Smart construction platform driven building engineering multi-job coordination operation system

Abstract

The application discloses a smart construction platform driven building engineering multi-work-type collaborative operation system, relates to the field of building engineering multi-work-type collaborative operation, and comprises the following: a data acquisition module used for acquiring space data, equipment resource occupation time data and environment parameter data of each work-type operation area; a data processing module used for constructing a three-dimensional geometric model of the work-type operation area and generating a time sequence distribution of equipment resource occupation; a conflict judgment module used for calculating geometric overlapping degree and time window overlapping degree of different work-type operation areas and judging space conflict and time conflict; a conflict resolution module used for executing space redivision and time window redistribution based on a priority order table; an output control module used for outputting control instructions to adjust construction equipment operation parameters; and a dynamic optimization module used for iteratively calculating and triggering an optimization cycle according to updated data. The application can improve the efficiency of multi-work-type collaborative operation under the premise of ensuring construction safety.

Description

Technical Field

[0001] This invention relates to the field of multi-trade collaborative operation in construction engineering, specifically a multi-trade collaborative operation system for construction engineering driven by an intelligent construction platform. Background Technology

[0002] With the exponential growth in the scale and complexity of modern construction projects, the efficiency and safety of multi-disciplinary collaborative operations have become core challenges for the industry. Driven by the wave of new urbanization and intelligent construction, the construction engineering field is undergoing a critical stage of transformation from extensive management to digitalization and intelligence. The integrated application of technologies such as intelligent construction platforms, the Internet of Things (IoT), and Building Information Modeling (BIM) provides a technological foundation for refined construction management.

[0003] However, in complex projects such as super high-rise buildings and large complexes, the frequency of cross-operations among multiple trades has surged, and the characteristics of three-dimensional construction space, dynamic equipment resources, and environmental safety sensitivity have become increasingly prominent. Traditional construction management models can no longer meet the needs of real-time collaboration and risk prediction. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a collaborative operation system for multiple trades in construction engineering driven by an intelligent construction platform.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, this invention discloses a multi-trade collaborative operation system for construction engineering driven by an intelligent construction platform, comprising:

[0007] The data acquisition module is used to acquire spatial data, equipment resource occupancy time data, and environmental parameter data of the work areas of each type of work through the multi-dimensional sensing unit;

[0008] The data processing module is used to construct a three-dimensional geometric model of the work area based on the spatial data and equipment resource occupancy time data, and generate a time series distribution of equipment resource occupancy.

[0009] The conflict determination module is used to calculate the geometric overlap A of different work areas based on the three-dimensional geometric model, and to calculate the time window overlap B based on the time sequence distribution of equipment resource occupancy; when the geometric overlap A exceeds a preset spatial threshold, a spatial conflict is determined to exist; when the time window overlap B exceeds a preset time threshold, a time conflict is determined to exist.

[0010] The conflict resolution module is used to perform multi-dimensional coupled analysis by combining environmental parameter data with preset safety thresholds to calculate the feasibility coefficient C of collaborative operation. If the feasibility coefficient C exceeds the preset collaborative operation threshold, a priority ranking table containing conflicting work types is generated. Based on the priority ranking table, the work areas of work types with time and space conflicts are spatially re-divided and time windows are reallocated.

[0011] The output control module is used to output control commands to the construction equipment and adjust the work area division and equipment working time window according to the control commands;

[0012] The dynamic optimization module is used to re-collect updated spatial and environmental parameter data after executing control commands and input them into the 3D geometric model for iterative calculation to generate new geometric overlap A' and time window overlap B'. When A' or B' exceeds the corresponding preset percentage, the dynamic optimization cycle of the collaborative operation scheme is triggered.

[0013] Secondly, this invention discloses a method for multi-trade collaborative operation in construction engineering driven by an intelligent construction platform, comprising the following steps:

[0014] The spatial data, equipment resource occupancy time data, and environmental parameter data of the work areas of each type of work are acquired through multi-dimensional sensing units.

[0015] Based on the spatial data and equipment resource occupancy time data, a three-dimensional geometric model of the work area for each type of work is constructed, and a time series distribution of equipment resource occupancy is generated.

[0016] The geometric overlap A of different work areas is calculated based on the three-dimensional geometric model, and the time window overlap B is calculated based on the time series distribution of equipment resource occupancy.

[0017] When the geometric overlap A exceeds the preset spatial threshold, a spatial conflict is determined to exist; when the time window overlap B exceeds the preset time threshold, a time conflict is determined to exist.

[0018] By combining environmental parameter data with preset safety thresholds, a multi-dimensional coupling analysis is performed to calculate the feasibility coefficient C of collaborative operations.

[0019] If the feasibility coefficient C exceeds the preset collaborative operation threshold, a priority ranking table containing conflicting jobs is generated. Based on the priority ranking table, the work areas of jobs with time and space conflicts are spatially re-divided and time windows are reallocated.

[0020] Output control commands to the construction equipment, and adjust the work area division and equipment working time window according to the control commands;

[0021] After executing control commands, updated spatial and environmental parameter data are re-acquired and input into the 3D geometric model for iterative calculations, generating new geometric overlap A' and time window overlap B'. When A' or B' exceeds the corresponding preset percentage, a dynamic optimization loop of the collaborative operation scheme is triggered.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] Based on the dual analysis of 3D geometric models and time-series data, the system captures in real time the risks of three-dimensional spatial interference and temporal conflicts of equipment resources in different work areas. Compared with traditional static models, the system significantly improves the comprehensiveness and timeliness of conflict detection through spatial projection overlap calculation and time window overlap analysis, effectively avoiding construction interruptions caused by human experience-based misjudgments.

[0024] Based on multiple indicators such as construction progress, quality impact, and resource scarcity, a work priority ranking table is dynamically generated. Through standardized evaluation and weight allocation mechanisms, the system can flexibly adjust resource allocation strategies according to the characteristics of each project stage, ensuring the continuity of key processes while optimizing the resource utilization efficiency of low-priority work.

[0025] By accumulating and analyzing historical adjustment data, the system constructs a dynamic performance evaluation model and autonomously generates classification optimization strategies. Based on a collaborative mechanism of spatial adjustment and time window compression, the system continuously optimizes construction plans, forming a closed-loop chain of "perception-decision-execution-verification," and gradually improving the adaptability and stability of multi-trade collaborative operations.

[0026] This effectively solved the technical problem of mismatch between the adjustment range and safety effect during the adjustment process based on control commands, avoided the equipment operation risks caused by excessive space compression, and eliminated resource idleness caused by rigid time window segmentation. Through a data-driven classification optimization strategy, the efficiency of multi-trade collaborative operations was improved while ensuring construction safety. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is an overall system block diagram of Embodiment 1 of the present invention;

[0029] Figure 2 This is a flowchart illustrating Embodiment 1 of the present invention;

[0030] Figure 3 This is a flowchart of the overall method in Embodiment 2 of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Application Overview: In the field of construction engineering, the efficiency and safety of multi-trade collaborative work have always been core issues for technological innovation. With the exponential growth in the scale and complexity of modern construction projects, the inherent information barriers in traditional construction management models are gradually being exposed. Systemic defects such as isolated work data for each trade, lagging dynamic resource scheduling, and insufficient prediction of environmental and safety risks lead to frequent process conflicts and resource mismatches during construction, severely hindering the overall project progress efficiency. While existing BIM-based construction management systems have achieved 3D visualization modeling, their static data update mechanisms and one-way information transmission modes are insufficient to support the dynamic collaborative needs of real-time multi-trade linkage.

[0033] The core challenges currently facing the industry are concentrated in three dimensions: First, construction data is scattered across fragmented management subsystems (such as schedule planning, equipment monitoring, and environmental sensing), lacking an intelligent central platform for full-domain data fusion, resulting in the inability to conduct collaborative analysis of key parameters such as space occupancy status, equipment operation sequence, and environmental safety thresholds; Second, traditional scheduling strategies rely excessively on manual experience and prediction, often falling into a lag cycle of passive response to conflicts when facing dynamic scenarios such as sudden changes in tower crane trajectories and cross-operations of multiple trades; Third, the rules for determining the priority of trades lack flexibility and fail to dynamically adapt to the characteristics of different project stages (such as the difference in trade weights between the main structure construction and decoration installation stages), resulting in a structural deviation between resource scheduling strategies and actual construction needs.

[0034] To address the aforementioned problems, this application proposes the following technical solutions.

[0035] Example 1:

[0036] like Figures 1-2As shown, the intelligent construction platform-driven multi-trade collaborative operation system for building engineering includes: a data acquisition module, used to acquire spatial data, equipment resource occupancy time data, and environmental parameter data of each trade's work area through multi-dimensional sensing units; a data processing module, used to construct a three-dimensional geometric model of the work area of ​​each trade based on the spatial data and equipment resource occupancy time data, and generate a time series distribution of equipment resource occupancy; a conflict judgment module, used to calculate the geometric overlap of different trades' work areas based on the three-dimensional geometric model, calculate the time window overlap based on the time series distribution, and determine spatial and temporal conflicts through threshold comparison; a conflict resolution module, used to perform multi-dimensional analysis to calculate the feasibility coefficient by combining environmental parameters and safety thresholds, and perform spatial re-division and time window reallocation based on a priority ranking table; an output control module, used to output control commands to adjust the operating parameters of construction equipment; and a dynamic optimization module, used to iteratively calculate and trigger optimization loops based on updated data.

[0037] The multi-dimensional sensing unit refers to IoT sensing devices deployed at the construction site, specifically a combination of LiDAR, RFID tags, and vibration sensors, used to capture spatial coordinates, equipment operating status, and environmental monitoring data in real time. The three-dimensional geometric model is a digital mirror containing spatial topological relationships, specifically implemented using point cloud data fusion algorithms, constructing a spatially correlated three-dimensional model through vertex coordinate interpolation. The time-series distribution refers to the temporal characteristic dataset of equipment resource occupancy, specifically implemented using timestamp marking methods, recording equipment identifiers, start times, and durations to form a calculable set of time intervals. The feasibility coefficient is a quantitative indicator of the safety risk of collaborative operations, specifically implemented using a normalized weighted algorithm, generating risk level parameters through standardized processing of deviations in environmental parameters such as dust concentration and noise intensity. The priority ranking table refers to the dynamic evaluation results of work scheduling priorities, specifically implemented using a multi-index weighted scoring method, generating a dynamically adjustable ranking sequence based on parameters such as construction progress and quality impact.

[0038] Specifically, spatial data is converted into point cloud datasets through LiDAR scanning, and equipment resource occupancy time data is uploaded with timestamp information by the equipment controller. The data processing module converts the point cloud data into a 3D geometric model and integrates time data to generate a time series distribution map. The conflict judgment module calculates the percentage of overlapping 3D projection areas of different work types' work areas, triggering a spatial conflict warning when it exceeds a preset threshold; it also calculates the overlap ratio of equipment time windows simultaneously, triggering a time conflict warning when it exceeds a time threshold. The conflict resolution module compares environmental monitoring data with safety thresholds, reducing the feasibility coefficient assessment value when dust concentration exceeds limits or noise intensity exceeds standards. By constructing a multi-dimensional evaluation system that includes construction progress and quality impact, a dynamic priority ranking table is generated to guide spatial area shrinkage and time window adjustment. The output control module sends trajectory offset commands and time interval commands to equipment such as tower cranes and concrete pump trucks. The dynamic optimization module continuously collects adjusted work data, restarting the conflict resolution process to form an optimization closed loop when the new geometric overlap or time overlap exceeds a preset ratio.

[0039] Compared to existing technologies, traditional BIM systems only achieve static model construction and cannot perform real-time spatial conflict detection. This solution achieves dual early warning of dynamic spatiotemporal conflicts through joint calculation of 3D geometric models and time series data. Existing scheduling systems rely on manual experience to adjust priorities; this solution generates a dynamic ranking table through multi-dimensional evaluation indicators, achieving data-driven intelligent decision-making. Traditional methods lack a closed-loop verification mechanism; this solution forms a complete control chain of "perception-decision-execution-verification" through a dynamic optimization module, ensuring continuous optimization of the scheduling strategy.

[0040] Through the above technical solutions, this application achieves real-time situational awareness and conflict prediction in multi-task workspaces, effectively reducing the probability of process conflicts. Deep coupling of environmental safety parameters and scheduling decisions avoids forced collaborative operations in high-risk environments. A dynamic priority evaluation mechanism automatically adapts scheduling strategies based on project stage characteristics, improving the rationality of resource allocation. A closed-loop optimization system ensures the system can continuously adapt to dynamic changes at the construction site, forming a progressively optimized collaborative operation plan.

[0041] This application further proposes a three-dimensional geometric model construction process that includes converting the spatial data of the work area into a set of polygon vertex coordinates, using a vertex coordinate interpolation algorithm to generate a three-dimensional geometric model with topological relationships when constructing the three-dimensional geometric model; the time series distribution includes equipment resource identifiers, start timestamps, and duration segments.

[0042] The set of polygon vertex coordinates refers to discretizing the geometry of the work area into a sequence of vertex coordinate data. Specifically, vertex coordinates can be obtained using LiDAR scanning or BIM model export. The topological connectivity of the vertex coordinates accurately describes the three-dimensional boundary of the work area. The vertex coordinate interpolation algorithm involves nonlinear interpolation calculation of the spatial coordinates between adjacent vertices. Specifically, cubic spline interpolation can be used to fill the surface shape between vertices, giving the generated geometric model continuous and smooth surface characteristics, supporting accurate spatial overlap calculations. A three-dimensional geometric model with topological relationships refers to vertices having clear adjacency relationships and surface continuity. This can be achieved through a vertex index list and edge connection table to implement the topological data structure, ensuring geometric integrity during dynamic adjustments. The equipment resource identifier is a unique coded sequence used to identify construction equipment. Specifically, it can be used to bind equipment entities using RFID tags or QR codes, achieving accurate association between equipment and time data. The start timestamp is a precise record of the start time of the equipment's work cycle. Specifically, it can be generated using a GPS synchronized clock to create millisecond-level time stamps, supporting high-precision time window calculations. The duration period refers to the continuous time period during which the equipment occupies resources. Specifically, it can be achieved by collecting the start and end times of the work cycle through the equipment's start and stop sensors, forming exclusive time interval data for the equipment resources.

[0043] Specifically, when constructing the 3D geometric model, the point cloud data obtained from LiDAR scanning is processed through boundary fitting and converted into a set of polygons composed of vertex coordinates. A cubic spline interpolation algorithm is then used to reconstruct the surface of the space between adjacent vertices, forming a 3D model with continuous surfaces. This model maintains the logical correlation of the geometric structure of the work area through a topological connection table between vertices, ensuring that subsequent spatial overlap calculations accurately reflect the overlap state of the real physical space. For time-series distributions, each equipment resource is assigned a unique identifier, and a high-precision clock records the start time stamp of the operation. Combined with the equipment operation status detected by pressure sensors, duration segment data is generated, forming an exclusive time occupancy record for the equipment resources.

[0044] Compared to existing technologies, traditional BIM systems use discrete point clouds to construct static 3D models, lacking topological connections between vertices, which easily leads to geometric structural breaks during model adjustments. This solution generates a continuous surface model with topological relationships through vertex coordinate interpolation algorithms, significantly improving the accuracy of spatial conflict detection. At the time data processing level, existing systems only record coarse time periods of equipment operation, while this solution achieves refined calculation of time window overlap by binding equipment identifiers with millisecond-level timestamps, effectively avoiding misjudgments caused by insufficient time precision in traditional methods.

[0045] Through the above technical solutions, this application solves the problem of conflict detection error caused by insufficient geometric accuracy of the three-dimensional model in traditional construction management systems, and realizes dynamic maintenance of the spatial topology of the work area; by associating equipment resource identifiers with high-precision time data, an accurate time window overlap calculation benchmark is established, avoiding missed detection of time conflicts in multi-equipment collaborative operations; the continuous surface model generated by the interpolation algorithm provides accurate input data for subsequent geometric overlap calculation, ensuring the reliability of spatial conflict determination.

[0046] This application further proposes a dual quantification calculation mechanism for geometric overlap and time window overlap in the collaborative operation system. Geometric overlap A is calculated by the intersection and union ratio of the three-dimensional spatial projection areas of different work types' work areas. Time window overlap B is calculated by the intersection of the time intervals occupied by different equipment resources and the ratio of the total occupied time. When the geometric overlap exceeds a preset spatial threshold, a spatial conflict judgment is triggered. When the time window overlap exceeds a preset time threshold, a time conflict judgment is triggered.

[0047] The geometric overlap A is calculated using the formula A = Σ(S_i∩S_j) / Σ(S_i∪S_j), where S_i and S_j represent the three-dimensional spatial projected areas of different work areas. Specifically, this can be achieved by calculating the overlap of polygon areas after projecting the surface of a three-dimensional model onto the same reference plane. Accurate intersection area data is generated through vertex coordinate interpolation and polygon Boolean operations. The time window overlap B is calculated using the formula B = Σ(T_m∩T_n) / Σ(T_m∪T_n), where T_m and T_n represent the time intervals occupied by different equipment resources. Specifically, this can be achieved by comparing timestamp intervals and accumulating overlap durations. Conflict analysis results within continuous time periods are generated through equipment resource identifier matching and time axis mapping techniques.

[0048] Specifically, during collaborative operations, the 3D geometric model reflects the three-dimensional boundary information of each work area in real time. After mapping the 3D space to a 2D plane using a projection algorithm, the area is calculated using a set of polygon vertex coordinates to dynamically capture the physical interference areas of different work areas. Simultaneously, equipment resource occupancy time data is analyzed using timestamps and duration segments to form a time-axis-based occupancy interval sequence. A time window overlap calculation model is used to quantify the competitive relationship of equipment resources in the time dimension. When the geometric overlap calculation result exceeds a preset threshold, it indicates a significant risk of spatial interference; when the time window overlap calculation result exceeds a preset threshold, it indicates a conflict in equipment resource scheduling, at which point the system will trigger a conflict resolution mechanism.

[0049] Compared to existing technologies, traditional methods often rely on manual visual inspection or static schedules for conflict assessment, lacking quantitative calculations of the degree of interference in three-dimensional space, and equipment time window conflicts depend solely on empirical estimates. This solution, through a dynamic calculation model of geometric overlap and time window overlap, transforms the degree of spatial interference into quantifiable proportional parameters and temporal conflicts into precise overlap duration ratios, significantly improving the objectivity and accuracy of conflict detection. For example, existing technologies typically rely on two-dimensional drawings to assess spatial conflicts between tower crane booms and scaffolding work areas, failing to reflect dynamic height changes during actual construction. This solution, however, can accurately identify potential interference areas in three-dimensional space through three-dimensional projected area calculations.

[0050] Through the above technical solution, this application effectively solves the technical problem of insufficient detection accuracy of spatial and temporal conflicts in traditional construction management. It achieves objective assessment of the degree of conflict through a dual-quantitative calculation model, providing accurate input parameters for subsequent priority ranking and resource scheduling optimization, reducing the risk of process delays caused by human judgment errors, and improving the safety and efficiency of multi-trade collaborative operations.

[0051] This application further proposes a process for generating the collaborative operation feasibility coefficient C, which includes: real-time monitoring values ​​of environmental parameter data, including noise intensity, dust concentration and vibration amplitude; normalizing the deviation values ​​of each environmental parameter data from the preset safety threshold to generate standardized deviation coefficients; and obtaining the collaborative operation feasibility coefficient C by weighted fusion calculation of the standardized deviation coefficients; where C∈[0,1], and the higher the value, the higher the risk of collaborative operation.

[0052] Among them, environmental parameter data refers to the noise, dust, and vibration indicators at the construction site collected in real time by sensors, specifically decibel meters, particulate matter concentration detectors, and accelerometers, used to quantify the impact of the working environment on the safety of multi-trade collaboration. Standardized deviation coefficients convert the differences between the measured values ​​of each environmental parameter and the safety threshold into dimensionless values, specifically using the range method or Z-score standardization algorithm to eliminate the interference of different physical dimensions on the comprehensive evaluation. Weighted fusion calculation refers to assigning weight coefficients according to the degree of influence of environmental parameters on construction safety, specifically using the analytic hierarchy process (AHP) or entropy weight method to determine the weight ratios, achieving coupled analysis of multi-dimensional parameters.

[0053] Specifically, during the feasibility assessment of collaborative operations, noise intensity, dust concentration, and vibration amplitude are monitored in real time and transmitted to the data processing module. When the instantaneous noise value in a certain work area exceeds a preset safety threshold, the deviation value of this parameter is extracted and standardized, transforming it into a comparable value within the range of 0 to 1. Subsequently, the system performs a weighted summation of the standardized deviation values ​​of the three environmental parameters based on the weight of dust's impact on visibility and the coefficient of vibration's effect on equipment stability, ultimately generating the collaborative operation feasibility coefficient C.

[0054] Compared to existing technologies, traditional methods typically monitor only a single environmental parameter or employ fixed threshold alarm mechanisms, failing to quantify the impact of multi-factor coupling on collaborative operations. This solution, however, achieves cross-dimensional comprehensive analysis of noise, dust, and vibration parameters through normalization and weighted fusion mechanisms, forming dynamic risk assessment indicators.

[0055] Through the above technical solution, this application can identify environmental safety risks in real time during multi-tasking collaborative operations and accurately determine the feasibility of the operation through a quantitative evaluation coefficient C. When the C value exceeds a preset threshold, the system automatically triggers a priority ranking and resource reallocation mechanism to avoid operation interruptions or safety accidents caused by deteriorating environmental conditions. For example, in a scenario where concrete pouring and steel structure installation are carried out in parallel, the operation sequence of the two trades is dynamically adjusted by continuously monitoring the impact of vibration amplitude on equipment positioning accuracy to ensure construction quality and personnel safety.

[0056] This application further proposes a priority ranking table generation process including: constructing a multi-dimensional evaluation index system, including construction progress urgency parameters, quality impact weight coefficients, resource scarcity indexes, and process complexity levels; configuring evaluation weights for each evaluation index, wherein the construction progress urgency parameter is inversely related to the remaining construction period, and the quality impact weight coefficient is positively related to the structural safety level; converting the evaluation indicators of each type of work into evaluation values ​​with unified dimensions through a standardization module; weighting and integrating the evaluation values ​​and evaluation weights to obtain a comprehensive score for each type of work, and generating a priority ranking table including conflicting types of work, with each type of work arranged in descending order of comprehensive score.

[0057] Among them, the construction progress urgency parameter refers to a quantitative indicator dynamically adjusted based on the difference between the remaining construction period and the planned construction period. Specifically, a reverse time decay function can be used to map the remaining construction period into an urgency scoring interval. The quality impact weighting coefficient is a parameter assigned based on the degree of influence of different work operations on the structural safety level of the building. Specifically, a structural mechanics analysis model can be used to quantify the load transfer impact of different processes on key components. The resource scarcity index is a dynamic indicator reflecting the supply and demand relationship of equipment resources at a specific construction stage. Specifically, it can be generated by statistically analyzing the average waiting time and call frequency of similar resources using historical scheduling data. The process complexity level is a level parameter based on the density of process connections and technical specification requirements in the work operation process. Specifically, a process decomposition tree model can be used to statistically analyze the number of standard operation steps and parallel operation constraints. The standardization module is a data processing unit that converts evaluation indicators of different dimensions into dimensionless values. Specifically, range standardization algorithms or Z-score standardization algorithms can be used to eliminate dimensional differences.

[0058] Specifically, during multi-trade collaborative operations in construction projects, when a spatiotemporal conflict is detected and the feasibility coefficient exceeds a threshold, the system initiates a priority ranking mechanism. First, the remaining construction period, structural safety correlation, equipment call records, and process parameters for each conflicting trade are extracted from the project management database. The urgency parameters for construction progress, the weighting coefficient for quality impact, the resource scarcity index, and the level of process complexity are calculated accordingly. The urgency parameter is calculated in reverse order of the remaining construction period; for example, processes with less than three days remaining receive a higher urgency score. The weighting coefficient for quality impact is matched using a structural safety level mapping table; for example, processes involving load-bearing wall construction are assigned a higher weight. Subsequently, the standardization module converts the raw data across the four dimensions into evaluation values ​​in the 0-1 range, ensuring comparability between different indicators. In the weighted fusion phase, the system dynamically adjusts the weight allocation based on the current project stage; for example, increasing the proportion of the quality impact weighting coefficient during the main structure construction stage. The final priority ranking table is arranged in descending order of the comprehensive score, providing a decision-making basis for subsequent spatial re-division and time window reallocation.

[0059] Compared to existing technologies, traditional construction management systems typically use fixed priority rules or single-dimensional indicators to prioritize conflicting work types, failing to dynamically adapt to changes in project stages and the combined influence of multiple factors. This solution, however, constructs a multi-dimensional evaluation system encompassing schedule, quality, resources, and processes, and combines standardization with a dynamic weighting mechanism. This achieves multi-dimensional quantification and dynamic adaptation of priority determination, effectively solving the problem of significant priority decision-making bias in complex construction scenarios using traditional methods.

[0060] Through the above technical solution, this application can generate an accurate priority ranking table based on a multi-dimensional dynamic evaluation system when conflicts occur in multi-task collaborative operations, providing an objective decision-making basis for resource reallocation, thereby reducing subjective errors in human experience judgment, improving the collaborative efficiency of spatial planning and time scheduling, and ensuring that the quality control and schedule requirements of key processes are given priority.

[0061] This application further proposes a spatial repartitioning process that includes retaining the original work area topology for jobs with a priority higher than the preset level, performing a region shrinkage operation on low-priority jobs, with the shrinkage magnitude being inversely proportional to the difference in the comprehensive score; and a time window reallocation process that includes allocating a continuous time window core segment to high-priority jobs and allocating discrete time periods containing buffer intervals to low-priority jobs.

[0062] The priority ranking table refers to a list of conflicting work types generated based on multi-dimensional evaluation indicators. Specifically, it can be implemented using a weighted fusion algorithm combining parameters such as construction progress urgency, quality impact weight coefficient, resource scarcity index, and process complexity level, quantifying the decision-making weight of different work types in the conflict resolution process. The topology preservation of spatial repartitioning refers to maintaining the spatial form and geometric relationship of the original work area for high-priority work types. This can be achieved using a polygon vertex coordinate interpolation algorithm to avoid secondary conflicts caused by spatial adjustments to high-value processes. The inverse relationship between the magnitude of area contraction and the difference in comprehensive scores means that the reduction ratio of the work area for low-priority work types decreases as the difference in their comprehensive scores from those of high-priority work types increases. This can be achieved by setting piecewise linear functions to constrain the contraction amount, ensuring that resource adjustments align with the principle of maximizing engineering benefits. The core segment of the continuous time window refers to allocating uninterrupted continuous work periods for high-priority work types. This can be achieved by using a time-series sliding window algorithm to extract the period with the lowest equipment resource occupancy density, ensuring the continuous construction needs of critical processes. Among them, the discrete time period buffer interval refers to the forced insertion of idle time between the work periods of low-priority jobs. Specifically, a time axis segmentation algorithm can be used to generate non-continuous time periods containing preset safety intervals to prevent conflicts in the use of equipment resources.

[0063] Specifically, when spatial or temporal conflicts are detected, the system first calls a priority ranking table to determine the decision-making order for the conflicting tasks. For spatial conflicts, the topology of the work area for high-priority tasks is preserved, while dynamic area shrinkage is performed on low-priority tasks based on the comprehensive score difference. The shrinkage magnitude is automatically calculated through a preset score difference-shrinkage mapping relationship to ensure that high-value processes are not affected by spatial compression. For temporal conflicts, a time window segmentation strategy is adopted, dividing the equipment resource occupancy time axis into core segments and buffer interval segments. High-priority tasks are prioritized for allocation to continuous core segments, while low-priority tasks can only select discrete windows containing buffer intervals in the remaining time period. The duration of the buffer interval is negatively correlated with the comprehensive score of the task. For example, in a conflict scenario between concrete pouring and steel structure installation, if the pouring process gains higher priority due to urgency, its work area boundary remains unchanged, while the steel structure installation area shrinks proportionally according to the score difference. At the same time, the pouring equipment obtains continuous use of tower crane resources, while the steel structure installation is allocated to fragmented time periods that include equipment cooling intervals.

[0064] Compared to existing technologies, traditional methods typically employ a fixed-ratio compression strategy during space adjustment, forcing high-value processes to relinquish workspace. This solution, however, optimizes resources while ensuring the spatial integrity of critical processes through dynamic shrinkage control. Existing time scheduling technologies often rely on first-come, first-served or manually assigned priority rules, failing to dynamically allocate continuous work windows based on real-time evaluation metrics. The buffer interval generation mechanism implemented in this solution effectively avoids safety hazards caused by competition for equipment resources.

[0065] Through the above technical solution, this application solves the problems of process interruption and resource waste caused by rigid adjustment strategies in multi-trade collaborative operations, and realizes flexible allocation of spatial resources and time windows in the conflict resolution process. By coordinating the control of dynamic contraction amplitude and buffer intervals, it ensures continuous operation of high-priority processes while providing safe operating windows for low-priority processes, effectively reducing construction safety risks caused by spatial overlap or equipment contention. Simultaneously, the nonlinear adjustment mechanism based on scoring differences avoids the loss of engineering benefits caused by traditional equal resource allocation.

[0066] This application further proposes output control commands including generating trajectory offset commands for construction equipment with spatial conflicts, the commands including the equipment movement direction and minimum safe distance; and generating operation interval commands for equipment resources with time conflicts, the commands including start and end markers of the buffer period.

[0067] The trajectory offset command refers to eliminating physical interference between equipment through spatial position adjustment. Specifically, it can use the BeiDou Navigation Differential Positioning System or UWB ultra-wideband positioning technology to calculate the vector direction of equipment movement, and combine it with the minimum safety distance parameter in the BIM model to generate the equipment movement path, which is used to plan the equipment obstacle avoidance trajectory in three-dimensional space. The work interval command refers to avoiding timing conflicts of equipment resources through time segment adjustment. Specifically, it can use a discrete event scheduling algorithm to dynamically allocate buffer time periods, and combine it with the Gantt chart model of the construction schedule to generate start and end marks of time windows, which is used to embed safety intervals in the process connection.

[0068] Specifically, when a spatial conflict is determined, the process of generating a trajectory offset instruction includes: extracting the spatial coordinate set of the conflicting devices based on a 3D geometric model; calculating the interference region between the devices using a convex hull collision detection algorithm; and iteratively solving for the optimal solution of the device movement direction using the gradient descent method, ensuring that the moved path satisfies the minimum safe distance constraint. For time-related conflict scenarios, the process of generating a work interval instruction includes: identifying overlapping time periods in the time series distribution; dividing continuous work time periods using a sliding window algorithm; shifting the work time periods of low-priority tasks to the later stages based on device resource priorities; and inserting buffer interval markers between adjacent work windows to avoid timing chaos caused by the superposition of device start-up and shutdown actions.

[0069] Compared to existing technologies, traditional construction scheduling systems typically rely on manually marking conflict areas and then manually adjusting equipment positions, resulting in low adjustment accuracy and slow response speed. This solution, by automatically generating vectorized movement directions and precise time window markers, achieves millimeter-level calibration of equipment trajectories and second-level optimization of process connections, thus eliminating the risk of secondary conflicts caused by manual intervention. Furthermore, the dynamic adaptation mechanism of minimum safe distance parameters and buffer time periods, compared to conflict resolution strategies with fixed thresholds, is more adaptable to the dynamic changes required in complex construction scenarios.

[0070] Through the above technical solutions, this application can effectively eliminate the risk of physical interference during equipment movement and ensure the safe operation of equipment in overlapping work areas; at the same time, it can precisely control the start-up and shutdown sequence of equipment to avoid resource contention caused by parallel operations of multiple trades. The spatial constraints of trajectory offset commands and the temporal constraints of work interval commands form a dual guarantee mechanism, which improves the reliability and execution efficiency of collaborative construction operations in multiple dimensions.

[0071] This application further proposes a dynamic optimization loop process including: constructing a historical database of work area adjustment records, storing the set of adjustment parameters for each spatial re-division, including spatial adjustment amount ΔS, time window offset ΔT, and corresponding feasibility coefficient change ΔC, and associating and recording the geometric overlap A' and environmental parameter data when the adjustment parameters are triggered; establishing an adjustment effectiveness evaluation function E=α·ΔS+β·ΔT-γ·ΔC based on the correlation between ΔS, ΔT, and ΔC, where α and β are the spatial and temporal adjustment weight coefficients, respectively, and γ is the feasibility improvement sensitivity coefficient; screening historical parameter groups that satisfy ΔC>preset improvement threshold and ΔS<current area ratio constraint, and generating classification optimization strategies from the historical parameter groups.

[0072] The adjustment parameter set refers to the quantitative adjustment indicators generated in each spatial repartitioning operation. Specifically, it can be stored in a database, including the spatial adjustment amount, time window offset, and feasibility coefficient change for each adjustment, with persistent storage of historical operation records achieved through structured data tables. The adjustment efficiency evaluation function is a mathematical model that quantitatively assesses the impact of spatial and temporal adjustments on the feasibility of collaborative operations. Specifically, it can use a linear weighting method to correlate the spatial adjustment amount, time window offset, and feasibility coefficient change, and use the coefficient configuration of parameters α, β, and γ to achieve adjustment efficiency evaluation in different dimensions. The classification optimization strategy refers to differentiated optimization schemes generated based on historical adjustment data. Specifically, it can use data mining algorithms to perform cluster analysis on historical parameter groups that meet preset conditions, and generate spatial and temporal optimization strategies based on the correlation between parameters.

[0073] Specifically, during the dynamic optimization cycle, the historical record database continuously accumulates the spatial adjustment amount ΔS, time window offset ΔT, and feasibility coefficient change ΔC for each adjustment operation, while also storing the geometric overlap A' and environmental parameter data triggered by the adjustment. The adjustment effectiveness evaluation function E=α·ΔS+β·ΔT-γ·ΔC quantifies and integrates the impact of adjustments in different dimensions through coefficient configuration. The spatial adjustment weight coefficient α and the time adjustment weight coefficient β are dynamically adjusted according to the characteristics of the engineering stage, and the feasibility improvement sensitivity coefficient γ is used to balance the relationship between the adjustment magnitude and the safety threshold. The screening module extracts effective parameter sets from historical data based on the conditions that ΔC > preset improvement threshold and ΔS < regional area ratio constraint. By analyzing the correlation patterns of ΔS, ΔT, and ΔC in the parameter sets, gradient adjustment schemes for spatial optimization or phase control rules for time optimization are generated, forming a data-driven classification optimization strategy.

[0074] Compared to existing technologies, traditional construction management systems lack mechanisms for accumulating and analyzing historical operational data for optimization decisions, and adjustment strategies are often handled in isolation based on single conflict scenarios. This solution, however, by constructing a historical record database and a performance evaluation model, can identify the impact patterns of different adjustment parameters on the feasibility of collaborative operations, thereby generating classification strategies with continuous optimization capabilities and avoiding repetitive and inefficient adjustment operations.

[0075] Through the above technical solutions, this application can achieve dynamic optimization strategy iteration based on historical data, effectively improving the decision-making efficiency of spatial repartitioning and time window reallocation. By quantitatively evaluating the impact of adjustment parameters on the feasibility coefficient, high-value adjustment patterns can be accurately identified, reducing resource consumption caused by ineffective adjustments. The classification optimization strategy generation mechanism enables the system to automatically adapt the optimal adjustment scheme for different conflict types, significantly shortening the response cycle of the dynamic optimization loop.

[0076] This application further proposes a process for generating a classification optimization strategy from historical parameter sets, including: a spatial optimization strategy: extracting parameter sets that are positively correlated with ΔS and ΔC, and generating a spatial repartitioning scheme for gradient adjustment step size; and a time optimization strategy: extracting parameter sets that are negatively correlated with ΔT and ΔC, and generating a time window compression scheme that includes phase control rules.

[0077] The spatial optimization strategy refers to a spatial adjustment plan based on the positive correlation between the amount of spatial adjustment and the degree of feasibility improvement in historical adjustment parameters. Specifically, it can be implemented using a gradient adjustment step size algorithm, dynamically setting the magnitude of each spatial contraction or expansion based on the correlation strength between ΔS and ΔC. This strategy achieves precise optimization of spatial partitioning by quantifying the impact of spatial adjustments on the feasibility of collaborative operations.

[0078] Time optimization strategies refer to time scheduling schemes based on the negative correlation between time window offset and the degree of feasibility improvement. Specifically, this can be implemented using phase control rules. By compressing time windows during high-conflict periods and reallocating discrete time slots, time overlap in equipment operations is reduced. This strategy leverages the mitigating effect of time adjustments on safety risks to establish a dynamic time window compression mechanism.

[0079] Specifically, during the execution of the spatial optimization strategy, when the system detects a positive correlation between ΔS (spatial adjustment amount) and ΔC (feasibility coefficient change), i.e., the larger the spatial adjustment range, the more significant the feasibility improvement, the more automatically a re-partitioning scheme with a gradient adjustment step size will be generated. For example, when there is a spatial conflict between the tower crane operation area and the rebar processing area, the system calculates the optimal gradient step size value by analyzing the correlation between the regional shrinkage range and the reduction in safety risk in historical adjustment records, and gradually reduces the spatial range of the low-priority work area.

[0080] The implementation of the time optimization strategy is reflected in the handling of the negatively correlated parameter set ΔT (time window offset) and ΔC. When the system detects an inverse correlation between the degree of time window compression and feasibility improvement, such as in a time conflict scenario between concrete pouring equipment and transportation equipment, phase control rules are used to decompose the core operation period into multiple discrete windows, with buffer intervals inserted in between. This approach ensures the continuous operation requirements of high-priority tasks while reducing environmental safety risks through time fragmentation.

[0081] Compared to existing technologies, traditional optimization methods typically employ fixed adjustment step sizes or empirical time window segmentation, lacking quantitative analysis of historical adjustment effects. Existing time scheduling schemes often only consider equipment utilization indicators, failing to establish a dynamic correlation between changes in environmental safety parameters and time adjustments. This solution achieves precise matching of spatial and temporal optimization by constructing a correlation model between adjustment parameters and feasibility coefficients.

[0082] Through the above technical solution, this application effectively solves the technical problem of mismatch between adjustment range and safety effect during dynamic optimization, avoids equipment operation risks caused by excessive space compression, and eliminates resource idleness caused by rigid time window segmentation. This solution improves the efficiency of multi-trade collaborative operations while ensuring construction safety through a data-driven classification optimization strategy.

[0083] Example 2:

[0084] like Figure 3 As shown, the multi-trade collaborative operation method for construction projects driven by an intelligent construction platform includes the following steps:

[0085] The spatial data, equipment resource occupancy time data, and environmental parameter data of the work areas of each type of work are acquired through multi-dimensional sensing units.

[0086] A three-dimensional geometric model of the work area for each type of work is constructed based on spatial data and equipment resource occupancy time data, and the time series distribution of equipment resource occupancy is generated.

[0087] The geometric overlap A of different work areas is calculated based on the three-dimensional geometric model, and the time window overlap B is calculated based on the time series distribution of equipment resource occupancy.

[0088] When the geometric overlap A exceeds the preset spatial threshold, a spatial conflict is determined to exist; when the temporal window overlap B exceeds the preset time threshold, a temporal conflict is determined to exist.

[0089] By combining environmental parameter data with preset safety thresholds, a multidimensional coupling analysis is performed to calculate the feasibility coefficient C of collaborative operation. If the feasibility coefficient C exceeds the preset collaborative operation threshold, a priority ranking table containing conflicting work types is generated. Based on the priority ranking table, the work areas of work types with time and space conflicts are spatially re-divided and time windows are reallocated.

[0090] Output control commands to construction equipment, and adjust the work area division and equipment working time window according to the control commands;

[0091] After executing the control command, the updated spatial data and environmental parameter data are re-acquired and input into the three-dimensional geometric model for iterative calculation, generating a new geometric overlap A' and time window overlap B'; when A' or B' exceeds the corresponding preset percentage, the dynamic optimization cycle of the collaborative operation scheme is triggered.

[0092] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0093] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0094] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-trade collaborative operation system for construction engineering driven by an intelligent construction platform, characterized in that: include: The data acquisition module is used to acquire spatial data, equipment resource occupancy time data, and environmental parameter data of the work areas of each type of work through the multi-dimensional sensing unit; The data processing module is used to construct a three-dimensional geometric model of the work area based on the spatial data and equipment resource occupancy time data, and generate a time series distribution of equipment resource occupancy. The conflict determination module is used to calculate the geometric overlap A of different work areas based on the three-dimensional geometric model, and to calculate the time window overlap B based on the time series distribution of equipment resource occupancy; when the geometric overlap A exceeds the preset spatial threshold, it is determined that there is a spatial conflict. When the overlap of time windows B exceeds the preset time threshold, a time conflict is determined to exist; The conflict resolution module is used to perform multi-dimensional coupled analysis by combining environmental parameter data with preset safety thresholds to calculate the feasibility coefficient C of collaborative operations. If the feasibility coefficient C exceeds the preset collaborative operation threshold, a priority ranking table containing conflicting jobs is generated. Based on the priority ranking table, the work areas of jobs with time and space conflicts are spatially re-divided and time windows are reallocated. The process of generating the collaborative operation feasibility coefficient C includes: the environmental parameter data includes real-time monitoring values ​​of noise intensity, dust concentration and vibration amplitude; the deviation values ​​of each environmental parameter data from the preset safety threshold are normalized to generate standardized deviation coefficients; the collaborative operation feasibility coefficient C is obtained by weighted fusion calculation of the standardized deviation coefficients; where C∈[0,1], the higher the value, the higher the risk of collaborative operation. The output control module is used to output control commands to the construction equipment and adjust the work area division and equipment working time window according to the control commands; The dynamic optimization module is used to re-collect updated spatial and environmental parameter data after executing control commands and input them into the 3D geometric model for iterative calculation to generate new geometric overlap A' and time window overlap B'; when A' or B' exceeds the corresponding preset percentage, the dynamic optimization cycle of the collaborative operation scheme is triggered. The dynamic optimization loop process includes: constructing a historical record database of work area adjustments for different job types, storing the set of adjustment parameters for each spatial re-division, including the spatial adjustment amount ΔS, the time window offset ΔT, and the corresponding change in feasibility coefficient ΔC, and associating and recording the geometric overlap A' and environmental parameter data when the adjustment parameters are triggered; establishing an adjustment effectiveness evaluation function E=α·ΔS+β·ΔT-γ·ΔC based on the correlation between ΔS, ΔT, and ΔC, where α and β are the spatial and temporal adjustment weight coefficients, respectively, and γ is the feasibility improvement sensitivity coefficient; selecting historical parameter groups that satisfy ΔC>preset improvement threshold and ΔS<current area ratio constraint, and generating classification optimization strategies from the historical parameter groups; The process of generating a classification optimization strategy from historical parameter sets includes: Spatial optimization strategy: Extract the parameter set that is positively correlated with ΔS and ΔC, and generate a spatial repartitioning scheme for gradient adjustment step size; Time optimization strategy: Extract the parameter set that is negatively correlated with ΔT and ΔC, and generate a time window compression scheme that includes phase control rules.

2. The intelligent construction platform-driven multi-trade collaborative operation system for building engineering according to claim 1, characterized in that, The process of constructing the three-dimensional geometric model includes: The spatial data of the work area is converted into a set of polygon vertex coordinates. When constructing the three-dimensional geometric model, a vertex coordinate interpolation algorithm is used to generate a three-dimensional geometric model with topological relationships. The time series distribution includes a device resource identifier, a start timestamp, and a duration segment.

3. The intelligent construction platform-driven multi-trade collaborative operation system for building engineering according to claim 1, characterized in that, The formula for calculating the geometric overlap A is A=Σ(S_i∩S_j) / Σ(S_i∪S_j), where S_i and S_j represent the three-dimensional spatial projection area of ​​the work area for different types of work, respectively. The formula for calculating the overlap of the time window B is B=Σ(T_m∩T_n) / Σ(T_m∪T_n), where T_m and T_n represent the time intervals occupied by different device resources.

4. The intelligent construction platform-driven multi-trade collaborative operation system for building engineering according to claim 1, characterized in that, The process of generating a priority sorting table includes: Construct a multi-dimensional evaluation index system, including parameters on the urgency of construction progress, weighting coefficients for the impact of quality, resource scarcity index, and process complexity level; Configure the evaluation weights for each evaluation indicator, where the urgency parameter of construction progress is inversely related to the remaining construction period, and the weight coefficient of quality impact is positively related to the structural safety level. The standardization module converts the evaluation indicators of each job type into evaluation values ​​with a unified dimension. The evaluation values ​​and evaluation weights are weighted and integrated to obtain the comprehensive score of each job, and a priority ranking table including conflicting jobs is generated. The jobs in the table are arranged in descending order of comprehensive score.

5. The intelligent construction platform-driven multi-trade collaborative operation system for building construction as described in claim 4, characterized in that, The spatial re-division process includes: retaining the original work area topology for jobs with a priority higher than the preset level, and performing a region shrinkage operation for low-priority jobs, with the shrinkage magnitude being inversely proportional to the difference in the comprehensive score. The process of reallocating the time window includes: allocating a continuous time window core segment to high-priority jobs and allocating discrete time periods containing buffer intervals to low-priority jobs.

6. The intelligent construction platform-driven multi-trade collaborative operation system for building engineering according to claim 1, characterized in that, The output control commands include: For construction equipment that has spatial conflicts, a trajectory offset instruction is generated, the instruction including the equipment movement direction and minimum safe distance; For equipment resources with time conflicts, a job interval instruction is generated, the instruction including the start and end markers of the buffer period.

7. A method for multi-trade collaborative operation in construction engineering driven by an intelligent construction platform based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: The spatial data, equipment resource occupancy time data, and environmental parameter data of the work areas of each type of work are acquired through multi-dimensional sensing units. Based on the spatial data and equipment resource occupancy time data, a three-dimensional geometric model of the work area for each type of work is constructed, and a time series distribution of equipment resource occupancy is generated. The geometric overlap A of different work areas is calculated based on the three-dimensional geometric model, and the time window overlap B is calculated based on the time series distribution of equipment resource occupancy. When the geometric overlap A exceeds the preset spatial threshold, a spatial conflict is determined to exist; When the overlap of time windows B exceeds the preset time threshold, a time conflict is determined to exist; By combining environmental parameter data with preset safety thresholds, a multi-dimensional coupling analysis is performed to calculate the feasibility coefficient C of collaborative operations. If the feasibility coefficient C exceeds the preset collaborative operation threshold, a priority ranking table containing conflicting jobs is generated. Based on the priority ranking table, the work areas of jobs with time and space conflicts are spatially re-divided and time windows are reallocated. Output control commands to the construction equipment, and adjust the work area division and equipment working time window according to the control commands; After executing the control command, the updated spatial data and environmental parameter data are re-acquired and input into the three-dimensional geometric model for iterative calculation, generating a new geometric overlap A' and time window overlap B'; when A' or B' exceeds the corresponding preset percentage, the dynamic optimization cycle of the collaborative operation scheme is triggered.

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