AI-based engineering cost accounting and price reasonableness commenting system and method

Abstract

The application provides an AI-based engineering cost accounting and price reasonableness commenting system and method, which comprises: a project confirmation module for determining a project engineering for cost accounting and locking a target engineering; a data acquisition module for acquiring target project implementation data information in an enterprise database according to the target engineering; a cost accounting module for assisting in cost budgeting of the target engineering by AI and calculating actual engineering cost by big data analysis according to target engineering implementation information; and an analysis and commenting module for commenting on the price reasonableness based on engineering cost budget data and engineering cost actual data in combination with a commenting model to obtain a price reasonableness commenting result. The application realizes automatic and efficient accounting of engineering cost based on AI, without human participation, effectively reducing accounting errors, and also enabling price reasonableness commenting based on the accounting result, reasonably controlling project cost and expenses, and further ensuring the core competitiveness of the enterprise.

Description

Technical Field

[0001] This invention relates to the field of project engineering management technology, and in particular to an AI-based system and method for engineering cost accounting and price reasonableness evaluation. Background Technology

[0002] Against the backdrop of my country's rapid economic development, and in order to meet the needs of market economic growth and people's lives, large-scale construction projects are increasing, leading to increasingly fierce competition in the construction industry. To gain a larger market share, construction companies need to improve project quality, rationally control project costs, and thus ensure their core competitiveness.

[0003] Construction cost accounting is an essential part of the construction process for construction companies, determining the efficiency and quality of construction work. It is the main method for cost control and a key guarantee for project quality. However, construction cost accounting is a professional and tedious task, and traditional manual operation methods can no longer meet the needs of construction companies. Therefore, with the rapid development of computer technology, calculation software is gradually being integrated into construction cost accounting to reduce the workload of manual calculation. However, manual calculation is not only inefficient but also prone to errors due to human assistance. This invention proposes an AI-based construction cost accounting and price reasonableness evaluation system and method. Based on AI, it achieves automated and efficient construction cost accounting without human assistance, effectively reducing calculation errors. At the same time, it can evaluate the price reasonableness based on the calculation results, enabling project cost control based on the price reasonableness evaluation results, thereby ensuring the core competitiveness of enterprises. Summary of the Invention

[0004] The purpose of this invention is to provide an AI-based system and method for engineering cost accounting and price reasonableness evaluation, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an AI-based engineering cost accounting and price reasonableness evaluation system, comprising: a project confirmation module, a data acquisition module, a cost accounting module, and an analysis and evaluation module;

[0006] The project confirmation module is used to identify the project engineering for cost accounting and to lock in the target project;

[0007] The data acquisition module is used to acquire project implementation data from the enterprise database based on the target project, and obtain the target project implementation information.

[0008] The cost accounting module is used to use AI to assist in the cost budgeting of the target project, obtain the project cost budget data, and to perform big data analysis based on the implementation information of the target project to calculate the actual project cost and obtain the actual project cost data.

[0009] The analysis and evaluation module is used to evaluate the price reasonableness by combining the project cost budget data and the actual project cost data with the evaluation model, and obtain the price reasonableness evaluation result.

[0010] Furthermore, when the cost accounting module uses AI to assist in cost budgeting for the target project, it issues a first accounting signal. The data acquisition module retrieves project-related data for the target project from the enterprise database based on the first accounting signal to obtain first target retrieval information. When the cost accounting module performs big data analysis based on the implementation information of the target project, it issues a second accounting signal according to the preset cost accounting rules for the project implementation process. The data acquisition module retrieves project implementation data for the target project from the enterprise database based on the second accounting signal to obtain second target retrieval information.

[0011] Furthermore, the cost accounting module includes: an overall budget unit and an actual calculation unit.

[0012] The overall budget unit is used to use AI to assist in predicting project costs based on information retrieved from the first target, obtain the estimated costs required for the implementation of the target project, and obtain project cost budget data.

[0013] The actual calculation unit is used to perform big data analysis based on the information retrieved from the second target, calculate the actual engineering cost of the current project implementation stage, and obtain the actual engineering cost data.

[0014] Furthermore, when the overall budget unit uses AI to assist in project cost prediction based on the information retrieved from the first target, it constructs a project cost prediction model based on AI technology, and uses the project cost prediction model to predict project costs based on the information retrieved from the first target, including:

[0015] Conduct project analysis for the target project to determine the cost composition of the target project;

[0016] Identify the construction company corresponding to the target project, and obtain historical data on the target project by acquiring historical data based on the construction company's cost composition.

[0017] Based on historical cost data of the target project, cost changes are predicted to obtain the projected cost of the target project.

[0018] The project cost budget data is obtained by calculating the project cost based on the predicted cost of the target project.

[0019] Furthermore, when the actual computing unit performs big data analysis based on the information retrieved from the second target, it employs an artificial intelligence analysis model to perform big data analysis on the information retrieved from the second target, including:

[0020] Determine the retrieval time of the second target retrieval information, and obtain the second target retrieval information corresponding to the previous retrieval time based on the retrieval time, so as to obtain the target retrieval information at the previous moment;

[0021] By combining the information retrieved from the second target with the information retrieved from the previous target, data analysis is performed to determine whether the information has changed and to obtain the data analysis results.

[0022] Based on the data analysis results, the information retrieved for the second target is filtered to obtain the first and second filtering results.

[0023] Based on the first screening results, cost calculations are performed to obtain the first cost data for the current project implementation phase.

[0024] Based on the second screening results, the target retrieval information from the previous moment is used to calculate and retrieve data references to obtain the second cost data for the current project implementation phase.

[0025] Based on the first cost data and the second cost data of the current project implementation phase, the actual project cost data is obtained.

[0026] Furthermore, the analysis and review module includes: a first review unit, a second review unit, and a third review unit;

[0027] The first review unit is used to review the price reasonableness of the project cost budget data using a first review model, and obtain the first price reasonableness review result;

[0028] The second review unit is used to review the price reasonableness based on the actual engineering cost data using the second review model, and obtain the second price reasonableness review result;

[0029] The third review unit is used to combine actual project cost data with project cost budget data using the third review model to analyze project implementation, evaluate whether the project implementation progress is reasonable, and obtain project implementation review results.

[0030] Furthermore, the first review unit uses a first review model to review the price reasonableness of the project cost budget data, including:

[0031] Based on the target project, obtain the competitor's quotation data, and combine the project cost budget data with the competitor's quotation data to make a review, and obtain the first review data;

[0032] Based on the target project, historical project data is acquired, and the project cost budget data is reviewed based on the historical project data to obtain the second review data;

[0033] The first and second review data are combined to obtain the first price reasonableness review result.

[0034] Furthermore, the second review unit uses a second review model to review the price reasonableness based on actual project cost data, including:

[0035] Analyze the actual project cost data and divide it into first-level actual cost data and second-level actual cost data according to its composition.

[0036] Based on actual project cost data, determine the corresponding construction nodes of the target project during the project construction process to obtain the current construction node;

[0037] Based on the current construction milestones and engineering cost budget data, project construction data and enterprise management data are analyzed separately to obtain the first analysis data and the second analysis data.

[0038] Based on the first analysis data, a third set of comments is made on the first actual cost data, and based on the second analysis data, a fourth set of comments is made on the second actual cost data.

[0039] The second price reasonableness assessment result is determined by combining the data from the third and fourth reviews.

[0040] Furthermore, the third review unit utilizes the third review model to combine actual project cost data with project cost budget data to conduct project implementation analysis and evaluate whether the project implementation progress is reasonable, including:

[0041] Based on the target project, the project implementation nodes are broken down according to the project cost budget data to determine the budget data for each implementation node;

[0042] Analyze the actual project cost data and the corresponding project construction progress to determine the current project construction milestones;

[0043] Based on the current project construction milestones, the budget data for the project implementation milestones is filtered to obtain the budget data for the target project implementation milestones;

[0044] The first evaluation data is obtained by summarizing the budget data based on the implementation milestones of the target project;

[0045] The initial assessment data is combined with actual project cost data for analysis and judgment to determine whether the project implementation schedule is reasonable, and the project implementation review results are obtained.

[0046] An AI-based method for engineering cost accounting and price reasonableness evaluation includes:

[0047] Identify the projects for which cost accounting will be conducted and lock in the target projects;

[0048] AI-assisted cost budgeting is used to obtain project cost budget data;

[0049] Based on the target project, project implementation data is retrieved from the enterprise database to obtain the target project implementation information;

[0050] Big data analysis is conducted based on the implementation information of the target project to calculate the actual project cost and obtain the actual project cost data.

[0051] The price reasonableness is evaluated by combining the project cost budget data and the actual project cost data with an evaluation model, and the price reasonableness evaluation result is obtained.

[0052] This invention enables engineering cost accounting and price reasonableness evaluation without human assistance, reducing the impact of human factors, improving the efficiency of engineering cost accounting and price reasonableness evaluation, reducing errors in engineering cost accounting, and eliminating the need for human evaluation of engineering cost budget data and actual engineering cost data, thereby improving the objectivity and accuracy of price reasonableness evaluation results. This allows for project cost control based on the price reasonableness evaluation results, thus ensuring the core competitiveness of enterprises.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the application.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a schematic diagram of the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0057] Figure 2 This is a schematic diagram of the cost accounting module in the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0058] Figure 3 This is a schematic diagram of the overall budget unit steps of the cost accounting module in the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0059] Figure 4 This is a schematic diagram of the actual calculation unit steps of the cost accounting module in the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0060] Figure 5 This is a schematic diagram of the analysis and evaluation module in the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0061] Figure 6 This is a schematic diagram of the first review unit step in the analysis and review module of the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0062] Figure 7 This is a schematic diagram of the second review unit step in the analysis and review module of the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0063] Figure 8 This is a schematic diagram of the third review unit step in the analysis and review module of the engineering cost accounting and price reasonableness evaluation system described in this invention;

[0064] Figure 9 This is a schematic diagram illustrating the steps of the engineering cost accounting and price reasonableness evaluation method described in this invention. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] like Figure 1 As shown, this embodiment of the invention provides an AI-based engineering cost accounting and price reasonableness evaluation system, including: a project confirmation module, a data acquisition module, a cost accounting module, and an analysis and evaluation module;

[0067] The project confirmation module is used to identify the project engineering for cost accounting and to lock in the target project;

[0068] The data acquisition module is used to acquire project implementation data from the enterprise database based on the target project, and obtain the target project implementation information.

[0069] The cost accounting module is used to use AI to assist in the cost budgeting of the target project, obtain the project cost budget data, and to perform big data analysis based on the implementation information of the target project to calculate the actual project cost and obtain the actual project cost data.

[0070] The analysis and evaluation module is used to evaluate the price reasonableness by combining the project cost budget data and the actual project cost data with the evaluation model, and obtain the price reasonableness evaluation result.

[0071] In the above technical solution, the project confirmation module, data acquisition module, cost accounting module, and analysis and review module are connected in sequence.

[0072] In the above technical solution, the enterprise database is an information database obtained by combining monitoring data obtained from project construction supervision and enterprise management information, including project construction monitoring data and enterprise management data.

[0073] The above-mentioned technical solution can realize project cost accounting and price reasonableness evaluation without human assistance, reducing the impact of human factors, improving the efficiency of project cost accounting and price reasonableness evaluation, reducing the error of project cost accounting, and eliminating the need for human evaluation of project cost budget data and actual project cost data, thereby improving the objectivity and accuracy of price reasonableness evaluation results. This enables project cost control based on the price reasonableness evaluation results, thereby ensuring the core competitiveness of enterprises. The project confirmation module locks in the target project, enabling a series of analyses within the project cost accounting and price reasonableness evaluation system. These analyses are conducted on the target project across the data acquisition, cost accounting, and analysis / evaluation modules. Even with multiple projects in the system, effective cost accounting and price reasonableness evaluation can still be performed, reducing data clutter and ensuring the system's orderliness. Furthermore, the data acquisition module allows for data retrieval based on the target project, eliminating the need for manual data collection and input, reducing time consumption, and minimizing the probability of errors from manual data entry. This ensures the accuracy of the target project implementation information, improves the efficiency of the data acquisition module in obtaining target project implementation information, and ultimately enhances the project's performance. The system improves the efficiency of cost accounting and price reasonableness evaluation. In the cost accounting module, AI assistance is used to quickly generate cost budgets for target projects and calculate actual project costs based on project implementation information. This enhances the efficiency of cost budgeting and calculation while reducing the likelihood of errors, effectively ensuring the accuracy of both budgeted and actual project cost data. Furthermore, the analysis and evaluation module, combined with an evaluation model, evaluates the price reasonableness of budgeted and actual project cost data without requiring subjective human intervention, reducing the impact of human factors. This still achieves the evaluation of budgeted and actual project cost data, clearly defining whether the data is reasonable. This guides construction companies to reasonably control project costs and expenses, thereby ensuring their core competitiveness.

[0074] In one embodiment of the present invention, when the cost accounting module uses AI to assist in cost budgeting for the target project, it issues a first accounting signal. The data acquisition module retrieves project-related data for the target project from the enterprise database based on the first accounting signal to obtain first target retrieval information. When the cost accounting module performs big data analysis based on the implementation information of the target project, it issues a second accounting signal according to preset cost accounting rules for the project implementation process. The data acquisition module retrieves project implementation data for the target project from the enterprise database based on the second accounting signal to obtain second target retrieval information.

[0075] In the above technical solution, the enterprise database is updated in real time based on the project construction status and enterprise management status.

[0076] In the above technical solution, the project-related data includes data information related to the entire project implementation, such as: construction progress, market changes, labor costs, material costs, equipment costs, site expenses, and period expenses.

[0077] In the above technical solution, the cost accounting rules for the project implementation process can be set according to needs, such as: periodic accounting based on time, phased accounting based on construction progress, etc.

[0078] In the above technical solution, the data acquisition module retrieves project implementation data for the target project from the enterprise database based on the second accounting signal, including:

[0079] The second accounting signal is verified to determine its validity and obtain the signal analysis results.

[0080] When the signal analysis result indicates that the second accounting signal is valid, the second accounting signal is parsed to obtain the second accounting signal parsing information;

[0081] The timing of the accounting node is determined based on the information from the second accounting signal analysis.

[0082] By combining the project monitoring data and enterprise management data with the accounting node time in the enterprise database, the project implementation data and enterprise management data corresponding to the accounting node time are obtained to obtain the first target data information and the second target data information.

[0083] The second target retrieval information is obtained by combining the data information of the first target and the data information of the second target.

[0084] The above technical solution establishes a communication connection between the cost accounting module and the data acquisition module through a first accounting signal and a second accounting signal. This enables the cost accounting module to automatically obtain the data information for accounting based on the data acquisition module during accounting, improving the efficiency of obtaining the first and second target retrieved information and providing a data foundation for the cost accounting module. This allows the cost accounting module to perform accounting efficiently. Furthermore, the first and second accounting signals enable the data acquisition module to acquire data information according to the needs of the cost accounting module, achieving on-demand acquisition. This reduces the redundancy of irrelevant information in the first and second target retrieved information, ensuring the accuracy of the first and second target retrieved information.

[0085] like Figure 2 As shown, in one embodiment of the present invention, the cost accounting module includes: an overall budget unit and an actual calculation unit.

[0086] The overall budget unit is used to use AI to assist in predicting project costs based on information retrieved from the first target, obtain the estimated costs required for the implementation of the target project, and obtain project cost budget data.

[0087] The actual calculation unit is used to perform big data analysis based on the information retrieved from the second target, calculate the actual engineering cost of the current project implementation stage, and obtain the actual engineering cost data.

[0088] The above technical solution achieves partitioned accounting of the cost accounting module through the overall budget unit and the actual calculation unit. This allows the overall accounting unit to clarify the overall price overview of the target project based on the overall project budget, and the actual calculation unit to realize the engineering cost accounting of intermediate nodes during the project implementation process. This enables timely understanding of the actual engineering cost overview of the project implementation, providing a reference for the construction company's construction efficiency and quality control. It allows the construction company to control costs based on the engineering cost budget data and the actual engineering cost data, reasonably control project costs and expenses, and thus ensure the company's core competitiveness.

[0089] like Figure 3 As shown, in one embodiment of the present invention, when the overall budget unit uses AI-assisted project cost prediction based on information retrieved from the first target, a project cost prediction model is constructed based on AI technology. The project cost prediction model is then used to predict project costs based on the information retrieved from the first target, including:

[0090] A1. Conduct project analysis for the target project to determine the cost composition of the target project;

[0091] A2. Identify the construction company corresponding to the target project, and obtain historical data of the target project based on the construction company's historical data according to the cost composition of the target project.

[0092] A3. Based on the historical cost data of the target project, predict the cost changes to obtain the predicted cost of the target project;

[0093] A4. Calculate the project cost budget data based on the predicted cost of the target project.

[0094] In the above technical solutions, the cost of the target project typically includes: labor costs, material costs, equipment costs, management costs, and temporary costs. Management costs include, for example, the salaries and benefits of management personnel, and office utilities. Temporary costs include, for example, temporary equipment and testing.

[0095] In the above technical solution, when a construction company acquires historical data based on the cost composition of the target project, it retrieves actual project cost data from completed projects to obtain the actual project cost data. This actual cost data is then effectively filtered based on the cost composition of the target project to obtain the historical cost data for the target project. The historical cost data for the target project refers to the data related to the target project within the historical actual project cost data.

[0096] The above technical solution includes predicting cost changes based on historical cost data for the target project, including:

[0097] The historical cost data for the target project is grouped according to the cost composition of the target project to obtain historical data by cost type.

[0098] Based on the historical data of the expense type, we analyze the characteristics of change and make data predictions based on the characteristics of change of the historical data of the expense type to obtain the first prediction data of the expense type.

[0099] Market data is acquired based on fee type, and the market data is matched with historical data of fee type to obtain the corresponding matching results;

[0100] Based on the corresponding matching results, conduct enterprise impact analysis and perform an overall assessment of the enterprise impact to obtain average predicted impact data and obtain the first impact factor; among which, enterprise impact is generally the preferential ratio of the actual cost data of the construction enterprise for the cost type to the market data.

[0101] We perform feature analysis on market data to obtain market change characteristics, and then make market predictions based on these characteristics to obtain market prediction information.

[0102] The predicted cost of the target project is obtained by combining the first forecast data of the cost type with market forecast information for forecast analysis, and then correcting it with the first influencing factor.

[0103] In the above technical solution, when calculating the predicted cost of the target project, the cost types are calculated sequentially according to the cost composition of the target project, and then the calculation results of the cost types are accumulated to obtain the project cost budget data.

[0104] In the above technical solution, the project cost prediction model is connected to enterprise databases and Internet databases. It can perform information matching in the enterprise database according to the cost composition of the target project to obtain historical data, or obtain market data in the Internet database according to the cost type in the cost composition of the target project.

[0105] The aforementioned technical solution employs AI assistance to enable the project cost prediction model to perform project cost prediction based on information retrieved from the primary target. This achieves automated AI analysis and calculation of big data, improving the efficiency and accuracy of project cost prediction. Furthermore, by determining the cost composition of the target project, analysis is conducted according to the actual situation of the target project, avoiding ineffective data analysis that impacts the efficiency of data analysis and calculation when different projects have different cost types. Additionally, by predicting cost changes based on historical cost data of the target project, predictions are made according to the changes in historical cost data, reducing the error in predicted costs and thus improving the accuracy of project cost budget data. Moreover, when predicting cost changes based on historical cost data of the target project, the historical cost data is grouped according to the cost composition of the target project, allowing for separate predictions by cost type. Furthermore, predictions are made by combining the changing characteristics of historical data for each cost type with consideration of market fluctuations and the impact of market conditions on construction companies based on cost types, optimizing the process of obtaining predicted costs for the target project and improving the accuracy of predicted costs.

[0106] like Figure 4 As shown, in one embodiment of the present invention, when the actual computing unit performs big data analysis based on the second target retrieved information, it employs an artificial intelligence analysis model to perform big data analysis on the second target retrieved information, including:

[0107] B1. Determine the retrieval time of the second target information, and obtain the second target information corresponding to the previous retrieval time based on the retrieval time, so as to obtain the target retrieval information at the previous moment;

[0108] B2. Combine the information retrieved from the second target with the information retrieved from the previous target to perform data analysis, determine whether the information has changed, and obtain the data analysis results.

[0109] B3. Based on the data analysis results, filter the information retrieved for the second target to obtain the first and second filtering results;

[0110] B4. Calculate the costs based on the first screening results to obtain the first cost data for the current project implementation phase;

[0111] B5. Based on the second screening results, calculate and retrieve the target retrieval information from the previous moment to obtain the second cost data for the current project implementation phase.

[0112] B6. Based on the first cost data and the second cost data of the current project implementation phase, obtain the actual project cost data.

[0113] In the above technical solution, the first screening result is the second target whose information has changed, and the second screening result is the second target whose information has not changed.

[0114] In the above technical solution, after obtaining the actual engineering cost data, the actual engineering cost data is combined with the second target retrieval information and retrieval time for data storage, forming an actual engineering cost data analysis library.

[0115] In the above technical solution, when calculating the cost for the first screening result, the cost is calculated based on the actual unit price according to the cost type. For example: labor cost = number of workers × labor cost per person, material cost = current construction material usage × material purchase unit price, equipment cost = current equipment used × equipment rental unit price, etc.

[0116] In the above technical solution, the artificial intelligence analysis model is a data analysis model built in advance for the data analysis of information retrieved for the second target. It has a trust relationship with the actual engineering cost data analysis database and can directly retrieve data information based on the retrieval time.

[0117] The aforementioned technical solution utilizes an artificial intelligence analysis model to perform big data analysis on the second target retrieved information. This not only efficiently and accurately obtains actual engineering cost data but also establishes a communication connection between the artificial intelligence analysis model and the actual engineering cost data analysis database. This allows for data retrieval based on big data analysis needs. Furthermore, during the big data analysis process, data information from the previous retrieval time's actual engineering cost data acquisition process is incorporated for analysis. This enables different cost data determinations based on whether the information has changed. By directly referencing the target retrieval information from the previous moment, calculations can be performed without redundant calculations for unchanged information. This improves the efficiency of acquiring second cost data in the current project implementation phase, thereby efficiently obtaining actual engineering cost data in a shorter timeframe and effectively enhancing the big data analysis efficiency of the actual calculation unit.

[0118] like Figure 5 As shown, in one embodiment of the present invention, the analysis and review module includes: a first review unit, a second review unit, and a third review unit;

[0119] The first review unit is used to review the price reasonableness of the project cost budget data using a first review model, and obtain the first price reasonableness review result;

[0120] The second review unit is used to review the price reasonableness based on the actual engineering cost data using the second review model, and obtain the second price reasonableness review result;

[0121] The third review unit is used to combine actual project cost data with project cost budget data using the third review model to analyze project implementation, evaluate whether the project implementation progress is reasonable, and obtain project implementation review results.

[0122] In the above technical solution, the review model includes: a first review model, a second review model, and a third review model. The first review unit, the second review unit, and the third review unit use AI to perform analysis and review through the first review model, the second review model, and the third review model, respectively.

[0123] The above technical solution uses an analysis and review module to review the project cost budget data and actual project cost data of the target project, thereby clarifying whether the target project is reasonably priced. Based on the first price reasonableness review results, the second price reasonableness review results, and the project implementation review results, the solution can guide construction companies to reasonably control project costs and expenses, thus ensuring the company's core competitiveness.

[0124] like Figure 6 As shown, in one embodiment of the present invention, the first review unit uses a first review model to review the price reasonableness of engineering cost budget data, including:

[0125] C1. Obtain competitor pricing data based on the target project, and combine the project cost budget data with the competitor pricing data for evaluation to obtain the first evaluation data;

[0126] C2. Obtain historical project data based on the target project, and comment on the project cost budget data based on the historical project data to obtain the second comment data;

[0127] C3. Combining the data from the first review and the second review, we obtain the first price reasonableness review result.

[0128] In the above technical solution, when evaluating project cost budget data in conjunction with competitor quotation data, the difference between the project cost budget data and competitor quotation data is calculated to obtain difference data. Then, based on the difference data, the first evaluation data is determined according to the first evaluation rule. The first evaluation rule is based on the mapping relationship between the difference data and the evaluation data.

[0129] The above technical solution includes commentary on engineering cost budget data based on historical project data, including:

[0130] Determine the project scale of the target project, and at the same time obtain the project scale of historical projects based on historical project data;

[0131] By comparing and analyzing the project scale of the target project with that of historical projects, the difference in project scale is determined.

[0132] The reconciliation factor is calculated based on the difference in project size, and the evaluation reconciliation factor is obtained.

[0133] By adjusting historical project data using a review harmonization factor, review analysis data is obtained.

[0134] Conduct a review and analysis of the project cost budget data and the review and analysis data to identify discrepancies.

[0135] The difference data is transformed according to the second review rule to obtain the second review data. The second review rule is based on the mapping relationship between the difference data and the review data.

[0136] In the above technical solution, when integrating the first review data and the second review data, weight analysis is performed on the reviews based on competitor pricing data and the reviews based on historical project data to determine the first weight and the second weight. Then, the first weight is combined with the first review data and the second weight is combined with the second review data to calculate the first price reasonableness review data. Finally, the first price reasonableness review result is obtained based on the first price reasonableness review data.

[0137] In the above technical solution, the first review unit uses the first review model to perform intelligent analysis based on the engineering cost budget data to obtain the first price reasonableness review result.

[0138] The above technical solution enables the first review unit to review the project cost budget data based on competitor quotation data and historical project data through the first review model. It provides reviews from multiple perspectives, eliminates the one-sidedness of the reviews, improves the comprehensiveness of the first price reasonableness review results, makes the first price reasonableness review results accurate, and can also provide a reference for controlling project costs and expenses, thereby improving the company's core competitiveness.

[0139] like Figure 7 As shown, in one embodiment of the present invention, the second review unit uses a second review model to review the price reasonableness based on actual project cost data, including:

[0140] D1. Analyze the actual project cost data, and divide the actual project cost data into first actual cost data and second actual cost data according to its composition.

[0141] D2. Based on the actual engineering cost data, determine the corresponding construction nodes of the target project during the project construction process, and obtain the current construction node;

[0142] D3. Based on the current construction milestones and the project cost budget data, analyze the project construction data and enterprise management data respectively to obtain the first analysis data and the second analysis data.

[0143] D4. Based on the first analysis data, comment on the first cost actual data to obtain the third comment data; based on the second analysis data, comment on the second cost actual data to obtain the fourth comment data.

[0144] D5. Determine the second price reasonableness assessment result by combining the data from the third and fourth reviews.

[0145] In the above technical solution, the second actual cost data is the management fee in the actual engineering cost data, and the first actual cost data is other expenses in the actual engineering cost data besides the management fee, such as material costs, equipment costs, etc.

[0146] In the above technical solution, the first analytical data refers to the project construction data at the current construction node, such as labor costs, material costs, equipment costs, temporary equipment, testing and inspection, etc. The second analytical data refers to the enterprise management data corresponding to the current construction node, such as management personnel salaries and benefits, office utilities, etc.

[0147] In the above technical solution, when commenting on the first cost actual data based on the first analysis data, the difference ratio between the first analysis data and the first cost actual data is calculated to obtain the third comment data. When commenting on the second cost actual data based on the second analysis data, the difference ratio between the second analysis data and the second cost actual data is calculated to obtain the fourth comment data.

[0148] In the above technical solution, when integrating the third and fourth review data, after performing weight analysis on the first and second analysis data, the weights of the first and second analysis data are combined to perform a comprehensive calculation on the first and second analysis data to obtain the second price reasonableness review data, and then the second price reasonableness review result is obtained based on the second price reasonableness review data.

[0149] In the above technical solution, the second review unit uses the second review model to perform intelligent analysis based on the engineering cost budget data to obtain the second price reasonableness review result.

[0150] The above technical solution uses a second review model to enable the second review unit to review the actual project cost data by combining project construction data and enterprise management data. This allows the target project to review the price reasonableness of the construction during the construction process, promptly identify construction issues, provide guidance for construction, and also provide a reference for construction companies to reasonably control project costs, avoid abnormal actual costs during construction, and effectively control the actual implementation cost of the target project.

[0151] like Figure 8 As shown, in one embodiment of the present invention, the third review unit uses a third review model to combine actual project cost data with project cost budget data to conduct project implementation analysis and evaluate whether the project implementation progress is reasonable, including:

[0152] E1. Based on the target project, break down the project implementation nodes according to the project cost budget data to determine the budget data for each project implementation node;

[0153] E2. Analyze the actual project cost data and the corresponding project construction progress to determine the current project construction milestones;

[0154] E3. Based on the current project construction nodes, filter the budget data for the project implementation nodes to obtain the budget data for the target project implementation nodes;

[0155] E4. Summarize the budget data based on the implementation milestones of the target project to obtain the first evaluation data;

[0156] E5. Analyze and judge the first assessment data in conjunction with the actual project cost data to determine whether the project implementation schedule is reasonable and obtain the project implementation review results.

[0157] In the above technical solution, when analyzing and judging the first assessment data in conjunction with actual engineering cost data, a harmonization factor is also used for adjustment analysis, including:

[0158] By using harmonic adjustment factors to perform positive and negative adjustments on the first evaluation data, the upper limit and lower limit of the current review data are obtained.

[0159] Based on the upper and lower limits of the current review data, the actual project cost data is analyzed to determine the relationship between the upper and lower limits of the current review data and the actual project cost data, and the first and second review results are obtained respectively.

[0160] The project implementation schedule is considered reasonable if the first review result indicates that the actual project cost is not less than the lower limit of the current review data, and the second review result indicates that the actual project cost is not greater than the upper limit of the current review data; otherwise, the project implementation schedule is considered unreasonable.

[0161] In the above technical solution, the budget data for the target project implementation node can be one or multiple.

[0162] In the above technical solution, the third review unit uses the third review model to realize intelligent analysis of project implementation by combining actual project cost data with project cost budget data.

[0163] The aforementioned technical solution utilizes a third-party evaluation model to combine actual project cost data with project cost budget data for project implementation analysis. This clarifies whether the project implementation progress is reasonable, allowing construction companies to make timely adjustments during project implementation based on the evaluation results. This ensures the quality of the target project during implementation, guides construction companies in rationally controlling project costs, and ultimately safeguards the company's core competitiveness. Furthermore, by breaking down project implementation nodes based on the project cost budget data, the budget is refined to the level of each construction node during project implementation. This makes the predicted allocation of project implementation transparent and allows for analysis according to project implementation nodes. This provides more intuitive evaluation data for the evaluation of actual project cost data. Additionally, the use of harmonization factors in the analysis and judgment process ensures that the initial evaluation data is adjusted within a reasonable range, improving the feasibility of the analysis and judgment and providing assurance for the project implementation evaluation results.

[0164] like Figure 9 As shown, this embodiment of the invention provides an AI-based method for engineering cost accounting and price reasonableness evaluation, including:

[0165] Step 1: Identify the project for which cost accounting will be performed and lock in the target project;

[0166] Step 2: Use AI to assist in cost budgeting of the target project and obtain project cost budget data;

[0167] Step 3: Obtain project implementation data from the enterprise database based on the target project to obtain the target project implementation information;

[0168] Step 4: Conduct big data analysis based on the implementation information of the target project to calculate the actual project cost and obtain the actual project cost data;

[0169] Step 5: Based on the project cost budget data and the actual project cost data, conduct a price reasonableness evaluation using the evaluation model to obtain the price reasonableness evaluation results.

[0170] The aforementioned technical solution enables engineering cost accounting without requiring human intervention in data acquisition and calculation, saving manpower and avoiding accounting errors caused by human negligence. Furthermore, the project confirmation module locks in the target project, allowing for monitoring and analysis based on that project. This ensures the orderly nature of engineering cost accounting and price reasonableness evaluation, enabling the system to systematically perform cost accounting and price reasonableness evaluation for multiple projects, reducing data chaos. Simultaneously, the data acquisition module allows for data retrieval based on the target project. This eliminates the need for manual data collection and input, improving data acquisition efficiency and thus enhancing the efficiency of project cost accounting and price reasonableness evaluation. Furthermore, the cost accounting module utilizes AI assistance, enabling rapid cost accounting with minimal errors, effectively ensuring the accuracy of both budgeted and actual project cost data. Additionally, the analysis and evaluation module assesses the reasonableness of both budgeted and actual project cost data, clarifying their validity and guiding construction companies to effectively control project costs, thereby safeguarding their core competitiveness.

[0171] Those skilled in the art should understand that the first, second, third, and fourth in this invention merely refer to different application stages.

[0172] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0173] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An AI-based engineering cost accounting and price reasonableness evaluation system, characterized in that, include: The module includes project confirmation, data acquisition, cost accounting, and analysis and review. The project confirmation module is used to identify the projects for cost accounting and to lock in the target projects; The data acquisition module is used to retrieve project implementation data from the enterprise database based on the target project, and obtain the target project implementation information. The cost accounting module is used to use AI to assist in the cost budgeting of the target project, build a project cost prediction model based on AI technology to obtain project cost budget data, and perform big data analysis based on the implementation information of the target project to calculate the actual project cost and obtain the actual project cost data. When the cost accounting module uses AI to assist in the cost budgeting of the target project, it sends out the first accounting signal. The data acquisition module retrieves the project-related data of the target project from the enterprise database based on the first accounting signal to obtain the first target retrieval information. The cost accounting module includes an overall budget unit and an actual calculation unit. The actual calculation unit is used to perform big data analysis based on the second target retrieval information to calculate the actual engineering cost of the current project implementation stage and obtain actual engineering cost data. When the cost accounting module performs big data analysis based on the target project implementation information, it issues a second accounting signal according to the preset engineering implementation process cost accounting rules. The data acquisition module retrieves engineering implementation data for the target project from the enterprise database based on the second accounting signal to obtain the second target retrieval information. Specifically, when the data acquisition module retrieves engineering implementation data for the target project from the enterprise database based on the second accounting signal, it verifies the second accounting signal to determine its validity and obtains the signal analysis result. When the signal analysis result indicates that the second accounting signal is valid, it parses the second accounting signal to obtain second accounting signal parsing information. Based on the second accounting signal parsing information, it determines the accounting node time. It then analyzes the project monitoring data and enterprise management data corresponding to the accounting node time in the enterprise database, combining the accounting node time, to obtain the first target data information and the second target data information. Finally, it combines the first target data information and the second target data information to obtain the second target retrieval information. The analysis and evaluation module is used to evaluate the price reasonableness by combining the project cost budget data and the actual project cost data with an evaluation model, and to obtain the price reasonableness evaluation result. It includes three evaluation units: the first evaluation unit, the second evaluation unit, and the third evaluation unit. The first evaluation unit uses the first evaluation model to evaluate the price reasonableness of the project cost budget data, and obtains the first price reasonableness evaluation result. The second evaluation unit uses the second evaluation model to evaluate the price reasonableness of the actual project cost data, and obtains the second price reasonableness evaluation result. The third evaluation unit uses the third evaluation model to combine the actual project cost data with the project cost budget data to conduct project implementation analysis, evaluate whether the project implementation progress is reasonable, and obtain the project implementation evaluation result.

2. The engineering cost accounting and price reasonableness evaluation system according to claim 1, characterized in that, The overall budget unit is used to use AI to assist in retrieving information based on the first target to predict project costs, obtain the estimated costs required for the implementation of the target project, and obtain project cost budget data.

3. The engineering cost accounting and price reasonableness evaluation system according to claim 2, characterized in that, When the overall budget unit uses AI-assisted project cost prediction based on the information retrieved from the first target, it performs project cost prediction using a project cost prediction model based on the information retrieved from the first target, including: Conduct project analysis for the target project to determine the cost composition of the target project; Identify the construction company corresponding to the target project, and obtain historical data on the target project by acquiring historical data based on the construction company's cost composition. Based on historical cost data of the target project, cost changes are predicted to obtain the projected cost of the target project. The project cost budget data is obtained by calculating the project cost based on the predicted cost of the target project.

4. The engineering cost accounting and price reasonableness evaluation system according to claim 2, characterized in that, When the actual computing unit performs big data analysis based on the information retrieved from the second target, it uses an artificial intelligence analysis model to perform big data analysis on the information retrieved from the second target, including: Determine the retrieval time of the second target retrieval information, and obtain the second target retrieval information corresponding to the previous retrieval time based on the retrieval time, so as to obtain the target retrieval information at the previous moment; By combining the information retrieved from the second target with the information retrieved from the previous target, data analysis is performed to determine whether the information has changed and to obtain the data analysis results. Based on the data analysis results, the information retrieved for the second target is filtered to obtain the first and second filtering results. Based on the first screening results, cost calculations are performed to obtain the first cost data for the current project implementation phase. Based on the second screening results, the target retrieval information from the previous moment is used to calculate and retrieve data references to obtain the second cost data for the current project implementation phase. Based on the first cost data and the second cost data of the current project implementation phase, the actual project cost data is obtained.

5. The engineering cost accounting and price reasonableness evaluation system according to claim 1, characterized in that, The first review unit uses a first review model to review the price reasonableness of the project cost budget data, including: Based on the target project, obtain the competitor's quotation data, and combine the project cost budget data with the competitor's quotation data to make a review, and obtain the first review data; Based on the target project, historical project data is acquired, and the project cost budget data is reviewed based on the historical project data to obtain the second review data; The first and second review data are combined to obtain the first price reasonableness review result.

6. The engineering cost accounting and price reasonableness evaluation system according to claim 1, characterized in that, The second review unit uses a second review model to review the reasonableness of prices based on actual project cost data, including: Analyze the actual project cost data and divide it into first-level actual cost data and second-level actual cost data according to its composition. Based on actual project cost data, determine the corresponding construction nodes of the target project during the project construction process to obtain the current construction node; Based on the current construction milestones and engineering cost budget data, project construction data and enterprise management data are analyzed separately to obtain the first analysis data and the second analysis data. Based on the first analysis data, a third set of comments is made on the first actual cost data, and based on the second analysis data, a fourth set of comments is made on the second actual cost data. The second price reasonableness assessment result is determined by combining the data from the third and fourth reviews.

7. The engineering cost accounting and price reasonableness evaluation system according to claim 1, characterized in that, The third review unit uses a third review model to combine actual project cost data with project cost budget data to analyze project implementation and evaluate whether the project implementation progress is reasonable, including: Based on the target project, the project implementation nodes are broken down according to the project cost budget data to determine the budget data for each implementation node; Analyze the actual project cost data and the corresponding project construction progress to determine the current project construction milestones; Based on the current project construction milestones, the budget data for the project implementation milestones is filtered to obtain the budget data for the target project implementation milestones; The first evaluation data is obtained by summarizing the budget data based on the implementation milestones of the target project; The initial assessment data is combined with actual project cost data for analysis and judgment to determine whether the project implementation schedule is reasonable, and the project implementation review results are obtained.

8. A method for engineering cost accounting and price reasonableness evaluation based on AI, characterized in that, include: Identify the projects for which cost accounting will be conducted and lock in the target projects; AI-assisted cost budgeting for the target project is employed. A project cost prediction model is constructed based on AI technology to obtain project cost budget data. This includes: issuing a first accounting signal; retrieving relevant project data from the enterprise database based on the first accounting signal to obtain the first target retrieval information; cost accounting including overall budgeting and actual calculations; during actual calculations, big data analysis is performed based on the second target retrieval information to calculate the actual project cost at the current implementation stage, obtaining the actual project cost data; during big data analysis based on the target project implementation information, a second accounting signal is issued according to preset project implementation process cost accounting rules, and project implementation data is retrieved from the enterprise database based on the second accounting signal. The process involves obtaining second target retrieval information. Specifically, when retrieving project implementation data from the enterprise database for the target project based on the second accounting signal, signal verification is performed on the second accounting signal to determine its validity, resulting in signal analysis results. If the signal analysis result indicates the second accounting signal is valid, the second accounting signal is parsed to obtain second accounting signal parsing information. Based on the second accounting signal parsing information, the accounting node time is determined. In the enterprise database, project monitoring data and enterprise management data are analyzed in conjunction with the accounting node time to obtain the corresponding project implementation data and enterprise management data for that accounting node time, yielding first target data information and second target data information. Finally, the second target retrieval information is obtained by combining the first target data information and the second target data information. Based on the target project, project implementation data is retrieved from the enterprise database to obtain the target project implementation information; Big data analysis is conducted based on the implementation information of the target project to calculate the actual project cost and obtain the actual project cost data. The pricing rationality is evaluated using a combination of project cost budget data and actual project cost data, resulting in a pricing rationality evaluation result. This includes: using a first evaluation model to evaluate the pricing rationality based on the project cost budget data, resulting in a first pricing rationality evaluation result; using a second evaluation model to evaluate the pricing rationality based on the actual project cost data, resulting in a second pricing rationality evaluation result; and using a third evaluation model to combine the actual project cost data with the project cost budget data to conduct project implementation analysis, evaluate the rationality of the project implementation progress, and obtain a project implementation evaluation result.

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