Project cost data analysis and application method

Abstract

The invention discloses a project cost data analysis and application method, which comprises the following steps of: 1, a planning stage: establishing a project data model by matching a historical case database, deducing expected investment income under different business form compositions and product grade combinations, and determining project products and development cycles to obtain project cost data; according to the project cost data analysis and application method, under the condition of less project information, the data model is quickly established by the system, and the project cost is optimized. Cost data and investment income analysis of a proposed project under different scheme selections can be visually displayed, and investors are assisted to further carry out project positioning and financial planning work to provide support; the method can also be used for guiding design to carry out scheme comparison and selection, selecting an optimal scheme in a cost controllable range and providing brand limitation of various materials and equipment in a project collection stage, and is a basis for carrying out refined cost management.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering cost, and in particular to a method for analyzing and applying project cost data. Background Art

[0002] With the current pace of real estate project development accelerating, the timeline from project approval to construction commencement is becoming increasingly tight. Some real estate development projects even require pre-sale conditions to be met within three to six months of obtaining land use rights. This makes the initial positioning decisions, cost design, and planning of projects even more crucial.

[0003] At this stage, the cost, investment, and benefits of the project are often calculated based on a series of indicators based on the project experience of the investor team. However, when comparing and selecting diverse project plans, it is often necessary to spend a lot of effort to adjust different business combinations, etc., which leads to cost changes. The time for decision-making and various uncertainties and risks are also relatively increased. There is no quickly applicable system tool that can provide effective data support and assist investors in making quick decisions. Therefore, we propose a method for project cost data analysis and application. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention discloses a method for project cost data analysis and application, the technical solution adopted is to include the following steps: Step 1: During the planning phase, a project data model is built by matching historical case databases. The expected investment returns under different business formats and product grade combinations are deduced. After determining the project products and development cycle, the cost data model for the entire project is completed, project costs are optimized, and contract planning recommendations or an overall project procurement plan are generated. Step 2: During the design phase, based on the data model planning results, set limits on design bidding and contracts to guide the design work. The design budget or scheme calculation is compared with the data model, and any parts that exceed the model cost range are optimized. Step 3: During the bidding phase, we provide recommendations on contract forms and versions, screen and evaluate material brands to ensure they fall within the scope of the data model, organize recommendations to refine contract terms and pre-control plans, review the reference price list to ensure it meets the model planning requirements, and organize strategic negotiations during the bid evaluation process. Step 4: During the construction phase, analyze changes based on database cases, apply online platforms to constrain contract execution timelines, integrate electronic document archiving and data collection, automatically collect price information and provide estimated data, conduct dynamic cost data monitoring, and provide graded early warning prompts. Step 5: During the settlement phase, mark special events and estimate their impact on the overall project, organize the basis for settlement negotiations, automatically aggregate contracts and change negotiation content, and reflect the project's estimated settlement amount in real time.

[0005] As a preferred technical solution of the method for project cost data analysis and application of the present invention, the cost data model of the entire project in the planning stage includes data models of construction cost, financial cost, management cost and sales cost.

[0006] As a preferred technical solution for the method of project cost data analysis and application of the present invention, the planning stage also includes optimizing or improving project costs through data models based on adjustments to own funds, development cycle and expected selling price, under the premise of fixed profits or profit margins.

[0007] As a preferred technical solution for the method of project cost data analysis and application of the present invention, the design stage also includes detailed agreements on building layout, structural form, limit indicators, and mechanical and electrical system configuration, and measures such as solution optimization and material replacement are taken for parts that exceed the model cost range.

[0008] As a preferred technical solution for the method of project cost data analysis and application of the present invention, the bidding stage also includes listing the change negotiation and claim content of similar cases in the database to improve contract terms, plan drawings or add pre-control plans to avoid similar content causing additional cost increases again.

[0009] As a preferred technical solution for the method of project cost data analysis and application of the present invention, the construction phase also includes multi-faceted management of change review, including matter review, cost review, and completion confirmation. The necessity and economic feasibility of the change are analyzed in combination with database cases, and the process is made transparent through the application of an online platform, so that the declaration and review of changes and payments are constrained to be carried out in accordance with the time limit agreed in the contract.

[0010] As a preferred technical solution for a method of project cost data analysis and application of the present invention, the construction phase also includes automatic archiving of project files, automatic generation of ledgers, traceable historical records, automatic price collection of contract-agreed survey materials, and provision of measurement data based on the construction progress.

[0011] As a preferred technical solution for a method of project cost data analysis and application of the present invention, the construction phase also includes automatically providing project dynamic cost data every week, providing model comparison financial reports every month, and issuing graded warnings for professional projects or sub-projects whose costs exceed the safety line, while listing pre-processing plans under different warning levels.

[0012] As a preferred technical solution for the method of project cost data analysis and application of the present invention, the settlement stage also includes estimating the impact of special events on the overall project based on the contract and database case processing plan, organizing the basis for settlement negotiations, and automatically collecting the contents of signed contracts, supplementary agreements, and completion confirmation change negotiations to reflect the project's estimated settlement amount in real time.

[0013] As a preferred technical solution of the project cost data analysis and application method of the present invention, the process of building a project data model includes the following steps: Step 1: Establish a database and standardize the data. Standardize the work content and unit price components of different project lists so that the lists entered into the database are standardized. Additionally, associate at least one time attribute with each list item, including but not limited to bidding time, construction time, and settlement time. The project location and non-project information parameters are then tagged. Step 2: Clean historical data, including removing duplicate data, correcting erroneous data, and filling in missing data. The cleaned data will be classified according to unified standards and entered into the database. During this process, the differences in contract systems, contract scope, and interface between different developers are taken into account. By re-dividing the checklist collection, a complete project's full-discipline checklist can be split into 17-107 checklist collections. By converting the checklist data under different contract systems into a checklist collection with a unified scope, subsequent data comparison and adaptation are facilitated. Step 3: Establish a data correlation analysis system and introduce objective data benchmarking objects. Use computer technology to quickly compile the entire list of proposed projects. Use matching algorithms to find highly similar projects in the standard list database. Adjust parameters based on project scale, time, and geographic location to generate a data model for the proposed projects. Use correlation analysis methods to verify the accuracy of production data to ensure that the data model can truly reflect the actual situation of the project. Step 4: When the project is established, based on the specific circumstances and needs of the project, use the established data model and database resources to quickly generate a cost data model for the project. This model can list in detail the total price of the project, the price of each sub-item, and provide the quantity and unit price of each estimated sub-item, as well as the corresponding process practices and material brand range recommendations. In addition, if investors have different needs, they can also combine them into a list format that suits their respective corporate contract systems.

[0014] Beneficial effects of the present invention: By allowing the system to quickly build a data model when there is less project information, the present invention can intuitively display the cost data and investment return analysis of the proposed project under different scheme selections, and assist investors in providing support for the further development of project positioning and financial planning; it can also be used to guide the design to compare schemes, select the optimal scheme within the controllable cost range, and provide various material and equipment brand restrictions in the project procurement stage, which is a basis for carrying out refined cost management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the engineering project cost estimation method of the present invention. DETAILED DESCRIPTION

[0016] Example 1

[0017] like Figure 1 As shown, the present invention discloses a method for project cost data analysis and application, and the technical solution adopted is to include the following steps: Step 1: During the planning phase, a project data model is built by matching the historical case database, and the expected investment returns under different business formats and product grade combinations are deduced. After determining the project products and development cycle, the cost data model of the entire project is completed. The cost data model of the entire project in the planning phase includes data models for construction costs, financial costs, management costs, and sales costs. The project cost is optimized and contract planning suggestions or an overall project procurement plan are generated. The planning phase also includes adjustments based on self-owned funds, development cycle, and expected selling price. Under the premise of fixed profits or profit margins, the data model is used to optimize or increase project costs; Step 2: Design phase: Based on the data model planning results, set limits on design bidding and contracting, guide the design work, compare the design budget or scheme calculation with the data model, and optimize the parts that exceed the model cost range. The design phase also includes detailed agreements on building layout, structural form, quota indicators, and mechanical and electrical system configuration. Measures such as scheme optimization and material replacement are taken for parts that exceed the model cost range. Step 3: During the bidding phase, recommendations on contract forms and versions are provided, material brands are screened and evaluated to ensure they fall within the specified scope of the data model, suggestions are compiled to improve contract terms and pre-control plans, list reference prices are reviewed to ensure they meet model planning requirements, and strategic negotiations are organized during the bid evaluation process. The bidding phase also includes listing change negotiations and claims from similar cases in the database to improve contract terms, plan drawings, or add pre-control plans to avoid additional costs caused by similar content. Step 4: During the construction phase, changes are analyzed in conjunction with database cases, and an online platform is used to constrain contract execution timelines. This integrates electronic document archiving and data collection, automatically collects price information and provides estimated data, conducts dynamic cost data monitoring, and provides graded early warning prompts. The construction phase also includes comprehensive management of change reviews, including item review, expense review, and completion confirmation. The necessity and economic feasibility of changes are analyzed in conjunction with database cases, and the online platform is used to make the process transparent. Change and payment declarations and reviews are constrained to be executed within the contractually agreed timelines. The construction phase also includes automatic archiving of project documents, automatic ledger generation, traceable historical records, automatic price collection of contract-agreed survey materials, and provision of estimated data based on the construction progress. The construction phase also includes automatic weekly provision of dynamic project cost data, monthly model comparison financial reports, and graded early warnings for specialized projects or sub-projects whose costs exceed safety limits, while also listing pre-processing plans for different warning levels.

[0018] Step 5: During the settlement phase, special events are marked and their impact on the overall project is estimated. The basis for settlement negotiations is organized, contracts and change negotiation contents are automatically aggregated, and the estimated project settlement amount is reflected in real time. The settlement phase also includes estimating the impact of special events on the overall project based on the contract and database case processing plan, organizing the basis for settlement negotiations, and automatically aggregating the contents of signed contracts, supplementary agreements, and completion confirmation change negotiations, and reflecting the estimated project settlement amount in real time.

[0019] The process of building a project data model includes the following steps: Step 1: Establish a database and standardize the data. Standardize the work content and unit price components of different project lists so that the lists entered into the database are standardized. Additionally, associate at least one time attribute with each list item, including but not limited to bidding time, construction time, and settlement time. The project location and non-project information parameters are then tagged. Step 2: Clean historical data, including removing duplicate data, correcting erroneous data, and filling in missing data. The cleaned data will be classified according to unified standards and entered into the database. During this process, the differences in contract systems, contract scope, and interface between different developers are taken into account. By re-dividing the checklist collection, a complete project's checklist covering all disciplines is split into 17 checklist collections. By converting the checklist data under different contract systems into a checklist collection with a unified scope, subsequent data comparison and adaptation are facilitated. Step 3: Establish a data correlation analysis system and introduce objective data benchmarking objects. Use computer technology to quickly compile the entire list of proposed projects. Use matching algorithms to find highly similar projects in the standard list database. Adjust parameters based on project scale, time, and geographic location to generate a data model for the proposed projects. Use correlation analysis methods to verify the accuracy of production data to ensure that the data model can truly reflect the actual situation of the project. Step 4: When the project is established, based on the specific circumstances and needs of the project, use the established data model and database resources to quickly generate a cost data model for the project. This model can list in detail the total price of the project, the price of each sub-item, and provide the quantity and unit price of each estimated sub-item, as well as the corresponding process practices and material brand range recommendations. In addition, if investors have different needs, they can also combine them into a list format that suits their respective corporate contract systems.

[0020] Example 2

[0021] The content of this embodiment is basically the same as that of Embodiment 1, and the difference lies in the process of building the project data model.

[0022] The process of building a project data model includes the following steps: Step 1: Establish a database and standardize the data. Standardize the work content and unit price components of different project lists so that the lists entered into the database are standardized. Additionally, associate at least one time attribute with each list item, including but not limited to bidding time, construction time, and settlement time. The project location and non-project information parameters are then tagged. Step 2: Clean historical data, including removing duplicate data, correcting erroneous data, and filling in missing data. The cleaned data will be classified according to unified standards and entered into the database. During this process, the differences in contract systems, contract scope, and interface between different developers are taken into account. By re-dividing the checklist collection, a complete project's checklist covering all disciplines is split into 57 checklist collections. By converting the checklist data under different contract systems into a checklist collection with a unified scope, subsequent data comparison and adaptation are facilitated. Step 3: Establish a data correlation analysis system and introduce objective data benchmarking objects. Use computer technology to quickly compile the entire list of proposed projects. Use matching algorithms to find highly similar projects in the standard list database. Adjust parameters based on project scale, time, and geographic location to generate a data model for the proposed projects. Use correlation analysis methods to verify the accuracy of production data to ensure that the data model can truly reflect the actual situation of the project. Step 4: When the project is established, based on the specific circumstances and needs of the project, use the established data model and database resources to quickly generate a cost data model for the project. This model can list in detail the total price of the project, the price of each sub-item, and provide the quantity and unit price of each estimated sub-item, as well as the corresponding process practices and material brand range recommendations. In addition, if investors have different needs, they can also combine them into a list format that suits their respective corporate contract systems.

[0023] Example 3

[0024] The content of this embodiment is basically the same as that of Embodiment 1, and the difference lies in the process of building the project data model.

[0025] The process of building a project data model includes the following steps: Step 1: Establish a database and standardize the data. Standardize the work content and unit price components of different project lists so that the lists entered into the database are standardized. Additionally, associate at least one time attribute with each list item, including but not limited to bidding time, construction time, and settlement time. The project location and non-project information parameters are then tagged. Step 2: Clean historical data, including removing duplicate data, correcting erroneous data, and filling in missing data. The cleaned data will be classified according to unified standards and entered into the database. During this process, the differences in contract systems, contract scope, and interface between different developers are taken into account. By re-dividing the checklist collection, a complete project's checklist covering all disciplines is split into 107 checklist collections. By converting the checklist data under different contract systems into a checklist collection with a unified scope, subsequent data comparison and adaptation are facilitated. Step 3: Establish a data correlation analysis system and introduce objective data benchmarking objects. Use computer technology to quickly compile the entire list of proposed projects. Use matching algorithms to find highly similar projects in the standard list database. Adjust parameters based on project scale, time, and geographic location to generate a data model for the proposed projects. Use correlation analysis methods to verify the accuracy of production data to ensure that the data model can truly reflect the actual situation of the project. Step 4: When the project is established, based on the specific circumstances and needs of the project, use the established data model and database resources to quickly generate a cost data model for the project. This model can list in detail the total price of the project, the price of each sub-item, and provide the quantity and unit price of each estimated sub-item, as well as the corresponding process practices and material brand range recommendations. In addition, if investors have different needs, they can also combine them into a list format that suits their respective corporate contract systems.

[0026] Components not described in detail herein are prior art.

[0027] Although the specific embodiments of the present invention have been described in detail above, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by ordinary technicians in this field, various changes can be made without departing from the purpose of the present invention. Modifications or deformations that do not involve creative work are still within the scope of protection of the present invention.

Claims

1. A method for project cost data analysis and application, characterized in that: The following steps are involved: Step 1: During the planning phase, a project data model is built by matching historical case databases. The expected investment returns under different business formats and product grade combinations are deduced. After determining the project products and development cycle, the cost data model for the entire project is completed, project costs are optimized, and contract planning recommendations or an overall project procurement plan are generated. Step 2: During the design phase, based on the data model planning results, set limits on design bidding and contracts to guide the design work. The design budget or scheme calculation is compared with the data model, and any parts that exceed the model cost range are optimized. Step 3: During the bidding phase, we provide recommendations on contract forms and versions, screen and evaluate material brands to ensure they fall within the scope of the data model, organize recommendations to refine contract terms and pre-control plans, review the reference price list to ensure it meets the model planning requirements, and organize strategic negotiations during the bid evaluation process. Step 4: During the construction phase, analyze changes based on database cases, apply online platforms to constrain contract execution timelines, integrate electronic document archiving and data collection, automatically collect price information and provide estimated data, conduct dynamic cost data monitoring, and provide graded early warning prompts. Step 5: During the settlement phase, mark special events and estimate their impact on the overall project, organize the basis for settlement negotiations, automatically aggregate contracts and change negotiation content, and reflect the project's estimated settlement amount in real time.

2. The method for project cost data analysis and application according to claim 1, characterized in that: The cost data model of the entire project in the planning stage of step 1 includes data models of construction cost, financial cost, management cost and sales cost.

3. The method for project cost data analysis and application according to claim 2, characterized in that: The planning stage also includes optimizing or increasing project costs through data models based on adjustments to own funds, development cycle and expected selling price, while maintaining a fixed profit or profit margin.

4. The method for project cost data analysis and application according to claim 1, characterized in that: The second step of the design phase also includes detailed agreements on building layout, structural form, limit indicators, and mechanical and electrical system configuration, and taking measures such as solution optimization and material replacement for parts that exceed the model cost range.

5. The method for project cost data analysis and application according to claim 1, characterized in that: The bidding stage in step three also includes listing the change negotiations and claims content of similar cases in the database to improve the contract terms, plan drawings or add pre-control plans to avoid additional costs caused by similar content again.

6. The method for project cost data analysis and application according to claim 1, characterized in that: The construction phase of step four also includes comprehensive management of change review, including item review, cost review, and completion confirmation. The necessity and economic feasibility of the change are analyzed in combination with database cases, and the process is made transparent through the online platform application, so that the declaration and review of changes and payments are carried out in accordance with the time limit stipulated in the contract.

7. The method for project cost data analysis and application according to claim 1, characterized in that: The construction phase of step four also includes automatic archiving of project files, automatic generation of ledgers, traceable historical records, automatic price collection of contract-agreed survey materials, and provision of estimated data based on the construction progress.

8. The method for analyzing and applying project cost data according to claim 1, characterized in that: The construction phase of step 4 also includes automatically providing dynamic project cost data every week, providing model comparison financial reports every month, and issuing graded warnings for professional projects or sub-projects whose costs exceed the safety line, while listing pre-processing plans under different warning levels.

9. The method for project cost data analysis and application according to claim 1, characterized in that: The settlement phase of step five also includes estimating the impact of special events on the overall project based on the contract and database case handling plan, organizing the basis for settlement negotiations, and automatically collecting the contents of signed contracts, supplementary agreements, and completion confirmation change negotiations to reflect the project's estimated settlement amount in real time.

10. The method for project cost data analysis and application according to claim 1, characterized in that: The process of building the project data model in step 1 includes the following steps: Step 1: Establish a database and standardize the data. Standardize the work content and unit price components of different project lists so that the lists entered into the database are standardized. Additionally, associate at least one time attribute with each list item, including but not limited to bidding time, construction time, and settlement time. The project location and non-project information parameters are then tagged. Step 2: Clean historical data, including removing duplicate data, correcting erroneous data, and filling in missing data. The cleaned data will be classified according to unified standards and entered into the database. During this process, the differences in contract systems, contract scope, and interface between different developers are taken into account. By re-dividing the checklist collection, a complete project's full-discipline checklist can be split into 17-107 checklist collections. By converting the checklist data under different contract systems into a checklist collection with a unified scope, subsequent data comparison and adaptation are facilitated. Step 3: Establish a data correlation analysis system and introduce objective data benchmarking objects. Use computer technology to quickly compile the entire list of proposed projects. Use matching algorithms to find highly similar projects in the standard list database. Adjust parameters based on project scale, time, and geographic location to generate a data model for the proposed projects. Use correlation analysis methods to verify the accuracy of production data to ensure that the data model can truly reflect the actual situation of the project. Step 4: When the project is established, based on the specific circumstances and needs of the project, use the established data model and database resources to quickly generate a cost data model for the project. This model can list in detail the total price of the project, the price of each sub-item, and provide the quantity and unit price of each estimated sub-item, as well as the corresponding process practices and material brand range recommendations. In addition, if investors have different needs, they can also combine them into a list format that suits their respective corporate contract systems.

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