Task list engineering quantity segmentation management and control method and system based on WBS and billing list

Abstract

The application discloses a task list engineering quantity segmentation control method and system based on WBS and a pricing list, relates to the technical field of engineering progress management, and comprises the following steps: an engineering project is decomposed into minimum task control units layer by layer through a WBS structure, and basic cost measurement is carried out on each unit based on a pricing list; scheduling constraints and overflow costs among units are generated, and costs are dynamically adjusted in real time through real-time input of engineering progress data; further, a prediction and an optimal scheme are generated based on actual data of completed engineering, and a project profit state and risks are comprehensively judged; through multidimensional evaluation and a sliding optimization algorithm, an optimal segmentation node is automatically identified, and segmentation control and scheme fine adjustment are realized. The method improves the fine management of engineering costs, risk early warning and dynamic optimization capability, and provides technical support for efficient, controllable and sustainable profit of complex engineering projects.

Description

Technical Field

[0001] This invention relates to project schedule management, and in particular to a method and system for segmented control of work order quantities based on WBS and pricing lists. Background Technology

[0002] In the field of engineering project management, with the continuous increase in the scale and complexity of projects, how to achieve refined and dynamic control over workload, schedule, and cost has become a focus of industry attention. Traditional project management relies heavily on manual experience and static tables, often resulting in problems such as coarse granularity and poor timeliness in project decomposition, cost accounting, resource scheduling, and schedule management. Especially during actual construction, projects are often affected by factors such as multi-disciplinary intersections, complex procedures, and diverse resource constraints, making it difficult for the original budget to accurately reflect actual consumption. Currently, although the industry widely uses WBS (Work Breakdown Structure) for hierarchical project decomposition and management, and adopts standardized cost and workload accounting using pricing lists, a unified data-driven mechanism is still lacking in the information correlation between various links, scheduling constraints, and process overflow cost management.

[0003] Furthermore, insufficient dynamic adjustment capabilities in project management lead to significant issues with actual project costs, schedule deviations, and resource waste, resulting in high uncertainty regarding profitability. Traditional segmented settlement and control methods often suffer from limitations such as data lag, untimely risk warnings, and difficulty in effectively optimizing and guiding subsequent construction processes. For large and complex projects, managers require highly efficient cost control and scheduling optimization tools that enable dynamic adjustments throughout the entire process and in different phases. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a task quantity segmentation control method based on WBS and pricing list, which solves the problem of insufficient segmented dynamic control and profitability intelligent decision-making in existing project management.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a task order quantity segmentation control method based on WBS and pricing list, which includes decomposing the project into engineering parts to obtain each smallest task control unit, and using the pricing list to measure the basic cost of each task control unit.

[0008] Based on the engineering decomposition process of the project, a scheduling constraint is generated for each task management unit, and the overflow cost of each scheduling relationship is quantified based on the scheduling relationship between every two task management units.

[0009] By inputting completed projects, the basic costs and overflow costs are adjusted, and a predicted plan and an optimal plan based on the completed projects are generated.

[0010] The optimal segmentation node is evaluated based on the prediction scheme, and the prediction scheme is fine-tuned based on the evaluation results. When the project progress reaches the optimal segmentation node, the optimal scheme based on the completed project is re-evaluated, and the remaining scheduling scheme is replaced.

[0011] As a preferred embodiment of the task quantity segmentation control method based on WBS and pricing list described in this invention, the project decomposition includes a WBS-based work breakdown structure, which decomposes the project into a series of manageable and quantifiable sub-task units from top to bottom according to a hierarchical structure, until the smallest executable and assessable work unit is formed.

[0012] As a preferred embodiment of the task order quantity segmentation control method based on WBS and pricing list described in this invention, the basic cost includes the difference between the total material cost and the total labor cost of each task control unit;

[0013] Based on the unit price of different materials in the pricing list, multiply it by the quantity of work in each task control unit to obtain the total material cost of each task control unit;

[0014] Based on the unit labor cost of different trades with different numbers of workers in the pricing list, establish the relationship between the project completion time and the total labor cost of each task control unit: Where E represents the total labor cost of the task management unit; This represents the total labor cost of the task control unit when there is only one worker. This represents the total construction time of the task control unit when there is only 1 worker, measured by the time it takes for a single worker to complete the entire task control unit's work; t represents the completion time of the task control unit's work, measured by selecting the maximum value of t based on time constraints for each stage. The number of workers is represented by an integer greater than 0.

[0015] As a preferred embodiment of the task quantity segmentation control method based on WBS and pricing list described in this invention, the scheduling constraints include: treating each smallest task control unit obtained from the decomposition of the project as a node in the network; generating the sequential execution relationship between each pair of units according to the logical dependencies and process flow of each unit during the project decomposition process, forming a directed edge to obtain a graph structure; wherein, the direction of the edge represents the constraint on the execution order.

[0016] The graph structure is simplified to obtain the simplified graph structure: if there is a direct connection between two nodes i and j with the direction i→j, and there are also multiple intermediate nodes forming an indirect connection between nodes i and j with the direction i→j, then the direct connection between nodes i and j is broken, thus achieving one simplification; the simplification process is continuously repeated to obtain the simplest form of the graph structure.

[0017] The constraints of each edge are used as the scheduling relationship between the two nodes. The increase and decrease of material and labor costs caused by the engineering transformation between the two nodes are quantified to obtain the overflow cost of the scheduling relationship between the two nodes.

[0018] The quantification process of the overflow cost includes the following steps: Step 1, calculating the increased cost: When generating each node in the graph structure, each node selects a material surplus evaluation function obtained by fitting historical data according to the corresponding engineering content; Based on the material surplus evaluation function, the sum of the cost of each material surplus and the corresponding removal cost is calculated using the engineering quantity of the task control unit.

[0019] The removal cost for each material is calculated by multiplying the remaining material quantity by the corresponding labor cost per unit.

[0020] Step 2, Calculate cost reduction: In the simplest form of the graph structure, generate the material cost utilization rate and calculate the cost reduction amount for removing the two nodes connected by each edge;

[0021] Based on the project content of the node pointed to by the arrow, compare the material surplus U1 of each type of node at the tail of the arrow with the sum of the project demand quantity of the node pointed to by the arrow and the corresponding material surplus U2. If U1 > U2, the sum of the material surplus cost corresponding to U2 and the removal cost corresponding to U2 is used as the cost reduction; if U1 ≤ U2, the sum of the material surplus cost corresponding to U1 and the removal cost corresponding to U1 is used as the cost reduction.

[0022] Step 3: Sum the increase cost and decrease cost between the two nodes in the scheduling relationship to obtain the overflow cost.

[0023] As a preferred embodiment of the task order quantity segmentation control method based on WBS and pricing list described in this invention, the process of adjusting the basic cost and overflow cost includes: replacing the theoretical material consumption and unit price in the original budget or list with the actual raw material usage and purchase unit price, and recalculating the total material cost; replacing the theoretical working hours and standard unit price with the actual labor hours and actual labor unit price, and recalculating the total labor cost.

[0024] Based on the deviation rate between the overflow cost and the calculation result between every two nodes in the completed project, the overflow cost between every two nodes in the unfinished project is adjusted according to the deviation rate.

[0025] As a preferred embodiment of the task order quantity segmentation control method based on WBS and pricing list described in this invention, the optimal embodiment includes scheduling each node with the minimum cost under the scheduling constraints, so as to obtain the scheduling results of all nodes and the minimum cost.

[0026] If the sum of the minimum cost and the cost already consumed is greater than a preset value, an early warning message is generated and the plan is terminated; if the sum of the minimum cost and the cost already consumed is not greater than the preset value, the project continues to be executed.

[0027] The prediction scheme includes: making the assumption that the completed amount of work is 0, generating the scheduling result as a comparison scheduling scheme; in the m comparison scheduling schemes, sliding and cutting out the length of the completed work, calculating the similarity between the length of the completed work and the scheduling scheme of the nodes in the completed work; selecting the scheduling scheme corresponding to the minimum value and the segment cut out from the scheme, which are respectively denoted as scheme 1 and segment 1.

[0028] In the node scheduling scheme of the completed project, randomly select any number of nodes, calculate the similarity with segment 1 after selection, and use it as the weight judgment coefficient; traverse all node combinations to obtain all weight judgment coefficients; input the weight judgment coefficients, scheme 1 and the node scheduling scheme of the completed project into the pre-trained neural network, and output the prediction scheme for the characteristics of the completed project.

[0029] Where m≥1 represents the number of scheduling schemes.

[0030] As a preferred embodiment of the task order quantity segmentation control method based on WBS and pricing list described in this invention, the optimal segmentation node is the position of the task control unit after the end of each task control unit and before the start of the next task control unit in the prediction scheme, which is taken as a candidate node.

[0031] Simulate each candidate node separately. Before the candidate node, the prediction scheme is executed according to the above scheme. After the candidate node, the optimal scheme is replaced according to the re-measured scheme at the candidate node to obtain the spliced ​​scheme. Calculate the total cost of the scheme.

[0032] The total cost and its corresponding candidate nodes are treated as elements, and the elements are arranged in ascending order according to the total cost to form a cost sequence. In the prediction scheme, the correlation between the task control units before and after each candidate node is analyzed sequentially through mutual information, and each candidate node is arranged in descending order according to the correlation to form a correlation sequence. After aligning the minimum values ​​of the cost sequence and the correlation sequence, they are slid towards each other in ascending order. During the sliding process, the candidate nodes corresponding to the aligned elements in the two sequences are identified. When the first candidate node is identified, it is taken as the optimal segmentation node.

[0033] Secondly, the present invention provides a task order quantity segmentation control system based on WBS and pricing list, including a decomposition module, which decomposes the project into the smallest task control unit, and uses the pricing list to measure the basic cost of each task control unit.

[0034] The quantification module generates scheduling constraints for each task management unit based on the engineering decomposition process of the project, and quantifies the overflow cost of each scheduling relationship based on the scheduling relationship between every two task management units.

[0035] The analysis module adjusts the base cost and overflow cost by inputting the completed projects, and generates a predicted plan and an optimal plan based on the completed projects.

[0036] The optimization module evaluates the optimal segmentation node based on the prediction scheme and fine-tunes the prediction scheme based on the evaluation results; when the project progress reaches the optimal segmentation node, it re-evaluates the optimal scheme based on the completed project and replaces the remaining scheduling scheme.

[0037] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the task order quantity segmentation control method based on WBS and pricing list as described in the first aspect of the present invention.

[0038] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the task order quantity segmentation control method based on WBS and pricing list as described in the first aspect of the present invention.

[0039] The beneficial effects of this invention are as follows: Through structured decomposition, intelligent scheduling, data-driven cost correction, and dynamic optimization of multiple solutions, it achieves refined management and control over all stages of an engineering project. Compared to traditional methods, it can respond in real time to changes in workload and cost, dynamically correct basic and excess costs, and improve forecast accuracy and risk control capabilities. By automatically identifying optimal stage nodes and intelligently adjusting scheduling, managers can obtain timely profit warnings and multi-solution decision-making suggestions during project execution, effectively preventing losses or reduced profits. This method improves resource utilization efficiency and the automation level of engineering management, providing a solid guarantee for the continuous profitability and high-quality delivery of engineering projects. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0041] Figure 1 This is a flowchart of a task order quantity segmentation control method based on WBS and pricing list. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0045] Reference Figure 1 As one embodiment of the present invention, this embodiment provides a method for segmented management of task order quantities based on WBS and pricing list, including the following steps:

[0046] S1: Decompose the project into its smallest control unit and measure the basic cost of each control unit using a pricing list.

[0047] Furthermore, based on the Work Breakdown Structure (WBS), engineering projects are hierarchically broken down from top to bottom into a series of manageable and quantifiable sub-task units, until the smallest executable and measurable work unit is formed. The overall project objectives are refined layer by layer, clearly defining the content, responsibility boundaries, and measurement standards for each task. This top-down decomposition method effectively transforms large and complex projects into multiple independently manageable, easily measurable, resource-allocation-friendly, and schedule-controllable smallest work units. This not only improves management granularity, reduces task omissions, and ensures the controllability of project progress and quality, but also lays a solid data and structural foundation for subsequent refined management processes such as cost accounting, scheduling optimization, dynamic adjustments, and risk analysis.

[0048] It should be noted that the basic cost includes the total material cost and total labor cost of each task control unit. The total material cost of each task control unit is obtained by multiplying the unit price of different materials in the pricing list by the quantity of work in each task control unit.

[0049] Based on the unit labor cost for different trades with varying numbers of workers in the pricing list (it should be noted that the work content within each smallest task control unit is planned as the same trade; therefore, "different trades" here refers to the trade specific to the current task control unit), establish the relationship between the project completion time and the total labor cost for each task control unit: Where E represents the total labor cost of the task management unit; This represents the total labor cost of the task control unit when there is only one worker. This represents the total construction time of the task control unit when there is only 1 worker. It is measured by the time it takes for a single worker to complete the entire task control unit's workload (total workload / individual construction speed); t represents the completion time of the task control unit's work. The maximum value of t is selected for measurement by imposing time constraints on each stage. The number of workers is represented by an integer greater than 0.

[0050] It is important to understand that each task control unit is subdivided into several key construction stages based on its work content (such as material preparation, basic work, main process, finishing, etc.), and the time constraints of each stage are extracted, including: material arrival time, completion time of prerequisite tasks, available time of necessary machinery / equipment, arrival time of necessary personnel, minimum / maximum time of process or safety restrictions, and stage time calculation; for each stage, the estimated time under the current organizational plan is calculated based on parameters such as actual workload, resource allocation and construction efficiency.

[0051] By combining the unit price of materials in the pricing list with the specific quantities of work, the total material cost for each unit can be accurately calculated, ensuring transparency and controllability of material consumption. Simultaneously, for each smallest task management unit, its work content is planned as a single trade, making labor cost accounting more scientific. A dynamic relationship model between "project completion time and labor cost" is established by combining the actual number of construction workers, trade unit prices, construction speed, and total project volume. This not only accurately reflects the impact of different construction organization methods on labor costs but also provides a decision-making basis for rationally allocating human resources, optimizing scheduling, and controlling labor expenditures. Overall, this design provides a quantifiable and traceable data foundation and algorithmic support for subsequent stages such as full-process dynamic cost control, resource allocation optimization, and intelligent scheduling.

[0052] S2: Based on the engineering decomposition process of the project, generate scheduling constraints for each task management unit, and quantify the overflow cost of each scheduling relationship based on the scheduling relationship between every two task management units.

[0053] Each smallest task management unit obtained from the project decomposition is treated as a node in the network. Based on the logical dependencies and technological processes of each unit during the project decomposition process (in the process of project decomposition, by sorting out the operation sequence, professional interfaces, process connections, and resource allocation of each task management unit, the logical dependencies and technological processes between each task unit are clarified. Specifically, this includes determining which task units must be started only after other units are completed (e.g., structural construction must be carried out after foundation construction is completed), which tasks can be carried out in parallel, and the key interface relationships between each process (e.g., material arrival depends on the completion of the preceding operation, installation operation requires the civil engineering acceptance, etc.). This systematic dependency and process analysis ensures that each stage of the project proceeds in an orderly manner according to a reasonable sequence, effectively avoiding risks such as resource conflicts, process waiting, and schedule delays, and achieving scientific and efficient construction organization), the sequential execution relationship between each pair of units is generated, forming directed edges, resulting in a graph structure; where the direction of the edge represents the constraint on the execution order. The graph structure is simplified to obtain a simplified graph structure: if there is a direct connection between two nodes i and j with an edge in the direction i→j, and there are also indirect connections between nodes i and j formed by multiple intermediate nodes with the same direction i→j, then the direct connection between nodes i and j is broken, achieving one simplification; this simplification process is repeated cyclically to obtain the simplest form of the graph structure. Each minimum task management unit is treated as a node in the graph, and directed edges are generated through logical dependencies and process flows, clearly and intuitively depicting the sequence and dependency paths in the engineering construction process. By simplifying the graph structure and breaking redundant direct edges with multiple indirect paths, the simplest graph structure is obtained, effectively eliminating redundant constraints and reducing network complexity. This not only improves the computational efficiency of scheduling and progress analysis but also facilitates the accurate calculation and optimization of core indicators such as critical paths, resource scheduling, cost accumulation, and overflow costs, laying a solid foundation for the informatization, automation, and intelligentization of engineering management.

[0054] The constraints of each edge are used as the scheduling relationship between the two nodes. The increase and decrease of material and labor costs caused by the engineering transformation between the two nodes are quantified to obtain the overflow cost of the scheduling relationship between the two nodes.

[0055] The quantification process of the overflow cost includes the following steps: Step 1, calculating the increased cost: When generating each node in the graph structure, each node selects a material surplus evaluation function obtained by fitting historical data based on the corresponding engineering content; according to the material surplus evaluation function, the sum of the cost of each type of material surplus and the corresponding removal cost is calculated using the engineering quantity of the task management unit. This step, because it measures the surplus cost and cleanup cost of each task management unit, generates an "increased cost" for each node when constructing the graph structure.

[0056] The setting of removal costs stems from the on-site cleanup and transfer of surplus materials, waste, and material residues in actual project management. It is an indispensable element in ensuring the accuracy, comprehensiveness, and economic optimization of project cost accounting. The removal cost for each type of material is calculated by multiplying the remaining material quantity by the corresponding labor cost per unit.

[0057] It is important to understand that, in the context of this invention and project management, the scope of an engineering project refers to the collection of specific work items and technical requirements that need to be completed for each task control unit or sub-project. This typically includes, but is not limited to: the construction techniques or work procedures involved (such as earthwork excavation, concrete pouring, rebar tying, equipment installation, etc.); the specific construction location, quantity of work, and its unit of measurement; the necessary types and quantities of raw materials, types and requirements of labor, and the equipment and tools used; the quality standards, technical parameters, and acceptance specifications to be achieved; the corresponding planned construction period and schedule milestones; and the relevant safety, environmental protection, and civilized construction requirements.

[0058] The data required for the material surplus assessment function mainly comes from the engineering management database of completed projects in the enterprise or industry, including but not limited to: the actual engineering quantity of task control units (such as sub-items, processes, and construction sections) of various engineering projects; the actual raw material purchase quantity, consumption quantity and on-site residual (surplus) data; material consumption details ledger, warehousing and outbound records, return and waste quantity statistics; related manual cleaning records and labor cost ledger. These data can be exported from project management system, enterprise ERP, BIM platform or manually collected and archived historical data to form a structured database. The fitting process generally includes the following steps: (1) Data cleaning and preprocessing: remove data outliers and duplicate values, unify engineering quantity and material measurement units; classify and organize datasets of different engineering types and different material types.

[0059] (2) Feature extraction: Extract feature variables such as “task control unit engineering quantity (Q)” and “actual material surplus (U)”; associate influencing factors such as material category, process link, and construction method.

[0060] (3) Function modeling and fitting: Select a suitable mathematical model, such as linear regression, multinomial regression, piecewise function, machine learning model (such as decision tree, SVR, neural network), etc.; use "Q" and "U" of the same type of unit in historical projects as independent and dependent variables, and use the least squares method or other optimization algorithms to train and fit the model to obtain an evaluation function describing the relationship between the quantity of work and the material surplus. Independent evaluation functions can be established according to different project types and material varieties.

[0061] (4) Model validation and optimization: Cross-validation is performed using a portion of the historical dataset to test the prediction accuracy of the fitted function; if the accuracy is not ideal, the data features can be optimized, influencing factors can be added, or the fitting method can be adjusted.

[0062] (5) Database / Platform Deployment: The fitted evaluation function and parameters are embedded as an evaluation module into the project management system or intelligent cost analysis platform to realize dynamic prediction of material surplus and cost measurement of new project task units.

[0063] Step 2, calculate cost reduction: In the simplest form of the graph structure, generate the material cost utilization rate and calculate the cost reduction amount for removing the two nodes connected by each edge.

[0064] Based on the project content of the node pointed to by the arrow, compare the material surplus U1 of each type of node at the tail of the arrow with the sum of the project demand of the node pointed to by the arrow and the corresponding material surplus U2. If U1 > U2, it means that the remaining material in the previous process is more than the amount that the next process can absorb. The subsequent node can only consume U2 of the material, and the cost saving is capped at U2. The remaining material is either left behind, discarded, or removed—the part exceeding U2 will not bring further savings. In this case, the sum of the material surplus cost corresponding to U2 and the removal cost corresponding to U2 is used as the cost reduction. If U1 ≤ U2, all the remaining material in the previous process can be absorbed and reused by the subsequent process. The maximum saving is the sum of the total material cost and removal cost corresponding to U1. In this case, the sum of the material surplus cost and the removal cost corresponding to U1 is used as the cost reduction.

[0065] The fundamental purpose of this algorithm is to accurately reflect the optimal reuse and cost-saving effect of material surplus during the connection of segmented projects, and to reasonably calculate the resulting cost savings (i.e., "cost reduction"). The design of this segmented node maximizes the use of previous surplus materials, minimizes waste and secondary cleanup expenses caused by leftover materials, and reflects the inherent logic of material flow and engineering economics. It avoids duplicate measurement, prevents "inflated" or overflowing cost-saving calculations, and ensures rigorous, traceable, and accurate cost accounting and engineering economics.

[0066] Step 3: Sum the increase cost and decrease cost between the two nodes in the scheduling relationship to obtain the overflow cost.

[0067] The key takeaway is that a refined cost quantification mechanism accurately reflects the additional costs incurred during the transition between project segments and tasks due to material surplus, manual cleanup, and resource scheduling, enabling dynamic assessment and control of "overflow costs" in scheduling relationships. By using a material surplus assessment function fitted to historical data, the remaining raw materials and removal labor costs for each task unit can be scientifically calculated in conjunction with the actual workload, ensuring the rationality of "increased costs" at each node. Furthermore, for project transitions between task units, the cost savings resulting from resource reuse or transfer between segment nodes are quantified through analysis of the reduction in material surplus utilization and removal costs. Ultimately, by combining increased and decreased costs, an accurate and dynamic reflection of the actual operational status of the project's overflow costs is obtained. This method not only improves the precision and real-time nature of cost accounting but also provides a solid data foundation and decision support for project segment optimization, resource allocation, and full-process profitability analysis, significantly enhancing the economic efficiency and intelligence of project management. In short, project content is a detailed description of "what should be done, to what extent, using what methods, using how many resources, and under what time and conditions" under a certain task management unit. It is the basic information unit for realizing refined project management, progress control and cost accounting.

[0068] S3: By inputting the completed projects, adjust the basic cost and overflow cost, and generate a predicted plan and an optimal plan based on the completed projects.

[0069] The process of adjusting the basic cost and overflow cost includes: replacing the theoretical material consumption and unit price in the original budget or list with the actual raw material usage and purchase unit price, and recalculating the total material cost; replacing the theoretical working hours and standard unit price with the actual labor hours and actual labor unit price, and recalculating the total labor cost.

[0070] Based on the deviation rate between the overflow cost and the calculation result between every two nodes in the completed project, the overflow cost between every two nodes in the unfinished project is adjusted according to the deviation rate.

[0071] By replacing the theoretical data in the original budget or bill of quantities with the actual raw material usage and purchase price, and the actual labor hours and labor price, the actual cost changes during project implementation can be reflected in real time, effectively correcting errors caused by initial estimation deviations, market fluctuations, or adjustments to construction techniques. Furthermore, based on the deviation rate between the overflow costs of every two nodes in the completed project and the original calculation results, the overflow costs of the unfinished project can be dynamically adjusted, enabling the cost prediction model to self-correct and continuously optimize. This not only improves the accuracy and flexibility of project cost prediction but also provides solid data support for managers to identify risks in a timely manner, optimize scheduling, and make scientific decisions, promoting refined management and economic improvement throughout the entire project process.

[0072] The optimal solution includes scheduling each node with the minimum cost while ensuring the remaining nodes meet the scheduling constraints, thereby obtaining the scheduling results and minimum cost for all nodes. If the sum of the minimum cost and the cost already consumed is greater than a preset value, an early warning message is generated and the solution is terminated; if the sum of the minimum cost and the cost already consumed is not greater than the preset value, the project continues.

[0073] The prediction scheme includes making the assumption that when the amount of completed work is 0, generating the scheduling result as a comparison scheduling scheme; among the m comparison scheduling schemes, sliding to extract the length of the completed work, calculating the similarity between the length of the completed work and the node scheduling scheme in the completed work; selecting the scheduling scheme corresponding to the minimum value and the segment extracted from the scheme, which are respectively denoted as Scheme 1 and Segment 1.

[0074] In the node scheduling scheme of the completed project, randomly select any number of nodes, calculate the similarity with segment 1 after selection, and use it as the weight judgment coefficient; traverse all node combinations to obtain all weight judgment coefficients; input the weight judgment coefficients, scheme 1 and the node scheduling scheme of the completed project into a pre-trained neural network, and output a prediction scheme for the characteristics of the completed project.

[0075] Here, m≥1 represents the number of scheduling schemes. By assuming "0 completed works" and generating multiple comparative scheduling schemes, the system can use the optimal scheduling under ideal conditions as a reference, effectively avoiding the risk of getting trapped in local optima due to the influence of existing progress paths. In this way, the predicted scheme always aims at the globally optimal cost, ensuring the maximum benefit and optimal schedule of subsequent projects. Using the sliding window technique, each comparative scheduling scheme is matched with the completed project progress in segments of the same length, ensuring that the scheduling features of completed nodes are fully incorporated into the predictive analysis. Through similarity calculation, the correspondence between completed schedules and optimal scheme segments can be accurately identified, providing a strong data foundation for subsequent scheme combinations and decisions. Randomly select any node combination from the completed project node scheduling schemes and compare its similarity with segment 1, quantifying the matching degree of each combination into a weighted judgment coefficient. This process essentially realizes multi-angle feature extraction and influence discrimination of scheduling segments in completed projects, eliminating misjudgments caused by data abstraction, and making the predicted scheme more consistent with the actual construction progress and resource allocation. Finally, the weighted judgment coefficients, the optimal segment plan, and the actual scheduling data are input into the neural network model to output a highly adaptive prediction plan. This model can deeply uncover the hidden patterns in the project progress, enabling dynamic optimization of subsequent task allocation, cost prediction, and schedule arrangement. This not only improves the foresight and scientific rigor of the plan but also greatly enhances its ability to cope with the uncertainties and variability of complex engineering projects. Through this method, the plan is ensured to operate under the global optimal objective while dynamically adjusting the prediction results based on the actual progress, effectively achieving "global-local" collaborative optimization. This innovative design significantly improves the intelligence of project management, data utilization, and risk control capabilities.

[0076] It's worth noting that intelligent scheduling and data-driven multi-scheme analysis enable cost optimization, risk warning, and decision support for subsequent project phases. For remaining task nodes, a cost minimization algorithm automatically generates the optimal scheduling plan while satisfying all scheduling constraints, ensuring overall costs remain within budget. By comparing the optimal cost with the costs already incurred in real time, potential cost overruns can be detected promptly, and automatic warnings are issued to safeguard project profitability and financial security. Simultaneously, multiple comparative scheduling schemes are set up. Through sliding windows and similarity analysis, historically completed projects are intelligently matched with potential scheduling segments. Combined with random node sampling and weighted judgment, a neural network model extracts the scheduling characteristics of completed projects, thereby outputting a dynamic prediction scheme that better reflects the actual project progress. This design not only enhances the scientific nature of scheme selection but also significantly improves the adaptability and foresight of project management, contributing to risk prevention and intelligent optimization throughout the entire project process.

[0077] In this embodiment, the neural network refers to a multi-layered structured model built based on artificial intelligence technology, capable of automatically learning and extracting complex correlation features from input data to achieve adaptive prediction and dynamic optimization of engineering scheduling schemes. This neural network typically includes an input layer, several hidden layers, and an output layer. Each layer consists of multiple neuron nodes, and signal transmission and feature transformation are performed through weighted connections and non-linear activation functions.

[0078] In practical applications, the input layer receives multi-dimensional feature data such as weight judgment coefficients, comparison scheme fragments, and the schedule of completed project nodes. The hidden layer automatically analyzes the nonlinear relationships between the input variables through deep feature learning, and the output layer generates the optimal prediction scheme based on the characteristics of the completed project. The neural network can employ common fully connected feedforward networks (such as multilayer perceptrons, MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), or, depending on the complexity of the actual project data, deep residual networks, graph neural networks, etc. Model training can utilize historical project data, continuously optimizing parameters through backpropagation and gradient descent to improve the accuracy and adaptability of the prediction scheme.

[0079] S4: Evaluate the optimal segmentation node according to the prediction scheme, and fine-tune the prediction scheme according to the evaluation results; when the project progress reaches the optimal segmentation node, re-evaluate the optimal scheme based on the completed project, and replace the remaining scheduling scheme.

[0080] The optimal segmentation node is defined as the position of the task management unit after the end of each task management unit in the prediction scheme and before the start of the next task management unit, which is taken as a candidate node. Simulations are performed on each candidate node, assuming that the prediction scheme is followed before the candidate node, and the optimal scheme, remeasured at the candidate node, is used after the candidate node to obtain the concatenated scheme, and the total cost of the scheme is calculated.

[0081] The total cost and its corresponding candidate nodes are treated as elements, arranged in ascending order of total cost to form a cost sequence. In the prediction scheme, mutual information analysis is used to analyze the correlation between the task control units before and after each candidate node, and each candidate node is arranged in descending order according to the correlation to form a correlation sequence. After aligning the minimum values ​​of the cost sequence and the correlation sequence, they are slid towards each other in ascending order. During the sliding process, candidate nodes corresponding to the aligned elements in the two sequences are identified. When the first candidate node is identified, it is taken as the optimal segmentation node. Existing technologies usually only use single indicators such as minimum cost and fastest progress as the basis, ignoring the strength of the process or resource coupling between nodes, which can easily lead to problems such as poor connection after project segmentation, resource conflicts, and actual cost rebound. In contrast, this scheme uses a sliding window method to dynamically compare after aligning the minimum values ​​of the two sequences. Each slide comprehensively considers cost and correlation between nodes, automatically selecting nodes that have both high resource utilization and reasonable connection, while also taking into account the optimal total project cost. This approach not only enhances the scientific rigor and intelligence of segmentation node selection but also significantly improves the robustness of the project plan and its overall profitability optimization capabilities. It effectively avoids traditional management bottlenecks such as local optima and global suboptimality caused by inappropriate segmentation strategies, achieving true "multi-objective dynamic optimization" in project segmentation decisions. Simultaneously, it reduces computational load, improving computational speed and reducing resource consumption.

[0082] Through multi-dimensional quantitative evaluation and dynamic intelligent decision-making, the optimal selection of segmented control nodes for engineering projects can be achieved, thereby improving the profitability and risk controllability of the entire project process. By setting the connection points between each task control unit as candidate nodes and simulating each candidate node, and splicing the predicted scheme before and after the node with the optimal scheme based on the latest data, the system can comprehensively evaluate the impact of different segmentation points on the overall cost and schedule.

[0083] By combining cost sequences and node relevance sequences with a sliding window alignment method, this approach prioritizes segments with lower total costs while highlighting key points that have the greatest impact on subsequent task continuity, achieving a dynamic balance between cost optimization and process coherence. Ultimately, the automatically determined optimal segments not only facilitate phased optimization of resource allocation and improved profitability but also enable intelligent fine-tuning of project schedules and proactive risk assessment, providing managers with a scientific basis for segmented decision-making and real-time dynamic adjustments. This innovative mechanism significantly enhances the informatization, intelligence, and overall optimization capabilities of project management.

[0084] This embodiment also provides a task order quantity segmentation management system based on WBS and pricing list, including:

[0085] The decomposition module breaks down the project into its smallest unit of control and uses a pricing list to measure the basic cost of each unit of control.

[0086] The quantification module generates scheduling constraints for each task management unit based on the engineering decomposition process of the project, and quantifies the overflow cost of each scheduling relationship based on the scheduling relationship between every two task management units.

[0087] The analysis module adjusts the base cost and overflow cost by inputting the completed projects, and generates a predicted plan and an optimal plan based on the completed projects.

[0088] The optimization module evaluates the optimal segmentation node based on the prediction scheme and fine-tunes the prediction scheme based on the evaluation results; when the project progress reaches the optimal segmentation node, it re-evaluates the optimal scheme based on the completed project and replaces the remaining scheduling scheme.

[0089] This embodiment also provides a computer device applicable to the task order quantity segmentation control method based on WBS and pricing list, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the task order quantity segmentation control method based on WBS and pricing list proposed in the above embodiment.

[0090] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0091] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the task quantity segmentation control method based on WBS and pricing list as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0092] In summary, this invention achieves dynamic segmented management and intelligent profitability optimization throughout the entire project lifecycle by introducing a series of innovative technologies, including Work Breakdown Structure (WBS)-based project decomposition, refined cost accounting using the bill of quantities, graph-based scheduling constraint modeling, dynamic quantification of spillover costs, data-driven cost and schedule adjustments, and intelligent evaluation of multi-objective optimization segmented nodes. This method not only significantly improves the refinement, automation, and foresight of project management but also effectively prevents risks such as cost overruns and resource waste, providing solid technical support and decision-making basis for the efficient implementation and sustainable profitability of large and complex projects.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A task order quantity segmentation control method based on WBS and pricing list, characterized by: include, The project is broken down into its smallest control unit, and the basic cost of each control unit is measured using a pricing list. Based on the engineering decomposition process of the project, a scheduling constraint is generated for each task management unit, and the overflow cost of each scheduling relationship is quantified based on the scheduling relationship between every two task management units. By inputting completed projects, the basic costs and overflow costs are adjusted, and a predicted plan and an optimal plan based on the completed projects are generated. The optimal segmentation node is evaluated based on the prediction scheme, and the prediction scheme is fine-tuned based on the evaluation results; when the project progress reaches the optimal segmentation node, the optimal scheme based on the completed project is re-evaluated, and the remaining scheduling scheme is replaced. The optimal segmentation node is the position of the task management unit after the end of each task management unit in the prediction scheme and before the start of the next task management unit, which is taken as a candidate node; Simulate each candidate node separately. Before the candidate node, the prediction scheme is executed according to the above scheme. After the candidate node, the optimal scheme is replaced according to the re-measured scheme at the candidate node to obtain the spliced ​​scheme. Calculate the total cost of the scheme. The total cost and its corresponding candidate nodes are treated as elements, and the elements are arranged in ascending order according to the total cost to form a cost sequence. In the prediction scheme, the correlation between the task control units before and after each candidate node is analyzed sequentially through mutual information, and each candidate node is arranged in descending order according to the correlation to form a correlation sequence. After aligning the minimum values ​​of the cost sequence and the correlation sequence, they are slid towards each other in ascending order. During the sliding process, the candidate nodes corresponding to the aligned elements in the two sequences are identified. When the first candidate node is identified, it is taken as the optimal segmentation node.

2. The task order quantity segmentation control method based on WBS and pricing list as described in claim 1, characterized in that: The project decomposition includes a Work Breakdown Structure (WBS) that breaks down the project into a series of manageable and quantifiable sub-task units from top to bottom in a hierarchical structure until the smallest executable and assessable work unit is formed.

3. The task order quantity segmentation control method based on WBS and pricing list as described in claim 2, characterized in that: The basic cost includes the total material cost and total labor cost of each of the task control units; Based on the unit price of different materials in the pricing list, multiply the quantity of work in each task control unit to obtain the total material cost of each task control unit. Based on the unit labor cost of different trades with different numbers of workers in the pricing list, establish the relationship between the project completion time and the total labor cost of each task control unit: Where E represents the total labor cost of the task management unit; This represents the total labor cost of the task control unit when there is only one worker. This represents the total construction time of the task control unit when there is only 1 worker, measured by the time it takes for a single worker to complete the entire task control unit's work; t represents the completion time of the task control unit's work, measured by selecting the maximum value of t based on time constraints for each stage. The number of workers is represented by an integer greater than 0.

4. The task order quantity segmentation control method based on WBS and pricing list as described in claim 3, characterized in that: The scheduling constraints include treating each smallest task management unit obtained from the decomposition of the project as a node in the network; Based on the logical dependencies and process flow of each unit during the engineering decomposition process, the sequential execution relationship between each pair of units is generated, forming directed edges to obtain a graph structure; where the direction of the edge represents the constraint on the execution order. The graph structure is simplified to obtain the simplified graph structure: if there is a direct connection between two nodes i and j with the direction i→j, and there are also multiple intermediate nodes forming an indirect connection between nodes i and j with the direction i→j, then the direct connection between nodes i and j is broken, thus achieving one simplification; the simplification process is continuously repeated to obtain the simplest form of the graph structure. The constraints of each edge are used as the scheduling relationship between the two nodes. The increase and decrease of material and labor costs caused by the engineering transformation between the two nodes are quantified to obtain the overflow cost of the scheduling relationship between the two nodes. The quantification process of the overflow cost includes the following steps: Step 1, calculating the increased cost: When generating each node in the graph structure, each node selects a material surplus evaluation function obtained by fitting historical data according to the corresponding engineering content; Based on the material surplus evaluation function, the sum of the cost of each material surplus and the corresponding removal cost is calculated using the engineering quantity of the task control unit. The removal cost for each material is calculated by multiplying the remaining material quantity by the corresponding labor cost per unit. Step 2, Calculate cost reduction: In the simplest form of the graph structure, generate the material cost utilization rate and calculate the cost reduction amount for removing the two nodes connected by each edge; Based on the project content of the node pointed to by the arrow, compare the material surplus U1 of each type of node at the tail of the arrow with the sum of the project demand quantity of the node pointed to by the arrow and the corresponding material surplus U2. If U1 > U2, the sum of the material surplus cost corresponding to U2 and the removal cost corresponding to U2 is used as the cost reduction; if U1 ≤ U2, the sum of the material surplus cost corresponding to U1 and the removal cost corresponding to U1 is used as the cost reduction. Step 3: Sum the increase cost and decrease cost between the two nodes in the scheduling relationship to obtain the overflow cost.

5. The task order quantity segmentation control method based on WBS and pricing list as described in claim 4, characterized in that: The process of adjusting the basic cost and overflow cost includes: replacing the theoretical material consumption and unit price in the original budget or list with the actual raw material usage and purchase unit price, and recalculating the total material cost; replacing the theoretical working hours and standard unit price with the actual labor hours and actual labor unit price, and recalculating the total labor cost. Based on the deviation rate between the overflow cost and the calculation result between every two nodes in the completed project, the overflow cost between every two nodes in the unfinished project is adjusted according to the deviation rate.

6. The task order quantity segmentation control method based on WBS and pricing list as described in claim 5, characterized in that: The optimal solution includes scheduling each node with the minimum cost while ensuring that the remaining nodes satisfy the scheduling constraints, thereby obtaining the scheduling results of all nodes and the minimum cost. If the sum of the minimum cost and the cost already consumed is greater than a preset value, an early warning message is generated and the plan is terminated; if the sum of the minimum cost and the cost already consumed is not greater than the preset value, the project continues to be executed. The prediction scheme includes making the assumption that the completed workload is 0, generating the scheduling result as a comparison scheduling scheme; In the m comparison scheduling schemes, the length of the completed project is slidably extracted, and the similarity between the scheme and the node scheduling scheme in the completed project is calculated. Select the scheduling scheme corresponding to the minimum value and the segment extracted from the scheme, and denote them as Scheme 1 and Segment 1, respectively; In the node scheduling scheme of the completed project, randomly select any number of nodes, calculate the similarity with segment 1 after selection, and use it as the weight judgment coefficient; traverse all node combinations to obtain all weight judgment coefficients; input the weight judgment coefficients, scheme 1 and the node scheduling scheme of the completed project into the pre-trained neural network, and output the prediction scheme for the characteristics of the completed project. Where m≥1 represents the number of scheduling schemes.

7. A task order quantity segmentation control system based on WBS and pricing list, based on the task order quantity segmentation control method based on WBS and pricing list as described in any one of claims 1 to 6, characterized in that: This includes a decomposition module, which breaks down the project into its smallest unit of control and calculates the basic cost of each unit of control using a pricing list. The quantification module generates scheduling constraints for each task management unit based on the engineering decomposition process of the project, and quantifies the overflow cost of each scheduling relationship based on the scheduling relationship between every two task management units. The analysis module adjusts the basic cost and overflow cost by inputting the completed projects, and generates a predicted plan and an optimal plan based on the completed projects. The optimization module evaluates the optimal segmentation node based on the prediction scheme and fine-tunes the prediction scheme based on the evaluation results; when the project progress reaches the optimal segmentation node, it re-evaluates the optimal scheme based on the completed project and replaces the remaining scheduling scheme.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the task order quantity segmentation control method based on WBS and pricing list as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the task order quantity segmentation control method based on WBS and pricing list as described in any one of claims 1 to 6.

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