Engineering cost data processing method and device

Abstract

The embodiment of the invention provides a project cost data processing method and device. The processing method comprises the following steps: acquiring first project cost data; cleaning the first project cost data to obtain first standard data; comparing the first standard data with set project cost data to obtain a comparison result; sorting the comparison result to obtain comparison result data; and outputting the comparison result data. According to the embodiment of the invention, through an automatic process, the processing time is shortened, the accuracy is improved, the data limitation is broken through, and full-dimension adaptation is realized.

Description

TECHNICAL FIELD

[0001] The embodiment of the application relates to the technical field of electric power data analysis, and particularly relates to an engineering cost data processing method and device. BACKGROUND

[0002] In engineering cost management of electric power, a traditional method mainly relies on manual file preparation, auditing and comparative analysis. In the prior art, part of engineering cost systems have basic functions of quantity calculation, difference adjustment and fee taking for generating a budget estimate, but generally lack multi-dimensional comparison capability. With rapid development of new energy, supporting power transmission and transformation projects increase year by year, and engineering cost data shows explosive growth (annual growth rate is more than 40%), but the data utilization rate is less than 15%. Under the engineering general contracting mode, multi-stage cost data such as design budget, construction drawing budget and completion settlement need to be compared in real time, and the traditional manual comparison is low in efficiency (single project takes more than 200 hours). SUMMARY

[0003] The technical problem to be solved by the embodiment of the application is to provide an engineering cost data processing method and device, which shortens processing time and improves accuracy through an automatic process, breaks through data limitations and realizes full-dimensional adaptation.

[0004] To solve the above technical problem, the technical scheme of the embodiment of the application is as follows: An engineering cost data processing method comprises the following steps: obtaining first engineering cost data; cleaning the first engineering cost data to obtain first standardized data; comparing the first standardized data with set engineering cost data to obtain a comparison result; arranging the comparison result to obtain comparison result data; outputting the comparison result data.

[0005] Optionally, the cleaning of the first engineering cost data to obtain first standardized data comprises the following steps: loading a preset semantic mapping rule library to identify actual semantics of the first engineering cost data; dynamically correcting the first engineering cost data according to the actual semantics of the first engineering cost data to realize row and column alignment; encapsulating the first engineering cost data after row and column alignment into first standardized data in a set format.

[0006] Optionally, the set engineering cost data comprises second standardized data, third standardized data or fourth standardized data. The obtaining process of the second standard data comprises: obtaining second engineering cost data; and cleaning the second engineering cost data to obtain the second standard data. The obtaining process of the third standard data comprises: obtaining general cost data; and reconstructing the general cost data to obtain the third standard data. The obtaining process of the fourth standard data comprises: obtaining quota cost data; and converting the quota cost data to obtain the fourth standard data.

[0007] Optionally, the first standard data and the second standard data are compared to obtain a comparison result, comprising: loading a first preset comparison rule to determine actual semantics of a comparison item; according to the actual semantics of the comparison item, sequentially traversing the first standard data and the second standard data to determine corresponding values of the comparison item; according to the first preset comparison rule, determining a comparison result of the corresponding values of the first standard data and the second standard data; the comparison result comprises original values, difference values, difference rates and traceability paths of the first standard data and the second standard data.

[0008] Optionally, the comparison result is arranged to obtain comparison result data, comprising: arranging the comparison result into a multi-dimensional comparison data body, the multi-dimensional comparison data body containing original values, difference values, difference rates and traceability paths of a comparison item, the first standard data and the second standard data; according to a hierarchical inclusion relationship of the comparison item, extracting corresponding subset data from the first standard data and the second standard data to establish an association relationship between the multi-dimensional comparison data body and the subset data; configuring a dynamic drilling interface on the multi-dimensional comparison data body, the dynamic drilling interface being used to trace and view an associated subset multi-dimensional comparison data body; comparing the difference rate of the multi-dimensional comparison data body with a set threshold value, marking comparison results exceeding the set threshold value to obtain the comparison result data.

[0009] Optionally, the first standard data and the third standard data are compared to obtain a comparison result, comprising: loading a second preset comparison rule to determine actual semantics of a comparison item; according to the actual semantics of the comparison item, sequentially traversing the first standard data and the third standard data to determine corresponding values of the comparison item; According to the second preset comparison rule, a comparison result of the respective corresponding values of the first standard data and the third standard data is determined; the comparison result includes original values, difference values, difference rates, and traceability paths of the first standard data and the third standard data.

[0010] Optionally, the comparison result is arranged to obtain comparison result data, including: The comparison result is arranged into a multi-dimensional comparison data body, and the multi-dimensional comparison data body includes comparison items, original values, difference values, difference rates, and traceability paths of the first standard data and the third standard data. According to the hierarchical inclusion relationship of the comparison items, corresponding subset data is extracted from the first standard data and the third standard data, and an association relationship between the multi-dimensional comparison data body and the subset data is established. A dynamic drilling interface is configured on the multi-dimensional comparison data body, and the dynamic drilling interface is used to trace and view the associated subset multi-dimensional comparison data body. The difference values and the difference rates of the multi-dimensional comparison data body are compared with a set threshold value, and the comparison result exceeding the set threshold value is marked to obtain the comparison result data.

[0011] Optionally, the first standard data and the fourth standard data are compared to obtain a comparison result, including: A third preset comparison rule is loaded to determine the actual semantics of the comparison items; According to the actual semantics of the comparison items, the first standard data and the fourth standard data are sequentially traversed to determine the corresponding values of the comparison items; According to the third preset comparison rule, a comparison result of the respective corresponding values of the first standard data and the fourth standard data is determined; the comparison result includes original values, difference values, difference rates, and traceability paths of the first standard data and the fourth standard data.

[0012] Optionally, the comparison result is arranged to obtain comparison result data, including: The comparison result is arranged into a multi-dimensional comparison data body, and the multi-dimensional comparison data body includes comparison items, original values, difference values, difference rates, and traceability paths of the first standard data and the fourth standard data. According to the hierarchical inclusion relationship of the comparison items, corresponding subset data is extracted from the first standard data and the fourth standard data, and an association relationship between the multi-dimensional comparison data body and the subset data is established. A dynamic drilling interface is configured on the multi-dimensional comparison data body, and the dynamic drilling interface is used to trace and view the associated subset multi-dimensional comparison data body. The difference value and the difference rate of the multi-dimensional comparison data body are compared with a set threshold value, and a comparison result exceeding the set threshold value is marked to obtain comparison result data.

[0013] An engineering cost data processing device is provided in the embodiment of the application, comprising: The acquisition module is configured to acquire first engineering cost data. The processing module is configured to clean the first engineering cost data to obtain first standard data, compare the first standard data with set engineering cost data to obtain a comparison result, arrange the comparison result to obtain comparison result data, and output the comparison result data.

[0014] The above-mentioned scheme of the embodiment of the application has at least the following beneficial effects: The above-mentioned scheme of the embodiment of the application greatly improves efficiency and solves the pain point of artificial dependence. The traditional order placement project comparison takes more than 200 hours and is prone to errors. The application shortens the processing time and improves accuracy through an automatic process. The application breaks through data limitations and realizes full-dimensional adaptation. The application covers power engineering files at all stages, supports 17 common data formats, does not require manual format conversion, breaks through format barriers, adapts to multiple scenarios, and meets the needs of new energy. The application can flexibly cope with power transmission, wind power, photovoltaic and other power engineering, meet the growing demand for new energy projects, dig data value, improve utilization rate, and convert scattered data into structured results to provide the basis for cost optimization, improve utilization rate, and meet real-time comparison needs. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 FIG. 1 is a flowchart of an engineering cost data processing method provided by an embodiment of the application.

[0016] Figure 2 FIG. 2 is a comparison flowchart of first standard data and second standard data of the engineering cost data processing method provided by an embodiment of the application.

[0017] Figure 3 FIG. 3 is a comparison flowchart of first standard data and third standard data of the engineering cost data processing method provided by an embodiment of the application.

[0018] Figure 4 FIG. 4 is a comparison flowchart of first standard data and fourth standard data of the engineering cost data processing method provided by an embodiment of the application.

[0019] Figure 5 FIG. 5 is a module diagram of the engineering cost data processing device provided by an embodiment of the application. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present application will be described herein below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0021] As Figure 1 shown, the embodiment of the present application provides a processing method of engineering cost data, comprising: Step 11, obtaining first engineering cost data; Step 12, cleaning the first engineering cost data to obtain first standard data; Step 13, comparing the first standard data with the set engineering cost data to obtain a comparison result; Step 14, arranging the comparison result to obtain comparison result data; Step 15, outputting the comparison result data.

[0022] Specifically, the first engineering cost data can be a full-stage file of power engineering, including design budget file, construction drawing budget file and completion settlement file, which can be 17 kinds of data formats commonly used in power engineering, including Excel format file, software output format file and database storage format data. It can be adapted to multiple types of power engineering scenes such as power transmission and transformation, thermal power generation, wind power generation, photovoltaic power generation, solar-thermal power generation and technical improvement.

[0023] In this example, the efficiency is greatly improved, and the pain points of manual dependence are solved; the traditional order comparison project consumes more than 200 hours, and is prone to errors, and the present application shortens the processing time and improves the accuracy through automatic process. Breakthrough data limit, realize full-dimensional adaptation. Covering the full-stage file of power engineering, supporting 17 kinds of commonly used data formats, without manual format conversion, breaking the format barrier. Adapt to multiple scenarios, meet the demand of new energy. It can flexibly deal with multiple types of power engineering such as power transmission and transformation, wind power and photovoltaic power generation, and meet the growing demand of new energy projects. Digging data value, improving the low utilization rate. The present application converts scattered data into structured results, provides basis for cost optimization, improves utilization rate and meets real-time comparison demand.

[0024] In an optional embodiment of the present application, in step 11, the first engineering cost data is obtained.

[0025] Specifically, the batch acquisition of the first engineering cost data is realized through a multi-source data access interface, and the interface supports three modes of local file uploading, database direct connection and cloud data pulling.

[0026] During the data acquisition process, the system automatically triggers the format checking mechanism to ensure data integrity through file header identification (such as verifying the.xls / .xlsx suffix of Excel files) and data signature verification (such as matching the exclusive verification code of software format files). If format abnormalities are detected, error logs are generated immediately and the user is prompted to re-upload.

[0027] After data acquisition, the data is temporarily stored in a distributed cache to avoid resource consumption caused by repeated reading, and meta-information such as data source, acquisition time, and data size is recorded to provide a foundation for subsequent path construction.

[0028] In this example, local upload, database direct connection, and cloud pulling are supported, breaking the limitations of traditional single access and allowing batch collection of multi-source cost data for power engineering (such as local budget files, database historical data, and cloud settlement files). This eliminates the need for manual cross-platform transfer, significantly reducing data collection labor and time costs, and adapting to multi-stage data collection needs.

[0029] Abnormal data is automatically intercepted through file header identification and data signature verification to prevent invalid data from entering subsequent stages, reduce rework caused by format errors, and solve the inefficiency of traditional manual collection, which involves entering data first and then discovering problems. This ensures data integrity and effectiveness from the source.

[0030] Data is temporarily stored in a distributed cache to avoid repeated reading and consumption of server resources, improving the efficiency of multi-round comparison scenarios. Meta-information such as data source and acquisition time is recorded to provide key evidence for subsequent path construction (such as locating the original source of data), solving the problem of missing meta-information in traditional stages and providing assistance for audit compliance management.

[0031] In an optional embodiment of the present application, in step 12, the first engineering cost data is cleaned to obtain first standardized data, including: In step 121, a preset semantic mapping rule library is loaded to identify the actual semantics of the first engineering cost data. Specifically, the preset semantic mapping rule library contains a power engineering-specific semantic matching algorithm that can identify different versions (such as 2018 and 2020) of files and enterprise-defined non-standard fields. Through field name matching and data correlation, combined with the preset semantic mapping rule library, the actual meaning of the first engineering cost data is confirmed field by field. Step 122: Based on the actual semantics of the first project cost data, dynamically correct the first project cost data to achieve row and column alignment; specifically, adopt dynamic column correction technology, and perform alignment based on the table structure feature configuration (this configuration records the common structural rules of the power cost table, such as the typical order of project code-project name-quantity-unit price-total price); if the field order of the first project cost data is disordered (such as project name-total price-quantity-unit price), according to the semantic recognition result, automatically adjust the quantity and unit price to the end of the project name and the beginning of the total price to match the standard structure; Step 123: Encapsulate the first project cost data after row and column alignment into first standard data in a set format; specifically, encapsulate the first project cost data after row and column alignment into a standard two-dimensional Java object list to ensure that the data can be directly used for subsequent comparison and calculation.

[0032] Specifically, the loaded preset semantic mapping rule library includes different versions of power engineering quota field mapping tables and enterprise non-standard field dictionaries. Through semantic matching algorithms, the semantics of standard fields are first identified by comparing field names. For non-standard fields, the actual semantics are confirmed and associated with the corresponding standard fields by associating them with the sheet page they belong to and the data relationship.

[0033] If the order of the first project cost data fields does not match the standard order of the table structure features, the system will automatically insert the missing fields (such as matching and supplementing the project code column from the associated project information table) based on the semantic recognition results, adjust the field order, and complete the row and column alignment without manual intervention.

[0034] The aligned field data is encapsulated into a standard two-dimensional Java object list. Each object contains attributes such as project code, project name, quantity, unit price, and total price. Each attribute value is accompanied by data type validation (e.g., the quantity must be numeric and the project code must conform to the specified coding rules) to ensure that there are no data type errors in subsequent comparison and calculation.

[0035] In this example, relying on the semantic mapping rule library with built-in power engineering-specific algorithms, it can accurately identify fields in different versions of files and non-standard fields of enterprises. Through field name matching and data association analysis, it clarifies the actual semantics of each type of data and associates it with standard fields, solving the problem of data misunderstanding caused by different meanings of the same field and the same meaning of different fields in traditional manual cleaning, and clearing semantic obstacles for cross-source data comparison.

[0036] Dynamic column correction technology based on table structure feature configuration can automatically identify and adjust disordered field order (such as correcting project name-total price-quantity-unit price to standard order), and can also supplement missing fields (such as matching project code columns). The entire process requires no manual intervention, completely eliminating the inefficient mode of manually configuring mapping rules in traditional cleaning, and greatly improving the efficiency of structure alignment.

[0037] The aligned data is encapsulated into a standard two-dimensional Java object list with data type validation. Attributes such as quantity and project code are format-verified (e.g., quantity must be numerical) to avoid comparison and calculation deviations caused by data type errors. This ensures that the data can be directly used in subsequent steps, solving the problem that traditional non-standardized data requires secondary processing before it can be used, and laying a data foundation for efficient comparison in the future.

[0038] In an optional embodiment of the present invention, the set engineering cost data includes: second standard data, third standard data, or fourth standard data; The process of obtaining the second standardized data includes: acquiring the second project cost data; cleaning the second project cost data to obtain the second standardized data; specifically, the second project cost data is another power project full-stage document, including design budget document, construction drawing budget document, and final settlement document; the cleaning process of the second project cost data cleaning and reuse step 12 is as follows: loading a preset semantic mapping rule library to identify actual semantics, dynamically correcting columns to achieve row and column alignment, and encapsulating it into a standard two-dimensional Java object list to ensure that the second standardized data and the first standardized data have the same structure. The process of obtaining the third standard data includes: acquiring general cost data; reconstructing the general cost data to obtain the third standard data; specifically, the general cost data is an Excel version of a general cost scheme template; the reconstruction of the general cost data first calls Aspose.Cells to read the data from each sheet in the Excel file, encapsulates it into a Java two-dimensional object list containing scheme code, project type, indicator name, and indicator value, and completes the initial structuring; then, a version compatibility layer is loaded, which has a built-in mapping table of differences between different versions of the general cost template, automatically eliminates version differences, and finally forms the third standard data; The process of obtaining the fourth standard data includes: acquiring the quota cost data; converting the quota cost data to obtain the fourth standard data; specifically, the quota cost data is a standard Excel version quota scheme file (including quota indicators such as unit area cost and main material consumption); the conversion of the quota cost data first uses a rule parsing matrix (XML format configuration, defining the quota hierarchy relationship and parsing logic) to convert the Excel quota scheme into a hierarchical feature map (such as total quota - building engineering quota - main plant quota - unit area cost quota); then, dynamic formulas are extracted from the Excel, the Apache CommonsMath mathematical engine is called to fill in the values, and the quota values ​​of each level are calculated to form the fourth standard data.

[0039] In this example, the cleaning process in step 12 is reused to process the second project cost data, ensuring that it shares the same source as the first standard data structure (both are standard two-dimensional Java object lists with type validation). This solves the problem of heterogeneous data structures across different projects, making direct comparison impossible. Whether comparing data from different stages of the same project (such as design estimates and final settlement) or data from different projects at the same stage, no additional manual structural adjustments are required. This lays the foundation for accurate calculation of differences and rates, improving the efficiency of cross-project / cross-stage comparisons.

[0040] To eliminate version barriers in general cost estimation, the Excel-based general cost estimation template is first structured using Aspose.Cells. Then, a version compatibility layer automatically eliminates differences between different template versions (such as the mapping of indicator fields between the 2020 and 2023 versions), avoiding the pain points of manual data format reconstruction and long version adaptation cycles in traditional general cost estimation comparisons. Simultaneously, the structured third-specification data, containing identifiers such as scheme codes and project types, can be quickly matched with the first-specification data, helping power transmission and transformation projects quickly benchmark against typical designs and improving error correction efficiency.

[0041] The system achieves intelligent conversion of quota indicators by transforming Excel quota schemes into hierarchical feature maps through a rule parsing matrix. Combined with the Apache CommonsMath engine, it calculates the quota values ​​for each level, solving the problems of manual indicator breakdown and error-prone formula calculations in traditional quota comparisons. The resulting fourth-standard data clearly presents the hierarchical relationship of quotas (e.g., total quota - building construction quota - main plant quota), which can be accurately correlated with the first-standard data, providing a structured basis for quota early warning and meeting the needs of real-time cost control.

[0042] like Figure 2 As shown, in an optional embodiment of the present invention, step 13, comparing the first specification data with the second specification data to obtain a comparison result, includes: Step 1311: Load the first preset comparison rule to determine the actual semantics of the items to be compared. Specifically, the first preset comparison rule is an XML format rule syntax tree, which can be edited through a visual interface. The rule defines the comparison dimensions (such as differences in quantity, unit price, and total price), the scope of the comparison items (such as building engineering - foundation engineering, installation engineering - equipment installation), and semantic association logic (such as clarifying the semantic mapping of corresponding items in different data). The XML syntax tree is parsed by the rule parsing engine to determine the items to be compared and their actual semantics. Step 1312: Based on the actual semantics of the items to be compared, the first and second standardized data are traversed sequentially to determine the corresponding values ​​of the items to be compared. Specifically, a bidirectional indexing algorithm is used to traverse the two types of standardized data. First, an index table for the first standardized data is established using the item code as the index key. Then, the second standardized data is traversed, and the corresponding item code is matched with the index key to extract the value. For items without a direct item code match, the corresponding value is determined based on the semantic association logic determined in step 1311 through semantic matching of item name and verification of data association relationship. During the traversal, the system automatically records the matching status (such as exact match, semantic match, no match). Unmatched items are marked separately and the user is prompted to confirm whether to supplement the comparison rules. Step 1313: Based on the first preset comparison rule, determine the comparison results of the corresponding values ​​of the first and second standardized data. The comparison results include the original values, difference values, difference rates, and traceability paths of the first and second standardized data. Specifically, calculate the difference value (e.g., second standardized data value - first standardized data value) and the difference rate (e.g., (difference value / first standardized data value) × 100%) according to the rules, record the original values ​​of the two types of data, and sort out the data sources and calculate the correlation to form a traceability path. For items with a difference rate exceeding a preset threshold, automatically mark them as key attention items and initially associate them with the deviation factor library to provide direction for subsequent analysis.

[0043] In this example, an XML-formatted rule syntax tree is used, which supports visual editing of comparison dimensions, project scope, and semantic association logic. Rules can be adjusted without modifying the core code (such as adding a material price difference comparison dimension), solving the pain points of hard-coded rules in traditional solutions and the need to modify Java code to add new dimensions. It can quickly respond to the comparison needs of different stages of power engineering (such as preliminary estimate-budget) and different professions (construction-installation), and improve the flexibility of business adaptation.

[0044] By employing a bidirectional indexing algorithm, fast and accurate matching is achieved based on item codes. For uncoded items, semantic matching is completed using semantic association logic. Simultaneously, the matching status is automatically recorded, and unmatched items are marked, avoiding the errors and omissions caused by differences in row and column structures in traditional manual matching. Compared to the traditional 8-hour manual mapping configuration, this significantly reduces matching time and improves the efficiency and accuracy of cross-data matching.

[0045] The comparison results cover the original values, differences, difference rates, and tracing paths (such as locating the data source sheet page and row number). Items exceeding the threshold are automatically marked and associated with the deviation factor library, overcoming the limitations of traditional comparisons that only display numerical differences and cannot trace the source. Users can quickly locate deviation items (such as a sub-item's total price exceeding the budget) and trace the root cause (such as an increase in the unit price of materials) by combining the tracing path, providing accurate evidence for cost control and audit review, and reducing deviation analysis time.

[0046] In an optional embodiment of the present invention, step 14 involves organizing the comparison results to obtain comparison result data, including: Step 1411: Organize the comparison results into a multidimensional comparison data body. The multidimensional comparison data body includes the comparison items, the original values ​​of the first standard data and the second standard data, the difference values, the difference rate, and the traceability path. Specifically, according to the hierarchical structure of project type-professional project-sub-item project, organize the comparison results into a multidimensional comparison data body in JSON format to clearly present the comparison information. Step 1412: Based on the hierarchical inclusion relationship of the comparison items, extract corresponding subset data from the first standard data and the second standard data, and establish the association relationship between the multidimensional comparison data body and the subset data; specifically, based on the hierarchical inclusion relationship of the comparison items (e.g., building engineering includes foundation engineering and main structure engineering, foundation engineering includes pile foundation engineering and concrete engineering), extract the comparison results of subset items from the two types of standard data, and establish the association relationship between the multidimensional data body and the subset data through the parent item code-sub-item code association field to form a hierarchical data structure; Step 1413: Configure a dynamic drill-down interface on the multidimensional comparison data body. The dynamic drill-down interface is used to trace and view the associated subset of multidimensional comparison data bodies. Specifically, the dynamic drill-down interface is designed based on RESTful API. The interface parameters include project code, comparison dimension, and drill-down level. Users can call the interface to realize multi-level drill-down from the total item to the sub-item and then to the detailed item, which meets the audit traceability requirements. Step 1414: Compare the difference rate of the multidimensional comparison data volume with a set threshold, and mark the comparison results that exceed the set threshold to obtain the comparison result data; Specifically, the threshold is set according to the engineering type, the difference rate of the multidimensional comparison data volume is compared with the threshold, and the items with the difference rate exceeding the threshold are marked as threshold-exceeding items (e.g., highlighted in red), and associated with the deviation reason input field, which supports users to select preset reasons or custom input, and finally form complete comparison result data.

[0047] In this example, the comparison results are organized into a multi-dimensional data body in JSON format, categorized by project type, professional project, and sub-project structure. This clearly presents key information such as original values ​​and difference rates, solving the problems of disorganized and hierarchical data in traditional comparison results. Users can quickly locate the target project (e.g., building construction - foundation engineering - concrete engineering), intuitively grasp the comparison situation, and reduce the cost of information interpretation.

[0048] By establishing the association between multidimensional data volumes and subset data through parent-child project coding, and combining it with a dynamic drill-down interface designed with RESTful API, multi-level tracing from the general item (such as unit project) to the detailed item (such as material unit price) is supported. This overcomes the limitations of traditional data processing, which only presents summary results and cannot pinpoint the root cause of deviations. Auditors or cost estimators can quickly trace overruns (such as total cost overruns) to the underlying cause (such as an increase in the price of a certain type of steel reinforcement), improving the efficiency of deviation analysis.

[0049] The system allows users to customize thresholds based on project type, automatically marking items exceeding the threshold (e.g., highlighted in red) and associating them with the deviation reason entry field. It supports selecting preset reasons or custom input, solving the problems of low efficiency, easy omissions, and lack of record-keeping for deviation reasons associated with traditional manual marking. This not only quickly identifies key areas of concern but also provides a basis for subsequent deviation rectification and data statistics, forming a closed-loop management system of comparison-marking-analysis-rectification.

[0050] like Figure 3 As shown, in an optional embodiment of the present invention, step 13, comparing the first specification data with the third specification data to obtain a comparison result, includes: Step 1321: Load the second preset comparison rule to determine the actual semantics of the project to be compared. Specifically, the second preset comparison rule is an XML rule syntax tree customized for general cost comparison scenarios, defining the project type matching logic (such as matching the corresponding general cost scheme according to the actual project type), the indicator mapping relationship (such as clarifying the correspondence between the actual project indicators and the general cost indicators), and the deviation warning threshold (such as the quantity difference rate ±10% and the unit price difference rate ±15%). The rule is parsed by the rule parsing engine to determine the project to be compared and its actual semantics. Step 1322: Based on the actual semantics of the project to be compared, sequentially traverse the first and third standard data to determine the corresponding values ​​of the project to be compared. Specifically, call the general cost-actual project matching algorithm in the scheme library adapter, combine it with the third standard data processed by the version compatibility layer, extract the core indicators of the corresponding scheme from the third standard data to build an index library, then traverse the first standard data, and determine the corresponding values ​​by semantically matching the index library according to project type-professional project-indicator name. For indicators that do not have direct corresponding values ​​in the general cost, calculate the corresponding reference values ​​in the general cost through the similar indicator mapping defined by the rules (such as the special cost of the actual project corresponding to the basic cost of the general cost × adjustment coefficient), to ensure the completeness of the comparison. Step 1323: According to the second preset comparison rule, determine the comparison results of the corresponding values ​​of the first standard data and the third standard data; the comparison results include the original values, difference values, difference rates, and traceability paths of the first standard data and the third standard data; specifically, calculate the difference values ​​and difference rates according to the rules, record the original values, and sort out the sources of the actual data and the general cost data to form a traceability path; according to the deviation warning threshold, mark the items whose difference rates are close to or exceed the threshold (e.g., mark those close to the threshold as deviations of concern, and those exceeding the threshold as deviations exceeding the threshold), and associate them with the relevant explanations in the general cost plan to provide a basis for judging the reasonableness of the deviations.

[0051] In this example, a customized XML rule syntax tree is used for general cost comparison, defining the matching logic for project types, the mapping relationship of indicators, and the deviation threshold. It can be flexibly adjusted through a visual interface (such as adding a unit cost comparison dimension), solving the problem that traditional general cost comparison rules are fixed and difficult to adapt to different project types, and accurately meeting the benchmarking needs of various power projects such as power transmission and transformation and new energy with general cost schemes.

[0052] Based on the general cost-actual project matching algorithm, and combined with the third standard data processed by the version compatibility layer, an index library is established to realize multi-level semantic matching of project type, professional project, and indicator name. For indicators without direct corresponding values, reference values ​​are calculated through similar indicator mapping formulas to avoid the problem of omissions caused by differences in indicator names in traditional comparisons, ensuring full indicator coverage of actual projects and general cost, and improving the comprehensiveness of comparison.

[0053] The comparison results include the original values, difference rates, and tracing paths (such as the source of the general cost estimate scheme and the actual data source), and are categorized by threshold levels to indicate deviations (deviations of concern / deviations exceeding the threshold). This is linked to the general cost estimate scheme description, overcoming the limitations of traditional benchmarking which only displays difference values ​​and lacks a basis for judgment. Users can quickly identify projects that deviate from the general design (such as a sub-item exceeding 10%), and, in conjunction with the scheme description, determine the reasonableness of the deviation, providing precise direction for cost optimization.

[0054] In an optional embodiment of the present invention, step 14 involves organizing the comparison results to obtain comparison result data, including: Step 1421: Organize the comparison results into a multidimensional comparison data body. The multidimensional comparison data body includes the comparison items, the original values ​​of the first standard data and the third standard data, the difference value, the difference rate, and the traceability path. Specifically, according to the general cost scheme-project type-indicator category hierarchy, organize the comparison results into a multidimensional comparison data body in JSON format, and supplement the cost category indicators with the unilateral cost composition field to clearly present the cost composition details. Step 1422: Based on the hierarchical inclusion relationship of the comparison items, extract corresponding subset data from the first and third standard data, and establish the association relationship between the multidimensional comparison data body and the subset data; specifically, based on the hierarchical inclusion relationship of the general cost scheme (e.g., the general cost scheme includes line engineering and substation engineering, and line engineering includes engineering quantity indicators, cost indicators, and material indicators), extract the comparison results of the subset items, and establish the hierarchical association between the multidimensional data body and the subset data through the scheme code-indicator code association field, forming a four-level data structure of scheme-engineering-indicator-details; Step 1423: Configure a dynamic drill-down interface on the multidimensional comparison data body. The dynamic drill-down interface is used to trace and view the associated subset of multidimensional comparison data bodies. Specifically, the dynamic drill-down interface supports filtering drill-down by indicator category and degree of deviation. Users can call the interface to penetrate from the total cost indicator to the comparison results of detailed items such as individual material costs and locate the root cause of the deviation. Step 1424: Compare the difference value and difference rate of the multidimensional comparison data body with the set threshold, and mark the comparison results that exceed the set threshold to obtain the comparison result data; Specifically, mark the items with the difference rate exceeding the threshold as exceeding the threshold deviation (e.g., highlighted in red), and associate them with the general cost-related instructions and deviation reason entry, support users to upload supporting materials, form a deviation management closed loop, and finally obtain the comparison result data.

[0055] In this example, the data is organized into a JSON format according to the hierarchy of general cost scheme - project type - indicator category, and supplemented with the unit cost composition field to clearly show the cost details (such as the total cost being composed of labor, materials and machinery costs). This solves the problem of traditional general cost comparison results being piled up with indicators and having an ambiguous composition, and helps users quickly grasp the differences between the actual project and the general scheme.

[0056] By establishing a four-level data structure (scheme-project-indicator-details) through scheme coding and indicator coding, and combining it with a dynamic drill-down interface that supports multi-condition filtering, it is possible to penetrate from the total cost indicator to the deviation of individual material costs, overcoming the limitations of traditional data processing that only presents surface differences and is difficult to pinpoint the root cause. Users can quickly trace deviation items (such as unit cost overruns) to specific materials (such as cable price increases), improving the depth of analysis.

[0057] By using a dual threshold system of difference value and difference rate to mark items exceeding the threshold, and linking it to a general cost description and reason entry point, it supports uploading supporting documents, solving the problems of vague standards and lack of records in traditional manual marking. This not only accurately identifies deviations requiring key attention but also forms a closed-loop management system of discovery, recording, analysis, and rectification, providing a systematic basis for cost optimization and benchmarking against general designs.

[0058] like Figure 4 As shown, in an optional embodiment of the present invention, step 13, comparing the first standard data with the fourth standard data to obtain a comparison result, includes: Step 1331: Load the third preset comparison rule to determine the actual semantics of the project to be compared. Specifically, the third preset comparison rule is an XML rule designed based on the quota feature map, which defines the mapping relationship between quota levels and actual projects (such as total quota - total cost of thermal power projects, quota of building projects - cost of main plant), quota index calculation logic (such as cost per unit area = total cost / building area), and semantic matching priority (such as project code exact matching > project name semantic matching > calculation association matching). The rule is parsed through the rule parsing matrix to determine the project to be compared and its actual semantics. Step 1332: Based on the actual semantics of the items to be compared, sequentially traverse the first and fourth standard data to determine the corresponding values ​​of the items to be compared. Specifically, call the multi-mode matcher group (including the precise matcher and formula derivator) in the quota comparison engine to traverse the data, precisely match the directly corresponding fields (such as building area), and calculate the quota indicators without directly corresponding fields (such as unit area cost = total cost / building area) through the formula derivator. At the same time, use a bidirectional correlation analyzer to establish a dynamic mapping between quota items and engineering data to ensure that no quota indicators are omitted. Step 1333: According to the third preset comparison rule, determine the comparison results of the corresponding values ​​of the first standard data and the fourth standard data; the comparison results include the original values, difference values, difference rates, and traceability paths of the first standard data and the fourth standard data; specifically, calculate the difference values ​​and difference rates according to the rules, record the original values, sort out the sources and calculation associations of the actual data and the quota data to form a traceability path; according to the quota deviation threshold, mark the items whose difference rates are close to or exceed the threshold, associate them with the quota indicator description, and prompt the user to review the relevant design or plan.

[0059] In this example, XML rules are designed based on the quota feature map to clarify the mapping logic between quota levels (such as total quota - building construction quota - main plant quota) and actual projects. Quota indicator calculation methods (such as unit area cost formulas) are also defined to solve the problems of ambiguity and chaotic hierarchical relationships in traditional quota comparison rules. Combined with semantic matching priority (prioritizing precise coding matching), it ensures that quota items accurately correspond to engineering data, adapting to the multi-level quota management needs of power engineering projects.

[0060] The system utilizes a multi-pattern matcher group to precisely match direct fields (such as building area), calculates indirect quota indicators (such as cost per unit area) using a formula derivative, and then establishes a dynamic mapping using a bidirectional correlation analyzer. This avoids the problem of omissions caused by the lack of direct correspondence between indicators in traditional comparisons. Both explicit quota indicators (such as steel consumption) and implicit calculation indicators (such as unit cost) are fully covered, ensuring comprehensive quota comparison.

[0061] The comparison results include the original values, difference rates, and traceability paths (such as the source of the quota data and the correlation between actual data calculations). Items exceeding the threshold are automatically marked and associated with the quota description, prompting for review of the design scheme. This overcomes the limitations of traditional quota comparisons, which only indicate that the limit has been exceeded but cannot trace the source. Users can quickly locate the root cause of the exceedance (such as the unit cost overrun of the main plant due to structural design deviations), optimize the scheme in a timely manner, and meet the real-time quota warning and compliance control requirements under the EPC (Engineering, Procurement, and Construction) model.

[0062] In an optional embodiment of the present invention, step 14 involves organizing the comparison results to obtain comparison result data, including: Step 1431: Organize the comparison results into a multidimensional comparison data body. The multidimensional comparison data body includes the comparison items, the original values ​​of the first standard data and the fourth standard data, the difference value, the difference rate, and the traceability path. Specifically, organize the data into a multidimensional comparison data body in JSON format according to the limit level, indicator type, and control dimension. Supplement the material indicators with material specification fields to ensure the consistency of indicator comparison. Step 1432: Based on the hierarchical inclusion relationship of the comparison items, extract corresponding subset data from the first standard data and the fourth standard data, and establish the association relationship between the multidimensional comparison data body and the subset data; specifically, based on the quota hierarchical inclusion relationship (total quota - construction engineering quota - main plant quota - sub-item quota), extract the comparison results of the subset items, and establish the hierarchical association between the multidimensional data body and the subset data through the quota code - sub-item code association field, clearly displaying the composition of the quota control details; Step 1433: Configure a dynamic drill-down interface on the multidimensional comparison data body. The dynamic drill-down interface is used to trace and view the associated subset of multidimensional comparison data bodies. Specifically, the dynamic drill-down interface supports drill-down driven by the limit warning level. Users can drill down from the total limit item to the detailed item to view specific design node information and locate the design root cause of the limit overrun. Step 1434: Compare the difference values and difference rates of the multi-dimensional comparison data body with the set thresholds, mark the comparison results that exceed the set thresholds, and obtain the comparison result data. Specifically, in combination with the dual-threshold control (warning threshold, overrun threshold) of quota management for marking, items that exceed the overrun threshold are marked as overrun deviations (such as red highlighting) and trigger the quota warning process. Items between the warning and overrun thresholds are marked as warning deviations (such as yellow highlighting) and optimization suggestions are prompted. At the same time, the quota adjustment process description and similar project cases are associated to provide reference for deviation rectification, and finally the comparison result data is formed.

[0063] In this example, it is sorted into a JSON format data body according to quota level - indicator type - control dimension, and the specification description (such as steel bar model, concrete strength grade) is supplemented for material indicators, solving the problem of misjudgment caused by vague definition and inconsistent specifications of traditional quota comparison indicators. Users can clearly distinguish the differences between different specification indicators (such as the quota comparison between HRB400E and HRB335 steel bars), ensuring the accuracy and reliability of the comparison results and providing a unified benchmark for quota control.

[0064] Based on the establishment of the hierarchical association between the total quota and the item quota according to the quota code - item code, combined with the dynamic drill-down interface driven by the warning level, it supports drilling through from the total quota over-expenditure item (such as the total cost of the main plant building exceeding the limit) to the detailed design node (such as the reinforcement of a certain frame beam exceeding the standard), solving the limitation of traditional sorting that only knows the total overrun and it is difficult to find the design root cause. Cost and design personnel can quickly locate the specific design link of the over-expenditure, improving the pertinence of rectification.

[0065] By grading and marking deviations through the warning and overrun dual thresholds, triggering the warning process when overrun, providing optimization suggestions when warning, and also associating the adjustment process description and similar cases, it solves the problem of no grading and lack of guidance in traditional manual marking. It can not only identify risks in a timely manner, but also provide an operation basis (such as the quota adjustment approval process) and reference cases for deviation rectification, forming a quota control closed-loop of discovering deviations - locating the root cause - rectifying and optimizing, adapting to the dynamic compliance requirements under the engineering general contracting model.

[0066] In an optional embodiment of the present invention, in step 15, output the comparison result data.

[0067] Specifically, it supports the output of comparison result data in multiple scenarios and multiple formats. The core output methods include: Client-side visualization: Comparison results are displayed via a hierarchical view combined with charts through a web or desktop client. A tree structure is used to show the hierarchical relationship between project type, professional project, and comparison project; clicking on nodes expands the multi-dimensional comparison data. Bar charts are used to compare original values, line charts to show the difference rate trend, and heatmaps to mark projects exceeding thresholds (red = exceeding limits, yellow = warning, green = normal). Custom filtering (such as filtering by difference rate range or project type) and sorting (such as descending by difference rate) are supported. A dynamic drill-down function is integrated; users can click the drill-down button to load subset data in real time, improving operational smoothness.

[0068] Standardized Report Export: Supports exporting standardized comparison reports in both Excel and PDF formats. Each report includes a cover page (project name, comparison type, time, compiler), table of contents (compared items and page numbers), comparison overview (project information, scope, thresholds, overall deviation statistics), detailed comparison results (multi-dimensional comparison data in a table, with threshold-exceeding items marked), deviation analysis (percentage of deviation types, preliminary cause analysis), and conclusions and recommendations (overall conclusions, rectification suggestions). Excel reports support data linking, and PDF reports support electronic signatures, meeting audit archiving requirements.

[0069] Interface data push: Comparison results (JSON format) are pushed to enterprise ERP systems, cost management platforms, OA systems, etc. via RESTful API to achieve data sharing and business collaboration. It supports scheduled push (such as pushing the day's results at midnight every day) and event-triggered push (such as pushing an alert immediately after the limit comparison is completed). The pushed data includes a complete multi-dimensional comparison data body, marked results and traceability path, which can trigger relevant business processes (such as the approval process for exceeding the threshold deviation).

[0070] Offline data storage: The comparison results are synchronously stored in a distributed database (such as Hadoop HBase) to build a historical comparison results knowledge base. It supports retrieval and statistical analysis by project type, comparison time, deviation rate range and other dimensions, providing data basis for enterprises to formulate cost management standards. At the same time, it supports data backup and recovery to ensure data security and traceability.

[0071] In this example, the results are presented using a combination of hierarchical views and charts. The tree structure clearly shows the pyramid-shaped data relationships, while bar charts and heatmaps intuitively present the differences. It supports custom filtering, sorting, and dynamic drill-down, solving the problems of obscure output data and cumbersome operations in traditional output. Users can quickly locate key items and improve analysis efficiency.

[0072] The Excel / PDF dual-format report contains complete modules. Excel supports data linkage, and PDF can be electronically signed. It is suitable for daily analysis and adjustment, and meets the compliance requirements for audit archiving. It avoids the pain points of inconsistent formats and time-consuming manual reporting in traditional methods.

[0073] The RESTful API pushes JSON-formatted data to multiple enterprise systems, supporting scheduled and event-triggered pushes. It can automatically trigger approval processes, break down data silos, solve the problems of traditional manual data transmission and delayed collaboration, and help with real-time management.

[0074] Distributed database storage builds a historical knowledge base, supports multi-dimensional retrieval and analysis, provides a basis for formulating cost standards, and ensures data security through backup and recovery, solving the problems of traditional data being easily lost and unable to be accumulated and reused.

[0075] Example 1 Construction cost management for a company's newly built 100MW photovoltaic power generation project in 2024; A method for processing engineering cost data includes: Step 21, Obtain the first project cost data (construction drawing budget for photovoltaic power generation project): The project's construction drawing budget data is stored in three different sources: Local files: Cost estimators upload the "Equipment and Installation Engineering Budget Sheet" and "Construction Engineering Budget Sheet" in Excel format via the client. The "Equipment and Installation Engineering Budget Sheet" includes fields such as project code, equipment name, quantity, unit price, and total price for equipment such as photovoltaic transformer foundations, photovoltaic panels, and cables. The "Construction Engineering Budget Sheet" covers project codes, project names, quantities, and comprehensive unit prices for projects such as photovoltaic field roads, support foundations, and integrated buildings. Some columns include non-standard fields for photovoltaic support anti-corrosion treatment customized by the enterprise. Direct database connection: Connect to the enterprise SQL Server database via JDBC interface to extract the project's preliminary "Design Estimate" document (exported to the database in Glodon format), which includes structured data such as sub-item engineering costs, measure project costs, and other project costs; Cloud retrieval: Obtain the "Special Budget for Photovoltaic Support Foundation" (PDF to Excel format) issued by a third-party consulting agency from the enterprise cloud drive. The data includes specific indicators such as foundation type, size, and cost per unit. The system automatically triggers format verification: The Excel file extension is verified to be .xlsx. It matches the unique check code for Guanglianda format files, intercepts a corrupted cable laying budget .xls file, generates an error log indicating that the file is corrupted, and asks the user to re-upload it. The 17 types of data obtained are temporarily stored in a Redis distributed cache, and metadata (such as "Equipment and Installation Engineering Budget Sheet", source: local upload, acquisition time: 2024-XX-XX, data volume: 15,000 rows) are recorded to provide a basis for subsequent tracing. Step 22, Set up the processing of project cost data (three types of standard data): (a) Second standard data (same as project design budget) Obtain the project's design budget document (database storage format) and reuse the cleaning process of the first project cost data: Semantic recognition: It was confirmed that the equipment purchase cost in the design estimate is semantically consistent with the equipment purchase cost in the "Equipment and Installation Engineering Budget Sheet" of the construction drawing budget, and the construction engineering cost in the design estimate is semantically matched with the construction engineering cost in the "Construction Engineering Budget Sheet". Dynamic column correction: Adjust the price difference contingency fund column in the budget document to follow other project expenses, matching the standard table structure order; Encapsulation: Encapsulate into a list of Java objects with the same origin as the first standard data structure to form the second standard data, which is used for cross-stage comparison between construction drawing budget and design estimate; (II) Third Standard Data (Typical Project Cost Scheme for Photovoltaic Power Generation) Obtain the 2023 version of the "Typical Engineering Cost Scheme for Photovoltaic Power Generation Projects" (Excel template) and reconstruct it: The Aspose.Cells function reads the data from the 100MW photovoltaic power generation project sheet in the template and encapsulates it into a list of two-dimensional Java objects containing schemeCode, projectType, indexName, and indexValue. The indexes include unit kilowatt equipment cost, unit kilowatt installation cost, and unit construction cost. Load the version compatibility layer to automatically eliminate the differences between the 2023 version and the enterprise's historical 2021 version of typical engineering cost schemes (such as the photovoltaic panel cleaning fee in the 2021 version being classified as other operating expenses in the 2023 version), forming third-party standardized data for benchmarking analysis of actual budgets and typical costs; (III) Fourth Standard Data (Quota Indicators for Photovoltaic Power Generation Projects) Obtain the "Design Indicators for New Energy Engineering (2024 Edition)" (Excel file, including a building cost per unit area of ​​photovoltaic field of less than or equal to 220 yuan / m²). 2 For indicators such as "single unit cost of transformer substation foundation less than or equal to 850 yuan", conversion is performed: The Excel quota scheme is converted into a hierarchical feature map through the rule parsing matrix (XML configuration): total quota (total quota of 450 million yuan for 100MW photovoltaic project) - equipment and installation engineering quota (equipment purchase cost of 280 million yuan, installation cost of 50 million yuan) - building engineering quota (building quota of 90 million yuan for photovoltaic field area). Extract the dynamic formula from Excel: Building cost limit for photovoltaic power plant area = Building cost per unit area × Plant area. Use the Apache CommonsMath engine to fill in the values ​​(plant area 41,000 m²). 2 The calculated limit is 0.902 billion yuan, forming the fourth set of standardized data for limit early warning; Step 23, Comparison of the first set of data with the three types of set data: (a) Comparison with the data in the second standard (construction drawing budget - design estimate) Loading rules: Load the first preset comparison rules (XML syntax tree), defining the comparison dimensions as differences in equipment quantity, differences in equipment unit price, differences in project quantity, differences in comprehensive unit price, and differences in total price. The comparison scope is equipment and installation engineering and building engineering. The semantic association logic is the substation foundation installation fee corresponding to the design estimate of the substation foundation installation fee in the construction drawing budget "Equipment and Installation Engineering Budget Table". Traversal and matching: A bidirectional index-like algorithm is used to traverse the data. Using the project code as the index, the system matches 020101001-PV transformer foundation, extracting 200 units of the first specification data and 195 units of the second specification data. For the price difference reserve without a code, the corresponding value is confirmed through semantic matching (budget of 1.3 million yuan and estimated cost of 1.1 million yuan). Calculation results: The difference in the number of transformer substation foundations is +5 units, with a difference rate of 2.56%; the difference in the price difference contingency fund is +200,000 yuan, with a difference rate of 18.18%. The traceability path is recorded (e.g., the source of transformer substation foundation data is: "Equipment and Installation Engineering Budget Sheet" page, transformer substation foundation row 35; "Design Estimate Sheet" page, transformer substation foundation row 32), and the price difference contingency fund is marked as a key focus item (difference rate exceeds the 15% threshold). (ii) Comparison with data from the third standard (construction drawing budget - typical project cost) Loading rules: Load the second preset comparison rules, define the project type matching logic: match the typical project cost of '100MW ground photovoltaic scheme' for a 100MW photovoltaic power generation project, and set the deviation thresholds as equipment quantity difference rate ±8%, unit price difference rate ±12%, and project quantity difference rate ±10%. Traversal matching: The typical cost-actual project matching algorithm is called to extract core indicators such as unit kilowatt equipment cost of 3.2 yuan / W and single unit installation cost of transformer foundation of 40 yuan from the third standard data. Traversing the first standard data, semantic matching yields the actual unit kilowatt equipment cost of 3.4 yuan / W and single unit installation cost of transformer foundation of 42 yuan. For the anti-corrosion treatment cost of photovoltaic brackets in the actual project (no direct indicator in typical cost), the reference value is calculated according to the rules: typical cost - bracket installation cost × 1.1 (anti-corrosion adjustment coefficient) = 80,000 yuan, and the actual value is 92,000 yuan. Calculation results: Unit kilowatt equipment cost difference value +0.2 yuan / W, difference rate 6.25% (normal), single unit installation cost difference rate of transformer substation foundation 5% (normal), photovoltaic bracket anti-corrosion treatment cost difference rate 15% (exceeding threshold), related typical cost descriptions require specific process descriptions for bracket anti-corrosion cost to confirm the reasonableness of the deviation; (III) Comparison with the data in the fourth standard (construction drawing budget - quota index) Loading rules: Load the third preset comparison rules, define the limit level mapping equipment and installation engineering limit - equipment purchase cost corresponding to the budget "Equipment and Installation Engineering Budget Table" equipment purchase cost, building engineering limit - unit area cost corresponding to the "Building Engineering Budget Table" building engineering cost / site area, limit index calculation logic single unit cost of transformer foundation = equipment cost + installation cost; Traversal matching: Invoke the multi-mode matcher group to accurately match the cost of a single transformer substation foundation (budget 860 yuan / unit, limit 850 yuan / unit); Calculate the unit area construction cost of the photovoltaic field using the formula derivative: Construction cost 9.2 million yuan / 41,000 m² (from the "Construction Engineering Budget Table"). 2 =224.39 yuan / m 2 (Limited to 220 yuan / m) 2 The bidirectional correlation analyzer confirmed that no quota indicators were omitted. Calculation results: The cost difference rate of a single transformer substation foundation is 1.18% (normal), and the cost difference rate of building area per unit area in the photovoltaic field is 1.99% (close to the threshold). The traceability path is recorded (the calculation basis for building cost per unit area is: page summary row 120 of the "Construction Engineering Budget Sheet" and the field area comes from the project planning documents). It is also noted that the building cost per unit area is close to the limit, and it is recommended to optimize the width of the field roads to reduce costs. Step 24, organizing the comparison results: (a) Compilation of data comparison results with the second standard Construct a multi-dimensional data body: Organize the data into a JSON format according to the project type (equipment and installation project) - professional project (substation foundation installation project) - sub-item project (photovoltaic substation foundation), including the original value (budget 200 units, estimated 195 units), difference value +5 units, difference rate 2.56%, and traceability path; Subset association: Extract the substation foundation equipment and substation foundation installation subset data under the substation foundation installation project, and associate the subset codes 02010100101 (substation foundation equipment) and 02010100102 (substation foundation installation) with the parent project code 020101001 to form a hierarchical structure. Configure dynamic drill-down interface: Configure RESTful API. When users call the interface, they can pass in the project code 020101001 and the drill-down level 1 to view the detailed comparison results of the transformer substation basic equipment and transformer substation basic installation (such as a difference of 30 yuan / unit in unit price of transformer substation basic equipment and a difference of 5 yuan / unit in installation fee). Threshold marker: The price difference reserve ratio of 18.18% exceeds the threshold, is highlighted in red and associated with the deviation reason entry field (the user selects that the recent increase in the price of transformer substation basic equipment has led to the increase in price difference), forming the comparison result data; (II) Compilation of data comparison results with the third standard The data is organized into typical engineering cost schemes, engineering types, and indicator categories. A field is added to specify the composition of equipment costs per kilowatt (e.g., budget 3.4 yuan / W = photovoltaic panel 2.0 yuan / W + transformer substation foundation 0.8 yuan / W + cable 0.6 yuan / W). Dynamic drilling can be performed to obtain details of the photovoltaic bracket anti-corrosion treatment cost (e.g., anti-corrosion material unit price 20 yuan / kg, usage 4600kg). An upload portal for supporting documentation related to photovoltaic bracket anti-corrosion treatment costs exceeding thresholds is provided, supporting the uploading of anti-corrosion process description documents. (III) Compilation of data comparison results with the fourth standard Data is organized by limit level and indicator type, supplementing the basic specifications of the transformer substation (e.g., the foundation of a 500kW centralized transformer substation); dynamic drilling can trace from the unit area building cost approaching the limit to the site road project exceeding the budget by 180,000 yuan (road width exceeding the design standard by 0.5m); the unit area building cost is marked as a yellow warning by dual thresholds, and linked to similar project cases (XX100MW photovoltaic project reduced the unit area building cost by 3 yuan / m by adjusting the road width from 4m to 3.5m). 2 ); Step 25, Output the comparison results data: (a) Client-side visual display The web client displays the hierarchical relationship of a 100MW photovoltaic project, including equipment and installation engineering, and substation foundation installation engineering, in a tree structure. Clicking on a node expands the multi-dimensional data volume. It uses bar charts to compare the equipment quantity between the budget and the preliminary estimate, line charts to show the trend of the difference rate of each item, and heat maps to mark items exceeding the threshold (price difference contingency in red, building cost per unit area in yellow). It supports filtering by difference rate > 5% to quickly locate key items of concern, and clicking the drill-down button to view the substation foundation equipment and installation details in real time. (ii) Exporting Standardized Reports Excel report: Includes cover (Project Name: XX100MW Photovoltaic Power Generation Project, Comparison Type: Multi-dimensional Comparison of Construction Drawings and Budgets), Comparison Overview (95 items compared in total, 15 exceeding the threshold, 8 approaching the threshold), Detailed Results Table (marking items exceeding and approaching the threshold), Deviation Analysis (cost overruns mainly concentrated in price difference contingency fund and photovoltaic bracket anti-corrosion treatment cost), Conclusions and Recommendations (recommendation to review the calculation basis of price difference contingency fund, optimize site road width and photovoltaic bracket anti-corrosion process). The Excel table supports clicking the trace path to jump to the original data sheet page; PDF Report: Add corporate electronic signature for audit archiving; (III) Interface data push The comparison results (in JSON format) are pushed to the enterprise ERP system and OA system via RESTful API: ERP system: Updated the project cost deviation amount field (total deviation 980,000 yuan), triggering the approval process for deviations exceeding 800,000 yuan; OA system: Pushes early warning notifications to the design manager and cost manager regarding the building cost per unit area approaching the limit, including details of the deviation and suggestions for rectification; (iv) Offline data storage The comparison results are synchronously stored in the Hadoop HBase database to build a historical comparison result knowledge base; it supports retrieval and statistical analysis by project type (photovoltaic power generation project), comparison time (2024), deviation rate range (>10%), etc., and can be used to statistically analyze the average deviation rate of the price difference reserve of photovoltaic power generation projects in 2024, which is 16%, providing data basis for enterprises to formulate the "Photovoltaic Power Generation Project Cost Control Standard"; at the same time, it supports data backup and recovery to ensure data security and traceability.

[0076] This invention replaces traditional manual comparison (which takes more than 200 hours per project), significantly shortens the time through automated processes (such as a processing speed of 500,000 rows per minute), and reduces human error through dynamic column correction and bidirectional indexing algorithms, thus solving the pain point of manual dependence and adapting to real-time management and control requirements.

[0077] It covers documents for all stages of power engineering, supports 17 data formats, and eliminates the need for manual format conversion; semantic mapping and dynamic column correction break down format and version barriers, structuring scattered data that increases by more than 40% annually but has a utilization rate of less than 15%, thereby improving data utilization and cross-source comparison capabilities.

[0078] It supports various types of power projects, including power transmission and transformation, and wind power. It can compare multiple stages of the same project (second standard data), general cost (third standard data), and quota indicators (fourth standard data) to meet the needs of new energy project growth and multi-level cost control.

[0079] Multidimensional data volume and dynamic drill-down enable root cause tracing from general items to detailed items. Dual threshold marking and cause entry form a comparison-analysis-rectification closed loop, helping to accurately locate deviations (such as material price differences and design overruns) and improve cost optimization and audit compliance.

[0080] Visualized displays, standardized reports, and system push notifications meet the needs of different roles. Offline storage builds a historical knowledge base, providing a basis for enterprises to formulate cost standards, breaking down data silos, and supporting long-term management decisions.

[0081] like Figure 5 As shown, an embodiment of the present invention provides a processing device 50 for engineering cost data, comprising: Module 51 is used to acquire the first project cost data; The processing module 52 is used to clean the first engineering cost data to obtain the first standard data; compare the first standard data with the set engineering cost data to obtain the comparison result; organize the comparison result to obtain the comparison result data; and output the comparison result data.

[0082] Optionally, the first project cost data is cleaned to obtain the first specification data, including: Load a preset semantic mapping rule library to identify the actual semantics of the first project cost data; Based on the actual semantics of the first project cost data, the first project cost data is dynamically corrected to achieve row and column alignment; The first project cost data, after being aligned with rows and columns, is encapsulated into first standardized data in a set format.

[0083] Optionally, the set engineering cost data includes: second standard data, third standard data, or fourth standard data; The process of obtaining the second standard data includes: acquiring the second project cost data; cleaning the second project cost data to obtain the second standard data; The process of obtaining the third standard data includes: acquiring general cost data; reconstructing the general cost data to obtain the third standard data; The process of obtaining the fourth standard data includes: acquiring the limit cost data; and converting the limit cost data to obtain the fourth standard data.

[0084] Optionally, the first standard data is compared with the second standard data to obtain the comparison results, including: Load the first preset comparison rule and determine the actual semantics of the items to be compared; Based on the actual semantics of the items to be compared, the first standard data and the second standard data are traversed sequentially to determine the corresponding values ​​of the items to be compared. According to the first preset comparison rule, the comparison results of the corresponding values ​​of the first standard data and the second standard data are determined; the comparison results include the original values, differences, difference rates and traceability paths of the first standard data and the second standard data.

[0085] Optionally, the comparison results are processed to obtain comparison result data, including: The comparison results are organized into a multidimensional comparison data volume, which includes comparison items, the original values ​​of the first and second standard data, the difference value, the difference rate, and the traceability path. Based on the hierarchical inclusion relationship of the comparison items, corresponding subset data are extracted from the first and second standardized data, and the association relationship between the multidimensional comparison data body and the subset data is established. A dynamic drill-down interface is configured on the multidimensional comparison data body, which is used to trace and view the associated subset of multidimensional comparison data bodies; The difference rate of the multidimensional comparison data is compared with a set threshold, and the comparison results that exceed the set threshold are marked to obtain the comparison result data.

[0086] Optionally, the first standard data is compared with the third standard data to obtain the comparison results, including: Load the second preset comparison rule to determine the actual semantics of the items to be compared; Based on the actual semantics of the items to be compared, the first and third standard data are traversed sequentially to determine the corresponding values ​​of the items to be compared. According to the second preset comparison rule, the comparison results of the corresponding values ​​of the first standard data and the third standard data are determined; the comparison results include the original values, differences, difference rates and traceability paths of the first standard data and the third standard data.

[0087] Optionally, the comparison results are processed to obtain comparison result data, including: The comparison results are organized into a multidimensional comparison data volume, which includes the comparison items, the original values ​​of the first and third standard data, the difference values, the difference rate, and the traceability path. Based on the hierarchical inclusion relationship of the comparison items, corresponding subset data are extracted from the first and third standard data, and the association relationship between the multidimensional comparison data body and the subset data is established. A dynamic drill-down interface is configured on the multidimensional comparison data body, which is used to trace and view the associated subset of multidimensional comparison data bodies; The difference values ​​and difference rates of the multidimensional comparison data are compared with a set threshold. The comparison results that exceed the set threshold are marked to obtain the comparison result data.

[0088] Optionally, the first standard data is compared with the fourth standard data to obtain the comparison results, including: Load the third preset comparison rule to determine the actual semantics of the items to be compared; Based on the actual semantics of the items to be compared, the first and fourth standard data are traversed sequentially to determine the corresponding values ​​of the items to be compared. According to the third preset comparison rule, the comparison results of the corresponding values ​​of the first standard data and the fourth standard data are determined; the comparison results include the original values, differences, difference rates and traceability paths of the first standard data and the fourth standard data.

[0089] Optionally, the comparison results are processed to obtain comparison result data, including: The comparison results are organized into a multidimensional comparison data volume, which includes comparison items, the original values ​​of the first standard data and the fourth standard data, the difference value, the difference rate, and the traceability path. Based on the hierarchical inclusion relationship of the comparison items, corresponding subset data are extracted from the first and fourth standard data, and the association relationship between the multidimensional comparison data body and the subset data is established. A dynamic drill-down interface is configured on the multidimensional comparison data body, which is used to trace and view the associated subset of multidimensional comparison data bodies; The difference values ​​and difference rates of the multidimensional comparison data are compared with a set threshold. The comparison results that exceed the set threshold are marked to obtain the comparison result data.

[0090] It should be noted that this device is a device corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0091] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing engineering cost data, characterized in that, include: Obtain the first project cost data; The first project cost data is cleaned to obtain the first standard data; The first standard data is compared with the set project cost data to obtain the comparison results; The comparison results are organized to obtain the comparison result data; Output the comparison result data.

2. The method for processing engineering cost data according to claim 1, characterized in that, The first project cost data is cleaned to obtain the first standard data, including: Load a preset semantic mapping rule library to identify the actual semantics of the first project cost data; Based on the actual semantics of the first project cost data, the first project cost data is dynamically corrected to achieve row and column alignment; The first project cost data, after being aligned with rows and columns, is encapsulated into first standardized data in a set format.

3. The method for processing engineering cost data according to claim 1, characterized in that, The set engineering cost data includes: second standard data, third standard data, or fourth standard data; The process of obtaining the second standard data includes: acquiring the second project cost data; cleaning the second project cost data to obtain the second standard data; The process of obtaining the third standard data includes: acquiring general cost data; reconstructing the general cost data to obtain the third standard data; The process of obtaining the fourth standard data includes: acquiring the limit cost data; and converting the limit cost data to obtain the fourth standard data.

4. The method for processing engineering cost data according to claim 3, characterized in that, The first standard data is compared with the second standard data to obtain the comparison results, including: Load the first preset comparison rule and determine the actual semantics of the items to be compared; Based on the actual semantics of the items to be compared, the first standard data and the second standard data are traversed sequentially to determine the corresponding values ​​of the items to be compared. According to the first preset comparison rule, the comparison results of the corresponding values ​​of the first standard data and the second standard data are determined; the comparison results include the original values, differences, difference rates and traceability paths of the first standard data and the second standard data.

5. The method for processing engineering cost data according to claim 4, characterized in that, The comparison results are organized to obtain comparison result data, including: The comparison results are organized into a multidimensional comparison data volume, which includes comparison items, the original values ​​of the first and second standard data, the difference value, the difference rate, and the traceability path. Based on the hierarchical inclusion relationship of the comparison items, corresponding subset data are extracted from the first and second standardized data, and the association relationship between the multidimensional comparison data body and the subset data is established. A dynamic drill-down interface is configured on the multidimensional comparison data body, which is used to trace and view the associated subset of multidimensional comparison data bodies; The difference rate of the multidimensional comparison data is compared with a set threshold, and the comparison results that exceed the set threshold are marked to obtain the comparison result data.

6. The method for processing engineering cost data according to claim 3, characterized in that, The first standard data is compared with the third standard data to obtain the comparison results, including: Load the second preset comparison rule to determine the actual semantics of the items to be compared; Based on the actual semantics of the items to be compared, the first and third standard data are traversed sequentially to determine the corresponding values ​​of the items to be compared. According to the second preset comparison rule, the comparison results of the corresponding values ​​of the first standard data and the third standard data are determined; the comparison results include the original values, differences, difference rates and traceability paths of the first standard data and the third standard data.

7. The method for processing engineering cost data according to claim 6, characterized in that, The comparison results are organized to obtain comparison result data, including: The comparison results are organized into a multidimensional comparison data volume, which includes the comparison items, the original values ​​of the first and third standard data, the difference values, the difference rate, and the traceability path. Based on the hierarchical inclusion relationship of the comparison items, corresponding subset data are extracted from the first and third standard data, and the association relationship between the multidimensional comparison data body and the subset data is established. A dynamic drill-down interface is configured on the multidimensional comparison data body, which is used to trace and view the associated subset of multidimensional comparison data bodies; The difference values ​​and difference rates of the multidimensional comparison data are compared with a set threshold. The comparison results that exceed the set threshold are marked to obtain the comparison result data.

8. The method for processing engineering cost data according to claim 3, characterized in that, The first standard data is compared with the fourth standard data to obtain the comparison results, including: Load the third preset comparison rule to determine the actual semantics of the items to be compared; Based on the actual semantics of the items to be compared, the first and fourth standard data are traversed sequentially to determine the corresponding values ​​of the items to be compared. According to the third preset comparison rule, the comparison results of the corresponding values ​​of the first standard data and the fourth standard data are determined; the comparison results include the original values, differences, difference rates and traceability paths of the first standard data and the fourth standard data.

9. The method for processing engineering cost data according to claim 8, characterized in that, The comparison results are organized to obtain comparison result data, including: The comparison results are organized into a multidimensional comparison data volume, which includes comparison items, the original values ​​of the first standard data and the fourth standard data, the difference value, the difference rate, and the traceability path. Based on the hierarchical inclusion relationship of the comparison items, corresponding subset data are extracted from the first and fourth standard data, and the association relationship between the multidimensional comparison data body and the subset data is established. A dynamic drill-down interface is configured on the multidimensional comparison data body, which is used to trace and view the associated subset of multidimensional comparison data bodies; The difference values ​​and difference rates of the multidimensional comparison data are compared with a set threshold. The comparison results that exceed the set threshold are marked to obtain the comparison result data.

10. A device for processing engineering cost data, characterized in that, include: The acquisition module is used to acquire the first project cost data; The processing module is used to clean the first project cost data to obtain the first standard data; The first standard data is compared with the set project cost data to obtain the comparison result; the comparison result is organized to obtain the comparison result data; and the comparison result data is output.

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