Rail vehicle database processing method, device, system and storage medium

Abstract

This application provides a method and apparatus for processing a rail vehicle database, a system for processing a rail vehicle database, and a storage medium, relating to the field of data processing technology. The method includes: classifying and storing acquired low-carbon and green technology data entries for rail vehicles based on a preset classification system for low-carbon and green technologies; wherein the classification system includes at least one of: energy-saving and consumption-reducing technology systems, low-carbon and zero-carbon technology systems, and carbon reduction and negative carbon technology systems; assigning evaluation values ​​to the data entries based on at least one of energy-saving contribution, techno-economic efficiency, and technological maturity, and storing the evaluation results in association with the corresponding data entries; receiving a query request containing a target technology system and a target application scenario, and, based on at least one of the technology classification system, the technology dimension corresponding to the evaluation result, and the target application scenario, selecting matching technology entries from the stored data entries as the output result.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method and apparatus for processing a rail vehicle database, a system for processing a rail vehicle database, and a storage medium. Background Technology

[0002] Currently, with the green and low-carbon transformation and development of the rail transit industry, a large number of energy-saving, low-carbon, zero-carbon, and carbon-reducing technologies for rail vehicles are emerging. Existing technologies typically use general database management systems to centrally store low-carbon and green technology data for rail vehicles. This data is often managed by data source, equipment physical location, or simply alphabetical order of names. This existing data management method results in a lack of inherent logical connections between data based on emission reduction mechanisms, creating data silos and making it difficult for users to quickly obtain targeted and comparable technical information.

[0003] To address the aforementioned data storage and classification issues, related technologies propose simple tagging management and retrieval of technical data based on technical categories or business attributes. This enables low-carbon and green technology data for rail vehicles to be archived according to certain classifications, initially alleviating the chaotic state of data accumulation.

[0004] In the process of implementing the embodiments of this application, at least the following problems were found in the related technology: The adoption of relevant technologies has, to some extent, enabled the classification, archiving, and basic retrieval of technical data. However, in practical applications, because low-carbon and green technologies for rail vehicles involve multiple interdisciplinary fields such as aerodynamics, materials, electrical engineering, and chemistry, simply managing them by broad categories and labels cannot directly obtain quantitative comparative information on the technology's contribution to emission reduction, economic investment, and feasibility of implementation. Original documents must be manually retrieved for offline evaluation, lacking the ability for multi-dimensional quantitative evaluation and scenario-based joint screening. This results in a serious disconnect between data management and practical application decisions, making it difficult to support complex engineering selection and investment decisions. Summary of the Invention

[0005] This application provides a method and apparatus for processing a rail vehicle database, a system for processing a rail vehicle database, and a storage medium.

[0006] The first aspect of this application provides a method for processing a rail vehicle database, applied to a server, comprising: classifying and storing acquired low-carbon and green technology data entries for rail vehicles based on a preset classification system for low-carbon and green technologies for rail vehicles; wherein the classification system includes at least one of energy-saving and consumption-reducing technology systems, low-carbon and zero-carbon technology systems, and carbon reduction and negative carbon technology systems; assigning evaluation values ​​to data entries based on at least one of energy-saving contribution, techno-economic efficiency, and technological maturity, and storing the evaluation results in association with the corresponding data entries; receiving a query request containing a target technology system and a target application scenario, and filtering out matching technology entries from the stored data entries as output results based on at least one of the technology classification system, the technology dimension corresponding to the evaluation results, and the target application scenario.

[0007] In one optional embodiment of this application, the technology maturity dimension includes at least one of the following: technology concept stage, laboratory verification stage, preliminary prototype stage, pilot application stage, mid-term application stage, mature application stage, high-efficiency optimization stage, industry-leading stage, and widespread application stage.

[0008] In one optional embodiment of this application, the basis for assigning a value to the energy-saving contribution dimension includes at least one of the changes in vehicle traction energy consumption, changes in auxiliary energy consumption, and pollutant emission reduction before and after the application of the technology.

[0009] In one optional embodiment of this application, the basis for assigning a value to the techno-economic dimension includes at least one of the initial investment cost, operating cost, maintenance cost, and decommissioning cost of the corresponding technology.

[0010] In one optional embodiment of this application, the target application scenario includes at least one of the train speed level, operating conditions, and energy storage utilization requirements.

[0011] In an optional embodiment of this application, the method for processing the rail vehicle database further includes: Receive update instructions or feedback information for stored data entries; Based on update instructions or feedback information, modify the corresponding data entries or their associated evaluation results.

[0012] In an optional embodiment of this application, the method for processing the rail vehicle database further includes: Use at least one of the following as index keywords: energy-saving and consumption-reducing technology system, low-carbon and zero-carbon technology system, and carbon reduction and negative carbon technology system, and associate it with the corresponding data entry.

[0013] A second aspect of this application provides a processing apparatus for a rail vehicle database, including a processor and a memory storing program instructions. The processor is configured to execute the rail vehicle database processing method as described in the first aspect of this application when running the program instructions.

[0014] A third aspect of this application provides a processing system for a rail vehicle database, comprising: a server body; and a rail vehicle database processing apparatus as described in the second aspect of this application, which is installed on the server body.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing program instructions that, when executed, cause a computer to perform a method for processing a rail vehicle database as described in the first aspect of this application.

[0016] The processing method and apparatus for the rail vehicle database, the processing system for the rail vehicle database, and the storage medium provided in the embodiments of this application have the following beneficial effects: This application embodiment quantifies and evaluates technical data by pre-setting multi-dimensional quantitative evaluation indicators, including emission reduction contribution, economic input, and implementation feasibility. It also performs joint screening based on scenario-based screening conditions, directly obtaining multi-dimensional quantitative comparison information. This overcomes the evaluation gap problem caused by simple label management, realizes multi-dimensional quantitative evaluation and scenario-based joint screening, and closely connects data management with actual application decisions, effectively supporting complex engineering selection and investment decisions. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of a method for processing a rail vehicle database provided in an embodiment of this application; Figure 2 This is a schematic diagram of a processing device for a rail vehicle database provided in an embodiment of this application.

[0018] Figure label: 800: Processing device for rail vehicle database; 801: Processor; 802: Memory; 803: Communication interface; 804: Bus. Detailed Implementation

[0019] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0020] Figure 1 This is a schematic diagram of the processing method for the rail vehicle database provided in the embodiments of this application. Any of the following methods can be executed in the rail vehicle database processing system, or in a server or terminal device that is communicatively connected to the rail vehicle database processing system.

[0021] Combination Figure 1 As shown in the figure, this application embodiment provides a method for processing a rail vehicle database, applied to a server, including: S01, based on the preset classification system of low-carbon and green technologies for rail vehicles, classify and store the acquired low-carbon and green technology data items for rail vehicles; wherein, the technology classification system includes at least one of the following: energy-saving and consumption-reducing technology system, low-carbon and zero-carbon technology system, and carbon reduction and negative carbon technology system.

[0022] S02, assign values ​​to data entries based on at least one of energy-saving contribution, techno-economic performance, and technological maturity, and store the evaluation results in association with the corresponding data entries.

[0023] S03, receive a query request containing the target technology system and the target application scenario, and based on at least one of the technology classification system, the technology dimension corresponding to the evaluation result, and the target application scenario, select matching technology items from the stored data items as the result output.

[0024] In this embodiment, a server refers to a computer device with data computing, storage, and network communication capabilities. The server undertakes all computing tasks related to receiving, classifying, evaluating, storing, and responding to external query requests for data filtering and output of low-carbon and green technology data for rail vehicles. Specific server forms include, but are not limited to, rack servers, blade servers, and virtual computing instances in cloud computing platforms. By applying this method to a server, the server's powerful data processing capabilities can be utilized to achieve high-speed processing and stable management of massive amounts of low-carbon and green technology data entries for rail vehicles.

[0025] In this embodiment of the application, a low-carbon and green technology data entry for rail vehicles refers to a data set that records information related to specific low-carbon and green technologies for rail vehicles. Typical information included in the data entry includes the technology name, a description of the technology principle, applicable vehicle models, research and development units, application cases, and various performance parameters. The methods for obtaining low-carbon and green technology data entries for rail vehicles include receiving data files in batches from research institutions or operating units through a data import interface, or having users manually enter data through a web form.

[0026] In this application embodiment, the classification system of low-carbon and green technologies for rail vehicles refers to a hierarchical classification standard for technologies based on their mechanisms and impacts on carbon emissions and energy consumption. The classification system includes at least one of the following: energy-saving and consumption-reducing technology system, low-carbon and zero-carbon technology system, and carbon reduction and negative carbon technology system. The energy-saving and consumption-reducing technology system refers to a collection of technologies aimed at reducing the absolute energy consumption of rail vehicle operation and auxiliary systems, including but not limited to aerodynamic drag reduction technology and / or lightweighting technology. Aerodynamic drag reduction technology reduces air resistance by optimizing the streamlined design of the train's exterior, while lightweighting technology reduces traction energy consumption by using aluminum alloy or carbon fiber composite materials to reduce the car body's weight. The low-carbon and zero-carbon technology system refers to a collection of technologies aimed at significantly reducing carbon emissions during rail vehicle operation, or even achieving zero carbon emissions, including but not limited to hydrogen energy technology and / or new pantograph technology. Hydrogen energy technology uses fuel cells to directly convert the chemical energy of hydrogen into electrical energy to drive the train, achieving zero carbon emissions. New pantograph technology optimizes pantograph-catenary contact to reduce arc loss and carbon dust emissions generated by carbon plate wear. The carbon reduction and carbon-negative technology system refers to a set of technologies aimed at actively absorbing, converting, or offsetting carbon emissions generated during the life cycle of rail vehicles. This includes, but is not limited to, regenerative braking carbon reduction technology and / or photovoltaic power generation technology. Regenerative braking carbon reduction technology converts the kinetic energy of train braking into electrical energy to feed back into the grid for use by other trains, thereby effectively reducing the overall carbon emissions of the grid. Photovoltaic power generation technology uses solar panels installed on the roofs of depots or stations to convert sunlight into electricity to offset some of the carbon emissions generated by grid power consumption. Specifically, the energy-saving and consumption-reducing technology system includes: aerodynamic drag reduction technology by optimizing train shape to reduce air resistance; lightweight technology by using aluminum alloy or carbon fiber composite materials to reduce the weight of the car body; energy efficiency improvement technology to improve the efficiency of motors and converters; wheel-rail drag reduction technology by optimizing wheel-rail contact to reduce resistance; and / or auxiliary equipment consumption reduction technology to reduce the energy consumption of auxiliary equipment such as air conditioning and lighting. The carbon reduction and carbon-negative technology system includes: photovoltaic power generation technology using solar panels installed on the roofs of depots or stations; and / or regenerative braking carbon reduction technology that converts the kinetic energy of train braking into electrical energy to feed back into the grid. The low-carbon and zero-carbon technology system includes: hydrogen energy technology that uses fuel cells to convert hydrogen into electricity, as well as various energy storage technologies such as flywheel energy storage, supercapacitor energy storage, and / or lithium-ion electrochemical energy storage.

[0027] In this embodiment, the classification and storage of acquired data entries includes: after receiving low-carbon and green technology data entries for rail vehicles, the server parses the technical classification identifier carried by the data entry or automatically matches the corresponding classification node based on the technical principle description of the data entry. Then, the data entry is written into the database into a data table or data partition corresponding to the energy-saving and emission-reduction technology system, the low-carbon and zero-carbon technology system, or the carbon reduction and negative carbon technology system. In other embodiments, multi-table association storage in a relational database or nested document structure storage in a non-relational database can also be used. Both methods can achieve physical or logical isolation of data based on the classification system and have roughly the same classification management effect as classification storage. Thus, classifying and storing low-carbon and green technology data entries for rail vehicles according to energy-saving and emission-reduction technology systems, low-carbon and zero-carbon technology systems, and carbon reduction and negative carbon technology systems changes the chaotic state of data stacked according to physical location or equipment type in the prior art. It transforms the originally disordered and scattered technical data into a structured technical map according to emission reduction mechanisms, significantly improving the directionality of subsequent data retrieval and laying the data organization foundation for multi-dimensional joint screening.

[0028] In this embodiment, the value assignment evaluation refers to the process of converting the technical performance reflected in the data entries of low-carbon and green technologies for rail vehicles into calculable and comparable values ​​or levels based on preset quantitative rules or evaluation models. Energy saving contribution refers to the degree to which low-carbon and green technologies for rail vehicles contribute to reducing energy consumption and pollutant emissions after application; it is a direct indicator of the technology's hard power in emission reduction. Techno-economic efficiency refers to the cost-effectiveness level of low-carbon and green technologies for rail vehicles throughout their entire life cycle, reflecting the investment feasibility and economic return capability of the technology. Technology maturity refers to the evolution of low-carbon and green technologies for rail vehicles from basic research to industrial application, reflecting the reliability and risk level of the technology. When conducting the value assignment evaluation, the server can use only one of the above three dimensions for evaluation; for example, for scenarios focusing on feasibility of implementation, only technology maturity can be used for value assignment. Alternatively, a combination of two or three dimensions can be used for comprehensive evaluation; for example, for investment decision-making scenarios, energy saving contribution and techno-economic efficiency can be jointly assigned, or for comprehensive evaluation scenarios, all three dimensions can be comprehensively assigned. Evaluation and assessment include, but are not limited to, constructing an analytic hierarchy process (AHP) model to obtain a comprehensive weight score, or establishing a multidimensional scaling space to calculate the relative distance between technical points and ideal points. These methods can transform qualitative descriptions into quantitative indicators. Specifically, the quantitative assessment of energy-saving contribution can be achieved by calculating the percentage reduction in traction energy consumption after the application of the technology. For example, the energy-saving contribution score = (traction energy consumption per unit turnover before application - traction energy consumption per unit turnover after application) / traction energy consumption per unit turnover before application × 100%. A higher score indicates a greater energy-saving contribution. The quantitative assessment of techno-economic efficiency can be based on full life-cycle cost analysis, such as calculating the payback period: Payback period = initial investment cost / (annual operating cost savings + annual maintenance cost savings). A shorter payback period indicates a higher economic efficiency. After the evaluation and assessment are completed, the server will associate the generated evaluation results with the corresponding data entries. This association storage includes adding an evaluation result field to the database record corresponding to the data entry and writing the value, or creating a foreign key pointing to the unique identifier of the data entry in an evaluation data table independent of the data entry's storage space and writing the evaluation result to that table. In other embodiments, node attribute associations in a graph database can also be used to bind evaluation results to technical entities. By storing evaluation results in association with data entries, the data entries for low-carbon and green technologies for rail vehicles are no longer merely textual descriptions, but possess multi-dimensional quantitative attributes. This enrichment of the data structure directly supports subsequent refined screening based on evaluation dimensions. Furthermore, the introduction of a multi-dimensional evaluation mechanism comprehensively measures the overall feasibility of the technology from multiple perspectives, including energy conservation, economics, and maturity, avoiding decision-making biases caused by single-dimensional evaluations.

[0029] In this embodiment, a query request refers to a data packet sent by a user terminal to a server via a network, carrying specific search criteria. The target technology system refers to the specific classification system to which the user expects to find low-carbon and green technologies for rail vehicles belong; for example, the user may specify the target technology system as a low-carbon or zero-carbon technology system in the query request. The target application scenario refers to the actual operating environment or working mode in which the low-carbon and green technologies for rail vehicles are intended to be used, such as passenger lines at a specific speed level or freight lines under specific climatic conditions. After receiving the query request, the server parses out the target technology system identifier and target application scenario parameters contained therein. Subsequently, the server constructs search logic based on at least one of the technology classification system, the technology dimension corresponding to the evaluation result, and the target application scenario. If filtering is based solely on a technology classification system, all technology entries are extracted from the corresponding category. If filtering is based on a target application scenario, the target application scenario parameters are matched with the applicable scenario parameters recorded in the data entries to extract technology entries whose applicable scenarios include the target application scenario. If filtering is based on the technology dimensions corresponding to the evaluation results, technology entries with evaluation values ​​greater than a preset threshold are selected. In practical applications, a combination of the above three conditions can be used, for example, selecting technology entries that belong to the low-carbon and zero-carbon technology system, have a technology maturity evaluation value greater than a preset threshold, and whose applicable target application scenario is high-speed. The filtering process is executed through a database query language or by traversing the data set in memory to perform conditional judgments. Specific methods for selecting matching technology entries from the stored data entries as output results include sorting the selected technology entries in descending order by the scores of a specific evaluation dimension and outputting them in list form, or encapsulating the selected technology entries into visual chart data and outputting them to the front-end page for rendering and display. In this way, by receiving query requests and filtering outputs based on multi-dimensional conditions, the data management end and the technology application end are directly connected, enabling the static database to have dynamic decision support capabilities. In particular, by combining the technology classification system, evaluation dimensions and actual application scenarios, the barriers between technical characteristics and actual application needs are broken down, ensuring that the output technical items not only meet the goals in terms of emission reduction mechanisms, but also meet the application requirements in terms of feasibility and economy, thus completely solving the problem of the disconnect between data management and application decision-making in existing technologies.

[0030] The processing method of the rail vehicle database provided in this application embodiment quantifies the technical data by pre-setting multi-dimensional quantitative evaluation indicators including emission reduction contribution, economic input and implementation feasibility, and performs joint screening based on scenario-based screening conditions. This directly obtains multi-dimensional quantitative comparison information, overcomes the evaluation gap problem caused by simple label management, realizes multi-dimensional quantitative evaluation and scenario-based joint screening, and closely connects data management with actual application decision-making, effectively supporting complex engineering selection and investment decisions.

[0031] Optionally, the technology maturity dimension includes at least one of the following stages: technology concept stage, laboratory verification stage, preliminary prototype stage, pilot application stage, mid-term application stage, mature application stage, high-efficiency optimization stage, industry-leading stage, and widespread application stage.

[0032] In this application embodiment, the technology maturity dimension is a classification standard for the specific position of low-carbon and green technologies for rail vehicles within their entire life cycle of research and development and application. The technology concept stage refers to the initial period when the technology is only in the theoretical conception or basic physicochemical principle verification stage, and has not yet formed a specific engineering design scheme. The laboratory verification stage refers to the stage of completing principle tests of core functional units and obtaining basic data in a controlled laboratory environment. The preliminary prototype stage refers to the stage of manufacturing a scaled-down or full-size prototype that can operate in a simulated environment but does not possess all engineering features. The pilot application stage refers to the stage of conducting small-scale on-vehicle trial runs of the technology on a single track line or a single train to initially verify the engineering feasibility. The mid-term application stage refers to the stage where the technology has been promoted on several lines or multiple trains, with operational data continuously accumulating and technical details still undergoing frequent iterative improvements. The mature application stage refers to the stage where technical indicators tend to be stable, have reached industry standards for mass procurement and deployment, and can meet the reliability requirements of regular operation. The high-efficiency optimization stage refers to the stage where, based on mature application, advanced control algorithms or new materials are introduced to further break through the original performance bottlenecks and achieve marginal gains in energy efficiency or emission reduction performance. The industry-leading stage refers to a stage where the technology's comprehensive performance indicators significantly surpass the current industry average, forming a technological barrier or leading industry standard. The stage of widespread application refers to the highest evolutionary stage where the technology has become a universal configuration or mandatory industry standard in the rail vehicle field, completely replacing traditional solutions in the vast majority of new and retrofit projects. The technology maturity dimension includes the types of verification documents that need to be submitted or the runtime test indicators that need to be met in at least one of the above stages. For example, in the pilot application stage, at least 5,000 hours of fault-free operation records on actual vehicles are required; in the mature application stage, a type test certification report from a third-party authoritative institution is required. In other embodiments, at least one of the above stages can be combined into four major stages—R&D, verification, promotion, and maturity—for coarse-grained evaluation, or further subdivided into seventeen sub-stages for ultra-fine evaluation. These schemes are essentially level classifications of the entire lifecycle of technology evolution, serving the same or similar progress indication function as the nine-stage division. During the evaluation and assignment process, the processor 21 can sequentially map these nine stages to values ​​one through nine, or use a logarithmic function to map and assign values ​​to the nine stages to widen the score gap between high maturity stages. These mapping algorithms can achieve the technical effect of transforming qualitative maturity stages into quantitative comparative values.

[0033] By subdividing technology maturity into nine stages and assigning values, the problem of confusion between high-potential early-stage technologies and reliable late-stage technologies caused by the extensive maturity assessment in existing technologies can be overcome. This approach accurately characterizes the risk level of technologies at different evolutionary stages, enabling investors and decision-makers to select technologies whose maturity level strictly matches their target needs based on their own risk preferences and project cycles. This significantly improves the granularity and accuracy of technology identification.

[0034] Optionally, the basis for assigning values ​​to the energy-saving contribution dimension includes at least one of the following: changes in vehicle traction energy consumption, changes in auxiliary energy consumption, and pollutant emission reductions before and after the application of the technology.

[0035] In this application embodiment, the change in vehicle traction energy consumption before and after the application of the technology refers to the difference in the total electrical or fossil energy consumed by the train traction system before and after adopting a certain low-carbon green technology when a rail vehicle performs the same passenger or freight turnover task. This difference directly reflects the extent to which the technology reduces the core power energy consumption of the train. The change in vehicle traction energy consumption before and after the application of the technology includes the reduction in constant-speed traction energy consumption when the train is running on a straight track and / or the reduction in acceleration traction energy consumption during the acceleration process. The change in vehicle auxiliary energy consumption before and after the application of the technology refers to the difference in energy consumption of non-traction loads such as the train's air conditioning system, lighting system, and air compressor system before and after adopting the new technology. The change in vehicle auxiliary energy consumption before and after the application of the technology includes the reduction in cooling energy consumption of variable frequency air conditioning compared to fixed frequency air conditioning and / or the reduction in energy consumption of LED lighting compared to traditional fluorescent lighting. Pollutant emission reduction refers to the total reduction in the mass of carbon dioxide, nitrogen oxides, particulate matter, and other harmful substances emitted into the external environment by rail vehicles during operation and maintenance after adopting low-carbon green technologies. Pollutant emission reduction includes carbon powder emission reduction due to pantograph carbon plate wear and / or transformer insulating oil leakage emission reduction. In other embodiments, the above-mentioned changes can be uniformly converted into changes in standard coal equivalent for comprehensive measurement, or various pollutants can be uniformly converted into carbon dioxide equivalent emission reductions based on global warming potential values ​​for comprehensive comparison, thus eliminating the normalization problem caused by different physical dimensions and possessing equivalent quantitative evaluation functions. When assigning values ​​based on energy-saving contributions, the processor uses a direct mapping method to convert the weighted sum of energy consumption changes and pollutant emission reductions into a percentage evaluation value, or uses a baseline method to calculate the relative energy saving rate based on pre-application energy consumption and map it into an evaluation value.

[0036] In this way, the above methods can achieve an objective projection of emission reduction benefits into the numerical space within the processor's computational logic. Using changes in traction energy consumption, auxiliary energy consumption, and / or pollutant emission reduction as the basis for assigning energy-saving contribution values, the actual emission reduction output of rail vehicles is comprehensively covered from three dimensions: main energy-consuming equipment, auxiliary energy-consuming equipment, and / or environmental impact. This eliminates the blind spot in evaluation that ignores auxiliary system energy consumption and pollution emissions by solely relying on traction energy consumption. This ensures that the assigned energy-saving contribution value highly matches the actual green emission reduction output of rail vehicles, guaranteeing the absolute objectivity and comprehensiveness of the evaluation conclusions in assessing environmental friendliness.

[0037] Optionally, the basis for assigning values ​​to the techno-economic dimension includes at least one of the initial investment cost, operating cost, maintenance cost, and decommissioning cost of the corresponding technology.

[0038] In this embodiment, initial investment cost refers to the total amount of funds invested in a one-time transaction during the design, manufacturing, or modification phase of a rail vehicle to introduce a low-carbon green technology, including patent licensing fees for technology introduction, procurement costs of new equipment, and / or construction costs for production line modifications. Operating cost refers to the total amount of funds continuously incurred during the entire lifecycle operation of the train, including fuel replenishment costs for new energy power systems and / or data communication costs for intelligent energy consumption management systems. Maintenance cost refers to the total cost of periodic inspections, parts replacements, and troubleshooting to maintain the normal operation of the technical equipment, including periodic equalization maintenance costs for energy storage batteries and / or calibration costs for complex sensors. Decommissioning cost refers to the total amount of funds required for the safe dismantling, hazardous substance treatment, and residual value recovery of the technical equipment after it reaches the end of its service life, including the cost of harmless treatment of used lithium batteries and / or the cost of dismantling and recycling large composite material car bodies. In other embodiments, a life-cycle cost model can be used to sum up at least one of the aforementioned costs based on the time value of money and applied to the same base year. Alternatively, an investment payback period model can be used to calculate the ratio of initial investment cost to annual operating cost savings to assess economic viability. These models are logically equivalent in financial evaluation and all reflect the economic returns of technological input. When assigning a techno-economic value, the processor directly takes the reciprocal of the calculated total life-cycle cost as a positive economic evaluation value, or calculates the internal rate of return and maps it to a preset score range. These calculation methods, once executed within the processor, all achieve the conversion of economic costs into comparable evaluation scores.

[0039] In this way, the initial investment cost, operating cost, maintenance cost and / or decommissioning cost are all incorporated into the technical and economic valuation basis, constructing a cost accounting closed loop covering the entire process of equipment introduction to withdrawal. This completely avoids the problem of high-priced, low-consumption technologies being misjudged and eliminated due to only considering the initial purchase cost, and also prevents the hidden financial risks caused by ignoring decommissioning costs. This ensures that the technical items output by the database have a solid guarantee in terms of long-term economic feasibility, and enhances the financial security of investment decisions.

[0040] Optionally, the target application scenario includes at least one of the train's speed level, operating conditions, and energy storage utilization requirements.

[0041] In this application embodiment, the train speed class refers to the classification standard of the highest design operating speed or average operating speed followed by rail vehicles under normal operating conditions. This includes intercity low-speed classes with speeds greater than or equal to the first speed (e.g., 160 km / h) and less than the second speed; urban medium-speed classes with speeds greater than the second speed (e.g., 250 km / h) and less than or equal to the third speed; and / or trunk line high-speed classes with speeds greater than or equal to the third speed (e.g., 350 km / h). The air resistance faced by trains at different speed classes increases quadratically, resulting in fundamentally different demands for aerodynamic drag reduction and low-carbon green technologies. Operating conditions refer to the train's running state mode under specific line conditions, including urban subway operating conditions with station spacing less than two kilometers and frequent starts and stops; high-speed railway operating conditions with longer station spacing and long-distance constant speed operation; and / or mountain railway operating conditions with continuous steep climbs. The traction and braking characteristics under different operating conditions place different requirements on the throughput frequency and power level of energy storage technologies. Energy storage and utilization requirements refer to the technical constraints on energy storage and release strategies imposed by the train power supply network or power system. These include requirements for high-rate utilization of supercapacitors to handle instantaneous high-power discharge during train startup, high-energy-density utilization of lithium batteries to support operation in overhead contactless sections, and / or high-cycle-life utilization of flywheel energy storage to absorb braking feedback energy at high frequencies. In other embodiments, scenarios can be divided into passenger dedicated line scenarios and freight heavy-haul scenarios according to line transportation functions, or into DC traction scenarios and AC traction scenarios according to power supply systems. Using these division methods, when processing query requests containing target application scenarios, the processor performs regular expression matching between the text tags of the target scenario and the applicable scenario attribute fields of each technical entry in the database, or inputs the target scenario parameters into a pre-trained vector space model to calculate semantic similarity and filter entries exceeding a threshold. Both of these implementation methods can complete data filtering based on scenario conditions.

[0042] In this way, by establishing the train's speed level, operating conditions, and / or energy storage and utilization requirements as the core dimensions of the target application scenario, the three most critical physical operating conditions that determine the adaptability of low-carbon and green technologies for rail vehicles are precisely extracted. This enables query requests to express extremely specific engineering requirements, and the database filtering results to directly connect with the train's actual operating physical environment. This eliminates the phenomenon of inflated matching results or failure to be implemented due to general queries, and greatly enhances the practical value of the database in engineering design and selection.

[0043] Optionally, the processing method for the rail vehicle database also includes receiving update instructions or feedback information for stored data entries; and modifying the corresponding data entries or their associated evaluation results according to the update instructions or feedback information.

[0044] In this embodiment, an update instruction refers to a data packet sent by maintenance personnel with database management privileges to the server via a terminal device, requesting modification of the content and attributes of existing data entries. This includes an upgrade instruction to advance a technology's maturity stage from the pilot application stage to the mid-term application stage, and / or an instruction to add details of a technology's application cases. Feedback information refers to evaluations and objections submitted by ordinary users regarding the accuracy or applicability of the data during database use. This includes user feedback indicating that a technology's energy-saving contribution score is too high, and / or user suggestions to supplement scenarios where a technology is not applicable. In other embodiments, the processor is configured with a timed crawler program to automatically obtain the latest technological advancements from external scientific research paper databases and / or news websites and automatically generate update instructions to trigger modifications. This automated mechanism replaces manual triggering, achieving data updates and preservation. After receiving an update instruction or feedback information, the processor first performs identity authentication and syntax parsing on the instruction or information, extracts the unique identifier of the data entry to be modified and the latest value of the field to be modified, and then, under the protection of the database transaction management mechanism, writes the new value into the corresponding record in memory and recalculates the associated evaluation result. If the modification fails, a transaction rollback is performed to ensure data consistency.

[0045] In this way, the introduction of update instructions and feedback information reception and modification mechanisms for stored data entries injects dynamic evolution capabilities into the database, enabling it to keep pace with the rapid evolution of low-carbon and green technologies in rail vehicles. At the same time, by leveraging the feedback loop from the general public, it continuously self-corrects and self-calibrates, ensuring the timeliness, accuracy, and vitality of the database content, and preventing decisions from deviating from the actual industry status due to outdated data.

[0046] Optionally, the processing method for the rail vehicle database also includes using at least one of the energy-saving and consumption-reducing technology systems, low-carbon and zero-carbon technology systems, and carbon reduction and negative carbon technology systems as index keywords to associate with the corresponding data entries.

[0047] In this embodiment, the index key refers to a specific data item identifier extracted in the database management system to accelerate data retrieval. Essentially, it establishes a mapping relationship between the physical storage location of data records and logical retrieval key values. Using energy-saving and emission-reducing technologies, low-carbon and zero-carbon technologies, and / or carbon reduction and negative-carbon technologies as index keys involves establishing an index structure for the classification system field at the database level. This includes using a B+ tree index structure to store the three major classification key values ​​and their corresponding row pointers, or using a hash index structure to map classification names to specific bucket addresses, or using an inverted index structure to construct an index table with the classification name as the key and a list of all data item identifiers containing that classification as the value. In other embodiments, bitmap indexing technology can be used to establish Boolean bitmaps for the three major classification systems, or sharding keys can be designed based on classification key values ​​for data distribution in a distributed database. These index construction methods can significantly reduce the scanning range of the retrieval logic in terms of query acceleration. When the processor performs the operation of establishing index associations, it triggers the index update process while storing data entries in memory. This process extracts the classification system name to which the current data entry belongs and inserts it into the corresponding index data structure in memory, thereby establishing a direct addressing channel between classification logic and physical data.

[0048] In this way, by directly setting energy-saving and consumption-reducing technology systems, low-carbon and zero-carbon technology systems, and / or carbon reduction and negative carbon technology systems as index keywords, the underlying technology classification system is given direct addressing capability at the physical level. This allows the database engine to instantly locate the target data set through the index structure without traversing the entire table when a target technology system is specified in a query request. This reduces the retrieval time complexity from linear to logarithmic or even constant level. When faced with retrieval requests for massive amounts of low-carbon and green technology data for rail vehicles, this greatly improves the concurrent response performance and resource utilization efficiency of the server itself.

[0049] In practical applications, databases can be built using common office software and lightweight database systems. Specifically, technical data is first collected from sources such as research literature and industry reports through data interfaces or manual input. This data is then cleaned using spreadsheet software such as Excel, including removing duplicates and standardizing field formats. Next, corresponding data tables are created in the Access database management system, defining fields such as text, number, and date based on data type. Finally, Access's external data import function is used to import the cleaned Excel file into the Access tables, completing the basic database setup. Furthermore, to ensure data security, a regular backup mechanism can be established. For example, in a Windows operating system environment, a scheduled task can be set up using the built-in Task Scheduler to call the database software's command-line tools or scripts to copy the database file to an external storage device or network location, thus achieving regular data backup.

[0050] Combination Figure 2 As shown, this application embodiment provides a rail vehicle database processing device 800, including a processor 801 and a memory 802. Optionally, the device may further include a communication interface 803 and a bus 804. The processor 801, communication interface 803, and memory 802 can communicate with each other via the bus 804. The communication interface 803 can be used for information transmission. The processor 801 can call logical instructions in the memory 802 to execute the rail vehicle database processing method of the above embodiment.

[0051] Furthermore, the logic instructions in the aforementioned memory 802 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0052] The memory 802, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 801 executes functional applications and data processing by running the program instructions / modules stored in the memory 802, that is, it implements the processing method of the rail vehicle database in the above embodiments.

[0053] The memory 802 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 802 may include high-speed random access memory and may also include non-volatile memory.

[0054] This application provides a rail vehicle database processing system, including a server body and the aforementioned rail vehicle database processing device 800. The rail vehicle database processing device 800 is installed on the server body. The installation relationship described herein is not limited to placement within the rail vehicle database processing system, but also includes installation connections with other components of the rail vehicle database processing system, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the rail vehicle database processing device 800 can be adapted to feasible rail vehicle database processing system bodies, thereby implementing other feasible embodiments.

[0055] This application provides a computer-readable storage medium storing computer-executable instructions configured to execute the above-described method for processing the rail vehicle database.

[0056] The technical solutions of this application embodiment can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this application embodiment. The aforementioned storage medium can be a non-transitory storage medium, including: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code.

[0057] The technical solutions of this application embodiment can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this application embodiment. The aforementioned storage medium can be a non-transitory storage medium, including: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code.

[0058] The foregoing description and accompanying drawings fully illustrate embodiments of this application to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., of the embodiments claimed, if they correspond to the method section of the embodiments claimed, then the relevant parts can be referred to the description of the method section.

[0059] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments claimed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0060] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0061] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method of processing a database of railway vehicles, characterized in that, Applied to servers, including: Based on a pre-defined classification system for low-carbon and green technologies of rail vehicles, the acquired data entries on low-carbon and green technologies of rail vehicles are classified and stored; wherein, the technology classification system includes at least one of the following: energy-saving and consumption-reducing technology system, low-carbon and zero-carbon technology system, and carbon reduction and negative carbon technology system; The data entries are evaluated based on at least one of energy-saving contribution, techno-economic performance, and technological maturity, and the evaluation results are associated with and stored with the corresponding data entries. The system receives a query request containing the target technology system and the target application scenario. Based on at least one of the technology classification system, the technology dimension corresponding to the evaluation result, and the target application scenario, it selects matching technology entries from the stored data entries as the result output.

2. The method according to claim 1, characterized in that, The technology maturity dimension includes at least one of the following stages: technology concept stage, laboratory verification stage, preliminary prototype stage, pilot application stage, mid-term application stage, mature application stage, high-efficiency optimization stage, industry-leading stage, and widespread application stage.

3. The method according to claim 1, characterized in that, The basis for assigning values ​​to the energy-saving contribution dimension includes at least one of the following: changes in vehicle traction energy consumption, changes in auxiliary energy consumption, and pollutant emission reduction before and after the application of the technology.

4. The method according to claim 1, characterized in that, The basis for assigning values ​​to the techno-economic dimension includes at least one of the following: initial investment cost, operating cost, maintenance cost, and decommissioning cost of the corresponding technology.

5. The method according to claim 1, characterized in that, The target application scenarios include at least one of the following: train speed level, operating conditions, and energy storage and utilization requirements.

6. The method according to any one of claims 1 to 5, characterized in that, Also includes: Receive update instructions or feedback information for the stored data entries; Based on the update instructions or feedback information, the corresponding data entries or their associated evaluation results are modified.

7. The method according to any one of claims 1 to 5, characterized in that, Also includes: At least one of the energy-saving and consumption-reducing technology systems, low-carbon and zero-carbon technology systems, and carbon reduction and negative carbon technology systems is used as an index key and associated with the corresponding data entry.

8. A processing device for a rolling stock database, comprising a processor and a memory having stored program instructions, characterized in that, The processor is configured to execute the processing method of the rail vehicle database as described in any one of claims 1 to 7 when running the program instructions.

9. A processing system of a rail vehicle database, characterized by Applied to servers, including: Server body; The processing apparatus for the rail vehicle database as described in claim 8 is installed on the server body.

10. A computer readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the processing method of the rail vehicle database as described in any one of claims 1 to 7.

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