current moment; After completing the data collection across the three dimensions mentioned above, the final task of this step is to aggregate the scattered vertical transportation data, material status data, and personnel efficiency data according to the time axis and spatial coordinates to generate a "spatiotemporal snapshot" reflecting the overall operational status of the project at the current moment. This snapshot is not a simple data accumulation, but a structured data set that has undergone logical association and status normalization processing.

[0052] The system uses the regions defined in step 1.1 as basic units, summarizing the status, required resources, currently available resources, and actual consumption of all tasks within each region. For example, for layer 45 of "Region A," the snapshot includes: task completion rate (based on the atomic tasks in step 1.1), remaining capacity of tower crane T-01 (from step 2.1), rebar inventory (from step 2.2), and rebar team attendance rate (from step 2.3). Through this aggregation, the system can present the real-time health status of any local area of ​​the project within milliseconds. This spatiotemporal snapshot becomes the direct input source for subsequent steps of deviation calculation and decision-making. It eliminates the cognitive lag caused by data delays, enabling scheduling decisions to be based on the latest on-site facts, rather than outdated reports.

[0053] Step 3: Calculate the schedule deviation index and trigger the capacity reallocation logic; Building upon the standardized data foundation established in the first two steps and the real-time spatiotemporal snapshots, this step enters the core analysis and decision-making phase. Its goal is to quantify the deviation between the current progress status and the planned objectives, and, in conjunction with resource constraints, determine whether systemic risks exist. By introducing a schedule deviation index, the qualitative concept of "lag" is transformed into a quantitative numerical indicator, triggering corresponding capacity reallocation algorithms and providing a scientific basis for subsequent plan adjustments. Specifically, this includes: Step 3.1: Calculate the actual progress completion rate of tasks in each region; First, using the spatiotemporal snapshot generated in step 2.4, calculate the actual progress completion rate for each atomic task defined in step 1.1. This metric is defined as the ratio of completed work to the planned total work. The calculation formula is as follows: ; in, This represents the actual progress completion rate of the i-th task at time t. This represents the amount of work completed by the i-th task up to time t; This represents the total planned workload for the i-th task, with dimensions consistent with the numerator.

[0054] For example, if a region plans to install 1000 steel beams, and 600 have been completed so far, then the average completion rate for related tasks in that region is 0.6. This calculation process requires traversing all task nodes in the snapshot of step 2.4 to ensure no omissions in coverage.

[0055] Step 3.2: Calculate the real-time supply capacity index of vertical transportation resources; Based on the vertical transportation equipment data collected in step 2.1, the real-time supply capacity index of vertical transportation resources in each region is calculated at the current moment. This index considers not only the theoretical maximum carrying capacity of the equipment but also deducts the impact of queuing time and current load. The calculation formula is as follows: ; in, This represents the total supply capacity index of the vertical transportation system at time t; n is the total number of devices participating in the service. Let k be the standard load capacity of the kth device in a single trip. The comprehensive efficiency coefficient of the kth equipment, with a value ranging from 0 to 1, reflects the equipment failure rate and operator proficiency. This represents the idle / available time window for the k-th device at the current moment. Let k be the average round-trip time of the k-th device. This formula calculates the overall supply ceiling of the system by summing the actual effective capacity of each device. If a region has dense tasks and... A lower value indicates that the region faces a transportation capacity bottleneck. This step transforms the raw data from step 2.1 into a supply capacity indicator with engineering significance, providing a denominator basis for subsequent matching degree analysis.

[0056] Step 3.3: Calculate the deviation index between the progress completion rate and transportation capacity; Obtaining task progress completion rate With resource supply capacity Next, this step combines the two to calculate the schedule deviation index. This index measures the degree of match between the current schedule status and resource supply capacity. The index rises significantly when schedule requirements are high and capacity is insufficient. The calculation formula is as follows: ; in, This represents the schedule deviation index of the i-th task at time t; This is the planned progress completion rate of the task, i.e., the theoretical value required by the time node; Theoretical transport capacity required to complete the remaining workload of this task; For safety, a value of 1.1 to 1.2 is used to reserve a certain buffer margin. If A value greater than 0 indicates that the actual progress lags behind the plan and the resource supply is insufficient to make up for the gap, with the deviation showing a positive growth trend; if A value less than or equal to 0 indicates that the progress is normal or ahead of schedule, or that resources are sufficient. This formula weights the progress lag and the resource shortage, avoiding the one-sidedness of a single-dimensional evaluation. Through this calculation, the system can accurately identify "high-risk" areas that are both lagging and lacking resources, providing clear triggering conditions for subsequent resource reallocation.

[0057] Step 3.4: Determine the deviation threshold and initiate the capacity reallocation logic; Finally, based on the schedule deviation index calculated in step 3.3... A dynamic threshold is set for judgment. If a certain area or task... Exceeding the preset threshold If the value is 0.15, the system determines that there is a serious risk of mismatch between schedule and resources in the area and immediately triggers the capacity reallocation logic. At this time, the system no longer waits passively but actively intervenes, reallocating the spare capacity from other idle equipment or neighboring areas collected in step 2.1 to the high-risk area. The reallocation logic prioritizes tasks on the critical path based on the task priority matrix in step 1.4.

[0058] For example, if the 45th layer of the core tube... The system automatically locks two idle tower cranes, removes non-urgent tasks from their task queues, and inserts urgent material handling tasks for the area. This process is entirely data-driven and requires no manual intervention. Through this judgment and triggering mechanism, the system transforms static monitoring into dynamic response, ensuring that corrective measures can be taken in the early stages of a problem to prevent further deviations.

[0059] Step 4: Generate optimized daily and weekly work plans based on the deviation index; After calculating the schedule deviation index and identifying high-risk areas, the system enters the plan refactoring phase. The core task of this step is to dynamically adjust the original weekly / daily plans using the capacity reallocation logic triggered in step 3.4, combined with the atomic task decomposition structure defined in step 1.1. This process is not a simple task postponement, but a rescheduling based on resource constraints, aiming to eliminate bottlenecks and ensure the continuity of subsequent workflows. Specifically, it includes: Step 4.1: Extract the task queue of high-risk areas and lock the critical path; First, based on the schedule deviation index calculated in step 3.3... And the threshold determination result in step 3.4, extract all from the spatiotemporal snapshot generated in step 2.4. The system generates a set of tasks. These tasks constitute a high-risk task queue that requires priority intervention. For each task in this queue, the system traces back to the three-level planning hierarchy established in step 1.1 to identify its critical path node. If a task is located on a non-critical path, but its delay will prevent subsequent critical nodes from starting (i.e., free float is exhausted), it is temporarily upgraded to a critical path task.

[0060] For example, the task of tying the core tube reinforcement on the 45th floor, though originally a routine procedure, was delayed due to vertical transportation delays, which in turn affected the formwork erection on the 46th floor. This task was then marked as urgent and critical. Through this dynamic path identification, the system can accurately locate the truly critical "bottleneck" tasks that hinder the overall progress, avoiding wasting limited scheduling resources on non-bottleneck stages. The extracted task queues and their critical path attributes become the input for the next step of time window rescheduling.

[0061] Step 4.2: Calculate the new task time window based on the capacity reallocation results; Following the critical path tasks identified in step 4.1, and utilizing the capacity reallocation scheme determined in step 3.4, the start and end times of each task are recalculated. Because vertical transport capacity has dynamically changed, the original planned time windows must be adjusted. New task durations... It no longer depends solely on process quotas; the efficiency factor of newly added transport capacity must also be taken into account. The calculation formula is as follows: ; in, Indicates the adjusted estimated duration of task i; Indicates the remaining workload of task i; The correction factor reflects the break-in efficiency of the newly allocated transport capacity (taken as 0.9 to 1.0). Additional capacity to be reallocated to this task; The baseline transport capacity originally allocated to this task is used. This formula allows the system to convert increased transport capacity into reduced time. Subsequently, based on the logical relationships of the work processes in step 1.1 (e.g., subsequent tasks can only begin after the preceding tasks are completed), the timeline of the entire task sequence is re-derived using the Critical Path Method (CPM). This process ensures that the new plan not only meets resource constraints but also conforms to the logical sequence of construction processes, avoiding situations where rushing the schedule violates process requirements.

[0062] Step 4.3: Generate a draft daily and weekly work plan with multi-period rolling optimization; Based on the new time window calculated in step 4.2, the system begins to generate a draft daily / weekly work plan. This draft uses a rolling update mechanism, with a planning cycle of 7 days, but focuses on detailing the execution instructions for the next 24 hours. The system prioritizes filling the high-risk tasks from step 4.1 into the time slots for the next 24 hours, ensuring they are executed during periods of maximum capacity. For non-critical tasks, they are flexibly scheduled according to their new time windows, avoiding peak periods or resource conflict points as much as possible.

[0063] For example, if the concrete pouring for the 45th floor requires the use of tower crane T-01, but T-01 is simultaneously being used for the steel structure hoisting for the 48th floor, the system will, based on the calculation results in step 4.2, fine-tune the concrete pouring task to the early morning of the following day and automatically notify the relevant work teams. The draft also includes expected output targets for each time period, which are directly derived from the product of the workload data in step 3.1 and the new time window. Through this multi-time-period rolling optimization, the system generates a draft plan that is both macroscopically guiding and microscopically operable, preparing for subsequent approval and issuance.

[0064] Step 4.4: Verify the feasibility of the plan and output the final execution plan; Finally, a full-element feasibility check is performed on the draft plan generated in step 4.3. The check includes: whether the total resources are overloaded, whether the process logic is closed-loop, and whether the personnel configuration is appropriate. The system again calls the real-time data from step 2.4 to simulate resource consumption under the new plan. If it is found that the required manpower exceeds the maximum number of on-site personnel collected in step 2.3, or the required materials exceed the inventory recorded in step 2.2, a rollback mechanism is triggered, automatically adjusting the task priority or extending the work period for that period. Only when all check indicators meet the safety thresholds is the draft plan confirmed as the final execution plan. The final execution plan is output in a structured document format, including the start and end times, responsible persons, required resource lists, and expected completion standards for each atomic task. This plan is no longer a static table, but an optimal solution after dynamic simulation, resource matching, and logical verification, directly guiding subsequent production activities on-site and ensuring the plan's feasibility.

[0065] Step 5: Execute resource reallocation commands and monitor the real-time response status; Once the plan is generated, it must be quickly translated into actual on-site action. This step focuses on converting the final execution plan output from step 4.4 into specific resource scheduling instructions, and monitoring the execution effect of these instructions in real time to ensure that resource flow is highly synchronized with the plan requirements. Specifically, this includes: Step 5.1: Generate a resource scheduling instruction sheet and distribute it to the execution terminal; Based on the final execution plan in step 4.4, the system automatically generates detailed resource scheduling instruction sheets. These sheets cover task assignments for vertical transportation equipment, material allocation routes, work areas for work teams, and personnel shift arrangements. These instruction sheets are pushed in real-time to the mobile terminals of on-site management personnel, the handheld devices of tower crane operators, and the mobile apps of labor teams via a wireless communication network. The instruction sheets clearly indicate the priority codes of the tasks; high-priority tasks are marked with a prominent red indicator, prompting operators to handle them first.

[0066] For example, the system sends an instruction to tower crane T-01: "Immediately stop non-urgent material hoisting and proceed to the 45th floor to load steel bars, priority P1," and to the steel bar team on the 45th floor: "Assemble 30 minutes in advance and prepare to receive materials." Through this targeted distribution, information flows directly through management levels to the very end of the operation, eliminating information attenuation and time delays caused by traditional layer-by-layer communication. The digital format of the instruction sheet allows the recipient to confirm with a single click, forming the basis for two-way feedback.

[0067] Step 5.2: Monitor the real-time trajectory of the vertical transportation equipment executing commands; After the instruction is issued, the system immediately initiates real-time monitoring of the vertical transportation equipment to verify whether the instruction in step 5.1 has been executed correctly. Utilizing the high-frequency monitoring network deployed in step 2.1, the system tracks the operating trajectory, load status, and task switching actions of each piece of equipment in real time. The system compares the task sequence in the planned instruction with the actual equipment logs. If it finds that the equipment has not executed the instruction (such as failing to proceed to the designated floor or failing to load the specified materials), it immediately records the abnormal event.

[0068] For example, if an instruction requires T-01 to hoist steel bars to the 45th floor, but the sensors show that it actually hoisted formwork to the 42nd floor, the system will mark it as "instruction deviation." Simultaneously, the system continuously collects the equipment's operating parameters during the execution of the new task, such as hoisting speed and slewing angle, to assess whether its operational efficiency meets the theoretical expectations in step 4.2. This real-time monitoring not only checks the execution status but also serves as a secondary verification of the instruction's feasibility, ensuring that resource scheduling instructions are fully implemented on-site.

[0069] Step 5.3: Track the status of material flow and personnel availability; Besides equipment, the availability of materials and personnel is equally crucial to the execution process. This step utilizes the data sources from steps 2.2 and 2.3 to track the actual flow of materials and the specific locations of personnel. For materials, the system updates their movement status from the storage yard to the work area in real time. Once materials arrive at the designated unloading point, the system automatically notifies the construction team to collect them and records the handover time. For personnel, the system uses smart wearable devices or mobile devices for location tracking to confirm whether each team has arrived at the work area specified in step 5.1 on time. If any team member is late or absent, the system immediately triggers an alert and attempts to relocate replacement personnel from nearby areas.

[0070] For example, if three members of the carpentry team on the 45th floor are absent, the system automatically checks the attendance data in step 2.3 and finds that there are surplus personnel on the 46th floor, then generates a cross-regional support instruction. Through this dual tracking of "things" and "people," the system ensures that all the elements required to execute the plan are in place, avoiding overall stagnation due to partial absences.

[0071] Step 5.4: Collect execution feedback data and build an instant response log; After completing the above monitoring actions, the final task of this step is to collect real-time feedback data generated during on-site execution and construct an instant response log. This data includes the actual start time of the task, the actual amount completed, equipment fault alarms, material loss, etc. The system compares this data with the planned data in step 4.4 item by item to calculate the execution deviation value. For example, if the plan requires the hoisting of 10 tons of steel bars to be completed within 2 hours, and the actual time taken is 2.5 hours, then the deviation is recorded as +0.5 hours. All deviation data is accompanied by a timestamp and a reason label (such as "traffic congestion", "equipment failure", "personnel lateness"). These instant response logs not only record the current execution status but also provide a basis for review in the subsequent step six.

[0072] Step 6: Output the execution result report and archive the scheduling decision file; This step serves as the final stage of the entire dynamic scheduling process. Its main task is to summarize and analyze the real-time response logs collected in step 5.4, generate a final execution result report, and archive all key data, decision-making logic, and adjustment results from this scheduling process. This step does not pursue system self-iteration or optimization, but rather focuses on knowledge accumulation and decision traceability, providing a reference template for scheduling in similar subsequent projects. Specifically, it includes: Step 6.1: Summarize the execution deviation data and generate a multidimensional statistical report; Based on the real-time response logs built in step 5.4, the system performs multi-dimensional statistics on the execution status throughout the entire lifecycle. Reports cover time dimensions (by hour, day, week), spatial dimensions (by floor, area), resource dimensions (by equipment, team), and task dimensions (by process type). Statistical indicators include plan completion rate, resource utilization rate, average deviation, and maximum deviation. For example, a report might show "Today, the total task completion rate for area A is 92%, the average utilization rate of vertical transportation equipment is 85%, and the maximum single task deviation is 15 minutes." These statistics intuitively reflect the actual performance of the plan formulated in step 4.4 during the execution process of steps 5.1 to 5.3. Through multi-dimensional cross-analysis, the system can identify which types of tasks are most prone to deviation and which areas have the most efficient resource allocation. These statistical reports not only showcase the results of project management but also serve as a direct basis for subsequent evaluation of the effectiveness of scheduling strategies, helping managers quickly grasp the overall status of project operation.

[0073] Step 6.2: Compile a case library for scheduling decision execution; To preserve the experience gained from this dynamic scheduling, the system automatically packages the deviation index calculated in step 3.3, the new time window generated in step 4.2, the instruction sheet issued in step 5.1, and the feedback log from step 5.4 into a complete "scheduling decision execution case." This case library is categorized and stored according to project code, date, and deviation type. Each case contains a complete decision-making chain: from problem discovery (deviation index exceeding the limit) to solution formulation (time window rescheduling), and then to execution monitoring (trajectory and feedback). For example, a case about "insufficient tower crane capacity causing core tube delay" details the process at the time. The system collects numerical data, the reassigned tower crane numbers, the adjusted schedule, and specific data on the recovered work hours. In this way, it accumulates a large amount of real-world engineering scheduling data, forming a valuable knowledge asset. These case studies are not used for automatic model training but are intended for human review and learning, helping future schedulers quickly find reference solutions and improve decision-making when encountering similar problems.

[0074] Step 6.3: Solidify resource constraint parameters and process quota benchmarks; Based on the results of this execution, the system solidifies the parameters of the initial correlation matrix in step 1.4. If the feedback data in step 5.4 shows that the efficiency of a certain type of task in actual execution is significantly higher or lower than the preset quota, the system will record this difference and apply it as a correction factor to the calculation formula in step 4.2 during the next planning process.

[0075] For example, if the system finds that rebar tying efficiency generally decreases by 20% in rainy weather, it will store the "rainy weather efficiency reduction factor" in its database. Similarly, for the actual transport capacity curve of vertical transportation equipment, if the measured value is consistently lower than the theoretical value, the system will also update it. The fundamental parameters used in the calculations. The solidification of these parameters is not achieved through an automatic iterative algorithm, but rather through baseline calibration based on historical data, ensuring that future planning is more closely aligned with actual on-site conditions. By solidifying these parameters, the system guarantees the continuity and accuracy of the scheduling basis, avoiding the uncertainty introduced by each re-estimation.

[0076] Step 6.4: Generate the final execution report and archive the entire process data; Finally, the system integrates all data, charts, logs, and case library indexes generated in steps one through five into a complete dynamic scheduling execution report. The report includes: an overall progress overview, a summary of deviation analysis, details of resource allocation, highlights and shortcomings of execution, and recommendations for fixed parameters. The report is output in a structured document format and sent to the project management platform and relevant departments for archiving via a data interface. The archived data package contains original records of the entire process from data collection, deviation calculation, plan generation to execution feedback, ensuring that every scheduling decision is traceable and auditable.

[0077] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A dynamic scheduling method driven by construction progress data for continued super high-rise buildings, characterized in that, include: A digital mapping base for three-level planning and four-line linkage is constructed. Based on the digital mapping base, the capacity status of vertical transportation equipment, the arrival status of materials, and the attendance rate of team members are collected in real time and aggregated to generate a spatiotemporal snapshot of the project at the current moment. Based on the project's spatiotemporal snapshot and the digital mapping base, the actual progress completion rate of tasks in each region and the real-time supply capacity of vertical transportation resources are calculated, and the progress deviation index is determined accordingly. When the schedule deviation index exceeds a preset threshold, the task queue in the high-risk area is extracted, and the task time window is recalculated and an optimized daily / weekly work plan is generated in combination with the reallocated vertical transportation capacity. The optimized daily / weekly work plan is converted into a resource scheduling instruction sheet and distributed to the field execution terminal. Monitor the execution process of the resource scheduling instruction sheet, collect execution feedback data, and output an execution result report.

2. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The digital mapping foundation for constructing a three-tiered plan and four-line linkage includes: Define the hierarchical decomposition relationship between the overall control plan, regional plans, and weekly / daily plans, and break down the project objectives into atomic tasks; Based on the aforementioned atomic tasks, data flow standards are established for the design line, procurement line, construction line, and acceptance line, so that the status of the four lines can be associated with the corresponding atomic tasks. Based on the data flow standard, the raw data from the multi-source system is cleaned and standardized, and then written into the central database; Based on the standardized data, an initial correlation matrix is ​​established between the atomic tasks and the required resources to form a baseline for resource requirements for task execution.

3. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The real-time collection of vertical transportation equipment capacity status, material arrival status, and team attendance rate includes: deploying sensors to monitor the task queues and remaining capacity of tower cranes and construction elevators; accessing supplier and logistics systems to obtain material transit and inventory status; and collecting the actual number of team members on duty and work efficiency through facial recognition and mobile terminals.

4. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The aggregation generates a spatiotemporal snapshot of the project at the current moment, including: taking regions as units, integrating atomic task status, vertical transportation capacity, material inventory and personnel attendance data to form a structured set of statuses with a unified timestamp.

5. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The calculation of the actual progress completion rate of tasks in each region and the real-time supply capacity of vertical transportation resources, and the determination of the progress deviation index based on this, includes: Based on the completed workload in the project's spatiotemporal snapshot and the planned total workload in the digital mapping base, calculate the actual progress completion rate of tasks in each region. Based on the idle time, single load capacity and round-trip cycle of vertical transportation equipment in the spatiotemporal snapshot of the project, the real-time supply capacity of vertical transportation resources is calculated. The difference between the actual progress completion rate and the planned progress completion rate is weighted and correlated with the ratio of the real-time supply capacity to the theoretically required transportation capacity to obtain the progress deviation index.

6. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The generation of optimized daily / weekly work plans includes: When the schedule deviation index exceeds a preset threshold, the corresponding high-risk area task queue is extracted from the project spatiotemporal snapshot, and the tasks affecting the path are identified. Based on the reallocated vertical transport capacity and the remaining workload of tasks in high-risk areas, the estimated duration of each task is recalculated. Based on the recalculated expected duration and process logic, a rolling mechanism is used to generate detailed work instructions for the next 24 hours; The detailed work instructions are verified for total resources and process logic. Once the verification is successful, the final execution plan is output.

7. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The optimized daily / weekly work plan is converted into a resource scheduling instruction sheet, which includes generating instruction content containing task priority, equipment assignment, material allocation route and personnel shift, and then pushing it to the tower crane operator, manager and team terminal via wireless network.

8. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, Monitoring the execution process of the resource scheduling instruction sheet includes: Based on the equipment assignment in the resource scheduling instruction sheet, track the actual operating trajectory and load status of the vertical transportation equipment to verify the consistency of instruction execution; Based on the material allocation route in the resource scheduling instruction sheet, confirm whether the materials have arrived at the designated work site on time; Verify whether the team members are in their positions as instructed, based on the personnel shift schedule in the resource scheduling instruction sheet.

9. The dynamic scheduling method driven by construction progress data for continued super high-rise buildings according to claim 1, characterized in that, The output execution result report includes: Collect and monitor the actual start time, completion amount, and reasons for deviations of tasks generated during the monitoring process to form execution feedback data; Based on the execution feedback data, a multi-dimensional statistical report covering time, space, resources, and task dimensions is generated. The multidimensional statistical reports, resource scheduling instruction sheets, and execution feedback data are packaged and solidified into a complete execution case for this scheduling.

10. A dynamic scheduling system driven by construction progress data for continuing the construction of super high-rise buildings, used to implement the method described in any one of claims 1-9, characterized in that, include: The data foundation building module is used to construct a digital mapping foundation for three-level planning and four-line linkage; The status awareness module is used to collect real-time data on the capacity status of vertical transportation equipment, the arrival status of materials, and the attendance rate of team members, and aggregate these data to generate a spatiotemporal snapshot of the project at the current moment. The deviation calculation module is used to calculate the actual progress completion rate, the real-time supply capacity of vertical transportation resources, and the progress deviation index based on the project's spatiotemporal snapshot and the digital mapping base. The plan generation module is used to extract the task queue of high-risk areas when the schedule deviation index exceeds a preset threshold, and generate an optimized daily / weekly work plan by combining the reallocated vertical transportation capacity. The instruction distribution module is used to convert the optimized daily / weekly work plan into a resource scheduling instruction sheet and distribute it to the field execution terminal; The execution monitoring module is used to monitor the execution process of the resource scheduling instruction sheet, collect execution feedback data, and output an execution result report.

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