Method and device for intelligent matching of bill of quantities and construction of cost index

Abstract

The present application relates to the technical field related to engineering cost, and specifically to a method and device for intelligent matching of bill of quantities and construction of cost index. Among them, the method includes: through intelligent matching technology, we can quickly and accurately identify and integrate data scattered in different projects, different personnel or "islands", and realize the interconnection and interoperability of data. The core of this method is to use advanced algorithms and models to extract, compare and match key information in the data, so as to discover the correlation, similarity and difference between the data. At the same time, we combine advanced data analysis technology to conduct in-depth mining and analysis of the integrated data, discover the laws and trends in the data, reveal the cost structure and change laws of engineering projects, and construct accurate cost indicators and indexes, which provide a scientific basis for the prediction, control and optimization of engineering costs, and promote engineering cost management into the intelligent era.

Description

Technical Field

[0001] The present application relates to the technical field related to bill of quantities data, and specifically to a method and device for intelligently matching bill of quantities and constructing a cost indicator index. Background Art

[0002] In the field of engineering cost, with the rapid development of the industry, a large amount of priced bill of quantities data has been accumulated. These data not only record the cost information of engineering projects in detail, but also condense rich practical experience, which is of great value for predicting, controlling and optimizing engineering costs. However, these data often have problems such as diverse forms, inconsistent content, and inconsistent naming, resulting in serious data islands, poor data sharing and interoperability, and greatly limiting the value of data.

[0003] Traditional data processing methods mainly rely on manual work, which is inefficient and prone to errors. Summary of the invention

[0004] In view of this, the embodiments of the present application are committed to providing a method and device for intelligent matching of bills of quantities and construction of cost indicator indexes, so as to reduce data silos, share data, improve interoperability, and better mine data value.

[0005] This application provides a method for intelligently matching a bill of quantities and constructing a cost index, including:

[0006] Obtaining raw data; the raw data includes a preset number of priced engineering lists, drawings, engineering technical standards and requirements;

[0007] Based on the preset data processing model, understand the original data, unify or associate the same entities corresponding to different expressions in the original data, and determine the type of engineering project for establishing indicators;

[0008] Determine the corresponding professional engineering categories based on different engineering project types as the basis for constructing the indicator hierarchy;

[0009] Based on the above-determined professional engineering categories, a cost indicator framework system is constructed; wherein the cost indicator framework system includes: a unified data structure and hierarchical rule base, a standardized data collection table, an indicator calculation table, a data mapping rule base, and an indicator list matching rule base;

[0010] Preprocessing the raw data: Preprocessing includes: marking and classifying the raw data, categorizing and organizing the data according to business types, stages, and periods, cleaning the data to remove duplicate, incomplete, or obviously erroneous data, converting the data, and unifying the format and naming of the data;

[0011] Based on the industry standard benchmarking and historical data analysis methods, the data collection table and indicator elements are determined through the original data;

[0012] Based on the data collection table, extract information from the original data, and store the extracted information according to the cost indicator framework system;

[0013] Based on the index list matching rule base, the extracted information is initially matched to obtain an initial matching cost index hierarchy table;

[0014] Convert the engineering quantity and comprehensive unit price data in the lists in the initial matching cost index hierarchy table, unify the measurement units of the same lists through the system's built-in formula, and convert the comprehensive unit price data into the comprehensive unit price of all costs to ensure that the price structure of all lists in the warehouse is consistent;

[0015] Based on the indicator calculation table, a list of various cost indicators is generated by category;

[0016] The data in the cost index list is further verified for accuracy by comparing it with the data of the same type of projects or the threshold set by the system, and the data in the cost index list is analyzed using statistical tools to obtain analysis results.

[0017] Based on the analysis results, revise the indicator list matching rule base or list matching position, adjust the indicator structure or list placement, and regenerate various cost indicator lists:

[0018] According to the regenerated cost index list, data visualization is performed to present the data in an intuitive form using charts and images;

[0019] Obtaining the results of the review of the cost index list by the preset personnel;

[0020] If the result indicates that the cost index list has passed the review, uploading the cost index list to the public index database;

[0021] Merge data in the public index database, merge the cost index lists entered by different batches and operators of the same single project, and generate a complete list of various cost indexes;

[0022] A complete list of various cost indicators for a single project, with a built-in benchmarking rule hierarchy, used to generate reference indicators for target projects;

[0023] Obtain user requests and respond based on the public indicator database, wherein the user requests include: indicator call requests, retrieval requests and query requests.

[0024] In some embodiments, it also includes:

[0025] Based on the preset deep learning model, it provides users with intelligent search and recommendation services.

[0026] In some embodiments, it also includes:

[0027] When initially matching the extracted information to data, the data that was not successfully matched is highlighted.

[0028] In some embodiments, it also includes:

[0029] Generate a report of unmatched data:

[0030] The unmatched data is analyzed to obtain the number, type and distribution of the unmatched data:

[0031] Based on the importance and urgency of the unsuccessfully matched data, the unsuccessfully matched data are divided into different priorities;

[0032] Prioritize the processing of the data with higher priority that has not been successfully matched:

[0033] The processing operations include: modifying the list description, adjusting the indicator level, and analyzing and adjusting the matching algorithm:

[0034] Re-do the initial matching.

[0035] In some embodiments, after converting the engineering quantity and comprehensive unit price data in the list in the initial matching cost index hierarchy table, the method further includes:

[0036] Select the comprehensive unit price of some of the total costs;

[0037] Determine whether the comprehensive unit price of the part of the total cost is consistent with expectations:

[0038] If they do not match, the preset conversion rules are adjusted and the conversion is performed again.

[0039] In some embodiments, the list of various types of cost indicators includes: total project investment indicators, single project economic indicators, unit project economic indicators, main engineering quantity indicators, unit project labor and material price indicators, functional indicators, and comprehensive unit price indicators of the list.

[0040] In some embodiments, it also includes: converting and unifying the measurement units of the list in the initial matching cost indicator hierarchy table.

[0041] The present application also provides a device for intelligently matching a bill of quantities and constructing a cost index, including:

[0042] A cost index list generation module is used to obtain raw data; wherein the raw data includes a preset number of priced engineering lists; based on a preset data processing model, the raw data is understood, the same entities corresponding to different expressions in the raw data are unified or associated, and the types of engineering projects for establishing indicators are determined; based on different engineering project types, the corresponding professional engineering categories are determined as the basis for component indicators; based on the professional engineering categories, a cost index framework system is constructed; wherein the cost index framework system includes: a unified data structure and hierarchical rule library, a standardized data collection table, an index calculation table, a data mapping rule library, and an index list matching rule library; pre-processing operations are performed on the raw data: the pre-processing operations include: marking and classifying the raw data, classifying and arranging the data according to business types, stages, and periods, cleaning the data to remove duplicate, incomplete or obviously erroneous data; converting the data to unify the format and unit of the data; based on the data collection table, information is extracted from the raw data, and the extracted information is stored according to the cost index framework system; based on the index list matching rule library, The extracted information is used for initial data matching to obtain an initial matching cost index hierarchy table; the engineering quantity and comprehensive unit price data in the list in the initial matching cost index hierarchy table are converted, the measurement units of the same list are unified through the system's built-in formula, and the comprehensive unit price data is converted into a comprehensive unit price of all costs to ensure that the price structure of all lists in the warehouse is consistent; based on the index calculation table, various cost index lists are generated in different categories; the data in the cost index list is compared with the data of the same type of projects to further verify the accuracy of the data, and the data in the cost index list is analyzed using statistical tools to obtain analysis results, and based on the analysis results, the index list matching rule library is revised, the index structure is adjusted, and various cost index lists are regenerated: according to the regenerated cost index list, data visualization processing is performed to present the data in an intuitive form by means of charts and images; the result of the review of the cost index list by the preset personnel is obtained; if the result indicates that the cost index list has passed the review, the cost index list is uploaded to the public index database;

[0043] The public indicator database module is used to merge data in the public indicator database, merge the cost indicator lists entered by different batches and different operators of the same single project, and generate a complete list of various cost indicators; provide a complete list of various cost indicators for a single project, with a built-in benchmarking rule hierarchy, to generate reference indicators for the target project; obtain user requests and respond based on the public indicator database, wherein the user requests include: indicator call requests, retrieval requests and query requests.

[0044] The present application provides an electronic device, including:

[0045] A processor, and a memory for storing a program executable by the processor;

[0046] The processor is used to implement the above-mentioned method of intelligent matching of bill of quantities and construction of cost indicator index by running the program in the memory.

[0047] The present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor is enabled to execute the method for intelligent matching of bills of quantities and construction of cost indicator index as described above.

[0048] The present application provides a method for intelligently matching bills of quantities and constructing cost index indices, which obtains original data; wherein the original data includes a preset number of priced bills of quantities; based on a preset data processing model, the original data is understood, the same entities corresponding to different expressions in the original data are unified or associated, and the types of engineering projects for which indicators are established are determined; based on different types of engineering projects, the corresponding professional engineering categories are determined; based on the professional engineering categories, a cost index framework system is constructed; wherein the cost index framework system includes: a unified data structure and hierarchical rule library, a standardized data collection table, an index calculation table, a data mapping rule library, and an index list matching rule library; the original data are mapped to the original data ... Preprocessing operations are performed on the data: Preprocessing operations include: marking and classifying the original data, categorizing and organizing according to business types, stages, and periods, cleaning the data to remove duplicate, incomplete, or obviously erroneous data; converting the data and unifying the format and naming of the data; extracting information from the original data based on the data collection table, and storing the extracted information according to the cost indicator framework system; performing initial data matching on the extracted information based on the indicator list matching rule library to obtain an initial matching cost indicator hierarchy table; converting the engineering quantity and comprehensive unit price data in the list of the initial matching cost indicator hierarchy table, and unifying the system's built-in formulas. The unit of measurement of the same list is used, and the comprehensive unit price data is converted into the comprehensive unit price of all costs to ensure that the price structure of all lists entered into the warehouse is consistent; based on the indicator calculation table, various cost indicator lists are generated by category; the data of the cost indicator list is compared with the data of the same type of projects to further verify the accuracy of the data, and the data of the cost indicator list is analyzed by using statistical tools to obtain the analysis results. Based on the analysis results, the indicator list matching rule library is revised, the indicator structure is adjusted, and various cost indicator lists are regenerated: according to the regenerated cost indicator list, data visualization is performed to use charts and images to visualize the data in an intuitive way. Present it in a form; obtain the result of the review of the cost index list by the preset personnel; if the result indicates that the cost index list has passed the review, upload the cost index list to the public index database; merge data in the public index database, merge the cost index lists entered by different batches and different operators of the same single project, and generate a complete list of various cost indexes; for a complete list of various cost indexes for a single project, a benchmarking rule hierarchy is built in to generate reference indicators for the target project; obtain user requests and respond based on the public index database, wherein the user requests include: index call requests, retrieval requests and query requests. With such a setting, in the solution provided by the present application, by obtaining and understanding the original data, the same entities with different expressions are unified or associated, and the standardization and integration of data are realized, which is convenient for subsequent processing and analysis. The phenomenon of data islands is reduced.Using the preset data processing model, the raw data is converted into a format that is easy to understand and analyze, which improves the availability and practicality of the data. Through the indicator elements and data collection tables, a cost indicator framework system with a unified data structure, hierarchical rule library, etc. is constructed to provide a standardized tool for cost analysis and management. The raw data is cleaned and converted to remove duplicate, incomplete or erroneous data, unify the data format and unit, and ensure the accuracy and consistency of the data. Using the indicator list matching rule library, the initial data matching is automatically performed to reduce manual operations and improve efficiency. The comprehensive unit price is converted into the comprehensive unit price of all costs to ensure that the price composition of all lists is consistent, which is convenient for cost comparison and control. According to the indicator calculation table, a multi-dimensional cost indicator list is generated to provide support for in-depth analysis and decision-making. By comparing the data of the same type of projects and using statistical tools for in-depth analysis, the data accuracy is verified and the inherent laws of the data are revealed. Based on the analysis results, the indicator structure and matching rule library are continuously revised and adjusted to achieve continuous optimization. The data is presented intuitively using charts and images to enhance the readability and understandability of the data and help users quickly understand the data. The accuracy and reliability of the data are ensured through the review of preset personnel, and the approved data is uploaded to the public indicator database. Merge data entered by different batches and different operators in the public indicator database to generate a unified list of cost indicators. Utilize the built-in benchmarking rule hierarchy to generate reference indicators for individual projects to support the budget preparation and cost control of target projects. Retrieve and respond to data from the public indicator database based on user requests, including indicator calls, retrievals, and queries, to improve the responsiveness and personalization of services. Based on this, the present application provides a solution to reduce data silos, share data, improve interoperability, and better mine the value of data. Specifically, it improves the processing efficiency, accuracy, and application value of engineering cost data, and provides strong technical support and decision-making basis for engineering cost management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0050] Figure 1 It is a flowchart of a method for intelligently matching bills of quantities and constructing a cost indicator index provided by an embodiment of the present application.

[0051] Figure 2 It is a structural diagram of a device for intelligently matching bills of quantities and constructing cost indicator indexes provided by an embodiment of the present application.

[0052] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] With the vigorous development of the field of engineering cost, a large amount of valuable priced bill of quantities data has been accumulated. These data not only record the detailed cost information of engineering projects, but also are the crystallization of many years of practical experience. They have far-reaching significance for the prediction, control and optimization of engineering costs. However, the current challenge is that these data have diverse forms, contents, naming, stages and levels, lack fixed and unified rules and systematic structured frameworks, forming "data islands", increasing the difficulty of data acquisition, integration and processing, and making data sharing and intercommunication difficult, which limits the value of data. In order to break this dilemma, we proposed a method of intelligent matching of bills of quantities, aiming to build an efficient and unified cost indicator system. Through intelligent matching technology, we can quickly and accurately identify and integrate data scattered in various "islands" to achieve data interconnection and intercommunication. The core of this method is to use advanced algorithms and models to extract, compare and match key information in the data, so as to discover the correlation, similarity and difference between the data. At the same time, we combine advanced data analysis technology to deeply mine and analyze the integrated data, discover the laws and trends in the data, reveal the cost structure and change laws of engineering projects, and generate cost indicators and indexes.

[0055] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0056] Figure 1 1 is a flow chart of a method for intelligently matching bills of quantities and constructing cost indexes provided by an embodiment of the present application. Figure 1 As shown, the method includes the following contents.

[0057] Step S101, obtaining original data; wherein the original data includes a preset number of priced engineering lists, drawings, engineering technical standards and requirements;

[0058] Among them, the Priced Bill of Quantities is a document used in the construction industry to list in detail all the work content and its corresponding prices in a project. It is an extension of the Bill of Quantities (BoQ), which not only includes the quantity of various materials, equipment and labor services required for the project, but also includes the specific unit price or total price of these items. That is, in practical applications, it is necessary to collect a certain number of Priced Bills of Quantities from various sources as the basic data for subsequent processing and analysis. Specifically, it is necessary to collect Priced Bills of Quantities extensively from multiple different channels or sources, and ensure that the number of collected lists reaches a certain scale. These collected Priced Bills of Quantities will become an important basis for subsequent data processing and analysis, and subsequent operations will be based on these data to achieve the goals of calculating, analyzing and constructing engineering cost indicators. Collecting from multiple different channels or sources can make the collected data as comprehensive, accurate and more realistic as possible, without being affected by a single factor of a certain channel or source, thereby causing large errors. Drawings are core documents in the field of engineering technology. They use precise graphics, symbols and annotations to describe in detail the structure, layout, size and technical requirements of engineering projects. These technical requirements are important factors that constitute and affect the cost of engineering projects. By collecting key technical parameter information in drawings, we can serve as a technical support for defining cost indicators. Engineering technical standards and requirements are the cornerstones for ensuring engineering quality, safety, reliability and environmental protection. Based on science and combined with practical experience, they provide clear and specific guidance for engineering design, construction, material selection and acceptance. Similarly, technical standards and requirements also play a vital role in the level of engineering cost.

[0059] Step S102, based on a preset data processing model, the original data is understood, the same entities corresponding to different expressions in the original data are unified or associated, and the type of engineering project for establishing the indicator is determined;

[0060] Specifically, we understand and analyze the acquired raw data based on the pre-set data processing model. In this process, we need to find out those situations in the raw data that are described in different ways but actually correspond to the same entity, and unify or associate these different expressions. The purpose of this is to ensure the consistency and accuracy of the data, avoid misunderstanding or incorrect use of data due to different expressions, and provide a more reliable foundation for subsequent data processing and analysis. At the same time, we lock in the types of engineering projects for establishing indicators based on our understanding of the data, such as housing construction engineering indicators, municipal engineering indicators, civil aviation professional engineering indicators, etc.

[0061] In some embodiments, in practical applications, the purpose of this step is to understand the data, find the commonality between the data, and lay the foundation for subsequent data structuring. Specifically, the commonality can be the same entity with a unified corresponding code. It should be noted that the same entity may have different names in different files, but it is limited by some prescriptive documents and industry rules and regulations. It has a specific code corresponding to it. This code is generally fixed. Therefore, the same entity expressed by different names in different files can be associated based on this code.

[0062] Step S103, determining the corresponding professional engineering categories based on different project types as the basis for constructing the indicator hierarchy, providing a clear framework for subsequent data management and analysis;

[0063] Determining the professional engineering categories corresponding to different project types helps to systematize and standardize complex engineering projects. For each project type, determine the professional engineering category to which it belongs based on factors such as the function, technical characteristics, and construction difficulty of the project. For example, housing construction projects are divided into residential buildings, office, judicial, and scientific research buildings, hotel buildings, transportation buildings, sanitary buildings, civil air defense buildings, airport production support facilities, freight stations, and maintenance rooms; municipal engineering is divided into road engineering, bridge and culvert engineering, tunnel engineering, pipeline network engineering, integrated pipeline corridor engineering, greening engineering, and new energy; civil aviation engineering is divided into airfield engineering, civil aviation professional weak current engineering, professional equipment and special vehicles, air traffic control engineering, and fuel supply engineering.

[0064] Step S104, based on the professional engineering categories determined above, construct a cost indicator framework system; wherein the cost indicator framework system includes: a unified data structure and hierarchical rule base, a standardized data collection table, an indicator calculation table, a data mapping rule base, and an indicator list matching rule base;

[0065] That is, based on the determined index elements and data collection tables, a framework system of cost index is established. This cost index framework system includes multiple parts, as follows:

[0066] 1. Unified data structure and hierarchical rule base: Ensure that the organization and classification of data are consistent and standardized, and store and manage data according to certain hierarchical rules.

[0067] 2. Standardized data collection form: used to standardize the data collection process and ensure that the collected data is accurate, complete and meets unified standards.

[0068] 3. Index calculation table: used to calculate various cost indicators and clarify the calculation methods and formulas of the indicators.

[0069] 4. Data mapping rule base: specifies the mapping relationship between data so that data from different sources can be associated and correspond to each other.

[0070] 5. Indicator list matching rule base: Determine the matching rules between indicator lists in order to accurately match and integrate data.

[0071] By constructing such a cost indicator framework system, we can achieve effective management, analysis and utilization of engineering cost data, and provide strong support for engineering cost decision-making.

[0072] In practical applications, first of all, identify the key indicators related to project cost, organize the key indicators according to logical relationships, and form a complete indicator system.

[0073] According to the indicator system, determine the data structure, including data type, data format, data length, etc. Ensure that the data structure can support the calculation and analysis of indicators. Develop grading and classification rules based on the importance and purpose of the indicators.

[0074] Divide the indicators into different levels and categories for targeted management and analysis. Design standardized data collection tables based on the data structure and classification rules to ensure that the collection tables cover all the data items required for key indicators. Design indicator calculation tables based on the indicator system and data structure. For complex calculation formulas or algorithms, provide detailed comments and examples to help users understand. At the same time, verify the indicator calculation tables through actual cases or simulated data to ensure the accuracy of the calculation formulas and the reliability of the calculation results. Determine the mapping relationship between data based on the indicator system and data structure, and organize the mapping relationship into a rule base for reference and query during data analysis and processing.

[0075] Step S105, preprocessing the raw data: the preprocessing includes: marking and classifying the raw data, categorizing and organizing the data according to business types, stages, and periods, cleaning the data to remove duplicate, incomplete, or obviously erroneous data; converting the data to unify the format and unit of the data;

[0076] In practical applications, the acquired raw data needs to be preprocessed. Preprocessing operations include the following aspects: 1. Marking and classification: Mark and classify the raw data according to business formats (such as residential buildings, transportation buildings, etc.), stages (such as investment estimates, design budgets, construction drawing budgets, bidding control prices, settlements, etc.), and periods (such as different years, quarters, etc.) to better classify and organize. 2. Data cleaning: Remove duplicate data from the raw data, as well as incomplete or obviously erroneous data to improve data quality. 3. Data conversion: Unify the format and unit of the raw data to make the data consistent and facilitate subsequent data processing and analysis. Through these preprocessing operations, the raw data can be made more standardized, accurate, and usable, providing a good foundation for subsequent data analysis and application.

[0077] Step S106, based on the industry standard benchmarking and historical data analysis method, determine the data collection table and indicator elements through the original data;

[0078] That is: with the help of industry standard benchmarking and historical data analysis, the original data mentioned above are used to clarify the indicator elements and data collection tables. Specifically, it is to use industry standards as a reference, analyze historical data, screen out key indicator elements from the original data, and determine the data collection table for collecting data related to these indicator elements. This ensures that the determined indicator elements and data collection tables are scientific, reasonable and practical, and can meet actual needs.

[0079] Specifically, determine the data screening criteria, use industry standard benchmarking and historical data analysis to determine the data screening criteria, and select appropriate indicator elements. Prepare for building and improving the data system and compiling data collection forms.

[0080] First, we study industry standards to understand the industry's development trends and best practices, and select the key indicators that are most relevant to the organization's business goals from industry standards. We use statistical methods and data mining techniques to conduct in-depth analysis of historical data to find out the patterns, trends, and outliers in the data. We then organize seminars to raise opinions and suggestions on data screening standards. Finally, we summarize and organize the opinions and suggestions to form a preliminary draft of data screening standards.

[0081] Integrate the results of industry standard benchmarking and historical data analysis to form a comprehensive list of data screening criteria. Based on the integrated list, formulate specific data screening criteria. Based on the goals and scope of data collection, design reasonable data collection forms and formulate detailed data collection processes.

[0082] Among them, indicator elements are key factors used to measure and evaluate the relevant characteristics of engineering cost. They can reflect certain specific characteristics or cost components of engineering projects. By determining appropriate indicator elements, a cost indicator system can be constructed more accurately, thereby better predicting, controlling and optimizing engineering cost.

[0083] The following are some detailed examples of indicator elements:

[0084] Housing construction index elements: such as cost per square meter, steel content, concrete content, wall-to-floor ratio, etc., their prices and usage will directly affect the project cost, and are important indicators and main material contents for measuring and judging the rationality of investment.

[0085] Municipal engineering indicator elements: such as unit length economic indicators, sewage treatment capacity economic indicators, water supply capacity economic indicators, heating area economic indicators, etc. These indicators not only help to evaluate the economic and social benefits of municipal engineering projects, but also provide an important basis for investment and management decisions.

[0086] Civil aviation engineering index elements: including unit economic index of land area, unit economic index of water collection area, unit economic index of runway area, unit economic index of runway length, unit economic index of total excavation volume, passenger throughput index, etc., are important parameters for measuring engineering cost.

[0087] Cost indicator elements: such as management fees, profits, fees, taxes, other engineering construction costs, etc., these are important components of the project cost.

[0088] For example, in a house construction project, "steel" is an indicator element, and its price fluctuation will affect the cost of the entire project; "building area" is an indicator element of engineering quantity, which determines the scale of the project and the amount of related materials and labor. Through the analysis and integration of these indicator elements, we can have a more comprehensive understanding of the composition and influencing factors of the project cost.

[0089] Step S107, extracting information from the original data based on the data collection table, and storing the extracted information according to the cost indicator framework system;

[0090] According to the requirements of the data collection table, relevant information is extracted from the original data. Then, the extracted information is sorted and stored according to the cost index framework system constructed previously. The purpose of this is to enable the information to be incorporated into the framework system in an orderly manner, which is convenient for subsequent query, analysis and use, and also ensures the consistency and standardization of the information.

[0091] Specifically, the system batch imports offline pre-cleaned (pre-processed) priced bills of quantities and corresponding data collection tables, and performs data parsing and marking in batches.

[0092] Before importing, ensure that the offline pre-cleaned priced bill of quantities import data format is one of the three formats that the system can recognize, Excel, XML and Glodon GBQ6.0 / 5.0 / 4.0. According to the data format, design the corresponding data import interface to support batch import and process large amounts of data. Write a data parsing algorithm for the imported data, extract key information according to the structure of the priced bill of quantities and data collection table, and add an exception handling mechanism during the parsing process to deal with possible data errors or format inconsistencies. Then mark the parsed data, design data marking rules and classification systems according to business needs, write a data marking algorithm, and automatically label or classify the data according to the key information obtained from the parsing. Then integrate the data parsing and marking functions into the system and realize automation through the batch processing mechanism. Users can select the data files to be imported through the system interface and trigger batch processing tasks. The system can also automatically read the files, perform data parsing and marking operations in the background, and save the results to the database.

[0093] Step S108, based on the index list matching rule base, perform initial data matching on the extracted information to obtain an initial matching cost index hierarchy table;

[0094] Specifically, the list data is cleaned online, and the system calls the above-mentioned indicator list matching rule library to automatically perform initial data matching, generate an initial matching cost indicator hierarchy table, highlight the data that has not been successfully matched, and generate an unmatched data report.

[0095] Get the pricing file that needs to be cleaned. Before performing any data operations, always back up the original data just in case. Check if there are duplicate records in the list and delete or merge them. Identify and handle missing or blank fields. This can include filling in default values, using algorithms to estimate values, or marking them as missing; load the hierarchical rule library and use the rules in the rule library to perform an initial match on the cleaned list data. Based on the initial matching results, build a list of cost indicators containing multiple levels; fill the successfully matched data into the corresponding positions in the hierarchy table. Identify the data that was not successfully matched from the initial matching results. Use different colors or tags to highlight the unmatched data in the hierarchy table or in a separate report.

[0096] In some embodiments, it also includes: generating a report on unmatched data; analyzing the unmatched data to obtain the number, type and distribution of the unmatched data; dividing the unmatched data into different priorities based on the importance and urgency of the unmatched data; and processing the unmatched data with higher priority first: wherein the processing operations include: correcting the list description, adjusting the indicator hierarchy, analyzing and adjusting the matching algorithm; and re-performing the initial match.

[0097] That is: according to the generated unmatched data report, manually adjust the unmatched data, improve the initial matching cost indicator hierarchy table, and ensure that each list can match a unique indicator hierarchy.

[0098] First, we need to conduct an overall analysis of the unmatched data, understand the number, type and distribution of unmatched data, and explore the reasons behind it. Possible reasons include inaccurate description of the list item name, unreasonable setting of the indicator level, defects in the matching algorithm, etc. Then, according to the importance and urgency of the unmatched data, it is divided into different priorities so that it can be focused in the subsequent adjustment process. For unmatched situations caused by inaccurate description of the list item name, the list description needs to be revised to make it more accurate and specific. For unmatched situations caused by unreasonable setting of the indicator level, the indicator level needs to be adjusted, and the division of the existing indicator level needs to be re-examined to see whether it is reasonable and comprehensive, and some indicator levels need to be added or merged according to actual needs; for unmatched situations caused by defects in the matching algorithm, it is necessary to analyze the advantages and disadvantages of the matching algorithm and the priority matching situation, introduce more advanced matching algorithms or technologies, and improve the matching accuracy; after completing the above adjustments, we need to match the unmatched data one by one, find the most suitable indicator level for each list according to the revised list description and indicator level, and consider setting special marks or making notes for lists that still cannot be matched. After completing the above manual adjustments, we will get a more complete and accurate cost index hierarchy table, which will ensure that each list can be matched to a unique index hierarchy, providing strong support for subsequent cost estimation and cost control.

[0099] Step S109, converting the engineering quantity and comprehensive unit price data in the lists in the initial matching cost index hierarchy table, unifying the measurement units of the same lists through the system's built-in formula, and converting the comprehensive unit price data into the comprehensive unit price of all costs to ensure that the price structure of all lists in the warehouse is consistent;

[0100] That is: the conversion operation of the engineering quantity data in the list in the initial matching cost index hierarchy table. Due to the diversity of the measurement units of the engineering quantity list, the calculation results of the engineering quantity of the same list are diverse. In order to ensure the horizontal comparability of the lists of different projects, it is necessary to use the system's built-in formula to convert the engineering quantities in the list into a unified measurement unit. For example, the measurement units of the 010302001 mud wall bored pile list are m / m³ / root. The data of the three different results cannot be directly compared. We need to determine a unified unit. For example, if we determine to use "m³", we need to convert "m" and "root" to "m³". To do this, we first need to understand in detail the conversion relationship between the system's built-in standard measurement unit list and the inventory list unit; analyze the list items in the hierarchy table one by one to understand the measurement unit currently used for each list. For each list item that needs to be converted, determine the conversion factor from the current measurement unit to the target standard measurement unit. Then manually enter the key conversion factor of each list item that needs to be converted in a fixed position in the system; use the unit conversion function provided by the system, combined with the key conversion factor that has been entered, to convert the measurement unit of the list item from the current unit to the target standard unit. After the conversion is completed, verify the conversion results to ensure that all list items have been correctly converted to the standard measurement unit; update the converted data to the cost indicator hierarchy table to ensure the consistency of the measurement unit of the entire table.

[0101] In this way, through clear steps and operations, the unified conversion of the list measurement units can be effectively achieved, and the accuracy and consistency of the data can be improved. The consistency of the measurement units in the cost index hierarchy table is ensured, which facilitates data analysis and comparison, and provides a reliable basis for the accurate calculation and evaluation of the project cost. At the same time, the steps of verifying and updating data also ensure the reliability of the conversion process and the quality of the data.

[0102] The conversion operation of the comprehensive unit price data in the list in the initial matching cost index hierarchy table. Specifically, it is to use the built-in formula of the system to convert the comprehensive unit price in the list into the comprehensive unit price of all costs, so as to ensure the consistency of the price composition of all incoming lists. In this way, through the conversion, the price composition of all lists is unified into the comprehensive unit price of all costs, eliminating the differences caused by inconsistent price composition, which is convenient for unified analysis and comparison. It ensures that the incoming lists have higher comparability in terms of price, so that the actual situation can be more accurately reflected when conducting cost index analysis and project comparison. The comprehensive unit price of all costs contains more cost elements, which helps to conduct more comprehensive cost analysis and provide stronger support for cost control and management. It provides more accurate and reliable data basis for project decision-making, which helps to make more scientific and reasonable decisions. It promotes the standardized management of cost data and improves the quality and reliability of data.

[0103] Among them, the comprehensive unit price data refers to the sum of labor costs, material and engineering equipment costs, construction machinery and equipment usage fees, enterprise management fees, profits, and risk costs within a certain range required to complete a specified list item. In addition to labor costs, material costs and engineering equipment costs, construction machinery and equipment usage fees, enterprise management fees, profits, and risk costs within a certain range, the "full cost comprehensive unit price" usually includes fees and taxes. Before conversion, it is necessary to clarify which specific cost items are included in the "full cost comprehensive unit price". A conversion formula needs to be built into the system according to the determined content. After the formula is set, the data conversion button is manually triggered to start the data conversion. This process may take some time, especially when your data set is large. In order to ensure the accuracy and completeness of the data during the conversion process, after the conversion is completed, some converted data are randomly checked to ensure that they are consistent with expectations. If any errors or inconsistencies are found, they are corrected immediately. That is: select part of the full cost comprehensive unit price; determine whether the said part of the full cost comprehensive unit price is consistent with expectations: if not, adjust the said preset conversion rules and re-convert.

[0104] Step S110, the data in the cost index list is further verified for accuracy by comparing the data of the same type of projects or the threshold set by the system, and the data in the cost index list is analyzed using statistical tools to obtain analysis results.

[0105] That is: according to the indicator calculation table of the aforementioned built-in system, various cost indicator lists are generated by category. Based on the "indicator calculation table of the built-in system", some indicator tables are generated by category, including: total project investment indicators, single project economic indicators, unit project economic indicators, main engineering quantity indicators, unit project labor and material price indicators, functional indicators, and list comprehensive unit price indicators. For the data in the generated cost indicator list, statistical tools (such as descriptive statistics and correlation analysis) are used for in-depth analysis to reveal the inherent laws and correlations between the data. At the same time, by comparing the data of the same type of projects, the commonalities and differences are identified, and the accuracy of the data is further verified.

[0106] Specifically, the statistical analysis of the data in the cost index list is described in detail as follows: Descriptive statistics: Descriptive statistics are used to summarize and describe the central tendency (such as mean, median), degree of dispersion (such as standard deviation, variance, interquartile range) and distribution form of the data in the cost index list. Correlation analysis: Through correlation analysis, the relationship between different cost indicators is explored, such as evaluating the correlation between material cost and labor cost, or analyzing the relationship between engineering quantity and total cost. Data comparison: Comparing the data of the same type of projects to find out the commonalities and differences between different projects, which helps to identify industry standards and project specificity. Accuracy verification: Through comparative analysis, the accuracy of the data is verified to ensure that the data collected in the cost index list reflects the actual cost without significant deviation or error. Data visualization: Use charts and images to visualize the statistical analysis results, such as using bar charts to compare the costs of different projects, or using scatter plots to show the correlation between two variables. In-depth analysis: Use more advanced statistical methods, such as regression analysis, to predict cost indicators and evaluate the impact of different factors on costs. Problem identification: If data anomalies or mismatches are found, use statistical tools to help identify the problem, and then make necessary data corrections or further investigations. Decision support: The results of statistical analysis can support decision making, such as budgeting, cost control, and resource allocation. Through these analyses, organizations can better understand the composition of project costs, optimize cost structures, and improve budget accuracy and cost-effectiveness for future projects.

[0107] Step S111: based on the analysis results, revise the index list matching rule base, adjust the index structure, and regenerate various cost index lists:

[0108] That is, by conducting self-checks on indicators, revising the matching rule base and adjusting the indicator structure, the cost indicators can more accurately reflect the actual situation of the project and improve the reliability and practicality of the cost indicators. The regenerated cost indicator lists of various types can better meet project needs and provide stronger support for the estimation, control and decision-making of engineering costs.

[0109] Specifically, carefully review the results of statistical analysis and data comparison to identify patterns, trends, outliers, and potential problems in the data. Update and optimize the indicator list matching rule base based on the analysis results. This may include adjusting the matching algorithm, modifying the matching conditions, or adding new matching rules. Re-evaluate and adjust the structure and classification of cost indicators based on project types, market changes, or new industry standards to ensure that they remain relevant and accurate. Regenerate various cost indicator lists using the revised rule base and adjusted indicator structure. This step ensures that the indicators are consistent with current market conditions and project requirements. Verify the regenerated cost indicator lists to ensure that they accurately reflect the relationship between engineering quantities and costs and that there are no calculation errors or omissions. Treat the revision process as a continuous optimization cycle and update it regularly based on new data and feedback.

[0110] Step S112, performing data visualization processing according to the regenerated cost index list, so as to present the data in an intuitive form by means of charts and images;

[0111] Specifically, according to the reborn indicator list, data visualization is performed, and the data is presented in an intuitive form using charts, images, etc., which is easy to understand and analyze, and to dig deeper into the laws and trends behind the data. When visualizing data according to the reborn indicator list, we first need to clarify which specific indicators are included in this list, and then present the complex reborn data in an intuitive form through bar charts, line charts, pie charts, radar charts, and scatter charts, so that users can quickly understand and analyze the laws and trends behind the data.

[0112] Step S113, obtaining the result of the review of the cost index list by a preset person;

[0113] Among them, auditors: The audit team is composed of preset personnel with relevant expertise and experience, who are responsible for evaluating the accuracy and completeness of the cost index list. Audit content: The auditor will check each item in the cost index list, including but not limited to engineering quantity conversion, unit index, price conversion, etc. Audit purpose: Ensure that the cost index list complies with industry standards, contract requirements and project budgets, without errors or omissions. Audit methods: May include manual inspection, verification using professional software, comparative analysis with historical data, etc. Result records: The audit results need to be recorded in detail, including the auditor's opinions, suggestions and any necessary corrections.

[0114] Step S114: if the result indicates that the cost index list has passed the review, the cost index list is uploaded to the public index database;

[0115] If the cost indicator list passes the review of the auditor, it is considered accurate and reliable. The approved cost indicator list is uploaded to the public indicator database, which is a platform for centralized storage and sharing of data. The public indicator database may have the functions of data storage, retrieval, update and management for use by project teams, management and other stakeholders. During the upload process, ensure the security and integrity of the data and take appropriate data encryption and backup measures. The cost indicator list uploaded to the database is accessible to authorized users, supporting data sharing and collaboration across departments or projects. The cost indicator list stored in the database can be used for future project budget preparation, cost analysis, trend forecasting and other related decision support.

[0116] In actual application, a clear approval process should be established according to the internal regulations of the organization. Approving personnel should carefully review the data to ensure that the data meets business requirements and standards; based on the approval results, the indicator data should be modified or confirmed accordingly. If there are problems with the data, it needs to be returned to the data collection and processing personnel for reprocessing. Before publishing, the indicator data should be checked again to ensure the accuracy and completeness of the data, and the indicator data should be uploaded to the public indicator database using appropriate tools or methods. At the same time, the integrity and security of the data during the upload process should be ensured, and the data in the public indicator database should be monitored regularly to ensure the accuracy and timeliness of the data.

[0117] Step S115, merging data in the public index database, merging cost index lists entered by different batches and different operators of the same single project, and generating a complete list of various cost indexes;

[0118] Use the database management system to merge data, use the name of the single project as the primary key, and merge the data entered by different batches and different operators. For the same indicator under the same single project, if there are multiple values, decide which value to use based on business logic; after the merger is completed, verify the data to ensure that the merged data remains accurate, consistent and complete. Finally, generate various complete cost indicators based on the merged data.

[0119] The process of using a database management system to merge data is an important part of ensuring data consistency and accuracy. The following are the detailed steps and considerations for the process: Determine the primary key: Set the name of the individual project as the primary key to ensure that each record can be uniquely identified based on the project name. Data deduplication: Before merging, check and remove duplicate records to ensure that only one record is retained for the indicator of each individual project. Data merging rules: Develop clear data merging rules, especially when there are multiple values ​​of the same indicator under the same individual project, and decide which value to use (for example, select the maximum value, minimum value, average value, or select the latest record based on the timestamp). Data integration: Integrate data entered by different batches and different operators based on the primary key to form a unified data view. Business logic application: Handle special situations according to business logic, such as priority issues between different data sources or data processing rules in specific situations. Data verification: After merging the data, perform data verification, including data accuracy, consistency, and completeness checks. If data problems are found during the merging process, execute a data cleaning process to correct or standardize the data. In some embodiments, data administrators or relevant business experts may be required to review the merged data to ensure that the data meets business requirements. In this way, various cost indicators can be generated based on the merged and verified data, such as unit cost, total cost, material cost, labor cost, etc. When the data is updated, the updated cost indicators are written back to the database to ensure that the data in the database is the latest and most accurate. Implement a data monitoring mechanism to promptly discover and handle problems that may arise during the data merging process. Record the detailed process and rules of data merging, including any records of manual intervention, for future auditing and backtracking. After the data is merged and updated, notify relevant users of changes in data status, especially decision makers who rely heavily on the data. Ensure compliance with data protection regulations during the data merging process to prevent data leakage or unauthorized access.

[0120] Through these steps, we can ensure that the cost index data in the database is accurate, consistent and complete, providing reliable data support for subsequent analysis, reporting and decision-making.

[0121] Step S116, a complete list of various cost indicators for a single project is provided, with a built-in benchmarking rule hierarchy, to generate reference indicators for the target project;

[0122] Specifically, first determine the basic parameters, characteristic parameters and quantitative parameters that need to be paid attention to according to the characteristics and needs of the target project; find the corresponding indicators in the indicator portrait, and set specific benchmarking benchmarks and evaluation standards with reference to the benchmarking rule hierarchy; according to the actual situation of the target project, conduct quantitative analysis and evaluation of each indicator, and determine the reference indicators for the target project; combine the reference indicators with the project plan, and formulate specific implementation measures and monitoring mechanisms.

[0123] Step S117, obtaining a user request and responding based on the public indicator database, wherein the user request includes: an indicator call request, a retrieval request, and a query request.

[0124] In actual applications, the system retrieves relevant indicator data from the database according to the user's query request and displays it through visualization tools (such as charts, dashboards, etc.); in addition to basic query functions, it also supports data filtering, sorting, exporting and other functions to meet users' different analysis needs. By classifying and labeling indicator data, users can quickly locate and filter the required data, and with the help of artificial intelligence and machine learning technology, provide users with intelligent search and recommendation services.

[0125] Providing intelligent search and recommendation services to users based on preset deep learning models usually includes the following key components: Cleaning, standardizing and formatting the database to adapt to the input requirements of the deep learning model to provide data support for intelligent search and recommendation services for users. Analyzing users' historical search and selection behaviors to better understand their needs and preferences. Developing search algorithms and using deep learning models to improve the relevance and accuracy of search results. Using deep learning models to predict items or indicators that users may be interested in and provide personalized recommendation lists. Considering users' current contextual information, such as time, location, project stage, etc., to provide more accurate recommendations. Allowing the model to adjust the recommendation strategy in real time based on user feedback to improve the accuracy of recommendations. If necessary, processing multiple types of data (such as text, images, sounds, etc.) to provide richer search and recommendation services. Continuously optimizing search algorithms and recommendation systems to ensure that the results provided best meet users' needs. Integrating intelligent search and recommendation services into the user interface so that users can easily access and use these services. Providing transparency and explanations of search results and recommended content to enhance users' trust in the system. Ensure compliance with data protection regulations and protect users' privacy in the process of providing intelligent services. We continuously monitor the system's operating status and regularly perform maintenance and updates to meet new challenges and needs. Through these services, users can find the information they need faster, improve work efficiency, and get more personalized and accurate recommendations, thereby improving the overall user experience.

[0126] The device embodiments of the present application can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0127] Figure 2 FIG. 1 is a block diagram of a device for intelligently matching bills of quantities and constructing cost indexes provided by an embodiment of the present application. Figure 2 As shown, the device comprises:

[0128] The cost index list generation module 21 is used to obtain raw data; wherein the raw data includes a preset number of priced project lists; based on a preset data processing model, the raw data is understood, the same entities corresponding to different expressions in the raw data are unified or associated, and the types of engineering projects for establishing indicators are established; based on different engineering project types, the corresponding professional engineering categories are determined; based on the above-determined engineering categories, a cost index framework system is constructed; wherein the cost index framework system includes: a unified data structure and hierarchical rule library, a standardized data collection table, an index calculation table, a data mapping rule library, and an index list matching rule library; the raw data is preprocessed: the preprocessing operation includes: marking and classifying the raw data, classifying and arranging according to business types, stages, and periods, cleaning the data to remove duplicate, incomplete or obviously erroneous data; converting the data, unifying the format and naming of the data; based on industry standards and historical data analysis methods, the index elements and data collection tables are determined through the raw data; based on the data collection table, information is extracted from the raw data, and the extracted information is processed according to the cost index Framework system, for storage; based on the index list matching rule library, the extracted information is initially matched to obtain the initial matching cost index hierarchy table; the comprehensive unit price data in the list in the initial matching cost index hierarchy table is converted, and the comprehensive unit price data is converted into the comprehensive unit price of all costs through the system's built-in formula to ensure that the price structure of all lists in the warehouse is consistent; based on the index calculation table, various cost index lists are generated in different categories; the data in the cost index list is further verified by comparing the data of the same type of projects, and the data in the cost index list is analyzed by using statistical tools to obtain the analysis results, and based on the analysis results, the index list matching rule library is revised, the index structure is adjusted, and various cost index lists are regenerated: according to the regenerated cost index list, data visualization processing is performed to present the data in an intuitive form by using charts and images; the result of the review of the cost index list by the preset personnel is obtained; if the result indicates that the cost index list has passed the review, the cost index list is uploaded to the public index database;

[0129] The public indicator database module 22 is used to merge data in the public indicator database, merge the cost indicator lists entered by different batches and different operators of the same single project, and generate a complete list of various cost indicators; for a complete list of various cost indicators of a single project, a built-in benchmarking rule hierarchy is provided to generate reference indicators for the target project; obtain user requests and respond based on the public indicator database, wherein the user requests include: indicator call requests, retrieval requests and query requests. Figure 3 To describe an electronic device according to an embodiment of the present application. Figure 3A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0130] like Figure 3 As shown, electronic device 300 includes one or more processors 310 and memory 320 .

[0131] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0132] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may run the program instructions to implement the method of intelligent matching of the bill of quantities and construction of the cost index index of each embodiment of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0133] In one example, the electronic device 300 may further include: an input device 330 and an output device 340 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0134] In addition, the input device 330 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 340 may output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0135] Of course, to simplify, Figure 3 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0136] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for intelligent matching of bills of quantities and constructing a cost indicator index according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0137] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0138] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the method for intelligent matching of bills of quantities and construction of cost indicator indexes according to various embodiments of the present application described in the above “Exemplary Method” section of this specification.

[0139] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0140] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for intelligently matching bill of quantities and constructing cost index, It is characterized in that include: Get the original data; The raw data includes a preset number of priced engineering lists, drawings, engineering technical standards and requirements; Based on the preset data processing model, understand the original data, unify or associate the same entities corresponding to different expressions in the original data, and determine the type of engineering project for establishing indicators; Determine the corresponding professional engineering categories based on different engineering project types as the basis for constructing the indicator hierarchy; Based on the above-determined professional engineering categories, a cost indicator framework system is constructed; wherein the cost indicator framework system includes: a unified data structure and hierarchical rule base, a standardized data collection table, an indicator calculation table, a data mapping rule base, and an indicator list matching rule base; Preprocessing the raw data: Preprocessing includes: marking and classifying the raw data, categorizing and organizing the data according to business types, stages, and periods, cleaning the data to remove duplicate, incomplete, or obviously erroneous data, converting the data, and unifying the format and naming of the data; Based on the industry standard benchmarking and historical data analysis methods, the data collection table and indicator elements are determined through the original data; Based on the data collection table, extract information from the original data, and store the extracted information according to the cost indicator framework system; Based on the index list matching rule base, the extracted information is initially matched to obtain an initial matching cost index hierarchy table; Convert the engineering quantity and comprehensive unit price data in the lists in the initial matching cost index hierarchy table, unify the measurement units of the same lists through the system's built-in formula, and convert the comprehensive unit price data into the comprehensive unit price of all costs to ensure that the price structure of all lists in the warehouse is consistent; Based on the indicator calculation table, a list of various cost indicators is generated by category; The data in the cost index list is further verified for accuracy by comparing it with the data of the same type of projects or the threshold set by the system, and the data in the cost index list is analyzed using statistical tools to obtain analysis results. Based on the analysis results, revise the indicator list matching rule base or list matching position, adjust the indicator structure or list placement, and regenerate various cost indicator lists: According to the regenerated cost index list, data visualization is performed to present the data in an intuitive form using charts and images; Obtaining the results of the review of the cost index list by the preset personnel; If the result indicates that the cost index list has passed the review, uploading the cost index list to the public index database; Merge data in the public index database, merge the cost index lists entered by different batches and operators of the same single project, and generate a complete list of various cost indexes; A complete list of various cost indicators for a single project, with a built-in benchmarking rule hierarchy, used to generate reference indicators for target projects; Obtaining user requests and responding based on the public indicator database, wherein the user requests include: indicator call requests, retrieval requests and query requests; The method of intelligently matching the bill of quantities and constructing the cost index index also includes: When initially matching the extracted information, the data that has not been successfully matched is highlighted; Generate a report of unmatched data: The unmatched data is analyzed to obtain the number, type and distribution of the unmatched data: Based on the importance and urgency of the unsuccessfully matched data, the unsuccessfully matched data are divided into different priorities; Prioritize the processing of the data with high priority that has not been successfully matched: The processing operations include: modifying the list description, adjusting the indicator level, and analyzing and adjusting the matching algorithm: Re-perform initial matching; The list of various cost indicators includes: total project investment indicators, single project economic indicators, unit project economic indicators, main project quantity indicators, unit project labor and material price indicators, functional indicators, and list comprehensive unit price indicators; The index elements include: housing construction index elements, municipal engineering index elements, civil aviation engineering index elements or cost index elements.

2. The method for intelligently matching bill of quantities and constructing cost index according to claim 1, It is characterized in that Also includes: Based on the preset deep learning model, it provides users with intelligent search and recommendation services.

3. The method for intelligently matching bill of quantities and constructing cost index according to claim 1, It is characterized in that After converting the engineering quantity and comprehensive unit price data in the list of the initial matching cost index hierarchy table, it also includes: Select the comprehensive unit price of some of the total costs; Determine whether the comprehensive unit price of the part of the total cost is consistent with expectations: If they do not match, the preset conversion rules are adjusted and the conversion is performed again.

4. The method for intelligently matching bill of quantities and constructing cost index according to claim 1, It is characterized in that Also includes: Convert and unify the measurement units of the list in the initial matching cost index hierarchy table.

5. A device for intelligently matching bill of quantities and constructing cost index, It is characterized in that include: A cost index list generation module is used to obtain raw data; the raw data includes a preset number of priced project lists; Based on the preset data processing model, understand the original data and unify or associate the same entities corresponding to different expressions in the original data; Based on the industry standard benchmarking and historical data analysis methods, the data collection table and indicator elements are determined through the original data; Based on the determined professional engineering categories, a cost indicator framework system is constructed; wherein the cost indicator framework system includes: a unified data structure and hierarchical rule library, a standardized data collection table, an indicator calculation table, a data mapping rule library, and an indicator list matching rule library; preprocessing operations are performed on the original data: the preprocessing operations include: marking and classifying the original data, classifying and arranging the data according to the business type, stage, and period, cleaning the data to remove duplicate, incomplete or obviously erroneous data; converting the data, unifying the format and naming of the data; based on the data collection table, extracting information from the original data, and storing the extracted information according to the cost indicator framework system; based on the indicator list matching rule library, performing initial data matching on the extracted information to obtain an initial matching cost indicator hierarchy table; converting the engineering quantity and comprehensive unit price data in the list in the initial matching cost indicator hierarchy table, through The system's built-in formula unifies the measurement units of the same list and converts the comprehensive unit price data into the comprehensive unit price of all costs to ensure that the price structure of all lists entered into the warehouse is consistent; based on the indicator calculation table, various cost indicator lists are generated by category; the data in the cost indicator list is compared with the data of the same type of projects to further verify the accuracy of the data, and the data in the cost indicator list is analyzed using statistical tools to obtain analysis results. Based on the analysis results, the indicator list matching rule library is revised, the indicator structure is adjusted, and various cost indicator lists are regenerated: according to the regenerated cost indicator list, data visualization processing is performed to present the data in an intuitive form by means of charts and images; the results of the review of the cost indicator list by the preset personnel are obtained; if the results indicate that the cost indicator list has passed the review, the cost indicator list is uploaded to the public indicator database; The public indicator database module is used to merge data in the public indicator database, merge the cost indicator lists entered by different batches and different operators of the same single project, and generate a complete list of various cost indicators; for a complete list of various cost indicators of a single project, a built-in benchmarking rule hierarchy is provided to generate reference indicators for target projects; obtain user requests and respond based on the public indicator database, wherein the user requests include: indicator call requests, retrieval requests and query requests; The device for intelligently matching the bill of quantities and constructing the cost index is also used for: When initially matching the extracted information, the data that has not been successfully matched is highlighted; Generate a report of unmatched data: The unmatched data is analyzed to obtain the number, type and distribution of the unmatched data: Based on the importance and urgency of the unsuccessfully matched data, the unsuccessfully matched data are divided into different priorities; Prioritize the processing of the data with high priority that has not been successfully matched: The processing operations include: modifying the list description, adjusting the indicator level, and analyzing and adjusting the matching algorithm: Re-perform initial matching; The list of various cost indicators includes: total project investment indicators, single project economic indicators, unit project economic indicators, main project quantity indicators, unit project labor and material price indicators, functional indicators, and list comprehensive unit price indicators; The index elements include: housing construction index elements, municipal engineering index elements, civil aviation engineering index elements or cost index elements.

6. An electronic device, It is characterized in that include: A processor, and a memory for storing a program executable by the processor; The processor is used to implement the method for intelligently matching the bill of quantities and constructing a cost indicator index as described in any one of claims 1 to 4 by running the program in the memory.

7. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the method for intelligent matching of bills of quantities and construction of cost indicator indexes as described in any one of claims 1 to 4.

Images