Engineering data dynamic matching method based on iBIM platform

Abstract

The invention discloses an engineering data dynamic matching method based on an iBIM platform. The method comprises the following steps that S1, existing engineering data are input and stored in a storage module; s2, an analysis module analyzes the engineering data stored in the storage module; s3, an auditing module calls the analyzed engineering data and audits the quality score based on the engineering data; s4, an engineering cost index data analysis model in the iBIM platform performs automatic matching analysis on the analyzed and audited engineering data based on an automatic matching rule to obtain a data analysis result; s5, matching and identifying abnormal data in the data analysis result by a matching rate analysis module, and automatically correcting an automatic matching rule according to the abnormal data to improve the automatic matching quality; and S6, according to a plurality of engineering data analysis results and the formed database, measuring and calculating the new project based on the measuring and calculating model, thereby effectively improving the accuracy and efficiency of engineering data dynamic matching, and reducing the labor cost.

Description

Technical Field

[0001] The present invention relates to the technical field of project cost engineering, and particularly relates to a method for dynamically matching engineering data based on an iBIM platform. Background Art

[0002] There are many problems in the traditional project cost during the process of engineering data collection and matching: First, it highly depends on manual operations, lacks informatization means, the data collection is fragmented, with low efficiency and prone to errors, and the original data is not effectively cleaned, affecting subsequent analysis; Second, the real-time and integrity are insufficient, the material price update lags behind, the supply chain information is difficult to track dynamically, the phenomenon of data islands is widespread, and the deviation between the cost estimate and the actual situation is large; Third, the cost is high and the sustainability is poor. The lack of intelligent technology leads to a large amount of manpower input and low efficiency. Due to the weak collection foundation of the database, the later maintenance cost is high and the application value is limited. These all reflect serious deficiencies in terms of informatization level, process standardization, and technology adaptability; Fourth, the traditional method cannot establish a feedback loop, resulting in low efficiency of engineering data matching. Summary of the Invention

[0003] Based on this, in view of various problems such as low matching accuracy encountered in the traditional project cost during the process of engineering data collection and matching, it is necessary to propose a method for dynamically matching engineering data based on an iBIM platform.

[0004] To achieve the above object, the present invention adopts the following technical solutions: A method for dynamically matching engineering data based on an iBIM platform, the iBIM platform includes a storage module, an analysis module, an audit module, and a matching rate analysis module, and the storage module, the analysis module, the audit module, and the matching rate analysis module are respectively connected with a human-computer interaction unit; the method for dynamically matching engineering data includes the following steps: S1. Input existing engineering data and store it in the storage module; S2. The analysis module analyzes the engineering data stored in the storage module; S3. The audit module retrieves the analyzed engineering data and conducts an audit based on the quality score of the engineering data; S4. The project cost index data analysis model in the iBIM platform conducts an automatic matching analysis on the analyzed and audited engineering data based on the automatic matching rule to obtain a data analysis result; S5. The matching rate analysis module identifies abnormal data in the data analysis result and automatically corrects the automatic matching rule according to the abnormal data to improve the quality of automatic matching; S6. Based on the database formed by the data analysis results of multiple engineering projects, a new project is estimated relying on a measurement model.

[0005] 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.

[0006] In some embodiments, step S3 specifically includes the following steps: S31. Classify according to the professional category and stage of the engineering data; S32. Match the corresponding inspection form according to the classification result of the engineering data; S33. Perform quality scoring according to the scores of the inspection items in the inspection form.

[0007] In some embodiments, the automatic matching rule in step S4 specifically includes the following steps: S41. Obtain the engineering specialty and matching rule to be matched; S42. Dynamically match the engineering data according to the engineering specialty and matching rule through the engineering cost index data analysis model to obtain the data analysis result.

[0008] In some embodiments, the data analysis result includes an automatic matching rate, an incorrect matching rate, and a correct matching rate, and a trend chart of the changes of the automatic matching rate, the incorrect matching rate, and the correct matching rate over time is displayed through the human-computer interaction unit.

[0009] In some embodiments, the calculation formula of the measurement model in step S6 is: Wherein, C is the engineering cost measurement result, K is the business format, H L is the labor cost, H Ma is the material cost, H Mc is the machinery cost, H R is the regulations and taxes, is the change in labor cost, is the change in material cost, is the difference between the measurement project and the historical sample in terms of characteristics such as grade positioning.

[0010] In some embodiments, the data analysis result further includes a matching rule, feature information, incorrect matching analysis, and unmatched analysis. The matching rule supports manual setting and includes two methods: matching by list code or by rule. And the user optimizes the rule according to the number of adjustments in the incorrect matching analysis; the matching rate analysis module supports manual selection of the engineering specialty and generates a matching rate trend function according to the fluctuation of the building material cost.

[0011] 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 project data, and the quality scoring unit is used to perform a quality score on the project data and generate a quality score report.

[0012] In some embodiments, the matching rate analysis module further includes an error collection unit, a list unit, and a keyword coding unit. Step S5 specifically includes the following steps: S51. Use the keyword coding unit to match and identify abnormal data based on the abnormal data recognition algorithm; S52. Use the error collection unit to collect the abnormal data to form an abnormal data list; S53. Use the list unit to establish a mapping relationship between the national standard list code and the index subject, and automatically classify and correct the abnormal data list.

[0013] In some embodiments, step S5 further includes the following steps: S54. According to the change of the recognition rule feedback to identify the improvement of the recognition rate, judge whether the automatic correction rule is reasonable, and prompt the rule optimization suggestion through the human-computer interaction unit.

[0014] The beneficial effects of the present invention compared with the prior art include: A project data dynamic matching method based on the iBIM platform provided by the present invention, through inputting existing project data and storing it in the storage module, parsing the project data stored in the storage module by the parsing module, retrieving the parsed project data by the audit module, and performing a quality score on the project data, and automatically matching and analyzing the project data according to the quality score by the matching rate analysis module and other technical features. Compared with the traditional method, it is not necessary to establish a model for the project, can automatically match and identify abnormal data in the existing project data, and perform automatic correction, thereby effectively improving the accuracy and efficiency of project data dynamic matching, and greatly reducing the manual intervention cost of project data cost calculation.

[0015] Other beneficial effects in the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a project data dynamic matching method based on the iBIM platform in an embodiment of the present invention; Figure 2 is a flowchart of data analysis, rule tuning, and data application of project data by three different methods; Figure 3 is a flowchart of automatically classifying and correcting the abnormal data list in an embodiment of the present invention. Detailed implementation mode

[0017] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. It should be noted that when an element is expressed as "connected" to another element, it can be directly on the other element, or there may be one or more intermediate elements therebetween.

[0018] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not used to limit the present invention.

[0019] Please refer to Figure 1 and Figure 2 , an embodiment of the present invention provides an engineering data dynamic matching method based on an iBIM platform. The iBIM platform includes a storage module, a parsing module, a review 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 review module, the review module is connected to the matching rate analysis module, and the matching rate analysis module is connected to the data acquisition module. Each module belongs to the same platform and shares a database. The storage module stores existing engineering data, and the parsing module is used to parse the information stored in the storage module; the review module includes a review key point unit, an online review unit, a quality scoring unit, and a quality data acquisition unit. The quality data acquisition unit is used to retrieve the parsed engineering data, and the quality scoring unit is used to perform quality scoring on the engineering data and generate a quality scoring report; the matching rate analysis module includes an error collection unit, a list unit, and a keyword coding unit for correcting abnormal data; the matching rate analysis module supports manual selection of engineering majors and generates a matching rate trend function according to the fluctuation of building material costs; the storage module, the parsing module, the review module, and the matching rate analysis module are also respectively connected to a human-computer interaction unit. The engineering data dynamic matching method includes the following steps: S1. Input existing engineering data and store it in the storage module; S2. Parse the engineering data stored in the storage module through the parsing module. The parsing steps include translating the engineering data file into an XML format file for easy reading by the computer system; S3. Retrieve the parsed engineering data through the review module and perform a review based on the quality score of the engineering data; Step S3 specifically includes the following steps: S31. Classify according to the professional category and stage of the engineering data; among them, the professional categories of the project include earthwork engineering, foundation pit support engineering, main project, fine decoration project, etc.; the stages of the engineering data include preliminary estimate, budget, and final settlement.

[0020] S32. Match the corresponding inspection form according to the classification result of the engineering data; S33. Conduct quality scoring according to the scores of the inspection items in the inspection form; the inspection form in step S33 specifically includes the inspection items of quality problems that may occur in the preliminary estimate, budget, and final settlement of project cost. The preparer uses the inspection items to compile or verify the project cost document, which is used to improve the accuracy of project cost compilation. The quality scoring is divided into two parts: automatic and semi-automatic scoring. The quality scoring is a 100-point system. The automatic scoring is associated with the cost reduction rate before and after the review of the project cost. Step-by-step deductions are made according to the size of the cost reduction rate; the semi-automatic deduction is associated with the inspection items. For each quality problem described by the inspection items in the inspection form found by the reviewer, the corresponding score is deducted. The project cost reduction rate refers to the percentage of the difference between the project settlement amount reported by the construction party (i.e., the submission amount) and the final settlement amount determined after the review (i.e., the approved amount) in the reported amount. Its core function is to quantify the settlement deviation degree and reflect the accuracy of the construction party's quotation and the quality of project management; the corresponding inspection items have corresponding inspection item scores. The project cost quality control personnel also review the project cost document prepared by the preparer according to the inspection form. The corresponding inspection items have corresponding quality scores. For each error-prone point mentioned in the inspection form, the corresponding score is deducted to form the quality score of the project cost document. The engineering data is reviewed according to the quality score of the engineering data, and the engineering data higher than the preset quality score is selected as the reviewed engineering data.

[0021] S4. Collect the reviewed engineering data, and perform automatic matching analysis on the parsed and reviewed engineering data through the project cost index data analysis model in the iBIM platform based on the automatic matching rule to obtain the data analysis result; step S4 specifically includes the following steps: S41. Import the engineering data into the iBIM platform for analysis, fill in the project scale data, and obtain the required engineering specialty and preset matching rules; among them, the engineering specialties include mechanical engineering, electrical engineering, civil engineering, chemical and metallurgical engineering, construction engineering, etc.; the preset matching rules include the bill of quantities code matching rule and the bill of quantities keyword matching rule. The bill of quantities code matching rule establishes a relationship with the cost index analysis framework through the 12-digit bill of quantities item code (national standard), where the first two digits are the professional engineering code, the 3rd - 4th digits are the project division code, the 5th - 6th digits are the sub-item project code, the 7th - 9th digits are the sub-item code, and the 10th - 12th digits are the bill of quantities sequence code; the bill of quantities keyword matching rule establishes a relationship with the cost index analysis framework through the unique combination of the name, feature description, and bill of quantities unit of the bill of quantities to form a relationship logic group.

[0022] S42. Dynamically match and automatically analyze the project data according to the project specialty and matching rules based on the project cost index data analysis model to obtain the data analysis result.

[0023] S5. Optimization and calibration of the automatic matching rule, identification of abnormal and incorrect matches: The matching rate analysis module matches and identifies abnormal data in the data analysis result to identify the recent matching data situation, and automatically calibrates the automatic matching rule according to the abnormal data, and optimizes the matching rule specifically. After the optimization is completed, the automatic matching quality can be effectively improved, and the accuracy of the project data is analyzed and verified through the project cost index data analysis model, and N project data analysis results are output; Among them, the abnormal data refers to the data items that are significantly deviated from the preset matching rules identified by the matching rate analysis module, including the incorrect matching items and unmatched items of the list data; the incorrect matching items of the list data refer to the inaccurate matching results caused by conflicts with the matching rules during the data matching process, and the incorrect project data obtained by incorrect matching. For example, the A-type data that should be associated is incorrectly associated with the B-type; the unmatched item refers to the data item that no corresponding relationship is found under the preset matching rules, which may be due to data missing, inconsistent format or the matching rules not covering the unmatched project data. The storage module includes a historical project database and a material cost database, which are used to store multi-dimensional project data and material cost information; Step S5 specifically includes the following steps: S51. Match and identify abnormal data through the keyword coding unit based on the abnormal data recognition algorithm; among them, the abnormal data refers to the list data that is not accurately classified or not classified under the coding or keyword rules; the iBIM platform will record the subsequent manual adjustment actions of humans to judge whether the list data has been correctly classified. (For example, if the list data is manually adjusted from item A to item B, it is recorded as misclassification; if it is adjusted from an empty item to item B, it is recorded as classification).

[0024] S52. Aggregate the abnormal data through the error aggregation unit to form an abnormal data list; S53. Establish the mapping relationship between the national standard list coding and the index subject through the list unit, and automatically classify and correct the abnormal data list; S54. Judge whether the automatic calibration rule is reasonable according to the change of the recognition rate feedback according to the recognition rule, and prompt the rule optimization suggestion through the human-computer interaction unit.

[0025] The workflow of automatically classifying and abnormally correcting the abnormal data list in step S53 is as Figure 3 shown, including the following steps: S531. Calculate the project cost valuation results for the preliminary estimate, budget estimate, and final settlement; S532. Determine whether the bill of quantities has a national standard code; S533. If the result of the determination in step S532 is yes, it is determined as an international coding system pricing document, and automatic cost index classification is performed based on the cost index analysis framework, according to the cost index classification standard, and in accordance with the international coding rules to obtain the engineering data analysis result; if the result of the determination in step S532 is no, it is determined as a market-based bill of quantities pricing document, and step S534 is executed; S534. Automatic cost index classification is performed based on the cost index analysis framework, according to the cost index classification standard, and in accordance with the keyword rules to obtain the engineering data analysis result. The engineering data analysis result includes the economic and technical index data of each construction project after being aggregated through the data framework, which can be used for data comparison between different projects to improve the efficiency of construction project cost control, and can also be used for the calculation of the construction cost of new projects.

[0026] The cost index analysis framework includes the relationship between the cost index classification level subjects, the relationship between the cost index classification subjects and the national standard code of the bill of quantities, and the relationship between the cost index classification subjects and the keywords of the bill of quantities. The cost index analysis framework already built into the existing iBIM platform analyzes the bill of quantities pricing results according to the analysis framework of the business type, specialty, and specialty component subjects. The bill of quantities pricing results establish a relationship with the specialty component subjects in the index classification standard through the national standard code of the bill of quantities and the list keyword matching rules.

[0027] In a preferred embodiment, the engineering cost index data analysis model in the iBIM platform includes the following components: 1. Index data framework: The index data framework is the core of the engineering cost index data analysis model. The data framework provides a structured basis for the collection, collation, cleaning, classification, and application of engineering cost data by defining the cost type, specialty, division, and organization method of the data, ensuring that various types of data are analyzed and stored in the same caliber, so as to support subsequent data query, benchmarking, calculation, and other applications.

[0028] 2. Project overview: The role of the project overview in the engineering cost index data analysis model is to provide the basic information framework of the project, ensuring the accuracy of the cost index data and the comprehensive reflection of the project characteristics. Through the project overview, key information such as the location, scale, time, business type, cost type, and single building situation of the project can be clarified, providing basic support for subsequent cost calculation, data query, and benchmarking. The specific construction steps of the project overview are as follows: A1. Obtain clear and complete various overview information (location, scale, time, business type, cost...) reflecting the project to obtain an overview template; A2. Incorporate the different index bases required for different business formats and specialties into the overview template; A3. Incorporate the key screening conditions for future data applications into the overview template.

[0029] 3. Construction business format: The role of the construction business format classification in the data analysis model of project cost indicators is to provide a targeted data classification and analysis framework for different building forms, ensuring that the data analysis model of project cost indicators can accurately reflect the characteristics and requirements of various construction business formats. Through business format classification, data can be collected, processed, and analyzed more targeted, providing data support for the cost control of construction projects. The construction business format specifically includes project business format and single-item business format; among them, the project business format reflects the overall business format attribute of the project and serves as the condition for data business format classification in the database; the single-item business format reflects the business format attribute of the single project and is not completely subordinate to the project business format; the construction business format reflects the usage function of the single-item business format, facilitating data application.

[0030] 4. Construction professional data interface: The main role of the construction professional data interface division rules in the data analysis model of project cost indicators is to ensure the structuring and purity of data, facilitating data reuse, analysis, and benchmarking, and providing accurate and reliable data support for cost control, cost calculation, etc. On the other hand, a set of stable interface division rules is also the link to integrate different types of result data. Specifically, it includes the contract interface of cost results and the subject interface of the data framework. The contract interface of cost results fully considers the contract interface situation of the existing project cost data samples and reduces the professional splitting work in subsequent data analysis; the subject interface of the data framework is application-oriented, and in combination with the existing data usage habits and the contract situation of cost results, it formulates an interface division that conforms to the enterprise's own data application status.

[0031] 5. Index data characteristics: Index data characteristics play a key role in the data analysis model of project cost indicators. It provides rich characteristic information highly correlated with cost sensitivity factors, enhancing the reliability and application efficiency of data. When compiling the index characteristic template, the core is to comprehensively collect the cost sensitivity factors at all levels of each specialty, while fully considering past project experience and new process information of existing projects to enrich the dimensions and content of characteristic items. The compilation process needs to follow the index data framework, level by level and item by item, to ensure the rationality and usability of characteristic information, clearly distinguish characteristic information from overview information, and highlight the cost sensitivity of characteristic information. Index data characteristics include project characteristics and brand characteristics. Project characteristics mainly reflect the cost sensitivity factors in aspects such as project features, special processes, and material and equipment selection; brand characteristics mainly reflect the cost sensitivity factors in the selection of material and equipment brands due to differences in project grades.

[0032] S6. Based on the analysis results of N engineering projects, a database is formed. According to the target project scale information, similar engineering data in the database is retrieved, and the project cost of the new project is calculated according to the measurement model, and the project measurement result is output.

[0033] In the preferred embodiment, the multiple engineering data analysis results in step S6 include but are not limited to business type, labor cost, material cost, machinery cost, and regulatory taxes, and a database is formed based on these data; the calculation formula for the measurement model in step S6 to measure the new project is as follows: Among them, C is the project cost measurement result, K is the business type, H L is the labor cost, H Ma is the material cost, H Mc is the machinery cost, H R is the regulatory taxes, is the change in labor cost, is the change in material cost, is the difference between the measurement project and the historical sample in terms of characteristics such as grade positioning. The business type refers to the type or functional classification of the engineering project, specifically the category divided according to the building use, functional positioning, and operation mode, including schools, hospitals, etc. The main influencing factors of the business type difference include functional complexity, decoration standard, and special technical requirements (such as the fire protection system for commercial buildings and the load requirements for industrial buildings). The higher the complexity, the larger the business type. The business type K The reference value (for residential buildings) ranges from 0.9 to 1.2, and other business types fluctuate up and down with it. For example, the business type K of commercial buildings ranges from 1.0 to 1.5, and the business type K of industrial buildings ranges from 1.1 to 1.6. On the other hand, the project scale also has an impact on the K value. 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 projects, the scale effect is significant, and the unit cost is reduced ( K <1).

[0034] The principle of the above formula is logically layered as follows: 1. Historical data dominant layer (accounting for 60% - 70%) Directly adopt the labor cost of the historical sample H L 、 Material cost H Ma Machinery cost H Mc、 Regulatory taxes and fees H R As the basis, reflecting the principle of "historical sample data having the greatest impact".

[0035] Assume that the mechanical costs and regulatory taxes and fees fluctuate less when the project characteristics are similar, and the historical values are adopted (if there are clear current data, the price adjustment of material costs and the price adjustment of labor costs can be added as adjustment items, but the historical data should be kept as the core; the price adjustment of material costs and the price adjustment of labor costs are the two costs that are most affected by market supply and demand changes. Through price adjustment, the data is converted to the current price level).

[0036] 2. Price Fluctuation Response Layer (accounting for 20% - 30%) Change in labor costs: , reflecting the increase in the current labor cost relative to the historical level (e.g., = +5%) indicates a 5% increase in labor cost).

[0037] Change in materials: , reflecting the increase in the current material cost relative to the historical level (e.g., an increase in the price of steel results in = +10%)).

[0038] Adjustment logic: Historical labor / material cost × change rate, directly added to the basic cost, reflecting the impact of "the change amplitude is the second".

[0039] 3. Feature Deviation Correction Layer (accounting for 5% - 15%) Refers to the differences in characteristics such as grade positioning between the measured project and the historical sample (e.g., if the historical sample is a mid - range residence and the current project is a high - end residence, costs for decoration, equipment, etc. need to be increased).

[0040] The business format / general situation is uniformly processed by K, and the feature deviations are separately split to avoid confusing the "consistent impact" and "differentiated impact".

[0041] The final measured result of the project cost can be used to guide procurement, achieve dynamic cost control, and serve as the benchmark for the tender control price, the warning line for construction costs, and the basis for final account settlement to verify the cost quality in the project preliminary estimate and final account stages.

[0042] The calculation formula provided in this embodiment only serves the calculation operation. Each variable in the model is based on each element of the project cost unit price, and the values of these elements are not fixed but change with time and the market supply and demand relationship. Therefore, when calculating the project to be built based on the data analysis results, it is necessary to adjust the variables in the formula to the price level that conforms to the time node and market supply and demand situation of the project to be built.

[0043] Specifically, in the prior art, the project cost database is an important tool for construction project cost management. It uses the project cost data of multiple regions, multiple types, and multiple projects, and classifies, cleans, summarizes, and organizes them through information technology. These data can be used to determine the investment estimate in the project proposal stage, determine the investment estimate in the feasibility study stage, prepare the preliminary design budget estimate in the preliminary design stage, etc., providing an important basis for investment decision-making, and at the same time, it can also perform certain verification and control on the cost quality in the pre-settlement stage.

[0044] Currently, most cost consulting enterprises have realized the importance of the project cost database and have started to build their own database platforms. However, due to the huge workload of data collection, collation, and analysis, and the need for professional technical support, the database construction of many enterprises is still in the initial stage. In addition, the issues of data sharing and application also need to be solved urgently. Although some enterprises have established databases, due to reasons such as limited sample size and high maintenance costs, the application of the database is restricted.

[0045] In the embodiment of the present invention, after logging in to the system, enter the iBIM platform, and enter the "Index Library" module through the console. The index library module includes index data analysis, and the enterprise index template can be entered through the index data analysis.

[0046] The iBIM platform can also be called the console, and the console includes "Enterprise Management", "System Background", "Template Library", "Digital Cost", "Approval Center", "Classroom", "Approval Process", "Project Management", "Index Library", "Bill of Quantities Library", "Report Center".

[0047] Enter the "Index Library" module through the console. The index library includes the completed project cost cases stored in the index data analysis unit, containing the data of existing projects such as geotechnical and foundation engineering, external decoration engineering, electrical engineering, and civil air defense engineering.

[0048] The quick access includes Enterprise Management, System Background, Template Library, Digital Cost, Approval Center, Classroom, Approval Process, Project Management, Index Library, Bill of Quantities Library, and Report Center, and enter the index library module through the console.

[0049] The application of data relies on the extensive accumulation of data in multiple dimensions. Only after having extensive data can the application of data exert corresponding value.

[0050] Project measurement: Rely on historical projects in the library to measure the cost of ongoing projects of the same type.

[0051] Data benchmarking: Rely on the data in the library to conduct multi-level index data benchmarking for similar projects. Index review: Rely on the index situation of project data in the library to review the index of the prepared cost results.

[0052] Beneficial effects: Compared with traditional cost calculation, there is no need to build a model for the project. The cost of the project can be calculated based on the matching existing building model data combined with the current material cost, thereby saving production costs.

[0053] In one embodiment, it further includes a login interface. The login interface includes the iBIM platform. The iBIM platform includes an index library, and the index library includes the index data analysis unit.

[0054] Specifically, the functions in this system include an attribute background, a node background, a dynamic configuration center, and dynamic adjustment of the approval process.

[0055] Login methods include: The login interface includes multiple login entrances. After external enterprise users enter the website address, they can choose WeChat, account password, or SMS path to log in.

[0056] Login method two includes: Internal enterprise users log in through the internal enterprise website → work space → iBIM platform.

[0057] Login method three includes: Enter the website address → resource acquisition → iBIM platform.

[0058] Construction of the data model: A set of refined index data models is a solid foundation for the construction of engineering cost index data. Systematically, comprehensively, and scientifically constructing a complete index data model system is of great significance for leading and promoting the subsequent precipitation and reuse of various data.

[0059] Overview: Project overview data is an important part of constructing the engineering cost index data model. Only by collecting and analyzing information such as the construction location, time, cost type, and structural form of the project can a comprehensive and accurate engineering cost index be formed. Characteristics: Cost index characteristics are an important part of the cost index data model. The detailed degree of the index characteristics directly affects the implementation effect of the application of cost index data.

[0060] The data framework, as the name implies, is the core of data model construction. The integrity and rationality of the data framework directly affect the accuracy and reliability of data in the future database. The data granularity directly affected by the data framework and subject level will have a great impact on future data analysis efficiency and data application effects.

[0061] Business type: Different building business types have significant differences in design, construction, material use, etc., which directly affect the project cost. Therefore, the building business type has become an important factor in the classification of the project cost index data model. By classifying and analyzing buildings of different business types, we can form project cost index data for different business types, providing accurate guidance for the cost estimation and management of various business type projects.

[0062] Interface: The interface division of indicator data subjects is also the 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 also affect the efficiency of data collection and analysis.

[0063] In one embodiment, the indicator data analysis unit includes a data analysis result unit, and the data analysis result unit can dynamically match the engineering data according to the engineering specialty to obtain the data analysis result.

[0064] Specifically, the project to be calculated is input into the system, and the system automatically matches the data of existing projects, finds similar projects, and calculates the corresponding cost by taking the average value. The automatic analysis of indicators in the database mainly includes the automatic classification and analysis of abnormal data lists by establishing the relationship between the national standard list codes and indicator subjects; the second is to sort out the characteristics of the collection list according to the indicator items and form a set of attribution logic. The attribution logic refers to dividing the data attribution of most professions into two standard actions, first attributing the standard division, and then attributing the list data. It is equivalent to attributing the parent set first, and then attributing the subset according to the category of the parent set.

[0065] The indicator data framework is the core of the indicator data model. The data framework provides a structured basis for the collection, organization, cleaning, classification and application of engineering cost data by defining the cost type, specialty, division and organization method of the data, ensuring that all types of data are analyzed and stored under the same caliber, thereby supporting subsequent data query, benchmarking, measurement and other applications.

[0066] In one embodiment, the data analysis results further include normal data, abnormal data, automatic matching rate, error matching rate, and correct matching rate. Among them, normal data refers to the engineering data that is correctly matched in the automatic matching result, and the automatic matching result represents the amount of engineering data that is automatically matched without manual intervention. The automatic matching rate refers to the percentage of the automatic matching result in the total amount of engineering data. The error matching rate refers to the percentage of the amount of engineering data that is incorrectly matched in the automatic matching result in the automatic matching result. The correct matching rate refers to the percentage of the amount of engineering data that is correctly matched in the automatic matching result in the automatic matching result.

[0067] Specifically, automatic matching is performed on various existing engineering data in the index template analysis, and the automatic matching rate, error matching rate, and correct matching rate are calculated.

[0068] In terms of the three dimensions of the automatic rate, error matching rate, and correct matching rate of the database, data statistics are carried out on the intelligent recognition situation of each branch. Different colors are used to represent the ratio increase, ratio decrease, and ratio selection.

[0069] In one embodiment, the time period to be matched in the data analysis results can be manually and freely selected.

[0070] Specifically, since the building materials cost in the market is in a fluctuating state, a recent time period can be selected as needed to make a function of the automatic matching rate with respect to time, so that the building materials cost is closer to the existing price and the calculation accuracy is improved. Query the trend of the intelligent recognition matching rate of the standard list.

[0071] In one embodiment, the data analysis results further include matching rules, feature information, error matching analysis, and unmatched analysis.

[0072] Specifically, setting the matching rules, feature information, error matching analysis, and unmatched analysis facilitates the calculation of various existing data. The feature information is the corresponding project feature information for the auxiliary index data application.

[0073] In one embodiment, the matching rules can be selected for manual setting.

[0074] Specifically, by manually setting the matching rules, the matching rate can be higher and the calculation accuracy of the project can be improved.

[0075] In one embodiment, the error matching analysis includes the branch name, the final matching standard branch name, and the number of adjustments.

[0076] Specifically, enter the template maintenance page, select the attribution list to be maintained as required. The intelligent recognition of the attribution list is divided into two recognition methods: "by code" and "by rule". Users can judge whether to add or optimize the code and rule information based on the frequencies of the adjustment times feedback by "error matching analysis (by rule)", "error matching analysis (by rule)", and "unmatched analysis".

[0077] List coding method: The list coding attribution method is simple, fast, and accurate, but it cannot solve the situation of list borrowing, etc. At the same time, it is necessary to build a national standard list library in the database for calling.

[0078] List rule method: In the early stage, the list rule method requires a large amount of sorting of the list recognition rules, which consumes a huge amount of labor, but the applicable scenarios after being formed are more extensive than the coding method.

[0079] In one embodiment, the error matching analysis includes querying by code and querying by rule.

[0080] Specifically, enter the "template maintenance" page, select the attribution list to be maintained as required. The intelligent recognition of the attribution list is divided into two recognition methods: "by code" and "by rule". Users can judge whether to add or optimize the code and rule information based on the frequencies of the adjustment times feedback by "error matching analysis (by code)", "error matching analysis (by rule)", and "unmatched analysis".

[0081] The list coding method includes sorting out the list recognition rules in the early stage.

[0082] The analysis of project cost indicators is mainly achieved through two methods: one is to manually analyze with the help of Excel tables offline. This method is flexible and easy to use, and is convenient for customized operations, but the efficiency is low when dealing with big data; the other is to establish a database and use a computer for automatic online analysis. This method has a fast processing speed and can efficiently process a large amount of data, but the technical threshold is relatively high, and attention needs to be paid to data quality and system maintenance. The two methods have their own characteristics and are suitable for different scenarios and requirements.

[0083] Offline Excel: Advantages: High flexibility; Easy to get started; No additional investment.

[0084] Disadvantages: Low efficiency when dealing with big data; Excel files are prone to leakage, and it is difficult to guarantee data security; Difficult to share and collaborate; Manual operations are prone to introduce human errors.

[0085] Online database: Advantages: Fast processing speed; High accuracy; Convenient for data sharing and collaboration; Relatively high data security.

[0086] Disadvantages: High technical threshold; Strong data dependence, and the accuracy highly depends on the integrity and accuracy of the input data; Lack of flexibility; High maintenance and upgrade costs.

[0087] In one embodiment, according to the change of the recognition rule to feedback the improvement of the recognition rate, it is judged whether the rule is reasonable.

[0088] Specifically, the matching rule can be set. The improvement of the recognition rate feedback by the change of the recognition rule will improve the accuracy of the system calculation. If the recognition rate decreases, it means that the set rule is unreasonable and needs to be adjusted.

[0089] Experimental example A comparative test is carried out between the traditional manual analysis method, the platform analysis method without a matching rate analysis module and a dynamic engineering data matching method based on the iBIM platform provided by the embodiment of the present invention. The processes of data analysis and measurement path comparison of engineering data by three different methods are as Figure 2 shown, and the total time required for the three different methods to process engineering data is shown in Table 1.

[0090] Method type Total workload duration (person / days) Traditional manual analysis method 15.8 Existing platform analysis method without a matching rate analysis module 18.3 Embodiment of the present invention 5.8 Before analysis, engineering data is first collected and reviewed, and then the analysis path is selected.

[0091] When the traditional manual analysis method is selected, the processes of data analysis and measurement path include: compiling an analysis framework table (5 person-days); filling in engineering scale data (0.3 person-days); filling in engineering cost data (5 person-days); analyzing and verifying data accuracy (1.5 person-days); outputting engineering data analysis results; continuing to repeat the above processes, then N data analysis results are output and archived in the company's public disk; searching for similar engineering data in the public disk according to the target engineering scale information (1 person-day); compiling a data measurement framework (1 person-day); manually measuring the target project cost (2 person-days); and finally outputting the engineering measurement results. The total workload time-consuming of selecting the traditional manual analysis method is about 15.8 person-days.

[0092] When the platform analysis method is selected, the engineering data is imported into the iBIM platform (0.5 person-day); the engineering scale data is filled in (0.3 person-day); the data is automatically analyzed according to the analysis framework (1 person-day); the two-way automatic matching rule is optimized and corrected; the identification of abnormal and incorrect matches is carried out; next, the existing platform analysis method and the analysis method of the embodiment of the present invention can be selected to analyze the engineering data: (1) When continuing to select the platform analysis method of the existing non-matching rate analysis module, the process of the data analysis and calculation path includes: unable to identify and judge only by the actual analysis of human perception due to the lack of a matching rate analysis module; completing the comprehensive rules supplement (15 person-days); after the optimization is completed, analyzing and verifying the data accuracy (0.5 person-day); outputting the engineering data analysis results; continuing to repeat the above process, then outputting N data analysis results and forming a database; retrieving similar project data from the database according to the target project scale information (0.5 person-day); calculating the target project cost according to the data framework and the target project scale information (0.5 person-day); and finally outputting the project calculation results. The total workload takes about 18.3 person-days.

[0093] (2) When continuing to select the method of the embodiment of the present invention with a matching rate analysis module, the process of the data analysis and calculation path includes: identifying the matching data situation of recent project 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, and performing targeted matching and rule optimization (2 person-days); after the optimization is completed, analyzing and verifying the data accuracy (0.5 person-day), and outputting the engineering data analysis results; continuing to repeat the above process, then outputting N data analysis results and forming a database; retrieving similar project data from the database according to the target project scale information (0.5 person-day); calculating the target project cost according to the data framework and the target project scale information (0.5 person-day); and finally outputting the project calculation results. The total workload takes about 5.8 person-days.

[0094] The test results show that the total workload of the method of the embodiment of the present invention is significantly lower than that of the traditional manual analysis method and the platform analysis method of the existing non-matching rate analysis module, and the processing efficiency of the engineering data of the method of the embodiment of the present invention is increased by up to 72% (the workload is reduced from 18.3 person-days to 5.8 person-days), realizing the intelligent analysis, dynamic matching and automatic correction of engineering data, greatly improving the matching efficiency of engineering data, and reducing the labor cost of project calculation.

[0095] The embodiments of the present invention have the following remarkable advantages: Compared with the traditional cost, the embodiments of the present invention do not need to build a model for the project, and can calculate the cost of the project by combining the current material cost with the existing building model data that matches, thus saving production costs. The embodiments of the present invention respectively analyze the matching rate, the correct matching rate and the wrong matching rate based on the actual correctly attributed data, un-attributed data and manually adjusted data. At the same time, the iBIM platform automatically records the wrong matching manual correction items of the code and the rule path respectively, providing a direction for optimizing and improving the matching efficiency in the future. On the other hand, the iBIM platform will also record the list content that should be matched but is not matched, which helps to continuously optimize and improve the matching efficiency in the future. The error collection unit identifies abnormal data through the keyword coding unit and corrects it. The list unit realizes the automatic classification and abnormal correction of the list by establishing the mapping relationship between the national standard list code and the index subject, optimizing the effect of automatic analysis, improving the accuracy of matching, improving the automatic analysis efficiency and the quality of the stored data, thus improving the quality of matching the existing project data and the accuracy of project measurement.

[0096] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; under the idea of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order, and there are many other changes in 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 of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for dynamic matching of engineering data based on iBIM platform, characterized in that: The iBIM platform includes a storage module, a parsing module, an audit module and a matching rate analysis module, wherein the storage module, the parsing module, the audit module and the matching rate analysis module are respectively connected to a human-computer interaction unit; the engineering data dynamic matching method includes the following steps: S1, inputting existing engineering data and storing it in the storage module; S2, the parsing module parses the engineering data stored in the storage module; S3, the audit module retrieves the parsed engineering data and performs an audit based on the quality score of the engineering data; S4. The engineering cost index data analysis model in the iBIM platform automatically matches and analyzes the parsed and reviewed engineering data based on the automatic matching rules to obtain the data analysis results; S5, the matching rate analysis module matches and identifies abnormal data in the data analysis results, and automatically corrects the automatic matching rules according to the abnormal data to improve the automatic matching quality; S6. Based on the analysis results of multiple engineering data, the database is formed and the new project is calculated based on the calculation model.

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, which are used to store multi-dimensional engineering data and material cost information.

3. The method for dynamic matching of engineering data based on iBIM platform according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31, classifying the engineering data according to the professional category and stage; S32, matching a corresponding inspection form according to the classification result of the engineering data; S33, performing quality scoring according to the scores of the inspection items 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 automatic matching rules in step S4 specifically include the following steps: S41. Obtain the engineering major and matching rules required for matching; S42. Dynamically match the engineering data according to the engineering specialty and matching rules through the engineering cost index data analysis model to obtain data analysis results.

5. The method for dynamic matching of engineering data based on the iBIM platform according to claim 4 is characterized in that: The data analysis results include automatic matching rate, error matching rate and correct matching rate, and the trend graphs of the automatic matching rate, error matching rate and correct matching rate changing with time are displayed through the human-computer interaction unit.

6. The method for dynamic matching of engineering data based on iBIM platform according to claim 1, characterized in that: The calculation formula of the measurement model in step S6 is: in, C The project cost calculation results are: K For business, H L For labor costs, H Ma For material costs, H Mc For machinery costs, H R For tax fees, Because labor costs vary, Because material costs change, To measure the differences between the project and historical samples in terms of grade positioning and other characteristics.

7. The method for dynamic matching of engineering data based on iBIM platform according to claim 4 is characterized in that: The data analysis results also include matching rules, feature information, error matching analysis and non-matching analysis. The matching rules support manual settings, including matching by list code or by rule, and the user optimizes the rules according to the number of adjustments in the error matching analysis. The matching rate analysis module supports manual selection of engineering majors and generates a matching rate trend function based on fluctuations in building materials costs.

8. The method for dynamic matching of engineering data based on iBIM platform according to claim 1, characterized in that: 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 perform quality scoring on the engineering data and generate a quality scoring report.

9. The method for dynamic matching of engineering data based on iBIM platform according to claim 1, characterized in that: 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: S51, identifying abnormal data through a keyword encoding unit based on an abnormal data identification algorithm matching; S52, collecting the abnormal data by the error collection unit to form an abnormal data list; S53. A mapping relationship between national standard list codes and indicator items is established through the list unit, and the abnormal data list is automatically classified and anomaly corrected.

10. The method for dynamic matching of engineering data based on iBIM platform according to claim 9, characterized in that: Step S5 also includes the following steps: S54, based on the feedback of recognition rate improvement of recognition rule changes, determine whether the automatic correction rule is reasonable, and prompt rule optimization suggestions through the human-computer interaction unit.

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