Train operation dynamic visualization method, system, medium and equipment

Abstract

The invention provides a train operation dynamic visualization method and system, a medium and equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining train operation data and a station data graph; sorting the train operation data along a set time sequence line, and taking the train operation unit data corresponding to each time node as an associated parameter of the time node on the time sequence line; for each train line, aligning the corresponding time sequence line with the station data line, taking the station as a fixed node, and taking the time node as an active node; taking the time information of the time node as a parameter, and sliding the time node along the aligned station data line to obtain the running dynamic state of the single train line; and integrating the running dynamic states of all the train lines, and rendering to obtain train running dynamic data. The train operation dynamic data can be conveniently output to operation and maintenance personnel, and the train operation dynamic data can be conveniently and visually analyzed.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, system, medium and device for visualizing the dynamic operation of a train. Background Art

[0002] Data on train operation, energy consumption, energy storage, etc. usually have a very large amount of data. Operation and maintenance personnel usually use Excel spreadsheets or platform reports to do this. However, the spreadsheet data is not intuitive, maintenance is time-consuming and labor-intensive, and the data in each aspect is relatively independent, making it impossible to achieve comprehensive correlation analysis. Summary of the Invention

[0003] The purpose of this application is to provide a train operation dynamic visualization method, system, computer-readable storage medium and electronic device, which can realize the visualization of train operation data and facilitate users to understand the train operation status in real time.

[0004] To solve the above technical problems, this application provides a method for visualizing train operation dynamics. The specific technical solutions are as follows:

[0005] Obtain train operation data and station data maps;

[0006] Sorting the train operation data along a set time series line, and using the train operation unit data corresponding to each time node on the time series line as an associated parameter of the time node;

[0007] For each train line, align the corresponding time series line with the station data line in the station data graph, treat the station as a fixed node and the time node as an active node;

[0008] Using the time information of the time node as a parameter, the time node is slid along the aligned station data line to obtain the operation dynamics of a single train line; wherein the sliding starts from the first station where the train starts running along the train line and is removed from the station data line when the train stops running;

[0009] Integrate the operation dynamics of all train lines and render the train operation dynamic data.

[0010] Optionally, before obtaining the train operation data and station data map, the following steps are also included:

[0011] Determine the location information of each train station;

[0012] The station distances between the train stations are proportionally scaled according to the location information to generate the station data map.

[0013] Optionally, when generating the station data map by scaling the station spacing between the train stations in equal proportion according to the location information, the method further includes:

[0014] For train lines that exceed the screen width, fold them according to the set folding rules;

[0015] The substation and the energy storage device are marked on the site data map according to the actual location data; the substation and the energy storage device are used to indicate the corresponding status data of the train.

[0016] Optionally, the train operation data is sorted along a set time series line, and on the time series line, the train operation unit data corresponding to each time node is used as an associated parameter of the time node, including:

[0017] Obtaining a train schedule, and constructing a time series line based on the train schedule;

[0018] The train operation data is divided into a plurality of train operation unit data based on a set duration; wherein each train operation unit data includes the start and end time, the current train number, the next train number, the running direction, the station time data and the arrival station data;

[0019] On the time series line, a number of time node intervals are obtained using the set time length as a division unit;

[0020] The train operation unit data is associated with the time node interval according to the corresponding start and end times and arrival data; each time node interval includes the current time node and a line segment extending to the next time node.

[0021] Optionally, for each train line, aligning the corresponding time series line with the station data line in the station data graph, treating the station as a fixed node, and treating the time node as an active node includes:

[0022] For each train line, determining an arrival time point in the time nodes, and associating the time node including the arrival time point with the station in the station data map as a reference time node;

[0023] The other time nodes between the reference time nodes are sequentially set between adjacent sites in the site data graph in chronological order.

[0024] Optionally, using the time information of the time node as a parameter, sliding the time node along the aligned station data line to obtain the operation dynamics of a single train line includes:

[0025] Confirm the time interval information corresponding to the time node;

[0026] If the time interval information of all time nodes between adjacent stations matches the train running time between adjacent stations, all time nodes between adjacent stations are removed;

[0027] Repeat the above steps until all time nodes except stations in a single train line are removed;

[0028] A sliding time point is set in a single train line, and all train operation unit data when the sliding time point slides along the single train line is used as the operation dynamics of the single train line; the operation dynamics include the real-time status data of the train at any moment, the traction substation, energy storage information and real-time energy consumption.

[0029] Optionally, after integrating the running dynamics of all train lines and rendering the train running dynamics data, it also includes:

[0030] Obtain historical train operation data and historical station data maps;

[0031] Obtaining historical train operation dynamic data based on the historical train operation data and the historical station data map rendering;

[0032] Integrating the historical site data graph and the site data graph to obtain an overlapping site data graph;

[0033] In the overlapped station data graph, the train operation dynamic data is displayed in a first display mode, and the historical train operation dynamic data is displayed in a second display mode;

[0034] The train operation dynamic data and the historical train operation dynamic data are compared to generate operation dynamic change data.

[0035] This application also provides a train operation dynamic visualization system, including:

[0036] Data acquisition module, used to obtain train operation data and station data map;

[0037] a data processing module, configured to sort the train operation data along a set time series line, and use the train operation unit data corresponding to each time node on the time series line as an associated parameter of the time node;

[0038] a data alignment module, for aligning, for each train line, a corresponding time series line with a station data line in the station data graph, treating the station as a fixed node and the time node as an active node;

[0039] A dynamic output module is configured to slide the time node along the aligned station data line using the time information of the time node as a parameter to obtain the operation dynamics of a single train line; wherein the sliding starts from the first station where the train starts running along the train line and is removed from the station data line when the train stops running;

[0040] The dynamic rendering module is used to integrate the operation dynamics of all train lines and render the train operation dynamic data.

[0041] The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-described method when executed by a processor.

[0042] The present application also provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned method when calling the computer program in the memory.

[0043] The present application provides a method for visualizing train operation dynamics, including: obtaining train operation data and a station data graph; sorting the train operation data along a set time series line, and on the time series line, using the train operation unit data corresponding to each time node as an associated parameter of the time node; for each train line, aligning the corresponding time series line with the station data line in the station data graph, using the station as a fixed node and the time node as an active node; using the time information of the time node as a parameter, sliding the time node along the aligned station data line to obtain the operation dynamics of a single train line; wherein the sliding starts from the first station where the train starts to run along the train line and is removed from the station data line when the train stops running; integrating the operation dynamics of all train lines and rendering the train operation dynamics data.

[0044] After obtaining the train operation data and station data diagram, this application associates the train operation data with the time series line, and uses the station as a fixed node and the time node as an active node to generate the operation dynamics of each train line, serializes and stores the train operation data, and associates and analyzes the data according to the time series, so as to facilitate the output of the train operation dynamic data to the operation and maintenance personnel and facilitate the visual analysis of the train operation dynamic data.

[0045] The present application also provides a train operation dynamic visualization system, a computer-readable storage medium and an electronic device, which have the above-mentioned beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0047] Figure 1 A flow chart of the train operation dynamic visualization method provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of train operation dynamic data comparison provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the structure of a train operation dynamic visualization system provided in an embodiment of the present application;

[0050] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] See also Figure 1 , Figure 1 This is a flow chart of a method for visualizing train operation dynamics provided in an embodiment of the present application, the method comprising:

[0053] S101: Obtain train operation data and station data map;

[0054] S102: sorting the train operation data along a set time series line, and using the train operation unit data corresponding to each time node on the time series line as an associated parameter of the time node;

[0055] S103: For each train line, align the corresponding time series line with the station data line in the station data graph, treat the station as a fixed node, and treat the time node as an active node;

[0056] S104: Using the time information of the time node as a parameter, the time node is slid along the aligned station data line to obtain the running dynamics of a single train line; wherein the sliding starts from the first station where the train starts running along the train line and is removed from the station data line when the train stops running;

[0057] S105: Integrate the running dynamics of all train lines and render to obtain train running dynamic data.

[0058] First, train operation data is obtained, such as train schedules, speed curves, switching stations, traction substations, energy storage, real-time energy consumption, and ledgers. A station data map can be generated in advance by determining the location information of each train station and proportionally scaling the station spacing between the train stations based on the location information.

[0059] Furthermore, when generating a station data map, train lines that exceed the screen width can be folded according to pre-set line-breaking rules. Substations and energy storage devices are annotated on the station data map based on their actual location data. These substations and energy storage devices are used to indicate the corresponding train status data.

[0060] Specifically, on the web page, you can use the offsetWidth property in JavaScript (a programming language) to obtain the width of the train line display element. On the web page, you can use window.innerWidth to obtain the width of the current browser window, which serves as a measure of the screen width. (Mobile apps can also use the same method to obtain the device screen width.) The obtained train line element width is compared with the screen width. If the line element width exceeds the screen width, it is determined that line wrapping is necessary.

[0061] When wrapping lines, you can break at a fixed number of characters, for example, every 10 characters. You can split the train route text string according to this number of characters, then insert the new lines into new lines for display. Alternatively, you can break the lines at specific stations or separators: If the train route text has distinct station names, you can use these separators as line breaks to display the lines across multiple lines.

[0062] Another way to break rows is to break them based on actual distance. For example, a row of 1000 pixels represents an actual distance of 10 km. If the originating station, A, is at position 0 pixels, and Station B is 2 km from Station A, then Station B is at position 200 pixels. Station C is 6 km from Station B, then Station C is at position 800 pixels. Station D is 5 km from Station B. At this point, the first row cannot be opened, and there are still 200 pixels left in the first row (representing 2 km). Therefore, Station C is located at 300 pixels in the second row (representing 3 km).

[0063] When executing step S102 , the purpose is to construct the time relationship of the train operation data, and to associate the train operation unit data with the time nodes by sorting along the time series line.

[0064] In a feasible implementation, the following steps may be included:

[0065] The first step is to obtain a train schedule and construct a time series line based on the train schedule;

[0066] Step 2: Divide the train operation data into a number of train operation unit data based on the set duration; wherein each train operation unit data includes the start and end time, current train number, next train number, operation direction, station time data and arrival data;

[0067] Step 3: On the time series line, a number of time node intervals are obtained using the set time length as a division unit;

[0068] Step 4: Associate the train operation unit data with the time node interval according to the corresponding start and end times and arrival data; each time node interval includes the current time node and a line segment extending to the next time node.

[0069] There are no restrictions on how to obtain train operation data. One feasible approach is to directly obtain multi-source heterogeneous fusion data. Multi-source heterogeneous fusion data includes five data sources: onboard sensor arrays (including inertial navigation, axle monitoring, and traction energy consumption modules), trackside detection base stations, signal system log servers, power monitoring systems, and passenger mobile terminal signaling data. This provides full-dimensional perception of the train's operating status. Unlike traditional single-source data collection methods, when obtaining multi-source heterogeneous fusion data, an edge computing gateway can be introduced to pre-process the raw data. FPGA chips can be used to implement millisecond-level anomaly data annotation, reducing cloud transmission pressure.

[0070] For example, if the timetable data is obtained from a data file, you can use a related library (such as pandas) in a programming language (such as Python) to read the timetable data in the file and load the data into a suitable data structure (such as DataFrame, list, etc.) in memory for subsequent processing.

[0071] Based on the time information in the train timetable (including the arrival and departure times of each train), the corresponding time points are marked on the timeline to construct a time series line, which will help to associate the train operation data with time for subsequent analysis and processing.

[0072] Determine a set duration as the division basis, for example, set the duration to 10 minutes. Starting from the train timetable, divide the data in sequence according to the set duration to obtain several train operation unit data.

[0073] See Table 1, which shows a train schedule provided by this application. It can be seen from Table 1 that trains have standard operating time points. Stations can also be mapped at the front end by converting them into pixels based on the distance between stations. Therefore, when dragging a sliding time point, the train's current location can be calculated and displayed based on each train line, such as which station it is at or between two stations.

[0074] Table 1 Train timetable

[0075]

[0076] Each train operation unit data can contain the following information:

[0077] Start and end time: the start and end time of the divided time period, which are determined according to the set duration. For example, for the first unit data, the start time may be the start time of the timetable, and the end time is the start time plus the set duration.

[0078] Current train number: The train number corresponding to this time period is extracted and determined based on the train number information in the timetable.

[0079] Next train: The next train following the current train, which can be obtained by searching the train records following the current train in the timetable.

[0080] Running direction: The running direction of the train (such as up, down, etc.) is determined from the relevant direction field in the timetable.

[0081] Station time data: includes the arrival and departure times of trains at various stations. It is extracted from the station time information of the corresponding train in the timetable and covers the relevant station times within the time period.

[0082] Arrival data: lists the names of the stations that the train arrives at during the time period, and is organized according to the station names and arrival times in the station time data.

[0083] Using the set duration as the unit of division, the constructed time series line is divided into several time nodes starting from the start time. For example, if the set duration is 10 minutes and the start time is 6:00 AM, the time nodes are 6:00, 6:10, 6:20, and so on. These time nodes divide the time series into multiple intervals for subsequent association with train operation unit data. In other embodiments of the present application, the division of time nodes does not necessarily need to be evenly divided.

[0084] For each train operation unit data, its start and end times are used to determine which time node interval on the time series line it falls into. For example, if a train operation unit data has a start and end time from 6:05 to 6:15, then it is associated with the interval consisting of the two time nodes 6:00 - 6:10 and 6:10 - 6:20.

[0085] At the same time, combined with the arrival data, we can see the stations where the train arrives during the time period, and further associate it with the time node interval, so as to facilitate subsequent analysis and display of the train operation status on the timeline. For example, you can intuitively see the train's driving status, arrival status and other information within the time node interval.

[0086] In step S103 , by aligning the time series line and the site data line, most of the time nodes on the time series line are blurred to form active nodes for easy viewing by the user.

[0087] Specifically, for each train line, the arrival time point in the time node is determined, the time node containing the arrival time point is used as the reference time node and associated with the station in the station data map, and then the other time nodes between the reference time nodes are set in sequence between adjacent stations in the station data map in chronological order.

[0088] For each train line, the arrival time of each station is extracted from the timetable. The timetable usually records the arrival and departure times of each train at each station. These arrival time points are marked on the constructed time series line. The time nodes are divided according to the set duration. For example, if the set duration is 5 minutes, the time nodes on the time series line are 06:00, 06:05, 06:10, 06:15, 06:20, 06:25, etc. It can be found that the arrival time points 06:00, 06:05, 06:10, 06:15, 06:20, and 06:25 are all located on the time nodes. The time nodes containing the arrival time points are used as reference time nodes and are associated with the corresponding stations respectively. For example, the reference time node 06:00 is associated with Site A, 06:05 with Site B, 06:10 with Site C, 06:15 with Site A, 06:20 with Site B, and 06:25 with Site C. This association can be stored in a dictionary or list to facilitate subsequent processing. Based on the associated reference time nodes and site order, reference time node pairs between adjacent sites are determined. For example, Site A (06:00) and Site B (06:05) are one pair of reference time nodes corresponding to adjacent sites, Site B (06:05) and Site C (06:10) are another pair, and so on.

[0089] Between the benchmark time nodes of each pair of adjacent sites, additional time nodes are set in chronological order. Assuming a set duration of 5 minutes, there are no other intermediate time nodes between the benchmark time nodes of Site A (06:00) and Site B (06:05), as they are exactly separated by the set duration. However, if the set duration is 3 minutes, between 06:00 (Site A) and 06:05 (Site B), the time nodes should be 06:00, 06:03, 06:06, and so on. In this case, the time nodes 06:03 and 06:06 are inserted between Site A and Site B. Time nodes can be inserted between adjacent benchmark time nodes through loops or list generation.

[0090] These added time nodes are sequentially set between adjacent stations in the station data graph. In the station data graph, the time node list between adjacent stations is updated and the newly added time nodes are inserted to fully present the time series of train operation.

[0091] Afterwards, the time information of the time node is used as a parameter, and the time node is slid along the aligned station data line to obtain the operation dynamics of a single train line; wherein, the sliding starts from the first station where the train starts to run along the train line, and is removed from the station data line when the operation ends.

[0092] This step aims to generate the operational dynamics of a single train line. Specifically, it may include the following steps:

[0093] The first step is to confirm the time interval information corresponding to the time node;

[0094] Step 2: If the time interval information of all time nodes between adjacent stations matches the train running time between adjacent stations, remove all time nodes between adjacent stations.

[0095] Repeat the above steps until all time nodes except stations in a single train line are removed.

[0096] At this time, a sliding time point is set in a single train line, and all train operation unit data when the sliding time point slides along the single train line is used as the operation dynamics of the single train line; the operation dynamics include the real-time status data of the train at any time, traction substation, energy storage information and real-time energy consumption.

[0097] First, you need to clearly define the specific time range represented by each time node. Time interval information refers to the start and end points of each time node, which facilitates analysis and comparison of time periods in subsequent steps. For example, if a time node is marked as "06:00," you need to determine whether it refers to a specific instant or a time period (e.g., from 06:00 to 06:05).

[0098] If the sum of the time intervals of all time nodes between two adjacent train stations is exactly equal to the actual train running time between these two stations, then these time nodes are considered redundant and can be removed. The purpose of this step is to eliminate unnecessary intermediate time nodes and simplify data representation.

[0099] Repeat the process of steps 1 and 2, continuously removing unnecessary intermediate time nodes, until only time nodes directly related to stations on a train line remain. This ensures that the dataset only contains key station information, facilitating subsequent analysis.

[0100] A sliding time point is defined, which can be slid along the train line's timeline. During this sliding process, all train operation unit data corresponding to that time point is collected. This data constitutes the train line's operational dynamics. This operational dynamics includes not only the train's real-time status (such as position and speed) at any given moment, but also the status of the traction substation, the condition of the energy storage system, and real-time energy consumption information. This provides a detailed data foundation for monitoring and analyzing the train line's operational efficiency and energy usage.

[0101] After obtaining the train operation data and station data diagram, the embodiment of the present application associates the train operation data with the time series line, and uses the station as a fixed node and the time node as an active node to generate the operation dynamics of each train line, serializes and stores the train operation data, and analyzes the data association according to the time series, so as to facilitate the output of the train operation dynamics data to the operation and maintenance personnel and facilitate the visual analysis of the train operation dynamics data.

[0102] In one feasible implementation, after acquiring train operation data and station data maps, data preprocessing can be performed. For example, a dynamic threshold adjustment algorithm can be used to set data filtering thresholds based on parameters such as the train's real-time location, operating time, and line load factor (for example, increasing the vibration data threshold by 30% during peak hours). This allows train operation data to be filtered based on the data filtering thresholds. Unlike traditional fixed threshold methods, setting dynamic data filtering thresholds effectively preserves key data features under specific operating conditions.

[0103] In addition, data cleaning can be performed by invoking a deep learning-based noise recognition model and adopting corresponding data cleaning strategies for different sensor characteristics to implement differentiated cleaning strategies. The following are several possible exemplary operation methods:

[0104] Adaptive Kalman filtering combined with wavelet transform is used to denoise the acceleration sensor data;

[0105] Construct a three-phase imbalance compensation algorithm based on power parameter data;

[0106] Implementing a spatiotemporal continuity verification mechanism for passenger flow data;

[0107] Specifically, when combining adaptive Kalman filtering with wavelet transform to denoise accelerometer data, the raw data sequence collected by the accelerometer is collected. This data contains actual acceleration information as well as various noises. An appropriate wavelet function and decomposition scale are selected. Based on the characteristics of the accelerometer data and the noise level, the wavelet basis functions (such as Daubechies or Symlet) and decomposition levels are determined. The optimal parameter combination is typically selected through experimentation and experience. Wavelet decomposition is performed on the raw data, breaking the signal into approximate coefficients and detail coefficients at different scales. The approximate coefficients reflect the low-frequency portion of the signal, while the detail coefficients reflect the high-frequency information at different scales, including the high-frequency components of the useful signal and noise. A dynamic model of the accelerometer data is established, consisting of a state equation and an observation equation. The state equation describes the dynamic changes in acceleration, while the observation equation expresses the relationship between the acceleration observed by the sensor and the actual acceleration. A preliminary estimate of the initial state and error covariance matrix of the Kalman filter is made. An adaptive algorithm is used to adjust the noise statistics of the Kalman filter. For example, the residual information is used to estimate the process noise covariance and measurement noise covariance in real time, so that the filter can better adapt to the changes of signals and noise.

[0108] The wavelet coefficients after wavelet transform preprocessing are used as the input of adaptive Kalman filter. During the iterative process of Kalman filter, the wavelet coefficients of each time step are filtered and estimated to remove the influence of noise.

[0109] After completing the Kalman filter, the filtered coefficients are reconstructed using wavelet transform to obtain the denoised acceleration signal.

[0110] When constructing a three-phase imbalance compensation algorithm based on power parameter data, we can collect power system parameter data such as three-phase voltage and current, including data during normal operation and when three-phase imbalance occurs. We analyze this collected data and calculate three-phase imbalance indicators, such as the ratio of the negative-sequence voltage amplitude to the positive-sequence voltage amplitude and the zero-sequence current, to understand the extent and characteristics of the three-phase imbalance.

[0111] Based on the power system's operational requirements and relevant standards, determine the three-phase imbalance compensation target, such as reducing the three-phase imbalance to below a specified threshold. Select an appropriate compensation strategy, such as reactive power compensation, active filtering, or interphase power transfer, to achieve this.

[0112] Based on the selected compensation strategy, a corresponding mathematical model is established. For example, for a method based on reactive power compensation, a control model for the reactive power compensation device is established, describing the relationship between the compensation device's output and the three-phase imbalance. Power parameter data is used to identify and optimize the compensation model's parameters. By minimizing the compensated three-phase imbalance, key model parameters, such as the compensation device's capacity and control coefficient, are determined.

[0113] When designing a three-phase imbalance compensation algorithm, the current three-phase imbalance can be calculated based on the real-time monitored three-phase power parameter data, and the control signal of the compensation device can be determined according to the compensation model to achieve dynamic compensation of the three-phase imbalance.

[0114] The algorithm is implemented in power system simulation software or actual power system test platform for simulation operation and testing.

[0115] When implementing a spatiotemporal continuity verification mechanism for passenger flow data, first collect passenger flow data, including information such as timestamps, locations (e.g., stations, stops), and the number of passengers entering and exiting the station. Clean the data to remove obvious errors or missing data records. Organize the data spatiotemporally, categorizing and sorting it by time series and spatial location (e.g., different stops).

[0116] Analyze the temporal and spatial variations in passenger flow and construct a spatiotemporal continuity model. One feasible implementation involves using a time series forecasting model (such as ARIMA) to describe temporal trends in passenger flow, and using spatial interpolation methods (such as Kriging) to reflect the spatial correlation of passenger flow across different stations. The model parameters are then determined and trained and calibrated using historical passenger flow data to ensure that the model accurately reflects the spatiotemporal variations in passenger flow.

[0117] Then, based on the spatiotemporal continuity model, validation rules are set. For example, in the temporal dimension, the change in passenger flow between adjacent time periods should not exceed a certain threshold; in the spatial dimension, passenger flow at adjacent stations should be relatively correlated and reasonable. Criteria for determining data anomalies are also set. For example, if a data point deviates from the value predicted by the spatiotemporal continuity model by more than a certain range, the data point is considered an anomaly.

[0118] Passenger flow data is individually verified for spatiotemporal continuity according to the verification rules. Any anomalous data points are marked and recorded. A variety of methods can be used to address anomalous data, such as correcting the data based on a spatiotemporal continuity model or interpolating or estimating data from surrounding stations or adjacent time periods to replace the anomalous data.

[0119] The above are only several exemplary data cleaning methods provided in this application. Those skilled in the art may adopt similar or other data cleaning methods based on this application, and no specific limitation is made here.

[0120] In an optional implementation, historical train operation dynamic data and current train operation dynamic data can also be compared to determine the data changes before and after the train operation optimization, including but not limited to operating efficiency, substation, energy storage, and summary data. The specific process can be as follows:

[0121] The first step is to obtain historical train operation data and historical station data maps;

[0122] Step 2: Rendering historical train operation dynamic data based on the historical train operation data and the historical station data graph;

[0123] Step 3: Integrate the historical site data graph and the site data graph to obtain an overlapping site data graph;

[0124] Step 4: In the overlapped station data graph, the train operation dynamic data is displayed in a first display mode, and the historical train operation dynamic data is displayed in a second display mode;

[0125] Step 5: Compare the train operation dynamic data with the historical train operation dynamic data to generate operation dynamic change data.

[0126] The historical site data map is matched with the current site data map to identify sites that are common to both datasets, known as overlapping sites. The information for these overlapping sites is then integrated into a new data map containing their locations, names, and related attributes (such as the number of platforms and tracks). This creates the overlapping site data map. It is important to note that the overlapping site data map can include sites that exist only in the historical site data map or the current site data map, namely abandoned or newly added sites.

[0127] Define two different display modes. For example, the first mode might use solid lines and bright colors (such as red) to display current train operation dynamics data, while the second mode might use dashed lines and lighter colors (such as gray) to display historical train operation dynamics data. Alternatively, different display patterns can be used to distinguish trains in different operation dynamics data. Based on the overlapping station data map, use data visualization tools to implement the two display modes. Plot the current train operation dynamics data and historical train operation dynamics data on the map according to the predetermined display mode, allowing users to intuitively distinguish between current and historical train operation status.

[0128] Use data comparison algorithms (such as time series analysis and data deviation calculation) to compare current train operation dynamics data with historical train operation dynamics data. For example, calculate the difference in train running time and stop time between the same stations.

[0129] Based on the comparison results, dynamic operation change data is generated. This data can be presented in the form of tables, charts or text reports to show the changing trends of train operations in different time periods, such as shortening or extending running time, improvement or deterioration of delays, etc.

[0130] In addition, it can also include the changes in substations, energy storage, etc. of the train operation dynamic data and historical train operation dynamic data. The train part includes the train traction energy consumption, train regeneration energy consumption, train auxiliary energy consumption, and train total energy consumption. The substation part includes forward and reverse power, current, and voltage. The substation part includes forward and reverse power, current, and voltage. The summary part only needs to show the comparative data before and after optimization, including the transmitted power, total energy consumption, and energy saving rate.

[0131] See also Figure 2 , Figure 2 This is a schematic diagram of the train operation dynamic data comparison provided in the embodiment of the present application. Figure 2 Different display patterns are used to distinguish trains in different running dynamic data.

[0132] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a train operation dynamic visualization system provided in an embodiment of the present application. The system includes:

[0133] Data acquisition module, used to obtain train operation data and station data map;

[0134] a data processing module, configured to sort the train operation data along a set time series line, and use the train operation unit data corresponding to each time node on the time series line as an associated parameter of the time node;

[0135] a data alignment module, for aligning, for each train line, a corresponding time series line with a station data line in the station data graph, treating the station as a fixed node and the time node as an active node;

[0136] A dynamic output module is configured to slide the time node along the aligned station data line using the time information of the time node as a parameter to obtain the operation dynamics of a single train line; wherein the sliding starts from the first station where the train starts running along the train line and is removed from the station data line when the train stops running;

[0137] The dynamic rendering module is used to integrate the operation dynamics of all train lines and render the train operation dynamic data.

[0138] Based on the above embodiment, as a preferred embodiment, it also includes:

[0139] The station data map generating module is used to determine the location information of each train station; and to proportionally scale the station spacing between the train stations according to the location information to generate the station data map.

[0140] Based on the above embodiment, as a preferred embodiment, the site data map generation module further includes:

[0141] The data graph processing unit is used to fold the train lines that exceed the screen width according to the set folding rules; the substation and energy storage equipment are marked on the station data graph according to the actual location data; the substation and the energy storage equipment are used to indicate the corresponding status data of the train.

[0142] Based on the above embodiment, as a preferred embodiment, the data processing module is a unit for performing the following steps:

[0143] Obtaining a train schedule, and constructing a time series line based on the train schedule;

[0144] The train operation data is divided into a plurality of train operation unit data based on a set duration; wherein each train operation unit data includes the start and end time, the current train number, the next train number, the running direction, the station time data and the arrival station data;

[0145] On the time series line, a number of time node intervals are obtained using the set time length as a division unit;

[0146] The train operation unit data is associated with the time node interval according to the corresponding start and end times and arrival data; each time node interval includes the current time node and a line segment extending to the next time node.

[0147] Based on the above embodiment, as a preferred embodiment, the data alignment module is a module for performing the following steps:

[0148] For each train line, determining an arrival time point in the time nodes, and associating the time node including the arrival time point with the station in the station data map as a reference time node;

[0149] The other time nodes between the reference time nodes are sequentially set between adjacent sites in the site data graph in chronological order.

[0150] Based on the above embodiment, as a preferred embodiment, using the time information of the time node as a parameter, the time node is slid along the aligned station data line to obtain the operation dynamics of a single train line, including:

[0151] Confirm the time interval information corresponding to the time node;

[0152] If the time interval information of all time nodes between adjacent stations matches the train running time between adjacent stations, all time nodes between adjacent stations are removed;

[0153] Repeat the above steps until all time nodes except stations in a single train line are removed;

[0154] A sliding time point is set in a single train line, and all train operation unit data when the sliding time point slides along the single train line is used as the operation dynamics of the single train line; the operation dynamics include the real-time status data of the train at any moment, the traction substation, energy storage information and real-time energy consumption.

[0155] Based on the above embodiment, as a preferred embodiment, it also includes:

[0156] A data query module is used to obtain historical train operation data and historical station data maps; render historical train operation dynamic data based on the historical train operation data and the historical station data maps; integrate the historical station data maps and the station data maps to obtain overlapping station data maps; in the overlapping station data maps, display the train operation dynamic data in a first display mode and display the historical train operation dynamic data in a second display mode; compare the train operation dynamic data with the historical train operation dynamic data to generate operation dynamic change data.

[0157] The present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in the above method embodiment.

[0158] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0159] The computer-readable storage medium provided in this embodiment includes the above-mentioned method, and the effect is the same as above.

[0160] This application also provides an electronic device, see Figure 4 , a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 4 As shown, a processor 1410 and a memory 1420 may be included.

[0161] The processor 1410 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1410 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 1410 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1410 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1410 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0162] The memory 1420 may include one or more computer-readable storage media, which may be non-transitory. The memory 1420 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 1420 is at least used to store the following computer program 1421, wherein, after the computer program is loaded and executed by the processor 1410, it can implement the relevant steps in the method performed by the electronic device side disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 1420 may also include an operating system 1422 and data 1423, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 1422 may include Windows, Linux, Android, etc.

[0163] In some embodiments, the electronic device may further include a display screen 1430 , an input / output interface 1440 , a communication interface 1450 , a sensor 1460 , a power supply 1470 , and a communication bus 1480 .

[0164] certainly, Figure 4 The structure of the electronic device shown does not constitute a limitation on the electronic device in the embodiment of the present application. In actual applications, the electronic device may include Figure 4 More or fewer components than shown, or combinations of certain components.

[0165] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems provided in the embodiments, since they correspond to the methods provided in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0166] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core ideas of this application. It should be noted that for those skilled in the art, without departing from the principles of this application, various improvements and modifications can be made to this application, and such improvements and modifications also fall within the scope of protection of this application.

[0167] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A train operation dynamic visualization method, characterized in that: include: Obtain train operation data and station data maps; Sorting the train operation data along a set time series line, and using the train operation unit data corresponding to each time node on the time series line as an associated parameter of the time node; For each train line, align the corresponding time series line with the station data line in the station data graph, treat the station as a fixed node and the time node as an active node; Using the time information of the time node as a parameter, the time node is slid along the aligned station data line to obtain the operation dynamics of a single train line; wherein the sliding starts from the first station where the train starts running along the train line and is removed from the station data line when the train stops running; Integrate the operation dynamics of all train lines and render the train operation dynamic data.

2. The train operation dynamic visualization method according to claim 1, characterized in that: Before obtaining train operation data and station data maps, it also includes: Determine the location information of each train station; The station distances between the train stations are proportionally scaled according to the location information to generate the station data map.

3. The train operation dynamic visualization method according to claim 2, characterized in that: When the station distances between the train stations are proportionally scaled according to the location information to generate the station data map, the method further includes: For train lines that exceed the screen width, fold them according to the set folding rules; The substation and the energy storage device are marked on the site data map according to the actual location data; the substation and the energy storage device are used to indicate the corresponding status data of the train.

4. The train operation dynamic visualization method according to claim 1, characterized in that: Sorting the train operation data along a set time series line, and using the train operation unit data corresponding to each time node on the time series line as the associated parameter of the time node includes: Obtaining a train schedule, and constructing a time series line based on the train schedule; The train operation data is divided into a plurality of train operation unit data based on a set duration; wherein each train operation unit data includes the start and end time, the current train number, the next train number, the running direction, the station time data and the arrival station data; On the time series line, a number of time node intervals are obtained using the set time length as a division unit; The train operation unit data is associated with the time node interval according to the corresponding start and end times and arrival data; each time node interval includes the current time node and a line segment extending to the next time node.

5. The train operation dynamic visualization method according to claim 1, characterized in that: For each train line, align the corresponding time series line with the station data line in the station data graph, treat the station as a fixed node, and treat the time node as an active node, including: For each train line, determining an arrival time point in the time nodes, and associating the time node including the arrival time point with the station in the station data map as a reference time node; The other time nodes between the reference time nodes are sequentially set between adjacent sites in the site data graph in chronological order.

6. The train operation dynamic visualization method according to claim 5, characterized in that: Using the time information of the time node as a parameter, the time node is slid along the aligned station data line to obtain the operation dynamics of a single train line, including: Confirm the time interval information corresponding to the time node; If the time interval information of all time nodes between adjacent stations matches the train running time between adjacent stations, all time nodes between adjacent stations are removed; Repeat the above steps until all time nodes except stations in a single train line are removed; A sliding time point is set in a single train line, and all train operation unit data when the sliding time point slides along the single train line is used as the operation dynamics of the single train line; the operation dynamics include the real-time status data of the train at any moment, the traction substation, energy storage information and real-time energy consumption.

7. The train operation dynamic visualization method according to claim 1, characterized in that: After integrating the running dynamics of all train lines and rendering the train running dynamic data, it also includes: Obtain historical train operation data and historical station data maps; Obtaining historical train operation dynamic data based on the historical train operation data and the historical station data map rendering; Integrating the historical site data graph and the site data graph to obtain an overlapping site data graph; In the overlapped station data graph, the train operation dynamic data is displayed in a first display mode, and the historical train operation dynamic data is displayed in a second display mode; The train operation dynamic data and the historical train operation dynamic data are compared to generate operation dynamic change data.

8. A train operation dynamic visualization system, characterized in that: include: Data acquisition module, used to obtain train operation data and station data map; a data processing module, configured to sort the train operation data along a set time series line, and use the train operation unit data corresponding to each time node on the time series line as an associated parameter of the time node; a data alignment module, for aligning, for each train line, a corresponding time series line with a station data line in the station data graph, treating the station as a fixed node and the time node as an active node; A dynamic output module is configured to slide the time node along the aligned station data line using the time information of the time node as a parameter to obtain the operation dynamics of a single train line; wherein the sliding starts from the first station where the train starts running along the train line and is removed from the station data line when the train stops running; The dynamic rendering module is used to integrate the operation dynamics of all train lines and render the train operation dynamic data.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the train operation dynamic visualization method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed, implements the steps of the train operation dynamic visualization method according to any one of claims 1 to 7.

Images