A method and system for quantitatively evaluating the parameter self-consistency of a construction scheme

Abstract

This invention discloses a method and system for quantitatively scoring the parameter self-consistency of construction schemes. The method includes: acquiring a parameter dataset of the construction scheme; constructing a rule set defining the logical constraints between parameters; verifying the parameters based on the rule set and calculating conflict deductions when each rule is not satisfied, wherein the conflict deductions are calculated based on continuous values ​​of the degree of non-compliance; calculating cross-coupling synergistic bonuses; calculating a comprehensive parameter self-consistency score based on the sum of conflict deductions and synergistic bonuses; and finally outputting the score result. The corresponding system includes modules for parameter extraction, constraint rule base, verification engine, score calculation, and visualization. This invention employs a deterministic rule engine and pioneers a cross-coupling synergistic bonus mechanism, achieving an objective, continuous, and repeatable quantitative evaluation of the logical self-consistency of internal parameters within a single construction scheme. This effectively overcomes the limitations of existing technologies, which can only perform scheme comparison or binary judgment and ignore parameter synergistic effects.

Description

Technical Field

[0001] This invention belongs to the field of construction management technology, and specifically relates to a method, system, computer-readable storage medium, and computer equipment for quantitatively scoring the self-consistency of parameters in construction technology schemes. Background Technology

[0002] With the intelligent development of construction management, the methods for compiling and evaluating construction technical solutions are undergoing profound changes. Existing technologies have achieved automated generation of construction solutions. For example, the "Intelligent Generation Method and System for Construction Organization Design Word Documents" (authorized) by Expressway Dejian achieves intelligent generation of construction organization design documents; Xu Hui Design's "Construction Management Method, System and Storage Medium Based on BIM and AI Integration" (CN121880427A) discloses a method for generating construction solutions based on BIM and AI integration. Simultaneously, intelligent review technologies for construction solutions have also been developed. For instance, China Railway Construction's "Intelligent Review System and Method for Construction Solutions" (CN118278891B, authorized) achieves intelligent review of construction solution documents; Shaanxi Construction Engineering's "Intelligent Dynamic Review Method and System for Construction Solutions Based on Multi-Model Collaboration" discloses a technical solution for intelligent review of construction solution documents and supporting drawings through multi-model collaboration; and Shanghai Construction Engineering's "Intelligent Review Method and System Based on Multi-Agent Collaboration" uses a large language model to drive quantitative evaluation and content optimization of construction solutions.

[0003] In the evaluation and comparison of construction schemes, numerous patents have employed multi-attribute decision-making methods to comprehensively evaluate and rank candidate schemes. Examples include scheme optimization methods based on the entropy weight method to determine objective weights, evaluation methods based on the AHP-TOPSIS combination, and comprehensive evaluation methods combining grey relational analysis and the entropy weight method. CCCC Fourth Harbor Engineering Co., Ltd.'s application for "Slope Protection Scheme Evaluation Method and System" discloses a technical solution for evaluating slope protection schemes by determining indicators, calculating the weights of each indicator, scoring each indicator, and calculating a comprehensive score. The evaluation method for stiffened arch bridge construction schemes based on the comprehensive stability coefficient establishes a comprehensive evaluation index system for selecting the optimal construction scheme. Furthermore, Glodon's "Network Plan Rationality Evaluation Dimension Design Method, Evaluation Method, System, and Device" (CN201910589539.8, authorized) discloses a technical solution for identifying hard and soft errors by evaluating and analyzing various dimensions of the construction schedule network plan. China Power Construction East China Institute's "Engineering Schedule Inspection System and Method" (CN120235439A) discloses a technical solution that breaks down engineering schedule inspection into eight modules to effectively judge the rationality of the schedule preparation. The inspection report will indicate whether the schedule is reasonable and list the work items that do not meet the rules detected according to the inspection rules. China Power Construction Northwest Institute's "Optimal Selection Method for Waste Disposal Site Design" (CN121881455A) discloses a technical solution for determining the optimal solution from multiple waste disposal site designs to be evaluated.

[0004] However, existing construction scheme evaluation technologies have the following limitations: First, existing evaluation technologies aim to compare and rank the options, outputting a "comprehensive score" and "ranking of superiority" for each candidate option. When only one construction option needs to be evaluated (such as a construction option developed internally by a company), existing technologies cannot provide a quantitative measure of the option's "absolute quality" or "self-consistency level," and can only output "cannot be compared" or "no comparable reference option."

[0005] Second, existing evaluation index systems are mostly based on expert experience, and the scoring of each index depends on the expert's subjective judgment and qualitative analysis. They lack objective, repeatable, and quantitative scoring criteria based on the inherent logical constraints between the internal parameters of the construction plan. Different experts may score the same plan significantly differently, resulting in a lack of objectivity and consistency in the scoring results.

[0006] Third, existing construction plan review technologies primarily focus on whether the plan meets regulatory requirements (external compliance). For example, Shaanxi Construction Engineering Group's technical plan constructs a knowledge graph of scaffolding construction specifications to form a compliance verification rule base, focusing on verifying whether the plan meets regulatory requirements. However, this type of technology does not involve evaluating the logical consistency of the plan's internal parameters and cannot identify "compliant but inconsistent" plans—that is, plans with logical contradictions in parameters such as schedule, resources, space, and cost, making the plan impossible to implement in practice.

[0007] Fourth, existing patents related to schedule inspection only make a binary judgment of "whether the rules are met" (pass / fail). For example, existing patents "list the work items that do not meet the rules as detected by the inspection rules" or "identify the hard and soft errors among them." Although these patents can find errors, they cannot quantify the "self-consistency" of the solution or distinguish between "serious nonconformity" and "minor nonconformity".

[0008] Fifth, existing evaluation techniques rely on independent scoring items, neglecting the positive correlations between parameters. In construction plans, synergistic relationships between certain parameters (such as resource reuse and space-schedule coupling) can offset or mitigate the unmet needs of other constraints, thereby improving the overall self-consistency of the plan. For example, sharing the same equipment across multiple processes can improve equipment utilization, and a reasonable match between space constraints and a moderate extension of the schedule can avoid conflicts. Existing techniques linearly weight and sum these independent scoring items, failing to capture the synergistic effects between parameters, resulting in evaluation results that do not accurately reflect the actual self-consistency level of the plan.

[0009] Therefore, there is an urgent need for an objective and repeatable quantitative scoring method for the logical self-consistency of internal parameters of construction schemes, which can output a self-consistency quantitative score in a single scheme evaluation scenario, upgrade "binary judgment" to "continuous quantitative scoring", and incorporate the positive correlation (synergistic effect) between parameters into the scoring system, so as to provide a deterministic and quantifiable decision-making basis for the feasibility assessment, scheme comparison and optimization of construction schemes. Summary of the Invention

[0010] The purpose of this invention is to provide a method and system for quantitatively evaluating the self-consistency of parameters in construction schemes, aiming to solve the technical problems of existing technologies that cannot objectively and repeatedly evaluate the self-consistency of a single scheme, can only make binary judgments, and ignore the positive correlation and synergistic effect between parameters.

[0011] To achieve the above objectives, in a first aspect, the present invention provides a method for quantitatively evaluating the parameter self-consistency of a construction scheme, comprising the following steps: S1. Obtain the parameter dataset of the target construction scheme. Extract structured parameters from the target construction plan document, including but not limited to: schedule parameters (planned schedule, duration of each process), resource parameters (number of equipment, output per shift, labor allocation), process parameters (process rate, workspace requirements), cost parameters (direct cost, indirect cost), and spatial parameters (work area size, passage width).

[0012] S2. Construct a set of parameter self-consistency constraint rules. Construct a rule set that defines the logical constraints between parameters. The rule base must contain at least the following core rules (R1-R12): R1 (Schedule-Resource-Quantity Constraint): Total schedule × Number of equipment × Output per shift ≥ Total quantity of work.

[0013] R2 (Resource-Space Constraint): The sum of the job widths of parallel devices ≤ the available width of the job surface.

[0014] R3 (Process-Time Constraint): Start time of subsequent process ≥ Completion time of preceding process + Technical interval.

[0015] R4 (Material Supply and Demand Balance Constraint): Material supply rate ≥ Material consumption rate.

[0016] R5 (Equipment Safety Distance Constraint): The distance between simultaneously operating equipment is ≥ the safety distance threshold.

[0017] R6 (Process-Space-Safety Constraints): Equipment operating radius ≤ Minimum size of the operating surface.

[0018] R7 (Cost-Resource-Schedule Consistency Constraint): Direct Cost × Resource Input Density × Schedule ≥ Total Cost Budget.

[0019] R8 (Labor-Time-Quantity Constraint): Labor quantity × Time efficiency × Time ≥ Total labor hours required.

[0020] R9 (Indirect Cost Ratio Constraint): Indirect cost / direct cost ≤ preset threshold.

[0021] R10 (parallelism coefficient constraint): The sum of the durations of each process ≤ Total duration × Parallelism coefficient.

[0022] R11 (Resource Demand Smoothing Constraint): Peak resource demand ≤ Supply ceiling × Smoothing coefficient.

[0023] R12 (Resource Demand Fluctuation Constraint): The rate of change in resource demand between adjacent time periods is ≤ a preset threshold.

[0024] S3. Perform constraint verification on a per-constraint basis and calculate individual conflict deductions. The parameter dataset is validated item by item based on the rule set. For each rule R_k, the formula for calculating the individual conflict deduction is: Score_conflict_k = w_k × δ_k × C_k Where w_k is the rule weight coefficient (adjustable), C_k is the base deduction score, and δ_k is a continuous value representing the degree of rule non-compliance (0 for complete compliance, 1 for complete non-compliance). For inequality constraints, δ_k is calculated as follows: δ_k = 1 - (actual value / theoretical minimum value).

[0025] S4. Calculate the cross-coupling synergistic bonus. The calculation of positive bonuses based on the positive correlation between parameters includes four types of synergy: 1. Resource reuse and collaboration (A): When multiple processes share the same type of resources and the resource time utilization rate U exceeds the threshold U_th, the bonus Score_A = 0.5 × (U / U_th) × B_A is awarded.

[0026] 2. Space-Schedule Coupling and Coordination (B): When space constraints lead to a reduction in equipment, but the deviation ε between the actual schedule extension ratio ΔT_act and the theoretical ratio ΔT_theo is within the threshold ε_th, the bonus score is Score_B = max(0, 1-ε / ε_th) × B_B.

[0027] 3. Synergy between process rate and cost allocation (C): When process speed increases lead to an increase in direct costs C_dir_inc, but a decrease in indirect costs C_ind_dec, and the net cost change rate ΔC_net=(C_dir_inc - C_ind_dec) / C_orig is within the threshold ΔC_th, the bonus score is Score_C = max(0, 1-ΔC_net / ΔC_th) × B_C.

[0028] 4. Resource-Space-Schedule Triangular Collaboration (D): When the degree of non-satisfaction of the three key rules R1, R2, and R10 is all <0.1, the bonus Score_D = (1-δ_R1)×(1-δ_R2)×(1-δ_R10)×B_D.

[0029] Total score for cross-coupling synergy bonus: Score_synergy = Score_A + Score_B + Score_C + Score_D.

[0030] S5. Comprehensive score of self-consistency of calculation parameters The formula for calculating the overall score is: Score_total = Score_max - Σ_k Score_conflict_k + Score_synergy Score_max is the preset maximum score (100 points by default). The total score is limited to between 0 and Score_max.

[0031] S6. Output parameter self-consistency score results The output includes a scoring report containing a comprehensive score, details of penalty points for rule conflicts, details of bonus points for collaboration, and a self-consistency level (e.g., excellent, satisfactory, need improvement, unsatisfactory).

[0032] Secondly, the present invention provides a parameter self-consistency quantitative scoring system for implementing the above method, comprising: 1. Parameter Extraction Module: Used to automatically or semi-automatically extract parameter datasets from construction plan documents.

[0033] 2. Constraint Rule Library Module: Used to store and manage constraint rules, supporting CRUD operations and version management.

[0034] 3. Verification Engine Module: This is a deterministic rule engine used to perform rule verification and calculate conflict deductions and collaborative bonuses.

[0035] 4. Scoring Calculation Module: Used to calculate the overall score based on the verification results.

[0036] 5. Visualization Module: Used to display the scoring dashboard, deduction / addition details, self-consistency level, and adjustment suggestions.

[0037] 6. Weight Management Module: Allows users to customize rule weight coefficients based on project type or process characteristics.

[0038] 7. Scoring Result Storage Module: Used to store historical scoring data, supporting retrieval and analysis.

[0039] Compared with the prior art, the present invention has the following beneficial effects: 1. Pioneering a quantitative evaluation of the self-consistency of a single scheme: The concept of "parameter self-consistency scoring" was first proposed, filling the technical gap in the objective and repeatable quantitative evaluation of the "absolute quality" of a single construction scheme.

[0040] 2. Upgrading from binary judgment to continuous quantification: By calculating the continuous value of the degree of non-compliance δ_k, the binary judgment of "pass / fail" of the rule is upgraded to a continuous quantification score, which can effectively distinguish the severity of the conflict.

[0041] 3. Pioneering Cross-Coupling Synergistic Scoring Mechanism: For the first time, the positive correlation (synergistic effect) between parameters is incorporated into the scoring system. Through four synergistic scoring mechanisms, the scoring results more realistically reflect the overall feasibility improvement brought about by reasonable matching between parameters. This is the core innovation of this invention.

[0042] 4. Objective and repeatable scoring: The scoring uses a deterministic rule engine based on mathematical formulas for calculation, without relying on expert subjective judgment or probability models, ensuring that the scoring results of the same solution are consistent under any circumstances.

[0043] 5. The results are interpretable and conducive to optimization: The detailed scoring results can directly locate the parameter links with the most serious logical conflicts and generate targeted adjustment suggestions, which strongly support the iterative optimization of the solution.

[0044] 6. The system is flexible and scalable: the constraint rule base and weight coefficients can be expanded and adjusted according to the actual engineering needs, making it highly adaptable. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 The overall flowchart of the parameter self-consistency quantitative scoring method provided in the embodiments of the present invention is shown.

[0047] Figure 2 This is a schematic diagram of the structure of 12 constraint rules in the constraint rule library provided in an embodiment of the present invention.

[0048] Figure 3 The calculation logic diagrams for four types of cross-coupling collaborative scoring provided in the embodiments of the present invention are as follows.

[0049] Figure 4 The module structure diagram of the parameter self-consistency quantitative scoring system provided in the embodiments of the present invention is shown.

[0050] Figure 5 This is a schematic diagram of the visualization interface for the parameter self-consistency scoring results provided in an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0052] Example 1: Calculation of Conflict Deduction Points Taking a municipal road earthwork excavation plan as an example: the construction period T=30 days, excavator N=1 unit, shift output P=100m³, and total project volume Q=30,000m³.

[0053] Verification of R1 rule: T×N×P=3,000m³ < Q, not satisfied.

[0054] The degree of dissatisfaction is δ_1 = 1 - (3,000 / 30,000) = 0.9.

[0055] If w_1=1.0 and C_1=100, then the conflict deduction Score_conflict_1 = 1.0 × 0.9 × 100 = 90 points.

[0056] If there is no bonus for collaboration, the total score is Score_total = 100 - 90 = 10 points (unsatisfactory).

[0057] The system outputs a detailed breakdown of deductions and a suggestion: "Increasing the number of excavators to 10 can reduce the conflict level to 0."

[0058] Example 2: Collaborative Bonus Application Example Taking a bridge pile foundation construction plan as an example: 2 rotary drilling rigs, 10m wide working face, 20-day construction period, 40 piles in total.

[0059] After verification, the main constraints R1, R2, R3, R8, etc. are all satisfied, and the total penalty for conflicts is 0.

[0060] Calculate collaborative bonus points: Resource reuse and collaboration: Equipment utilization rate U=85% > threshold 70%, Score_A ≈ 3.04 points.

[0061] Resource-space-schedule triangular coordination: δ_R1=δ_R2=δ_R10=0, Score_D = 15 points.

[0062] Total synergy bonus Score_synergy ≈ 18.04 points.

[0063] The total score is Score_total = 100 - 0 + 18.04 = 118.04 points. Taking the full score of 100, the grade is "Excellent".

[0064] Example 3: Complete Calculation Example of Four Types of Collaborative Bonus Points A certain municipal integrated utility tunnel project plan simultaneously embodies multiple collaborative relationships: Resource reuse and collaboration: Tunnel boring machine relocation and reuse, Score_A≈3.04 points.

[0065] Space-Schedule Coordination: Due to space constraints, sequential operations were adopted, resulting in an actual extension of 20% in the schedule, while the theoretical extension should have been 20%. The deviation ε = 20% > the threshold of 10%, therefore Score_B = 0 points.

[0066] Process-cost synergy: Process speed-up increases direct costs by 150,000 and decreases indirect costs by 120,000, resulting in a net cost change rate of 2.5% < threshold 5%, with a score of 5.

[0067] Triangular synergy: δ_R1≈0.05, δ_R2=0.06, δ_R10≈0.04 all <0.1, Score_D≈12.85 points.

[0068] Total synergy bonus Score_synergy ≈ 3.04+0+5+12.85 ≈ 20.89 points.

[0069] Example 4: System Implementation The parameter extraction module can extract structured data from Word documents using regular expressions or pre-trained models (such as BERT). The constraint rule library is stored in JSON format. The validation engine is a deterministic rule engine, executing rule calculations sequentially. The visualization module uses web front-end technology to display results in dashboards, tables, and other formats. The weight management module provides an interface that allows users to preset weight templates for different engineering types such as bridges and tunnels.

[0070] Example 5: Verification and Comparison Ten completed project plans were selected (5 well-executed, 5 with problems) for comparative verification: 1. Traditional binary check method: only outputs "pass / fail", and cannot distinguish differences within groups or sort them.

[0071] 2. Conflict-only deduction method: The average score of the good group was 85.7, and the average score of the problem group was 52.3, with a difference of 33.4 points.

[0072] 3. The method of this invention (deduction + synergistic bonus): The average score of the good group was 94.2, and the average score of the problem group was 51.8, with the gap widening to 42.4 points. Some solutions with minor conflicts in the good group received bonus points due to synergistic effects, and the scores (92, 96, 89 points) more closely reflected their actual good implementation on site, proving the effectiveness of the synergistic bonus mechanism.

[0073] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating the parameter self-consistency of a construction scheme, characterized in that, Includes the following steps: S1. Obtain the parameter dataset of the target construction plan; S2. Construct a set of parameter self-consistency constraint rules, wherein each rule in the set of constraint rules defines a logical constraint relationship that must be satisfied between two or more parameters; S3. Based on the constraint rule set, perform rule-by-rule verification on the parameter dataset, and calculate the single-item conflict deduction Score_conflict_k = w_k × δ_k × C_k when each rule is not satisfied, where w_k is the rule weight coefficient, δ_k is the continuous value of the degree of rule non-satisfaction, and C_k is the base deduction score. S4. Calculate the cross-coupling synergistic bonus, which is a positive bonus calculated based on the positive correlation between parameters; S5. Calculate the parameter self-consistency comprehensive score based on the sum of the individual conflict deductions and the cross-coupling synergy bonus: Score_total = Score_max - ΣScore_conflict + Score_synergy, where Score_max is the preset full score value; S6. Output the score result containing the comprehensive score of the self-consistency of the parameters.

2. The method according to claim 1, characterized in that, In step S4, the cross-coupling collaborative scoring is calculated based on at least one of the following collaborative relationships: (a) Resource reuse and collaboration: When multiple processes share the same type of resource and the resource time utilization rate exceeds a preset threshold, a bonus will be awarded; (b) Space-Schedule Coordination: Bonus points are awarded when the number of parallel devices is reduced due to space constraints, and the deviation between the actual and theoretical schedule extension ratios is within a preset range. (c) Synergy between process rate and cost allocation: When the increase in direct costs due to the increase in process rate is partially offset by the decrease in indirect costs, and the net cost change rate is lower than a preset threshold, a bonus is awarded. (d) Resource-space-schedule triangular coordination: When the degree of non-compliance of the three key constraints of schedule-resource-workload, resource-space, and parallel coefficient is all below the threshold, bonus points are awarded.

3. The method according to claim 1 or 2, characterized in that, The constraint rule set mentioned in step S2 includes at least one of the following: schedule-resource-quantity constraint rules, resource-space constraint rules, process-time constraint rules, material supply and demand balance constraint rules, equipment safety distance constraint rules, process-space-safety constraint rules, cost-resource-schedule consistency constraint rules, labor-schedule-quantity constraint rules, indirect cost ratio constraint rules, parallel coefficient constraint rules, resource demand smoothness constraint rules, and resource demand fluctuation constraint rules.

4. The method according to claim 1, characterized in that, The scoring results output in step S6 also include: a detailed list of individual conflict deductions for each constraint rule, a detailed list of cross-coupling and collaborative bonuses, and a self-consistency level based on the comprehensive scoring.

5. A parameter self-consistency quantitative scoring system for a construction scheme, used to implement the method according to any one of claims 1 to 4, characterized in that, include: The parameter extraction module is used to extract parameter datasets from construction plan documents; The constraint rule library module is used to store and manage the constraint rules for construction scheme parameters; The verification engine module is used to verify the parameter dataset obtained by the parameter extraction module according to the rules in the constraint rule base module, and to calculate the deduction for single-item conflict and the bonus for cross-coupling collaboration. The scoring calculation module is used to calculate the parameter self-consistency comprehensive score based on the calculation results of the verification engine module; The visualization module is used to display the output results of the scoring calculation module.

6. The system according to claim 5, characterized in that, The verification engine module is a rule engine that performs deterministic mathematical operations based on a preset calculation formula.

7. The system according to claim 5, characterized in that, It also includes a weight management module, which allows users to customize and adjust the weight coefficients of the constraint rules.

8. The system according to claim 5, characterized in that, The visualization module displays at least one of the following: a comprehensive score dashboard for parameter self-consistency, a detailed table of penalty points for rule conflicts, a detailed table of bonus points for collaboration, self-consistency level labels, and parameter adjustment suggestions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

10. 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 method as described in any one of claims 1 to 4.

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