A multi-dimensional data-based economic calculation system for the whole life cycle of rail transit

Abstract

The application discloses a kind of track traffic full life cycle economy estimation system based on multidimensional data, it is related to track traffic engineering economic estimation technical field, the system includes following component parts: data acquisition module, time sequence linkage mapping module, substage estimation model module, feedback correction module and dynamic output module;The application is through covering track traffic full life cycle, establishes effective association, to substage estimation model accurately calculates each stage economic cost, provides solid data support for economic analysis;Dynamic feedback correction mechanism is built, real-time response deviation changes, improve estimation accuracy;Whole-process closed-loop optimization design, solve prior art defects, adapt to project dynamic change, provide timely reliable reference for project full cycle economic management.

Description

Technical Field

[0001] This invention relates to the field of economic calculation technology for rail transit engineering, specifically to an economic calculation system for the entire life cycle of rail transit based on multi-dimensional data. Background Technology

[0002] As a core infrastructure of urban integrated transportation systems, rail transit is characterized by large investment scale, long construction period, complex operation and maintenance, and a full life cycle covering multiple key aspects such as planning and design, engineering construction, operation and maintenance, upgrading and renovation, and decommissioning. Its full life cycle economic calculation is crucial to the scientific nature of project investment decisions, the effectiveness of cost control, and the rationality of resource allocation. With the advancement of digital transformation in the rail transit industry, massive amounts of multi-dimensional data are generated at each stage. However, how to integrate this data to achieve accurate economic calculation has become an urgent problem for the industry, placing higher demands on the comprehensiveness, interconnectivity, and dynamism of the calculation system.

[0003] Existing economic calculation technologies for rail transit have many shortcomings. Most calculation schemes focus only on a single link or a portion of the process, lacking full coverage of all stages throughout the entire lifecycle and failing to reflect the complete trajectory of project economic costs. Data collection standards are inconsistent across stages, resulting in isolated data lacking effective correlation. The temporal linkage between preceding and subsequent stages is not established, ignoring the impact of calculation results from preceding stages on later stages, leading to fragmented calculation logic. Furthermore, existing calculation models lack detailed, tiered calculation mechanisms, with insufficiently targeted indicator settings and computational logic, and lack dynamic feedback and correction mechanisms. This makes it impossible to adjust calculation parameters and model coefficients in real time, hindering the correction of cross-stage deviations and resulting in low accuracy of calculation results. In addition, some systems' output calculation reports lack temporal fusion and incremental optimization capabilities, exhibiting inconsistent data connections and failing to meet the needs of dynamic management throughout the project's entire lifecycle.

[0004] In summary, existing economic calculation technologies for rail transit suffer from problems such as incomplete coverage, insufficient data linkage, low accuracy of calculation models, and a lack of dynamic correction and optimization mechanisms. This leads to significant discrepancies between the calculated results and actual conditions, failing to provide reliable support for investment decisions, cost control, and operation management throughout the entire lifecycle of rail transit projects. Therefore, there is an urgent need to construct a full lifecycle economic calculation system that can integrate multi-dimensional data, establish temporal linkages, achieve tiered accurate calculations, and dynamically correct and optimize. This system would fill the existing technological gaps and improve the scientific rigor and effectiveness of economic management for rail transit projects. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a rail transit full life cycle economic calculation system based on multi-dimensional data. It can accurately calculate the economic costs of each stage by standardizing the processing of data from multiple stages, establishing data correlations, and constructing a dynamic feedback mechanism to correct calculation deviations in real time and improve accuracy. The system's closed-loop optimization solves the problems of fragmented calculations and poor data connection in traditional methods, providing strong support for the full life cycle economic management of rail transit projects.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a rail transit full life cycle economic calculation system based on multi-dimensional data, the system comprising the following components: a data acquisition module, a time-series linkage mapping module, a tiered calculation model module, a feedback correction module, and a dynamic output module; The data acquisition module collects data from all stages of the rail transit lifecycle, including planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning, and processes the collected data to generate standardized data. The time-series linkage mapping module: receives standardized data, adds a unique time-series identifier to each data item, including a stage code, timestamp, and business identifier; establishes a mapping relationship between the calculation output data of the preceding stage and the calculation input data of the following stage according to the business connection logic between each stage, generates time-series linkage coefficients; and integrates the standardized data and time-series linkage coefficients into a parameter package. The tiered calculation model module includes built-in calculation sub-models for planning and design, engineering construction, operation and maintenance, renovation and upgrading, and scrapping and disposal. Based on the integrated parameter package, it uses a time-series linkage weighted calculation algorithm to perform economic calculations for each stage and generate calculation values ​​for each stage. The feedback correction module receives the measured values ​​from each stage, performs deviation analysis using a cross-order deviation dynamic correction algorithm, generates correction parameters, and adjusts the coefficients of the measurement sub-model and the judgment criteria for the mapping relationship based on the correction parameters. The dynamic output module receives the calculated values, correction parameters, and deviation analysis details from each stage, integrates them to generate a full life cycle economic calculation report, and uses adaptive time-series fusion and incremental optimization technology to iteratively optimize the full life cycle economic calculation report, and feeds the optimized full life cycle economic calculation report back to the data acquisition module.

[0007] Furthermore, the process of collecting data from each stage of the entire life cycle of rail transit—planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning—in the data acquisition module is as follows: For each stage of planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning, business correlation, economic cost, basic attributes, and time series data are collected respectively; for the planning and design stage, feasibility study report, cost estimate, and planning indicator data are collected; for the engineering construction stage, bidding, construction, materials and equipment, and construction schedule data are collected; for the operation and maintenance stage, passenger flow, energy consumption, equipment maintenance, and labor cost data are collected; for the renovation and upgrading stage, equipment aging, renovation cost, and renovation period data are collected; and for the decommissioning stage, asset residual value, disposal cost, and environmental treatment data are collected; the collected data is preprocessed to remove duplicate data, fill in missing data, and correct abnormal data; the preprocessed data is converted into a unified storage and transmission format; and the data in the unified storage and transmission format is standardized by unifying field definitions, measurement units, and codes to generate standardized data.

[0008] Furthermore, the process of establishing the mapping relationship between the calculation output data of the preceding stage and the calculation input data of the following stage in the time-series linkage mapping module, and generating the time-series linkage coefficient, is as follows: The business connection logic of each stage is sorted out, the preceding and following correspondences of each stage (planning and design, engineering construction, operation and maintenance, renovation and upgrading, and disposal) are clarified, and the business sequence of each stage is defined; the calculation output data of the preceding stage and the calculation input data of the following stage are extracted, and the data association dimension between the two is matched; a mapping relationship between the preceding output and the following input data is established based on the data association dimension; and the time-series linkage coefficient is generated by combining the tightness of the mapping relationship, the degree of influence of the preceding data on the following calculation, and the time-series linkage.

[0009] Furthermore, the calculation sub-models for each stage of the hierarchical calculation model module, including planning and design, engineering construction, operation and maintenance, renovation and upgrading, and disposal, all include a data access layer, a calculation index layer, and a calculation logic layer. The data access layer is used to adapt to the data access specifications of the parameter package and uniformly receive and parse the data in the parameter package. The calculation index layer configures the economic calculation indicators for each stage, providing indicator basis for the economic calculation of each stage. The calculation logic layer sets the calculation operation logic for each stage and performs economic calculation operations for each stage based on the accessed data and calculation indicators.

[0010] Furthermore, the calculation formula for the time-series linkage weighted calculation algorithm in the graded calculation model module is as follows: ,in, The calculated value is for one of the following stages: planning and design, engineering construction, operation and maintenance, renovation and upgrading, and disposal. For the first Each step is based on its own calculated access parameters; This serves as the basis for calculating the weighting coefficients in this step; For the first The number of preceding steps that have an impact on each step; For the first The calculated values ​​of each preceding step; No. The first pre-processing step is for the first The time-series linkage coefficient of each link; by integrating the basic calculation results of this link with the calculation results of the preceding links, the cross-link time-series linkage of economic calculation of each link is realized, taking into account the basic role of the data of this link in the calculation results and the correlation influence of the preceding links on the calculation results, so that the economic calculation results of each link are more in line with the actual business connection logic of the entire life cycle of rail transit, and improve the accuracy and rationality of the calculation results.

[0011] Furthermore, the calculation formula for the cross-order deviation dynamic correction algorithm in the feedback correction module is as follows: ,in, For the first Corrected parameters generated in the next iteration; These are dynamic weighting coefficients; For the first The cross-order deviation value of the next iteration; For the first The correction parameters for each iteration are dynamically calculated by integrating cross-order deviations and historical correction trends to generate correction parameters. This enables reverse adaptation and adjustment of the coefficients and mapping relationship judgment criteria of the measurement sub-model, quantitatively correcting the measurement deviations at each stage, and ensuring the accuracy and adaptability of the economic measurement results for the entire life cycle of rail transit.

[0012] Furthermore, the specific steps in the feedback correction module for adjusting the coefficients of the measurement sub-model and the judgment criteria for the mapping relationship based on the correction parameters are as follows: Analyze the correction parameters to clarify the adjustment direction and magnitude of the measurement sub-model coefficients, as well as the adjustment dimensions of the mapping relationship judgment criteria; correct the measurement coefficients of the measurement sub-model at each stage according to the adjustment direction and magnitude; correct various indicators of the mapping relationship judgment criteria based on the corrected measurement coefficients, wherein the mapping relationship judgment criteria include data association dimension matching degree, business connection logic fit degree, and business ownership consistency of front-end and back-end data; and store the corrected measurement sub-model coefficients and mapping relationship judgment criteria.

[0013] Furthermore, the process of integrating and generating a full life-cycle economic calculation report in the dynamic output module is as follows: The module connects to the tiered calculation model module and the feedback correction module to receive the calculated values, correction parameters, and deviation analysis details for each stage, and performs integrity verification on the received data. For the verified data, it is categorized and organized according to the dimensions and data types of each stage of the life-cycle, forming calculation result datasets, correction parameter datasets, and deviation analysis datasets for each stage. The categorized datasets are then filled into the corresponding chapters of the report: the calculation result datasets are filled into the corresponding stage calculation chapters, and the correction parameter datasets and deviation analysis datasets are filled into the corresponding explanatory chapters. The data from each chapter is integrated and organized, and full life-cycle economic indicators are extracted to generate a complete full life-cycle economic calculation report.

[0014] Furthermore, the specific steps for iteratively optimizing the full life cycle economic calculation report using adaptive time-series fusion and incremental optimization technology in the dynamic output module are as follows: First, obtain the initial full life cycle economic calculation report, historical optimization records, and time-series data correlations. Second, align the data according to the time-series logic of each stage of the full life cycle using adaptive time-series fusion and incremental optimization technology, dynamically adapt the data correlations, and fuse the corrected data with the original calculation results. Third, perform local updates and iterative optimizations on the parts of the full life cycle economic calculation report that have time-series deviations or inconsistent data connections. Finally, synchronously transmit the optimized full life cycle economic calculation report to the data acquisition module.

[0015] Compared with existing technologies, this rail transit full life cycle economic calculation system based on multi-dimensional data has the following advantages: I. This system covers all key stages of the entire lifecycle of rail transit, integrates multi-dimensional data, and performs standardized processing to break down data silos between stages, ensuring data uniformity and usability. Based on a time-series linkage mapping mechanism, it clarifies the pre- and post-stage correspondences of each stage, establishes effective correlations between data, and quantifies the degree of impact, ensuring the calculation process fully reflects business connection logic. Coupled with a tiered calculation model, it configures dedicated calculation indicators and operational logic for different stages, and combines a time-series linkage weighted calculation method, integrating the basic data of each stage with the influencing factors of pre-stage stages, to achieve accurate calculation of economic costs at each stage. It ensures the comprehensiveness of the calculation, strengthens the correlation between stages, effectively solves the problems of fragmented traditional calculations and poor data connectivity, and provides data support for economic analysis at each stage of the project.

[0016] Second, this system constructs a dynamic feedback correction mechanism, generates correction parameters based on cross-order deviation analysis, and optimizes the coefficients and mapping relationship judgment criteria of the calculation sub-model in reverse, enabling the calculation system to respond to deviation changes in real time and improve calculation accuracy. At the same time, the dynamic output module integrates the calculation results, correction parameters, and deviation details to form a complete calculation report. Through adaptive time-series fusion and incremental optimization technology, it aligns the data time-series logic and corrects connection deviations, achieving iterative improvement of the report. The closed-loop optimization design of the entire process solves the defects of existing technologies, such as lack of dynamic adjustment capabilities and lagging calculation results. By optimizing the data collection and calculation links through feedback, the calculation system adapts to the dynamic changes of the entire project life cycle, providing timely and reliable economic references for project investment decisions, cost control, and operation management, and helping to improve the economic management level of the entire life cycle of rail transit projects.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A block diagram of the modules of a rail transit life-cycle economic calculation system based on multi-dimensional data; Figure 2 A flowchart of a rail transit life-cycle economic calculation system based on multi-dimensional data; Figure 3 This is a data transmission diagram of a rail transit life-cycle economic calculation system based on multi-dimensional data. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1 The data acquisition module comprehensively collects data for all stages of new rail transit lines, including planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning. It collects business-related data, economic cost data, basic attribute data, and time-series data for each stage. Business-related data includes information on collaborating units and approval records for each stage. Economic cost data includes expenses such as preliminary research fees, scheme design fees, building material procurement costs, construction labor costs, equipment maintenance costs, personnel salaries, renovation material costs, dismantling costs, and environmental treatment costs. Basic attribute data involves line design standards, equipment model parameters, and construction process requirements. Time-series data records the start time and completion time of key nodes for each stage. The collected raw data undergoes rigorous preprocessing, meticulously removing redundant data and accurately supplementing missing key information, such as maintenance cycle data for some equipment and detailed labor costs for some construction stages. Abnormal data exceeding reasonable limits is rigorously corrected, such as building material procurement quotations that significantly deviate from market prices and time-series records that do not conform to conventional construction cycles. The preprocessed data is converted into a unified storage and transmission format to eliminate format differences between different data sources. Then, through standardized processing of unified field definitions, units of measurement and coding, all data are kept consistent in terms of field meaning, calculation units and coding rules, thus generating standardized data.

[0022] The time-series linkage mapping module receives standardized data output from the data acquisition module and adds a unique time-series identifier to each standardized data piece, including a stage code, a timestamp, and a business identifier. The stage code clearly identifies the entire lifecycle stage to which the data belongs, the timestamp records the specific time the data was generated or collected, and the business identifier distinguishes the specific business type corresponding to the data. This identifier enables full traceability of the data, facilitating subsequent verification of the data source and flow path. The module also streamlines the business connection logic of each stage, clearly defining the business sequence as follows: planning and design as a prerequisite for engineering construction, engineering construction as a prerequisite for operation and maintenance, operation and maintenance as a prerequisite for renovation and upgrading, and renovation and upgrading as a prerequisite for disposal. Finally, the module extracts the calculated output data of each prerequisite stage and the calculated input data of the corresponding subsequent stages, accurately matching the data correlation dimensions between them. For example, data on building material usage in the engineering construction stage is compared with equipment loss rate data in the operation and maintenance stage, and data on equipment aging in the operation and maintenance stage is compared with the renovation scope data in the renovation and upgrading stage. A mapping relationship is established between the preceding output and the following input data based on data correlation dimensions. This ensures that data from the preceding stage accurately impacts the calculations in the following stage. By combining the tightness of the mapping relationship, the degree of influence of the preceding data on the following calculations, and the time sequence connection, a time-series linkage coefficient is generated for each stage, quantifying the influence of the preceding stage on the following stage. Finally, the standardized data and time-series linkage coefficients are integrated into a parameter package. This eliminates the need for additional data correlation processing in the tiered calculation model module, allowing it to directly call complete and clearly correlated data for calculations. This significantly improves calculation efficiency while ensuring the continuity and accuracy of data connection between each stage, avoiding calculation deviations caused by data disconnect between the preceding and following stages.

[0023] The tiered calculation model module calls upon built-in calculation sub-models for each stage of planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning. Each calculation sub-model includes a data access layer, a calculation index layer, and a calculation logic layer. These three layers have clear division of labor and work collaboratively. The data access layer adapts to the data access specifications of the parameter package, uniformly receiving and parsing standardized data and time-series linkage coefficients in the parameter package. This ensures that data of different types and sources can be efficiently identified and processed, avoiding calculation interruptions or data omissions due to data access incompatibility, and opening up data input channels for subsequent calculation work. The calculation indicator layer configures economic calculation indicators for each stage. The planning and design stage includes indicators such as preliminary research fees, scheme design fees, and feasibility study fees. The engineering construction stage includes indicators such as building material purchase costs, construction labor costs, machinery rental fees, and supervision fees. The operation and maintenance stage involves indicators such as equipment maintenance costs, personnel salaries, energy consumption costs, and site usage fees. The renovation and upgrading stage includes indicators such as renovation material costs, construction costs, equipment replacement costs, and technical consulting fees. The scrapping and disposal stage includes indicators such as dismantling costs, environmental treatment costs, and waste transportation costs. Clear calculation indicators provide a clear calculation basis for the economic calculation of each stage. The calculation logic layer sets the calculation and operation logic for each stage. Combining the accessed data and calculation indicators, it uses a time-series linked weighted calculation algorithm to carry out the economic calculation of each stage. The formula is: ,in, This is the calculated value for the j-th stage; For the first Each step is based on its own calculated access parameters; This serves as the basis for calculating the weighting coefficients in this step; For the first The number of preceding steps that have an impact on each step; For the first The calculated values ​​of each preceding step; No. The first pre-processing step is for the first The algorithm calculates the time-series linkage coefficients for each stage, taking into account both the basic access parameters of each stage and the calculation results of the preceding stages. This ensures that the core role of the data in each stage is guaranteed, while also taking into full account the impact of the preceding stages on the current stage. This allows the calculation results to comprehensively reflect the economic status of each stage throughout the entire lifecycle, avoiding the bias of calculation results caused by relying solely on one's own data or ignoring the impact of the preceding stages. The generated calculation values ​​for each stage are more in line with actual business scenarios.

[0024] The feedback correction module receives the calculated values ​​from each stage from the tiered calculation model module. It then uses a cross-stage deviation dynamic correction algorithm to perform a comprehensive deviation analysis on the calculated values ​​of each stage, comparing the rationality of the connection between calculated data from different stages. This includes assessing the matching degree between calculated values ​​from the engineering construction stage and the planning and design stage, as well as the smoothness of the connection between calculated values ​​from the operation and maintenance stage and the engineering construction stage. The module accurately identifies cross-stage deviations, clarifies the stage and extent of the deviation, and generates targeted correction parameters. The formula is as follows: ,in, For the first Corrected parameters generated in the next iteration; These are dynamic weighting coefficients; For the first The cross-order deviation value of the next iteration; For the first The next iteration refines the parameters. Analyzing these parameters clarifies the adjustment direction and magnitude of the coefficients for each stage's calculation sub-model, as well as the adjustment dimensions of the mapping relationship judgment criteria, ensuring that adjustments are based on clear evidence. Based on the adjustment direction and magnitude, the calculation coefficients of each stage's calculation sub-model are refined, making the calculation logic of the sub-model more closely aligned with actual business data patterns. Then, based on the refined calculation sub-model coefficients, the various indicators of the mapping relationship judgment criteria are revised, optimizing the association rules between preceding and subsequent stage data, making the mapping relationship more accurate. Finally, the refined calculation sub-model coefficients and mapping relationship judgment criteria are stored, providing an optimized model foundation for subsequent calculations, preventing similar deviations from recurring, improving the adaptability of the calculation model and the accuracy of the calculation results, ensuring that subsequent full lifecycle economic calculations continuously approach actual conditions, and reducing the impact of accumulated deviations on the overall calculation results.

[0025] The dynamic output module interfaces with the tiered calculation model module and the feedback correction module, receiving calculation values, correction parameters, and deviation analysis details from each stage. It rigorously verifies the completeness of the received data, checking for missing data, anomalies, logical contradictions, and other issues to ensure that the data entering the report generation stage is comprehensive, accurate, and flawless, guaranteeing the reliability of the full life-cycle economic calculation report from the outset. The verified data is systematically categorized and organized according to the dimensions and data types of each stage of the life-cycle, forming calculation result datasets, correction parameter datasets, and deviation analysis datasets for each stage. The categorized datasets have a clear structure and logical order, facilitating subsequent report filling and integration. The categorized datasets are then used to populate the corresponding chapters of the report. The data from each chapter is integrated and organized to extract core economic indicators such as total economic cost throughout the life-cycle, economic share of each stage, peak costs of key stages, and cost change trends, generating a complete full life-cycle economic calculation report. This report is comprehensive, data-rich, and highlights key points, providing clear reference for investment decisions, resource allocation, and schedule planning for new routes. The system acquires the initial full life-cycle economic calculation report, historical optimization records, and time-series data correlations. Through adaptive time-series fusion and incremental optimization techniques, it aligns data according to the time-series logic of each stage of the life-cycle, dynamically adapts data correlations, and merges the corrected data with the original calculation results. It then performs local updates and iterative optimizations on sections of the report with time-series deviations and inconsistencies in data continuity, resulting in a more coherent and accurate report. The optimized full life-cycle economic calculation report is then fed back to the data acquisition module. Figure 1 As shown, this provides a clear direction for subsequent data collection, guiding the data collection module to focus on relevant data types with deviations in previous calculations, improving the targeting and accuracy of data collection, forming a closed-loop mechanism of data collection-calculation-correction-optimization-re-collection, continuously improving the overall quality of the full life cycle economic calculation, and enabling the calculation report to provide more reliable and guiding support for various decisions throughout the entire life cycle of new rail transit lines.

[0026] Example 2 The data acquisition module collects data throughout the entire lifecycle of existing rail transit lines, comprehensively covering the historical planning and design phases, the historical engineering construction phases, the current operation and maintenance phases, the current upgrading and renovation phases, and the estimated decommissioning and disposal phases, ensuring that the data covers the entire lifecycle without any blind spots. The system collects data on the following aspects: historical planning and design data (including past route planning schemes, historical preliminary survey economic costs, original basic attributes of the route, and historical planning timeline data); construction data (including past construction contracts, historical building material procurement and labor costs, original basic attributes of the project, and historical construction progress timeline data) to help reconstruct the economic input and implementation during the route construction phase; operation and maintenance data (including current daily operation scheduling, recent equipment maintenance and personnel compensation costs, current basic attributes of operating equipment, and recent operation timeline data) to accurately present the current operational status and economic consumption of the route; upgrade and renovation data (including proposed equipment upgrade plans, estimated upgrade materials and construction costs, current basic attributes of equipment to be upgraded, and planned upgrade implementation timeline data) to provide a basis for calculating the economic input required for current upgrades; and disposal data (including estimated disposal process data, estimated dismantling and environmental treatment costs, estimated basic attributes of scrapped equipment, and disposal timeline data) to predict economic expenditures at the route's final stage. The collected historical, existing, and projected data undergo meticulous preprocessing to remove duplicate historical records and redundant data, reducing the burden of data storage and processing. This process also supplements missing key operational parameters and projected update parameters in the existing data, preventing incomplete calculations due to data gaps and correcting anomalies caused by recording errors in historical data. The preprocessed data is then converted into a unified storage and transmission format. Standardization processes, including unified field definitions, units of measurement, and coding, ensure consistency in statistical definitions and calculation standards among historical, existing, and projected data, generating standardized data.

[0027] The time-series linkage mapping module receives the generated standardized data and adds a unique time-series identifier to each data entry, including a stage code, a timestamp, and a business identifier. The stage code distinguishes different stages such as planning and design history and engineering construction history. The timestamp accurately records the historical period, current period, or estimated period corresponding to the data. The business identifier clearly defines the specific business content corresponding to the data. Through this identifier, accurate traceability and management of data from different periods and different businesses can be achieved, facilitating subsequent verification of the data's authenticity and relevance. The module also analyzes the business connection logic of each stage of the existing line, clarifying the clear business sequence: the planning and design history stage is the precursor to the engineering construction history stage; the engineering construction history stage is the precursor to the current operation and maintenance stage; the current operation and maintenance stage is the precursor to the current upgrade and renovation stage; and the current upgrade and renovation stage is the precursor to the estimated decommissioning and disposal stage. Combined with the historical operation trajectory of the existing line, this ensures that the business connection logic is completely consistent with the actual situation. Extract the calculation output data from each preceding stage and the corresponding calculation input data from the following stages to accurately match the correlation dimensions of each data set. For example, this involves comparing construction quality data from the historical stages of engineering construction with equipment failure frequency data from the current stages of operation and maintenance; comparing the service life data of equipment from the current stages of operation and maintenance with the renovation depth data from the current stages of upgrading and renovation; and comparing the renovation effect data from the current stages of upgrading and renovation with the estimated scrapping age data from the stages of scrapping and disposal. This makes the data correlation between stages at different times more targeted. Based on the data correlation dimensions, establish corresponding mapping relationships between preceding outputs and subsequent inputs to ensure that the impact of historical data on current and future stages can be accurately transmitted. Combining the tightness of the mapping relationship, the degree of impact of preceding data on subsequent calculations, and the time-series connection, generate the corresponding time-series linkage coefficients for each stage. By integrating standardized data and time-series linkage coefficients into a parameter package, the phased calculation model module can directly call the integrated complete data without the need for additional correlation and organization of data from different periods and stages. This significantly improves the efficiency of retesting and calculation, while ensuring the continuity and accuracy of data connection across different periods and stages. It avoids one-sided calculation results caused by data fragmentation, allowing retesting to comprehensively consider the mutual influence of each stage of the entire line life cycle.

[0028] The tiered measurement model module calls built-in measurement sub-models corresponding to each stage. Each measurement sub-model includes a data access layer, a measurement indicator layer, and a measurement logic layer, adapting to the complex scenario of historical, current, and estimated data coexisting in existing line retesting. The data access layer adapts to the data access specifications of the parameter package, uniformly receiving and parsing historical, current, and estimated data in the parameter package. Regardless of the period or business type of the data, it can achieve efficient and accurate parsing, avoiding access failures or parsing errors caused by complex data types. This opens up a data input channel for retesting and ensures that all relevant data can be fully utilized. The calculation indicator layer calls upon economic calculation indicators for each stage. The planning and design historical stage includes indicators such as historical preliminary research costs, historical scheme design costs, and historical feasibility study costs. The engineering construction historical stage includes indicators such as historical building material purchase costs, historical construction labor costs, and historical machinery rental costs. The operation and maintenance current stage includes indicators such as recent equipment maintenance costs, recent personnel salaries, and recent energy consumption costs. The renovation and upgrading current stage includes indicators such as estimated renovation material costs, estimated construction costs, and estimated equipment replacement costs. The scrapping and disposal estimated stage includes indicators such as estimated dismantling costs, estimated environmental treatment costs, and estimated waste transportation costs. Calculation indicators are set for different stages at different times to fit their actual conditions, ensuring that the calculation caliber is highly matched with the business characteristics of each stage and avoiding calculation deviations caused by uniform indicators. The calculation logic layer, based on the calculation logic of each stage and combined with the accessed data and calculation indicators, uses a time-series linkage weighted calculation algorithm to perform economic calculations for each stage. This algorithm fully considers the historical, current, and estimated data of each stage, and also reasonably incorporates the impact of the calculation results of the preceding stages. This allows the economic situation of historical stages to be transmitted to current and future stages through linkage coefficients, and the calculation results of the current stage can also provide a reference for future stages. The generated calculation values ​​for each stage are comprehensive and accurate, and can truly reflect the economic input and consumption of the existing line at each stage of its entire life cycle. Figure 3 As shown, this provides high-quality basic data for subsequent feedback corrections and report generation.

[0029] The feedback correction module receives the calculated values ​​from each stage and uses a cross-stage deviation dynamic correction algorithm to conduct a comprehensive deviation analysis. It focuses on comparing the connection deviations between the current calculated values ​​of operation and maintenance stages and the historical calculated values ​​of engineering construction stages; the matching deviations between the current calculated values ​​of renovation and upgrading stages and the current calculated values ​​of operation and maintenance stages; and the logical deviations between the calculated values ​​of the estimated disposal stage and the current calculated values ​​of renovation and upgrading stages. This accurately identifies the specific location and severity of cross-stage deviation problems, analyzes the causes of the deviations (such as historical data recording errors, inaccurate mapping relationships, and unreasonable calculation indicator settings), and generates targeted correction parameters. The correction parameters are then analyzed to clarify the adjustment direction and magnitude of the coefficients of each stage's calculation sub-model, as well as the adjustment dimensions of the mapping relationship judgment criteria, ensuring that the adjustments are accurate and based on evidence. The calculation coefficients of the sub-models for each stage are revised based on the adjustment direction and magnitude. This allows the calculation logic of the sub-models to better adapt to the historical operating trajectory and current actual conditions of the existing lines, reducing calculation deviations caused by the rigidity of model coefficients. Based on the revised calculation model coefficients, various indicators of the mapping relationship judgment criteria between each stage are adjusted, optimizing the association rules of data between stages at different times, and making the quantification of the impact of upstream stages on downstream stages more accurate. The revised calculation model coefficients and mapping relationship judgment criteria are stored for future use, providing an optimized model foundation for subsequent re-testing or calculations in later stages of the line. This improves the adaptability of the calculation model to the complex situations of the existing lines, ensuring that the calculation results continuously approach the actual economic conditions of the existing lines, and providing accurate data support for the subsequent operation and planning of the existing lines.

[0030] The dynamic output module interfaces with the tiered calculation model module and the feedback correction module, receiving calculation values, correction parameters, and deviation analysis details from each stage. It performs rigorous integrity checks on the received data, focusing on whether historical, current, and estimated data comprehensively cover each stage, and whether the correction parameters and deviation analysis details are complete and logically consistent, ensuring no data gaps or logical inconsistencies. This guarantees the comprehensiveness and reliability of the retest report from the outset. The verified data is categorized and organized according to the dimensions and data types of each stage of the entire lifecycle, forming calculation result datasets, correction parameter datasets, and deviation analysis datasets for each stage. These categorized datasets clearly distinguish between historical, current, and estimated data, allowing report readers to quickly locate the information they need. The categorized datasets are then populated into the corresponding chapters of the report. The data from each chapter is integrated and analyzed to extract core economic indicators such as the cumulative economic cost throughout the entire lifecycle of the existing line, the proportion of economic investment in each historical stage, the current level of operation and maintenance costs, the estimated economic investment in the renovation and upgrading stage, the estimated cost of subsequent decommissioning, and the cost change trends at each stage. This generates a complete lifecycle economic calculation report, comprehensively presenting the complete economic trajectory of the existing line from planning and construction to estimated decommissioning, providing clear and intuitive reference for decision-making. The initial calculation report, historical optimization records, and time-series data correlations are obtained. Through adaptive time-series fusion and incremental optimization techniques, historical, existing, and predicted data are aligned according to the time-series logic of each stage, dynamically adapting data correlations, fusing corrected data with the original calculation results, and performing local updates and iterative optimizations on parts of the report with time-series deviations and data inconsistencies, making the report more coherent, logically rigorous, and accurate. The optimized lifecycle economic calculation report is then fed back to the data acquisition module, such as... Figure 2 As shown, this report provides a clear direction for subsequent data collection work on this line, guiding the data collection module to focus on the data collection quality of links with large deviations, improving the accuracy and relevance of data collection, and forming a closed-loop optimization mechanism. Simultaneously, the report provides precise data support for the operation and maintenance adjustments, upgrade and renovation plan optimization, and decommissioning planning of the existing line, helping relevant parties to rationally formulate operation strategies, optimize renovation plans, and plan decommissioning timing, achieving economical and efficient operation of the existing line throughout its entire lifecycle.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A rail transit full life-cycle economic calculation system based on multi-dimensional data, characterized in that, The system comprises the following components: a data acquisition module, a time-series linkage mapping module, a multi-level measurement model module, a feedback correction module, and a dynamic output module; The data acquisition module collects data from all stages of the rail transit lifecycle, including planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning, and processes the collected data to generate standardized data. The time-series linkage mapping module: receives standardized data, adds a unique time-series identifier to each data item, including a stage code, timestamp, and business identifier; establishes a mapping relationship between the calculation output data of the preceding stage and the calculation input data of the following stage according to the business connection logic between each stage, generates time-series linkage coefficients; and integrates the standardized data and time-series linkage coefficients into a parameter package. The tiered calculation model module includes built-in calculation sub-models for planning and design, engineering construction, operation and maintenance, renovation and upgrading, and scrapping and disposal. Based on the integrated parameter package, it uses a time-series linkage weighted calculation algorithm to perform economic calculations for each stage and generate calculation values ​​for each stage. The feedback correction module receives the measured values ​​from each stage, performs deviation analysis using a cross-order deviation dynamic correction algorithm, and generates correction parameters. And adjust the coefficients of the measurement sub-model and the judgment criteria of the mapping relationship in reverse according to the correction parameters; The dynamic output module receives the calculated values, correction parameters, and deviation analysis details from each stage, and integrates them to generate a full life-cycle economic calculation report. Adaptive time-series fusion and incremental optimization techniques are used to iteratively optimize the full life cycle economic calculation report, and the optimized full life cycle economic calculation report is fed back to the data acquisition module.

2. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The data acquisition module collects data from each stage of the entire life cycle of rail transit, including planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning. The process is as follows: For each stage of planning and design, engineering construction, operation and maintenance, renovation and upgrading, and decommissioning, business association, economic cost, basic attributes, and time series data are collected respectively; the collected data is preprocessed to remove duplicate data, fill in missing data, and correct abnormal data. The preprocessed data is converted into a unified storage and transmission format; the data in the unified storage and transmission format is then standardized, with unified field definitions, units of measurement, and encoding, to generate standardized data.

3. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The process of establishing a mapping relationship between the calculation output data of the preceding stage and the calculation input data of the following stage in the time-series linkage mapping module, and generating the time-series linkage coefficient, is as follows: The business connection logic of each stage is sorted out, clarifying the preceding and following correspondences of each stage (planning and design, engineering construction, operation and maintenance, renovation and upgrading, and disposal), and clarifying the business sequence of each stage; the calculation output data of the preceding stage and the calculation input data of the following stage are extracted, and the data correlation dimensions of the two are matched; a mapping relationship between the preceding output and the following input data is established based on the data correlation dimensions; and the time-series linkage coefficient is generated by combining the tightness of the mapping relationship, the degree of influence of the preceding data on the following calculation, and the time-series linkage.

4. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The tiered calculation model module includes calculation sub-models for each stage of planning and design, engineering construction, operation and maintenance, renovation and upgrading, and disposal. Each sub-model comprises a data access layer, a calculation index layer, and a calculation logic layer. The data access layer is used to adapt to the data access specifications of the parameter package and uniformly receive and parse the data in the parameter package. The calculation index layer configures the economic calculation indicators for each stage. The calculation logic layer sets the calculation operation logic for each stage and performs economic calculation operations for each stage based on the accessed data and calculation indicators.

5. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The calculation formula for the time-series linkage weighted calculation algorithm in the graded calculation model module is as follows: ,in, This is the calculated value for the j-th stage; For the first Each step is based on its own calculated access parameters; This serves as the basis for calculating the weighting coefficients in this step; For the first The number of preceding steps that have an impact on each step; For the first The calculated values ​​of each preceding step; No. The first pre-processing step is for the first The timing linkage coefficient of each link.

6. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The calculation formula for the cross-order deviation dynamic correction algorithm in the feedback correction module is as follows: ,in, For the first Corrected parameters generated in the next iteration; These are dynamic weighting coefficients; For the first The cross-order deviation value of the next iteration; For the first The correction parameters for the next iteration.

7. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The specific steps in the feedback correction module for adjusting the coefficients of the measurement sub-model and the judgment criteria for the mapping relationship based on the correction parameters are as follows: Analyze the correction parameters to clarify the adjustment direction and magnitude of the measurement sub-model coefficients, as well as the adjustment dimensions of the mapping relationship judgment criteria; correct the measurement coefficients of the measurement sub-model at each stage according to the adjustment direction and magnitude; correct the various indicators of the mapping relationship judgment criteria according to the corrected measurement coefficients; and store the corrected measurement sub-model coefficients and the mapping relationship judgment criteria.

8. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The process of integrating and generating a full life cycle economic calculation report in the dynamic output module is as follows: connecting to the tiered calculation model module and the feedback correction module, receiving the calculation values, correction parameters and deviation analysis details of each stage respectively, and verifying the integrity of the received data. For the data that has passed the verification, it is classified and sorted according to the dimensions and data types of each stage of the entire life cycle, forming the calculation result dataset, correction parameter dataset and deviation analysis dataset of each stage; the classified datasets are then filled into the corresponding chapters of the report, the data of each chapter are integrated and sorted, the economic indicators of the entire life cycle are extracted, and a complete economic calculation report of the entire life cycle is generated.

9. The rail transit full life cycle economic calculation system based on multi-dimensional data according to claim 1, characterized in that, The specific steps for iteratively optimizing the full life cycle economic calculation report using adaptive time-series fusion and incremental optimization technology in the dynamic output module are as follows: First, obtain the initial full life cycle economic calculation report, historical optimization records, and time-series data correlations. Second, align the data according to the time-series logic of each stage of the full life cycle using adaptive time-series fusion and incremental optimization technology, dynamically adapt the data correlations, and fuse the corrected data with the original calculation results. Third, perform local updates and iterative optimizations on the parts of the full life cycle economic calculation report that have time-series deviations or inconsistent data connections. Finally, synchronously transmit the optimized full life cycle economic calculation report to the data acquisition module.

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