Multi-dimensional index dynamic application optimization method, system and device for technical and economic data, medium and product

Abstract

The invention discloses a multi-dimensional index dynamic application optimization method, system and device for technical and economic data, a medium and a product, and relates to the technical and economic data processing field, the method comprises the following steps: extracting technical and economic data from a technical and economic integrated management system; matching and calculating the extracted technical and economic data to obtain a multi-dimensional index; respectively storing the extracted technical and economic data and the multi-dimensional indexes in a mixed database architecture; constructing a multi-dimensional dynamic billboard based on the technical and economic data and the multi-dimensional indexes stored in the hybrid database architecture; acquiring a role type of the user; and configuring the data authority based on the role types of the users, so that the users of different role types can only access the data dimensions and modules corresponding to the data authority in the multi-dimensional dynamic billboard. According to the technical-economic data query method and device, the technical-economic data query efficiency and accuracy in the technical-economic integrated management system can be improved, so that actual requirements are better met, and the overall efficiency of project management is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical data processing field, in particular to a multi-dimensional index dynamic application optimization method, system, device, medium and product of technical data. BACKGROUND

[0002] With the rapid development of information technology, a large number of researches have been carried out in the field of engineering management and data processing. The data volume of modern engineering projects is increasingly large and complex, and the traditional query method is not up to the task when faced with massive data. Some advanced engineering management systems known to the inventors have introduced multi-dimensional dynamic dashboards and intelligent query technology to significantly improve the efficiency and accuracy of data processing. Current researches have also gradually focused on this problem, exploring the optimization of data management and query processes through big data analysis, artificial intelligence and other technical means to adapt to changing management needs. These research results provide valuable experience and reference for the improvement of technical and economic comprehensive management systems.

[0003] With the increasing amount of data in the technical and economic comprehensive management system, querying these data becomes more and more complex and difficult, which is specifically manifested in: 1. Due to the large amount and variety of data, traditional query methods cannot meet the efficient and accurate needs, resulting in users spending more time and effort to find specific data, thereby reducing work efficiency.

[0004] 2. The requirements of project management units and economic research institutes at all levels for data management and application are also changing, and the existing system may not be able to update in time to adapt to these new needs, causing inconvenience to users.

[0005] 3. The increase in data query difficulty seriously affects the efficiency of engineering management, which may lead to decision-making errors and thus affect the overall progress and quality of the project. SUMMARY

[0006] To solve the above problems, the present application provides a multi-dimensional index dynamic application optimization method, system, device, medium and product of technical data.

[0007] To achieve the above purpose, the present application provides the following solutions: In a first aspect, the present application provides a multi-dimensional index dynamic application optimization method of technical data, comprising: extracting technical data from a technical and economic comprehensive management system; matching and calculating the extracted technical data to obtain multi-dimensional indexes; storing the extracted technical data and the multi-dimensional indexes in a hybrid database architecture, respectively; construct a multi-dimensional dynamic board based on the technical data and the multi-dimensional indexes stored in the hybrid database architecture; the multi-dimensional dynamic board comprises a homepage overview module, a technical index analysis library module, and a power grid infrastructure construction cost resource library module; the multi-dimensional dynamic board supports linkage filtering and analysis according to at least two dimensions of time, project, organization, voltage level, engineering type, and region; obtain a role type of a user; configure data permissions based on the role type of the user, so that users of different role types can only access data dimensions and modules corresponding to the data permissions in the multi-dimensional dynamic board.

[0008] In a second aspect, the present application provides a multi-dimensional index dynamic application optimization system for technical data, comprising: a data extraction module configured to extract technical data from a technical comprehensive management system; a data matching and calculation module configured to match and calculate the extracted technical data to obtain multi-dimensional indexes; a data storage module configured to store the extracted technical data and the multi-dimensional indexes in a hybrid database architecture, respectively; a dynamic board construction module configured to construct a multi-dimensional dynamic board based on the technical data and the multi-dimensional indexes stored in the hybrid database architecture; the multi-dimensional dynamic board comprises a homepage overview module, a technical index analysis library module, and a power grid infrastructure construction cost resource library module; the multi-dimensional dynamic board supports linkage filtering and analysis according to at least two dimensions of time, project, organization, voltage level, engineering type, and region; a role type obtaining module configured to obtain a role type of a user; an access configuration module configured to configure data permissions based on the role type of the user, so that users of different role types can only access data dimensions and modules corresponding to the data permissions in the multi-dimensional dynamic board.

[0009] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-dimensional index dynamic application optimization method for technical data provided above.

[0010] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the multi-dimensional index dynamic application optimization method for technical data provided above.

[0011] In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the technical data multi-dimension index dynamic application optimization method provided above.

[0012] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides a technical data multi-dimension index dynamic application optimization method, system, device, medium and product. By extracting technical data from a technical comprehensive management system and matching and calculating the extracted technical data, multi-dimension indexes are obtained, which can improve the query efficiency and accuracy of technical data in the technical comprehensive management system, and then facilitate the construction of a unified data dimension system and index caliber, and provide a consistent data base for multi-dimension dynamic dashboards. By storing the extracted technical data and multi-dimension indexes in a hybrid database architecture, the efficiency of engineering management can be improved. By storing the technical data and multi-dimension indexes based on the hybrid database architecture, a multi-dimension dynamic dashboard including a home page overview module, a technical index analysis library module and a power grid infrastructure cost resource library module is constructed to support linkage filtering and analysis according to at least two dimensions of time, project, organization, voltage level, engineering type and region, facilitate the display and access of different data dimensions and modules based on different user role types, meet the decision-making needs of different management levels, ensure data security and on-demand access, adapt to data extraction, processing and storage technical solutions, support efficient data query and analysis, and better meet actual needs and improve the overall efficiency of engineering management. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 A flowchart of a technical data multi-dimension index dynamic application optimization method provided by an embodiment of the present application is shown in the figure. Figure 2 A functional module diagram of a technical data multi-dimension index dynamic application optimization system provided by an embodiment of the present application is shown in the figure. Figure 3 A structural diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0015] With reference to the drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0016] In order to make the above objectives, characteristics and advantages of the present application more apparent, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0017] In one exemplary embodiment, the present application provides a method for dynamically applying and optimizing multi-dimensional indicators of technical data. The method is executed by a computer device, which can be a terminal or a server, or both. In the embodiments of the present application, the method is applied to a server. As shown in FIG. 1, the method comprises the following steps. Figure 1 Step 100: extracting technical data from a technical comprehensive management system.

[0018] Step 101: matching and calculating the extracted technical data to obtain multi-dimensional indicators.

[0019] Step 102: storing the extracted technical data and multi-dimensional indicators in a hybrid database architecture.

[0020] Step 103: constructing a multi-dimensional dynamic board based on the technical data and multi-dimensional indicators stored in the hybrid database architecture. The multi-dimensional dynamic board comprises a homepage overview module, a technical indicator analysis library module and a power grid infrastructure cost resource library module. The multi-dimensional dynamic board supports linkage filtering and analysis according to at least two dimensions of time, project, organization, voltage level, engineering type and region.

[0021] Step 104: obtaining the role type of a user.

[0022] Step 105: configuring data permissions based on the role type of the user, so that users of different role types can only access the data dimensions and modules corresponding to the data permissions in the multi-dimensional dynamic board.

[0023] By implementing the above steps 100-105, the present application can improve the data query efficiency of the technical comprehensive management system, better meet the actual needs of project management units at all levels and research institutes, and improve the overall efficiency of engineering management.

[0024] ​In an exemplary embodiment of the present application, in order to solve the problem in the prior art that due to the large amount and variety of data, the traditional query method cannot meet the efficient and accurate demand, resulting in the user needing to spend more time and effort in finding specific data, thereby reducing work efficiency, the implementation process of the above step 100 can be described as follows: from the technical and economic management system, a mixed data extraction mode is adopted, and batch ETL processing and real-time stream processing are synchronously performed to extract technical and economic data. The batch ETL processing is used for periodically synchronizing historical and incremental business data. The real-time stream processing is used for capturing real-time change data of key indicators in business data.

[0025] In actual application process, in order to obtain project basic information, engineering cost data and other requirements from cost analysis data and project management data, in the embodiment, the adaptation scene, data extraction efficiency, accuracy and maintenance cost of mainstream data extraction technologies (such as ETL tools, API interface calling, database direct connection and the like) are compared and analyzed; combined with the characteristics of data sources (such as multi-system heterogeneous data, real-time / quasi-real-time data requirements), the adaptive technical and economic data extraction technology is selected to ensure the accuracy and timeliness of project and engineering data extraction, and to avoid the distortion of board data caused by untimely or incorrect data extraction. Based on this, the advantages and disadvantages and applicable scenes of each extraction technology selection are described as follows: (1) Scheme A: Batch ETL (Kettle / Airflow).

[0026] 1) Scheme A description: technical and economic data is extracted from technical and economic management systems such as cost analysis system and project management platform by ETL tool regularly, and loaded to target database after cleaning and conversion.

[0027] 2) Advantages of scheme A include: mature and stable, suitable for periodic full / incremental synchronization; data cleaning rules are easy to maintain; support complex conversion logic, adapt to heterogeneous data sources.

[0028] 3) Disadvantages of scheme A include: high delay, unable to meet the requirement of minute-level real-time monitoring; fixed scheduling period, slow response to temporary data demand.

[0029] 4) Applicable scene of scheme A includes: batch migration of historical data; periodic update of power grid infrastructure whole process cost resource library; non-real-time report statistics.

[0030] (2) Scheme B: Real-time stream processing (Kafka + Flink).

[0031] 1) Scheme B description: real-time data is collected by Kafka, and stream calculation is performed by Flink to directly update the indicator result.

[0032] 2) Advantages of Solution B include: low latency, enabling second / minute-level metric updates; high throughput, supporting real-time processing of massive data; support for windowed computation, suitable for real-time trend analysis.

[0033] 3) Disadvantages of Solution B include: complex architecture, requiring more computing resources; high technical capability requirements for the team.

[0034] 4) Applicable scenarios for Solution B include: real-time refresh of key metrics on the homepage; abnormality early warning for technical indicators; high-frequency data updates for dynamic dashboards.

[0035] (3) Solution C: Hybrid data extraction mode (batch ETL + real-time stream).

[0036] 1) Solution C explanation: Core business data uses ETL batch synchronization, and key indicators use real-time stream processing.

[0037] 2) Advantages of Solution C include: balancing batch processing and real-time requirements; more reasonable resource allocation; higher system stability.

[0038] 3) Disadvantages of Solution C include: complex architecture, requiring unified data governance strategies; need to maintain two sets of data processing processes.

[0039] 4) Applicable scenarios for Solution C include: full business scenario coverage; both historical data analysis and real-time monitoring needs.

[0040] Based on the above description, the comparison results of the three extraction technology selection options, Solution A-Solution C, are shown in Table 1.

[0041] Table 1: Comparison of extraction technology selection

[0042] Based on Table 1, the hybrid data extraction mode is the most suitable for the research content of the present application, as it can ensure the complete loading of historical data and meet the real-time updating needs of key indicators, and is the best balance point in the current business scenario.

[0043] In an exemplary embodiment of the present application, in order to solve the problem that the requirements of project management units at various levels and research institutes (i.e., economic research institutes) for data management and application are changing constantly, the existing system fails to update in time to adapt to these new demands, causing inconvenience to users, a unified data dimension system and index caliber are constructed to provide consistent data basement for multi-dimensional dynamic dashboards. The implementation process of the above step 101 of the present application can be described as follows: mixed computing mode is used to match and calculate the extracted technical and economic data to obtain multi-dimensional indexes. Among them, for non-real-time indexes in technical and economic data, batch calculation is performed by configuring visual calculation rules of ETL tool built-in calculation components. For real-time indexes in technical and economic data, millisecond-level dynamic calculation is performed through a stream computing framework combined with a custom UDF function to ensure the unity of multi-dimensional technical and economic index caliber.

[0044] In actual application process, in the research of multi-dimensional indexes of technical and economic data, data matching and calculation are core links, which need to process business requirements such as cross-system data association (such as matching of project information and cost data), complex index calculation (such as unit capacity cost, cost deviation rate) and the like. Based on this, the embodiment analyzes three types of matching and calculation technical solutions from the perspective of business adaptability.

[0045] (1) Scheme A: SQL script calculation + associated query.

[0046] 1) Scheme A description: based on the SQL script of the database layer, the matching and calculation of technical and economic data are realized. By joining the project table, the cost detail table, the index parameter table and other multi-source data, using SQL aggregation functions (SUM, AVG, COUNT) and custom functions, the core index calculation such as unit cost and investment deviation rate is completed, and the data matching of key fields such as project number and voltage level is realized through WHERE condition and fuzzy matching (LIKE).

[0047] 2) Advantages of scheme A include: compatible with existing relational database architecture, no need to introduce new tools, reduce technical stack complexity, adapt to the basic requirement of "structured storage of business data" in the project; the calculation logic is intuitive, the script can be directly written based on the business table structure, which is convenient for developers to understand and debug, especially suitable for fixed dimension index calculation in "project management module" (such as average cost of a certain voltage level project in a year); support batch calculation, SQL script can be scheduled through timing task to meet the periodic data update and index recalculation requirements of "power grid infrastructure cost resource library".

[0048] 3) The disadvantages of solution A include: complex multi-dimensional calculation (such as cross-year, cross-region, multi-voltage level combined indicators) requires nested SQL, performance decreases significantly with data volume growth, making it difficult to support "dynamic dashboard real-time indicator refresh" scenarios; data matching relies on strict key field consistency (such as uniform project number format), if source system data has format differences (such as some systems have project number prefixes and some do not), additional cleaning scripts need to be written, and adaptability is weak; lack of visual calculation logic management, when technical and economic indicator formula is adjusted (such as cost deviation rate calculation scope update), SQL script needs to be modified one by one, maintenance cost is high.

[0049] 4) The applicable scenarios of solution A include: fixed dimension, low frequency update of technical and economic indicators, such as "annual power grid infrastructure project total investment statistics" and "quarterly unit cost average calculation of certain type of engineering"; data format specification, key field uniformity cross-table matching scenarios, such as "project information table and cost settlement table matching through unique project number association"; non-real-time historical data backtracking calculation, such as "5-year power grid infrastructure project cost trend review analysis".

[0050] (2) Solution B: ETL tool built-in calculation component (Kettle / Sqoop).

[0051] 1) Solution B explanation: rely on the visual components of ETL tools to complete technical and economic data matching and calculation. Use "data cleaning components" (such as field format conversion, null value filling) to handle source system data differences, use "association components" (such as MergeJoin, Lookup) to realize cross-system data matching (such as associating "engineering detail data" in the cost analysis system with "project attribute data" in the project management platform), and then use "calculation components" (such as formula calculator, aggregator) to configure indicator calculation logic (such as cost deviation rate = (actual cost - budget cost) / budget cost x 100%).

[0052] 2) The advantages of solution B include: visual configuration reduces technical threshold, business personnel can participate in calculation logic analysis, and adapts to the needs of multi-department cooperation in "technical and economic indicator library" (such as joint confirmation of indicator calculation caliber by research institute specialists and IT personnel); support complex data matching rules, handle source system data format differences (such as uniform project number prefix, voltage level name standardization) through conditional judgment components (such as IF-ELSE), reduce data preprocessing workload; calculation and data synchronization process integration, can complete indicator calculation simultaneously in data extraction, loading process, avoid data delay caused by flow in multiple systems, adapt to "power grid infrastructure cost resource library" incremental update scenarios.

[0053] 3) Disadvantages of Solution B include: insufficient real-time performance, ETL tools are mostly based on batch task scheduling, making it difficult to support "minute-level index updates for dynamic dashboard on the homepage" requirements such as "real-time display of current quarter power grid infrastructure project investment completion rate"; complex index calculations (such as multi-factor weighted scoring indicators) require nested multiple components, complex configuration logic, difficult maintenance in later stages, and lack of intuitive error positioning mechanism in the debugging process; weak support for unstructured data matching, if PDF format cost report data extraction and matching (such as extracting project number from the report and associating database data), additional OCR tools need to be integrated, compatibility is poor.

[0054] 4) Applicable scenarios of Solution B include: semi-real-time incremental data matching and calculation, such as "updating the latest cost data of power grid infrastructure projects daily and calculating the cumulative investment completion rate simultaneously"; cross-system matching scenarios with non-uniform data formats but configurable rules, such as "unifying the '220kV' voltage level in system A with the '220kV' in system B to the standard format for matching"; moderately complex technical and economic indicators calculation, such as "total project cost = construction cost + installation cost + equipment purchase cost + other costs" "unit capacity cost = total project cost / construction capacity".

[0055] (3) Solution C: Stream computing framework (Flink / SparkStreaming) + custom UDF.

[0056] 1) Solution C explanation: Real-time technical and economic data matching and calculation based on stream computing framework. Through "stream data access components", real-time incremental data of source systems (such as "real-time cost change data" of cost analysis system, "project status update data" of project management platform) is collected, historical data is cached using "state management mechanism" (such as caching project budget cost for real-time calculation of cost deviation rate), and complex index calculation logic (such as multi-dimensional weighted technical and economic indicator scoring model) is written through custom UDF (user-defined function), while supporting real-time data matching based on dynamic rules (such as dynamically adjusting matching field priority based on project type and region).

[0057] 2) The advantages of solution C include: low latency and high throughput, millisecond-level data matching and calculation can be achieved, which perfectly meets the demand of "real-time refresh of key indicators on the homepage", such as "real-time display of current power infrastructure project investment completion rate and cost deviation warning data"; support for dynamic calculation rules, which can be updated in real time through a rule engine (such as Drools) without restarting the service, adapting to scenarios where index rules in the "technical and economic index library" are frequently adjusted (such as policy changes leading to updates of cost deviation rate thresholds); compatible with multiple types of data matching, which can be combined with FlinkSQL to realize structured data correlation, and integrated with external API interfaces to realize unstructured data matching (such as calling OCR interfaces to extract cost report data and correlate database project information), adapting to complex business scenarios.

[0058] 3) The disadvantages of solution C include: high technical threshold, requiring a professional stream computing development team, and professional operation and maintenance support for framework deployment and cluster maintenance (such as resource scheduling and fault recovery), increasing project costs; state management consumes a lot of memory resources, if a large amount of historical data (such as nearly 3 years of power infrastructure project cost data) needs to be cached for real-time comparison and calculation, it is easy to cause cluster memory overflow, and additional distributed storage (such as HDFS) needs to be configured, increasing the complexity of the architecture; batch calculation efficiency is lower than that of ETL tools, for historical data full recalculation scenarios (such as "recalculating nearly 10 years of power infrastructure project unit cost indicators"), it needs to be converted to batch processing mode, and compatibility needs to be optimized.

[0059] 4) The applicable scenarios of solution C include: real-time technical and economic indicator calculation, such as "minute-level update of investment completion rate and cost deviation warning indicators on the homepage dynamic dashboard", "real-time monitoring of technical and economic indicator anomalies (such as sudden increase of cost deviation rate of a project above the threshold)"; dynamic adjustment of calculation rules, such as "updating the cost deviation rate calculation range according to policy documents and applying it to the indicator calculation in real time"; real-time matching of multiple heterogeneous data sources, such as "real-time correlation of "engineering progress data" from the power dispatching system and "cost payment data" from the cost system to calculate the matching degree of engineering progress and cost payment".

[0060] Based on the above description, the comparison results of the technology selection for matching and calculation are shown in Table 2.

[0061] Table 2 Comparison results of technology selection for matching and calculation

[0062] Based on the above description, the application combines the business characteristics of multi-dimensional index research with technical data, and uses a hybrid mode of "ETL for batch calculation and stream framework for real-time calculation" for matching and calculation. Among them, for batch data matching and calculation that is not real-time (such as historical data backtracking and daily incremental index updating), the built-in calculation component of the ETL tool is used to reduce collaboration costs and adapt to the periodic update needs of the "power grid infrastructure full-process cost resource library"; for index calculation with high real-time requirements (such as homepage dynamic dashboard and abnormal early warning), stream calculation framework + custom UDF is used to meet the millisecond-level response requirement and adapt to the "technical index library real-time monitoring" scenario. The two are connected through a unified data governance platform to ensure consistency of calculation rules (such as index formula and matching field), avoid analysis deviation caused by data coverage differences, and perfectly meet the integrated construction goal of "business + real-time analysis" of the project.

[0063] In an exemplary embodiment of the present application, considering that the present application involves storage of a large amount of project and engineering information and needs to support high-frequency data query and calculation, the storage capacity, query performance, concurrent processing capability, scalability and cost of different types of databases (such as relational databases MySQL, PostgreSQL, non-relational databases MongoDB, Redis, time series databases InfluxDB, etc.) are compared and analyzed; combined with data types (such as structured data, semi-structured data) and business needs (such as mass data storage, fast query response), the appropriate database type and building scheme (such as single-machine deployment, cluster deployment) are selected. Based on this, in this embodiment, the way of database building technology selection is: (1) Scheme A: Relational database (MySQL / PostgreSQL).

[0064] 1) Scheme A Explanation: Use a mature relational database to design table structures according to business modules, such as project information table, engineering cost data table, index calculation result table, etc.

[0065] 2) Advantages of Scheme A include: strong transaction consistency, ensuring the accuracy of cost data entry, update, and deletion; strong SQL query capability, supporting complex multi-table association, suitable for multi-dimensional statistical analysis of technical indexes; mature ecology, low development and maintenance cost, and high team acceptance.

[0066] 3) Disadvantages of Scheme A include: mass historical data query performance degradation, especially in multi-dimensional aggregation analysis; limited scalability in high-concurrency read-write scenarios.

[0067] 4) Applicable scenarios of Scheme A include: storage of structured business data such as project management and cost analysis; analysis scenarios mainly based on annual / quarterly reports; modules with complex business logic and high data consistency requirements.

[0068] (2) Solution B: Time series database (InfluxDB / TimescaleDB).

[0069] 1) Solution B explanation: For the time series characteristics of technical and economic indicators, store data such as cost trends and indicator changes in a time series database.

[0070] 2) Advantages of Solution B include: high read and write performance of time series data, supporting millions of writes per second; high compression rate, effectively reducing historical data storage costs; built-in time window aggregation functions suitable for trend analysis and real-time monitoring.

[0071] 3) Disadvantages of Solution B include: weak non-time dimension query capability; complex business relationship modeling is not as flexible as relational databases.

[0072] 4) Applicable scenarios of Solution B include: technical and economic indicator trend analysis (unit capacity cost, single kilometer cost, etc.); multi-time granularity (year / month / day) cost fluctuation monitoring; real-time display modules such as homepage dynamic investment trend line chart and early warning monitoring.

[0073] (3) Solution C: Hybrid architecture (PostgreSQL + TimescaleDB extension).

[0074] 1) Solution C explanation: Core business data is stored in PostgreSQL, and time series indicators are processed through TimescaleDB extension.

[0075] 2) Advantages of Solution C include: a database that meets both business transaction processing and time series analysis needs; avoids the complexity of data synchronization across databases; retains SQL flexibility, allowing complex indicator calculations directly at the database layer.

[0076] 3) Disadvantages of Solution C include: higher requirements for database configuration and maintenance; requires professional DBA support for performance tuning.

[0077] 4) Applicable scenarios of Solution C include: coexistence of homepage multi-dimensional statistics and trend analysis; technical and economic indicator library needs to associate business attributes and time series in the same query; hybrid query requirements for power grid infrastructure full-process cost resource library.

[0078] Based on the above database construction technology selection analysis, the comparison results of each technology are shown in Table 3.

[0079] Table 3 Comparison of database construction technology selection results

[0080] Based on the above description, the database architecture finally confirmed in this application is a hybrid architecture (PostgreSQL + TimescaleDB), which can both meet the transactional management needs of cost data and efficiently support time series analysis of technical and economic indicators, perfectly matching the project's integrated "business + analysis" construction goal. Specifically, the structured business data in the technical and economic data is stored in the relational database PostgreSQL. Multi-dimensional indicators are stored through the TimescaleDB extension module.

[0081] In an exemplary embodiment of this application, to accurately measure the technical and economic performance of a project, auxiliary technical and economic indicators are used as the data foundation for a multi-dimensional technical and economic data dashboard, ultimately forming a complete and systematic technical and economic data display system. This requires designing a multi-dimensional dynamic dashboard framework that covers multi-dimensional filtering and visualization, meeting the decision-making needs of different management levels, and establishing a role-based data access control mechanism to ensure data security and on-demand access. It also requires adaptable data extraction, processing, and storage technologies to support efficient data querying and analysis. Based on this, this embodiment takes the expected construction plan as an integration of business needs and technical solutions as an example. Based on the research results of the first two directions, the overall framework, core functions, and display scheme of the multi-dimensional dynamic dashboard in step 103 are clarified, forming a feasible construction blueprint. The specific content is as follows: 1. Sorting out the technical and economic data display system.

[0082] 1.1 Definition of data dimensions.

[0083] First, we organize the main dimensions that the multi-dimensional dynamic dashboard needs to display, including two modules: project management and cost analysis data. The core is the cost analysis data, which contains various cost data for individual projects under the project. This can be used as the basic cost data module, mainly collecting basic information such as project code, voltage level, construction nature, region, and individual project type, as well as detailed data on various major costs of individual projects, such as dynamic and static investment, construction costs, installation costs, equipment purchase costs, and other costs. Based on the cost analysis reports and cost analysis data tables from previous years, various technical and economic indicators, such as unit capacity cost and unit length cost, are used as the technical and economic indicator data module to support horizontal comparison and trend analysis of cost levels. Combining the above, we also need to organize a dashboard-like module to integrate some key data for real-time display of the overall project cost overview, core technical and economic indicator trends, and anomaly warning information, forming a decision support layer. This module will aggregate key indicators such as the number of projects and total investment scale, and support drill-down analysis by voltage level, region, time period, and other dimensions.

[0084] Based on the above analysis, we can summarize the three modules included in the data dimensions; (1) Homepage Overview Module.

[0085] It covers key indicators, number of projects, total investment scale and abnormal warning information, presenting the overall situation in intuitive charts; it supports multi-dimensional filtering and interactive linkage based on project type, completion time, regional companies and other dimensions, enabling data drill-down analysis. (2) Technical and economic indicators analysis module.

[0086] It covers core technical and economic indicators such as cost per unit capacity and cost per unit length, and uses line charts to show the historical trends and bar charts to compare differences between different regions or voltage levels; it supports filtering by multiple dimensions such as time period, construction type, and region. (3) Power grid infrastructure cost resource library module.

[0087] It covers detailed data on power grid infrastructure construction costs, including the dynamic and static investment, construction costs, installation costs, equipment purchase costs, and other expenses for each individual project. It supports querying and exporting by voltage level, region, and other criteria. After confirming the general content of each module, organize the display data involved in each module.

[0088] (1) The data and filtering dimensions that need to be displayed in the homepage overview module.

[0089] 1) Data dimensions.

[0090] It mainly covers core data indicators such as project investment, total project investment of each regional company (in RMB 100 million), key indicators (number of single projects exceeding the budget), number of projects uploaded by each regional company, substation project cost level (dynamic total investment cost), overhead line cost level (dynamic total investment cost), and cable line cost level (dynamic total investment cost).

[0091] 2) Filtering dimensions.

[0092] The user can filter various data on the homepage, mainly covering dimensions such as single project type, completion date selection, and regional company selection, and supports multi-condition linked filtering.

[0093] (2) Data and filtering dimensions required to be displayed in the technical and economic indicator analysis library module.

[0094] 1) Data dimensions.

[0095] The technical and economic indicator analysis database mainly covers indicators for three core engineering types: substation engineering, overhead lines, and cable lines. Substation engineering includes trend analysis of 17 key technical and economic indicators, such as unit capacity cost, cable cost, cable fire protection, cable auxiliary facilities, station grounding, special commissioning, main control and communication building, main transformer system, framework and foundation, cable trench, fire protection system, firewall, site leveling, foundation treatment, retaining walls and water retaining walls, external roads, and slope protection. Overhead line engineering includes trend analysis of 7 key technical and economic indicators, such as cost per kilometer, foundation engineering, pole and tower engineering, grounding engineering, stringing engineering, accessory engineering, and main body investment. Cable line engineering includes trend analysis of 4 key technical and economic indicators, such as cost per unit length, construction cost per unit length, equipment purchase cost per unit length, and installation cost per unit length.

[0096] 2) Filtering dimensions.

[0097] It covers filtering by year / last three years, month, and regional company, and also allows selection of individual regional company and completion year. Within each project type, it covers filtering by voltage level and indicators.

[0098] (3) Data and filtering dimensions required to be displayed in the power grid infrastructure cost resource library module.

[0099] 1) Data dimensions.

[0100] The data mainly consists of four major dimensions: project investment, cost data asset structure (number of projects), preliminary design budget investment amount (ten thousand yuan), and final settlement investment amount (ten thousand yuan). Among them, project investment includes statistics on the number of projects, the number of engineering works, and the number of cost documents; the cost data asset structure (number of projects) includes the number and percentage of engineering works at different voltage levels, the number of engineering works covered by different engineering types, and the number of engineering works covered by different regional companies; the preliminary design budget investment amount (ten thousand yuan) and the final settlement investment amount (ten thousand yuan) include trend statistics on voltage level, engineering type, and construction management unit.

[0101] 2) Filtering dimensions.

[0102] It covers filtering by regional company and completion date.

[0103] Based on the above description, the contents of the three modules—Homepage Overview, Technical and Economic Indicator Analysis Library, and Power Grid Infrastructure Cost Resource Library—are summarized in Table 4.

[0104] Table 4. Main contents of the Homepage Overview, Technical and Economic Indicator Analysis Library, and Power Grid Infrastructure Cost Resource Library modules

[0105] 1.2 Data source and algorithm confirmation.

[0106] Based on the above defined data dimensions, the data sources of each data dimension are confirmed, and the calculation formula. According to the actual system construction content, each data dimension is mainly from the project management, cost analysis data, project, engineering basic information, and project engineering cost analysis data. The calculation formula involved mainly includes the total or average level of each cost, so the following table 5 shows the content.

[0107] Table 5 Data source and algorithm

[0108] For example, the calculation method includes: Transformer engineering-earthwork cost (ten thousand yuan) index calculation formula =

external earthwork quantity (m3 / station External earthwork unit price (including transportation and consumption) (yuan / m 3 ) + purchased earthwork quantity (m3 / station Purchased earthwork unit price (including transportation and purchase) (yuan / m 3 )

[0109] Overhead line tower material cost (ten thousand yuan) index calculation formula = angle steel tower_tower material cost + steel pipe tower_tower material cost + steel pipe pole (base)_tower material cost.

[0110] Overhead line single kilometer cost (ten thousand yuan / km) index calculation formula = overhead line engineering completion final investment / total line length (fold single).

[0111] Cable line cable intermediate joint and terminal material cost (ten thousand yuan) index calculation formula = cable intermediate joint total price (ten thousand yuan) + cable terminal joint total price (ten thousand yuan).

[0112] 2. Visualization display method and multi-dimensional dynamic dashboard design.

[0113] Combined with the data display dimension, data source characteristics and user usage habits, the core needs of multi-dimensional dynamic dashboard in front-end design, back-end design, database design, chart design and instrument panel design are determined. According to the three modules of homepage, technical and economic index analysis library and power grid infrastructure cost resource library that have been confirmed, the display method of homepage is confirmed first.

[0114] 2.1 Home page.

[0115] Covering key indicators, project quantity, total investment scale and abnormal early warning information, as a dashboard, it involves the investment situation of multiple regional units, reporting situation, and the number of projects reported by each regional unit of the center can be integrated with Shanghai map to display the number of reported projects of each regional company. The left and right sides are mainly column charts, pie charts and line charts, and the main involved display methods are shown in Table 6.

[0116] Table 6 Main display methods involved in the home page

[0117] 2.2 Technical index analysis library.

[0118] Covering unit capacity cost, unit length cost and other core technical indexes, the trend of changes over the years is shown through line charts, and the differences between different regions or voltage levels are compared. The main involved display methods are shown in Table 7.

[0119] Table 7 Main display methods involved in the technical index analysis library

[0120] 2.3 Power grid infrastructure cost resource library.

[0121] Covering power grid infrastructure cost data details, including dynamic and static investment, construction engineering cost, installation engineering cost, equipment purchase cost and other cost composition of each single project, mainly column chart, line chart, pie chart + text, etc. The display method is shown in Table 8.

[0122] Table 8 Display method of power grid infrastructure cost resource library

[0123] 3. Data authority system construction.

[0124] According to the job responsibilities and work needs of different user groups, the user role types are divided; based on the role type, the data dimension range accessible by each role is determined, and the current actual system involves the role situation as shown in Table 9. Based on the actual identity information of the user, the role and corresponding permission system are established, and the roles of users of different levels can perform different operations. The computer authorizes the menu related functions, and different roles get the operation, viewing permission of the functions according to their permissions and authorization of the functions.

[0125] Table 9 Role type

[0126] Among them, the institute responsible role is the main management personnel, responsible for the audit and other operations of all regional company upload projects, and the management department leaders and management department specialists and institute leaders are mainly management roles, which are the main users of multi-dimensional dynamic dashboards. The institute specialists, institute leaders, management department specialists, and management department leaders can view all regional project data and all operation permissions. The project unit specialists are only involved in actual business and are not considered as users of the multi-dimensional dynamic dashboard. Only the management personnel are considered, and if needed in the future, the permission division will be considered.

[0127] Based on the above description, the method provided by the application realizes the visualization of technical data, selects technologies from the aspects of front-end design, back-end design, database design, chart design, and dashboard design, to support efficient data storage and query, ensure data interaction and response speed, and provide a variety of charts and dashboards to ensure intuitive display of technical data and user friendliness.

[0128] In addition, to meet the data query needs of different user groups, in-depth communication is carried out with relevant departments (such as city companies, research institutes, and district companies) to understand specific technical data query needs. Multi-dimensional technical data dynamic dashboard optimization design is carried out, covering multiple dimensions such as time, project, and organization, to ensure that users can flexibly view and analyze the required data. At the same time, the application includes user role division, function permission allocation, and permission management system to ensure that each user group can only access and operate the data within its authorized range, thereby providing safe and customized data display content to effectively support decision-making and management needs.

[0129] Based on the same inventive concept, the embodiments of the application also provide a multi-dimensional index dynamic application optimization system for technical data, which is used to realize the multi-dimensional index dynamic application optimization method of technical data as described above. The solution provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more multi-dimensional index dynamic application optimization system embodiments of technical data provided below can refer to the limitations of the multi-dimensional index dynamic application optimization method of technical data in the above text, and will not be repeated here.

[0130] In one exemplary embodiment, as shown in Figure 2 a multi-dimensional index dynamic application optimization system for technical data is provided, which includes a data extraction module, a data matching calculation module, a data storage module, a dynamic dashboard construction module, a role type acquisition module, and an access configuration module.

[0131] The data extraction module is configured to extract technical data from the technical management system. The data matching and calculation module is configured to match and calculate the extracted technical data to obtain multi-dimensional indexes. The data storage module is configured to store the extracted technical data and the multi-dimensional indexes in a hybrid database architecture. The dynamic dashboard construction module is configured to construct a multi-dimensional dynamic dashboard based on the technical data and the multi-dimensional indexes stored in the hybrid database architecture. The multi-dimensional dynamic dashboard includes a homepage overview module, a technical index analysis library module and a power grid infrastructure cost resource library module. The multi-dimensional dynamic dashboard supports linkage filtering and analysis according to at least two dimensions of time, project, organization, voltage level, engineering type and region. The role type acquisition module is configured to acquire a role type of a user. The access configuration module is configured to configure data permissions based on the role type of the user, so that users of different role types can only access data dimensions and modules corresponding to the data permissions in the multi-dimensional dynamic dashboard.

[0132] In an exemplary embodiment, a computer device, which can be a server or a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 3 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store multi-dimensional index dynamic application optimization data of technical data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a multi-dimensional index dynamic application optimization method for technical data.

[0133] Those skilled in the art can understand that Figure 3 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the diagram, or combine certain components, or have a different arrangement of components.

[0134] In an exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0135] In an exemplary embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, implements the steps of any of the above method embodiments.

[0136] In an exemplary embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any of the above method embodiments.

[0137] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0138] It can be understood by those skilled in the art that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (Resistive Random Access Memory, RRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0139] The database involved in each of the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each of the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0140] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as there is no contradiction.

[0141] The principles and implementation manners of the present application are described by using specific examples herein, and the above embodiments are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A method for dynamic application and optimization of multi-dimensional indicators of technical and economic data, characterized in that, include: Extract technical and economic data from the integrated technical and economic management system; The extracted technical and economic data are matched and calculated to obtain multi-dimensional indicators; The extracted technical and economic data and the multi-dimensional indicators are stored in a hybrid database architecture respectively; Based on the technical and economic data and the multi-dimensional indicators stored in the hybrid database architecture, a multi-dimensional dynamic dashboard is constructed. The multi-dimensional dynamic dashboard includes a homepage overview module, a technical and economic indicator analysis library module, and a power grid infrastructure cost resource library module. The multi-dimensional dynamic dashboard supports linked filtering and analysis based on at least two dimensions, including time, project, organization, voltage level, project type, and region. Get the user's role type; Configure data permissions based on the user's role type, so that users of different role types can only access the data dimensions and modules in the multi-dimensional dynamic dashboard that correspond to the data permissions.

2. The method for dynamic application optimization of multi-dimensional indicators of technical and economic data according to claim 1, characterized in that, Extract technical and economic data from the integrated technical and economic management system, including: The technical and economic data is extracted from the integrated technical and economic management system using a hybrid data extraction mode, which simultaneously performs batch ETL processing and real-time stream processing. The batch ETL processing is used to periodically synchronize historical and incremental business data, while the real-time stream processing is used to capture real-time changes in key indicators within the business data.

3. The method for dynamic application optimization of multi-dimensional indicators of technical and economic data according to claim 1, characterized in that, The extracted technical and economic data are matched and calculated to obtain multi-dimensional indicators, including: A hybrid computing model is used to match and calculate the extracted technical and economic data to obtain multi-dimensional indicators. For non-real-time indicators in the technical and economic data, batch calculation is performed by configuring visual calculation rules through the built-in calculation component of the ETL tool. For real-time indicators in the technical and economic data, millisecond-level dynamic calculation is performed by combining a stream computing framework with a custom UDF function to ensure the consistency of the multi-dimensional technical and economic indicators.

4. The method for dynamic application optimization of multi-dimensional indicators of technical and economic data according to claim 1, characterized in that, The hybrid database architecture includes the relational database PostgreSQL and the TimescaleDB extension module; The structured business data in the technical and economic data is stored in the relational database PostgreSQL; the multi-dimensional indicators are stored through the TimescaleDB extension module.

5. The method for dynamic application optimization of multi-dimensional indicators of technical and economic data according to claim 1, characterized in that, The homepage overview module includes key indicators, number of projects, total investment scale, and abnormal warning information; The technical and economic indicators analysis module covers the cost per unit capacity and the cost per unit length; the content of the technical and economic indicators analysis module is displayed in the form of line charts and bar charts. The power grid infrastructure cost resource library module includes detailed power grid infrastructure cost data; the display methods of the content in the power grid infrastructure cost resource library module include bar charts, line charts, and pie charts with text; the power grid infrastructure cost resource library module supports conditional queries and export of content including voltage level and region.

6. The method for dynamic application optimization of multi-dimensional indicators of technical and economic data according to claim 5, characterized in that, The data dimensions displayed in the homepage overview module include engineering investment, total engineering investment of each regional company, key indicators, number of projects uploaded by each regional company, cost level of substation projects, cost level of overhead lines, and cost level of cable lines. The homepage overview module's filtering dimensions include selection of individual project type, completion date, and regional companies, supporting multi-dimensional linked filtering. The data dimensions displayed in the technical and economic index analysis library module include: substation projects, overhead lines, and cable lines; The filtering dimensions of the technical and economic indicator analysis library module include year, month, regional company, voltage level and indicator, and support multi-dimensional linkage filtering; The data dimensions of the power grid infrastructure construction cost resource library module include: project investment status, cost data asset structure, preliminary design budget investment amount, and final settlement investment amount. The filtering dimensions of the power grid infrastructure cost resource database module include regional company and completion date.

7. A multi-dimensional indicator dynamic application optimization system for technical and economic data, characterized in that, include: The data extraction module is used to extract technical and economic data from the integrated technical and economic management system. The data matching and calculation module is used to match and calculate the extracted technical and economic data to obtain multi-dimensional indicators. The data storage module is used to store the extracted technical and economic data and the multi-dimensional indicators in a hybrid database architecture respectively; The dynamic dashboard construction module is used to construct a multi-dimensional dynamic dashboard based on the technical and economic data and multi-dimensional indicators stored in the hybrid database architecture. The multi-dimensional dynamic dashboard includes a homepage overview module, a technical and economic indicator analysis library module, and a power grid infrastructure cost resource library module. The multi-dimensional dynamic dashboard supports linked filtering and analysis based on at least two dimensions, including time, project, organization, voltage level, project type, and region. The role type acquisition module is used to obtain the user's role type; The access configuration module is used to configure data permissions based on the user's role type, so that users of different role types can only access the data dimensions and modules in the multi-dimensional dynamic dashboard that correspond to the data permissions.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the method for dynamic application optimization of multi-dimensional indicators of technical and economic data as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for dynamic application optimization of multi-dimensional indicators of technical data as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for dynamic application optimization of multi-dimensional indicators of technical data as described in any one of claims 1-6.

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