Rail flash butt welding single-source power supply mode switching method based on energy consumption optimization

Abstract

This invention proposes a method for switching single-source power supply modes in rail flash welding based on energy consumption optimization. It relates to the field of mode switching technology, determining the energy consumption paths of various single-source power supply modes, modeling and calculating energy conversion efficiency, and collecting relevant parameters and recording historical welding process curves in real time. The method integrates basic energy consumption data for each power supply mode to obtain comprehensive energy consumption information. By combining this basic energy consumption data with a short-term prediction model, it predicts key energy consumption indicators for future periods and calculates the comprehensive evaluation value of each power supply mode to obtain the target power supply mode with optimal energy consumption. Finally, it makes decisions and executes power supply mode switching based on the target power supply mode, preset thresholds, and welding interval requirements, obtaining target rail flash welding power supply energy consumption effect data. This invention achieves scientific switching, energy consumption optimization, and improved construction efficiency for single-source power supply modes in rail flash welding.

Description

Technical Field

[0001] This invention proposes a method for switching the single-source power supply mode of rail flash welding based on energy consumption optimization, which relates to the field of mode switching technology, specifically to the field of switching the single-source power supply mode of rail flash welding based on energy consumption optimization. Background Technology

[0002] Rail flash welding is a core process in railway track laying and maintenance. Its power supply mode directly affects welding quality, energy consumption, and construction economics. Currently, the mainstream power supply modes are mains power, diesel generators, and energy storage batteries. Existing power supply mode switching relies heavily on manual experience, focusing only on single economic costs while neglecting the integrity of the energy consumption path and energy conversion efficiency. This leads to blind switching decisions and a lack of scientific basis. Furthermore, current technology lacks a systematic energy consumption data integration and prediction mechanism, resulting in disorganized energy consumption data, significant interference from invalid data, and the absence of a multi-dimensional comprehensive evaluation system, making it impossible to accurately select the optimal energy supply mode. In addition, unreasonable switching timing can easily affect welding quality, and the lack of a sound effect monitoring and anomaly investigation mechanism after switching makes it difficult to achieve a closed-loop energy consumption optimization, leading to high construction energy consumption and low energy utilization efficiency. Summary of the Invention

[0003] This invention provides a method for switching single-source power supply modes for rail flash welding based on energy consumption optimization, in order to solve the above-mentioned problems: This invention proposes a method for switching single-source power supply modes for rail flash welding based on energy consumption optimization. The method includes: S1. By determining the energy consumption path of various single-source power supply modes, modeling and calculating the energy conversion efficiency, and collecting relevant parameters in real time and recording historical welding process curves, the basic data of power supply mode energy consumption are integrated to obtain comprehensive energy consumption-related information. S2. By combining basic energy consumption data with short-term prediction models, key energy consumption indicators for future periods are predicted and comprehensive evaluation values ​​of various power supply modes are calculated to obtain the target power supply mode with optimal energy consumption. S3. The power supply mode switching is decided and executed by combining the target power supply mode with the preset threshold and welding interval requirements to obtain the power supply energy consumption effect data of the target rail flash welding.

[0004] Furthermore, the system includes: The welding fundamental analysis module is used to determine the energy consumption path of various single-source power supply modes, model and calculate the energy conversion efficiency, and collect relevant parameters and record historical welding process curves in real time. It integrates the basic energy consumption data of power supply modes to obtain comprehensive energy consumption-related information. The energy consumption prediction module is used to predict key energy consumption indicators for future periods by combining basic energy consumption data with short-term prediction models, calculate the comprehensive evaluation value of each power supply mode, and obtain the target power supply mode with optimal energy consumption. The mode switching module is used to make decisions and execute power supply mode switching based on the target power supply mode, a preset threshold, and welding interval requirements, so as to obtain the power supply energy consumption effect data of the target rail flash welding.

[0005] The beneficial effects of this invention are as follows: This method solves the technical problems of existing rail flash welding power supply mode switching that only focus on single economic cost, ignore energy consumption optimization, and make blind switching decisions; it achieves precise optimization selection of power supply mode, breaking the limitations of traditional single-index optimization; it improves the scientificity and rationality of power supply mode switching, avoiding energy waste caused by ineffective and blind switching; it reduces the overall energy consumption during rail flash welding construction, while improving energy utilization efficiency; it constructs a complete energy consumption optimization closed loop, ensuring that the energy consumption optimization effect is traceable and can be improved, adapting to the energy consumption control needs of different construction scenarios. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of a single-source power supply mode switching method for rail flash welding based on energy consumption optimization. Detailed Implementation

[0007] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0008] In one embodiment of the present invention, a method for switching single-source power supply mode for rail flash welding based on energy consumption optimization is proposed, the method comprising: S1. By determining the energy consumption path of various single-source power supply modes, modeling and calculating the energy conversion efficiency, and collecting relevant parameters in real time and recording historical welding process curves, the basic data of power supply mode energy consumption are integrated to obtain comprehensive energy consumption-related information. S2. By combining basic energy consumption data with short-term prediction models, key energy consumption indicators for future periods are predicted and comprehensive evaluation values ​​of various power supply modes are calculated to obtain the target power supply mode with optimal energy consumption. S3. The power supply mode switching is decided and executed by combining the target power supply mode with preset thresholds and welding interval requirements to obtain energy consumption data for the target rail flash welding. Figure 1 As shown.

[0009] The working principle and technical effects of the above-mentioned technical solution are as follows: This method solves the problem of messy and incomplete energy consumption data by clarifying the core energy consumption basis of various power supply modes; then, it uses short-term prediction technology to predict future energy consumption changes, and combines a comprehensive evaluation system to quantify the energy consumption performance of each power supply mode, realizing a shift from passive response to proactive prediction; finally, it formulates scientific switching decisions to ensure the switching process is safe and efficient, while collecting operational effect data to form a closed-loop optimization mechanism, ensuring that the energy consumption optimization effect is verifiable and iterative. The entire process revolves around the actual construction scenario of rail flash welding, organically combining energy consumption data, prediction models, and switching decisions to achieve dynamic optimization and adjustment of the power supply mode.

[0010] This method addresses the technical problems of existing rail flash welding power supply mode switching, which only focuses on single economic costs, ignores energy consumption optimization, and involves blind switching decisions. It achieves precise optimization selection of power supply modes, breaking the limitations of traditional single-indicator optimization; it improves the scientificity and rationality of power supply mode switching, avoiding energy waste caused by ineffective and blind switching; it reduces the overall energy consumption during rail flash welding construction, while improving energy utilization efficiency; and it constructs a complete energy consumption optimization closed loop, ensuring that the energy consumption optimization effect is traceable and can be improved, adapting to the energy consumption control needs of different construction scenarios.

[0011] In one embodiment of the present invention, S1 includes: The complete energy consumption transmission paths for three single-source power supply modes—mains power, diesel generator, and energy storage battery—are preset for flash welding of steel rails. Energy conversion efficiency calculation models are established for each complete energy consumption transmission path to obtain the efficiency of the mains power path, diesel path, and energy storage path. By combining the efficiency of the mains power path, diesel path, and energy storage path with the welding equipment control system, historical welding process curves under different welding conditions and welding stages are recorded. Energy consumption correlation feature data were extracted from historical welding process curves; The energy consumption-related characteristic data are classified and organized, outlier values ​​are checked and verified, invalid data is removed, and data standardization is completed to obtain comprehensive energy consumption-related information.

[0012] The working principle and technical effects of the above technical solution are as follows: First, a complete energy transfer path is preset for three mainstream single-source power supply modes, clearly defining each stage of energy transfer. Then, a dedicated energy conversion efficiency calculation model is established for each path to accurately quantify the energy loss of each path, avoiding confusion in energy consumption calculations for different paths. Next, combined with the welding equipment control system, the path efficiency is correlated with welding conditions and welding stages, recording welding process curves in real time under different scenarios to ensure the correlation between process curves and energy consumption data. Feature data directly related to energy consumption is extracted from the process curves, focusing on the core data dimensions for energy consumption optimization. Finally, through data processing, anomaly verification, and standardization, invalid and abnormal data are eliminated to ensure the accuracy, completeness, and standardization of energy consumption-related information, avoiding deviations in subsequent optimization due to data issues.

[0013] This method addresses the technical problems in existing technologies, such as fuzzy energy consumption paths, inaccurate energy conversion efficiency calculations, disorganized energy consumption data, and invalid data interfering with subsequent optimization. It achieves systematic integration and standardized processing of basic energy consumption data, improving the reliability and usability of energy consumption data; it clarifies the energy consumption characteristics of different power supply modes and the energy consumption patterns under different welding scenarios; it reduces the risk of energy consumption optimization decision errors caused by data deviations, improving the stability and reliability of the entire method; and it standardizes the energy consumption data processing flow.

[0014] In one embodiment of the present invention, the complete energy transfer path for the three single-source power supply modes of the preset rail flash welding—mains power, diesel generator, and energy storage battery—includes: The complete energy transmission path of the mains power supply mode is as follows: the primary energy of the grid is generated by the power plant, then transmitted through the power transmission and distribution network, enters the rectifier module of the welding equipment, and is supplied to the rail flash welding load via the welding DC bus. The complete energy transmission path of the diesel generator power supply mode is as follows: diesel fuel enters the diesel internal combustion engine for combustion and does work, driving the generator to generate electricity. The electrical energy is rectified by the welding equipment module and supplied to the rail flash welding load through the welding DC bus. The complete energy transfer path of the energy storage battery power supply mode is to connect the battery charging module through the front-end power supply to charge and store the energy storage battery. The energy storage battery releases electrical energy through the battery discharge module and supplies the rail flash welding load through the welding equipment rectifier module and welding DC bus. Based on the preset links of energy transfer in the complete energy transfer path, mark the energy loss nodes in each link to obtain the complete energy transfer path.

[0015] The complete energy transfer path of the energy storage battery power supply mode involves connecting the front-end power supply to the battery charging module to charge and store energy in the battery. The energy storage battery then releases electrical energy through the battery discharge module, which is then supplied to the rail flash welding load via the welding equipment rectifier module and the welding DC bus. This includes: The front-end power supply selects mains power or diesel generator according to the on-site power supply conditions. After the electrical energy output by the front-end power supply is connected to the battery charging module, it is rectified and regulated by the charging module to convert the electrical energy into a charging voltage and current suitable for the energy storage battery, so as to perform constant current and constant voltage charging for the energy storage battery and complete the energy storage. The electrical energy stored in the energy storage battery is discharged and controlled by the battery discharge module to ensure stable output of discharge voltage and current. The discharged electrical energy is connected to the rectifier module of the welding equipment, and after rectification, it is converted into DC power required for welding. Then, it is transmitted to the rail flash welding load through the welding DC bus. Simultaneously, the electrical energy parameters of each stage of charging and discharging are recorded to clarify the energy loss at each stage, ensuring the integrity and traceability of the power supply path of the energy storage battery.

[0016] The working principle and technical effects of the above technical solution are as follows: It clarifies the complete energy transmission path for three single-source power supply modes, accurately locates the energy loss nodes in each path, and especially refines the entire process of the energy storage battery power supply mode, ensuring that the energy consumption path is complete and traceable. It systematically analyzes the entire energy transmission process for the three modes—mains power, diesel generator, and energy storage battery—from the energy source to the welding load, clarifying the energy conversion method and transmission process at each stage. Considering the specific characteristics of the energy storage battery power supply mode, it refines the complete charging and discharging process, selects a suitable front-end power supply based on the site construction conditions, and ensures the stability of energy conversion through the control of charging and discharging modules. Simultaneously, it records the electrical parameters at each stage, achieving full traceability of the energy consumption path. Finally, it marks the energy loss nodes at each stage of each path, clearly identifying the key locations of energy loss and avoiding problems such as missing loss nodes and inaccurate energy consumption calculations due to unclear energy consumption paths.

[0017] This method addresses the technical problems in existing technologies, such as ambiguous energy transmission paths, unclear loss nodes, incomplete power supply paths for energy storage batteries, and lack of energy traceability. It standardizes and clarifies the energy transmission paths for three power supply modes, identifying energy loss nodes at each stage. It refines the entire process of energy storage battery power supply, improving its stability and controllability, and avoiding energy waste caused by improper charging and discharging control. It ensures the traceability of the energy storage battery power supply path, facilitating subsequent investigation of energy consumption anomalies and optimization of the energy consumption path. Furthermore, it provides targeted directions for subsequent energy consumption optimization, enabling targeted reduction of energy loss at each loss node and improving overall energy utilization efficiency.

[0018] In one embodiment of the present invention, the method of recording historical welding process curves under different welding conditions and stages by combining the mains power path efficiency, diesel path efficiency, and energy storage path efficiency with the welding equipment control system includes: The efficiency of the mains power path, diesel path, and energy storage path are preset into the welding equipment control system. Through the current and voltage acquisition modules of the welding equipment control system, the operating parameters of different welding conditions and different welding stages of rail flash welding are monitored in real time to obtain welding operation monitoring data. Based on the welding operation monitoring data, record the welding current, welding voltage, welding time, power supply mode type and corresponding path energy conversion efficiency data under each working condition and stage, and generate historical welding process curves; Historical welding process curves are categorized, stored, and labeled.

[0019] This includes generating historical welding process curves based on welding operation monitoring data, recording welding current, welding voltage, welding duration, power supply mode type, and energy conversion efficiency data of corresponding paths under various operating conditions and stages. Real-time analysis of welding operation monitoring data is performed to extract the peak value, effective value, average value of welding voltage, and welding duration corresponding to each welding condition and welding stage, thereby clarifying the current power supply mode type and the path energy conversion efficiency data corresponding to that mode. The extracted data are linked and integrated in chronological order, with time as the horizontal axis and welding current, welding voltage, and energy conversion efficiency as the vertical axis to construct a two-dimensional process curve model. Mark the corresponding welding conditions, welding stages and power supply modes in the curve model, and supplement auxiliary information such as the acquisition time of each data point and the equipment operating status to generate a complete historical welding process curve. The generated process curves are validated to ensure that the curve data is consistent with the original data from welding operation monitoring, thus avoiding curve distortion caused by data deviation.

[0020] The working principle and technical effects of the above technical solution are as follows: The path efficiency of the three power supply modes is preset into the welding equipment control system, establishing a correlation between path efficiency and equipment operation; the operating parameters of different welding conditions (light load, heavy load, etc.) and different welding stages (flash welding, upsetting, etc.) are monitored in real time through the equipment's built-in current and voltage acquisition modules, ensuring the real-time nature and comprehensiveness of the monitoring data; the monitoring data is then analyzed to extract core parameters related to energy consumption, clarifying the correspondence between parameters and power supply modes and path efficiency; a two-dimensional process curve model is constructed through time series correlation and integration, and relevant auxiliary information is labeled to ensure the integrity and readability of the curve; finally, data verification ensures the accuracy of the curve data, avoiding errors in subsequent energy consumption feature extraction due to data deviation; the curves are classified, stored, and labeled for easy retrieval, querying, and analysis of energy consumption patterns under different scenarios.

[0021] This method addresses the technical problems in existing technologies, such as non-standardized recording of welding process curves, incomplete data, low correlation with energy consumption parameters, distorted curve data, and difficulty in traceability and querying. It achieves standardized generation, accurate verification, and categorized storage of welding process curves, improving their reliability and usability. It deeply binds path efficiency with welding conditions and stages, clearly presenting the energy consumption variation patterns under different scenarios, providing an accurate and comprehensive data source for energy consumption correlation feature extraction. It avoids energy consumption analysis biases caused by distorted curve data, improving the accuracy of energy consumption optimization. Simultaneously, it standardizes the management process of process curves, facilitating rapid traceability of energy consumption in different welding scenarios.

[0022] In one embodiment of the present invention, S2 includes: By combining comprehensive energy consumption information with the moving average method or short-term prediction model, key energy consumption indicators are predicted to obtain energy consumption indicator prediction data. A multi-dimensional comprehensive evaluation system is constructed based on energy consumption index prediction data; Energy consumption data for each power supply mode are calculated based on a multi-dimensional comprehensive evaluation system. Based on energy consumption data, the energy consumption performance of each power supply mode is analyzed and screened to obtain the target power supply mode with optimal energy consumption.

[0023] The working principle and technical effects of the above technical solution are as follows: Using comprehensive energy consumption information as input, the system employs a moving average method or short-term prediction model to predict key energy consumption indicators (such as load power, electricity price, carbon emission factor, etc.) for future periods, breaking the limitations of traditional real-time response and achieving proactive optimization. Subsequently, based on the predicted data, a multi-dimensional comprehensive evaluation system centered on minimizing energy consumption is constructed, taking into account economic costs, carbon emissions, and other related dimensions to avoid the one-sidedness caused by optimizing a single indicator. Next, according to the evaluation system, energy consumption-related data for the three power supply modes are quantitatively calculated, transforming the multi-dimensional data into comparable comprehensive evaluation values, achieving accurate quantification of the energy consumption performance of each power supply mode. Finally, through the analysis, comparison, and screening of the comprehensive evaluation values, combined with welding construction requirements, the target power supply mode with optimal energy consumption is determined, ensuring the scientific and targeted nature of the switching decision.

[0024] This method addresses the technical problems of existing technologies, such as lack of predictability in power supply mode switching, single evaluation dimensions, and inability to accurately select the optimal power supply mode. It achieves short-term accurate prediction of key energy consumption indicators, improving the foresight of power supply mode switching and avoiding reactive responses to deteriorating energy consumption. It constructs a multi-dimensional comprehensive evaluation system, breaking the limitations of single cost optimization and achieving synergistic optimization of energy consumption, cost, and carbon emissions. It improves the accuracy and scientific rigor of power supply mode selection, ensuring that the selected power supply mode achieves optimal energy consumption while also considering construction needs. It reduces energy waste caused by blind switching or improper selection, improves energy utilization efficiency, and promotes the green, efficient, and economical development of rail flash welding construction.

[0025] In one embodiment of the present invention, the calculation of energy consumption-related data for each power supply mode based on a multi-dimensional comprehensive evaluation system includes: By using a multi-dimensional comprehensive evaluation system, the evaluation dimensions and weights are set according to the preset core objectives, and the dimension weight setting data is obtained. Calculate energy consumption data for the three power supply modes based on energy consumption index prediction data: By combining a weighted summation formula with dimensional weight settings, energy consumption-related data from each dimension are transformed into a single comprehensive evaluation value.

[0026] This involves setting the weights of evaluation dimensions based on preset core objectives using a multi-dimensional comprehensive evaluation system, and obtaining the dimension weight setting data, including: The core objective of the pre-set multi-dimensional comprehensive evaluation system is to minimize energy consumption, and the evaluation system includes three evaluation dimensions: energy loss, economic cost, and carbon emissions. Based on the needs of on-site construction scenarios, weight evaluation standards for each evaluation dimension are set. Under normal construction scenarios, the weight of energy consumption loss dimension is higher than that of economic cost and carbon emission dimensions. Under construction scenarios in areas with strict environmental control, the weight of carbon emission dimension is increased. Under construction scenarios at high altitudes, the weight of economic cost dimension is appropriately adjusted. The weight evaluation algorithm is used to quantify and assign values ​​to each dimension, ensuring that the sum of the weights of the three dimensions is 1. The assignment results are organized and archived to form complete dimension weight setting data. The weight setting data can be dynamically adjusted according to changes in the construction scenario to ensure the adaptability of the evaluation system.

[0027] Among them, energy consumption-related data for three power supply modes are calculated based on energy consumption index prediction data, including: Based on the energy consumption index prediction data, we calculated the energy consumption related data of each dimension for three power supply modes: mains power, diesel generator, and energy storage battery. Energy consumption loss data calculation includes combining the predicted welding load power, runtime and energy conversion efficiency of each path to calculate the total energy consumption and unit welding energy consumption for each power supply mode in the future period. The calculation of economic cost data includes combining the predicted mains electricity price and diesel price to calculate the unit energy consumption electricity cost and diesel consumption cost, and combining the cycle life of energy storage batteries and charging costs to calculate the battery depreciation cost; Carbon emission data calculation includes combining the predicted grid carbon emission factor to calculate the unit energy consumption carbon emission and total carbon emission of the grid power supply mode; combining the diesel carbon emission coefficient and fuel consumption rate to calculate the carbon emission data of the diesel generator power supply mode; and combining the carbon emission data of the upstream power supply to calculate the source carbon emission data of the energy storage battery power supply mode. The calculated data from various dimensions were categorized and organized to form complete energy consumption-related data for the three power supply modes.

[0028] The working principle and technical effects of the above technical solution are as follows: The core objective of the comprehensive evaluation system is clearly defined as minimizing energy consumption. Three core evaluation dimensions are identified: energy loss, economic cost, and carbon emissions, balancing energy optimization with actual construction needs. The weights of each dimension are dynamically set based on the differences in various on-site construction scenarios, ensuring the evaluation system can adapt to the optimization needs of different scenarios and avoiding evaluation bias caused by fixed weights. Quantitative values ​​are assigned through a weight evaluation algorithm to ensure the rationality and standardization of weight settings. Subsequently, based on energy consumption index prediction data, specific data for the three power supply modes under the three evaluation dimensions are calculated. For each mode's characteristics, combined with parameters such as path efficiency, load power, price, and carbon emission factors, relevant data on energy loss, economic cost, and carbon emissions are accurately calculated, ensuring the comprehensiveness and accuracy of data calculation. Finally, through a weighted summation formula, the multi-dimensional data is transformed into a single comprehensive evaluation value, achieving accurate quantification of the energy consumption performance of each power supply mode.

[0029] This method addresses the technical problems of fixed weights in existing comprehensive evaluation systems, inability to adapt to different construction scenarios, incomplete calculation of energy consumption-related data, and biased evaluation results. It achieves dynamic adjustment of evaluation dimension weights, improving the scenario adaptability of the comprehensive evaluation system and meeting the energy consumption optimization needs of different construction scenarios. It refines the calculation process for energy consumption-related data for each power supply mode, ensuring the comprehensiveness and accuracy of data calculation and avoiding evaluation bias caused by missing data. It transforms multi-dimensional data into a single comprehensive evaluation value, simplifying the comparison of different power supply modes and improving evaluation efficiency and accuracy. Finally, it ensures that the comprehensive evaluation results truly reflect the energy consumption performance of each power supply mode, further enhancing the targeting and effectiveness of energy consumption optimization.

[0030] In one embodiment of the present invention, the step of analyzing and screening the energy consumption performance of various power supply modes based on energy consumption-related data to obtain the target power supply mode with optimal energy consumption includes: The comprehensive evaluation values ​​of the three power supply modes are compared and ranked to obtain a comparative evaluation sequence. Candidate target power supply modes are determined based on the comparative evaluation sequence; Feasibility verification of candidate target power supply modes was conducted to obtain feasibility verification data; If a candidate target power supply mode meets all feasibility requirements, it is determined as the target power supply mode with optimal energy consumption. If the requirements are not met, the next lowest power supply mode with the best comprehensive evaluation value will be selected in turn, and the verification process will be repeated until the optimal energy consumption target power supply mode that meets the requirements is determined.

[0031] This includes conducting feasibility verification on candidate target power supply modes and obtaining feasibility verification data, including: Retrieve relevant parameters of the candidate target power supply mode to verify whether its output power can meet the load power requirements of each welding stage of rail flash welding, and ensure that the power is stable and without fluctuation during the welding process; Verify the stability of its output voltage, confirm that the voltage fluctuation range meets the operating requirements of the welding equipment, and avoid voltage abnormalities affecting welding quality; Verify the operability of the power supply mode switching process, confirm that the switching time and energy consumption during the switching process are within the preset reasonable range, and that the switching process will not damage the welding equipment or interrupt the welding process. Simultaneously verify the operational safety of this power supply mode and investigate potential risks such as equipment failure and energy leakage; All verification results and data are recorded in detail to form feasibility verification data, which serves as the basis for judging whether the candidate target power supply mode is qualified.

[0032] The working principle and technical effects of the above technical solution are as follows: The comprehensive evaluation values ​​of the three power supply modes are compared and ranked, and the power supply mode with the lowest comprehensive evaluation value and optimal energy consumption is initially selected as the candidate target. Subsequently, a comprehensive feasibility verification is conducted on the candidate target, focusing on the core needs of welding construction, and verifying it from four key dimensions: power adaptability, voltage stability, switching operability, and operational safety. This ensures that the candidate mode can adapt to the load requirements of each stage of welding, will not affect welding quality, and that the switching process is safe, efficient, and without potential operational risks. The verification results are recorded in detail to form feasibility verification data, which serves as the core basis for judging whether the candidate mode is qualified. If a candidate mode does not meet the feasibility requirements, the power supply mode with the second lowest comprehensive evaluation value is selected in turn, and the verification process is repeated until a target power supply mode that is both energy-efficient and meets construction requirements is determined, ensuring the scientific and practical nature of the selection results.

[0033] This method addresses the technical problems of existing power supply mode selection, which focuses solely on energy consumption while neglecting construction feasibility, potentially leading to decreased welding quality or construction interruption. It achieves a synergistic balance between optimal energy consumption and construction feasibility, avoiding disruptions to normal construction due to blindly pursuing energy optimization. It enhances the rigor and reliability of power supply mode selection by identifying potential construction risks and equipment malfunctions through multi-dimensional feasibility verification. It ensures that the selected target power supply mode can stably adapt to welding construction needs, reducing energy consumption while guaranteeing welding quality and construction safety. Furthermore, it improves the selection process, establishing a standardized procedure for comparison and ranking, candidate selection, feasibility verification, and iterative confirmation, thereby enhancing the accuracy and practicality of the selection results.

[0034] In one embodiment of the present invention, S3 includes: Based on the target power supply mode with optimal energy consumption, retrieve relevant data of the currently running power supply mode, calculate the difference in comprehensive evaluation value between the current power supply mode and the target power supply mode, and determine the switching threshold in combination with on-site construction needs; When the difference in the comprehensive evaluation value exceeds the preset threshold, and the short-term prediction model confirms that there are no abnormal characteristics, the power supply mode switching decision is initiated. The welding current status is monitored by the welding equipment control system, and a switching operation is performed during welding intervals or before the start of a welding process. After the switch is completed, the energy consumption monitoring module is activated to collect the mode operation data under the target power supply mode in real time; Based on the operational data of the model, the operational effect analysis was performed to obtain the power consumption effect data of the flash welding of the target rail.

[0035] The working principle and technical effects of the above technical solution are as follows: Based on the target power supply mode, relevant data of the current operating power supply mode are retrieved, and the difference in the comprehensive evaluation value between the two is calculated to clarify the energy consumption optimization space for switching; combined with the on-site construction requirements, a reasonable switching threshold is determined to avoid energy waste and equipment damage caused by frequent switching due to small differences; when the difference exceeds the threshold, and the short-term prediction model confirms that there are no abnormal characteristics such as power supply anomalies or energy consumption fluctuations, a switching decision is initiated to ensure the rationality of the switching timing; subsequently, the welding current status is monitored in real time through the welding equipment control system, and the switching operation is performed during the welding interval or before welding begins to avoid the impact of current fluctuations on welding quality during the switching process, while reducing energy consumption loss during the switching process; after the switching is completed, the energy consumption effect monitoring module is activated to collect the operating data of the target mode in real time, providing data support for energy consumption effect analysis; finally, by analyzing the operating data, energy consumption effect data is obtained to verify the effectiveness of energy consumption optimization.

[0036] This method addresses the technical problems in existing technologies, such as unreasonable timing of power supply mode switching, impact on welding quality during switching, energy waste due to frequent switching, and lack of post-switching effect monitoring. It achieves precise power supply mode switching, avoids frequent switching through threshold control, and reduces equipment wear and energy waste. It ensures the safety and stability of the switching process, preventing switching operations from affecting welding quality and guaranteeing normal construction. Through post-switching energy consumption effect monitoring, it achieves verifiable and traceable energy consumption optimization results. It constructs a complete switching, monitoring, and feedback closed loop, enhancing the iterative optimization capability of the entire method. It can adjust subsequent strategies based on actual operational results, further improving energy consumption optimization levels and reducing construction energy consumption and costs.

[0037] In one embodiment of the present invention, the step of analyzing the operational effect based on the mode operation data to obtain the power consumption effect data for flash welding of the target rail includes: The mode operation data under the target power supply mode is classified and organized to obtain energy consumption data. Compare and analyze the energy consumption data and comprehensive evaluation values ​​to calculate energy consumption analysis data; Based on energy consumption analysis data, determine whether the preset target has been achieved and obtain target judgment information; Based on energy consumption analysis data, perform operational anomaly analysis and obtain an operational performance analysis report; Based on the target judgment information and the operation effect analysis report, obtain the power consumption effect data of the flash welding of the target rail.

[0038] This involves comparing and analyzing energy consumption data and comprehensive evaluation values ​​to calculate energy consumption analysis data, including: Extract core data such as actual energy consumption loss, unit welding energy consumption, economic cost, and carbon emissions from the energy consumption data and compare them one by one with the comprehensive evaluation values ​​of each power supply mode and the predicted data of the corresponding dimensions. Calculate the difference and deviation rate between actual energy consumption and predicted energy consumption, the difference and deviation rate between actual economic cost and predicted economic cost, and the difference and deviation rate between actual carbon emissions and predicted carbon emissions. Calculate the energy consumption optimization difference, cost saving difference, and carbon emission reduction difference between the current target power supply mode and the power supply mode before switching; All calculated differences, deviation rates, and other data are categorized and organized to form complete energy consumption analysis data, clearly reflecting the difference between the actual operating effect and the predicted effect of the target power supply mode, as well as the optimization effect after the switch.

[0039] This includes analyzing operational anomalies based on energy consumption data and obtaining an operational performance analysis report, including: Anomaly identification is performed on energy consumption analysis data to screen out abnormal data points, such as excessive deviation between actual and predicted data, abnormally high energy consumption loss, and abnormally low energy conversion efficiency. For abnormal data points, combine relevant information such as the operating parameters of the target power supply mode, the on-site construction environment, and the equipment operating status to investigate the causes of the abnormality, including equipment failure, unreasonable parameter settings, environmental factors, and abnormal energy consumption path loss. A detailed analysis of the causes of the anomalies was conducted to clarify the extent to which the anomalies affected the energy consumption optimization effect, and targeted adjustment suggestions and solutions were proposed. The results of anomaly identification, anomaly cause analysis, impact assessment, and adjustment suggestions are compiled and summarized to form a standardized operational effectiveness analysis report.

[0040] The working principle and technical effects of the above technical solution are as follows: The operational data of the target power supply mode after the switch are classified and organized, and core data related to energy consumption are selected to ensure the relevance and usability of the data. Then, the organized actual data is compared one by one with the predicted data and comprehensive evaluation values ​​in S2, calculating various differences and deviation rates to clearly present the difference between the actual operating effect and the predicted effect. Simultaneously, the energy consumption optimization, cost savings, and carbon emission reduction effects after the switch are quantified, clarifying the actual value of energy consumption optimization. Next, based on the energy consumption analysis data obtained from the comparative analysis, it is determined whether the preset energy consumption optimization target has been achieved, forming target judgment information. Then, for abnormal data points in the energy consumption analysis data, combined with the actual situation on site, the causes of the abnormalities are comprehensively investigated, clarifying the impact of the abnormalities on the energy consumption optimization effect, proposing targeted adjustment suggestions, and forming a standardized operational effect analysis report. Finally, the target judgment information and analysis report are integrated to form complete energy consumption effect data, achieving verifiable and traceable energy consumption optimization results.

[0041] This method addresses the technical problems in existing technologies, such as the lack of effectiveness verification after power supply mode switching, the inability to judge the effectiveness of energy consumption optimization, the inability to promptly investigate abnormal problems, and the lack of basis for subsequent optimization. It achieves quantitative comparison and accurate judgment of energy consumption optimization effectiveness, clearly understanding the actual optimization effect after switching and verifying the effectiveness of the entire method. Through anomaly identification and cause investigation, it promptly identifies problems in the operation process, avoiding energy waste and equipment damage caused by anomalies, and improving the stability of power supply mode operation. It generates standardized operation effect analysis reports, promoting continuous improvement in energy consumption optimization levels. It perfects the complete closed loop of optimization, execution, monitoring, analysis, and iteration, further enhancing the scientific rigor and practicality of the entire method, ensuring the continuous achievement of energy consumption optimization goals, while reducing energy consumption and costs during construction and improving energy utilization efficiency.

[0042] According to one embodiment of the present invention, the system includes: The welding fundamental analysis module is used to determine the energy consumption path of various single-source power supply modes, model and calculate the energy conversion efficiency, and collect relevant parameters and record historical welding process curves in real time. It integrates the basic energy consumption data of power supply modes to obtain comprehensive energy consumption-related information. The energy consumption prediction module is used to predict key energy consumption indicators for future periods by combining basic energy consumption data with short-term prediction models, calculate the comprehensive evaluation value of each power supply mode, and obtain the target power supply mode with optimal energy consumption. The mode switching module is used to make decisions and execute power supply mode switching based on the target power supply mode, a preset threshold, and welding interval requirements, so as to obtain the power supply energy consumption effect data of the target rail flash welding.

[0043] The working principle and technical effects of the above technical solution are as follows: This system addresses the problem of messy and incomplete energy consumption data by clearly defining the core energy consumption basis of various power supply modes; it then uses short-term prediction technology to predict future energy consumption changes, and combines this with a comprehensive evaluation system to quantify the energy consumption performance of each power supply mode, achieving a shift from passive response to proactive prediction; finally, it formulates scientific switching decisions to ensure a safe and efficient switching process, while simultaneously collecting operational data to form a closed-loop optimization mechanism, ensuring that the energy consumption optimization effect is verifiable and iterative. The entire process revolves around the actual construction scenario of rail flash welding, organically combining energy consumption data, prediction models, and switching decisions to achieve dynamic optimization and adjustment of the power supply mode.

[0044] This system addresses the technical problems of existing rail flash welding power supply mode switching, which only focuses on single economic costs, ignores energy consumption optimization, and involves blind switching decisions. It achieves precise optimization selection of power supply modes, breaking the limitations of traditional single-indicator optimization; it improves the scientificity and rationality of power supply mode switching, avoiding energy waste caused by ineffective and blind switching; it reduces the overall energy consumption during rail flash welding construction, while improving energy utilization efficiency; and it constructs a complete energy consumption optimization closed loop, ensuring that the energy consumption optimization effect is traceable and can be improved, adapting to the energy consumption control needs of different construction scenarios.

[0045] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for switching single-source power supply modes for flash welding of rails based on energy consumption optimization, characterized in that, The method includes: S1. By determining the energy consumption path of various single-source power supply modes, modeling and calculating the energy conversion efficiency, and collecting relevant parameters in real time and recording historical welding process curves, the basic data of power supply mode energy consumption are integrated to obtain comprehensive energy consumption-related information. S2. By combining basic energy consumption data with short-term prediction models, key energy consumption indicators for future periods are predicted and comprehensive evaluation values ​​of various power supply modes are calculated to obtain the target power supply mode with optimal energy consumption. S3. The power supply mode switching is decided and executed by combining the target power supply mode with the preset threshold and welding interval requirements to obtain the power supply energy consumption effect data of the target rail flash welding.

2. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 1, characterized in that, S1 includes: The complete energy transfer path for three single-source power supply modes—mains power, diesel generator, and energy storage battery—is pre-defined for flash welding of steel rails. For each complete energy transfer path, an energy conversion efficiency calculation model is established to obtain the efficiency of the mains power path, the diesel path, and the energy storage path. By combining the efficiency of the mains power path, diesel path, and energy storage path with the welding equipment control system, historical welding process curves under different welding conditions and welding stages are recorded. Energy consumption correlation feature data were extracted from historical welding process curves; The energy consumption-related characteristic data are classified and organized, outlier values ​​are checked and verified, invalid data is removed, and data standardization is completed to obtain comprehensive energy consumption-related information.

3. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 2, characterized in that, The complete energy transfer path for the three single-source power supply modes of the pre-set rail flash welding—mains power, diesel generator, and energy storage battery—includes: The complete energy transmission path of the mains power supply mode is as follows: the primary energy of the grid is generated by the power plant, then transmitted through the transmission and distribution network, enters the rectifier module of the welding equipment, and is supplied to the flash welding load of the rail via the welding DC bus. The complete energy transmission path of the diesel generator power supply mode is as follows: diesel fuel enters the diesel internal combustion engine for combustion and does work, driving the generator to generate electricity. The electrical energy is rectified by the welding equipment module and supplied to the rail flash welding load through the welding DC bus. The complete energy transfer path of the energy storage battery power supply mode is to connect the battery charging module through the front-end power supply to charge and store the energy storage battery. The energy storage battery releases electrical energy through the battery discharge module and supplies the rail flash welding load through the welding equipment rectifier module and welding DC bus. Based on the preset links of energy transfer in the complete energy transfer path, mark the energy loss nodes in each link to obtain the complete energy transfer path.

4. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 2, characterized in that, The method, which combines the efficiency of the mains power path, diesel path, and energy storage path with the welding equipment control system, records historical welding process curves under different welding conditions and stages, including: The efficiency of the mains power path, diesel path, and energy storage path are preset into the welding equipment control system. Through the current and voltage acquisition modules of the welding equipment control system, the operating parameters of different welding conditions and different welding stages of rail flash welding are monitored in real time to obtain welding operation monitoring data. Based on the welding operation monitoring data, record the welding current, welding voltage, welding time, power supply mode type and corresponding path energy conversion efficiency data under each working condition and stage, and generate historical welding process curves; Historical welding process curves are categorized, stored, and labeled.

5. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 1, characterized in that, S2 includes: By combining comprehensive energy consumption information with the moving average method or short-term prediction model, key energy consumption indicators are predicted to obtain energy consumption indicator prediction data. A multi-dimensional comprehensive evaluation system is constructed based on energy consumption index prediction data; Energy consumption data for each power supply mode are calculated based on a multi-dimensional comprehensive evaluation system. Based on energy consumption data, the energy consumption performance of each power supply mode is analyzed and screened to obtain the target power supply mode with optimal energy consumption.

6. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 5, characterized in that, The energy consumption data for each power supply mode calculated based on the multi-dimensional comprehensive evaluation system includes: By using a multi-dimensional comprehensive evaluation system, the evaluation dimensions and weights are set according to the preset core objectives, and the dimension weight setting data is obtained. Calculate energy consumption data for the three power supply modes based on energy consumption index prediction data: By combining a weighted summation formula with dimensional weight settings, energy consumption-related data from each dimension are transformed into a single comprehensive evaluation value.

7. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 5, characterized in that, The process of analyzing and filtering the energy consumption performance of each power supply mode based on energy consumption-related data to obtain the target power supply mode with optimal energy consumption includes: The comprehensive evaluation values ​​of the three power supply modes are compared and ranked to obtain a comparative evaluation sequence. Candidate target power supply modes are determined based on the comparative evaluation sequence; Feasibility verification of candidate target power supply modes was conducted to obtain feasibility verification data; If a candidate target power supply mode meets all feasibility requirements, it is determined as the target power supply mode with optimal energy consumption. If the requirements are not met, the next lowest power supply mode with the best comprehensive evaluation value will be selected in turn, and the verification process will be repeated until the optimal energy consumption target power supply mode that meets the requirements is determined.

8. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 1, characterized in that, S3 includes: Based on the target power supply mode with optimal energy consumption, retrieve relevant data of the currently running power supply mode, calculate the difference in comprehensive evaluation value between the current power supply mode and the target power supply mode, and determine the switching threshold in combination with on-site construction needs; When the difference in the comprehensive evaluation value exceeds the preset threshold, and the short-term prediction model confirms that there are no abnormal characteristics, the power supply mode switching decision is initiated. The welding current status is monitored by the welding equipment control system, and a switching operation is performed during welding intervals or before the start of a welding process. After the switch is completed, the energy consumption monitoring module is activated to collect the mode operation data under the target power supply mode in real time; Based on the operational data of the model, the operational effect analysis was performed to obtain the power consumption effect data of the flash welding of the target rail.

9. The method for switching single-source power supply mode for rail flash welding based on energy consumption optimization according to claim 8, characterized in that, The process of analyzing the operational effect based on the mode operation data to obtain the power consumption effect data for flash welding of the target rail includes: The mode operation data under the target power supply mode is classified and organized to obtain energy consumption data. Compare and analyze the energy consumption data and comprehensive evaluation values ​​to calculate energy consumption analysis data; Based on energy consumption analysis data, determine whether the preset target has been achieved and obtain target judgment information; Based on energy consumption analysis data, perform operational anomaly analysis and obtain an operational performance analysis report; Based on the target judgment information and the operation effect analysis report, obtain the power consumption effect data of the flash welding of the target rail.

10. A system for implementing the energy consumption optimization-based single-source power supply mode switching method for rail flash welding as described in claim 1, characterized in that, The system includes: The welding fundamental analysis module is used to determine the energy consumption path of various single-source power supply modes, model and calculate the energy conversion efficiency, and collect relevant parameters in real time and record historical welding process curves to integrate the basic energy consumption data of power supply modes and obtain comprehensive energy consumption-related information. The energy consumption prediction module is used to predict key energy consumption indicators for future periods by combining basic energy consumption data with short-term prediction models, calculate the comprehensive evaluation value of each power supply mode, and obtain the target power supply mode with optimal energy consumption. The mode switching module is used to make decisions and execute power supply mode switching based on the target power supply mode, a preset threshold, and welding interval requirements, so as to obtain the power supply energy consumption effect data of the target rail flash welding.

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