Train whole-course recorded data optimization method based on time completion technology

Abstract

The invention discloses a train whole-course recorded data optimization method based on a time complementation technology, and the method comprises the steps: S1, combining Beidou positioning data with locomotive real-time operation data, and carrying out the integration to form an initial data set; s2, carrying out formatting processing; s3, generating a data set with complete and consistent time dimensions; s4, extracting total energy consumption and renewable energy consumption data from the time-aligned data set according to the railway bureau energy consumption management requirement, loading Beidou positioning data, and generating and uploading the Beidou positioning data to a railway bureau energy consumption management server in real time; s5, in the running process of the train, the electric energy measuring equipment outputs real-time energy consumption data and running coordinate data to a cab in real time; s6, generating the train basic traction energy consumption data file and the data file uploaded by the railway bureau according to a unified standard format; and S7, storing the standardized train basic traction energy consumption data file in a locomotive depot database. The method provides important technical support for efficient collaborative management of traction energy consumption data of the railway bureau and the locomotive depot.

Description

Technical Field

[0001] The present invention relates to the technical field of data optimization, and particularly to a method for optimizing train full-course traction energy consumption record data based on time filling technology. Background Art

[0002] With the development of railway transportation and the modernization of rail transit, the collection and management of train operation traction energy consumption data have gradually become an important part of the railway transportation industry. Traction energy consumption data is not only an important basis for optimizing train operation efficiency and improving energy utilization rate, but also a key data source for railway bureaus and locomotive depots to carry out train dispatching management, performance evaluation, and energy conservation and emission reduction work.

[0003] Currently, there are multiple sources of traction energy consumption data collection devices during train operation, such as traction energy consumption collection devices, renewable energy detection devices, and direct power supply energy consumption record devices. Due to different collection standards and time accuracies, the collected data is often inconsistent on the time axis. Similar problems also exist in the recording of the Beidou positioning system and real-time locomotive operation data. Time differences or clock drifts between data collection devices can cause misalignment of the time axes of multi-source data. In addition, during the data collection process, partial time period data may be lost due to equipment failures or signal losses, posing challenges to subsequent data analysis and integration work.

[0004] On the other hand, current railway bureaus and locomotive depots have different management requirements for traction energy consumption data. Railway bureaus pay more attention to the standardized management and macro dispatching decisions of traction energy consumption data, while locomotive depots need to conduct refined analysis and management of traction energy consumption in combination with operation data to improve operation efficiency and the operation level of drivers. However, existing technologies are difficult to balance these two types of requirements and usually only support data processing methods for single scenarios, resulting in insufficient adaptability of traction energy consumption data in different systems. In addition, the output support for real-time traction energy consumption data is also a weak link in current technologies. Many traction energy consumption data collection devices cannot provide data to the cab in real time for driving assistance, which directly affects the refined management ability of train operation.

[0005] In summary, existing technologies have significant defects in multi-source traction energy consumption data time alignment, standardized processing, and two-way adaptation of the requirements of railway bureaus and locomotive depots, and are difficult to meet the requirements of modern railway transportation systems for efficient, accurate, and flexible energy consumption data management. Therefore, there is an urgent need for an improved method to solve the above problems and enhance the integrity, consistency, and adaptability of energy consumption data in collection, processing, and application. Summary of the Invention

[0006] An object of the present invention is to propose an optimization method for train full - journey recorded data based on time filling technology, which provides important technical support for the efficient collaborative management of traction energy consumption by railway bureaus and locomotive depots.

[0007] An optimization method for train full - journey recorded data based on time filling technology according to an embodiment of the present invention includes the following steps:

[0008] S1. Collect the total energy consumption, regenerative energy consumption, and direct power supply energy consumption data generated during the train operation through an electric energy measurement device, record the real - time energy consumption of the locomotive in seconds, and at the same time, combine the Beidou positioning data and the real - time operation data of the locomotive to integrate and form an initial data set;

[0009] S2. Perform formatting processing on the total energy consumption, regenerative energy consumption, and direct power supply energy consumption data in the initial data set, and calibrate them according to the national electric energy measurement standard. At the same time, perform unified formatting processing on the Beidou positioning data and the real - time operation data of the locomotive, so that the initial data set meets the technical performance requirements and has a processing basis;

[0010] S3. Based on the time filling technology, perform unified time - axis alignment processing on the initial data set, eliminate the time misalignment caused by the time accuracy difference of data collection devices, and fill the missing time intervals caused by data collection interruption or data loss, generating a data set with a complete and consistent time dimension;

[0011] S4. Extract the total traction energy consumption and regenerative energy consumption data from the time - aligned data set according to the traction energy consumption management requirements of the railway bureau, and load the Beidou positioning data, generate and upload them to the traction energy consumption management server of the railway bureau in real - time. According to the traction energy consumption management requirements of the locomotive depot, extract the total energy consumption, regenerative energy consumption, and direct power supply energy consumption data from the time - aligned data set, and load the real - time operation data of the locomotive, generate a train basic traction energy consumption data file, and store it in the locomotive depot management system;

[0012] S5. During the train operation, the electric energy measurement device outputs real - time traction energy consumption data and operation coordinate data to the cab in real - time;

[0013] S6. Generate the train basic traction energy consumption data file and the data file uploaded by the railway bureau in accordance with a unified standard format, so as to ensure the compatibility and consistency of the traction energy consumption data between the railway bureau and the locomotive depot management systems;

[0014] S7. Store the standardized train basic traction energy consumption data file in the locomotive depot database, and provide two - way calls when needed, including providing real - time traction energy consumption data support to the railway bureau and providing offline energy consumption analysis and management support to the locomotive depot.

[0015] Optionally, the step S1 includes the following steps:

[0016] S11. Use an electric energy measurement device to collect the electric energy data generated during the train operation. The collected electric energy data includes the total traction energy consumption data E t , the renewable energy consumption data E r and the direct power supply energy consumption data E d ;

[0017] S12. Mark the collected electric energy data, including the total energy consumption data E t , the renewable energy consumption data E r and the direct power supply energy consumption data E d with time in seconds. The data for each second is represented by a timestamp T i :

[0018] (T i , E t (i), E r (i), E d (i));

[0019] Among them, T i represents the timestamp of data collection, in seconds, E t (i) represents the total energy consumption data at the i-th second, in kWh, E r (i) represents the renewable energy consumption data at the i-th second, in kWh, E d (i) represents the direct power supply energy consumption data at the i-th second, in kWh;

[0020] S13. Collect and load the Beidou positioning data and the real-time train operation data V i , and integrate them to form an initial data set D i containing spatio-temporal and operation information:

[0021] D i = (T i , E t (i), E r (i), E d (i), P i , V i );

[0022] Among them, P i = (X i , Y i ) represents the Beidou positioning information at the i-th second, X i , Y i represent the corresponding longitude and latitude respectively, and V i represents the train operation speed at the i-th second;

[0023] S14. During the recording process, associate the electric energy data with the Beidou positioning data P iMatch to confirm the accuracy of kilometer coordinates in meters while maintaining the timestamp T i with a second-level resolution, and finally construct a complete initial dataset D i .

[0024] Optionally, the S2 step includes the following steps:

[0025] S21. Format the total energy consumption data, renewable energy consumption data, and direct power supply energy consumption data in the initial dataset D i according to the national electric energy measurement standard, and unify all energy consumption data units to kilowatt-hours;

[0026] S22. Uniformly format the Beidou positioning data P i =(X i ,Y i ) in the initial dataset, convert the longitude and latitude into the standard coordinate system format, and make the data accuracy reach six decimal places;

[0027] S23. Unify and format the locomotive running speed data in the initial dataset, and unify the speed unit to meters per second;

[0028] S24. Calibrate the formatted energy consumption data, Beidou positioning data, and locomotive running speed data, and merge them into a formatted initial dataset D′ i :

[0029] D′ i =(T i ,E′ t (i),E′ r (i),E′ d (i),P′ i ,V′ i );

[0030] where D′ i represents a complete initial dataset that meets the national electric energy measurement standard and has been uniformly formatted.

[0031] Optionally, the S3 step includes the following steps:

[0032] S31. Detect the synchronization of the timestamps in the formatted initial dataset D′ i and the timestamps of the Beidou positioning data P′ i , calculate the time deviation ΔT 源 of each record in the multi-source data, and generate a reference time axis T 参考 by statistically analyzing all time deviations, and use the reference time axis as a unified time benchmark:

[0033] ΔT 源,j =T北斗,j -T 能耗,j , j = 1, 2, …, N;

[0034] Among them, T 北斗,j represents the timestamp of the j-th Beidou data, and T 能耗,j represents the timestamp of the j-th energy consumption data, and ΔT 源,j represents the multi-source time deviation of the j-th record;

[0035] S32. Construct a filling model based on dynamic time programming to optimize the continuity and rationality of the interpolated data during the time axis alignment, and define the objective function of the filling model:

[0036]

[0037] Among them, T 参考,i represents the i-th reference timestamp on the unified time axis, C 补齐 is the objective function of the time filling model, f(E′ t (i)) represents the prediction function of the energy consumption data, performs non-linear interpolation based on historical data, and α is the interpolation smoothing coefficient of the energy consumption data;

[0038] S33. Insert data points at the positions corresponding to the missing timestamp T i,k based on the optimization result of the dynamic time filling model, and adjust the deviation between the filled data and the reference time axis through dynamic time programming. The interpolation results are as follows:

[0039] T i,k = T i + k·ΔT 标准 , k = 1, 2, …, n;

[0040] E″ t (i, k) = f(E′ t (i));

[0041] E″ r (i, k) = g(E′ r (i));

[0042] E″ d (i, k) = h(E′ d (i));

[0043] Among them, f(E′ t (i)), g(E′ r (i)), h(E′ d (i)) respectively represent the interpolation prediction functions of the energy consumption data, and ΔT 标准 = 1 second, which is the standard value of the time interval;

[0044] S34. Match the interpolated data with the reference time axis to generate an optimized data set that is complete and consistent in the time dimension.

[0045]

[0046] Optionally, the S4 step includes the following steps:

[0047] S41. Extract the total energy consumption data, renewable energy consumption data, and Beidou positioning data from the optimized data set after time completion to generate an energy consumption data file F that contains the management requirements of the railway administration. : 铁路局

[0048] F 铁路局 = {(T i , E″ t (i), E″ r (i), P′ i ) | i = 1, 2, …, N};

[0049] Among them, F 铁路局 represents the data file that meets the energy consumption management requirements of the railway administration, and P′ i represents the Beidou positioning information;

[0050] S42. Upload the generated F 铁路局 data file to the energy consumption management server of the railway administration in real time through the data interface and record the upload status flag S 上传 :

[0051]

[0052] Among them, S 上传 (i) represents the upload status flag of the i-th data;

[0053] S43. Extract the total energy consumption data, renewable energy consumption data, direct power supply energy consumption data, and real-time train operation data from the optimized data set after time completion to generate a train basic traction energy consumption data file F ; 机务段

[0054] S44. Store the train basic traction energy consumption data file F 机务段 data file in the energy consumption management system of the locomotive depot and add a file index Index 机务段 according to the requirements of the management system;

[0055] S45. Monitor the status of the stored train basic traction energy consumption data file F 机务段 and the uploaded F 铁路局 file, and feedback the success rates of storage and upload to the management system through the data interface Rate成功 :

[0056]

[0057] Among them, Rate 成功 represents the proportion of successful data transmission and is used for real-time monitoring and adjustment of the data management process.

[0058] Optionally, the S6 step includes the following steps:

[0059] S61. Standardize the train basic traction energy consumption data file F 机务段 and the data file F 铁路局 uploaded by the railway administration, so that the two conform to the unified data format standard StdFormat. The processed data are respectively represented as and

[0060] S62. Perform consistency verification on the fields of the standardized data files to make the number, name, and order of the two fields consistent:

[0061]

[0062] Among them, CheckConsistency represents the result of field consistency verification. When the result is 0, readjust the standardization process.

[0063] S63. Merge the and after consistency verification to generate a data file F 合并 in a unified format. The merging method is field-by-field merging, and the merging result is expressed as:

[0064] F 合并 = {(T i , E″ t (i), E″ r (i), E″ d (i), P′ i , V′ i , Source) | i = 1, 2,..., N};

[0065] Among them, the Source field represents the data source, and the value is "locomotive depot" or "railway administration". T i , E″ t (i), E″ r (i), E″ d (i), P′ i , V′ i represent the timestamp, total energy consumption, regenerative energy consumption, direct power supply energy consumption, Beidou positioning information, and operating speed data respectively;

[0066] S64. Perform integrity verification on the merged data file F 合并 to generate a verification report;

[0067] S65. Store the merged unified format data file F 合并 in the energy consumption management system databases of the locomotive depot and the railway administration, and generate file indexes Index 机务段 and Index 铁路局 respectively.

[0068] Optionally, the S7 step includes the following steps:

[0069] S71. Store the standardized train basic traction energy consumption data file in the locomotive depot energy consumption management database, and simultaneously generate file index information, including file identifier, timestamp, and data verification information;

[0070] S72. When the railway administration issues a real-time energy consumption data request, extract the standardized energy consumption data that meets the call conditions from the locomotive depot database, and output it to the railway administration energy consumption management system in real time according to the requested time range;

[0071] S73. When performing offline energy consumption analysis in the locomotive depot, extract the complete energy consumption data file from the database for the locomotive depot to use for offline analysis, management, and optimization;

[0072] S74. Monitor the status of the real-time call and offline call processes, record the success and failure status of the calls, and generate a call status report, the content of which includes call success rate, call time range, and integrity information of the call results;

[0073] S75. Store the log records generated during the call process in the call log system for query and optimization management. The log information includes call time, call results, and feedback information, providing support for the quality monitoring of real-time and offline calls.

[0074] The beneficial effects of the present invention are:

[0075] (1) The present invention adopts the time filling technology, and through the dynamic time planning algorithm, it aligns the total energy consumption, renewable energy consumption, direct power supply energy consumption data with the Beidou positioning data on the unified time axis, effectively solving the time misalignment problem caused by the time accuracy difference of the acquisition devices, clock drift, and data loss. By detecting, marking, and dynamically filling the data time interval, it ensures the consistency and integrity of multi-source data in the time dimension. Compared with the traditional method, the time alignment accuracy is improved to the second level, filling the data missing and analysis error problems caused by the inconsistent time dimension in the traditional energy consumption management technology.

[0076] (2) In the process of generating and managing the energy consumption data file of the present invention, through a unified standardized processing mechanism, the two-way adaptability of the energy consumption data management requirements of the railway administration and the locomotive depot is ensured. On the railway administration side, a standardized energy consumption file is generated for macro management, and on the locomotive depot side, a traction energy consumption file combined with real-time operation data is generated for refined analysis and driver operation management. The two-way adaptation mechanism solves the data compatibility problem caused by different management requirements in the prior art, making the transmission and application of energy consumption data in different management systems more efficient and reliable.

[0077] (3) The present invention realizes the real-time output function of energy consumption data. The electric energy measurement device can output the energy consumption data in seconds and the Beidou positioning data to the cab in real time, providing high-precision basic data support for the driving assistance device, thereby improving the intelligent level of train operation management. At the same time, the locomotive depot can carry out offline analysis and performance evaluation work based on the stored complete energy consumption data file, providing a decision-making basis for optimizing the driver's operation. Through this function, the real-time energy consumption monitoring accuracy is significantly improved, and the applicability of offline analysis is further enhanced, providing an important technical support for the efficient collaborative management of the railway administration and the locomotive depot. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0079] Figure 1 is a flowchart of an optimization method for train full-course recording data based on time filling technology proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0081] Refer to Figure 1 , an optimization method for train full-course recording data based on time filling technology, includes the following steps:

[0082] S1. Collect the total energy consumption, regenerative energy consumption, and direct power supply energy consumption data generated during the train operation through the electric energy measurement device, record the real-time energy consumption of the locomotive in seconds, and at the same time, integrate the Beidou positioning data and the real-time operation data of the locomotive to form an initial data set;

[0083] S2. Format the total energy consumption, renewable energy consumption, and direct power supply energy consumption data in the initial dataset, calibrate them according to the national electric energy measurement standard, and uniformly format the Beidou positioning data and the real-time train operation data at the same time, so that the initial dataset meets the technical performance requirements and has a processing basis;

[0084] S3. Based on the time filling technology, perform unified time axis alignment processing on the initial dataset, eliminate the time misalignment caused by the time accuracy difference of data acquisition devices, fill the missing time intervals caused by data acquisition interruption or data loss, and generate a dataset with a complete and consistent time dimension;

[0085] S4. Extract the total energy consumption and renewable energy consumption data from the time-aligned dataset according to the energy consumption management requirements of the railway administration, and load the Beidou positioning data, generate and upload it to the energy consumption management server of the railway administration in real time. For the energy consumption management requirements of the locomotive depot, extract the total energy consumption, renewable energy consumption, and direct power supply energy consumption data from the time-aligned dataset, and load the real-time train operation data, generate a train basic traction energy consumption data file, and store it in the locomotive depot management system;

[0086] S5. During the train operation, the electric energy measurement device outputs the real-time energy consumption data and operation coordinate data to the cab in real time;

[0087] S6. Generate the train basic traction energy consumption data file and the data file uploaded by the railway administration according to the unified standard format, so as to ensure the compatibility and consistency of the data between the railway administration and the locomotive depot management system;

[0088] S7. Store the standardized train basic traction energy consumption data file in the locomotive depot database, and provide two-way calls when needed, including providing real-time energy consumption data support to the railway administration and providing offline energy consumption analysis and management support to the locomotive depot.

[0089] In this embodiment, step S1 includes the following steps:

[0090] S11. Use the electric energy measurement device to collect the electric energy data generated during the train operation. The collected electric energy data includes the total energy consumption data E t , the renewable energy consumption data E r , and the direct power supply energy consumption data E d ;

[0091] S12. Mark the collected electric energy data including the total energy consumption data E t , the renewable energy consumption data E r , and the direct power supply energy consumption data E d with time in seconds. Each second of data is represented by a time stamp T i :

[0092] (T i ,E t (i),E r (i),E d (i));

[0093] Among them, T i represents the timestamp of data acquisition, in seconds, and E t (i) represents the total energy consumption data at the i-th second, in kilowatt-hours, and E r (i) represents the renewable energy consumption data at the i-th second, in kilowatt-hours, and E d (i) represents the direct power supply energy consumption data at the i-th second, in kilowatt-hours;

[0094] S13. Collect and load Beidou positioning data and real-time locomotive operation data V i , and integrate them to form an initial dataset D i containing spatio-temporal and operation information:

[0095] D i =(T i ,E t (i),E r (i),E d (i),P i ,V i );

[0096] Among them, P i =(X i ,Y i ) represents the Beidou positioning information at the i-th second, and X i ,Y i represent the corresponding longitude and latitude respectively, and V i represents the locomotive operation speed at the i-th second;

[0097] S14. Match the electrical energy data with the Beidou positioning data P i during the recording process, confirm the accuracy of the kilometer coordinates in meters, and at the same time maintain the second-level resolution of the timestamp T i , and finally construct the complete initial dataset D i .

[0098] In this embodiment, step S2 includes the following steps:

[0099] S21. Format the total energy consumption data, renewable energy consumption data, and direct power supply energy consumption data in the initial dataset D i according to the national electrical energy measurement standard, and unify the unit of all energy consumption data to kilowatt-hours;

[0100] S22. For the Beidou positioning data P in the initial dataset i =(Xi ,Y i ) Perform unified formatting processing to convert longitude and latitude into the standard coordinate system format, so that the data accuracy reaches six decimal places;

[0101] S23. Unify the units and format the locomotive operation speed data in the initial dataset, and unify the speed unit to meters per second;

[0102] S24. Calibrate the formatted energy consumption data, Beidou positioning data, and locomotive operation speed data, and merge them into the formatted initial dataset D′ i :

[0103] D′ i =(T i ,E′ t (i),E′ r (i),E′ d (i),P′ i ,V′ i );

[0104] Among them, D′ i represents the complete initial dataset that meets the national electric energy measurement standard and has been unified and formatted.

[0105] In this embodiment, step S3 includes the following steps:

[0106] S31. Detect the synchronization of the timestamps in the formatted initial dataset D′ i and the timestamps of the Beidou positioning data P′ i , calculate the time deviation ΔT 源 of each record in the multi-source data, and generate a reference time axis T 参考 through statistical analysis of all time deviations, and use the reference time axis as the unified time benchmark:

[0107] ΔT 源,j =T 北斗,j -T 能耗,j ,j = 1,2,…,N;

[0108] Among them, T 北斗,j represents the timestamp of the jth Beidou data, T 能耗,j represents the timestamp of the jth energy consumption data, and ΔT 源,j represents the multi-source time deviation of the jth record;

[0109] S32. Construct a filling model based on dynamic time warping to optimize the continuity and rationality of the interpolated data during the time axis alignment process, and define the objective function of the filling model:

[0110]

[0111] Among them, T 参考,i represents the i-th reference timestamp on the unified timeline, and C 补齐 is the objective function of the time filling model, and f(E′ t (i)) represents the prediction function of energy consumption data, which performs non-linear interpolation based on historical data, and α is the smoothing coefficient of energy consumption data interpolation;

[0112] S33. Based on the optimization result of the dynamic time filling model, insert data points at the positions corresponding to the missing timestamps T i,k , and adjust the deviation between the filled data and the reference timeline through dynamic time programming. The interpolation results are as follows:

[0113] T i,k = T i + k·ΔT 标准 , k = 1, 2, …, n;

[0114] E″ t (i, k) = f(E′ t (i));

[0115] E″ r (i, k) = g(E′ r (i));

[0116] E″ d (i, k) = h(E′ d (i));

[0117] Among them, f(E′ t (i)), g(E′ r (i)), h(E′ d (i)) respectively represent the interpolation prediction functions of energy consumption data, and ΔT 标准 = 1 second, which is the standard value of the time interval;

[0118] S34. Match the interpolated data with the reference timeline to generate an optimized data set with a complete and consistent time dimension

[0119]

[0120] In this embodiment, step S4 includes the following steps:

[0121] S41. Extract the total energy consumption data, renewable energy consumption data, and Beidou positioning data from the optimized data set after time filling to generate an energy consumption data file F that contains the management requirements of the railway administration: 铁路局 :

[0122] F 铁路局 = {(T i,E″ t (i),E″ r (i),P′ i )∣i=1,2,…,N};

[0123] Among them, F 铁路局 represents a data file that meets the energy consumption management requirements of the railway administration, and P′ i represents Beidou positioning information;

[0124] S42. Upload the generated F 铁路局 data file to the energy consumption management server of the railway administration in real time through the data interface, and record the upload status flag S 上传 :

[0125]

[0126] Among them, S 上传 (i) represents the upload status flag of the i-th piece of data;

[0127] S43. Extract the total energy consumption data, renewable energy consumption data, direct power supply energy consumption data, and real-time train operation data from the optimized data set after time filling, and generate a train basic traction energy consumption data file F ; 机务段 ;

[0128] S44. Store the train basic traction energy consumption data file F 机务段 data file in the energy consumption management system of the locomotive depot, and add a file index Index 机务段 ;

[0129] S45. Monitor the status of the stored train basic traction energy consumption data file F 机务段 and the uploaded F 铁路局 file, and feedback the success rate Rate 成功 of storage and upload to the management system through the data interface:

[0130]

[0131] Among them, Rate 成功 represents the proportion of successful data transmission, and is used for real-time monitoring and adjustment of the data management process.

[0132] In this embodiment, the S6 step includes the following steps:

[0133] S61. Standardize the train basic traction energy consumption data file F 机务段 and the data file F 铁路局 uploaded by the railway administration so that the two conform to the unified data format standard StdFormat, and the processed data are respectively represented as and

[0134] S62. Perform consistency check on the fields of the standardized data file to make the number, name, and order of the two sets of fields consistent:

[0135]

[0136] Among them, CheckConsistency represents the result of the field consistency check. When the result is 0, readjust the standardization process.

[0137] S63. Combine the and to generate a data file F in a unified format 合并 , and the combination method is corresponding field combination. The combination result is expressed as:

[0138] F 合并 ={(T i , E″ t (i), E″ r (i), E″ d (i), P′ i , V′ i , Source) | i = 1, 2,..., N};

[0139] Among them, the Source field represents the data source, and the value is "locomotive depot" or "railway administration". T i , E″ t (i), E″ r (i), E″ d (i), P′ i , V′ i represent the timestamp, total energy consumption, renewable energy consumption, direct power supply energy consumption, Beidou positioning information, and operating speed data respectively;

[0140] S64. Perform integrity check on the combined data file F 合并 to generate a check report;

[0141] S65. Store the combined data file F in the unified format in the energy consumption management system databases of the locomotive depot and the railway administration, and generate file indexes Index 合并 and Index 机务段 and Index 铁路局 respectively.

[0142] In this embodiment, step S7 includes the following steps:

[0143] S71. Store the standardized train basic traction energy consumption data file in the energy consumption management database of the locomotive depot, and simultaneously generate file index information, including file identifier, timestamp, and data verification information;

[0144] S72. When the railway administration issues a real-time energy consumption data request, extract the standardized energy consumption data that meets the call conditions from the locomotive depot database, and output it to the energy consumption management system of the railway administration in real time according to the requested time range;

[0145] S73. When performing offline energy consumption analysis in the locomotive depot, extract the complete energy consumption data file from the database for the locomotive depot to use for offline analysis, management, and optimization;

[0146] S74. Monitor the status of the real-time call and offline call processes, record the success and failure status of the calls, and generate a call status report, the content of which includes the call success rate, call time range, and integrity information of the call results;

[0147] S75. Store the log records generated during the call process in the call log system for query and optimization management. The log information includes the call time, call results, and feedback information, providing support for the quality monitoring of real-time and offline calls.

[0148] Embodiment 1:

[0149] In the embodiment, at 10:30 am on June 21, 2024, a train numbered CR400AF-121 departed from the South Station of City A and executed the high-speed railway mission from City A to City B. The planned running time of the train for the whole journey was 4 hours and 28 minutes, and it stopped at the West Station of City C and the South Station of City D along the way. To verify the effectiveness of the method of the present invention, real-time data collection, optimization, and analysis were carried out during the train operation.

[0150] Within the first hour after departure, in the section where the train was traveling from the South Station of City A to Tianjin West Station, the running speed was maintained at about 300 kilometers per hour. During this period, the electric energy measurement device recorded the total energy consumption, regenerative energy consumption, and direct power supply energy consumption data at a frequency of once per second. At the same time, the Beidou positioning system synchronously collected the longitude and latitude position information of the train. At a certain moment, the data recorded by the electric energy measurement device was as follows:

[0151] The total energy consumption was 18.4 kWh, the regenerative energy consumption was 3.1 kWh, the direct power supply energy consumption was 1.2 kWh, and the longitude and latitude were 39.9042, 116.4074.

[0152] At 11:45 am, the train left West Station of City C and entered a mountainous area with a complex operating line. Within this section, the operating speed fluctuated. The real-time data record showed that within 15 consecutive seconds, there was an inconsistency in the timestamps of the Beidou positioning data and the energy consumption data. The timestamp record of the Beidou positioning data showed from 11:45:02 to 11:45:16, while the timestamp of the energy consumption data only recorded from 11:45:02 to 11:45:12. Through the time filling technology of the present invention, the system automatically detected the data loss and filled in the energy consumption data from 11:45:13 to 11:45:16 through the dynamic interpolation algorithm.

[0153] The generated data record after filling is as follows:

[0154] Timestamp 11:45:13: Total energy consumption is 20.6 kWh, renewable energy consumption is 3.5 kWh, and direct power supply energy consumption is 1.5 kWh.

[0155] Timestamp 11:45:14: Total energy consumption is 20.8 kWh, renewable energy consumption is 3.6 kWh, and direct power supply energy consumption is 1.6 kWh.

[0156] Timestamp 11:45:15: Total energy consumption is 20.9 kWh, renewable energy consumption is 3.6 kWh, and direct power supply energy consumption is 1.6 kWh.

[0157] Timestamp 11:45:16: Total energy consumption is 21.0 kWh, renewable energy consumption is 3.7 kWh, and direct power supply energy consumption is 1.7 kWh.

[0158] The filled data successfully solved the problem of missing energy consumption data caused by unstable mountain signals, ensuring the integrity of the train operation data in the time dimension.

[0159] At 13:00 pm, the train entered the buffer zone in front of South Station of City D. The system automatically uploaded the standardized energy consumption data generated after filling to the energy consumption management server of the railway administration. In the uploaded data file, it was recorded that the total energy consumption of the whole journey of the train from South Station of City A to South Station of City D was 874.6 kWh, the renewable energy consumption was 145.2 kWh, and the direct power supply energy consumption was 73.8 kWh. The railway administration called this data in real time for train operation energy consumption analysis and found that the renewable energy utilization rate of this train was as high as 16.6%, which was significantly better than the average value of the same type of trains.

[0160] On the locomotive depot side, after the train arrived at South Station of City D, the locomotive depot system automatically extracted the complete train operation energy consumption file from the database and conducted an offline analysis of the driver's operation mode in combination with the Beidou positioning data and the running speed data. The analysis results showed that the driver carried out an early deceleration operation before entering South Station of City D, with a significant energy-saving effect and an energy-saving potential of 5.4%.

[0161] Through the verification of this experiment, the method of the present invention has successfully solved the problems of time dislocation and loss of multi-source data in the traditional method, and realized the two-way adaptation requirements of the railway bureau and the locomotive depot. At the same time, through the supplementation and optimization of real-time energy consumption data, the accuracy and efficiency of train operation energy consumption management have been effectively improved.

[0162] The present invention adopts a time supplementation technology, and through a dynamic time planning algorithm, the total energy consumption, regenerative energy consumption, direct power supply energy consumption data and Beidou positioning data are aligned on a unified time axis, effectively solving the time dislocation problem caused by the time accuracy difference of acquisition devices, clock drift and data loss. By detecting, marking and dynamically supplementing the data time interval, the consistency and integrity of multi-source data in the time dimension are ensured. Compared with the traditional method, the time alignment accuracy is improved to the second level, filling the data missing and analysis error problems caused by the inconsistent time dimension in the traditional energy consumption management technology.

[0163] In the process of generating and managing the energy consumption data file, the present invention ensures the two-way adaptability of the energy consumption data management requirements of the railway bureau and the locomotive depot through a unified standardization processing mechanism. On the railway bureau side, a standardized energy consumption file is generated for macro management, and on the locomotive depot side, a traction energy consumption file combined with real-time operation data is generated for refined analysis and driver operation management. The two-way adaptation mechanism solves the data compatibility problem caused by different management requirements in the prior art, making the transmission and application of energy consumption data in different management systems more efficient and reliable.

[0164] The present invention realizes the real-time output function of energy consumption data. The electric energy measurement device can output the energy consumption data in seconds and the Beidou positioning data to the cab in real time, providing high-precision basic data support for the driving assistance device, thereby improving the intelligent level of train operation management. At the same time, the locomotive depot can carry out offline analysis and performance evaluation work according to the stored complete energy consumption data file, providing a decision-making basis for optimizing the driver's operation. Through this function, the real-time energy consumption monitoring accuracy is significantly improved, and the applicability of offline analysis is further enhanced, providing an important technical support for the efficient collaborative management of the railway bureau and the locomotive depot.

[0165] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing train full-course record data based on time completion technology, characterized in that: The steps include: S1. Collect the total energy consumption, renewable energy consumption and direct power supply energy consumption data generated during the operation of the train through the power measurement equipment, record the real-time energy consumption of the locomotive in seconds, and combine the Beidou positioning data and the real-time operation data of the locomotive to form an initial data set; S2. Format the total energy consumption, renewable energy consumption and direct power supply energy consumption data in the initial data set, and calibrate them according to the national electric energy measurement standards. At the same time, uniformly format the Beidou positioning data and locomotive real-time operation data to make the initial data set meet the technical performance requirements and have a processing basis; S3. Perform unified time axis alignment processing on the initial data set based on time completion technology, eliminate time dislocation caused by time accuracy differences of data acquisition devices, fill in time interval missing caused by data acquisition interruption or data loss, and generate a data set with complete and consistent time dimension; S4. To meet the traction energy consumption management needs of the railway bureau, the total energy consumption and renewable energy consumption data are extracted from the time-aligned data set, and the Beidou positioning data is loaded to generate and upload to the railway bureau's traction energy consumption management server in real time. To meet the energy consumption management needs of the electric locomotives in the locomotive depot, the total energy consumption, renewable energy consumption and direct power supply energy consumption data are extracted from the time-aligned data set, and the real-time operation data of the locomotive is loaded to generate the basic traction energy consumption data file of the train, and store it in the electric locomotive management system of the locomotive depot; S5. During the operation of the train, the power measurement equipment outputs real-time energy consumption data and operation coordinate data to the cab in real time; S6. Generate the basic traction energy consumption data file of the train and the data file uploaded by the railway bureau in a unified standard format to make the traction energy consumption data compatible and consistent between the railway bureau and the locomotive depot management system; S7. Store the standardized basic train traction energy consumption data files in the locomotive depot database and provide two-way calls when needed, including providing real-time traction energy consumption data support to the railway bureau and providing offline traction energy consumption analysis and management support to the locomotive depot.

2. The method for optimizing train full-course record data based on time completion technology according to claim 1 is characterized in that: The S1 step includes the following steps: S11. Use power measurement equipment to collect power data generated during train operation. The collected power data includes total energy consumption data E t , Renewable energy consumption data E r And direct power supply energy consumption data E d ; S12. Collected power data including total energy consumption data E in seconds t , Renewable energy consumption data E r And direct power supply energy consumption data E d The data is time-stamped, and each second of data is timestamped by T i express: (T i ,E t (i),E r (i),E d (i)); Among them, T i Indicates the timestamp of data collection, in seconds, E t (i) represents the total energy consumption data at the i-th second, in kilowatt-hours, E r (i) represents the renewable energy consumption data at the i-th second, in kilowatt-hours, E d (i) represents the direct power supply energy consumption data at the i-th second, in kilowatt-hours; S13. Collect and load Beidou positioning data and locomotive real-time operation data V i , integrating to form an initial data set D containing spatiotemporal and operational information i : D i =(T i ,E t (i),E r (i),E d (i),P i ,V i ); Among them, P i =(X i ,Y i ) represents the Beidou positioning information at the i-th second, X i ,Y i Represent the corresponding longitude and latitude, V i represents the locomotive running speed at the i-th second; S14. During the recording process, the power data and Beidou positioning data P i Matching, confirming the accuracy of kilometer coordinates in meters while maintaining the timestamp T i The second-level resolution is finally used to construct the complete initial dataset D i .

3. The method for optimizing train full-course record data based on time completion technology according to claim 1 is characterized in that: The S2 step includes the following steps: S21. For the initial data set D i The total energy consumption data, renewable energy consumption data and direct power supply energy consumption data are formatted according to the national electric energy measurement standards, and all energy consumption data units are unified into kilowatt-hours; S22. Beidou positioning data P in the initial data set i =(X i ,Y i ) to uniformly format the longitude and latitude into a standard coordinate system format, so that the data accuracy reaches six decimal places; S23. Unify and format the locomotive running speed data in the initial data set, and unify the speed unit into meters per second; S24. Calibrate the formatted energy consumption data, Beidou positioning data and locomotive running speed data, and merge them into the formatted initial data set D′ i : D′ i =(T i ,E′ t (i),E′ r (i),E′ d (i),P′ i ,V′ i ); Among them, D′ i It represents a complete initial data set that complies with national electric energy measurement standards and is uniformly formatted.

4. The method for optimizing train full-course record data based on time completion technology according to claim 1 is characterized in that: The S3 step includes the following steps: S31. Format the initial data set D′ i The timestamp in the Beidou positioning data P′ i The timestamp is used for synchronization detection, and the time deviation ΔT of each record in the multi-source data is calculated. 源 , by counting all time deviations to generate a reference time axis T 参考 , using the reference time axis as the unified time base: ΔT 源,j =T 北斗,j -T 能耗,j ,j=1,2,…,N; Among them, T 北斗,j Indicates the timestamp of the jth Beidou data, T 能耗,j Indicates the timestamp of the j-th energy consumption data, ΔT 源,j represents the multi-source time deviation of the j-th record; S32. Construct a completion model based on dynamic time planning, optimize the continuity and rationality of interpolation data during time axis alignment, and define the completion model objective function: Among them, T 参考,i represents the i-th reference timestamp on the unified time axis, C 补齐 is the objective function of the time completion model, f(E′ t (i)) represents the prediction function of energy consumption data, which performs nonlinear interpolation based on historical data, and α is the interpolation smoothing coefficient of energy consumption data; S33. Optimization results based on dynamic time completion model in missing timestamp T i,k Data points are inserted at the corresponding positions, and the deviation between the padded data and the reference time axis is adjusted through dynamic time planning. The interpolation results are as follows: T i,k =T i +k·ΔT 标准 ,k=1,2,…,n; E″ t (i,k)=f(E′ t (i)); E″ r (i,k)=g(E′ r (i)); E″ d (i,k)=h(E′ d (i)); Among them, f(E′ t (i)), g(E′ r (i)), h(E′ d (i)) represent the interpolation prediction function of energy consumption data, ΔT 标准 =1 second, which is the standard value of time interval; S34. Match the interpolated data with the reference time axis to generate an optimized data set with complete and consistent time dimension 5. The method for optimizing train full-course record data based on time completion technology according to claim 1 is characterized in that: The S4 step includes the following steps: S41. Optimized dataset after time completion Extract total energy consumption data, renewable energy consumption data and Beidou positioning data from the data to generate an energy consumption data file containing the management needs of the railway bureau. 铁路局 : F 铁路局 ={(T i ,E″ t (i),E″ r (i),P′ i )∣i=1,2,…,N}; Among them, F 铁路局 Represents a data file that meets the energy consumption management requirements of the railway bureau, P′ i Indicates Beidou positioning information; S42. The generated F 铁路局 The data file is uploaded to the railway bureau energy consumption management server in real time through the data interface, and the upload status mark S is recorded. 上传 : Among them, S 上传 (i) indicates the upload status mark of the i-th data; S43. Optimized dataset after time completion Extract the total energy consumption data, renewable energy energy consumption data, direct power supply energy consumption data and locomotive real-time operation data from the data to generate the train basic traction energy consumption data file F 机务段 ; S44. The train basic traction energy consumption data file F 机务段 The data files are stored in the locomotive depot energy consumption management system, and the file index is added according to the requirements of the management system. 机务段 ; S45. The stored train basic traction energy consumption data file F 机务段 And uploaded F 铁路局 The file status is monitored and the success rate of storage and upload is fed back to the management system through the data interface. 成功 : Among them, Rate 成功 Indicates the proportion of successfully transmitted data and is used to monitor and adjust data management processes in real time.

6. The method for optimizing train full-course record data based on time completion technology according to claim 1 is characterized in that: The step S6 comprises the following steps: S61. Train basic traction energy consumption data file F 机务段 and data files uploaded by the Railway Administration 铁路局 Standardization is performed to make them conform to the unified data format standard StdFormat. The processed data are represented as and S62. Perform consistency check on the standardized data file fields to ensure that the number, name and order of fields in the two are consistent: Among them, CheckConsistency represents the result of the field consistency check. When the result is 0, the standardization process is readjusted. S63. and Merge to generate a unified format data file F 合并 , the merging method is to merge the fields accordingly, and the merging result is expressed as: F 合并 ={(T i ,E″ t (i),E″ r (i),E″ d (i),P′ i ,V′ i ,Source)∣i=1,2,…,N}; The Source field indicates the source of the data, and its value is "Locomotive Depot" or "Railway Bureau". i , E″ t (i) E″ r (i) E″ d (i), P′ i , V′ i Respectively represent timestamp, total energy consumption, renewable energy consumption, direct power supply consumption, Beidou positioning information and operating speed data; S64. The merged data file F 合并 Perform integrity check and generate a check report; S65. The merged unified format data file F 合并 The energy consumption management system database stored in the locomotive depot and railway bureau generates file indexes respectively. 机务段 and Index 铁路局 .

7. The method for optimizing train full-course record data based on time completion technology according to claim 1 is characterized in that: The S7 step includes the following steps: S71. Store the standardized train basic traction energy consumption data file in the locomotive depot energy consumption management database, and generate file index information, including file identifier, timestamp and data verification information; S72. When the railway bureau issues a request for real-time energy consumption data, the standardized energy consumption data that meets the call conditions is extracted from the locomotive depot database and output to the railway bureau energy consumption management system in real time according to the requested time range; S73. When the locomotive depot performs offline energy consumption analysis, a complete energy consumption data file is extracted from the database for offline analysis, management and optimization of the locomotive depot; S74. Monitor the status of real-time and offline calls, record the success and failure status of the calls, and generate a call status report, including the call success rate, call time range and call result integrity information; S75. The log records generated during the call process are stored in the call log system for query and optimization management. The log information includes the call time, call results and feedback information, providing support for quality monitoring of real-time and offline calls.

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