An engineering data dynamic matching method based on an iBIM platform

Abstract

The application discloses an engineering data dynamic matching method based on an iBIM platform, and comprises the following steps: S1, inputting existing engineering data and storing the engineering data in a storage module; S2, an analysis module analyzing the engineering data stored in the storage module; S3, an auditing module calling the analyzed engineering data and auditing the quality score based on the engineering data; S4, an engineering cost index data analysis model in the iBIM platform automatically matching and analyzing the analyzed and audited engineering data based on automatic matching rules, and obtaining a data analysis result; S5, a matching rate analysis module matching and identifying abnormal data in the data analysis result, and automatically correcting the automatic matching rules according to the abnormal data, so that the automatic matching quality is improved; and S6, according to a plurality of engineering data analysis results, a database formed by the engineering data analysis results, and a measurement model, a new project is measured, so that the accuracy and efficiency of the engineering data dynamic matching are effectively improved, and the labor cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of engineering cost technology, specifically to a dynamic matching method for engineering data based on the iBIM platform. Background Technology

[0002] Traditional engineering cost estimation suffers from numerous problems in the process of engineering data collection and matching: First, it relies heavily on manual operation, lacks information technology, and suffers from fragmented, inefficient, and error-prone data collection. The raw data is not effectively cleaned, affecting subsequent analysis. Second, it lacks real-time performance and completeness. Material price updates are lagging, supply chain information is difficult to track dynamically, data silos are common, and cost estimates deviate significantly from actual costs. Third, it is costly and unsustainable. The lack of intelligent technology leads to high manpower input and low efficiency. The database suffers from weak data collection foundations, resulting in high maintenance costs and limited application value. These factors collectively reflect its serious deficiencies in information technology level, process standardization, and technology adaptability. Fourth, traditional methods cannot establish a feedback loop, leading to low efficiency in engineering data matching. Summary of the Invention

[0003] Therefore, it is necessary to propose a dynamic matching method for engineering data based on the iBIM platform to address various problems encountered in the process of engineering data collection and matching in traditional engineering cost estimation, such as the low accuracy of matching.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A dynamic matching method for engineering data based on an iBIM platform, wherein the iBIM platform includes a storage module, a parsing module, an auditing module, and a matching rate analysis module, and the storage module, parsing module, auditing module, and matching rate analysis module are respectively connected to a human-computer interaction unit; the dynamic matching method for engineering data includes the following steps:

[0006] S1. Input existing engineering data and store it in the storage module;

[0007] S2. The parsing module parses the engineering data stored in the storage module;

[0008] S3. The audit module retrieves the parsed project data and audits it based on the quality score of the project data;

[0009] The engineering cost index data analysis model in the S4 and iBIM platforms automatically matches and analyzes the parsed and approved engineering data based on automatic matching rules to obtain data analysis results.

[0010] S5. The matching rate analysis module identifies abnormal data in the data analysis results and automatically corrects the automatic matching rules based on the abnormal data to improve the quality of automatic matching.

[0011] S6. Based on the analysis results of multiple engineering data, a database is formed, and calculations are performed on new projects using a calculation model.

[0012] In some embodiments, the storage module includes a historical engineering database and a material cost database for storing multi-dimensional engineering data and material cost information.

[0013] In some embodiments, step S3 specifically includes the following steps:

[0014] S31. Classify the engineering data according to their professional categories and stages;

[0015] S32. Match the corresponding inspection form according to the classification results of the engineering data;

[0016] S33. Score the quality based on the inspection item scores in the inspection form.

[0017] In some embodiments, the automatic matching rule in step S4 specifically includes the following steps:

[0018] S41. Obtain the required engineering specialties and matching rules;

[0019] S42. The engineering cost index data analysis model is used to dynamically match the engineering data according to the engineering profession and matching rules to obtain the data analysis results.

[0020] In some embodiments, the data analysis results include automatic matching rate, incorrect matching rate, and correct matching rate, and the trend graphs of automatic matching rate, incorrect matching rate, and correct matching rate over time are displayed through a human-computer interaction unit.

[0021] In some embodiments, the calculation formula for the measurement model in step S6 is: in, C For the project cost calculation results, K As a business format, H L For labor costs, H Ma For material costs, H Mc For machinery costs, H R In order to regulate taxes and fees, Due to changes in labor costs, Due to changes in material costs, To measure the differences between the project and historical samples in terms of characteristics such as grade positioning.

[0022] In some embodiments, the data analysis results further include matching rules, feature information, error matching analysis, and non-matching analysis. The matching rules can be manually set, including matching by list coding or by rule, and the user can optimize the rules based on the number of adjustments in the error matching analysis. The matching rate analysis module supports manual selection of engineering disciplines and generates a matching rate trend function based on the fluctuation of building material costs.

[0023] In some embodiments, the audit module includes an audit key point unit, an online audit unit, a quality scoring unit, and a quality data collection unit. The quality data collection unit is used to retrieve the parsed engineering data, and the quality scoring unit is used to score the engineering data and generate a quality scoring report.

[0024] In some embodiments, the matching rate analysis module further includes an error collection unit, a list unit, and a keyword encoding unit. Step S5 specifically includes the following steps:

[0025] S51. Abnormal data is identified by matching and identifying the abnormal data recognition algorithm based on the keyword encoding unit;

[0026] S52. The error collection unit collects the abnormal data to form an abnormal data list;

[0027] S53. Establish a mapping relationship between national standard list codes and indicator items through the list unit, and automatically classify and correct abnormal data lists.

[0028] In some embodiments, step S5 further includes the following steps: S54, based on the feedback of the recognition rate improvement according to the change of recognition rules, determine whether the automatic correction rules are reasonable, and provide rule optimization suggestions through the human-computer interaction unit.

[0029] The beneficial effects of this invention compared to the prior art include:

[0030] This invention provides a dynamic matching method for engineering data based on the iBIM platform. This method incorporates the following technical features: inputting existing engineering data and storing it in the storage module; parsing the stored engineering data through the parsing module; retrieving the parsed engineering data through the review module and scoring its quality; and automatically matching and analyzing the engineering data based on the quality score through the matching rate analysis module. Compared to traditional methods, this approach eliminates the need to build a model of the project, automatically identifies and corrects abnormal data in existing engineering data, and effectively improves the accuracy and efficiency of dynamic matching of engineering data. It also significantly reduces the manual intervention costs in engineering cost estimation.

[0031] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0032] Figure 1 This is a flowchart of a dynamic matching method for engineering data based on the iBIM platform in an embodiment of the present invention;

[0033] Figure 2 The flowcharts show three different methods for data analysis, rule optimization, and data application in engineering data.

[0034] Figure 3 This is a flowchart of an embodiment of the present invention for automatically classifying and correcting abnormal data lists. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. It should also be noted that when an element is described as "connected" to another element, it can be directly on the other element, or one or more intermediate elements may exist between them.

[0036] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0037] Please see Figure 1 and Figure 2This invention provides a dynamic matching method for engineering data based on an iBIM platform. The iBIM platform includes a storage module, a parsing module, an auditing module, and a matching rate analysis module connected according to the data flow direction. The storage module is connected to the parsing module, the parsing module is connected to the auditing module, the auditing module is connected to the matching rate analysis module, and the matching rate analysis module is connected to the data acquisition module. All modules belong to the same platform and share a database. The storage module stores existing engineering data, and the parsing module parses the information stored in the storage module. The auditing module includes an audit point unit, an online auditing unit, a quality scoring unit, and a quality data acquisition unit. The quality data acquisition unit retrieves the parsed engineering data, and the quality scoring unit scores the engineering data and generates a quality scoring report. The matching rate analysis module includes an error collection unit, a list unit, and a keyword encoding unit for correcting abnormal data. The matching rate analysis module supports manual selection of engineering specialties and generates a matching rate trend function based on building material cost fluctuations. The storage module, parsing module, auditing module, and matching rate analysis module are also connected to a human-computer interaction unit. The dynamic matching method for engineering data includes the following steps:

[0038] S1. Input existing engineering data and store it in the storage module;

[0039] S2. The parsing module parses the engineering data stored in the storage module. The parsing steps include interpreting the engineering data file into an XML format file so that the computer system can read it.

[0040] S3. Retrieve the parsed project data through the audit module and conduct an audit based on the quality score of the project data; Step S3 specifically includes the following steps:

[0041] S31. Classify the engineering data according to the professional category and stage; wherein, the professional category of the project includes earthwork engineering, foundation pit support engineering, main structure engineering, interior decoration engineering, etc.; the stage of the engineering data includes preliminary estimate, budget, and settlement.

[0042] S32. Match the corresponding inspection form according to the classification results of the engineering data;

[0043] S33. Quality scoring is conducted based on the scores of the inspection items in the inspection form. The inspection form in step S33 specifically includes inspection items for potential quality issues that may occur in the preliminary and final settlement of project costs. The compilers use these inspection items to prepare or verify the project cost documents, thereby improving the accuracy of project cost preparation. Quality scoring is divided into automatic and semi-automatic scoring. Quality scoring is based on a 100-point scale. Automatic scoring is linked to the project cost reduction rate before and after review, with tiered deductions based on the reduction rate. Semi-automatic deductions are linked to the inspection items; for each quality issue described in an inspection item in the inspection form that the reviewer identifies, the corresponding points are deducted. The project cost reduction rate refers to the percentage difference between the project settlement amount submitted by the contractor (i.e., the submitted amount) and the final settlement amount determined after review (i.e., the approved amount) relative to the submitted amount. Its core function is to quantify the degree of settlement deviation, reflecting the accuracy of the contractor's quotation and the quality of project management. Each inspection item has a corresponding score, and the project cost quality control personnel also review the project cost documents prepared by the compilers according to the inspection form. Each inspection item has a corresponding quality score. For each error point mentioned in the inspection form, the corresponding score is deducted to form the quality score of the project cost document. The project data is reviewed based on the quality score, and project data with a higher than the preset quality score is selected as the reviewed project data.

[0044] S4. Collect the reviewed project data, and use the project cost index data analysis model in the iBIM platform to automatically match and analyze the parsed and reviewed project data based on automatic matching rules to obtain the data analysis results; Step S4 specifically includes the following steps:

[0045] S41. Import the project data into the iBIM platform for analysis, fill in the project scale data, and obtain the required matching engineering specialties and preset matching rules. Engineering specialties include mechanical engineering, electrical engineering, civil engineering, chemical and metallurgical engineering, and architectural engineering. Preset matching rules include list code matching rules and list keyword matching rules. List code matching rules establish a relationship with the cost index analysis framework using a 12-digit list item code (national standard): the first two digits are the professional engineering code, the 3rd and 4th digits are the engineering section code, the 5th and 6th digits are the sub-item code, the 7th and 9th digits are the sub-item code, and the 10th to 12th digits are the list sequence code. List keyword matching rules establish a relationship with the cost index analysis framework by uniquely combining the list name, feature description, and unit of quantity in the list.

[0046] S42. Based on the engineering cost index data analysis model, the engineering data is dynamically matched and automatically analyzed according to the engineering profession and matching rules to obtain the data analysis results.

[0047] S5. Optimization and correction of automatic matching rules, identification of abnormal and incorrect matching: The matching rate analysis module identifies abnormal data in the data analysis results to identify recent matching data, and automatically corrects the automatic matching rules based on the abnormal data. The matching rules are optimized in a targeted manner. After optimization, the quality of automatic matching can be effectively improved. The accuracy of engineering data is verified by analyzing the engineering cost index data analysis model, and N engineering data analysis results are output.

[0048] The abnormal data refers to data items identified by the matching rate analysis module that significantly deviate from the preset matching rules, including incorrectly matched and unmatched items in the bill of quantities data. Incorrectly matched items refer to inaccurate matching results due to conflicts with the matching rules during the data matching process, resulting in incorrectly matched engineering data, such as incorrectly associating data of category A with category B. Unmatched items refer to data items for which no corresponding relationship was found under the preset matching rules, possibly due to missing data, inconsistent formats, or incomplete matching rules. The storage module includes a historical engineering database and a material cost database for storing multi-dimensional engineering data and material cost information.

[0049] Step S5 specifically includes the following steps:

[0050] S51. Abnormal data is identified through keyword coding units based on an abnormal data identification algorithm. Abnormal data refers to list data that has not been accurately assigned or has not been assigned under coding or keyword rules. The iBIM platform will record subsequent manual adjustments to determine whether the list data has been correctly assigned. (For example, if list data is manually adjusted from item A to item B, it is recorded as an incorrect assignment; if it is adjusted from an empty item to item B, it is recorded as correctly assigned.)

[0051] S52. The error collection unit collects the abnormal data to form an abnormal data list;

[0052] S53. Establish a mapping relationship between national standard list codes and indicator items through the list unit, and automatically classify and correct abnormal data lists.

[0053] S54. Based on the feedback on the improvement of recognition rate according to the changes in recognition rules, determine whether the automatic correction rules are reasonable, and provide suggestions for rule optimization through the human-computer interaction unit.

[0054] The workflow for automatically classifying and correcting the abnormal data list in step S53 is as follows: Figure 3 As shown, it includes the following steps:

[0055] S531. Preliminary estimate, budget, and settlement produce the project cost pricing results;

[0056] S532. Determine if the bill of quantities has a national standard code;

[0057] S533. If the judgment result of step S532 is yes, then it is judged to be an international coding system pricing document. Based on the cost index analysis framework, according to the cost index classification standard and in accordance with the international coding rules, the cost index is automatically classified to obtain the engineering data analysis results. If the judgment result of step S532 is no, then it is judged to be a market-based list pricing document, and step S534 is executed.

[0058] S534. Based on the cost index analysis framework, and according to the cost index classification standards and keyword rules, the cost index is automatically classified to obtain engineering data analysis results. The engineering data analysis results include economic and technical indicator data of individual construction projects collected through the data framework. These data can be used for data comparison between different projects, improving the efficiency of construction cost control, and can also be used for calculating the construction cost of new projects.

[0059] The cost index analysis framework includes the hierarchical relationship of cost index classification subjects, the relationship between cost index classification subjects and the national standard codes of the bill of quantities, and the relationship between cost index classification subjects and keywords in the bill of quantities. The existing cost index analysis framework built into the iBIM platform analyzes the bill of quantities pricing results according to the analysis framework of business type, profession, and professional component subjects. The bill of quantities pricing results establish relationships with the national standard codes of the bill of quantities, the keyword matching rules, and the professional component subjects in the index classification standards.

[0060] In a preferred embodiment, the engineering cost index data analysis model in the iBIM platform includes the following components:

[0061] 1. Indicator Data Framework: The indicator data framework is the core of the engineering cost indicator data analysis model. The data framework provides a structured foundation for the collection, sorting, cleaning, classification and application of engineering cost data by defining the cost type, specialty, division and organization of the data. It ensures that all types of data are analyzed and stored under the same caliber, thereby supporting subsequent data query, benchmarking, calculation and other applications.

[0062] 2. Project Overview: The role of the project overview in the project cost index data analysis model is to provide a basic information framework for the project, ensuring the accuracy of cost index data and a comprehensive reflection of project characteristics. Through the project overview, key information such as the project's location, scale, time, business type, cost type, and individual unit details can be clearly defined, providing fundamental support for subsequent cost calculations, data queries, and benchmarking. The specific steps for constructing the project overview are as follows:

[0063] A1. Obtain clear and complete overview information reflecting various aspects of the project (location, scale, time, business type, cost, etc.) to create an overview template;

[0064] A2. Include the different base values ​​of indicators required for different business formats and professions into the overview template;

[0065] A3. Incorporate key screening criteria for future data applications into the overview template.

[0066] 3. Building Formats: The role of building format classification in the engineering cost index data analysis model is to provide a targeted data classification and analysis framework for different building types, ensuring that the engineering cost index data analysis model can accurately reflect the characteristics and needs of various building formats. Through format classification, data can be collected, processed, and analyzed more specifically, thereby providing data support for cost control of construction projects. Building formats specifically include project formats and individual formats; project formats reflect the overall format attributes of a project and serve as a condition for data format classification in the database; individual formats reflect the format attributes of a single project and are not entirely subordinate to project formats; building formats reflect the usage functions of individual formats, facilitating data application.

[0067] 4. Construction Professional Data Interface: The main role of the construction professional data interface division rules in the engineering cost index data analysis model is to ensure the structure and purity of the data, thereby facilitating data reuse, analysis, and benchmarking, and providing accurate and reliable data support for cost control and cost estimation. On the other hand, a stable set of interface division rules also serves as a link between different types of deliverable data. Specifically, this includes: the contract interface for cost deliverables and the subject interface for the data framework. The contract interface for cost deliverables fully considers the existing contract interface situation of cost data samples, reducing the work of professional segmentation in subsequent data analysis; the subject interface of the data framework is application-oriented, combining existing data usage habits and cost deliverable contract situations to create an interface division that reflects the current data application status of the enterprise.

[0068] 5. Indicator Data Characteristics: Indicator data characteristics play a crucial role in the engineering cost indicator data analysis model. They provide rich feature information highly correlated with cost-sensitive factors, enhancing data reliability and application efficiency. When compiling indicator feature templates, the core focus is on comprehensively collecting cost-sensitive factors from all professional levels, while fully considering past project experience and new process information from existing projects to enrich the dimensions and content of feature items. The compilation process must follow the indicator data framework, proceeding hierarchically and item by item to ensure the rationality and usability of feature information, clearly distinguishing between feature information and overview information, and highlighting the cost sensitivity of feature information. Indicator data characteristics include engineering characteristics and brand characteristics. Engineering characteristics mainly reflect cost-sensitive factors due to project characteristics, special processes, and material and equipment selection; brand characteristics mainly reflect cost-sensitive factors in the selection of material and equipment brands due to differences in project level.

[0069] S6. Based on the analysis results of N engineering data, a database is formed. Based on the target engineering scale information, similar engineering data in the database are retrieved. Based on the calculation model, the engineering cost of the new project is calculated, and the engineering calculation results are output.

[0070] In a preferred embodiment, the analysis results of multiple engineering data in step S6 include, but are not limited to, business type, labor costs, material costs, machinery costs, and regulatory taxes, and a database is formed based on these data; the calculation formula for the calculation model to measure the new project in step S6 is as follows: in, C For the project cost calculation results, K As a business format, H L For labor costs, H Ma For material costs, H Mc For machinery costs, H R In order to regulate taxes and fees, Due to changes in labor costs, Due to changes in material costs, To measure the differences between the project and historical samples in terms of features such as grade and positioning. Business type refers to the type or functional classification of an engineering project, specifically categories based on building use, functional positioning, and operating model, including schools, hospitals, etc. The main influencing factors of differences in business type include functional complexity, decoration standards, and special technical requirements (such as fire protection systems for commercial projects and load requirements for industrial projects). The higher the complexity, the larger the business type. K The benchmark value (for residential properties) ranges from 0.9 to 1.2, with other business types fluctuating around this benchmark, such as commercial properties. K The value range is 1.0~1.5, for industrial business formats. KThe value range is 1.1 to 1.6. On the other hand, the project size also affects... K The values ​​have an impact relationship; in small projects, the scale effect is small, and the allocated upfront costs (such as design and supervision) and management costs are higher. K >1); In large-scale projects, the scale effect is significant, and the unit cost is reduced ( K <1).

[0071] The principle behind the above formula can be logically divided into the following layers:

[0072] 1. Historical data-driven layer (accounting for 60%-70%)

[0073] Directly using historical sample labor costs H L 、 Material costs H Ma Machinery costs H Mc 、 Taxes and Fees H R As a foundation, it embodies the principle that "historical sample data has the greatest impact".

[0074] Assuming that machinery costs and regulatory fees fluctuate relatively little when project characteristics are similar, historical values ​​will be used (if current data is available, adjustments for material costs can be added). Adjustment of labor costs As an adjustment item, but historical data must be kept as the core; adjustments to material costs Adjustment of labor costs These two are the fees most affected by market supply and demand fluctuations; the data is converted to the current price level through price adjustments.

[0075] 2. Price fluctuation response layer (accounting for 20%-30%)

[0076] Changes in labor costs: This reflects the current increase in labor costs relative to historical levels (e.g., =+5%) indicates a 5% increase in labor costs.

[0077] Material changes: This reflects the current increase in material costs relative to historical levels (such as the increase in steel prices leading to...). =+10%)).

[0078] Adjustment logic: Historical labor / material costs × change rate, directly added to the base cost, reflecting the impact of "secondary change magnitude".

[0079] 3. Feature bias correction layer (5%-15%)

[0080] This refers to the differences between the measured project and historical samples in terms of characteristics such as grade positioning (e.g., the historical sample was a mid-range residence, while the current project is a high-end residence, requiring additional costs for decoration, equipment, etc.).

[0081] Business types / overviews are handled uniformly by K, while characteristic deviations are separated to avoid confusion between "consistent effects" and "differentiated effects".

[0082] The final project cost calculation results can be used to guide procurement, achieve dynamic cost control, and serve as a benchmark for bidding control price, a construction cost early warning line, and a basis for final settlement, so as to verify the cost quality in the project pre-settlement stage.

[0083] The calculation formula provided in this embodiment only serves the calculation action. The variables in the model are based on the elements that make up the unit price of the project cost. The values ​​of these elements are not fixed, but change with time and with the changes in market supply and demand. Therefore, when calculating the project under construction based on the data analysis results, it is necessary to adjust the variables in the formula to a price level that conforms to the time node of the project under construction and the market supply and demand situation.

[0084] Specifically, in existing technologies, engineering cost databases are important tools for construction project cost management. They utilize engineering cost data from multiple regions, types, and projects, classifying, cleaning, summarizing, and organizing this data through information technology. This data can be used to determine investment estimates in the project proposal stage, determine investment estimates in the feasibility study stage, and prepare preliminary design estimates in the preliminary design stage, providing important basis for investment decisions. It can also be used to verify and control the cost quality in the pre-settlement and final settlement stages.

[0085] Currently, most cost consulting firms have recognized the importance of engineering cost databases and have begun to establish their own database platforms. However, due to the enormous workload of data collection, organization, and analysis, and the need for professional technical support, many firms' database construction is still in its early stages. Furthermore, issues related to data sharing and application urgently need to be addressed. While some firms have established databases, their application is limited due to factors such as limited sample sizes and high maintenance costs.

[0086] In this embodiment of the invention, after logging into the system, one enters the iBIM platform and accesses the "Indicator Library" module through the console. The Indicator Library module includes indicator data analysis, through which one can access enterprise indicator templates.

[0087] The iBIM platform can also be called the console, which includes "Enterprise Management", "System Backend", "Template Library", "Digital Cost Estimation", "Approval Center", "Classroom", "Approval Process", "Project Management", "Indicator Library", "List Library", and "Report Center".

[0088] Access the "Indicator Library" module through the console. The indicator library includes the indicator data analysis unit, which stores completed engineering cost cases, containing data on existing projects such as geotechnical and foundation engineering, exterior decoration engineering, electrical engineering, and civil defense engineering.

[0089] Quick access points include Enterprise Management, System Backend, Template Library, Digital Cost Estimation, Approval Center, Classroom, Approval Process, Project Management, Indicator Library, List Library, and Report Center. The Indicator Library module can be accessed through the console.

[0090] The application of data relies on the extensive accumulation of data across multiple dimensions. Only after possessing a wide range of data can the application of data realize its corresponding value.

[0091] Project cost estimation: Cost estimation is performed on similar projects under construction based on historical projects in the database.

[0092] Data benchmarking: Based on the data in the database, benchmark multi-level indicator data of similar projects.

[0093] Indicator review: The indicators of the cost estimates being compiled are reviewed based on the data indicators of the projects in the database.

[0094] Beneficial effects: Compared to traditional cost estimates, there is no need to build a model for the project. The cost can be calculated based on existing building model data and current material costs, thus saving production costs.

[0095] In one embodiment, a login interface is also included, the login interface including the iBIM platform, the iBIM platform including an indicator library, and the indicator library including the indicator data analysis unit.

[0096] Specifically, the system includes an attribute backend, a node backend, a dynamic configuration hub, and dynamic adjustment of the approval process.

[0097] Login methods include: The login interface includes multiple login entrances, and external enterprise users can log in by entering the URL and choosing WeChat, account and password or SMS path.

[0098] The second login method includes: internal enterprise users can log in via the enterprise's internal website → workspace → iBIM platform.

[0099] Login method three includes: Enter the URL → Resource Acquisition → iBIM Platform.

[0100] Data Model Construction: A refined indicator data model is the solid foundation for the construction of engineering cost indicator data; systematically, comprehensively and scientifically constructing a complete indicator data model system is of great significance for guiding and promoting the subsequent accumulation and reuse of various types of data.

[0101] Overview: Project overview data is a crucial component in constructing project cost index data models. Only by collecting and analyzing information such as the project's construction location, time, cost type, and structural form can comprehensive and accurate project cost indicators be generated.

[0102] Features: Cost indicator features are an important part of the cost indicator data model, and the level of detail of the indicator features directly affects the effectiveness of the application of cost indicator data.

[0103] A data framework, as the name suggests, is the core of data model construction. The completeness and rationality of the data framework directly affect the accuracy and reliability of the data in the future database. Furthermore, the data granularity directly affected by the data framework and subject hierarchy will have a significant impact on the efficiency of future data analysis and the effectiveness of data application.

[0104] Building type: Different building types exhibit significant differences in design, construction, and material usage, which directly impact project costs. Therefore, building type becomes a crucial factor in classifying project cost index data models. By classifying, statistically analyzing, and reporting the different building types, project cost index data specific to each type can be generated, providing precise guidance for cost estimation and management of projects in each type.

[0105] Interface: The interface division of indicator data subjects is also a key component of the indicator data model. Whether the interface division rules are scientific and reasonable will directly affect the future data application effect and the efficiency of data collection and analysis.

[0106] In one embodiment, the indicator data analysis unit includes a data analysis result unit, which can dynamically match engineering data according to engineering disciplines to obtain data analysis results.

[0107] Specifically, the project to be calculated is input into the system. The system automatically matches similar projects based on existing project data, and calculates the corresponding cost by averaging the results.

[0108] The automatic analysis of the database's indicators mainly includes: firstly, establishing the relationship between national standard list codes and indicator items to achieve automatic classification and analysis of abnormal data lists; secondly, sorting out the characteristics of the collection lists based on indicator items and forming a set of attribution logic. This attribution logic refers to dividing data attribution into two standard actions for most specialties: first, attributing to the standard sub-sets, and then attributing to the list data. This is equivalent to first attributing to the parent set, and then attributing to the subsets based on the category attributed to the parent set.

[0109] The indicator data framework is the core of the indicator data model. By defining the cost type, specialty, division and organization of the data, the data framework provides a structured foundation for the collection, sorting, cleaning, classification and application of engineering cost data, ensuring that all types of data are analyzed and stored under the same caliber, thereby supporting subsequent data query, benchmarking, calculation and other applications.

[0110] In one embodiment, the data analysis results further include normal data, abnormal data, automatic matching rate, incorrect matching rate, and correct matching rate. Normal data refers to the correctly matched engineering data in the automatic matching results, which represent the amount of engineering data automatically matched without manual intervention. The automatic matching rate is the percentage of automatically matched data out of the total engineering data. The incorrect matching rate is the percentage of incorrectly matched engineering data out of the automatic matching results. The correct matching rate is the percentage of correctly matched engineering data out of the automatic matching results.

[0111] Specifically, the system automatically matches data from various existing projects in the indicator template analysis, and calculates the automatic matching rate, error matching rate, and correct matching rate.

[0112] The database uses three dimensions—automatic matching rate, incorrect matching rate, and correct matching rate—to statistically analyze the intelligent recognition performance of different parts of the database. Different colors are used to represent increases and decreases in these ratios.

[0113] In one embodiment, the data analysis results can be manually selected to match the desired time period.

[0114] Specifically, since building material costs fluctuate in the market, a recent time period can be selected as needed to automatically match the rate as a function of time, making the building material costs closer to current prices and improving the accuracy of the calculation. The standard list intelligently identifies and queries the matching rate trend.

[0115] In one embodiment, the data analysis results further include matching rules, feature information, incorrect matching analysis, and non-matching analysis.

[0116] Specifically, setting matching rules, feature information, mismatch analysis, and non-match analysis facilitates calculations on various types of existing data. The feature information refers to the corresponding item feature information for the application of auxiliary indicator data.

[0117] In one embodiment, the matching rules can be manually set.

[0118] Specifically, by manually setting matching rules, the matching rate can be increased, thereby improving the accuracy of engineering calculations.

[0119] In one embodiment, the error matching analysis includes the part name, the final matching standard part name, and the number of adjustments.

[0120] Specifically, after entering the template maintenance page, select the desired maintenance list. The intelligent identification of the list is divided into two identification methods: "by code" and "by rule". Users can determine whether to add or optimize code and rule information based on the frequency of adjustment feedback from "Error Match Analysis (by rule)", "Error Match Analysis (by rule)" and "No Match Analysis".

[0121] List coding method: List coding is a simple, quick and accurate method, but it cannot solve the problem of list borrowing. It also requires the database to have a built-in national standard list library for use.

[0122] List rule approach: The list rule approach requires a lot of manual work to sort out the list recognition rules in the early stage, but the applicable scenarios after it is completed are more extensive than the coding method.

[0123] In one embodiment, the error matching analysis includes querying by code and querying by rule.

[0124] Specifically, go to the "Template Maintenance" page, select the list of categories to be maintained, and the intelligent identification of the category list is divided into two identification methods: "by code" and "by rule". Users can determine whether to add or optimize code and rule information based on the frequency of adjustment feedback from "Error Match Analysis (by code)", "Error Match Analysis (by rule)" and "Unmatch Analysis".

[0125] The list encoding method includes the prior sorting out of list identification rules.

[0126] Engineering cost index analysis is mainly achieved in two ways: First, manual analysis using Excel spreadsheets is used offline. This method is flexible, easy to use, and facilitates customization, but it is less efficient when handling large datasets. Second, an online automated analysis is performed using a database. This method is fast and can efficiently handle large amounts of data, but it has a relatively high technical threshold and requires attention to data quality and system maintenance. Each method has its own characteristics and is suitable for different scenarios and needs.

[0127] Offline Excel: Advantages: High flexibility; easy to learn and operate; no additional investment required.

[0128] Disadvantages: Inefficient when processing large amounts of data; Excel files are easily leaked, making data security difficult to guarantee; difficult to share and collaborate on; manual operation is prone to human error.

[0129] Online databases: Advantages: fast processing speed; high accuracy; easy data sharing and collaboration; high data security.

[0130] Disadvantages: High technical threshold; strong data dependence, accuracy is highly dependent on the completeness and accuracy of the input data; lack of flexibility; high maintenance and upgrade costs.

[0131] In one embodiment, the recognition rate is improved based on the feedback of changes in the recognition rules to determine whether the rules are reasonable.

[0132] Specifically, matching rules can be set, and changes to the recognition rules will improve the accuracy of the system's calculations if the recognition rate increases. If the recognition rate decreases, it means that the set rules are unreasonable and need to be adjusted.

[0133] Experimental Example

[0134] A comparative experiment was conducted between traditional manual analysis methods, existing platform analysis methods without matching rate analysis modules, and a dynamic matching method for engineering data based on the iBIM platform provided in this embodiment of the invention. The flowcharts comparing the data analysis and calculation paths of the three different methods are as follows: Figure 2 As shown in Table 1, the total time required to process engineering data using the three different methods is as follows.

[0135] Method type Total workload time (person / day) Traditional manual analysis methods 15.8 Existing platform analysis methods without a matching rate analysis module 18.3 Embodiments of the present invention 5.8 Before analysis, first collect and review the engineering data, and then select the analysis path.

[0136] When choosing the traditional manual analysis method, the data analysis and calculation process includes: compiling the analysis framework table (5 person-days); filling in project scale data (0.3 person-days); filling in project cost data (5 person-days); analyzing and verifying data accuracy (1.5 person-days); outputting the project data analysis results; repeating the above process to output N data analysis results, which are then archived on the company's public storage; searching for similar project data on the public storage based on the target project scale information (1 person-day); compiling the data calculation framework (1 person-day); manually calculating the target project cost (2 person-days); and finally outputting the project calculation results. The total workload of choosing the traditional manual analysis method is approximately 15.8 person-days.

[0137] When selecting the platform analysis method, the following steps are performed: importing engineering data into the iBIM platform (0.5 person-days); filling in engineering scale data (0.3 person-days); automatically analyzing the data according to the analysis framework (1 person-day); optimizing and correcting the bidirectional automatic matching rules; identifying abnormal and incorrect matches; and then selecting either the existing platform analysis method or the analysis method of this invention to analyze the engineering data.

[0138] (1) When continuing to select the existing platform analysis method without a matching rate analysis module, the data analysis and calculation path includes: due to the lack of a matching rate analysis module, it is impossible to identify and can only be judged by human judgment based on actual analysis; perform comprehensive rule completion (15 people / day); after optimization, analyze and verify the accuracy of the data (0.5 people / day); output the engineering data analysis results; continue to repeat the above process to output N data analysis results and form a database; retrieve similar engineering data from the database based on the target engineering scale information (0.5 people / day); calculate the target engineering cost based on the data framework and target engineering scale information (0.5 people / day); finally output the engineering calculation results. The total workload takes about 18.3 people / day.

[0139] (2) When continuing to select the method of this embodiment of the invention with the matching rate analysis module, the data analysis and calculation path includes: identifying the matching data status of recent engineering data through the matching rate analysis module and performing quality scoring (0.5 person / day); identifying abnormal data through the matching rate analysis module, and automatically correcting the abnormal data through the matching rate analysis module, performing targeted matching and rule optimization (2 person / day); after optimization, analyzing and verifying the accuracy of the data (0.5 person / day), and outputting the engineering data analysis results; continuing to repeat the above process, outputting N data analysis results and forming a database; retrieving similar engineering data from the database based on the target engineering scale information (0.5 person / day); calculating the target engineering cost based on the data framework and target engineering scale information (0.5 person / day); and finally outputting the engineering calculation results. The total workload takes approximately 5.8 person / day.

[0140] Experimental results show that the total workload and time consumption of the method in the embodiments of the present invention are significantly lower than those of traditional manual analysis methods and existing platform analysis methods without matching rate analysis modules. Furthermore, the method in the embodiments of the present invention improves the processing efficiency of engineering data by up to 72% (reducing the workload from 18.3 person-days to 5.8 person-days), realizes intelligent parsing, dynamic matching and automated correction of engineering data, greatly improves the matching efficiency of engineering data and reduces the human resource cost of engineering calculation.

[0141] The embodiments of this invention have the following significant advantages: Compared with traditional cost estimation methods, the embodiments of this invention do not require building a model of the project. They can calculate the project cost based on existing building model data and current material costs, thus saving production costs. The embodiments of this invention analyze the matching rate, correct matching rate, and incorrect matching rate based on actual correctly assigned data, unassigned data, and manually adjusted data. Simultaneously, the iBIM platform automatically records manually corrected incorrect matching items in codes and rule paths, providing direction for subsequent optimization and improvement of matching efficiency. Furthermore, the iBIM platform also records list items that should have been matched but were not, which helps in continuous optimization and improvement of matching efficiency. The error collection unit identifies and corrects abnormal data through the keyword coding unit, and the list unit establishes a mapping relationship between national standard list codes and indicator items to achieve automatic list classification and anomaly correction, optimizing the effect of automatic analysis, improving matching accuracy, improving automatic analysis efficiency and the quality of data entered into the database, thereby improving the quality of matching existing project data and the accuracy of project calculations.

[0142] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above. For the sake of brevity, they are not provided in detail. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic matching of engineering data based on the iBIM platform, characterized in that, The iBIM platform includes a storage module, a parsing module, an approval module, and a matching rate analysis module, which are each connected to a human-computer interaction unit; the dynamic matching method for engineering data includes the following steps: S1. Input existing engineering data and store it in the storage module; S2. The parsing module parses the engineering data stored in the storage module and interprets the engineering data file into an XML format file; S3. The audit module retrieves the parsed project data and audits it based on the quality score of the project data. The quality score includes automatic scoring and semi-automatic scoring. The automatic scoring is related to the cost reduction rate of the project before and after the audit, and a tiered deduction system is applied according to the size of the reduction rate. The semi-automatic deduction is related to the inspection items. For each quality problem found by the auditor, the corresponding score is deducted. The engineering cost index data analysis model in the S4 and iBIM platforms automatically matches and analyzes the parsed and approved engineering data based on automatic matching rules to obtain data analysis results. The matching rules include list code matching rules and list keyword matching rules. The list code matching rules establish a relationship between the 12-digit list item code and the cost index analysis framework. The list keyword matching rule establishes a relationship between the list name, feature description, and bill of quantities unit and the cost index analysis framework; the data analysis results include automatic matching rate, error matching rate, and correct matching rate; the automatic matching rule specifically includes the following steps: S41, obtain the engineering discipline and matching rule to be matched; S42, dynamically match the engineering data according to the engineering discipline and matching rule through the engineering cost index data analysis model to obtain the data analysis results; S5. The matching rate analysis module identifies abnormal data in the data analysis results and automatically corrects the automatic matching rules based on the abnormal data to improve the quality of automatic matching. Step S5 includes identifying abnormal data based on the abnormal data identification algorithm, collecting the abnormal data to form an abnormal data list, establishing a mapping relationship between list codes, list keywords and indicator items, and automatically classifying and correcting the abnormal data list. Step S5 specifically includes the following steps: S51. Abnormal data is identified by matching and identifying the abnormal data recognition algorithm based on the keyword encoding unit; S52. Abnormal data is collected through the error collection unit to form an abnormal data list; S53. Establish a mapping relationship between national standard list codes and indicator items through list units, and automatically classify and correct abnormal data lists. S6. Based on the analysis results of multiple engineering data, a database is formed, and a calculation model is used to calculate the new project; the calculation model includes a historical data-driven layer, a price fluctuation response layer, and a feature deviation correction layer; the calculation formula of the calculation model is: in, C For the project cost calculation results, K As a business format, H L For labor costs, H Ma For material costs, H Mc For machinery costs, H R In order to regulate taxes and fees, Due to changes in labor costs, Due to changes in material costs, To measure the differences in grade positioning characteristics between the project and historical samples.

2. The method for dynamic matching of engineering data based on the iBIM platform according to claim 1, characterized in that, The storage module includes a historical engineering database and a material cost database, used to store multi-dimensional engineering data and material cost information.

3. The method for dynamic matching of engineering data based on the iBIM platform according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Classify the engineering data according to their professional categories and stages; S32. Match the corresponding inspection form according to the classification results of the engineering data; S33. Score the quality based on the inspection item scores in the inspection form.

4. The method for dynamic matching of engineering data based on the iBIM platform according to claim 1, characterized in that, The human-computer interaction unit displays trend graphs of automatic matching rate, error matching rate, and correct matching rate over time.

5. The method for dynamic matching of engineering data based on the iBIM platform according to claim 1, characterized in that, The data analysis results also include matching rules, feature information, error matching analysis, and non-matching analysis. The matching rules can be manually set, including matching by list code or by rule, and users can optimize the rules based on the number of adjustments in the error matching analysis. The matching rate analysis module supports manual selection of engineering specialties and generates a matching rate trend function based on the fluctuation of building material costs.

6. The method for dynamic matching of engineering data based on the iBIM platform according to claim 1, characterized in that, The review module includes a review key point unit, an online review unit, a quality scoring unit, and a quality data collection unit. The quality data collection unit is used to retrieve the parsed engineering data, and the quality scoring unit is used to score the engineering data and generate a quality scoring report.

7. The method for dynamic matching of engineering data based on the iBIM platform according to claim 1, characterized in that, Step S5 also includes the following steps: S54, based on the feedback on the improvement of the recognition rate according to the changes in the recognition rules, determine whether the automatic correction rules are reasonable, and provide suggestions for rule optimization through the human-computer interaction unit.

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