Data entry method of engineering cost software

Abstract

The invention relates to the technical field of engineering cost, and discloses a data entry method for engineering cost software, which comprises the following steps of: S1, importing an engineering quantity list file in an Excel format through an interface of the engineering cost software, and automatically analyzing a data structure of the engineering quantity list file; s2, based on a preset intelligent matching algorithm, performing dynamic mapping on the Excel data field and a data field of a project cost software function module; s3, adopting a data type self-adaptive rule to automatically classify and convert numerical values, texts and compound data in the Excel; s4, based on a data association generation algorithm, automatically establishing a hierarchical relationship and a dependency relationship between the engineering quantity list items; s5, executing one-button data import, and writing the processed structured data into a project cost software database in batches; according to the method, high-precision automatic mapping of the Excel field and the software module is realized through an intelligent matching algorithm and a dynamic weight adjustment mechanism based on the feature vector similarity, the matching accuracy is greatly improved, and the manual correction frequency is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of engineering cost technology, specifically to a data entry method for engineering cost software. Background Technology

[0002] Construction cost refers to the construction price of a project. It is the total cost expected or actually required to complete the construction of a project. Therefore, in this sense, construction cost is the fixed asset investment of a construction project. From the perspective of contracting, construction cost refers to the price of the project, that is, the price of the construction and installation work and the total price of the construction project formed through bidding and other transaction methods in the land, equipment, technical labor and contracting markets to complete a project.

[0003] Traditional engineering cost estimation software commonly suffers from the following technical defects when importing Excel bills of quantities: data structure parsing relies on fixed template matching, and when there are differences between the descriptions of Excel column names and system preset fields, manual configuration of mapping relationships is required for each column, resulting in low operational efficiency and a high error rate; the construction of item association relationships depends entirely on manual selection, especially when facing multi-level bill of quantities structures, making it difficult to automatically identify the project coding rules and textual semantic relationships between parent and child items; data conflict detection often adopts a fixed threshold mechanism, which cannot dynamically adapt to the numerical distribution characteristics of different project types, and is prone to false alarms and missed alarms. To address these issues, a new data entry method for engineering cost estimation software is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a data entry method for engineering cost estimation software, addressing the following common technical defects in traditional engineering cost estimation software when importing Excel bills of quantities, as mentioned in the background: data structure parsing relies on fixed template matching; when there are differences between the Excel column names and the system's preset fields, manual configuration of mapping relationships is required for each column, resulting in low operational efficiency and a high error rate; the construction of item association relationships relies entirely on manual selection, especially when facing multi-level bill of quantities structures, making it difficult to automatically identify the project coding rules and textual semantic relationships between parent and child items; data conflict detection often uses fixed threshold mechanisms, which cannot dynamically adapt to the numerical distribution characteristics of different project types, easily leading to false alarms and missed alarms.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a data entry method for engineering cost estimation software, comprising the following steps:

[0006] S1. Import the bill of quantities file in Excel format through the engineering cost software interface and automatically parse its data structure;

[0007] S2. Based on a preset intelligent matching algorithm, dynamically map Excel data fields to data fields of engineering cost software functional modules;

[0008] S3. Uses data type self-adaptation rules to automatically classify and convert numerical, text, and composite data in Excel;

[0009] S4. Based on the data association generation algorithm, automatically establish the hierarchical relationship and dependency relationship between the items in the bill of quantities;

[0010] S5. Perform one-click data import to write the processed structured data into the engineering cost software database in batches.

[0011] Preferably, in S1 above, the algorithm for parsing the data structure is as follows: The field types of the bill of quantities are identified by calculating the similarity of the column feature vectors of the Excel table, using the following formula:

[0012]

[0013] in, For Excel eigenvectors of the column For engineering cost estimation software Preset feature vectors for each target field and These are the corresponding feature weights.

[0014] Preferably, in S2 above, the intelligent matching algorithm includes a dynamic weight adjustment mechanism, and the weight update formula is:

[0015]

[0016] in, For the updated weights, For historical matching weights, For learning rate, This refers to the number of manual corrections. This represents the total number of matches.

[0017] Preferably, in S3 above, the data type self-adaptation rule includes:

[0018] S301. Use regular expressions to identify numerical data;

[0019] S302. For composite data, extract the numerical value and unit using a delimiter priority algorithm. The priority formula is:

[0020]

[0021] in, Frequency of occurrence of historical units The total number of all units. This refers to the context-related weight.

[0022] Preferably, in S4 above, the data association generation algorithm includes: constructing a tree structure based on the similarity between parent and child node identifiers and item codes, and the node relationship determination formula is:

[0023]

[0024] in, For encoding matching weight coefficients, The degree of overlap in the coding prefixes of project A and project B. The text similarity between Project A and Project B.

[0025] Preferably, in step S5 above, a data conflict detection step is also included before the import is performed:

[0026] For data items with abnormal numerical ranges, mismatched units, or logical contradictions, an alarm is generated using an outlier detection model. The model formula is as follows:

[0027]

[0028] in, and The mean and standard deviation of similar data. The unit conflict penalty coefficient, The value of the current data point. This is a conflict indicator for units.

[0029] Preferably, the feature vectors described above include column name semantic features, data distribution features, and contextual position features, wherein the weights of the contextual position features are dynamically allocated based on the correlation between adjacent columns within the table.

[0030] As a preferred option, the above also includes a version compatibility handling step: when the Excel template version is inconsistent with the engineering cost software version, the version mapping table is automatically invoked to interpolate and fill missing or newly added fields or mark them as retained.

[0031] Preferably, the above also includes generating a three-dimensional visualization data topology map to intuitively display the dependencies and resource allocation paths between items in the bill of quantities.

[0032] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0033] 1. This invention achieves high-precision automatic mapping between Excel fields and software modules through an intelligent matching algorithm based on feature vector similarity and a dynamic weight adjustment mechanism, greatly improving the matching accuracy and significantly reducing the number of manual corrections. At the same time, it adopts a data type self-adaptation rule that combines regular expressions and delimiter priority algorithms to effectively process numerical, text and composite data.

[0034] 2. This invention automatically constructs a tree-like hierarchical structure of the bill of quantities through a composite scoring mechanism of project code similarity and text similarity, which improves efficiency and reduces the logical error rate compared to the traditional manual association method.

[0035] 3. This invention integrates an outlier model based on statistical distribution and unit conflict detection, which can identify numerical out-of-bounds or unit mismatch problems before data import, significantly reducing the risk of error in subsequent engineering quantity calculations. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 Schematic diagram of the method of the present invention. Detailed Implementation

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Example

[0040] In existing technologies, engineering cost estimation software generally suffers from the following technical defects when importing Excel bills of quantities: data structure parsing relies on fixed template matching. When there are differences between the descriptions of Excel column names and system preset fields, the mapping relationship needs to be manually configured column by column, resulting in low operational efficiency and a high error rate; the construction of item association relationships relies entirely on manual selection, especially when facing multi-level bill of quantities structures, making it difficult to automatically identify the project coding rules and textual semantic relationships between parent and child items; data conflict detection mostly adopts a fixed threshold mechanism, which cannot dynamically adapt to the numerical distribution characteristics of different project types, and is prone to false alarms and missed alarms.

[0041] Please see Figure 1 This invention provides a technical solution: a data entry method for engineering cost estimation software, comprising the following steps:

[0042] S1. Import the Excel-formatted bill of quantities file through the engineering cost software interface and automatically parse its data structure. The algorithm for parsing the data structure is as follows: identify the field types of the bill of quantities by calculating the similarity of the column feature vectors of the Excel table. The formula is:

[0043]

[0044] in, For Excel eigenvectors of the column For engineering cost estimation software Preset feature vectors for each target field and These are the corresponding feature weights. The feature vector includes column name semantic features, data distribution features, and contextual position features. The weights of the contextual position features are dynamically allocated based on the correlation between adjacent columns in the table.

[0045] The specific implementation includes: Data import: Users upload Excel-formatted bill of quantities files to the software system through a dedicated interface provided by the engineering cost estimation software (such as a file import function or plugin). This interface supports dragging and dropping files or selecting file paths;

[0046] Data structure analysis: The software automatically analyzes the structure of the Excel file and identifies key fields (such as "project code", "quantity", "unit of measurement" etc.).

[0047] Column feature analysis: By analyzing the column names, data formats (such as numbers and text), and data distribution patterns (such as whether they contain dates or codes) of the Excel spreadsheet, the corresponding functional module of each column is determined; Context association: By combining the logical relationship between adjacent columns (for example, "Project Code" and "List Name" are usually adjacent), the field type is further confirmed; Intelligent matching of preset fields: The columns in the Excel are compared with the preset field library inside the engineering cost software. For example, the "Quantity" column in the Excel is automatically associated with the "Quantity" field in the software.

[0048] S2. Based on a preset intelligent matching algorithm, the data fields in Excel are dynamically mapped to the data fields of the engineering cost software's functional modules. The intelligent matching algorithm includes a dynamic weight adjustment mechanism, and the weight update formula is as follows:

[0049]

[0050] in, For the updated weights, For historical matching weights, For learning rate, This refers to the number of manual corrections. The total number of matches is shown. Through an intelligent matching algorithm based on feature vector similarity and a dynamic weight adjustment mechanism, high-precision automatic mapping between Excel fields and software modules is achieved, greatly improving the matching accuracy and significantly reducing the number of manual corrections.

[0051] The specific implementation includes: Field matching: The software has a built-in intelligent algorithm that automatically matches fields in Excel (such as "Project Name" and "Quantity") with data fields inside the engineering cost estimation software (such as "Project Information" and "Quantity Record"); the algorithm dynamically adjusts the matching priority based on historical matching records or user feedback. For example, if the user has modified the matching relationship of the "Quantity" field multiple times, the system will prioritize recognizing the mapping rule of that field;

[0052] Conflict handling: If there are differences between Excel fields and software fields (e.g., the Excel column name "Material Cost" corresponds to the software's "Material Unit Price"), the system will pop up a prompt, suggesting that the user confirm or reselect the mapping relationship to avoid data errors.

[0053] S3. Employs data type self-adaptation rules to automatically classify and convert numeric, text, and composite data in Excel. These data type self-adaptation rules include:

[0054] S301. Use regular expressions to identify numerical data;

[0055] S302. For composite data, extract the numerical value and unit using a delimiter priority algorithm. The priority formula is:

[0056]

[0057] in, Frequency of occurrence of historical units The total number of all units. Context-related weights are applied; a data type self-adaptation rule combining regular expressions and delimiter priority algorithms is adopted to effectively handle numerical, text, and composite data.

[0058] The specific implementation includes: data classification: identifying purely numerical content (such as "project quantity = 100") and directly converting it into a numerical type supported by the software; processing plain text information (such as "project name = teaching building A") to ensure compliance with the software's character limits and format requirements; splitting content containing units or symbols (such as "50 meters" or "300 yuan / m²") and extracting and storing the numerical value and unit separately.

[0059] Unit standardization: Inconsistent unit conversions (such as converting "meter" to "kilometer" or "foot"), or unit differences that require manual verification (such as the conversion between "ton" and "kilogram").

[0060] S4. Based on a data association generation algorithm, automatically establish the hierarchical and dependency relationships between items in the bill of quantities. The data association generation algorithm includes: constructing a tree structure based on the similarity of parent-child node identifiers and project codes. The formula for determining node relationships is:

[0061]

[0062] in, For encoding matching weight coefficients, The degree of overlap in the coding prefixes of project A and project B. The text similarity between project A and project B is calculated. Through a composite scoring mechanism of project coding similarity and text similarity, a tree-like hierarchical structure of the bill of quantities is automatically constructed, which improves efficiency and reduces the logical error rate compared with the traditional manual association method.

[0063] The specific implementation includes: Coding analysis: By analyzing the structure of the project codes (such as “01-02-03” to represent hierarchical relationships) or related words in the text description (such as “auxiliary project” and “main building”), the parent-child relationship between projects is automatically identified; for example, the code “01-02” may belong to a sub-project of the “01” level, and the system will automatically generate a tree structure;

[0064] Dependency derivation: Analyze the logical dependencies between projects (such as "concrete pouring" depending on "reinforcing bar installation"), establish a dependency graph, and ensure logical consistency during data entry.

[0065] S5. Perform one-click data import to batch write the processed structured data into the engineering cost software database. A data conflict detection step is also included before the import process.

[0066] For data items with abnormal numerical ranges, mismatched units, or logical contradictions, an alarm is generated using an outlier detection model. The model formula is as follows:

[0067]

[0068] in, and The mean and standard deviation of similar data. The unit conflict penalty coefficient, The value of the current data point. It serves as a unit conflict indicator; integrating an outlier model based on statistical distribution and unit conflict detection, it can identify numerical out-of-bounds or unit mismatch issues before data import, significantly reducing the risk of errors in subsequent engineering quantity calculations;

[0069] The specific implementation includes: One-click import: After the user confirms the mapping relationship and data conversion rules, they can click the "One-click import" button, and the system will write the processed data into the database of the engineering cost software in batches;

[0070] Conflict detection: Numerical anomalies are checked to see if the data exceeds a reasonable range (e.g., the quantity of work is negative or too large); unit mismatches are detected, for example, if the "area" field is entered in "meters", the system will mark it as an error; logical contradictions are detected, such as "material usage" and "quantity of work" not matching, the system will prompt that manual review is required; alarm handling is provided, an alarm list is generated for abnormal data, and users can choose to ignore, modify or skip it.

[0071] This method also includes a version compatibility handling step: when the Excel template version is inconsistent with the engineering cost software version, the version mapping table is automatically invoked to fill missing or newly added fields with interpolation or mark them as retained; the field mapping table mechanism is as follows: when the Excel template version does not match the software version: missing fields are filled with default values ​​or field values ​​from the previous version; newly added fields are marked as "pending confirmation" and prompted to the user for processing.

[0072] This method also includes generating a 3D visualization data topology map to intuitively display the dependencies and resource allocation paths between bill of quantities items; topology map construction: using the D3.js or Three.js library to generate 3D graphics, where nodes represent bill of quantities items and edges represent dependencies; path display: displaying resource allocation paths (such as "material procurement → construction → acceptance") through color or arrow direction.

[0073] In summary, this invention achieves high-precision automatic mapping between Excel fields and software modules through an intelligent matching algorithm based on feature vector similarity and a dynamic weight adjustment mechanism, significantly improving matching accuracy and greatly reducing the number of manual corrections. It also employs a data type self-adaptation rule combining regular expressions and delimiter priority algorithms to effectively handle numerical, text, and composite data. Furthermore, through a composite scoring mechanism combining project code similarity and text similarity, it automatically constructs a tree-like hierarchical structure for the bill of quantities, improving efficiency and reducing logical error rates compared to traditional manual association methods. Finally, it integrates an outlier model based on statistical distribution and unit conflict detection, which can identify numerical out-of-bounds or unit mismatch issues before data import, significantly reducing the risk of errors in subsequent quantity calculations.

[0074] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

Claims

1. A data entry method for engineering cost estimation software, characterized in that, The following steps are involved: S1. Import the bill of quantities file in Excel format through the engineering cost software interface and automatically parse its data structure; S2. Based on a preset intelligent matching algorithm, dynamically map Excel data fields to data fields of engineering cost software functional modules; S3. Uses data type self-adaptation rules to automatically classify and convert numerical, text, and composite data in Excel; S4. Based on the data association generation algorithm, automatically establish the hierarchical relationship and dependency relationship between the items in the bill of quantities; S5. Perform one-click data import to write the processed structured data into the engineering cost software database in batches.

2. The data entry method for engineering cost estimation software according to claim 1, characterized in that: In S1, the algorithm for parsing the data structure is as follows: The field types of the bill of quantities are identified by calculating the similarity of the column feature vectors of the Excel spreadsheet. The formula is: ; in, For Excel eigenvectors of the column For engineering cost estimation software Preset feature vectors for each target field and These are the corresponding feature weights.

3. The data entry method for engineering cost estimation software according to claim 1, characterized in that: In S2, the intelligent matching algorithm includes a dynamic weight adjustment mechanism, and the weight update formula is: ; in, For the updated weights, For historical matching weights, For learning rate, This refers to the number of manual corrections. This represents the total number of matches.

4. The data entry method for engineering cost estimation software according to claim 1, characterized in that: In S3, the data type self-adaptation rules include: S301. Use regular expressions to identify numerical data; S302. For composite data, extract the numerical value and unit using a delimiter priority algorithm. The priority formula is: ; in, Frequency of occurrence of historical units The total number of all units. This refers to the context-related weight.

5. The data entry method for engineering cost estimation software according to claim 1, characterized in that: In S4, the data association generation algorithm includes: constructing a tree structure based on the similarity of parent-child node identifiers and item codes, and the node relationship determination formula is: ; in, For encoding matching weight coefficients, The degree of overlap in the coding prefixes of project A and project B. The text similarity between Project A and Project B.

6. The data entry method for engineering cost estimation software according to claim 1, characterized in that: In S5, a data conflict detection step is also included before the import is performed: For data items with abnormal numerical ranges, mismatched units, or logical contradictions, an alarm is generated using an outlier detection model. The model formula is as follows: ; in, and The mean and standard deviation of similar data. The unit conflict penalty coefficient, The value of the current data point. This is a conflict indicator for units.

7. The data entry method for engineering cost estimation software according to claim 2, characterized in that: The feature vector includes column name semantic features, data distribution features, and contextual position features, wherein the weight of the contextual position features is dynamically allocated based on the correlation between adjacent columns in the table.

8. The data entry method for engineering cost estimation software according to claim 1, characterized in that: It also includes version compatibility handling steps: when the Excel template version is inconsistent with the engineering cost software version, the version mapping table is automatically called to fill in missing or newly added fields or mark them as retained.

9. The data entry method for engineering cost estimation software according to claim 1, characterized in that: It also includes generating a 3D visualization data topology map, which intuitively displays the dependencies and resource allocation paths between items in the bill of quantities.

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