Railway longitudinal section design method and system

Abstract

This invention relates to the field of railway design technology, and provides a method and system for railway longitudinal profile design. The method includes acquiring measured information of the longitudinal profile to be designed, including the elevation and mileage corresponding to each discrete point; determining a set of potential gradient change points of the longitudinal profile to be designed based on the measured information; constructing a multi-segment continuous linear model using the least squares method based on the set of potential gradient change points of the longitudinal profile to be designed, thereby obtaining a continuous piecewise linear model; solving the continuous piecewise linear model to obtain an initial railway longitudinal profile design scheme; and optimizing the initial railway longitudinal profile design scheme using linear programming to obtain an optimized railway longitudinal profile design scheme. This invention integrates point-line spacing iteration and linear optimization algorithms, combined with the regulations and characteristics of railway line design, and can achieve automatic operation, making the highly experience-based, professional, and cumbersome longitudinal profile design process simple and easy to use.

Description

Technical Field

[0001] This invention relates to the field of railway design technology, and more specifically, to a railway longitudinal profile design method and system. Background Technology

[0002] Current railway longitudinal profile design primarily relies on manual methods, involving segmental gradient adjustments based on measured mileage-elevation data. Designers, limited by time, energy, and equipment, often rely on experience to study a limited number of options, making it difficult to guarantee overall optimization. This approach suffers from high workload, low efficiency, and dependence on manual experience. Furthermore, there is a lack of specific algorithmic research on automated design fitting in this field, both domestically and internationally. Key issues such as gradient change point identification and elevation control still depend on manual experience, resulting in a highly experience-based, specialized, and cumbersome design process that fails to meet the demand for improved intelligent longitudinal profile design of existing lines. Summary of the Invention

[0003] The purpose of this invention is to provide a railway longitudinal profile design method and system to improve the above-mentioned problems.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] On the one hand, embodiments of this application provide a railway longitudinal profile design method, the method comprising:

[0006] Obtain the measured information of the longitudinal profile to be designed, including the elevation and mileage corresponding to each discrete point;

[0007] The set of potential slope change points of the longitudinal section to be designed is determined based on the measured information of the longitudinal section to be designed.

[0008] Based on the set of potential slope change points of the longitudinal section to be designed, a multi-segment continuous linear model is constructed using the least squares method to obtain a continuous piecewise linear model.

[0009] Solving the continuous piecewise linear model yields an initial railway longitudinal profile design scheme, which includes the starting point mileage, starting point elevation, slope length, and slope gradient for each slope segment.

[0010] The initial railway longitudinal profile design scheme is optimized using linear programming to obtain an optimized railway longitudinal profile design scheme, which includes a gradient table.

[0011] Secondly, embodiments of this application provide a railway longitudinal profile design system, the system comprising:

[0012] The acquisition module is used to acquire the measured information of the longitudinal profile to be designed, including the elevation and mileage corresponding to each discrete point;

[0013] The first processing module is used to determine the set of potential slope change points of the longitudinal section to be designed based on the measured information of the longitudinal section to be designed.

[0014] The second processing module is used to construct a multi-segment continuous linear model based on the set of potential slope change points of the longitudinal section to be designed using the least squares method, and obtain a continuous piecewise linear model.

[0015] The third processing module is used to solve the continuous piecewise linear model to obtain the initial railway longitudinal profile design scheme, which includes the starting point mileage, starting point elevation, slope length and slope of each slope segment.

[0016] The fourth processing module is used to optimize the initial railway longitudinal profile design scheme using linear programming to obtain an optimized railway longitudinal profile design scheme, which includes a gradient table.

[0017] Thirdly, embodiments of this application provide a railway longitudinal profile design device, the device including a memory and a processor. The memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the above-described railway longitudinal profile design method.

[0018] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described railway longitudinal profile design method.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention determines the set of potential slope change points of the longitudinal section to be designed by using measured information of the longitudinal section to be designed, and obtains the railway longitudinal section design scheme by performing multi-segment continuous linear fitting using least squares. It realizes the automated selection of slope change points and fitting of the line shape, etc., reducing the need for a large number of office personnel and reducing labor costs. The algorithm automatically calculates and avoids the quality problems of manual operation, reduces the dependence of the results on the experience of designers, realizes data standardization and unified solution, and facilitates later inspection and verification.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the railway longitudinal profile design method described in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the railway longitudinal profile design system described in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the railway longitudinal section design equipment structure described in an embodiment of the present invention.

[0026] Figure 4 A schematic diagram for iteratively solving for potential slope change points.

[0027] Figure 5 A flowchart illustrating the process of obtaining the optimized slope table.

[0028] The diagram is labeled as follows: 800, Railway longitudinal section design equipment; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component; 901, Acquisition module; 902, First processing module; 903, Second processing module; 904, Third processing module; 905, Fourth processing module. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] Example 1:

[0032] This embodiment provides a railway longitudinal profile design method. It can be understood that in this embodiment, a scenario can be set up, such as: in the survey of existing railways, it is necessary to draw the topographic longitudinal profile of the railway line centerline based on the measured centerline mileage and rail surface elevation data, and design the slope line on the topographic longitudinal profile.

[0033] See Figure 1 The figure shows that the method includes steps S1, S2, S3, S4, and S5, which specifically include:

[0034] Step S1: Obtain the measured information of the longitudinal section to be designed, including the elevation and mileage corresponding to each discrete point;

[0035] Step S2: Determine the set of potential slope change points of the longitudinal section to be designed based on the measured information of the longitudinal section to be designed;

[0036] Step S2 further includes steps S21, S22, S23, S24, and S25, which specifically include:

[0037] Step S21: Obtain the preset minimum slope length;

[0038] In this step, the preset minimum slope length is obtained to ensure that the selected slopes meet the basic requirements for slope length in railway engineering specifications, and to avoid unreasonable design due to excessively short slopes.

[0039] Step S22: Select the starting point and the ending point from the measured information of the longitudinal section to be designed. The starting point includes the discrete point corresponding to the minimum mileage, and the ending point includes the discrete point corresponding to the maximum mileage.

[0040] Step S23: Connect the starting point and the ending point to obtain the first connecting line;

[0041] Step S24: Calculate the distance from discrete points within the mileage range of the starting point and the ending point to the first connecting line to obtain the first distance information;

[0042] Step S25: Determine the set of potential slope change points of the longitudinal section to be designed based on the first distance information and the preset minimum slope length.

[0043] Step S25 further includes steps S251, S252, S253, S254, S255, and S256, which specifically include:

[0044] Step S251: Select the discrete point corresponding to the maximum value in the first distance information as the first potential slope change point;

[0045] In this step, by calculating the distance from discrete points to the connecting lines, the points that deviate most from the overall trend (i.e., locations where the terrain or track elevation changes significantly) are identified. These points are potential slope change points.

[0046] Step S252: Connect the starting point with the first potential slope change point to obtain the second connection line;

[0047] Step S253: Connect the first potential slope change point with the termination point to obtain the third connection line;

[0048] Step S254: Calculate the distance from discrete points within the mileage range of the second connecting line to the second connecting line, and obtain the second distance information;

[0049] Step S255: Calculate the distance from discrete points within the mileage range of the third connecting line to the third connecting line, and obtain the third distance information;

[0050] Step S256: Select the discrete points corresponding to the maximum values ​​of the second and third distance information as slope change points, and repeat the process until the slope length is less than the preset minimum slope length to obtain the set of potential slope change points for the longitudinal section to be designed.

[0051] In this step, through iterative iteration, the line segment is continuously divided into smaller sub-segments, and the maximum distance point is repeatedly calculated until the slope length approaches the minimum limit. This ultimately forms a set of potential slope change points covering the entire longitudinal profile, ensuring that no key slope change locations are missed. It should be noted that this invention uses a point-line distance iteration method to screen potential slope change points, solving the problem of traditional manual slope change point search relying on experience and being highly subjective. Through quantitative point-line distance calculation and iterative logic, the selection of slope change points is standardized and scientific. At the same time, through the dual logic of maximum distance optimization and minimum slope length constraint, the location of abrupt changes in terrain or track elevation is accurately captured, ensuring that the slope change points are consistent with the actual alignment trend.

[0052] In this embodiment, a specific implementation method is as follows: Figure 4As shown, assuming the minimum slope length is *a* meters, the measured information of the longitudinal profile to be designed includes five discrete points: A, B, C, D, and E. Connecting the first point A and the last point E of the measured data forms a straight line AE. For all measured points whose mileage is within AE and whose mileage distance from points A and E meets the slope value constraints, calculate the distance to the straight line AE. Include the point with the largest distance as a potential slope change point, denoted as point C. The measured data is then divided into sets AC and CE based on mileage. For each set, find the point with the largest point-to-line distance using the above method, include it as a potential slope change point, and re-divide the data set based on the mileage value of this point. Iterate through the process to find slope change points until the slope length of the new dataset is about to be less than *a*. This process identifies the set of potential slope change points.

[0053] Step S3: Based on the set of potential slope change points of the longitudinal section to be designed, a multi-segment continuous linear model is constructed using the least squares method to obtain a continuous piecewise linear model;

[0054] In this step, the continuous piecewise linear model is specifically as follows:

[0055]

[0056] In the above formula, β represents the parameter to be estimated in the continuous piecewise linear model, used to characterize the linear relationship of each slope segment; x and y represent the mileage and elevation of the discrete points, respectively; b m This represents the mileage value corresponding to the m-th potential change-of-slope point.

[0057] It is understandable that the longitudinal profile of a railway consists of multiple slope segments, and adjacent slope segments need to be continuous at the slope change point. This step constructs a continuous piecewise linear model using the least squares method to ensure that each slope segment is smoothly connected at the slope change point, avoiding abrupt changes in elevation and meeting the requirements of railway engineering for the smoothness of the longitudinal profile. At the same time, this invention replaces the experience-based operation of manual slope adjustment with the least squares method, reducing human error and making the design results more accurate and consistent, providing core technical support for improving design efficiency and quality.

[0058] Step S4: Solve the continuous piecewise linear model to obtain the initial railway longitudinal profile design scheme, which includes the starting point mileage, starting point elevation, slope length, and slope of each slope segment.

[0059] Step S4 further includes steps S41, S42, S43, and S44, which specifically include:

[0060] Step S41: Matrix the continuous piecewise linear model to obtain the matrix equation;

[0061] In this step, the mathematical expression of the multi-segment continuous linear model is converted into matrix form. Utilizing the standardization and efficiency of matrix operations, the model solution process is simplified, avoiding the tediousness of piecewise calculations. The specific matrix equation is as follows:

[0062] Aβ=Y

[0063] In the above formula, A represents a known matrix constructed from the potential slope change point mileage and segmented interval information, which is used to characterize the position and weight of the linear relationship of different slope segments in the matrix, ensuring the continuity of adjacent slope segments at the slope change point; β represents the parameter matrix to be estimated; Y represents a known matrix composed of measured elevation data, which is the observation vector of the model.

[0064] Step S42: Solve the matrix equation based on the least squares residual sum of squares criterion to obtain the parameters to be estimated;

[0065] In this step, the solution for the parameters to be estimated is as follows:

[0066] β=(A T A) -1 A T Y

[0067] In the above formula, since A and Y are known quantities, the parameters to be estimated can be solved.

[0068] Step S43: Substitute the parameters to be estimated into the continuous piecewise linear model to obtain the piecewise continuous linear fitting model of the railway longitudinal profile.

[0069] In this step, the estimated parameter β obtained from the solution is substituted back into the original model to restore the specific linear expression of each slope segment, forming a complete and continuous longitudinal profile fitting model. This ensures that the elevations of adjacent slope segments are consistent at the slope change points, clarifies the mathematical relationships of each slope segment, and realizes the transformation from abstract parameters to specific line shapes. This lays the foundation for extracting practical engineering parameters (such as slope and slope length), while ensuring the smoothness and continuity of the longitudinal profile.

[0070] Step S44: Extract the starting point mileage, starting point elevation, slope length and slope of each slope segment according to the segmented continuous linear fitting model to obtain the initial railway longitudinal profile design scheme.

[0071] In this step, key parameters such as starting point mileage, elevation, slope length, and gradient required for engineering design are extracted from the fitted model, forming a design scheme that can be directly used for railway construction or renovation. This meets the specific requirements of engineering practice for parameters, realizes the automated transformation from discrete data to continuous alignment and then to practical parameters, greatly reduces manual intervention, and provides key technical support for the intelligent design of longitudinal profiles.

[0072] Step S5: Optimize the initial railway longitudinal profile design scheme using linear programming to obtain an optimized railway longitudinal profile design scheme, which includes a gradient table.

[0073] Step S5 further includes steps S51, S52, S53, S54, S55, S56, S57, and S58, which specifically include:

[0074] Step S51: Based on the initial railway longitudinal profile design scheme, determine whether there are at least two potential gradient change points within the minimum gradient section length, and obtain the judgment result;

[0075] In this step, historical design records are obtained as a supplement to potential gradient change points. The railway longitudinal profile design scheme is checked to see if there are multiple gradient change points within the minimum gradient length, which would affect the train ride smoothness and thus further optimize the design scheme.

[0076] Step S52: When the judgment result is that there are at least two potential slope change points, the potential slope change points are screened to obtain the screened potential slope change points.

[0077] In this step, multiple slope change points within the short slope section are simplified, retaining the slope change point with the smallest deviation from the measured elevation, eliminating redundant nodes, and ensuring that the design scheme will not affect the smoothness of train operation.

[0078] Step S53: Construct constraints based on the screened potential slope change points;

[0079] In this step, the longitudinal profile is segmented based on the screened potential slope change points. For each segment, to ensure the continuity of the fitted line segment, the line segment needs to pass through the screened potential slope change points, and there are certain restrictions on the starting and ending points. Therefore, the specific constraints are as follows:

[0080]

[0081] In the above formula, k and b are the slope and intercept of each segment, respectively; (x0, y0) represent the coordinates of the slope change point corresponding to the current segment; u i and u max These represent the height difference of the line segment above the i-th measured point and the starting limit value, respectively; v i and v max These represent the height difference and the drop limit of the line segment below the i-th measured point, respectively.

[0082] Step S54: Construct observation equations based on the measured information of the longitudinal section to be designed;

[0083] In this step, the observation equation is specifically as follows:

[0084] kx i +by i =u i -v i

[0085] In the above formula, (x i y i ) represents the mileage and elevation corresponding to the i-th discrete point.

[0086] Step S55: Obtain the starting quantity parameters, ending quantity parameters, and penalty coefficient;

[0087] Step S56: Construct a linear programming model based on the starting quantity parameter, the ending quantity parameter, the penalty coefficient, the constraint conditions, and the observation equation;

[0088] In this step, an objective function is established based on the starting quantity parameter, the ending quantity parameter, and the penalty coefficient. The specific process is as follows:

[0089]

[0090] In the above formula, S represents the target value; M represents the penalty coefficient for track descent, which is a dynamically adjusted positive number used to prioritize reducing the number and magnitude of track descent points. The core of the objective function is to minimize S, and under the premise of satisfying the limits of track descent and slope continuity, to minimize the sum of track descent and (penalized) track descent, thereby controlling the track adjustment range and prioritizing construction feasibility.

[0091] Step S57: Solve the linear programming model to obtain the slope parameters, which include the slope and intercept of the slope.

[0092] In this step, the linear programming model outputs accurate slope parameters, avoiding errors from manual calculations, and the coordination between parameters (such as the continuity of adjacent slope segments) is better.

[0093] Step S58: Construct a slope table based on the slope parameters corresponding to each slope segment.

[0094] In this step, the slope parameters (slope, intercept) obtained by solving are converted into a practical slope table for engineering, which includes key information such as the mileage of the slope change point, elevation, slope length, and slope, forming a result document that can be directly used for construction. This realizes the transformation from abstract mathematical parameters to concrete engineering results. The standardized output format facilitates subsequent review, verification, and construction application, reducing the workload of manual processing.

[0095] Following step S58, steps S59, S510, and S511 are further included, which specifically include:

[0096] Step S59: Perform a standard test on the slope table to obtain the test results;

[0097] In this step, the railway longitudinal profile design problem is not a general discrete point linear fitting problem. It also needs to consider the constraints of railway specifications, conduct a comprehensive check on the generated gradient table according to the railway design specifications, identify whether there are any parameters that do not conform to the specifications, avoid engineering risks caused by human oversight, and provide a clear direction for subsequent optimization.

[0098] Step S510: When the test result does not meet the specifications, obtain the preset multi-constraint conditions to modify the slope table and obtain the modified adjustment parameters;

[0099] In this step, the preset multiple constraints include, but are not limited to: 1. Shift point mileage handling strategy: Remove or adjust the mileage of shift points near transition curves, turnouts, and regulators; round potential shift points to the nearest mileage value. 2. Gradient difference handling strategy: After fitting, start from the second slope segment and judge each slope segment one by one. If the gradient difference between the current slope segment and the previous slope segment is less than the empirical threshold, it is merged into the previous slope segment that meets the requirements. In addition, the angle between each slope segment and the previous slope segment is calculated, and if the angle is less than the empirical threshold, it is also merged into the previous slope segment. 3. Gradient value limit: Judge the gradient of each slope segment one by one. If it is greater than the limit, refit; retain the result to three decimal places. 4. Vertical curve addition: When the gradient difference between adjacent slope segments is large, add circular or parabolic vertical curves at the corresponding positions according to the line maintenance rules.

[0100] For detected violations, the parameters in the slope table (such as the mileage of the slope change point and the slope value) are adjusted based on preset multiple constraints to generate preliminary adjustment parameters that meet the specifications.

[0101] Step S511: Send the modified adjustment parameters to the linear programming model for solution to obtain the optimized slope table.

[0102] In this step, the adjusted parameters are re-input into the linear programming model. Global optimization of the parameters is achieved through model solving, ensuring that the modified slope table conforms to specifications and meets multiple objectives such as rise and fall control and slope continuity. This invention achieves a closed loop of "detection-modification-re-solution" through a cyclic optimization mechanism, ensuring that the final output slope table simultaneously meets engineering specifications and actual construction needs. This avoids imbalances in other parameters caused by adjusting a single constraint, guaranteeing the overall optimality of the design results. The specific process for obtaining the optimized slope table in this invention is as follows: Figure 5 As shown.

[0103] Example 2:

[0104] like Figure 2 As shown, this embodiment provides a railway longitudinal profile design system. The system includes an acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904, and a fourth processing module 905, specifically including:

[0105] The acquisition module 901 is used to acquire the measured information of the longitudinal profile to be designed, including the elevation and mileage corresponding to each discrete point;

[0106] The first processing module 902 is used to determine the set of potential slope change points of the longitudinal section to be designed based on the measured information of the longitudinal section to be designed.

[0107] The second processing module 903 is used to construct a multi-segment continuous linear model based on the set of potential slope change points of the longitudinal section to be designed using the least squares method, and obtain a continuous piecewise linear model.

[0108] The third processing module 904 is used to solve the continuous piecewise linear model to obtain an initial railway longitudinal profile design scheme, which includes the starting point mileage, starting point elevation, slope length and slope of each slope segment.

[0109] The fourth processing module 905 is used to optimize the initial railway longitudinal profile design scheme using linear programming to obtain an optimized railway longitudinal profile design scheme, which includes a gradient table.

[0110] In one specific embodiment of this disclosure, the first processing module further includes a first acquisition unit, a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit, specifically including:

[0111] The first acquisition unit is used to acquire the preset minimum slope length;

[0112] The first processing unit is used to filter out the starting point and the ending point from the measured information of the longitudinal section to be designed. The starting point includes discrete points corresponding to the minimum mileage value, and the ending point includes discrete points corresponding to the maximum mileage value.

[0113] The second processing unit is used to connect the starting point and the ending point to obtain a first connection line;

[0114] The third processing unit is used to calculate the distance from discrete points within the mileage range of the starting point and the ending point to the first connecting line, and obtain the first distance information;

[0115] The fourth processing unit is used to determine the set of potential slope change points of the longitudinal section to be designed based on the first distance information and the preset minimum slope length.

[0116] In one specific embodiment of this disclosure, the fourth processing unit further includes a fifth processing unit, a sixth processing unit, a seventh processing unit, an eighth processing unit, a ninth processing unit, and a tenth processing unit, specifically comprising:

[0117] The fifth processing unit is used to filter out the discrete points corresponding to the maximum values ​​in the first distance information as the first potential slope change points;

[0118] The sixth processing unit is used to connect the starting point with the first potential slope change point to obtain a second connection line;

[0119] The seventh processing unit is used to connect the first potential slope change point with the termination point to obtain a third connection line;

[0120] The eighth processing unit is used to calculate the distance from discrete points within the mileage range of the second connecting line to the second connecting line, and obtain the second distance information;

[0121] The ninth processing unit is used to calculate the distance from discrete points within the mileage range of the third connecting line to the third connecting line, and obtain the third distance information;

[0122] The tenth processing unit is used to filter out the discrete points corresponding to the maximum values ​​of the second and third distance information as slope change points, and repeats the process until the slope length is less than the preset minimum slope length, thus obtaining a set of potential slope change points for the longitudinal section to be designed.

[0123] In one specific embodiment of this disclosure, the third processing module further includes an eleventh processing unit, a twelfth processing unit, a thirteenth processing unit, and a fourteenth processing unit, specifically including:

[0124] The eleventh processing unit is used to matrixify the continuous piecewise linear model to obtain a matrix equation.

[0125] The twelfth processing unit is used to solve the matrix equation based on the least squares residual sum of squares criterion to obtain the parameters to be estimated.

[0126] The thirteenth processing unit is used to substitute the parameters to be estimated into the continuous piecewise linear model to obtain a piecewise continuous linear fitting model of the railway longitudinal profile.

[0127] The fourteenth processing unit is used to extract the starting point mileage, starting point elevation, slope length and slope of each slope segment according to the segmented continuous linear fitting model, so as to obtain the initial railway longitudinal profile design scheme.

[0128] In one specific embodiment of this disclosure, the fourth processing module is followed by a judgment unit, a fifteenth processing unit, a sixteenth processing unit, a seventeenth processing unit, a second acquisition unit, an eighteenth processing unit, a nineteenth processing unit, and a twentieth processing unit, specifically including:

[0129] The judgment unit is used to determine whether there are at least two potential slope change points within the minimum slope length based on the initial railway longitudinal profile design scheme, and to obtain the judgment result.

[0130] The fifteenth processing unit is used to filter the potential slope points when the judgment result is that there are at least two potential slope change points, and obtain the filtered potential slope change points.

[0131] The sixteenth processing unit is used to construct constraints based on the screened potential slope change points;

[0132] The seventeenth processing unit is used to construct observation equations based on the measured information of the longitudinal section to be designed.

[0133] The second acquisition unit is used to acquire the starting quantity parameter, the ending quantity parameter, and the penalty coefficient;

[0134] The eighteenth processing unit is used to construct a linear programming model based on the starting quantity parameter, the ending quantity parameter, the penalty coefficient, the constraint conditions, and the observation equation.

[0135] The nineteenth processing unit is used to solve the linear programming model to obtain slope parameters, which include the slope and intercept of the slope.

[0136] The twentieth processing unit is used to construct a slope table based on the slope parameters corresponding to each slope segment.

[0137] In one specific embodiment of this disclosure, the twentieth processing unit is further followed by a detection unit, a third acquisition unit, and a twenty-first processing unit, which specifically includes:

[0138] The detection unit is used to perform standardized testing on the slope table and obtain the test results.

[0139] The third acquisition unit is used to acquire preset multiple constraints to modify the slope table when the detection result does not meet the standard, and obtain the modified adjustment parameters.

[0140] The twenty-first processing unit is used to send the modified adjustment parameters to the linear programming model for solving, so as to obtain the optimized slope table.

[0141] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0142] Example 3:

[0143] Corresponding to the above method embodiments, this embodiment also provides a railway longitudinal profile design device. The railway longitudinal profile design device described below and the railway longitudinal profile design method described above can be referred to each other.

[0144] Figure 3 This is a block diagram illustrating a railway longitudinal profile design device 800 according to an exemplary embodiment. (See diagram below.) Figure 3 As shown, the railway longitudinal profile design device 800 may include: a processor 801 and a memory 802. The railway longitudinal profile design device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0145] The processor 801 controls the overall operation of the railway longitudinal profile design equipment 800 to complete all or part of the steps in the aforementioned railway longitudinal profile design method. The memory 802 stores various types of data to support the operation of the railway longitudinal profile design equipment 800. This data may include, for example, instructions for any application or method operating on the railway longitudinal profile design equipment 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the railway longitudinal profile design equipment 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0146] In an exemplary embodiment, the railway longitudinal profile design device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the railway longitudinal profile design method described above.

[0147] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the railway longitudinal profile design method described above. For example, the computer-readable storage medium may be the memory 802 including program instructions described above, which may be executed by the processor 801 of the railway longitudinal profile design device 800 to complete the railway longitudinal profile design method described above.

[0148] Example 4:

[0149] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the railway longitudinal profile design method described above.

[0150] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the railway longitudinal profile design method described in the above method embodiments.

[0151] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0152] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0153] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for designing the longitudinal profile of a railway, characterized in that, include: Obtain the measured information of the longitudinal profile to be designed, including the elevation and mileage corresponding to each discrete point; The set of potential slope change points of the longitudinal section to be designed is determined based on the measured information of the longitudinal section to be designed. Based on the set of potential slope change points of the longitudinal section to be designed, a multi-segment continuous linear model is constructed using the least squares method to obtain a continuous piecewise linear model. Solving the continuous piecewise linear model yields an initial railway longitudinal profile design scheme, which includes the starting point mileage, starting point elevation, slope length, and slope gradient for each slope segment. The initial railway longitudinal profile design scheme is optimized using linear programming to obtain an optimized railway longitudinal profile design scheme, which includes a gradient table, comprising: Based on the initial railway longitudinal profile design scheme, it is determined whether there are at least two potential slope change points within the minimum slope length, and the determination result is obtained, including: obtaining historical design records as a supplement to potential slope change points; When the judgment result is that there are at least two potential slope change points, the potential slope change points are screened to obtain the screened potential slope change points. The screened potential slope change points include the slope change point with the smallest deviation from the measured elevation. Based on the screened potential slope change points, constraints are constructed, specifically as follows: ； In the above formula, k and b are the slope and intercept of each segment, respectively; , () indicates the coordinates of the slope change point corresponding to the current segment; and These represent the height difference of the line segment above the i-th measured point and the starting limit value, respectively; and These represent the height difference and the drop limit of the line segment below the i-th measured point, respectively. An observation equation is constructed based on the measured information of the longitudinal section to be designed. The observation equation is as follows: ； In the above formula, ( , () represents the mileage and elevation corresponding to the i-th discrete point; Obtain the starting distance parameters, ending distance parameters, and penalty coefficient; A linear programming model is constructed based on the starting quantity parameter, the ending quantity parameter, the penalty coefficient, the constraints, and the observation equation. Specifically, the linear programming model is as follows: ； In the above formula, S represents the target value; M represents the penalty coefficient for the landing amount, which is a dynamically adjusted positive number used to prioritize reducing the number and magnitude of landing points. The core of the objective function is to minimize S, and to achieve the minimum sum of the landing amount and the (penalized) landing amount while satisfying the landing amount limit and the slope continuity. Solving the linear programming model yields slope parameters, which include the slope and intercept of the slope. A slope table is constructed based on the slope parameters corresponding to each slope segment.

2. The railway longitudinal profile design method according to claim 1, characterized in that, Based on the measured information of the longitudinal section to be designed, determine the set of potential slope change points of the longitudinal section to be designed, including: Get the preset minimum slope length; The starting point and the ending point are selected from the measured information of the longitudinal section to be designed. The starting point includes the discrete point corresponding to the minimum mileage, and the ending point includes the discrete point corresponding to the maximum mileage. Connect the starting point and the ending point to obtain the first connecting line; Calculate the distance from discrete points within the mileage range of the starting point and the ending point to the first connecting line to obtain the first distance information; The set of potential slope change points for the longitudinal section to be designed is determined based on the first distance information and the preset minimum slope length.

3. The railway longitudinal profile design method according to claim 1, characterized in that, Solving the continuous piecewise linear model yields an initial railway longitudinal profile design scheme, including: The continuous piecewise linear model is matrixed to obtain the matrix equation; The matrix equation is solved based on the least squares residual sum of squares criterion to obtain the parameters to be estimated. Substituting the parameters to be estimated into the continuous piecewise linear model, a piecewise continuous linear fitting model of the railway longitudinal profile is obtained. Based on the piecewise continuous linear fitting model, the starting point mileage, starting point elevation, slope length, and slope gradient of each slope segment are extracted to obtain the initial railway longitudinal profile design scheme.

4. The railway longitudinal profile design method according to claim 1, characterized in that, After constructing a slope table based on the slope parameters corresponding to each slope segment, it includes: The slope table was subjected to standardized testing, and the test results were obtained. When the test results do not meet the specifications, the slope table is modified by obtaining preset multiple constraints to obtain the modified adjustment parameters; The modified adjustment parameters are sent to the linear programming model for solution to obtain the optimized slope table.

5. A railway longitudinal profile design system, characterized in that, include: The acquisition module is used to acquire the measured information of the longitudinal profile to be designed, including the elevation and mileage corresponding to each discrete point; The first processing module is used to determine the set of potential slope change points of the longitudinal section to be designed based on the measured information of the longitudinal section to be designed. The second processing module is used to construct a multi-segment continuous linear model based on the set of potential slope change points of the longitudinal section to be designed using the least squares method, and obtain a continuous piecewise linear model. The third processing module is used to solve the continuous piecewise linear model to obtain the initial railway longitudinal profile design scheme, which includes the starting point mileage, starting point elevation, slope length and slope of each slope segment. The fourth processing module is used to optimize the initial railway longitudinal profile design scheme using linear programming to obtain an optimized railway longitudinal profile design scheme, which includes a gradient table. The fourth processing module includes: The judgment unit is used to determine whether there are at least two potential slope change points within the minimum slope length based on the initial railway longitudinal profile design scheme, and to obtain the judgment result, including: obtaining historical design records as a supplement to potential slope change points; The fifteenth processing unit is used to filter the potential slope points when the judgment result is that there are at least two potential slope points, and obtain the filtered potential slope points. The filtered potential slope points include the slope point with the smallest deviation from the measured elevation. The sixteenth processing unit is used to construct constraints based on the screened potential slope change points, and the constraints are specifically as follows: ； In the above formula, k and b are the slope and intercept of each segment, respectively; , () indicates the coordinates of the slope change point corresponding to the current segment; and These represent the height difference of the line segment above the i-th measured point and the starting limit value, respectively; and These represent the height difference and the drop limit of the line segment below the i-th measured point, respectively. The seventeenth processing unit is used to construct observation equations based on the measured information of the longitudinal section to be designed. The specific observation equations are as follows: ； In the above formula, ( , () represents the mileage and elevation corresponding to the i-th discrete point; The second acquisition unit is used to acquire the starting quantity parameter, the ending quantity parameter, and the penalty coefficient; The eighteenth processing unit constructs a linear programming model based on the starting quantity parameter, the ending quantity parameter, the penalty coefficient, the constraints, and the observation equation. Specifically, the linear programming model is as follows: ； In the above formula, S represents the target value; M represents the penalty coefficient for the landing amount, which is a dynamically adjusted positive number used to prioritize reducing the number and magnitude of landing points. The core of the objective function is to minimize S, and to achieve the minimum sum of the landing amount and the (penalized) landing amount while satisfying the landing amount limit and the slope continuity. The nineteenth processing unit solves the linear programming model to obtain slope parameters, which include the slope and intercept of the slope. The twentieth processing unit constructs a slope table based on the slope parameters corresponding to each slope segment.

6. The railway longitudinal profile design system according to claim 5, characterized in that, The first processing module includes: The first acquisition unit is used to acquire the preset minimum slope length; The first processing unit is used to filter out the starting point and the ending point from the measured information of the longitudinal section to be designed. The starting point includes discrete points corresponding to the minimum mileage value, and the ending point includes discrete points corresponding to the maximum mileage value. The second processing unit is used to connect the starting point and the ending point to obtain a first connection line; The third processing unit is used to calculate the distance from discrete points within the mileage range of the starting point and the ending point to the first connecting line, and obtain the first distance information; The fourth processing unit is used to determine the set of potential slope change points of the longitudinal section to be designed based on the first distance information and the preset minimum slope length.

7. The railway longitudinal profile design system according to claim 5, characterized in that, The third processing module includes: The eleventh processing unit is used to matrixify the continuous piecewise linear model to obtain a matrix equation. The twelfth processing unit is used to solve the matrix equation based on the least squares residual sum of squares criterion to obtain the parameters to be estimated. The thirteenth processing unit is used to substitute the parameters to be estimated into the continuous piecewise linear model to obtain a piecewise continuous linear fitting model of the railway longitudinal profile. The fourteenth processing unit is used to extract the starting point mileage, starting point elevation, slope length and slope of each slope segment according to the segmented continuous linear fitting model, so as to obtain the initial railway longitudinal profile design scheme.

8. The railway longitudinal profile design system according to claim 5, characterized in that, Following the twentieth processing unit, the following is included: The detection unit is used to perform standardized testing on the slope table and obtain the test results. The third acquisition unit is used to acquire preset multiple constraints to modify the slope table when the detection result does not meet the standard, and obtain the modified adjustment parameters. The twenty-first processing unit is used to send the modified adjustment parameters to the linear programming model for solving, so as to obtain the optimized slope table.

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