Power transmission and transformation project data index verification deepening method and system

Abstract

The invention discloses a power transmission and transformation project data index verification deepening method and system, and relates to the field of project cost management and cost control. The method comprises the following steps: determining extraction rules of technical and economic indexes and index data of different transmission and transformation project types; establishing a database, and dividing the database into a plurality of tables according to power transmission and transformation project types; according to an extraction rule, extracting and averaging historical engineering data of each technical-economic index, collecting engineering information at the same time, and storing the engineering information in a table of a power transmission and transformation engineering type to which the engineering information belongs, so as to obtain a historical library; determining examination nodes and to-be-examined project information of the power transmission and transformation project; matching the to-be-reviewed project information with project information in a historical library, and comparing index data; and when the deviation of the technical and economic indexes meets the dynamic early warning rule, early warning is triggered. According to the invention, engineering index verification deepening can be carried out based on the historical data of the power transmission and transformation project, and the accuracy and normalization of project review are ensured.

Description

Technical Field

[0001] This application relates to the fields of engineering cost management and cost control, and in particular to a method and system for deepening the verification of data indicators in power transmission and transformation projects. Background Technology

[0002] Numerous research achievements have been made both domestically and internationally in the fields of engineering cost management and cost control. Advanced engineering management concepts and technologies, such as full life-cycle cost management, refined cost control, and intelligent review systems, have been successfully applied in numerous large-scale infrastructure projects. To achieve the goal of controlling costs through meticulous budgeting, implementing the concept of optimal full life-cycle cost, and innovating cost management and technology, it is necessary to strengthen the refined control of key aspects such as preliminary design review, budget audit, and settlement supervision of power transmission and transformation projects. Research should be conducted on in-depth schemes for verifying engineering indicators based on historical data of power transmission and transformation projects. This will provide strong technical support for the cost effectiveness supervision of power grid infrastructure projects, ensure the scientific nature and effectiveness of cost management, and promote the high-quality development of power grid infrastructure projects. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for deepening the verification of data indicators in power transmission and transformation projects, which can deepen the verification of engineering indicators based on historical data of power transmission and transformation projects, and ensure the accuracy and standardization of project review.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for deepening the verification of data indicators in power transmission and transformation projects, including: Determine the technical and economic indicators for different types of power transmission and transformation projects; Determine the extraction rules for each technical and economic indicator data; Build a database and divide it into multiple tables according to the type of power transmission and transformation project; Based on the extraction rules, the historical engineering data of each technical and economic indicator are extracted and averaged. At the same time, engineering information is collected and stored in the tables of their respective power transmission and transformation engineering types to obtain a historical database. Identify the review milestones for power transmission and transformation projects, as well as the information on projects awaiting review within those milestones; The project information to be reviewed is matched with the project information in the historical database to obtain the average historical project data of each technical and economic indicator of the matched project in the historical database; Extract the technical and economic indicators of the project under review from the review nodes, and compare the technical and economic indicators of the project under review with the historical average of the technical and economic indicators of the matching projects in the historical database to obtain the deviation of each technical and economic indicator. An early warning is triggered when the deviation of technical and economic indicators meets the dynamic early warning rules.

[0005] Secondly, this application provides a data index verification and deepening system for power transmission and transformation projects, including: The indicator determination module is used to determine the technical and economic indicators for different types of power transmission and transformation projects; The rule determination module is used to determine the extraction rules for each technical and economic indicator data; The database construction module is used to build the database and divide the database into multiple tables according to the type of power transmission and transformation project; The data extraction module is used to extract and average the historical engineering data of each technical and economic indicator according to the extraction rules, and at the same time collect engineering information and store it in the tables of their respective power transmission and transformation engineering types to obtain a historical database. The pending review information determination module is used to determine the review nodes of power transmission and transformation projects, as well as the pending review information of the projects within the review nodes; The matching module is used to match the project information to be reviewed with the project information in the historical database to obtain the historical average of the technical and economic indicators of the matched projects in the historical database. The comparison module is used to extract the technical and economic indicators of the project under review in the review node, and compare the technical and economic indicators of the project under review with the historical average of the technical and economic indicators of the matching projects in the historical database to obtain the deviation of each technical and economic indicator. The early warning triggering module is used to trigger an early warning when the deviation of technical and economic indicators meets the dynamic early warning rules.

[0006] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and system for deepening the verification of data indicators in power transmission and transformation projects. A historical database is established by extracting historical engineering data for various technical and economic indicators. The data of the project under review is compared with the data in the historical database to deepen the verification of engineering indicators. Combined with the power grid project review process (feasibility study, preliminary design), the review nodes of the power transmission and transformation project are determined. A phased comparative review is conducted for different review nodes to ensure that the rules match the actual needs of the project. At the same time, dynamic early warning rules are used to ensure the timeliness and accuracy of early warning prompts, thereby ensuring the accuracy and standardization of project review. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments 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.

[0008] Figure 1 An application environment diagram for a method for deepening the verification of data indicators in power transmission and transformation projects, provided in an embodiment of this application; Figure 2 A flowchart illustrating a method for deepening the verification of data indicators in power transmission and transformation projects, provided as an embodiment of this application; Figure 3 This is a schematic diagram of the functional modules of a data index verification and deepening system for power transmission and transformation projects provided in an embodiment of this application. Detailed Implementation

[0009] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0011] The data index verification and deepening method for power transmission and transformation projects provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send review nodes and projects to be reviewed to server 104. After receiving the review nodes and projects to be reviewed, server 104 determines the technical and economic indicators for different power transmission and transformation project types; determines the extraction rules for each technical and economic indicator data; builds a database and divides the database into multiple tables according to the power transmission and transformation project type; extracts and averages the historical project data of each technical and economic indicator according to the extraction rules, and collects project information and stores it in the table of its respective power transmission and transformation project type to obtain a historical database; determines the review nodes of the power transmission and transformation projects and the project information to be reviewed in the review nodes; matches the project information to be reviewed with the project information in the historical database to obtain the average historical project data of each technical and economic indicator of the matched projects in the historical database; extracts the technical and economic indicator data of each project to be reviewed in the review nodes, and compares the technical and economic indicator data of each project to be reviewed with the average historical project data of each technical and economic indicator of the matched projects in the historical database to obtain the deviation of each technical and economic indicator; and triggers an early warning when the deviation of the technical and economic indicator meets the dynamic early warning rules. Server 104 can send the triggered warnings back to terminal 102. Furthermore, in some embodiments, the method for deepening the verification of power transmission and transformation engineering data indicators can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly process the review nodes and projects to be reviewed, or server 104 can obtain the review nodes and projects to be reviewed from the data storage system and process them accordingly.

[0012] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0013] In one exemplary embodiment, such as Figure 2 As shown, a method for deepening the verification of data indicators in power transmission and transformation projects is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 208.

[0014] Step 201: Determine the technical and economic indicators for different types of power transmission and transformation projects.

[0015] Step 202: Determine the extraction rules for each technical and economic indicator data.

[0016] Step 203: Build a database and divide it into multiple tables according to the type of power transmission and transformation project.

[0017] Step 204: According to the extraction rules, extract and average the historical engineering data of each technical and economic indicator, collect engineering information, and store it in the tables of their respective power transmission and transformation engineering types to obtain the historical database.

[0018] Step 205: Determine the review nodes for power transmission and transformation projects, as well as the information on projects awaiting review within those review nodes.

[0019] Step 206: Match the project information to be reviewed with the project information in the historical database to obtain the historical average of each technical and economic indicator of the matched project in the historical database.

[0020] Step 207: Extract the technical and economic indicators of the project under review from the review node, and compare the technical and economic indicators of the project under review with the historical average of the technical and economic indicators of the matching projects in the historical database to obtain the deviation of each technical and economic indicator.

[0021] Step 208: Trigger an early warning when the deviation of the technical and economic indicators meets the dynamic early warning rules.

[0022] In another exemplary embodiment of this application, step 201 above involves the screening and confirmation of key indicators in the accurate construction of the indicator system.

[0023] This application is based on the existing technical and economic indicator system for power grid engineering. Currently, the technical and economic laboratory has cost analysis data tables, which were formed through on-site actual surveys and analyses. The key technical and economic indicators covered not only have a significant impact on cost, but also have quantifiable and comparable characteristics, including core contents such as engineering technology and expenses.

[0024] Given that the core objective of this application is review and comparison, it is necessary to further screen the indicators that can be automatically extracted. We will prioritize using the Bowei cost estimation software, taking the latest pre-planned cost documents as the analysis object, and temporarily limiting the stage to the preliminary budget stage. Based on the above analysis, the types of power transmission and transformation projects are defined as: substation projects, overhead line projects, and cable line projects; the technical and economic indicators include: technical indicators and cost indicators.

[0025] Finally, the key technical and cost indicators for three types of projects—substation engineering, overhead lines, and cable lines—were extracted, as detailed in Tables 1-3.

[0026] Table 1 Technical and Economic Indicators for Substation Engineering

[0027] Table 2 Technical and Economic Indicators for Overhead Line Projects

[0028] Table 3 Technical and Economic Indicators for Cable Line Engineering

[0029] The three tower material quantities (t) in Table 2, numbered 10 to 12, refer to the tower material quantities of different models.

[0030] In another exemplary embodiment of this application, the indicator extraction rules are formulated as follows: After obtaining the above-mentioned extractable indicators, it is necessary to further organize the extraction rules for each indicator to ensure the accuracy of indicator reading. The data extraction technology of this application mainly targets the structured data (such as the quantity of equipment and cost amount) and semi-structured data (such as the description of basic project information) in the preliminary cost estimate documents of power transmission and transformation projects (such as the 2018 pre-planned version cost document). The extraction rules for each technical and economic indicator data are one or more of the following: regular expression extraction, rule-based parsing engine, natural language processing (NLP) extraction, and Bowei cost estimation software extraction. A specific comparison of the four extraction rules is shown in Table 4.

[0031] Table 4 Comparison of the four extraction rules

[0032] Based on the actual needs of the project, the extraction target was the preliminary cost estimate file of the Bowei software version 18, which needed to be adapted to a microservice architecture and intranet deployment. The final solution determined was a combination of Bowei cost estimation software extraction rules and a rule-based parsing engine. (1) Core indicators (such as total equipment cost and construction cost breakdown) are directly extracted using Bowei cost estimation software to ensure accuracy.

[0033] (2) Edge metrics not supported by the interface (such as concrete usage in cable trenches and pipe hole size in meters) are extracted by a rule-based parsing engine to adapt to differences in table format.

[0034] (3) Both are split into independent microservice modules. The interface integration service is responsible for interacting with the Bowei software, and the rule parsing service is responsible for supplementing extraction. The modules coordinate through intranet service calls, taking into account both efficiency and compatibility.

[0035] The specific extraction rules for each technical and economic indicator are shown in Tables 5-7.

[0036] Table 5 Rules for Extracting Indicators for Substation Engineering

[0037] Table 6 Rules for Extracting Technical and Economic Indicators for Overhead Line Projects

[0038] Table 7 Rules for Extracting Technical and Economic Indicators for Cable Line Engineering

[0039] In another exemplary embodiment of this application, the database of this application needs to store three types of core data: basic project information (such as project name and voltage level), technical and economic indicator data (such as equipment quantity and cost amount), and comparison and review results (such as deviation rate and warning level). It also needs to meet the requirements of concurrent access of multiple modules under microservice architecture, data security in intranet environment, and efficient query of indicator data (such as filtering by project type and pre-plan version). Candidate database solutions include relational databases (MySQL, PostgreSQL), time series databases (InfluxDB), and non-relational databases (MongoDB), as shown in Table 8.

[0040] Table 8 Database Comparison

[0041] Considering the needs for multi-module collaboration under a microservice architecture, ease of operation and maintenance in an intranet environment, and the requirement for structured storage of core data, PostgreSQL was ultimately chosen as the primary database. Therefore, step 203 above can be replaced by steps 301-305.

[0042] Step 301: Set up a PostgreSQL relational database.

[0043] Step 302: Define that structured data is stored in a PostgreSQL relational database, and semi-structured data is stored using the JSONB type of the PostgreSQL relational database.

[0044] Data storage is divided as follows: basic project information, technical and economic indicators (substation / overhead line / cable line indicator table), comparison and review results, and other structured data are stored in PostgreSQL, while semi-structured data (such as early warning content) are stored in PostgreSQL's JSONB type, balancing query efficiency and flexibility.

[0045] The storage field information for each table should be clearly defined to ensure that the fields in each indicator table match the corresponding project type. Based on the aforementioned information and the characteristics of the project types, the field structures of the indicator tables for substation projects, overhead line projects, and cable line projects should be designed separately. Tables 9-11 show the field designs based on basic project information and indicator content.

[0046] Table 9 Data Storage Field Information for Substation Projects

[0047] Table 10 Data storage field information for overhead line projects

[0048] Table 11 Data storage field information for cable line engineering

[0049] Step 303: Encapsulate PostgreSQL relational database operations into data access service micro-modules, and define data extraction microservices and comparison and review microservices to call the PostgreSQL relational database through the intranet RESTful interface.

[0050] Microservice adaptation: Database operations are encapsulated into a "data access service" micro-module. Other microservices (such as data extraction service and comparison review service) call this module through the intranet RESTful interface, avoiding direct access to the database and achieving centralized control of permissions.

[0051] Step 304: Enable SSL encrypted transmission for PostgreSQL relational database.

[0052] Security measures: Enable SSL encryption for PostgreSQL to prevent data theft during intranet transmission; configure role-based access control (RBAC) so that different microservice modules only have corresponding data operation permissions (e.g., the data extraction service only has write permissions, and the comparison and review service only has read permissions).

[0053] Step 305: Create multiple tables according to the pre-plan version and the type of power transmission and transformation project.

[0054] Performance optimization: For historical project data, create partitioned tables based on "pre-plan version (e.g., 2018 pre-plan) + project type (e.g., substation project)"; create indexes for frequently queried fields (e.g., project voltage level, completion time) to ensure that multi-dimensional filtering response time is less than 1 second.

[0055] In another exemplary embodiment of this application, the data source is confirmed and classified as follows: The database (technical and economic indicator database) studied in this application needs to support the reading of historical project indicators. Since the core revolves around the preliminary cost estimate documents of the 2018 pre-planned version of the project, the historical project data is mainly taken from power transmission and transformation projects prepared in accordance with the 2018 pre-planned version in recent years. Combining the aforementioned extraction rules, the technical and economic data of these projects are first extracted. Since the study needs to realize the review and comparison of the projects to be reviewed, it is also necessary to collect basic project information that can be matched with the projects to be reviewed. This information can be read from the individual project information of each project stored in the technical and economic laboratory project management system. Specifically, the source is the basic information filled in when manually creating the project, as well as the preliminary cost estimate documents of the 2018 pre-planned version of the project.

[0056] By analyzing the contents of Tables 1 to 7 in the structured budget document, key indicators for each individual project are extracted and formed into a structured database. Data cleaning and standardization are then performed to ensure the comparability of the indicators, ultimately constructing a typical project reference model. Step 104 above, which involves extracting and averaging historical project data for each technical and economic indicator, specifically includes: extracting historical project data for each technical and economic indicator for each individual project by analyzing the contents of the structured budget document; cleaning and standardizing the historical project data to obtain standardized historical project data; and calculating the average value of the standardized historical project data for each technical and economic indicator.

[0057] In another exemplary embodiment of this application, the project information includes: project name, project type, project voltage level, project pre-plan version, and project completion time.

[0058] After obtaining the above information, considering that different project types correspond to different indicators, in order to ensure the accuracy of the information stored in the database, the database needs to be divided into different tables according to the project type, specifically: substation project indicator table, overhead line indicator table, and cable line indicator table. In another exemplary embodiment of this application, the phased comparative review method is designed as follows: Based on the various indicators that have been sorted out, the nodes of the engineering comparative review of the review system are first identified, and then the engineering review stages of the current review system are analyzed. Currently, the engineering budget review mainly covers the following stages: (1) Monthly plan submission; (2) Monthly plan review; (3) Monthly plan release in a balanced manner; (4) Submission of application materials; (5) Preliminary review of materials; (6) Formal review; (7) Submission of closing documents; (8) Review of final documents; (9) Review comments and evaluations; (10) Archiving of results and data.

[0059] It is necessary to further clarify the upload nodes of the engineering cost documents and the nodes that need to be reviewed and compared in each stage: among them, the (1)-(3) stages are mainly based on the declaration of basic engineering information. Starting from the "submission of review materials" stage, various documents required for engineering review (including engineering cost documents) will be uploaded. This node is the starting point for comparison and review. Starting from this node, the basic engineering information (engineering type, engineering voltage level, engineering pre-plan version) is obtained first for engineering matching. After the matching is completed, the cost documents of the project to be reviewed are extracted. The index data is obtained in combination with the index extraction rules. Then, it is compared with the historical engineering indexes in the database to calculate the deviation value and deviation rate, and the warning prompt is triggered according to the threshold.

[0060] In summary, the stages involved in the comparative review are as follows: (1) Submission of application materials; (2) Preliminary review of materials; (3) Formal review; (4) Submission of closing documents; (5) Review of closing documents.

[0061] At the aforementioned nodes, the system will automatically capture the indicator data of the project to be reviewed and similar historical projects, and compare and analyze key parameters such as engineering technical indicators, project cost composition, and main material usage item by item.

[0062] In another exemplary embodiment of this application, the data matching and calculation technology is the core of realizing the "comparison of indicators between the project under review and historical projects". It needs to complete two major tasks: one is "project matching" (selecting projects similar to the project under review from the historical database, such as projects with the same voltage level and the same type), and the other is "indicator calculation" (calculating the deviation value and deviation rate between the indicators under review and historical indicators, and triggering an early warning). Candidate technical solutions are shown in Table 12.

[0063] Table 12 Candidate Technical Solutions

[0064] The final technical solution adopted was a combination of "layered engineering matching + dynamic indicator calculation," adapted to both microservice architecture and intranet environment, as detailed below: (1) Layered engineering matching technology 1) First layer (fast filtering): The "rule-based matching algorithm" is adopted. Through the "project matching service" micro-module, candidate historical projects are quickly filtered from the PostgreSQL database according to the hard rules of "pre-plan version (e.g., 18 pre-plan) + project type (e.g., substation project) + voltage level (e.g., 220kV)". The response time is controlled within 0.5 seconds. 2) Second layer (precise matching): For candidate projects, a "similarity-based matching algorithm" is used. Through the "similarity calculation service" micro-module, features such as "project capacity (e.g., main transformer capacity), construction region, and completion time (last 3 years)" are integrated to calculate the similarity between the project to be reviewed and the candidate projects (maximum score of 100 points). Historical projects with a similarity score ≥ 80 points are selected as the final comparison samples. 3) Intranet efficiency optimization: Deploy Redis cache on the backend of "Similarity Calculation Service" to cache feature data of high-frequency candidate projects (such as similar projects in the past year) to avoid repeated database queries. The cache validity period is set to 24 hours and updated regularly.

[0065] (2) Dynamic index measurement technology 1) Core Algorithm: The algorithm adopts a "dynamic calculation algorithm based on statistical distribution". Through the "indicator calculation service" micro-module, the indicator data of the final comparison sample is statistically analyzed and the dynamic threshold is calculated according to the "3σ principle" (i.e., threshold = historical indicator mean ± 3 × standard deviation). If the indicator to be reviewed exceeds this range, an early warning is triggered. 2) Special scenario adaptation: If the historical sample size of a certain indicator is less than 30 (such as the cost of special structures), it will automatically switch to "static calculation algorithm based on threshold". The static threshold is set with reference to the State Grid's "Guidelines for Cost Management of Transmission and Transformation Projects" to ensure that the calculation is comprehensive. 3) Microservice collaboration: The "Indicator Calculation Service" obtains historical indicator data from the "Data Access Service". After the calculation is completed, it writes the deviation value, deviation rate, warning level and other results into the database. At the same time, it calls the "Warning Notification Service" to generate warning information, realizing full-process automation.

[0066] 4) Intranet Deployment Guarantee: All technical modules are deployed on intranet servers. The "Similarity Calculation Service" and "Indicator Measurement Service" are developed using lightweight frameworks (such as Spring Boot) to avoid excessive resource consumption. Modules communicate asynchronously through intranet message queues (such as RabbitMQ) to prevent a failure of one service from affecting the overall process and to ensure system stability.

[0067] In step 206 above, the information of the project to be reviewed is matched with the project information in the historical database. Specifically, this includes: using a rule-based matching algorithm, according to hard rules such as the pre-plan version, power transmission and transformation project type and voltage level, to select candidate projects that match the information of the project to be reviewed from the historical database; using a similarity-based matching algorithm, to calculate the similarity between the project to be reviewed and the candidate projects, and to select candidate projects with a similarity greater than or equal to the similarity threshold as the matching projects.

[0068] Step 208 above triggers an early warning when the deviation of the technical and economic indicators meets the dynamic early warning rules. Specifically, this includes: when the number of historical engineering data entries for the technical and economic indicators is greater than or equal to the quantity threshold, statistical analysis is performed on the historical engineering data of each technical and economic indicator for the matching projects in the historical database, and the upper and lower limits of the dynamic early warning threshold are calculated according to the 3σ principle; if the deviation of the technical and economic indicators is greater than the upper limit of the dynamic early warning threshold, or the deviation of the technical and economic indicators is less than the lower limit of the dynamic early warning threshold, an early warning is triggered; when the number of historical engineering data entries for the technical and economic indicators is less than the quantity threshold, the deviation rate of the technical and economic indicators is determined based on the deviation of the technical and economic indicators; if the deviation rate of the technical and economic indicators is greater than the deviation rate threshold, an early warning is triggered.

[0069] Therefore, based on the aforementioned review nodes, this application has formulated early warning rules and notification methods: A reasonable deviation threshold is set by combining historical engineering data, and a tiered early warning mechanism is automatically triggered for indicators exceeding the threshold. In the system interface, high, medium, and low risks are respectively identified by red, yellow, and blue, and a comparative analysis report is generated simultaneously to assist reviewers in quickly locating abnormal data. Early warning information will flow with the review process, providing key notifications during the pre-review and formal review stages to ensure early detection and correction of problems, thereby improving review efficiency and accuracy.

[0070] The warning message mainly includes: (1) For projects with many deviation items: prompt "Multiple indicators of Project XX are higher / lower than the average of similar projects in history, with a deviation rate of X%, mainly concentrated in sub-items such as construction cost / equipment purchase cost / installation cost"; (2) For projects where the deviation value / rate exceeds the set threshold: prompt "The deviation rate of XX indicator of XX project is 8% / XX compared with the historical project XX indicator, which exceeds the preset threshold. It is recommended to conduct a key review". The main components of the comparative analysis report are shown in Table 13.

[0071] Table 13 Comparative Analysis Report

[0072] The technical advantages of this application are: (1) Accurate construction of the indicator system: Based on the State Grid's three-level indicator system and combined with the settlement data of the past three years, we selected engineering quantity, cost and comprehensive technical and economic indicators that are both representative and practical, so as to ensure that the indicator benchmarks can truly reflect the project cost level.

[0073] (2) Efficiency and reliability of data processing: The historical data sources of power transmission and transformation projects are complex. The focus is on solving the problems of data cleaning, integration and standardization to ensure data quality. At the same time, an appropriate extraction and storage scheme is designed to meet the needs of subsequent indicator calculation and comparison.

[0074] (3) Adaptability of review rules and early warning mechanism: In conjunction with the power grid project review process (feasibility study, preliminary design), a phased comparative review method is formulated to ensure that the rules match the actual needs of the project, and at the same time, reasonable threshold standards are determined to achieve the timeliness and accuracy of early warning prompts.

[0075] The beneficial effects of this application are as follows: 1. Based on the historical construction scale and project types of power transmission and transformation projects, and in conjunction with the State Grid's three-level indicator system, key engineering quantity-related technical and economic indicators benchmarks were identified from the construction settlements of projects that have been settled in the past three years. These indicators include material usage, equipment quantity, and quota consumption, in order to establish scientific and reasonable reference standards.

[0076] 2. In accordance with the requirements of State Grid and government documents, and based on settlement data from previous years, we conducted data sorting of cost-related and comprehensive indicators, covering key elements such as land acquisition costs, compensation costs, land area, and detailed construction and installation costs. This resulted in a comprehensive set of auxiliary technical and economic indicators and benchmarks, providing scientific basis and support for the feasibility study and preliminary design review of power grid projects.

[0077] 3. Based on the data analyzed in the pending cost estimates and preliminary budgets, and in conjunction with technical and economic indicators, we will sort out and analyze the data calculation rules for the technical and economic indicators of the projects under review. Simultaneously, we will utilize the project review system to design an indicator extraction scheme for the cost documents and conduct comparative research with established technical and economic indicator benchmarks, including developing an early warning scheme for data exceeding thresholds, to ensure the accuracy and standardization of project reviews.

[0078] Based on the same inventive concept, this application also provides a system for verifying and deepening the verification of data indicators in power transmission and transformation projects, used to implement the aforementioned method for deepening the verification of data indicators in power transmission and transformation projects. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the system for deepening the verification of data indicators in power transmission and transformation projects provided below can be found in the limitations of the method for deepening the verification of data indicators in power transmission and transformation projects described above, and will not be repeated here.

[0079] In one exemplary embodiment, such as Figure 3 As shown, a data index verification and deepening system for power transmission and transformation projects is provided, including: an index determination module, a rule determination module, a database construction module, a data extraction module, a pending information determination module, a matching module, a comparison module, and an early warning triggering module.

[0080] The system comprises the following modules: **Indicator Determination Module:** This module determines the technical and economic indicators for different types of power transmission and transformation projects. **Rule Determination Module:** This module determines the extraction rules for each technical and economic indicator data. **Database Construction Module:** This module constructs a database and divides it into multiple tables according to the power transmission and transformation project type. **Data Extraction Module:** This module extracts and averages historical project data for each technical and economic indicator based on the extraction rules, while simultaneously collecting project information and storing it in the corresponding tables for each power transmission and transformation project type to obtain a historical database. **Pending Review Information Determination Module:** This module determines the review nodes for power transmission and transformation projects and the pending project information within those nodes. **Matching Module:** This module matches the pending project information with the project information in the historical database to obtain the average historical project data for each technical and economic indicator of the matched projects. **Comparison Module:** This module extracts the technical and economic indicator data for each pending project from the review nodes and compares this data with the average historical project data for each technical and economic indicator of the matched projects in the historical database to obtain the deviation of each technical and economic indicator. **Early Warning Trigger Module:** This module triggers an early warning when the deviation of the technical and economic indicators meets the dynamic early warning rules.

[0081] As an optional implementation, the indicator determination module, rule determination module, database construction module, data extraction module, pending review information determination module, matching module, comparison module, and early warning triggering module are split into independent microservices.

[0082] This application focuses on "adapting to microservice architecture, meeting the deployment requirements of intranet environments, and supporting the entire process of verifying technical and economic indicators for power transmission and transformation projects" as its core objectives. It outlines its strategic direction around the entire data processing chain, specifically including three core directions: (1) High-efficiency data processing technology: Focus on the structured parsing of multi-source cost documents (such as Tables 1 to 7 of the budget estimate), break through the bottleneck of unstructured data extraction, ensure the accuracy and integrity of data from source to storage, and adapt to offline data processing scenarios in intranet environment.

[0083] (2) Microservice technology integration direction: The functional modules such as data extraction, indicator calculation, comparison review, and early warning prompts are split into independent microservices to achieve low coupling and high cohesion between modules, which facilitates subsequent system expansion and maintenance and meets the elastic deployment requirements of microservice architecture.

[0084] (3) Secure and controllable technology direction: In response to the data security requirements of the intranet environment, research technologies such as data transmission encryption, hierarchical access control, and operation log traceability to ensure the storage security and compliant use of historical engineering data and engineering data to be reviewed, and to avoid data leakage or unauthorized access.

[0085] This application conducts in-depth research on engineering indicator verification based on historical data of power transmission and transformation projects, providing strong technical support for the cost and effectiveness supervision of power grid infrastructure projects, ensuring the scientific nature and effectiveness of cost management, and promoting the high-quality development of power grid infrastructure projects.

[0086] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for deepening the verification of data indicators in power transmission and transformation projects, characterized in that, include: Determine the technical and economic indicators for different types of power transmission and transformation projects; Determine the extraction rules for each technical and economic indicator data; Build a database and divide it into multiple tables according to the type of power transmission and transformation project; Based on the extraction rules, the historical engineering data of each technical and economic indicator are extracted and averaged. At the same time, engineering information is collected and stored in the tables of their respective power transmission and transformation engineering types to obtain a historical database. Identify the review milestones for power transmission and transformation projects, as well as the information on projects awaiting review within those milestones; The project information to be reviewed is matched with the project information in the historical database to obtain the average historical project data of each technical and economic indicator of the matched project in the historical database; Extract the technical and economic indicators of the project under review from the review nodes, and compare the technical and economic indicators of the project under review with the historical average of the technical and economic indicators of the matching projects in the historical database to obtain the deviation of each technical and economic indicator. An early warning is triggered when the deviation of technical and economic indicators meets the dynamic early warning rules.

2. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, The types of power transmission and transformation projects include: substation projects, overhead line projects, and cable line projects; The technical and cost indicators include: technical indicators and cost indicators.

3. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, The extraction rules for each technical and economic indicator data are one or more of the following: regular expression extraction, rule-based parsing engine, natural language processing extraction, and Bowei cost estimation software extraction.

4. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, Build a database and divide it into multiple tables according to the type of power transmission and transformation project, specifically including: Setting up a PostgreSQL relational database; Structured data is stored in a PostgreSQL relational database, while semi-structured data is stored using the JSONB type in the PostgreSQL relational database. Encapsulate PostgreSQL relational database operations into data access service micro-modules, and define data extraction microservices and comparison and review microservices to call the PostgreSQL relational database through an intranet RESTful interface; Enable SSL encrypted transmission for PostgreSQL relational databases; Multiple tables were created based on the pre-planned version and the type of power transmission and transformation project.

5. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, The historical engineering data for each technical and economic indicator were extracted and averaged, specifically including: By analyzing the contents of the budget estimate document in a structured manner, historical engineering data of various technical and economic indicators for each individual project are extracted. The historical engineering data is cleaned and standardized to obtain standardized historical engineering data; Calculate the average value of standardized historical engineering data for each technical and economic indicator.

6. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, The project information includes: project name, project type, project voltage level, project planning version, and project completion time.

7. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, Matching the project information to be reviewed with project information in the historical database, specifically including: A rule-based matching algorithm is used to select candidate projects that match the information of the project to be reviewed from the historical database according to hard rules such as the pre-plan version, the type of power transmission and transformation project, and the voltage level. A similarity-based matching algorithm is used to calculate the similarity between the project to be reviewed and the candidate projects. Candidate projects with a similarity greater than or equal to the similarity threshold are selected as matching projects.

8. The method for deepening the verification of data indicators in power transmission and transformation projects according to claim 1, characterized in that, An early warning is triggered when the deviation of technical and economic indicators meets the dynamic early warning rules, specifically including: When the number of historical engineering data entries for technical and economic indicators is greater than or equal to the quantity threshold, statistical analysis is performed on the historical engineering data of each technical and economic indicator for the matching projects in the historical database, and the upper and lower limits of the dynamic early warning threshold are calculated based on the 3σ principle. If the deviation of the technical and economic indicators is greater than the upper limit of the dynamic early warning threshold, or if the deviation of the technical and economic indicators is less than the lower limit of the dynamic early warning threshold, an early warning will be triggered. When the number of historical engineering data entries for technical and economic indicators is less than the quantity threshold, the deviation rate of the technical and economic indicators is determined based on the deviation of the technical and economic indicators. If the deviation rate of the technical and economic indicators exceeds the deviation rate threshold, an early warning will be triggered.

9. A data index verification and deepening system for power transmission and transformation projects, characterized in that, include: The indicator determination module is used to determine the technical and economic indicators for different types of power transmission and transformation projects; The rule determination module is used to determine the extraction rules for each technical and economic indicator data; The database construction module is used to build the database and divide the database into multiple tables according to the type of power transmission and transformation project; The data extraction module is used to extract and average the historical engineering data of each technical and economic indicator according to the extraction rules, and at the same time collect engineering information and store it in the tables of their respective power transmission and transformation engineering types to obtain a historical database. The pending review information determination module is used to determine the review nodes of power transmission and transformation projects, as well as the pending review information of the projects within the review nodes; The matching module is used to match the project information to be reviewed with the project information in the historical database to obtain the historical average of the technical and economic indicators of the matched projects in the historical database. The comparison module is used to extract the technical and economic indicators of the project under review in the review node, and compare the technical and economic indicators of the project under review with the historical average of the technical and economic indicators of the matching projects in the historical database to obtain the deviation of each technical and economic indicator. The early warning triggering module is used to trigger an early warning when the deviation of technical and economic indicators meets the dynamic early warning rules.

10. The data index verification and deepening system for power transmission and transformation projects according to claim 9, characterized in that, The modules for determining indicators, rules, database construction, data extraction, pending information determination, matching, comparison, and early warning triggering are separated into independent microservices.

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