A building design project schedule management system

Abstract

The application relates to the technical field of project management, in particular to a building design project progress management system. The system comprises the following modules: a topological attribute weighting module for calculating a topological proportion to generate a weighted maturity parameter; a progress state evaluation module for comparing a completion index and constructing a space grid boundary; a space interference blocking module for screening pixel coordinates to generate a cross-professional conflict index; a node fault tolerance calculation module for extracting a pressure-resistant fault tolerance weight according to a graph element complexity; and a scheduling network rearrangement module for correcting a scheduling slack time to generate a progress distribution table. In the application, a lagging node is located by calculating a component maturity and a space completion index, and an interference boundary is constructed; a conflict early warning mechanism is established by excavating a cross-professional drawing collision hidden danger; a scheduling pressure-resistant fault tolerance limit is calculated in combination with a graph element complexity; an index and a process slack time are dynamically reconstructed, the delay of traditional manual information circulation is reduced, and adaptive flexible deployment of project progress in a multi-specialty cross-scene is realized.

Description

Technical Field

[0001] This invention relates to the field of project management technology, and in particular to a project schedule management system for architectural design projects. Background Technology

[0002] Project management technology is a systematic approach that comprehensively coordinates resource planning, task allocation, schedule, and cost budgeting with a specific goal in mind. Its core involves organizing and scheduling personnel, materials, and funds within time and budget constraints. It maintains the sequential progress of various business processes by tracking and comparing each node throughout the entire project lifecycle, from initiation, planning, execution, monitoring to closure. Traditional architectural design project schedule management systems focus on controlling time nodes and arranging personnel tasks during stages such as scheme establishment, preliminary design, and construction drawing preparation within the architectural design cycle. This typically involves manually filling out electronic spreadsheets, drawing Gantt charts, creating progress charts, or having project leaders hold regular offline meetings to collect actual drawing times from designers. The handover status of drawings between architecture, structure, and plumbing / electrical disciplines is confirmed through paper approval forms and email attachments. Specialists then manually enter the collected drawing submission dates into a local computer's progress log to compare the current time node with the preset deadline.

[0003] Traditional architectural design project schedule management relies heavily on manual filling out of spreadsheets and drawing Gantt charts to control time nodes. It also relies on offline meetings and email attachments to confirm the handover status of drawings from multiple disciplines, and specialists manually enter and compare progress data. This manual workflow is prone to information delays and coordination errors. When faced with complex architectural structures, it is difficult to provide dynamic early warnings of spatial collisions and interference and to adaptively and flexibly adjust the scheduling network. As a result, the risk of project node delays increases dramatically and the overall management efficiency is seriously low. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a project schedule management system for architectural design.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a building design project progress management system includes: The topology attribute weighting module calculates the ratio of the total number of connection points of the primary component to be evaluated within the spatial topology connection relationship matrix of building components to the global maximum connection point parameter, obtains the spatial topology weighting ratio, and multiplies it by the basic attribute filling base to obtain the weighted component maturity parameter. The progress status assessment module calculates the regional objective completion index based on the weighted component maturity parameter and the spatial topology weighting ratio. Based on the regional objective completion index that has not reached the milestone standard parameter, it calculates and generates the lag time parameter in combination with the preset time mapping benchmark, and constructs the bounding box mesh boundary using the vertex spatial coordinate data of the primary component to be evaluated. The spatial interference blocking module filters the initial spatial pixel coordinates within the bounding box mesh boundary and generates a cross-professional component conflict matrix data index based on the reporting ratio of the primary component attributes to be evaluated that have not met the data improvement standard parameters. The node fault tolerance calculation module adjusts the cross-professional component conflict matrix data index, accumulates the total number of three-dimensional primitives to obtain the primitive summary parameters, calculates the ratio of the total number of three-dimensional primitives to the primitive summary parameters, obtains the complexity ratio and extracts the complement share, and obtains the stress resistance fault tolerance weight parameter. The scheduling network reordering module multiplies the lag time parameter with the stress tolerance weight parameter to obtain the graph contraction index parameter, calculates the difference between the original relaxation time data and the graph contraction index parameter, obtains the reconstructed relaxation time data, overwrites the original relaxation time data, and generates a global schedule allocation table.

[0006] As a further aspect of the present invention, the weighted component maturity parameter specifically includes the structural stability coefficient value, the spatial hub rating score, and the core skeleton completion index; the bounding box mesh boundary includes the virtual three-dimensional protection contour, the dynamic collision interference interval, and the model extension warning area; the cross-disciplinary component conflict matrix data index specifically refers to the axis interference alarm entries, the pipeline collision positioning coordinate group, and the time-dependent blocking label; the pressure resistance and fault tolerance weight parameter specifically includes the node delay absorption quota, the drawing reconstruction buffer time, and the scheduling pressure resistance quota; and the global progress allocation table includes the terminal task update sequence, the designer scheduling overload details, and the construction period node network diagram.

[0007] As a further aspect of the present invention, the topology attribute weighting module includes: The relation parsing submodule obtains the spatial topology connection relationship matrix of building components, extracts the primary components to be evaluated within the spatial topology connection relationship matrix of building components, performs network traversal, summarizes network connection features, and constructs the total number of connection points. The ratio calculation submodule, based on the total number of connection points, analyzes the global maximum connection point parameter in the preset sample set, evaluates the extreme value boundary association state, calculates the ratio of the total number of connection points to the global maximum connection point parameter, and extracts the spatial topology weighted ratio. The attribute weighting submodule calls the spatial topology weighting ratio, monitors the basic attribute filling base corresponding to the primary component to be evaluated, identifies the non-empty mapping state, calculates the product of the basic attribute filling base and the spatial topology weighting ratio, and generates a weighted component maturity parameter.

[0008] As a further aspect of the present invention, the process of parsing the global maximum connection point parameters within the preset sample set specifically includes: Obtain a historical building information model, extract the building component association records within the historical building information model, and merge the building component association records to generate a preset sample set; The pre-set sample set contains multiple historical topology relationship tables, and the building component connection data and the component connection line identifiers attached to the building component connection data are extracted from each historical topology relationship table. Based on the topology endpoint nodes corresponding to the component connection identifiers, the total number of connections of the topology endpoint nodes is summarized to generate historical connection point statistics for each building component connection data. The statistical values ​​of multiple historical connection points are stored in a numerical comparison sequence. The statistical values ​​of the historical connection points in the numerical comparison sequence are traversed and compared. Non-maximum value items in the numerical comparison sequence are filtered out. The maximum value result remaining in the numerical comparison sequence is extracted and set as the global maximum connection point parameter.

[0009] As a further aspect of the present invention, the progress status assessment module includes: The completion calculation submodule calls the weighted component maturity parameter and the spatial topology weighting ratio, performs the algebraic mapping operation between the weighted component maturity parameter and the spatial topology weighting ratio, summarizes the node attribute status, and generates regional objective completion indicators. The time consumption assessment submodule extracts milestone standard parameters from a preset sample set based on the regional objective completion indicators, performs a numerical difference comparison between the regional objective completion indicators and the milestone standard parameters, filters out regional objective completion indicators that have not reached the milestone standard parameters, extracts the time difference by calculating the time conversion equivalent corresponding to the target numerical difference, and generates the lag time consumption parameter. The boundary construction submodule collects vertex spatial coordinate data in the digital model space for the primary component to be evaluated, which is associated with the hysteresis time parameter, constructs a tensor structure sequence, parses the extreme value limits of the vertex spatial coordinate data, and generates a bounding box mesh boundary.

[0010] As a further aspect of the present invention, the process of extracting milestone standard parameters from the preset sample set specifically includes: The project analyzes multiple historical node plan tables contained in the pre-set sample set and extracts the project stage identifier, time base value and historical progress achievement ratio recorded in each historical node plan table. Extract the construction stage attributes and current node time parameters of the primary component to be evaluated, perform character matching comparison between the construction stage attributes and the project stage identifier, and filter out unmatched table data; For the matched and retained table data, calculate the absolute value of the difference between the current node's time consumption parameter and the time base value, and filter the target time base value with the smallest absolute value of difference within the matched and retained table data. Read the historical progress achievement ratio associated with the target time base value mapping, and establish the extracted historical progress achievement ratio as the milestone standard parameter.

[0011] As a further aspect of the present invention, the space interference blocking module includes: The spatial positioning submodule obtains the initial spatial pixel coordinates within the cross-disciplinary collaborative mapping environment, determines the geometric positional relationship between the initial spatial pixel coordinates and the bounding box mesh boundary, and generates a spatial pixel mapping set. The position comparison submodule, based on the spatial pixel mapping set, filters the coordinates of initial spatial pixels located within the bounding box grid boundary, extracts the corresponding boundary center quantity and bounding box span, obtains the attribute filling ratio of the primary component to be evaluated, calls the data provision standard parameter, compares the attribute filling ratio with the data provision standard parameter, calculates and obtains the multidimensional interference deviation, and generates a spatial pixel filter set. The conflict index submodule, based on the spatial pixel filter set, filters the attribute reporting ratios that do not meet the data submission standard parameters, and generates a cross-professional component conflict matrix data index.

[0012] As a further aspect of the present invention, the node fault tolerance calculation module includes: The element accumulation submodule adjusts the cross-professional component conflict matrix data index recorded by the scheduling server, filters the total number of 3D elements of the nodes to be allocated in the set of associated secondary nodes on the target path, performs an accumulation operation on multiple total number of 3D elements, calculates the sum of multiple total number of 3D elements, and generates element summary parameters. The complexity calculation submodule calls the primitive summary parameters, performs a numerical comparison between the total number of 3D primitive parameters and the primitive summary parameters, calculates the ratio of the total number of 3D primitive parameters to the primitive summary parameters, and generates the complexity ratio. The fault tolerance extraction submodule obtains the numerical constant benchmark in the preset reference sample set, calls the complexity ratio, calculates the difference between the numerical constant benchmark and the complexity ratio, calculates the complement share corresponding to the complexity ratio, and generates the stress-resistant fault tolerance weight parameter.

[0013] As a further aspect of the present invention, the process of obtaining the numerical constant benchmark within the preset reference sample set specifically comprises: The preset reference sample set is parsed, and multiple historical scheduling status tables contained in the preset reference sample set are extracted. The historical primitive complexity limit value recorded in each of the historical scheduling status tables is read. Construct an indicator reference sequence, input multiple historical primitive complexity limit values ​​into the indicator reference sequence, and perform a numerical comparison operation on the historical primitive complexity limit values ​​within the indicator reference sequence; Non-maximum value items in the index reference sequence are filtered out, the full load extreme value data retained in the index reference sequence are extracted, the full load state corresponding to the full load extreme value data is quantized to unit 1, and set as the numerical constant benchmark.

[0014] As a further aspect of the present invention, the scheduling network reordering module includes: The indicator calculation submodule obtains the lag time parameter, calls the stress resistance and fault tolerance weight parameter, calculates the product of the lag time parameter and the stress resistance and fault tolerance weight parameter, and obtains the graph contraction indicator parameter. The compression and reconstruction submodule determines the original relaxation time data of the node to be assigned, calls the graph contraction index parameter, calculates the damping compression difference, calculates the difference between the original relaxation time data and the damping compression difference, and obtains the reconstruction relaxation time data. The time allocation submodule calls the reconstructed relaxation time data, overwrites the original relaxation time data with the reconstructed relaxation time data, and generates a global progress allocation table.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the maturity parameters of building components are weighted by calculating the fill base number based on the connection topology of building components and basic attributes. Lagging nodes are located by comparing the differences between objective completion indicators and milestone parameters. Bounding box mesh boundaries are constructed using vertex coordinates. Spatial pixel coordinates within the boundary are screened to deeply mine potential interference risks in cross-disciplinary drawings and establish conflict indexes. Combining the complexity of three-dimensional primitives, the compressive strength and fault tolerance weight parameters are extracted. Simultaneously, the shrinkage index of the graph is derived by combining the data on lagging time consumption. The scheduling relaxation time is dynamically corrected to reconstruct the global schedule allocation network, eliminating the information delay drawbacks caused by manual coordination. This achieves spatial interference early warning and adaptive and flexible allocation of the scheduling network for cross-disciplinary design schedules. Attached Figure Description

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the topology attribute weighting module of the present invention; Figure 3 This is a flowchart of the progress status assessment module of the present invention; Figure 4 This is a flowchart of the spatial interference blocking module of the present invention; Figure 5 This is a flowchart of the node fault tolerance calculation module of the present invention; Figure 6 This is a flowchart of the scheduling network reordering module of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Please see Figure 1 A project schedule management system for architectural design includes: The topology attribute weighting module calculates the ratio of the total number of connection points of the primary component to be evaluated within the spatial topology connection relationship matrix of building components to the global maximum connection point parameter, obtains the spatial topology weighting ratio, and multiplies it by the basic attribute filling base to obtain the weighted component maturity parameter. The progress status assessment module calculates the regional objective completion index based on the weighted component maturity parameter and the spatial topology weighting ratio. It also calculates and generates the lag time parameter based on the regional objective completion index based on the parameter that has not reached the milestone standard, combined with the preset time mapping benchmark, and constructs the bounding box mesh boundary using the vertex spatial coordinate data of the primary component to be evaluated. The spatial interference blocking module filters the initial spatial pixel coordinates within the bounding box grid boundary and generates a cross-professional component conflict matrix data index based on the proportion of primary component attributes to be evaluated that have not met the data improvement standard parameters. The node fault tolerance calculation module adjusts the cross-professional component conflict matrix data index, accumulates the total number of 3D primitive parameters to obtain the primitive summary parameters, calculates the ratio of the total number of 3D primitive parameters to the primitive summary parameters, obtains the complexity ratio and extracts the complement share, and obtains the stress resistance fault tolerance weight parameter. The scheduling network reordering module multiplies the lag time parameter with the stress tolerance weight parameter to obtain the graph contraction index parameter, calculates the difference between the original relaxation time data and the graph contraction index parameter, obtains the reconstructed relaxation time data, overwrites the original relaxation time data, and generates a global schedule allocation table.

[0020] The weighted component maturity parameters specifically include the structural stability coefficient value, spatial hub rating score, and core skeleton completion index. The bounding box mesh boundary includes the virtual 3D protection contour, dynamic collision interference interval, and model extension warning area. The cross-disciplinary component conflict matrix data index specifically refers to the axis interference alarm entries, pipeline collision positioning coordinate group, and time-dependent blocking label. The pressure resistance and fault tolerance weight parameters specifically include the node delay absorption quota, drawing reconstruction buffer time, and scheduling pressure resistance quota. The global schedule allocation table includes the terminal task update sequence, designer scheduling overload details, and schedule node network map.

[0021] Please see Figure 2 The topology attribute weighting module includes: The relation parsing submodule obtains the spatial topology connection relationship matrix of building components, extracts the primary components to be evaluated within the spatial topology connection relationship matrix of building components, performs network traversal, summarizes network connection features, and constructs the total number of connection points. The system receives a data packet containing the spatial topology connection matrix of building components, transmitted by the underlying Building Information Modeling (BIM) parsing engine via a high-bandwidth external device interconnect bus. This data packet is encapsulated in a 1024x1024 sparse matrix format, with the data type of the matrix elements uniformly set to 32-bit single-precision floating-point numbers. It then triggers the built-in graph neural network feature extraction hardware accelerator, using hard-wired multi-threaded addressing logic to scan the aforementioned spatial topology connection matrix row by row, extracting the physical memory addresses of the primary component nodes to be evaluated, each with its own independent state flag and a flag value equal to 1. Next, it activates the network traversal execution component deployed based on a field-programmable gate array (FPGA) framework, using the extracted primary component node memory address as the starting root node, and initiates a breadth-first network traversal algorithm with a search depth limit of 5 layers. During the execution of the traversal operator, it calls a parallelized edge data collection thread pool to read and summarize the network connection features between the current node and its neighboring nodes in real time. The feature vector contains Euclidean distance spatial coordinates of dimension 3, shear strength parameters of physical connection point materials in megapascals, and mechanical transfer attenuation coefficient parameters. Subsequently, the compiled network connection feature vector sequence is input into the multilayer perceptron feature fusion module for nonlinear mapping processing. This multilayer perceptron includes an input layer with an input dimension of 256, two fully connected hidden layers with 128 and 64 computational nodes respectively, and a single-node output layer. A modified linear unit activation function with a leakage parameter set to 0.01 is enforced between each hidden layer to effectively suppress gradient vanishing during feature transfer. The output of the multilayer perceptron is connected to a 64-bit hardware accumulator register. Discrete counting statistics are performed on all valid connection features with confidence scores higher than the 0.85 threshold. Finally, a stable scalar data representing the total number of connection points, representing the core metric of the primary component network to be evaluated, is constructed and output within the hardware accumulator register.

[0022] The ratio calculation submodule analyzes the global maximum connection point parameter in the preset sample set based on the total number of connection points, evaluates the extreme value boundary association state, calculates the ratio of the total number of connection points to the global maximum connection point parameter, and extracts the spatial topology weighted ratio. The system loads the read-only data path of the distributed non-volatile storage cluster containing the pre-set sample set, wakes up the background data mining daemon process to traverse the historical building information model data warehouse, and concurrently extracts the building component association record fields from 150 historical building information model files through the mapping simplification mechanism of the distributed computing framework. The extracted unstructured record fields are imported into the memory cleaning layer for noise reduction and standardization alignment. After calling the natural language processing component to remove damaged records with missing key identifiers, the system merges them using primary key constraints to generate structured pre-set sample set data blocks. The internal tensor processing core is then driven to parse the 500 historical topology relationship tables contained in the pre-set sample set in parallel. A deterministic finite state automaton regular expression matcher is used to extract the building component connection data and the accompanying 16-digit hexadecimal component connection identifier code stored in each historical topology relationship table. Based on the 16-digit hexadecimal component connection identifier code, the corresponding topology endpoint node's physical memory base address is reverse-addressed in the global high-speed hash mapping table, activating multi-threading. The atomic adder counter summarizes the total number of valid connections for all topology endpoint nodes, generates corresponding historical connection point statistics, and pushes them into the value comparison sequence queue in the first-level data cache. The built-in sorting operator is called to perform radix sorting and full traversal comparison operations on the value comparison sequence queue, strictly filtering out non-maximum value items in the queue, extracting the maximum value result remaining at the top of the queue, and writing it to a specified zero-offset address in global shared memory, thus fixing it as the global maximum connection point parameter. The total number of connection point statistics transmitted through the input bus is obtained, and after verifying the integrity of its cyclic redundancy check code data, the total number of connection point statistics and the aforementioned global maximum connection point parameter are synchronously sent to a high-precision floating-point divider to execute a hardware-level division instruction to calculate their ratio. This ratio is input to the boundary association state evaluation and calibration function for extreme value boundary association state evaluation, ensuring that the final ratio strictly falls within the closed interval of 0.0 to 1.0, and the calibrated result data stream is extracted as a spatial topology weighted ratio and output to the downstream video memory interface. To clearly demonstrate the underlying historical topology data storage structure of the system, as shown in Table 1, some historical topology association parameters are extracted for reference and verification.

[0023] Table 1. Historical Topological Association Parameters: ; As shown in Table 1, the system has performed high-precision quantitative recording of the association status and connection point statistics of different types of historical components.

[0024] The attribute weighting submodule calls the spatial topology weighting ratio, monitors the basic attribute filling cardinality corresponding to the primary component to be evaluated, identifies the non-empty mapping state, calculates the product of the basic attribute filling cardinality and the spatial topology weighting ratio, and generates the weighted component maturity parameter. The system continuously monitors the data-ready interrupt signal of the downstream video memory interface, calls and loads the space topology weighting ratio (in double-precision floating-point format) output from the upstream module into the local working register; it starts the asynchronous memory direct access controller and continuously monitors the update pulse signal of the basic attribute fill base corresponding to the primary component to be evaluated on the memory address bus; when a valid pulse is captured, it activates the state recognition logic gate array, extracts the memory block header flag of the basic attribute fill base, performs a bitwise AND operation to identify its non-empty mapping state, and ensures that the basic attribute fill base is non-zero and not suspended by the system; after confirming that the non-empty mapping state is valid, it synchronously pushes the called space topology weighting ratio and the obtained basic attribute fill base into the high-speed buffer stack of the floating-point multiplication calculation unit; and triggers the internal clock frequency to 2. The 5 GHz arithmetic logic unit executes the product instruction of the basic attribute filling radix and the spatial topology weighting ratio. To eliminate floating-point truncation errors caused by the underlying hardware calculation, the preliminary product result is sent to a hardware-level high-precision numerical rounding compensator for truncation compensation correction. The corrected residual is controlled within a tolerance band of 0.001. Subsequently, the high-precision corrected product result is serialized and encapsulated into a weighted component maturity parameter object with a timestamp and data source identifier header. Finally, the generated weighted component maturity parameter is pushed to the data persistence layer of the global state monitor for archiving and storage through the internal message queue telemetry transmission protocol bus, and the memory resources occupied by the local working register are released simultaneously to reset the computing power channel for the next round of cycle calculation.

[0025] Please see Figure 3 The progress status assessment module includes: The completion calculation submodule calls the weighted component maturity parameter and the spatial topology weighting ratio, performs the algebraic mapping operation between the weighted component maturity parameter and the spatial topology weighting ratio, summarizes the node attribute status, and generates regional objective completion indicators. A long-lived connection with the global state monitor's data persistence layer is established via the Transmission Control Protocol (TCP). Structured Query Language (SCL) commands are used to concurrently invoke archived weighted component maturity parameters and associated spatial topology weighted ratio data frames within the current period. These two types of data frames are then fed into the built-in algebraic mapping operation engine, initiating a data tensor reorganization process. The weighted component maturity parameters are converted into one-dimensional row vectors, and the spatial topology weighted ratios are converted into corresponding one-dimensional column vectors. The matrix multiplication acceleration core performs the dot product algebraic mapping operation on the row and column vectors, outputting a preliminary comprehensive node attribute tensor. To achieve a deep aggregation of node attribute states, this comprehensive node attribute tensor is input into the convolutional neural network model deployed in this module. This computational network has… The system comprises a perceptual input layer with an input dimension of 512, three hidden feature extraction layers with 256, 128, and 64 neurons respectively, and a normalized exponential output layer. During data transmission in the hidden layers, modified linear units are uniformly used as activation functions to ensure the high-dimensional nonlinear expressive power of the feature space. The network model output performs a weighted summation on the features of each dimension, and the output range is strictly clamped to a floating-point value between 0.0 and 100.0. Finally, this floating-point value is multiplied twice by a preset regional environment penalty coefficient (default value is 0.95) to generate an objective completion index that accurately reflects the current construction completion level of the region, and this index is written into a read-only data blackboard for downstream modules to poll and read.

[0026] The time consumption assessment submodule extracts milestone standard parameters from a pre-set sample set based on regional objective completion indicators, performs a numerical difference comparison between regional objective completion indicators and milestone standard parameters, filters regional objective completion indicators that have not reached milestone standard parameters, extracts time difference by calculating the time conversion equivalent corresponding to the target numerical difference, and generates lag time consumption parameters. The system polls and listens for status changes on the data blackboard, extracting the latest generated regional objective completion indicators based on read commands. Simultaneously, it launches a background sample retrieval engine to parse 300 historical node plan table data clusters within a pre-set sample set. It extracts the project stage identifier, hourly time base values, and historical progress achievement percentages recorded in Extensible Markup Language (XML) format from each table cluster. Using a hash character matching algorithm, it extracts the construction stage attributes and current node time parameters of the primary component to be evaluated in the current time slice, performing a high-precision string difference comparison with the aforementioned project stage identifiers. Non-matching table data with a similarity lower than 95% are forcibly filtered out from the candidate memory pool. For the high-confidence table data remaining after matching, it activates a cyclic difference calculation operator to calculate the current node time one by one. The absolute difference between the parameter and each historical time base value is used to retrieve and lock the target time base value with the smallest absolute difference in the matched and retained table data using the bubble sort method. The historical progress achievement ratio of the target time base value associated with the foreign key in the relational database is read and established as the current milestone standard parameter. Subsequently, the regional objective completion index and the extracted milestone standard parameter are sent to the numerical difference comparison device. If the regional objective completion index is lower than the red line threshold set by the milestone standard parameter, a lag interruption is triggered. After the calculator captures the interruption, it immediately extracts the time difference between the standard timestamp corresponding to the milestone standard parameter and the actual time-consuming timestamp of the current node, converts the time difference into a floating-point value in working days, thereby generating a high-precision lag time-consuming parameter and updating it to the global anomaly alarm register.

[0027] The boundary construction submodule collects vertex spatial coordinate data in the digital model space for the primary component to be evaluated, which is related to the lag time parameter, constructs a tensor structure sequence, analyzes the extreme value limits of the vertex spatial coordinate data, and generates the bounding box mesh boundary. Upon receiving a hysteresis latency parameter warning signal from the global anomaly alarm register, and for the primary component to be evaluated associated with the hysteresis latency parameter foreign key, a bidirectional data stream is established with the digital model space rendering engine by calling the 3D graphics application programming interface; a vertex data retrieval command is issued to collect the vertex spatial coordinate data of all polygon meshes on the surface of the component in the digital model space, with the data format being a set of tuples containing floating-point values ​​for the horizontal, vertical, and triangular axes; the collected hundreds of thousands of vertex spatial coordinate data are fed into the tensor construction pipeline, and reshaped into a tensor structure sequence of dimension N by 3 according to the hierarchical relationship of the component, where N represents the total number of valid vertices; the parallel extremum analysis algorithm deployed in the computation shader is activated, along the world coordinate system... The tensor structure sequence is scanned synchronously along three orthogonal axes, extracting the maximum and minimum coordinate values ​​on the horizontal, vertical, and axial axes respectively, and then resolving the six vertex coordinate extreme values ​​with absolute limits. Based on these six vertex coordinate extreme values, a safety buffer margin of 50 mm is extended outwards on each of the six vertex coordinate extreme values. A cuboid bounding box containing six orthogonal planes is automatically constructed in the virtual coordinate system using a topological geometry reconstruction tool. Finally, a mesh discretization function is called to divide each surface of the cuboid bounding box into uniform triangular meshes with a side length of 10 mm. The normal vectors and position coordinates of the mesh intersections are extracted to generate a high-precision bounding box mesh boundary data packet that can be directly called by the interferometric detection algorithm and cached in the high-speed shared video memory of the graphics processing unit.

[0028] Please see Figure 4 The space interference blocking module includes: The spatial positioning submodule obtains the initial spatial pixel coordinates within the cross-disciplinary collaborative mapping environment, determines the geometric positional relationship between the initial spatial pixel coordinates and the bounding box mesh boundary, and generates a spatial pixel mapping set. The socket listening port of the cross-disciplinary collaborative drafting environment is opened, and the initial spatial pixel coordinate data stream broadcast in real time by the design terminals of each discipline is obtained through the User Datagram Protocol (UDP). This coordinate data stream contains absolute spatial location information with timestamps and a discipline affiliation identifier. The geometric intersection test core in the graphics processing unit is activated, and the acquired initial spatial pixel coordinate data stream and the bounding box mesh boundary data packet residing in the high-speed shared video memory are read in batch parallel. For each initial spatial pixel coordinate, a 3D point containment test algorithm based on ray casting is invoked to project a ray from the pixel coordinate to the forward direction, and the total number of intersections between the ray and all triangular faces on the bounding box mesh boundary is calculated. The algorithm counts the number of intersection points. It uses a hardware-level bitwise operator to determine the parity of the total number of intersection points. If the total number of intersection points is odd, the initial spatial pixel coordinates are determined to be inside the bounding box mesh boundary; if the total number of intersection points is even, they are determined to be outside. For all coordinate data whose geometric positional relationships are determined to be inside the bounding box mesh boundary, the original professional attribution identifier is extracted and a bounding box internal state label is added. These are then repackaged into a key-value pair sequence with a structured index. Finally, all generated high-dimensional key-value pair sequences are input into a specified set of a distributed in-memory database to generate a persistent spatial pixel mapping set that supports high-concurrency queries, while simultaneously releasing the cache resources of the geometric intersection test core.

[0029] The location comparison submodule, based on the spatial pixel mapping set, filters the coordinates of initial spatial pixels located within the bounding box mesh boundary, extracts the corresponding boundary center quantity and bounding box span, obtains the attribute reporting ratio of the primary component to be evaluated, calls the data provision standard parameter, and compares the attribute reporting ratio with the data provision standard parameter using the formula: ; The multidimensional interference deviation is obtained through calculation, and a spatial pixel filter set is generated. in, The multidimensional interference bias is obtained by taking the square root of the sum of the squares of the attribute bias ratio and the squares of the spatial offset ratio. The attribute reporting ratio is obtained by statistically analyzing the attribute completion rate of the primary component to be evaluated. To improve the standard parameters, they were obtained by extracting baseline data from the scheduling system. The coordinate parameters of the initial spatial pixel points along the target axis are obtained through analysis of a cross-disciplinary collaborative mapping environment. The projection of the boundary center along the same axis is obtained by extracting the centroid position of the bounding box mesh boundary. The bounding box span is obtained by measuring the axial span of the bounding box mesh boundary; A cursor mechanism based on an in-memory database reads the spatial pixel mapping set, activates a conditional filtering engine to filter out the initial spatial pixel coordinates with bounding box internal state labels, extracts the underlying parameters of the bounding box mesh boundary associated with the filtering results, uses a 3D centroid calculation module to extract the boundary center quantity, and obtains the bounding box span by measuring the maximum span of the boundary on both the horizontal and vertical axes. Simultaneously, the business logic interface is called to obtain the current attribute reporting ratio of the primary component to be evaluated, and the current funding standard parameters are retrieved from the read-only node library of the scheduling system. Then, high-precision numerical deviation calculations are performed on the above parameters to obtain the preprocessed attribute reporting ratio parameters and funding standard parameters. After measurement, the difference between the attribute reporting ratio parameter and the data provision standard parameter is calculated, and this difference is divided by the data provision standard parameter to obtain the attribute deviation ratio. Then, the spatial deviation between the initial spatial pixel coordinate parameter and the boundary center parameter is calculated, and this spatial deviation is divided by the bounding box span to obtain the spatial offset ratio. Next, the obtained attribute reporting ratio (value 0.80), data provision standard parameter (value 1.00), initial spatial pixel coordinate parameter along the target axis (value 150), boundary center projection along the same axis (value 100), and bounding box span (value 250) are substituted into the multidimensional interferometric deviation formula for combined calculation. The calculated multidimensional interference deviation is 0.282. This result indicates that the current component's overall deviation in both spatial and attribute dimensions is within the second-level warning range. The advantage of this formula lies in its ability to orthogonally integrate dimensionless attribute progress deviation and spatial physical deviation within Euclidean space. This effectively avoids the cross-disciplinary collaboration blind spot caused by one dimension meeting the standard while another dimension lags significantly, significantly improving the comprehensiveness of collision detection and the scientific rigor of fault tolerance assessment. After calculation, data with multidimensional interference deviation results below the 0.25 safety threshold are removed, and the remaining high-risk points are used to generate a spatial pixel filter set. To clearly illustrate the computational details of the interference data, specific parameters are shown in Table 2.

[0030] Table 2 Spatial Multidimensional Interference Deviation Parameters: ; As shown in Table 2, the high-risk pixel locations that cause substantial interference were accurately located through the fusion calculation of multi-dimensional parameters.

[0031] The conflict index submodule, based on the spatial pixel filter set, filters the attribute reporting ratios that do not meet the data submission standards and generates a cross-professional component conflict matrix data index. Establish a direct memory access channel with the spatial pixel filter set to continuously monitor for new data row additions within the filter set. When an event stream is triggered, activate the attribute verification state machine to read the actual attribute reporting ratio data of the primary component to be evaluated associated with each spatial pixel in the filter set, line by line. Call the standard data submission interface of the scheduling system to extract the corresponding current data submission standard parameter red line value. Use a hardware comparator module to strictly filter out abnormal records where the actual attribute reporting ratio is less than the data submission standard parameter red line value. For these abnormal records that do not meet the data submission standard parameters, extract the conflicting professional codes and spatial three-dimensional coordinate data they contain. The system extracts multi-dimensional conflict elements and time-series lag labels; it then feeds these elements into a hash encoder, performs irreversible encoding mapping using the Secure Hash Algorithm Version 256 standard, and generates a unique 64-bit hash identifier string. Using this hash identifier string as the primary key, and with conflict professional codes as column families and spatial coordinates and time-series labels as column qualifiers, it dynamically constructs a wide table structure in a non-relational database. Finally, it solidifies and outputs the mapping relationships within the wide table, generating a highly efficient and scalable cross-professional component conflict matrix data index, which is then distributed to the scheduling server's system bus for subsequent reordering calls.

[0032] Please see Figure 5 The node fault tolerance calculation module includes: The element accumulation submodule adjusts the cross-professional component conflict matrix data index recorded by the scheduling server, filters the total number of 3D elements parameters of the nodes to be assigned in the set of associated secondary nodes on the target path, performs an accumulation operation on multiple total number of 3D elements parameters, calculates the sum of multiple total number of 3D elements parameters, and generates the element summary parameters. The system intercepts the cross-disciplinary component conflict matrix data index update signal broadcast by the scheduling server on the system bus, and dynamically adjusts the conflict matrix addressing pointer in local memory using a built-in index parsing script. Following the topological flow of the target critical path, it executes a depth-first search algorithm of a graph neural network to extract a set of secondary nodes with strong connectivity to the target path. It scans the state attributes of nodes marked as unassigned within this set of secondary nodes, extracting the total quantity parameter of 3D primitives configured in their underlying database. This parameter is a 64-bit unsigned integer. The extracted total quantity parameters of multiple 3D primitives are then fed into a multi-channel parallel accumulator. In the array, the accumulator array supports single-clock-cycle synchronous addition of up to 16 data channels. When the accumulator performs the summation operation, the overflow protection detection circuit is activated synchronously to ensure that the accumulation process does not exceed the upper limit of the register's bit width. After completing the calculation cycle of all parameter channels, the sum of multiple stereoscopic primitive total parameters output by the accumulator array is extracted. The sum is sent to the format verification module for double verification of non-negativity and non-zero. After the verification passes, a version number and timestamp metadata header are added to the value to generate standardized primitive summary parameters, and it is locked in the optimal reading area of ​​the high-speed shared cache.

[0033] The complexity calculation submodule calls the primitive summary parameter, performs a numerical comparison between the total number of 3D primitives parameter and the primitive summary parameter, calculates the ratio of the total number of 3D primitives parameter to the primitive summary parameter, and generates the complexity ratio. A read request is sent to the high-speed shared cache unit, invoking the primitive summary parameters within the optimal read region that is currently locked. The data prefetch channel is synchronously driven to read the total primitive parameters corresponding to each node to be allocated in batches from local memory pages. These two sets of parameters are then sent in parallel to the floating-point operation core queue, executing a high-precision division micro-instruction sequence. Specifically, using the total primitive parameters of the current node to be allocated as the numerator and the total primitive parameters of the entire set as the denominator, the Newton iterative division hardware unit performs numerical comparison and ratio calculation. After 5 iterations, the output precision reaches [precision value missing]. The complexity ratio is calculated to six decimal places. To eliminate division-to-zero anomalies caused by extreme data, a non-zero denominator assertion check is enforced before division. Subsequently, the calculated complexity ratio is input into the reverse mapping filter to filter out abnormal calculation results that exceed the threshold boundary of 0.0 to 1.0. Finally, the verified and compliant complexity ratio is converted into a 32-bit floating-point data frame format and pushed into a first-in-first-out queue buffer, waiting for the pop-up request from the downstream fault-tolerant extraction submodule, providing accurate data measurement basis for the system's fault tolerance space allocation.

[0034] The fault tolerance extraction submodule obtains the numerical constant benchmark in the preset reference sample set, calls the complexity ratio, calculates the difference between the numerical constant benchmark and the complexity ratio, calculates the complement share corresponding to the complexity ratio, and generates the stress-resistant fault tolerance weight parameter. A pre-set reference sample set loading command is triggered, and historical scheduling status table data is read from a non-volatile database cluster via a streaming protocol. The multi-dimensional feature data within the table is mapped to a low-dimensional tensor space. Principal component analysis (PCA) is used to remove feature redundancy, and an index reference sequence containing all full-load extreme value data is extracted and reconstructed. A binary search searcher is used to locate and extract the full-load extreme value data retained in the highest percentile interval of the index reference sequence. This full-load extreme value data is then sent to a quantization and normalization processing unit, where its absolute numerical domain is forcibly linearly stretched and truncated to a dimensionless unit 1 state, thereby obtaining a precise numerical constant benchmark and writing it into a global static variable table. Subsequently… A pop command is sent to the first-in-first-out queue buffer, invoking the complexity percentage generated by the previous module; the extracted numerical constant baseline (fixed value 1.0) and the complexity percentage are synchronously fed into the subtraction logic gate array; precise subtraction instructions are executed to calculate the subtraction difference between the numerical constant baseline and the complexity percentage, thereby calculating and extracting the complement share of the complexity percentage in the overall probability space; this complement share is input into the adaptive weight scaling function, dynamically fine-tuned based on the current system load state, and finally generates a stress-resistant and fault-tolerant weight parameter with stress-resistant buffer characteristics, which is then sent to the listening port of the scheduling network reordering module through the inter-process communication mechanism.

[0035] Please see Figure 6 The scheduling network reordering module includes: The indicator calculation submodule obtains the lag time parameter, calls the stress resistance and fault tolerance weight parameter, calculates the product of the lag time parameter and the stress resistance and fault tolerance weight parameter, and obtains the graph contraction indicator parameter. The system listens to the designated data receiving port of the inter-process communication mechanism, obtains the 32-bit floating-point value of the lag time parameter through the memory unpacking mechanism, and synchronously initiates a data request to the global parameter configuration center through a system call to retrieve the stress-tolerant weight parameter output by the upstream module and verified by the state. It then starts the multiplication acceleration hardware circuit to perform multi-threaded concurrent multiplication operations on the lag time parameter and the stress-tolerant weight parameter. To ensure that the product result does not cause subsequent progress network collapses in extreme lag scenarios, the output of the hardware multiplication circuit is sent to a pre-configured nonlinear convergence activation function (using a hyperbolic tangent function for threshold limiting). The product data after limiting is imported into the numerical steady-state verification unit to perform boundary rationality verification. Finally, the data output is reconstructed, and priority tags and lifecycle timestamps are added to the result data to obtain the graph contraction index parameter. This graph contraction index parameter is then written to a designated memory address block of the dual-port random access memory so that the next-level compression and reconstruction operator can achieve high-speed data retrieval with zero latency, providing core driving data for the dynamic damping contraction of the scheduling network.

[0036] The compression and reconstruction submodule determines the original relaxation time data of the nodes to be assigned, calls the graph contraction index parameter, and uses the formula: ; The damping compression difference is obtained through calculation, the difference between the original relaxation time data and the damping compression difference is calculated, and the reconstructed relaxation time data is obtained. in, The damped compression difference is obtained by extracting the nonlinear scaling factor from the original relaxation time data, multiplying it by the contraction index parameter in the graph, and then superimposing the compressive bias constant. The contraction index parameter is obtained by multiplying the lag time parameter by the stress tolerance weight parameter. The original relaxation time data was obtained by extracting the node scheduling sufficiency attribute of the nodes to be assigned. The buffer limit is obtained by setting a convergence threshold based on the progress time limit. The margin is obtained by calculating the available time for the current process. The stiffness coefficient is obtained by calibrating the compressive rebound characteristics of the scheduling plan; The node scheduling leeway attribute of the nodes to be assigned in the database dictionary is analyzed to determine the status of the original relaxation time data. The graph contraction index parameter is retrieved via a high-speed channel of dual-port random access memory, and the schedule time limit convergence threshold is extracted to set the buffer limit. The available leeway time for the current process is calculated to set the margin, and the stress rebound characteristics of the scheduling plan are calibrated to set the stiffness coefficient. Then, the algebraic calculation derivation process of the formula is entered to obtain the reconstruction parameters. The obtained graph contraction index parameter (value 2.0), original relaxation time data (value 14), buffer limit (value 2), margin (value 10), and stiffness coefficient (value 5) are substituted into the damped compression difference formula for combined calculation. The final damped compression difference was found to be 3.732. This result indicates that, under the current compressibility and fault tolerance weights, the scheduling margin of the target node requires a forced compression of 3.732 units of time. The advantage of this formula lies in its ability to dynamically adjust the elastic rebound of the schedule network under compression by introducing a nonlinear convergence factor and a stiffness feedback mechanism, thus avoiding the risk of secondary critical path breakage caused by traditional linear compression. Finally, the difference between the original relaxation time data and the damped compression difference was calculated to obtain the reconstructed relaxation time data. The specific network reconstruction calculations are shown in Table 3.

[0037] Table 3. Network Graph Reconstruction Parameters: ; As shown in Table 3, the damping compression of different state nodes was scientifically calculated and allocated.

[0038] The time allocation submodule calls the reconstructed relaxation time data, overwrites the original relaxation time data with the reconstructed relaxation time data, and generates a global progress allocation table. The system extracts high-precision reconstructed relaxation time data buffer frames output by the arithmetic logic unit, activates the system kernel-level data overwrite instruction set, and uses the overwrite operator to write the reconstructed relaxation time data into the physical memory address block where the original relaxation time data is located, forcibly erasing the original inefficient slack time state to achieve data overwrite. After the physical memory overwrite is completed, a system-level interrupt is triggered to wake up the scheduling and routing engine. The scheduling and routing engine reads the updated full relaxation time snapshot and uses an improved critical path network mapping component based on the Dijkstra algorithm to reorganize the temporal topology connection relationship of all task nodes with hourly granularity. The reorganized topology data stream is input into the global table generation renderer, and combined with the preset terminal task update sequence and designer scheduling reload details rules, a high-dimensional structured data table containing multi-level Gantt chart mapping parameters and a set of network graph coordinates of project nodes is generated. Finally, the structured data table is broadcast to all control terminals through the message publish-subscribe system, officially generating and distributing a global schedule allocation table with stress resistance and fault tolerance, completing the adaptive closed-loop rescheduling control of the underlying architecture.

[0039] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A project schedule management system for architectural design, characterized in that, The system includes: The topology attribute weighting module calculates the ratio of the total number of connection points of the primary component to be evaluated within the spatial topology connection relationship matrix of building components to the global maximum connection point parameter, obtains the spatial topology weighting ratio, and multiplies it by the basic attribute filling base to obtain the weighted component maturity parameter. The progress status assessment module calculates the regional objective completion index based on the weighted component maturity parameter and the spatial topology weighting ratio. Based on the regional objective completion index that has not reached the milestone standard parameter, it calculates and generates the lag time parameter in combination with the preset time mapping benchmark, and constructs the bounding box mesh boundary using the vertex spatial coordinate data of the primary component to be evaluated. The spatial interference blocking module filters the initial spatial pixel coordinates within the bounding box mesh boundary and generates a cross-professional component conflict matrix data index based on the reporting ratio of the primary component attributes to be evaluated that have not met the data improvement standard parameters. The node fault tolerance calculation module adjusts the cross-professional component conflict matrix data index, accumulates the total number of three-dimensional primitives to obtain the primitive summary parameters, calculates the ratio of the total number of three-dimensional primitives to the primitive summary parameters, obtains the complexity ratio and extracts the complement share, and obtains the stress resistance fault tolerance weight parameter. The scheduling network reordering module multiplies the lag time parameter with the stress tolerance weight parameter to obtain the graph contraction index parameter, calculates the difference between the original relaxation time data and the graph contraction index parameter, obtains the reconstructed relaxation time data, overwrites the original relaxation time data, and generates a global schedule allocation table.

2. The architectural design project progress management system according to claim 1, characterized in that, The weighted component maturity parameters specifically include structural stability coefficient, spatial hub rating score, and core skeleton completion index. The bounding box mesh boundary includes virtual 3D protection contour, dynamic collision interference interval, and model extension warning area. The cross-disciplinary component conflict matrix data index specifically refers to axis interference alarm entries, pipeline collision positioning coordinate group, and time-dependent blocking label. The pressure resistance and fault tolerance weight parameters specifically include node delay absorption quota, drawing reconstruction buffer time, and scheduling pressure resistance quota. The global progress allocation table includes terminal task update sequence, designer scheduling overload details, and construction period node network map.

3. The architectural design project progress management system according to claim 1, characterized in that, The topology attribute weighting module includes: The relation parsing submodule obtains the spatial topology connection relationship matrix of building components, extracts the primary components to be evaluated within the spatial topology connection relationship matrix of building components, performs network traversal, summarizes network connection features, and constructs the total number of connection points. The ratio calculation submodule, based on the total number of connection points, analyzes the global maximum connection point parameter in the preset sample set, evaluates the extreme value boundary association state, calculates the ratio of the total number of connection points to the global maximum connection point parameter, and extracts the spatial topology weighted ratio. The attribute weighting submodule calls the spatial topology weighting ratio, monitors the basic attribute filling base corresponding to the primary component to be evaluated, identifies the non-empty mapping state, calculates the product of the basic attribute filling base and the spatial topology weighting ratio, and generates a weighted component maturity parameter.

4. The architectural design project progress management system according to claim 3, characterized in that, The process of parsing the global maximum connection point parameters within the preset sample set is as follows: Obtain a historical building information model, extract the building component association records within the historical building information model, and merge the building component association records to generate a preset sample set; The pre-set sample set contains multiple historical topology relationship tables, and the building component connection data and the component connection line identifiers attached to the building component connection data are extracted from each historical topology relationship table. Based on the topology endpoint nodes corresponding to the component connection identifiers, the total number of connections of the topology endpoint nodes is summarized to generate historical connection point statistics for each building component connection data. The statistical values ​​of multiple historical connection points are stored in a numerical comparison sequence. The statistical values ​​of the historical connection points in the numerical comparison sequence are traversed and compared. Non-maximum value items in the numerical comparison sequence are filtered out. The maximum value result remaining in the numerical comparison sequence is extracted and set as the global maximum connection point parameter.

5. The architectural design project progress management system according to claim 3, characterized in that, The progress status assessment module includes: The completion calculation submodule calls the weighted component maturity parameter and the spatial topology weighting ratio, performs the algebraic mapping operation between the weighted component maturity parameter and the spatial topology weighting ratio, summarizes the node attribute status, and generates regional objective completion indicators. The time consumption assessment submodule extracts milestone standard parameters from a preset sample set based on the regional objective completion indicators, performs a numerical difference comparison between the regional objective completion indicators and the milestone standard parameters, filters out regional objective completion indicators that have not reached the milestone standard parameters, extracts the time difference by calculating the time conversion equivalent corresponding to the target numerical difference, and generates the lag time consumption parameter. The boundary construction submodule collects vertex spatial coordinate data in the digital model space for the primary component to be evaluated, which is associated with the hysteresis time parameter, constructs a tensor structure sequence, parses the extreme value limits of the vertex spatial coordinate data, and generates a bounding box mesh boundary.

6. The architectural design project progress management system according to claim 5, characterized in that, The process of extracting milestone standard parameters from the pre-set sample set is as follows: The project analyzes multiple historical node plan tables contained in the pre-set sample set and extracts the project stage identifier, time base value and historical progress achievement ratio recorded in each historical node plan table. Extract the construction stage attributes and current node time parameters of the primary component to be evaluated, perform character matching comparison between the construction stage attributes and the project stage identifier, and filter out unmatched table data; For the matched and retained table data, calculate the absolute value of the difference between the current node's time consumption parameter and the time base value, and filter the target time base value with the smallest absolute value of difference within the matched and retained table data. Read the historical progress achievement ratio associated with the target time base value mapping, and establish the extracted historical progress achievement ratio as the milestone standard parameter.

7. The architectural design project progress management system according to claim 5, characterized in that, The space interference blocking module includes: The spatial positioning submodule obtains the initial spatial pixel coordinates within the cross-disciplinary collaborative mapping environment, determines the geometric positional relationship between the initial spatial pixel coordinates and the bounding box mesh boundary, and generates a spatial pixel mapping set. The location comparison submodule, based on the spatial pixel mapping set, filters the coordinates of initial spatial pixels located within the bounding box mesh boundary, extracts the corresponding boundary center quantity and bounding box span, obtains the attribute reporting ratio of the primary component to be evaluated, calls the data provision standard parameter, compares the attribute reporting ratio with the data provision standard parameter, and uses the formula: ； The multidimensional interference deviation is obtained through calculation, and a spatial pixel filter set is generated. in, For multidimensional interference bias, For the attribute reporting ratio, To improve the standard parameters, These are the initial spatial pixel coordinate parameters. For the boundary center quantity, For bounding box span; The conflict index submodule, based on the spatial pixel filter set, filters the attribute reporting ratios that do not meet the data submission standard parameters, and generates a cross-professional component conflict matrix data index.

8. The architectural design project progress management system according to claim 7, characterized in that, The node fault tolerance calculation module includes: The element accumulation submodule adjusts the cross-professional component conflict matrix data index recorded by the scheduling server, filters the total number of 3D elements of the nodes to be allocated in the set of associated secondary nodes on the target path, performs an accumulation operation on multiple total number of 3D elements, calculates the sum of multiple total number of 3D elements, and generates element summary parameters. The complexity calculation submodule calls the primitive summary parameters, performs a numerical comparison between the total number of 3D primitive parameters and the primitive summary parameters, calculates the ratio of the total number of 3D primitive parameters to the primitive summary parameters, and generates the complexity ratio. The fault tolerance extraction submodule obtains the numerical constant benchmark in the preset reference sample set, calls the complexity ratio, calculates the difference between the numerical constant benchmark and the complexity ratio, calculates the complement share corresponding to the complexity ratio, and generates the stress-resistant fault tolerance weight parameter.

9. The architectural design project progress management system according to claim 8, characterized in that, The process of obtaining the numerical constant benchmark within the preset reference sample set is as follows: The preset reference sample set is parsed, and multiple historical scheduling status tables contained in the preset reference sample set are extracted. The historical primitive complexity limit value recorded in each of the historical scheduling status tables is read. Construct an indicator reference sequence, input multiple historical primitive complexity limit values ​​into the indicator reference sequence, and perform a numerical comparison operation on the historical primitive complexity limit values ​​within the indicator reference sequence; Non-maximum value items in the index reference sequence are filtered out, the full load extreme value data retained in the index reference sequence are extracted, the full load state corresponding to the full load extreme value data is quantized to unit 1, and set as the numerical constant benchmark.

10. The architectural design project progress management system according to claim 8, characterized in that, The scheduling network reordering module includes: The indicator calculation submodule obtains the lag time parameter, calls the stress resistance and fault tolerance weight parameter, calculates the product of the lag time parameter and the stress resistance and fault tolerance weight parameter, and obtains the graph contraction indicator parameter. The compression and reconstruction submodule determines the original relaxation time data of the node to be assigned, calls the graph contraction index parameter, calculates the damping compression difference, calculates the difference between the original relaxation time data and the damping compression difference, and obtains the reconstruction relaxation time data. The time allocation submodule calls the reconstructed relaxation time data, overwrites the original relaxation time data with the reconstructed relaxation time data, and generates a global progress allocation table.

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