Price price separation result online data management method and system and storage medium

Abstract

The invention relates to a bid price separation result online data management method and system, and a storage medium. The bid price separation result online data management method comprises the following steps: S1, receiving bid price separation data comprising a total bidding quotation and details thereof; s2, based on a predefined metadata standard, performing standardization processing on detail composition in the bid price separation data to obtain structured bid price separation detail data; according to the method, a closed-loop process is formed through standardized input, multi-dimensional splitting, intelligent verification, dynamic linkage, quality evaluation and tracing archiving, the whole process from data receiving to archiving and automatic treatment are achieved, and the problems of process splitting, high manual dependency degree, low efficiency and difficulty in tracing caused by a traditional offline processing mode are fundamentally solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of project management and data governance, in particular to a method and system for online data governance of bid price separation results and a storage medium. BACKGROUND

[0002] In the bidding stage of a project, bid price separation refers to splitting the total bid price according to the logic of "cost (labor, materials, machinery, management fees, etc.) + profit + risk premium" to clearly define the composition of each item price, and to provide a basis for subsequent cost control, resource allocation and profit prediction.

[0003] Currently, the processing of bid price separation results mainly relies on offline Excel tables or simple online input methods, which have problems such as non-uniform data caliber, frequent conflicts, lack of dynamic linkage, difficulty in quantitatively evaluating data quality, and poor traceability. In particular, when the total bid price changes, the split results of each item cannot be automatically updated, and manual recalculation and verification are still required, which not only is inefficient, but also is prone to logical errors such as "the sum of the total bid price and the item prices does not match", which seriously hinders the cost control accuracy and decision-making efficiency in the bidding stage of a project.

[0004] Therefore, the prior art lacks a standardized, automated and traceable online data governance method for bid price separation results, which cannot effectively solve the core problem of "scattered, chaotic, incorrect and uncontrollable" bid price separation data. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide an online data governance method, system and storage medium for bid price separation results, which realizes unified caliber, intelligent verification, dynamic linkage and quality quantitative evaluation of bid price separation data, improves data governance efficiency and accuracy, and provides reliable data support for subsequent cost control.

[0006] To achieve the above purpose, the present application provides an online data governance method for bid price separation results, comprising the following steps: S1: receiving bid price separation data containing a total bid price and its detailed composition; S2: based on a predefined metadata standard, standardizing the detailed composition in the bid price separation data to obtain structured bid price separation detailed data; S3: based on a pre-set multi-dimensional split model, mapping and splitting the structured bid price separation detailed data to obtain the bid price separation amount of each item project, and setting a reference value interval for each item project in combination with external reference data; S4: according to a pre-set intelligent verification rule, detecting conflicts of the bid price separation amount of each item project obtained after mapping and splitting; S5: In response to the change of the total bid price, the bid price separation amount of each sub-project is dynamically updated according to the preset linkage rule; S6: Based on the multi-dimensional quality evaluation model, the bid price separation data after conflict detection and dynamic update are quantitatively scored; S7: The whole process operation and data change receiving the quantitative score from the bid price separation data are traced and recorded, and the final management qualified bid price separation data are standardized and archived.

[0007] By adopting this technical scheme, the closed-loop process of standardized input-multi-dimensional splitting-intelligent checking-dynamic linkage-quality evaluation-tracing and archiving is formed, the whole process from data receiving to archiving is realized, and the problems of process fragmentation, high artificial dependence, low efficiency and difficult tracing caused by traditional offline processing mode are fundamentally solved.

[0008] Further, the S2 step includes: According to the metadata dictionary containing basic information, price composition and auxiliary information, and the standardized caliber template preset according to the project type, the containing range and measurement unit of the detail composition data in the bid price separation data are unified; Through the structured interface input or file import function, the unstructured detail composition data is converted into structured bid price separation detail data.

[0009] By adopting this technical scheme, the metadata dictionary and the standardized caliber template are used to forcibly unify the data caliber and format, ensuring that the data input by different projects and different personnel has consistent structure and semantics, providing a high-quality structured data basis for all subsequent processing steps, and improving the data quality from the source.

[0010] Further, in the S3 step: The multi-dimensional splitting model is a three-dimensional model based on project level, cost type and risk level, wherein the project level includes departmental engineering and sub-project engineering, the cost type includes labor, material, machinery, management fee, profit and risk fund; The setting of the reference value interval for each sub-project in combination with external reference data is calculated by accessing one or more external reference data sources in the industry quota library and enterprise historical project database, based on the industry quota reference price or enterprise historical average price, and introducing a preset deviation coefficient.

[0011] By adopting this technical solution, the three dimensions and their constituent elements of the multi-dimensional splitting model are defined, giving the splitting process a clear structure and hierarchy. At the same time, the method for setting the benchmark value range is clarified. By accessing authoritative external data sources (industry quota database, enterprise historical database) and combining them with deviation coefficient calculation, the set benchmark values ​​are both industry standard and enterprise adaptability, thus improving the rationality of data splitting.

[0012] Furthermore, the formula for the multi-dimensional splitting model is as follows: B_{i,j,k}=C_{L,i,j,k}+C_{M,i,j,k}+C_{E,i,j,k}+C_{A,i,j,k}+P_{i,j,k}+R_{i,j,k} Where B_{i,j,k} represents the total amount of the price separation results for the i-th sub-project, the j-th sub-item, and the k-th risk level; C_{L,i,j,k}, C_{M,i,j,k}, C_{E,i,j,k}, C_{A,i,j,k}, P_{i,j,k}, and R_{i,j,k} correspond to the amounts of labor, materials, machinery, management expenses, profit, and risk funds under each dimension, respectively. The formula for calculating the benchmark value range is as follows: B_min=B_std×(1-α), B_max=B_std×(1+β) Where B_std is the industry benchmark price or the company's historical average price; α is the lower limit deviation coefficient; and β is the upper limit deviation coefficient.

[0013] By adopting this technical solution, mathematical expressions for multi-dimensional splitting models and benchmark value interval calculations are provided, making the splitting and benchmark setting process fully quantifiable and programmable, enhancing the feasibility and determinism of the technical solution, and providing a precise numerical calculation basis for subsequent intelligent verification.

[0014] Furthermore, in step S4, the intelligent verification rules include: Basic logic verification: used to verify the consistency between the total bid price in step S1 and the sum of the bid prices of all sub-items obtained in step S3; Benchmark deviation verification: used to determine whether the price separation amount of each sub-item project obtained in step S3 exceeds the corresponding benchmark value range set in step S3; Cross-dimensional consistency check: Used to check the consistency of the split amount under different dimensions in the multi-dimensional split model of the same cost type in the S3 step.

[0015] By adopting this technical solution, a three-level intelligent verification rule system consisting of basic logic verification, benchmark deviation verification, and cross-dimensional consistency verification was constructed. This system systematically covers the main types of data conflicts and can automatically detect most common data problems, significantly reducing the workload of manual verification and the risk of error omission.

[0016] Furthermore, the basic logic verification and judgment formula is as follows: ΔB=|B_total-Σ(i=1 to n)Σ(j=1 to m)Σ(k=1 to p)B_i,j,k| Where B_total is the total bid price in step S1; when ΔB is greater than the preset allowable error, it is judged as a conflict. The formula for determining the benchmark deviation is: Dev=(B_{i,j,k}-B_std) / B_std×100% Wherein, B_{i,j,k} is the total amount of the price separation results for the i-th sub-project, the j-th sub-item project, and the k-th risk level; B_std is the industry quota benchmark price or the company's historical average price; when Dev exceeds the range determined by the deviation coefficients α and β set in step S3, it is judged as a conflict.

[0017] By adopting this technical solution, specific judgment formulas for basic logic verification and benchmark deviation verification are provided, providing clear and quantitative numerical standards for conflict detection. This makes the verification process completely objective, can be automatically executed, and can accurately locate problematic data items, thereby improving the accuracy and efficiency of verification.

[0018] Furthermore, cross-dimensional consistency verification uses a cosine similarity algorithm to calculate the consistency between the split amounts of the same cost type in different dimensions.

[0019] By adopting this technical solution, the method can effectively quantify and compare the similarity of the amount vectors of the same cost type under different dimensions, thereby intelligently identifying abnormal logical deviations and enhancing the ability to verify the inherent consistency of the data.

[0020] Furthermore, in step S5, the linkage rules include: Proportional linkage: When no sub-item of the change is specified, the change amount of the total bid price in step S1 is allocated according to the proportion of the original bid price separation amount of each sub-item obtained in step S3. Targeted linkage: When a changed sub-item is specified, only the price separation amount of the specified target sub-item obtained in step S3 is updated.

[0021] By adopting this technical solution, two update rules, proportional linkage and targeted linkage, are defined to ensure that when the total bid price changes, the system can automatically and intelligently adjust the amount of each item according to the preset rules without manual recalculation. This effectively avoids logical errors caused by mismatch between the total price and the item sums, and ensures the consistency and accuracy of the data during the dynamic adjustment process.

[0022] This invention also provides an online data governance system for pricing separation results, used to implement an online data governance method for pricing separation results, including: The data receiving module is used to receive price-separated data, which includes the total bid price and its details; The standardization processing module, connected to the data receiving module, is used to standardize the detailed composition of the price separation data based on predefined metadata standards, and output structured price separation detailed data. The splitting and benchmark setting module is connected to the standardization processing module. It is used to map and split the structured price separation details data based on the preset multi-dimensional splitting model to obtain the price separation amount of each sub-item project, and set the benchmark value range for each sub-item project in combination with the accessed external benchmark data. The intelligent verification module, connected to the splitting and benchmark setting module, is used to perform conflict detection on the price separation amount of each sub-item project according to the preset intelligent verification rules. The dynamic linkage module is connected to the data receiving module and the splitting and benchmark setting module respectively. It is used to update the bid price of each sub-item according to the preset linkage rules in response to changes in the total bid price. The quality assessment module, connected to the intelligent verification module and the dynamic linkage module, is used to quantitatively score the processed price separation data based on a multi-dimensional quality assessment model. The traceability and archiving module, connected to the aforementioned modules, is used to trace and record the entire process of operations and data changes, and to standardize and archive the final qualified pricing separation data.

[0023] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an online data governance method for pricing separation results.

[0024] Compared with the prior art, the present invention has the following advantages: 1. By unifying data standards and formats from the source and through preset verification and linkage rules, the core problems of data being "scattered, disordered, incorrect, and inconsistent" have been systematically solved, shortening the governance cycle from several days to several hours, significantly improving data processing efficiency, and fundamentally ensuring the overall quality and usability of data.

[0025] 2. A three-level intelligent verification system consisting of basic logic, benchmark deviation, and cross-dimensional consistency, along with proportional and targeted linkage rules, has been constructed. This system enables automated detection of data conflicts and intelligent response to price changes. It can automatically identify the vast majority of data errors and automatically update details when the total price is adjusted. This replaces more than 90% of manual verification and calculation work, reducing the risk of logical conflicts and omissions caused by human error to a very low level and ensuring a high degree of accuracy and consistency of data during processing and adjustment.

[0026] 3. For the first time, a comprehensive quantitative score was given to the accuracy, completeness, and consistency of the price separation data, providing an objective and measurable standard for data quality. At the same time, combined with the recording of the entire process operation log, the traceability of each step of the operation from data entry to final archiving was realized, which effectively improved the credibility of the data after governance. This not only provided a solid data foundation for project cost control and profit forecasting, but also greatly improved the efficiency and reliability of subsequent audits, reviews, and risk management. Attached Figure Description

[0027] Fig. 1 This is a flowchart illustrating the online data governance method for bid price separation results in this invention. Fig. 2 This is a schematic diagram of the architecture of the online data governance system for the bid price separation results in this invention. Detailed Implementation

[0028] The following is in conjunction with the appendix Figs. 1-2 The present invention will be further described with reference to specific embodiments.

[0029] Example: Remediation of the pricing separation results in a municipal road construction project; A construction company participated in the bidding for a municipal road project. The preliminary results of the price separation that were completed needed to be processed to ensure the accuracy and reliability of the data. The company's cost engineer used the online data processing system for price separation results (hereinafter referred to as the processing system) of this invention to perform the operation.

[0030] (S1: Receive price separation data including the total bid price and its details) Log in to the management system, create a project file named "XX Municipal Road Project", and enter the total bid price of the project into the system: 120 million yuan; then, upload an Excel file containing detailed cost breakdowns of each item, including labor, materials, and machinery, to the system; the system's data receiving module successfully received this bid price data, which consists of the total price and the detailed breakdown. (S2: Based on predefined metadata standards, the detailed composition of the price separation data is standardized to obtain structured price separation detailed data) The system activates the standardization processing module; this module calls the predefined standardized templates and metadata dictionary for municipal engineering projects to automatically process the uploaded detailed data. 1. Unified Rules: Based on the template, the unit of measurement for "steel" will be unified to "tons", and it will be clarified that "management fees" only include on-site management fees and do not include headquarters management fees.

[0031] 2. Structured Conversion: Automatically parses unstructured data (such as merged cells) in Excel files and converts it into structured, price-separated detailed data with a hierarchical relationship of "project-sub-section-item" and stores it in the database; for example, the costs of the "asphalt concrete surface layer" item under the "road engineering" sub-section are converted into structured records with clear fields. (S3: Based on the preset multi-dimensional splitting model, the structured price separation details data are mapped and split to obtain the price separation amount of each sub-item project, and a benchmark value range is set for each sub-item project in combination with external benchmark data.) The splitting and baseline setting module performs the following operations: 1. Mapping and Splitting: The splitting and benchmark setting module loads a preset multi-dimensional splitting model. This model includes three dimensions: project level (sub-project, sub-item project), cost type (labor, materials, machinery, management fees, profit, risk fund), and risk level (high, medium, low). The module automatically maps the structured data obtained in the previous step into this model. For example, the total price of the "pipeline installation" sub-item under the "drainage project" sub-project is split according to the cost type dimension and associated with the "medium" risk level, ultimately forming a price separation amount B_{i,j,k} containing multi-dimensional labels. The calculation formula is as follows: B_{i,j,k}=C_{L,i,j,k}+C_{M,i,j,k}+C_{E,i,j,k}+C_{A,i,j,k}+P_{i,j,k}+R_{i,j,k} Where B_{i,j,k} is the total amount of the price separation results for the i-th sub-project, the j-th sub-item, and the k-th risk level; C_{L,i,j,k}, C_{M,i,j,k}, C_{E,i,j,k}, C_{A,i,j,k}, P_{i,j,k}, and R_{i,j,k} correspond to the amounts of labor, materials, machinery, management expenses, profit, and risk funds under each dimension.

[0032] 2. Set the baseline value range: Meanwhile, this module accesses the industry quota library and the enterprise historical project database through a data interface to obtain the industry quota benchmark price or enterprise historical average price (B_std) corresponding to a certain sub-item project (such as "asphalt concrete surface layer"). For example, it is 120 yuan / square meter. Based on the preset deviation coefficient (lower limit α=5%, upper limit β=10%), according to the formula B_min=B_std×(1-α) and B_max=B_std×(1+β), the reasonable price range of the sub-item project is calculated to be [114-132] yuan / square meter, and it is used as the benchmark value range to associate with the data. (S4: Based on the preset intelligent verification rules, perform conflict detection on the price separation amount of each sub-item project obtained after mapping and splitting) The intelligent verification module initiates automated verification based on preset intelligent verification rules and displays the results intuitively on the interface: 1. Basic Logic Verification: The module calculates the sum of the prices of all sub-projects and compares it with the entered total price of 120 million yuan; it calculates the difference ΔB=|B_total-Σ(i=1 to n)Σ(j=1 to m)Σ(k=1 to p)B_i,j,k|; in this example, the verification passes (ΔB=0). The system prompts that if ΔB is greater than the preset allowable error (e.g., 0 yuan), it will locate the specific difference item.

[0033] 2. Benchmark Deviation Verification: The module checks whether the amount of each item falls within its corresponding benchmark value range. The check found that the material cost of the "DN500 drainage pipe installation" item was entered as 115 yuan / meter, while its corresponding industry benchmark price B_std is 100 yuan / meter. The deviation rate Dev = (115-100) / 100×100% = 15%. Since 15% > β(10%), the system determines that this item is a benchmark deviation conflict and displays it automatically. At the same time, it also displays the inquiry form number attached when it was uploaded: XJ-2025-032 for verification.

[0034] 3. Cross-dimensional consistency verification: The module uses a cosine similarity algorithm to calculate the consistency of prices for the same cost type (such as "C30 commercial concrete") in different sub-projects; if the calculated similarity is lower than a preset threshold (such as 0.95), the system will prompt "abnormal price difference for the same material". (S5: In response to changes in the total bid price, dynamically update the bid price of each sub-item according to the preset linkage rules.) In a subsequent bid clarification, the tendering party requested an adjustment to the bid price, and the engineer revised the total bid price for the project to 118 million yuan; after the total bid price was changed, the dynamic linkage module was automatically triggered: By selecting the proportional linkage rule, the system calculates a total price change of 2 million yuan. Then, based on the proportion of the original amount of each item, it automatically distributes the 2 million yuan reduction to all items, instantly completing the recalculation and update of all price separation amounts.

[0035] After the update is complete, the system will automatically re-execute the intelligent verification to ensure that the adjusted data still meets the consistency between the total price and the sum of the items (ΔB returns to zero). (S6: Based on a multi-dimensional quality assessment model, quantitatively score the price separation data after conflict detection and dynamic updates) Once all conflict warnings have been confirmed or corrected by engineers, the quality assessment module will be activated; the module will then score the current dataset based on a multi-dimensional quality assessment model. Model from accuracy A (No unresolved conflicts) Integrity C (Completeness of required fields), consistency I (Cross-dimensional verification results) Scoring across three dimensions.

[0036] Substitute into the formula Q = w 1× A + w 2× C + w 3× I (The weights in this example are taken as follows) w 1 = 0.5 w 2 = 0.3, w Calculate using 3=0.2).

[0037] Ultimately, the system's data score was... Q =99.6 points; the system is based on the settings ( Q A score of ≥90 indicates high quality), thus the treatment results for this batch are considered high-quality data. (S7: Trace and record the entire process of receiving pricing separation data and quantitative scoring, and archive the final qualified pricing separation data in a standardized manner.) The traceability and archiving module performs the final operation: 1. Traceability Records: The module will completely save all key operations (operator, time, content, and values ​​before and after data changes) and related attachments (inquiry form, adjustment instructions) in the form of an immutable log throughout the entire process.

[0038] 2. Standardized Archiving: The module packages the final qualified price separation data, quality assessment report, and complete operation logs into the project knowledge base in a unified format of "Project Number_Price Separation_Version Number" to complete standardized archiving. At the same time, through the system interface, the data package is automatically synchronized to the enterprise's cost control system, providing an immediate and reliable data foundation for subsequent construction cost control. Implementation effect

[0039] Through the implementation of this embodiment, the governance of pricing separation data for the municipal road project has been fundamentally improved: the governance cycle has been shortened from about 4 working days required by the traditional offline model to less than 3 hours; the inherent conflicts and logical errors in the data have been systematically detected and corrected, and the governed data can be directly used for accurate cost analysis; the digital traceability of the entire process has also greatly facilitated subsequent auditing and review. The present invention has been described in detail above with reference to the accompanying drawings and embodiments. Those skilled in the art can make various modifications to the present invention based on the above description. Therefore, certain details in the embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention shall be defined by the appended claims.

Claims

1. A method for online data governance of pricing separation results, characterized in that, Includes the following steps: S1: Receive price separation data including the total bid price and its details; S2: Based on predefined metadata standards, standardize the detailed composition of the price separation data to obtain structured price separation detailed data; S3: Based on the preset multi-dimensional splitting model, the structured price separation details data are mapped and split to obtain the price separation amount of each sub-item project, and the benchmark value range is set for each sub-item project in combination with external benchmark data. S4: Based on the preset intelligent verification rules, perform conflict detection on the price separation amount of each sub-item project obtained after mapping and splitting; S5: In response to changes in the total bid price, dynamically update the bid price of each sub-item according to the preset linkage rules; S6: Based on a multi-dimensional quality assessment model, quantitatively score the price separation data after conflict detection and dynamic updating; S7: Trace and record the entire process of receiving pricing separation data and quantitative scoring, and archive the final qualified pricing separation data in a standardized manner.

2. The online data governance method for pricing separation results according to claim 1, characterized in that, Step S2 includes: Based on the metadata dictionary containing basic information, price composition and auxiliary information, and the standardized templates preset according to project type, the scope and unit of measurement of the detailed composition data in the price separation data are unified. By using the structured interface for data entry or the file import function, unstructured detailed data can be transformed into structured price-separated detailed data.

3. The online data governance method for pricing separation results according to claim 1, characterized in that, In step S3: The multi-dimensional decomposition model is a three-dimensional model built on project level, cost type and risk level. The project level includes sub-projects and sub-items, and the cost type includes labor, materials, machinery, management fees, profit and risk fund. Setting benchmark value ranges for each sub-project by combining external benchmark data involves accessing one or more external benchmark data sources, such as industry quota libraries or enterprise historical project databases, and calculating based on industry quota benchmark prices or enterprise historical average prices, while introducing preset deviation coefficients.

4. The online data governance method for pricing separation results according to claim 3, characterized in that, The formula for the multi-dimensional splitting model is as follows: B_{i,j,k}=C_{L,i,j,k}+C_{M,i,j,k}+C_{E,i,j,k}+C_{A,i,j,k}+P_{i,j,k}+R_{i,j,k} Where B_{i,j,k} represents the total amount of the price separation results for the i-th sub-project, the j-th sub-item, and the k-th risk level; C_{L,i,j,k}, C_{M,i,j,k}, C_{E,i,j,k}, C_{A,i,j,k}, P_{i,j,k}, and R_{i,j,k} correspond to the amounts of labor, materials, machinery, management expenses, profit, and risk funds under each dimension, respectively. The formula for calculating the benchmark value range is as follows: B_min=B_std×(1-α), B_max=B_std×(1+β) Where B_std is the industry benchmark price or the company's historical average price; α is the lower limit deviation coefficient; and β is the upper limit deviation coefficient.

5. The online data governance method for pricing separation results according to claim 4, characterized in that, In step S4, the intelligent verification rules include: Basic logic verification: used to verify the consistency between the total bid price in step S1 and the sum of the bid prices of all sub-items obtained in step S3; Benchmark deviation verification: used to determine whether the price separation amount of each sub-item project obtained in step S3 exceeds the corresponding benchmark value range set in step S3; Cross-dimensional consistency check: Used to check the consistency of the split amount under different dimensions in the multi-dimensional split model of the same cost type in the S3 step.

6. The online data governance method for pricing separation results according to claim 5, characterized in that, The basic logic verification and judgment formula is: ΔB=|B_total-Σ(i=1 to n)Σ(j=1 to m)Σ(k=1 to p)B_i,j,k| Where B_total is the total bid price in step S1; when ΔB is greater than the preset allowable error, it is judged as a conflict. The formula for determining the benchmark deviation is: Dev=(B_{i,j,k}-B_std) / B_std×100% Wherein, B_{i,j,k} is the total amount of the price separation results for the i-th sub-project, the j-th sub-item project, and the k-th risk level; B_std is the industry quota benchmark price or the company's historical average price; when Dev exceeds the range determined by the deviation coefficients α and β set in step S3, it is judged as a conflict.

7. The online data governance method for pricing separation results according to claim 5, characterized in that, Cross-dimensional consistency verification uses the cosine similarity algorithm to calculate the consistency between the split amounts of the same cost type in different dimensions.

8. The online data governance method for pricing separation results according to claim 1, characterized in that, In step S5, the linkage rules include: Proportional linkage: When no sub-item of the change is specified, the change amount of the total bid price in step S1 is allocated according to the proportion of the original bid price separation amount of each sub-item obtained in step S3. Targeted linkage: When a changed sub-item is specified, only the price separation amount of the specified target sub-item obtained in step S3 is updated.

9. An online data governance system for pricing separation results, used to implement the online data governance method for pricing separation results according to any one of claims 1-8, characterized in that, include: The data receiving module is used to receive price-separated data, which includes the total bid price and its details; The standardization processing module, connected to the data receiving module, is used to standardize the detailed composition of the price separation data based on predefined metadata standards, and output structured price separation detailed data. The splitting and benchmark setting module is connected to the standardization processing module. It is used to map and split the structured price separation details data based on the preset multi-dimensional splitting model to obtain the price separation amount of each sub-item project, and set the benchmark value range for each sub-item project in combination with the accessed external benchmark data. The intelligent verification module, connected to the splitting and benchmark setting module, is used to perform conflict detection on the price separation amount of each sub-item project according to the preset intelligent verification rules. The dynamic linkage module is connected to the data receiving module and the splitting and benchmark setting module respectively. It is used to update the bid price of each sub-item according to the preset linkage rules in response to changes in the total bid price. The quality assessment module, connected to the intelligent verification module and the dynamic linkage module, is used to quantitatively score the processed price separation data based on a multi-dimensional quality assessment model. The traceability and archiving module, connected to the aforementioned modules, is used to trace and record the entire process of operations and data changes, and to standardize and archive the final qualified pricing separation data.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the online data governance method for pricing separation results as described in any one of claims 1-8.

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