Bridge engineering bidding cost management and control system and method based on list dynamic adjustment

Abstract

The invention discloses a bridge engineering bidding and tendering cost management and control system and method based on list dynamic adjustment. The system comprises a data acquisition module, a bridge engineering exclusive list template library, a dynamic adjustment module, a cost accounting module, a bidding and tendering linkage module, a storage module and an interaction module. According to the method, the bridge engineering exclusive list template library is constructed, the dynamic adjustment of the list is realized based on the preset trigger condition, and the adjustment coefficient is calculated in combination with multi-dimensional data and a scientific algorithm (machine learning and multiple linear regression), so that the real-time performance and adaptability of the cost list are remarkably improved. Market, engineering technology, history and process data are integrated through the data acquisition module, a data closed loop is formed, a comprehensive basis is provided for cost accounting, deviation analysis and risk assessment, meanwhile, the total data stored in the storage module can be fed back to follow-up similar projects, and a cost management and control model is continuously optimized.

Description

Technical Field

[0001] This invention relates to the field of bridge engineering information technology, specifically to a bridge engineering bidding cost control system and method based on dynamic adjustment of the bill of quantities. Background Technology

[0002] Bridge engineering projects are characterized by their large scale, high technical complexity, and long construction period. Cost control during the bidding stage directly impacts project investment efficiency and construction compliance. Currently, cost control in bridge engineering bidding primarily employs the traditional static bill of quantities model, which has significant limitations: First, the bill of quantities is based on initial design data and market prices, making it difficult to respond to dynamic fluctuations in the market prices of building materials, labor, and machinery, easily leading to significant deviations between cost estimates and actual conditions. Second, in the face of unforeseen circumstances such as corrections to geological survey data and changes in engineering design, the static bill of quantities cannot promptly add or delete items or calibrate prices, resulting in delayed cost control. Third, multi-dimensional data (market dynamics, engineering technology, historical projects, and bidding process data) is stored in a scattered manner and lacks an effective integration mechanism, making it difficult to support accurate cost accounting and risk assessment. Fourth, the cost compliance verification of bid prices during the bid evaluation stage relies on manual operation, which is inefficient and makes it difficult to accurately identify bids below cost. Furthermore, the calibration of the bidding control price lacks scientific data support, easily leading to unreasonable pricing issues. These shortcomings result in a high cost deviation rate during the bridge engineering bidding stage, serious resource waste, and potential risks associated with non-standard bidding processes.

[0003] The technical problems that need to be solved in this field are as follows: 1. The traditional static bill of quantities model cannot adjust the cost list in real time based on multi-dimensional dynamic data (market price fluctuations, geological / design changes, bidding stage transitions, etc.), resulting in low cost estimation accuracy and difficulty in adapting to the complex variables of bridge engineering.

[0004] 2. The lack of a unified collection and integration mechanism for multi-dimensional data (market, engineering technology, historical, and process data) leads to fragmented data, resulting in a lack of comprehensive basis for cost accounting and insufficient scientific rigor in cost deviation analysis and risk assessment.

[0005] 3. Cost control is disconnected from the bidding process; the calibration of the bidding control price lacks dynamic data support; the generation of bid prices is inefficient and it is difficult to mark cost-sensitive items; compliance verification during the bid evaluation stage relies on manual labor, resulting in low accuracy and efficiency.

[0006] 4. Cost risk identification is lagging behind, and there is a lack of a graded early warning mechanism for specific scenarios of bridge engineering, making it impossible to respond in a timely manner to the risk of cost overruns caused by market fluctuations, geological changes, etc. Summary of the Invention

[0007] Therefore, to address the aforementioned shortcomings of existing systems, this invention provides a cost control system and method for bridge engineering bidding based on dynamic adjustment of the cost list. This technology constructs a dedicated cost list template library for bridge engineering and dynamically adjusts the cost list based on preset trigger conditions. It combines multi-dimensional data with scientific algorithms (machine learning, multiple linear regression) to calculate adjustment coefficients, significantly improving the real-time performance and adaptability of the cost list. It achieves multi-dimensional data collaboration to support scientific decision-making: the data acquisition module integrates market, engineering technology, historical, and process data to form a data closed loop, providing comprehensive evidence for cost accounting, deviation analysis, and risk assessment. Simultaneously, the full amount of data stored in the storage module can be fed back into subsequent similar projects to continuously optimize the cost control model.

[0008] This invention is implemented by constructing a bridge engineering bidding cost control system based on dynamic adjustment of the bill of quantities, comprising: The data acquisition module is used to collect multi-dimensional data from the entire process of bridge engineering bidding. The multi-dimensional data includes market dynamic data, bridge engineering technical parameter data, bidding process data, and historical project data. A dedicated bill of quantities template library for bridge engineering pre-stores basic templates for bidding cost lists based on different bridge types, structural forms, and construction techniques. These basic templates include standardized items for sub-item engineering lists, measure item lists, and other item lists. The dynamic adjustment module is communicatively connected to the data acquisition module and the list template library, and is used to dynamically adjust the cost list based on preset trigger conditions and the multi-dimensional data to generate a real-time updated target cost list. The preset trigger conditions include market price fluctuation threshold trigger, engineering design change trigger, geological exploration data correction trigger, and bidding stage transition trigger. The cost accounting module is communicatively connected to the dynamic adjustment module and is used to complete cost calculation, cost deviation analysis and cost risk assessment at each stage of bridge engineering bidding based on the target cost list and preset accounting rules. The bidding linkage module is connected to the cost accounting module and the external bidding platform to synchronize the cost accounting results to the bidding process, realize the generation of bid price, calibration of bidding control price and verification of cost compliance during the bid evaluation stage; The storage module is used to store multi-dimensional data, cost lists of various versions, cost accounting results, and bidding and tendering linkage records; The interactive module allows users to configure and adjust parameters, view cost data, and receive early warning information from the system.

[0009] According to the system of the present invention, the data acquisition module comprises: The market data submodule is used to capture dynamic data on building material market prices, construction machinery rental prices, and labor costs in real time, and compare them with preset benchmark prices. The engineering technology submodule is used to collect geological survey report data, design drawing parameters, construction organization design schemes, and on-site construction condition data for bridge engineering. The process data submodule is used to collect information on bidding announcements, bid submission data, Q&A clarification data, and bid evaluation progress data. The historical data submodule is used to retrieve stored bidding cost data, construction process cost deviation data, and settlement data for similar bridge projects.

[0010] According to the system of the present invention, the dynamic adjustment module comprises: The trigger recognition submodule is used to monitor the multi-dimensional data in real time and generate an adjustment command when the preset trigger conditions are met. The bill of quantities matching submodule matches the corresponding basic template from the bill of quantities template library based on the specific type and construction requirements of the bridge project. The parameter calculation submodule calculates the adjustment coefficients of the list items according to the adjustment instructions and multi-dimensional data using a preset algorithm, which includes a cost prediction algorithm based on machine learning and a multiple linear regression algorithm. The bill of quantities update submodule adds, deletes, adjusts prices, and calibrates quantities for the basic template bill of quantities based on the adjustment coefficients, generating a target cost bill of quantities.

[0011] According to the system of the present invention, the cost accounting module includes: The cost calculation submodule is used to calculate the cost of each sub-item of the project, the cost of the measures project, the fees and taxes, and generate the total cost based on the target cost list. The deviation analysis submodule is used to compare the cost differences between the target cost list and the historical project cost list, analyze the causes of the differences, and output a deviation report. The risk assessment submodule is used to identify cost overrun risk points based on cost deviation data and market dynamic data, and generate risk level assessment results and response suggestions.

[0012] According to the system of the present invention, the bidding linkage module includes: The quotation generation submodule is used to automatically generate a bid quotation plan based on cost accounting results and bidding strategy parameters, and mark cost-sensitive items. The control price calibration submodule is used to dynamically calibrate the bidding control price by combining the market average level and project characteristics to ensure the rationality of the control price; The bid evaluation verification submodule is used to verify the compliance of the cost composition of bid prices during the bid evaluation stage, identify bids that are below cost price, and issue warnings.

[0013] The system according to the present invention further includes an early warning module, which is communicatively connected to the cost accounting module and the dynamic adjustment module. The early warning module is used to send tiered early warning information to the user when the cost deviation exceeds a preset threshold or the dynamic adjustment range is abnormal. The early warning information includes text prompts, pop-up reminders, and email notifications.

[0014] According to the system of the present invention, the interaction module also supports user-defined adjustment rules, including setting price fluctuation thresholds, adjustment coefficient calculation weights, and cost risk assessment indicators.

[0015] A method for controlling the bidding and tendering costs of bridge engineering projects based on dynamic adjustment of the bill of quantities includes the following steps: S1. Collect multi-dimensional data of the entire process of bridge engineering bidding, including market dynamic data, bridge engineering technical parameter data, bidding process data and historical project data; S2. Based on the specific type, structural form, and construction technology of the bridge project, match the corresponding basic cost list template from the pre-stored bridge project-specific list template library; S3. Monitor the multi-dimensional data, and when the preset triggering conditions are met, dynamically adjust the basic template in combination with the multi-dimensional data to generate a target cost list. The preset triggering conditions include market price fluctuation threshold triggering, engineering design change triggering, geological exploration data correction triggering, and bidding stage conversion triggering. S4. Based on the target cost list and preset accounting rules, complete the cost calculation, cost deviation analysis and cost risk assessment for each stage of bridge engineering bidding; S5. Synchronize cost accounting results to the bidding process to achieve bid price generation, bid control price calibration, and cost compliance verification during the bid evaluation stage; S6. Store the multi-dimensional data, cost lists of each version, cost accounting results and bidding linkage records, and display the relevant data and early warning information to the user through the interactive interface.

[0016] According to the method described in this invention, The specific process of dynamically adjusting the basic template in step S3 includes: S31. When the trigger recognition submodule detects that the preset trigger conditions are met, it generates an adjustment command; S32. The parameter calculation submodule calculates the adjustment coefficient of the list item based on the adjustment instruction and multi-dimensional data using a cost prediction algorithm based on machine learning or a multiple linear regression algorithm. S33. The bill of quantities update submodule performs add / delete operations, price corrections, and quantity calibrations on the bill of quantities items in the basic template based on the adjustment coefficients, and generates a target cost bill of quantities.

[0017] According to the method described in this invention, The specific process of cost risk assessment in step S4 is as follows: Based on cost deviation data and market dynamic data, identify cost overrun risk points, classify risk levels according to the probability of occurrence and the degree of impact of the risks, and generate corresponding risk level response suggestions; The specific process of generating the bid price in step S5 is as follows: Based on the cost accounting results and the bidding strategy parameters configured by the user, at least one bid price plan is automatically generated, and cost-sensitive items in the plan are marked for the user's decision-making reference.

[0018] The present invention has the following advantages through the above embodiments: 1. Improve cost control accuracy and reduce cost deviation: By building a dedicated cost list template library for bridge engineering and dynamically adjusting the list based on preset trigger conditions, and combining multi-dimensional data with scientific algorithms (machine learning, multiple linear regression) to calculate adjustment coefficients, the real-time performance and adaptability of the cost list are significantly improved. In the implementation example, the final bid price deviated from the target cost list by only 0.28%, which is significantly lower than the historical average cost deviation (4.5%) of similar projects.

[0019] 2. Achieve multi-dimensional data collaboration to support scientific decision-making: By integrating market, engineering technology, historical and process data through the data acquisition module, a data closed loop is formed, providing a comprehensive basis for cost accounting, deviation analysis and risk assessment. At the same time, the full amount of data retained by the storage module can be fed back to subsequent similar projects to continuously optimize the cost control model.

[0020] 3. Streamline cost control and bidding processes to improve efficiency and compliance: The bidding linkage module enables automatic generation of multiple bid price proposals, dynamic calibration of the bidding control price, and intelligent verification of bid evaluation compliance, reducing manual intervention. For example, in the implementation example, two bids below cost price were quickly eliminated, shortening the bid evaluation time by approximately 3 hours, while ensuring that the bidding control price met market standards.

[0021] 4. Accurately identify cost risks and proactively mitigate losses: A tiered early warning mechanism and risk assessment indicators are established specifically for bridge engineering scenarios. Based on dynamic data, cost overrun risks are identified in real time, and corresponding response suggestions are generated. For example, in one implementation instance, the risk of rising steel prices was identified in advance, and a price-locking agreement was used to avoid approximately 12 million yuan in additional costs, ensuring the project's investment returns.

[0022] 5. Highly adaptable and flexible in operation: It supports users to customize and adjust rules (price fluctuation threshold, risk weight, etc.) through the interactive module, and matches exclusive list templates for different bridge types, structural forms and construction processes, which can be widely used in various bridge engineering bidding scenarios. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the system module execution flow of this application. Detailed Implementation

[0024] The following will be combined with the appendix Figure 1 This invention will be described in detail, and the technical solutions in the embodiments of this invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0025] Example 1: This invention provides a bridge engineering bidding cost control system based on dynamic adjustment of the bill of quantities. The system comprises: The data acquisition module is used to collect multi-dimensional data from the entire process of bridge engineering bidding. The multi-dimensional data includes market dynamic data, bridge engineering technical parameter data, bidding process data, and historical project data. A dedicated bill of quantities template library for bridge engineering pre-stores basic templates for bidding cost lists based on different bridge types, structural forms, and construction techniques. These basic templates include standardized items for sub-item engineering lists, measure item lists, and other item lists. The dynamic adjustment module is communicatively connected to the data acquisition module and the list template library, and is used to dynamically adjust the cost list based on preset trigger conditions and the multi-dimensional data to generate a real-time updated target cost list. The preset trigger conditions include market price fluctuation threshold trigger, engineering design change trigger, geological exploration data correction trigger, and bidding stage transition trigger. The cost accounting module is communicatively connected to the dynamic adjustment module and is used to complete cost calculation, cost deviation analysis and cost risk assessment at each stage of bridge engineering bidding based on the target cost list and preset accounting rules. The bidding linkage module is connected to the cost accounting module and the external bidding platform to synchronize the cost accounting results to the bidding process, realize the generation of bid price, calibration of bidding control price and verification of cost compliance during the bid evaluation stage; The storage module is used to store multi-dimensional data, cost lists of various versions, cost accounting results, and bidding and tendering linkage records; The interactive module allows users to configure and adjust parameters, view cost data, and receive early warning information from the system.

[0026] In the system of this embodiment, the data acquisition module includes: The market data submodule is used to capture dynamic data on building material market prices, construction machinery rental prices, and labor costs in real time, and compare them with preset benchmark prices. The engineering technology submodule is used to collect geological survey report data, design drawing parameters, construction organization design schemes, and on-site construction condition data for bridge engineering. The process data submodule is used to collect information on bidding announcements, bid submission data, Q&A clarification data, and bid evaluation progress data. The historical data submodule is used to retrieve stored bidding cost data, construction process cost deviation data, and settlement data for similar bridge projects.

[0027] In the system of this embodiment, the dynamic adjustment module includes: The trigger recognition submodule is used to monitor the multi-dimensional data in real time and generate an adjustment command when the preset trigger conditions are met. The bill of quantities matching submodule matches the corresponding basic template from the bill of quantities template library based on the specific type and construction requirements of the bridge project. The parameter calculation submodule calculates the adjustment coefficients of the list items according to the adjustment instructions and multi-dimensional data using a preset algorithm, which includes a cost prediction algorithm based on machine learning and a multiple linear regression algorithm. The bill of quantities update submodule adds, deletes, adjusts prices, and calibrates quantities for the basic template bill of quantities based on the adjustment coefficients, generating a target cost bill of quantities.

[0028] In the system of this embodiment, the cost accounting module includes: The cost calculation submodule is used to calculate the cost of each sub-item of the project, the cost of the measures project, the fees and taxes, and generate the total cost based on the target cost list. The deviation analysis submodule is used to compare the cost differences between the target cost list and the historical project cost list, analyze the causes of the differences, and output a deviation report. The risk assessment submodule is used to identify cost overrun risk points based on cost deviation data and market dynamic data, and generate risk level assessment results and response suggestions.

[0029] In the system of this embodiment, the bidding and tendering linkage module includes: The quotation generation submodule is used to automatically generate a bid quotation plan based on cost accounting results and bidding strategy parameters, and mark cost-sensitive items. The control price calibration submodule is used to dynamically calibrate the bidding control price by combining the market average level and project characteristics to ensure the rationality of the control price; The bid evaluation verification submodule is used to verify the compliance of the cost composition of bid prices during the bid evaluation stage, identify bids that are below cost price, and issue warnings.

[0030] The system in this embodiment also includes an early warning module, which is communicatively connected to the cost accounting module and the dynamic adjustment module. The early warning module is used to send tiered early warning information to the user when the cost deviation exceeds a preset threshold or the dynamic adjustment range is abnormal. The early warning information includes text prompts, pop-up reminders and email notifications.

[0031] In the system of this embodiment, the interaction module also supports user-defined adjustment rules, including setting price fluctuation thresholds, adjustment coefficient calculation weights, and cost risk assessment indicators.

[0032] Example 2: This invention provides a method for controlling the bidding costs of bridge engineering projects based on dynamic adjustment of the bill of quantities, comprising the following steps: S1. Collect multi-dimensional data of the entire process of bridge engineering bidding, including market dynamic data, bridge engineering technical parameter data, bidding process data and historical project data; S2. Based on the specific type, structural form, and construction technology of the bridge project, match the corresponding basic cost list template from the pre-stored bridge project-specific list template library; S3. Monitor the multi-dimensional data, and when the preset triggering conditions are met, dynamically adjust the basic template in combination with the multi-dimensional data to generate a target cost list. The preset triggering conditions include market price fluctuation threshold triggering, engineering design change triggering, geological exploration data correction triggering, and bidding stage conversion triggering. S4. Based on the target cost list and preset accounting rules, complete the cost calculation, cost deviation analysis and cost risk assessment for each stage of bridge engineering bidding; S5. Synchronize cost accounting results to the bidding process to achieve bid price generation, bid control price calibration, and cost compliance verification during the bid evaluation stage; S6. Store the multi-dimensional data, cost lists of each version, cost accounting results and bidding linkage records, and display the relevant data and early warning information to the user through the interactive interface.

[0033] In the method of this embodiment, the specific process of dynamically adjusting the basic template in step S3 includes: S31. When the trigger recognition submodule detects that the preset trigger conditions are met, it generates an adjustment command; S32. The parameter calculation submodule calculates the adjustment coefficient of the list item based on the adjustment instruction and multi-dimensional data using a cost prediction algorithm based on machine learning or a multiple linear regression algorithm. S33. The bill of quantities update submodule performs add / delete operations, price corrections, and quantity calibrations on the bill of quantities items in the basic template based on the adjustment coefficients, and generates a target cost bill of quantities.

[0034] The specific process of cost risk assessment in step S4 is as follows: Based on cost deviation data and market dynamic data, identify cost overrun risk points, classify risk levels according to the probability of occurrence and the degree of impact of the risks, and generate corresponding risk level response suggestions; The specific process of generating the bid price in step S5 is as follows: Based on the cost accounting results and the bidding strategy parameters configured by the user, at least one bid price plan is automatically generated, and cost-sensitive items in the plan are marked for the user's decision-making reference.

[0035] The following section, in conjunction with the accompanying drawings, explains the bridge engineering bidding cost control system based on dynamic adjustment of the bill of quantities in this application; Data Acquisition Link: Through four sub-modules, it connects to the external market, engineering site, bidding platform and historical database respectively to achieve comprehensive collection of multi-dimensional data.

[0036] Dynamic adjustment link: The list template library provides basic templates for dynamic adjustment. After the trigger identification submodule detects the preset conditions, it links the parameter calculation and list update submodules to generate the target list.

[0037] Cost control process: The target list is input into the cost accounting module to complete the calculation, deviation analysis and risk assessment. The results are synchronized to the bidding linkage module to connect with external processes, and the early warning module is triggered at the same time.

[0038] Interaction and storage link: Users configure parameters and receive alerts through the interaction module, while the storage module retains data throughout the process and provides support for the historical data sub-module.

[0039] The implementation process for cost control in bridge engineering bidding based on dynamic adjustment of the list is a corresponding methodology and steps, clearly presenting the entire process from data collection to bidding linkage.

[0040] Dynamic branch adjustment: By using the "whether the triggering condition is met" judgment node, the template can be flexibly switched between dynamic updating and static maintenance, which fits the preset triggering condition features of the claims.

[0041] The entire cost accounting process sequentially completes three core operations: cost calculation, deviation analysis, and risk assessment, and embeds early warning judgment nodes, covering the core cost control process of the claims.

[0042] The bidding and tendering linkage operation presents three key actions in parallel: bid price generation, control price calibration, and bid evaluation verification, reflecting the deep linkage with the external bidding and tendering process.

[0043] Cyclic mechanism: Before the bidding process is completed, the data collection stage is returned to repeat the dynamic adjustment to ensure the real-time nature of cost control.

[0044] The following describes an implementation example of a bridge engineering bidding cost control system and method based on dynamic adjustment of the bill of quantities. I. Project Background: This project involves the planning and construction of a cross-river interchange in a key city (hereinafter referred to as the "Target Project"). The project is a two-way six-lane bridge with a steel-concrete composite beam structure for the main bridge and prestressed concrete box girder structures for the approach bridges. The total length is 1800 meters, and the total investment is approximately 860 million yuan. The project faced multiple challenges during the bidding stage: the prices of building materials (such as steel and concrete) fluctuated wildly due to market supply and demand; geological surveys revealed discrepancies between the rock strata distribution in some areas and the initial report; and historical bidding costs for similar bridge projects showed significant deviations. The traditional static bill of quantities model was insufficient to meet the needs of precise cost control. Therefore, the project construction unit introduced a "Bridge Engineering Bidding Cost Control System Based on Dynamic Bill of Quantities Adjustment," applying corresponding control methods throughout the entire bidding cost management process.

[0045] II. System Deployment and Preliminary Preparations (a) System module configuration: During implementation, the entire system should be deployed according to its technical characteristics described above: 1. Data Acquisition Module: Connects to the local building materials market price monitoring platform (to obtain real-time prices of main materials such as steel and concrete), the BIM model system of the engineering design unit (to extract bridge technical parameters), and the provincial public resource trading platform (to collect bidding process data), and imports historical cost data (including itemized list prices and construction process deviation records) of three similar cross-river bridge projects in the province over the past 5 years.

[0046] 2. Matching the bill of quantities template library: Based on the structural form of the target project "steel-concrete composite beam main bridge + prestressed concrete approach bridge", the "basic template of cost list for large-scale cross-river interchange bridge" is matched from the bridge engineering exclusive bill of quantities template library. This template includes 12 sub-item lists such as main bridge steel component processing and installation, approach bridge box girder pouring, and foundation engineering, as well as 6 types of measure items such as scaffolding erection and large machinery transportation.

[0047] 3. Interaction and Early Warning Parameter Configuration: Set price fluctuation thresholds (steel unit price fluctuation ±5%, concrete unit price fluctuation ±3% trigger adjustment) and cost deviation early warning threshold (±8%) through the interaction module, and configure risk assessment indicators (market price risk weight 40%, design change risk weight 30%, geological condition risk weight 30%).

[0048] III. Implementation Process (Corresponding Methods and Steps) (a) Data Acquisition Phase (Step S1) Multi-dimensional data collection is accomplished through four sub-modules of the data acquisition module: Market data: The price of steel in the month the bidding announcement was released was 4,800 yuan / ton (up 6.2% from the previous month), the price of C50 concrete was 420 yuan / cubic meter (unchanged from the previous month), and the rental price of large tower cranes was 12,000 yuan / month (down 2% from the previous month).

[0049] Engineering technical data: Obtain the revised geological survey report (the burial depth of the rock strata at the No. 3 pier of the main bridge increased by 3 meters compared with the initial data), the cross-sectional dimensions of the main bridge steel-concrete composite beam (width 25 meters, height 3.5 meters), the span arrangement of the approach bridge (30-meter standard span), and other parameters.

[0050] Process and historical data: Key time nodes of the project bidding announcement (registration deadline, bid submission date) were collected, and historical cost deviation data of steel component installation in similar projects were extracted (average cost overrun of 4.5%, mainly due to rising steel prices).

[0051] (II) List Template Matching and Dynamic Adjustment Phase (Steps S2-S3): Basic template matching: Based on the structural characteristics of the project, after matching the basic template, supplement the special construction list items for the main bridge steel-concrete composite beam (such as steel component factory processing, on-site hoisting and splicing).

[0052] Trigger condition identification and execution adjustment: Because the unit price of steel increased by 6.2%, exceeding the preset ±5% threshold, a market price fluctuation adjustment instruction was triggered (step S31).

[0053] The parameter calculation submodule uses a multiple linear regression algorithm, combined with the increase in steel prices and the proportion of steel costs in similar historical projects (the cost of main bridge steel components accounts for 35% of the total cost of sub-items), to calculate the adjustment coefficient of 1.04 for the main bridge steel component installation list item and 1.0 for the approach bridge concrete pouring list item (concrete price has not fluctuated) (step S32).

[0054] The bill of quantities update submodule adjusts the basic template: the unit price for processing and installing the main bridge steel components is increased from 1200 yuan / square meter to 1248 yuan / square meter; at the same time, based on the revised geological data, a special bill of quantities item for rock strata excavation at pier #3 is added (unit price 850 yuan / cubic meter), generating the target cost bill of quantities (step S33).

[0055] (III) Cost Accounting Stage (Step S4): 1. Cost Calculation: Based on the target cost list, the cost calculation submodule calculates the total cost of the sub-items of the project as 520 million yuan, the cost of the measures project as 110 million yuan, the fees and taxes as 80 million yuan, and the total target cost of the project as 710 million yuan (step S4).

[0056] 2. Deviation Analysis: The deviation analysis submodule compares the target cost list with the historical cost list of similar projects and finds that the cost of the main bridge steel components is 6.8% higher than the historical average (caused by rising steel prices and higher precision requirements for steel component processing in this project), while the cost of the approach bridge foundation is 3.2% lower (caused by the use of new drilling equipment in the foundation construction of this project, which improves efficiency). A difference analysis report is output (step S41).

[0057] 3. Risk Assessment: The risk assessment submodule, in conjunction with the rising trend of steel prices and the geological changes at the No. 3 pier site, identifies the risk points of "cost overrun of steel components" (risk level II, medium risk) and "delay and increased costs due to complex geological conditions for foundation construction" (risk level III, low risk), and generates corresponding suggestions (such as signing a price lock-in agreement with the steel supplier and optimizing the construction plan for the No. 3 pier site) (step S42).

[0058] 4. Warning Trigger: Since the cost deviation of the main bridge steel components is 6.8%, which does not exceed the preset warning threshold of ±8%, no warning is triggered. If the steel price continues to rise by 2% during subsequent monitoring, a Level II warning will be triggered (step S5).

[0059] (iv) Bidding and tendering linkage stage (step S5): 1. Bid Price Generation: The bid price generation submodule automatically generates three bid price schemes based on the target cost list and the bidding strategy parameters submitted by the construction unit (such as the expected profit margin of 8%) (Scheme 1: based entirely on the target cost + 8% profit; Scheme 2: compressing the cost of the measures project by 2%, with a profit margin of 7.5%; Scheme 3: locking the cost of steel components, and other sub-items based on the target cost + 7% profit), and marks "steel component processing and installation" and "rock stratum excavation at pier #3" as cost-sensitive items (step S61).

[0060] 2. Control Price Calibration: The control price calibration submodule, taking into account the average market profit margin (6%-10%) and the technical difficulty of the project, calibrates the bidding control price from the initial 750 million yuan to 730 million yuan, ensuring that the control price is in line with the market level and avoids investment waste due to excessively high prices (step S62).

[0061] 3. Evaluation and Verification: During the evaluation stage, the evaluation and verification submodule performs compliance verification on the 12 received bid documents. It finds that the main bridge steel component price of 2 bid documents is lower than 15% of the target cost list price, and judges them as "bids below cost price". A warning is issued and the evaluation committee is advised to reject the two bid documents (step S63).

[0062] (V) Data storage and cyclic adjustment (steps S7-S9): 1. The storage module will retain and archive all data, including multi-dimensional data, target cost list (including adjustment records), cost accounting report, and verification results of 12 tender documents, and synchronize them to the historical data sub-module (step S7).

[0063] 2. Display cost data and early warning information to the construction unit and the bid evaluation committee through the interactive interface, and support users to customize and adjust the list items for design changes in subsequent bidding stages (such as second Q&A) (step S8).

[0064] 3. Since the submission of tender documents has not been completed, the system returns to the data collection stage and continues to monitor the dynamics of steel prices. If subsequent price fluctuations meet the trigger conditions, the dynamic adjustment of the list will be initiated again (step S9).

[0065] IV. Implementation Results: 1. Improved accuracy of cost control: After the target project bidding control price was calibrated, the final winning bid price was 728 million yuan, with a deviation of only 0.28% from the target cost list, which is significantly lower than the historical average cost deviation (4.5%) of similar projects.

[0066] 2. Timely and effective risk warning: Through the system's identification of the risk of "cost overrun of steel components", the construction unit signed price lock-in agreements with three steel suppliers in advance, avoiding an additional cost of about 12 million yuan caused by a 3% increase in steel prices in the following two months.

[0067] 3. Improved bidding efficiency: During the bid evaluation stage, two invalid bids were quickly eliminated through compliance verification, shortening the bid evaluation time by approximately 3 hours, while ensuring the reasonableness and compliance of the bid prices.

[0068] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A bridge engineering bidding cost management system based on dynamic adjustment of the list, characterized in that, The application relates to a bridge engineering bidding cost dynamic adjustment system. The system comprises: a data acquisition module for acquiring multi-dimensional data of the whole process of bridge engineering bidding; a bridge engineering exclusive list template library for pre-storing bidding cost list basic templates based on different bridge types, structure forms and construction processes; a dynamic adjustment module in communication connection with the data acquisition module and the list template library, for dynamically adjusting the cost list based on preset trigger conditions and multi-dimensional data to generate a real-time updated target cost list; a cost accounting module in communication connection with the dynamic adjustment module, for completing cost calculation, cost deviation analysis and cost risk assessment of each stage of bridge engineering bidding based on the target cost list and preset accounting rules; a bidding linkage module in communication connection with the cost accounting module and an external bidding platform, for synchronizing the cost accounting result to the bidding process to realize bid price generation, bidding control price calibration and cost compliance verification in the evaluation stage; a storage module for storing multi-dimensional data, various versions of cost lists, cost accounting results and bidding linkage records; 2. The system of claim 1, wherein, an interactive module for allowing a user to configure adjustment parameters, view cost data and receive early warning information output by the system. The data acquisition module comprises: a market data submodule for real-time grabbing dynamic data of building material market prices, engineering machinery rental prices and labor costs and comparing the data with preset benchmark prices; an engineering technology submodule for acquiring geological survey report data, design drawing parameters, construction organization design schemes and on-site construction condition data of bridge engineering; a process data submodule for acquiring bidding announcement information, bid document submission data, question and clarification data and evaluation progress data; 3. The system of claim 1, wherein, a historical data submodule for calling stored bidding cost data, construction process cost deviation data and settlement data of similar bridge engineering. The dynamic adjustment module comprises: a trigger identification submodule for real-time monitoring of the multi-dimensional data and generating an adjustment instruction when the preset trigger condition is met; a list matching submodule for matching a corresponding basic template from the list template library based on the specific type and construction demand of bridge engineering; a parameter calculation submodule for calculating an adjustment coefficient of a list item through a preset algorithm according to the adjustment instruction and multi-dimensional data, wherein the preset algorithm comprises a cost prediction algorithm based on machine learning and a multivariate linear regression algorithm; 4. The system of claim 1, wherein, a list updating submodule for adding, deleting, price correcting and engineering quantity calibrating list items of the basic template according to the adjustment coefficient to generate a target cost list. The cost accounting module comprises: a cost calculation submodule for calculating sub-item engineering cost, measure item cost, rules and taxes based on the target cost list to generate total cost; a deviation analysis submodule for comparing the cost difference between the target cost list and historical engineering list, analyzing the difference causes and outputting a difference report; 5. The system of claim 1, wherein, a risk assessment submodule for identifying cost overrun risk points based on cost deviation data and market dynamic data to generate risk level assessment results and countermeasures. The bidding linkage module comprises: The offer generation submodule is configured to automatically generate a bidding offer scheme according to the cost accounting result and the bidding strategy parameter, and mark a cost-sensitive item; The control price calibration submodule is configured to dynamically calibrate the bidding control price in combination with the market average level and the project characteristics, so as to ensure the reasonableness of the control price; The bid evaluation checking submodule is configured to perform compliance checking on the cost composition of the bidding offer in the bid evaluation stage, identify the bid documents lower than the cost price, and issue a warning.

6. The system of claim 1, wherein, The early warning module is in communication connection with the cost accounting module and the dynamic adjustment module, and is configured to send a hierarchical early warning information to a user when a cost deviation exceeds a preset threshold or a dynamic adjustment amplitude is abnormal, wherein the early warning information includes a text prompt, a pop-up window reminder, and an email notification.

7. The system of claim 1, wherein, The interaction module also supports user-defined adjustment rules, including setting a price fluctuation threshold, an adjustment coefficient calculation weight, and a cost risk assessment index.

8. A bridge engineering bidding cost management method based on dynamic adjustment of the list, characterized in that, The method comprises the following steps: S1. Collecting multi-dimensional data of the whole process of bridge engineering bidding, wherein the multi-dimensional data includes market dynamic data, bridge engineering technical parameter data, bidding process data, and historical engineering data; S2. Matching a corresponding cost list basic template from a pre-stored bridge engineering exclusive list template library based on the specific type, structure form, and construction process of the bridge engineering; S3. Monitoring the multi-dimensional data, and when a preset trigger condition is met, dynamically adjusting the basic template in combination with the multi-dimensional data to generate a target cost list, wherein the preset trigger condition includes a market price fluctuation threshold trigger, an engineering design change trigger, a geological survey data correction trigger, and a bidding stage conversion trigger; S4. Completing cost calculation, cost deviation analysis, and cost risk assessment for each stage of bridge engineering bidding based on the target cost list and a preset accounting rule; S5. Synchronizing the cost accounting result to the bidding process to realize bidding offer generation, bidding control price calibration, and bid evaluation stage cost compliance checking; S6. Storing the multi-dimensional data, each version of the cost list, the cost accounting result, and the bidding linkage record, and displaying relevant data and early warning information to the user through an interactive interface.

9. The method of claim 8, wherein The specific process of dynamically adjusting the basic template in step S3 includes: S31. Generating an adjustment instruction when the trigger identification submodule detects that the preset trigger condition is met; S32. The parameter calculation submodule calculates the adjustment coefficient of the list item by using a cost prediction algorithm based on machine learning or a multiple linear regression algorithm according to the adjustment instruction and the multi-dimensional data; S33. The list updating submodule performs addition, deletion, price correction, and quantity calibration on the list items of the basic template according to the adjustment coefficient to generate a target cost list.

10. The method of claim 8, wherein The specific process of cost risk assessment in step S4 is to identify cost overrun risk points based on cost deviation data and market dynamic data, divide risk levels according to risk occurrence probability and impact degree, and generate corresponding risk level response suggestions. The specific process of the bid offer generation in step S5 is: at least one set of bid offer scheme is automatically generated according to the cost accounting result and the bid strategy parameters configured by the user, and the cost sensitive items in the scheme are marked for the user decision reference.

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