Method, device, equipment, medium and product for evaluating heavy-haul train operation

Abstract

This disclosure relates to an evaluation method, apparatus, equipment, medium, and product for heavy-haul train operation, applied in the field of assisted driving technology. In this disclosure, operation item data for a target operating section of a heavy-haul train is acquired; based on the operation item data and preset standard ranges for the operation items, the degree of execution of the operation items by the heavy-haul train in the target operating section is determined; if the degree of execution of the operation items is determined to be in compliance with operating specifications, the operation evaluation result of the heavy-haul train in the target operating section is determined based on the operation item data and preset standard ranges for the operation items. By first determining the degree of execution of the operation items, i.e., whether the current heavy-haul train meets standardized operation, and then determining the operation evaluation result, i.e., the level of precision operation of the current heavy-haul train, the operation level is evaluated from multiple evaluation dimensions, thereby allowing analysis of the safety status of the heavy-haul train based on the operation evaluation results. Therefore, the operational safety of heavy-haul trains can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of assisted driving technology, and in particular to an evaluation method, apparatus, equipment, medium and product for heavy-duty train operation. Background Technology

[0002] Heavy-haul trains are complex mechanical systems. Compared to regular freight trains, they have longer braking and release distances, longer inflation and deflation times, and greater longitudinal impacts. This places higher demands on train operation and control. Improper operation can easily generate huge longitudinal impacts, causing abnormal stops. Accurate evaluation of heavy-haul train operation is particularly important for guiding the safety of heavy-haul trains.

[0003] Generally, the evaluation methods for heavy-haul train handling primarily focus on single performance indicators that assess train operational safety. However, such evaluation methods cannot accurately and effectively assess the handling skills of heavy-haul trains. Therefore, they can negatively impact the operational safety of heavy-haul trains. Summary of the Invention

[0004] This disclosure provides an evaluation method, apparatus, equipment, medium, and product for the handling of heavy-haul trains, which is beneficial to improving the operational safety of heavy-haul trains.

[0005] Firstly, this disclosure provides an evaluation method for the handling of heavy-haul trains, including: Acquire the control point data for the target operating section of the heavy-haul train; Based on the control item data and the preset control item specification range, the degree of control item execution of the heavy-haul train in the target travel section is determined; If the execution level of the control item is determined to be in accordance with the control specifications, the control evaluation result of the heavy-haul train in the target travel section is determined based on the control item data and the preset standard range of the control item. The range of the standard range of the control item is greater than the range of the standard range of the control item.

[0006] Optionally, determining the handling evaluation result of the heavy-haul train in the target operating section based on the handling point data and the preset standard range of handling points includes: The standard range of the control point is determined based on the environmental parameters of the target driving range and the braking and relief parameters in the control point data; The control evaluation result of the heavy-haul train in the target operating section is determined by comparing the standard interval of the control point with the control point data.

[0007] Optionally, the step of comparing the standard interval based on the control point and the control point data to determine the control evaluation result of the heavy-haul train in the target operating interval includes: Based on the optimal value distance model and the standard interval of the control point, the braking position and the relief position in the control point data are compared and calculated to determine the evaluation sample matrix of the braking position and the relief position in the target driving interval. Based on the weights of the braking position, the weights of the release position, and the evaluation sample matrix, the handling evaluation result of the heavy-haul train in the target operating section is determined.

[0008] Optionally, the step of comparing the standard interval based on the control point and the control point data to determine the control evaluation result of the heavy-haul train in the target operating interval includes: Based on a multi-objective optimization model, the braking position and braking speed in the control point data are calculated, as well as the release position and release speed of the control point, to determine the actual range of the control point. The similarity of the heavy-haul train in the target travel section is determined by superimposing and comparing the standard interval and the actual interval of the control point. The similarity is used as the evaluation result of the manipulation.

[0009] Optionally, determining the degree of execution of the control items by the heavy-haul train in the target operating section based on the control item data and the preset control item specification range includes: The manipulation item data is traversed based on the normed interval of the manipulation item to determine the manipulation item data that satisfies the normed interval; Based on the control item data and corresponding control item weights that satisfy the specified interval, the degree of control item execution of the heavy-haul train in the target operating interval is determined.

[0010] Optionally, acquiring the control point data for the target operating section of the heavy-haul train includes: Collect train operation data of the heavy-haul train in the target operating section; The train operation data is cleaned to obtain the first train operation data; Traction calculations are performed on the first train's operating data to determine the control point data.

[0011] Secondly, this disclosure provides an evaluation device for heavy-haul train operation, comprising: The acquisition module is used to acquire the control point data of the target operating section of the heavy-haul train; The first determining module is used to determine the degree of execution of the control items by the heavy-haul train in the target travel section based on the control item data and the preset control item specification range; The second determining module is used to determine the operation evaluation result of the heavy-haul train in the target travel section based on the operation item data and the preset standard range of the operation item, when it is determined that the execution degree of the operation item meets the operation specifications. The range of the specification range of the operation item is greater than the range of the standard range of the operation item.

[0012] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the evaluation method for heavy-load train handling described above.

[0013] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the evaluation method for heavy-load train handling described above.

[0014] Fifthly, this disclosure provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of the evaluation method for heavy-load train handling described above.

[0015] This disclosure involves acquiring control item data for the target operating section of a heavy-haul train; determining the degree of control item execution of the heavy-haul train within the target operating section based on the control item data and preset standard ranges for the control items; and, if the degree of control item execution is determined to be in compliance with control specifications, determining the control evaluation result of the heavy-haul train within the target operating section based on the control item data and preset standard ranges for the control items, where the range of the standard range for the control items is greater than the range of the standard range for the control items. By first determining the degree of control item execution—that is, whether the current heavy-haul train meets standardized control requirements—and then determining the control evaluation result—that is, the level of precision control achieved by the current heavy-haul train—the control level of the heavy-haul train is objectively evaluated from multiple evaluation dimensions and factors influencing the control level. Based on the control evaluation result, the safety status of the current heavy-haul train can be determined. Therefore, the operational safety of heavy-haul trains can be improved. Attached Figure Description

[0016] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings: Figure 1 This is a flowchart of an evaluation method for heavy-haul train handling provided in this disclosure.

[0017] Figure 2Another flowchart for an evaluation method of heavy-haul train handling provided in this disclosure.

[0018] Figure 3 This is a schematic diagram of the structure of an evaluation device for heavy-load train operation provided in this disclosure. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solution of this application, the application scenario of this application will be described first below.

[0020] Heavy-haul trains are complex mechanical systems, with longer braking and release distances, longer charging and venting times, and greater longitudinal impacts compared to conventional freight trains, thus placing higher demands on train operation and control. Due to complex track conditions, improper operation of heavy-haul trains on critical sections such as undulating slopes, small-radius curves, phase breaks, long downhill sections, and temporary speed limits can easily generate significant longitudinal impacts, causing abnormal stops and making operation significantly more difficult than on undulating slopes. Furthermore, the varying signal conditions caused by workshop interventions and the inherent characteristics of heavy-haul combined train systems can lead to differences in the following levels of operation: For example, the level of safe and stable operation, such as the accuracy of "benchmarking operation" under complex track conditions; punctuality, including target speed control under time and kilometer constraints; and energy-efficient operation under safety and stability constraints, i.e., the ability to optimally allocate spare time and potential energy in sections without long downhill slopes. Accurate evaluation of heavy-haul train operation can provide safe operation guidance and improve the level of heavy-haul train operation, which is of great significance for ensuring and improving the transportation efficiency of heavy-haul trains.

[0021] Currently, evaluation methods for heavy-haul train handling primarily focus on single performance indicators that assess train operational safety. For example, they evaluate the accuracy of patterned handling under complex track conditions, but lack further assessment of target speed accuracy under spatiotemporal constraints, delay recovery adjustment levels under signal disturbances, and energy-efficient handling levels under safety and stability constraints. Such evaluation methods cannot truly and effectively assess the handling level of heavy-haul trains, thus impacting their operational safety.

[0022] To address the aforementioned technical problems, this disclosure provides a method, apparatus, equipment, medium, and product for evaluating the operation of heavy-haul trains. This disclosure involves acquiring operation item data for the target operating section of the heavy-haul train; determining the degree of operation item execution of the heavy-haul train in the target operating section based on the operation item data and preset standard ranges for the operation items; and, if the degree of operation item execution is determined to be compliant with operating specifications, determining the operation evaluation result of the heavy-haul train in the target operating section based on the operation item data and preset standard ranges for the operation items, where the range of the standard range for the operation items is greater than the range of the standard range for the operation items. By first determining the degree of operation item execution—that is, whether the current heavy-haul train meets standardized operation—and then determining the operation evaluation result—that is, the level of precision operation of the current heavy-haul train—the operation level of the heavy-haul train is objectively evaluated from multiple evaluation dimensions and factors influencing the operation level. Based on the operation evaluation result, the safety status of the current heavy-haul train can be determined. Therefore, the operational safety of heavy-haul trains can be improved.

[0023] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0026] Example 1 Figure 1 This is a flowchart illustrating an evaluation method for heavy-haul train handling provided in this disclosure. Figure 1 As shown, the method includes: S101: Obtain the control point data for the target operating section of the heavy-haul train.

[0027] Specifically, data cleaning can be performed on the actual engineering data during the operation of heavy-haul trains to remove redundant and invalid data. Traction calculations can then be performed on the cleaned data to obtain valid data for operational evaluation, namely, operational item data. Operational item data refers to indicators whose values ​​change or affect operations during train operation.

[0028] S102: Based on the control item data and the preset control item specification range, determine the degree of control item execution of the heavy-haul train in the target operating section.

[0029] Specifically, the operation standardization evaluation mainly evaluates the operation items in different stages of train operation using a scoring system. The result is the degree of execution of the operation items. The standardization evaluation is the evaluation standard for judging whether there is any violation of the operation items. If the operation item data is within the standard range, it is considered to be in compliance with the standard. The standard range is relatively wide and is the passing standard for operation.

[0030] S103: If the degree of execution of the control items is determined to be in accordance with the control specifications, the control evaluation result of the heavy-haul train in the target operating section is determined based on the control item data and the preset standard range of the control items.

[0031] Specifically, determining the degree of execution of control items as conforming to control specifications means that the standardization evaluation of the degree of execution of control items is the passing standard, requiring a precise control evaluation of the control item data. The range of the standardized interval for control items is larger than the range of the standard interval for control items. This further narrows the scope of the control evaluation, and the determined control evaluation results are used to assess the safety and stability of heavy-haul trains in challenging driving conditions, namely long downhill slopes, making the control evaluation results more targeted.

[0032] This disclosure involves acquiring control item data for the target operating section of a heavy-haul train; determining the degree of control item execution of the heavy-haul train within the target operating section based on the control item data and preset standard ranges for the control items; and, if the degree of control item execution is determined to be in compliance with control specifications, determining the control evaluation result of the heavy-haul train within the target operating section based on the control item data and preset standard ranges for the control items, where the range of the standard range for the control items is greater than the range of the standard range for the control items. By first determining the degree of control item execution—that is, whether the current heavy-haul train meets standardized control requirements—and then determining the control evaluation result—that is, the level of precision control achieved by the current heavy-haul train—the control level of the heavy-haul train is objectively evaluated from multiple evaluation dimensions and factors influencing the control level. Based on the control evaluation result, the safety status of the current heavy-haul train can be determined. Therefore, the operational safety of heavy-haul trains can be improved.

[0033] Example 2 Based on the above embodiments, an exemplary method for determining the handling evaluation results of a heavy-haul train in a target operating section according to the handling item data and the preset standard range of handling items includes: The standard range of the control points is determined based on the environmental parameters of the target driving range and the braking and mitigation parameters in the control point data.

[0034] Specifically, in this embodiment, the environmental parameters of the target driving section include driving route conditions and the number of brakes on the driving route. Based on the driving route conditions, the number of brakes on the driving route, the optimal braking position, and the control release position, the standard range of the control point can be determined.

[0035] By comparing the standard interval and the data of the control points, the control evaluation results of the heavy-haul train in the target operating interval are determined.

[0036] Example: Based on the optimal value distance model and the standard interval of the control point, the braking position and the release position in the control point data are compared and calculated to determine the evaluation sample matrix of the braking position and the release position of the target driving interval.

[0037] Specifically, after determining the standard interval, based on the optimal value distance model, the actual control point data and the standard interval are substituted into the calculation to obtain the braking position evaluation value and the release position evaluation value. Table 1 shows the standard intervals for the braking position and release position of the 12 brake levers.

[0038] The braking position evaluation value and the release position evaluation value can be calculated using the following formulas:

[0039] in, Braking position evaluation value or release position evaluation value For actual manipulation point data, This is the lower limit of the standard range. The upper limit of the standard range. The mean of the manipulated item data is normally distributed.

[0040] Table 1.12 Standard Ranges for Braking and Releasing Positions

[0041] After determining the braking position evaluation value and the release position evaluation value, the evaluation sample matrix of the braking position and the release position in the target driving section is determined based on the optimal value distance model.

[0042] The evaluation sample matrix can be determined using the following formula:

[0043] In this context, each row of the evaluation sample matrix X represents the manipulated evaluation value in the long downhill section, i.e. Each column of the matrix represents the evaluation value of the braking and release positions for each brake under a given number of brakes. In this embodiment, odd-numbered columns represent braking position evaluation values, and even-numbered columns represent release position evaluation values.

[0044] Based on the weights of braking position, release position, and evaluation sample matrix, the handling evaluation results of heavy-haul trains in the target operating section are determined.

[0045] Specifically, the impact of different braking and release point accuracy on the overall handling evaluation results of long downhill slopes varies, so the weights of braking and release points are determined. Based on the weights and the evaluation sample matrix, the handling evaluation results of heavy-haul trains in the target operating section are determined.

[0046] The manipulation evaluation result can be determined using the following formula:

[0047] in, To manipulate the evaluation results, To evaluate the sample matrix, For weights.

[0048] Example: Based on a multi-objective optimization model, the braking position and braking speed in the control point data are calculated, as well as the release position and release speed of the control point, to determine the actual range of the control point.

[0049] Specifically, using a multi-objective optimization manipulation model, under given constraints such as line constraints and control quantity constraints, the actual range of speed and position of the manipulation point is solved through quadratic programming. In this embodiment, the actual range and the standard range can also be represented by curves.

[0050] The similarity of heavy-haul trains in the target operating section is determined by superimposing and comparing the standard interval and the actual interval of the control point; the similarity is used as the control evaluation result.

[0051] Specifically, the similarity of the curves is evaluated by overlaying and comparing the standard interval and the actual interval of the manipulated item point, using the discrete Friesian distance. In each sampling method, the discrete traversal interval t is... The maximum distance can be obtained under each sampling method. The Fraser distance is the value of the sampling method corresponding to the minimum value of the maximum distance.

[0052] The similarity can be calculated using the following formula.

[0053]

[0054] in, For similarity, For points on the actual curve, These are points on the standard curve.

[0055] The curve similarity results are standardized and transformed into manipulation evaluation results. In this embodiment, the manipulation evaluation results are the evaluation results of non-long downhill slopes.

[0056] The manipulation evaluation result can be calculated using the following formula:

[0057]

[0058] in, To manipulate the evaluation results; To calculate the coefficients; The result is the discrete Fréchet distance calculation result; This is the actual curve; This is the standard curve.

[0059] Example 3 Based on the above embodiments, an exemplary method for determining the degree of control point execution of a heavy-haul train in a target operating section according to control point data and preset control point specification intervals includes: The manipulation item data is traversed based on the canonical interval of the manipulation item to determine the manipulation item data that meets the canonical interval.

[0060] Specifically, based on the standardized guidelines for heavy-haul railway operation, and in conjunction with traction regulations and technical regulations, the standardized range of operation items can be determined. These operation items can include starting and stopping phases, phase transitions, operating condition switching, safety locking, signaling, and cyclic braking on long downhill slopes. Each operation item's data is iterated through according to its standardized range to determine if any violations exist. A violation results in a score of 0, while a valid violation results in a score of 1. The score for each operation item can be determined using the following formula:

[0061] in, To determine if a violation of the manipulation rules satisfies the function.

[0062] Table 2 shows some of the manipulation items.

[0063] Based on the control item data and corresponding control item weights that meet the specifications, the degree of control item execution of heavy-haul trains in the target operating section is determined.

[0064] Specifically, the degree of execution of the manipulation point can be calculated and determined using the following formula:

[0065] in, To control the degree of execution of the item, To meet the number of manipulation points in the standard interval, As weight, To determine if a violation of the manipulation rules satisfies the function, To meet the score requirements of the manipulation items in the standard interval.

[0066] Example 4 Based on the above embodiments, an exemplary method for obtaining control point data for a target operating section of a heavy-haul train includes: Collect train operation data of heavy-haul trains in the target operating section; clean the train operation data to obtain the first train operation data; perform traction calculation on the first train operation data to determine the control point data.

[0067] Specifically, data cleaning can be performed on the actual engineering data during the operation of heavy-haul trains to remove redundant and invalid data, and to supplement any missing data. Traction calculations can then be performed on the cleaned data to obtain control point data. In this embodiment, control point data may include running kilometer markers, train speed, track speed limits, track signals, train operating conditions, train traction / braking force, train pipe pressure, track gradient, gradient length, braking time, braking distance, braking position, release time, release distance, release position, and operating energy consumption.

[0068] Example 5 Based on the above embodiments, this embodiment provides an application example. Figure 2 Another flowchart is provided for an evaluation method of heavy-haul train handling according to this disclosure. Figure 2 As shown, the method includes: S201: Obtain train operation data throughout the entire process.

[0069] Specifically, data cleaning can be performed on the actual engineering data during the operation of heavy-haul trains to remove redundant and invalid actual engineering data. Traction calculations can then be performed on the cleaned data to obtain effective data for operation evaluation, namely operation data.

[0070] S202: Based on the static control item database and the entire process control data, determine whether the driver's operation is in violation of regulations and assess the degree of the driver's standardized operation.

[0071] Specifically, the entire process of operation data is traversed and searched in a pre-configured static operation item library to determine whether the driver's operation is in violation of regulations. For each operation item, it is determined whether the operation is in violation of regulations. If it is in violation, points are deducted according to the corresponding score and weight. The final score is the score of the standardized operation level.

[0072] S203: Based on the full-process operation data, obtain the relief position, braking position, relief speed and braking speed of long downhill slopes, compare with the standard range, and evaluate the operation level of long downhill slopes.

[0073] Specifically, after determining the standard interval, based on the optimal value distance model, the actual control point data and the standard interval are substituted into the calculation to obtain the braking position evaluation value and the relief position evaluation value. Comparison with the standard interval allows for the assessment of handling level on long downhill slopes. This further narrows the scope of handling evaluation, and the determined handling evaluation results are used to assess the safety and stability of heavy-haul trains on challenging sections of road, namely long downhill slopes, making the handling evaluation results more targeted.

[0074] S204: Based on a multi-objective optimization model, the similarity between the actual vehicle curve and the standard curve is used as the evaluation index to assess the handling level.

[0075] Specifically, a multi-objective optimization control model is used. Under constraints such as track constraints, traction constraints, and train dynamic constraints, a speed-position curve is obtained through quadratic programming as the control standard curve for non-long downhill slopes. The optimization objectives include the safety, speed achievement, energy efficiency, and smoothness of heavy-haul trains. The actual control speed-position curve is superimposed and compared with the control standard curve to evaluate the curve similarity, and the similarity is used as the evaluation result.

[0076] Example 6 Figure 3 This is a schematic diagram of the structure of an evaluation device for heavy-haul train operation provided in this disclosure. Figure 3 As shown, the device 300 includes: an acquisition module 310, a first determination module 320, and a second determination module 330.

[0077] The acquisition module 310 is used to acquire the control point data of the target travel section of the heavy-haul train; The first determining module 320 is used to determine the degree of execution of the control items by the heavy-haul train in the target travel section based on the control item data and the preset control item specification range; The second determining module 330 is used to determine the operation evaluation result of the heavy-haul train in the target travel section based on the operation item data and the preset standard range of the operation item when the degree of execution of the operation item is determined to be in accordance with the operation specification. The range of the specification range of the operation item is greater than the range of the standard range of the operation item.

[0078] Optionally, the second determining module is used to: The standard range of the control point is determined based on the environmental parameters of the target driving range and the braking and relief parameters in the control point data; The control evaluation result of the heavy-haul train in the target operating section is determined by comparing the standard interval of the control point with the control point data.

[0079] Optionally, the second determining module is used to: Based on the optimal value distance model and the standard interval of the control point, the braking position and the relief position in the control point data are compared and calculated to determine the evaluation sample matrix of the braking position and the relief position in the target driving interval. Based on the weights of the braking position, the weights of the release position, and the evaluation sample matrix, the handling evaluation result of the heavy-haul train in the target operating section is determined.

[0080] Optionally, the second determining module is used to: Based on a multi-objective optimization model, the braking position and braking speed in the control point data are calculated, as well as the release position and release speed of the control point, to determine the actual range of the control point. The similarity of the heavy-haul train in the target travel section is determined by superimposing and comparing the standard interval and the actual interval of the control point. The similarity is used as the evaluation result of the manipulation.

[0081] Optionally, the first determining module is used to: The manipulation item data is traversed based on the normed interval of the manipulation item to determine the manipulation item data that satisfies the normed interval; Based on the control item data and corresponding control item weights that satisfy the specified interval, the degree of control item execution of the heavy-haul train in the target operating interval is determined.

[0082] Optionally, the acquisition module is used for: Collect train operation data of the heavy-haul train in the target operating section; The train operation data is cleaned to obtain the first train operation data; Traction calculations are performed on the first train's operating data to determine the control point data.

[0083] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of the evaluation method for heavy-load train handling described in the above embodiments.

[0084] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the evaluation method for heavy-load train handling described in the above embodiments.

[0085] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the evaluation method for heavy-load train handling described in the above embodiments.

[0086] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0087] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media can include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CDs). ROM, DVD ROM, Blu-ray discs, etc.

[0088] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0089] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0090] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0091] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0092] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0093] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0094] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for evaluating the handling of heavy-haul trains, characterized in that, include: Acquire the control point data for the target operating section of the heavy-haul train; Based on the control item data and the preset control item specification range, the degree of control item execution of the heavy-haul train in the target travel section is determined; If the execution level of the control item is determined to be in accordance with the control specifications, the control evaluation result of the heavy-haul train in the target travel section is determined based on the control item data and the preset standard range of the control item. The range of the standard range of the control item is greater than the range of the standard range of the control item.

2. The method according to claim 1, characterized in that, The step of determining the handling evaluation result of the heavy-haul train in the target operating section based on the handling item data and the preset standard range of handling items includes: The standard range of the control point is determined based on the environmental parameters of the target driving range and the braking and relief parameters in the control point data; The control evaluation result of the heavy-haul train in the target operating section is determined by comparing the standard interval of the control point with the control point data.

3. The method according to claim 2, characterized in that, The comparison between the standard section based on the control point and the control point data to determine the control evaluation result of the heavy-haul train in the target operating section includes: Based on the optimal value distance model and the standard interval of the control point, the braking position and the relief position in the control point data are compared and calculated to determine the evaluation sample matrix of the braking position and the relief position in the target driving interval. Based on the weights of the braking position, the weights of the release position, and the evaluation sample matrix, the handling evaluation result of the heavy-haul train in the target operating section is determined.

4. The method according to claim 2, characterized in that, The comparison between the standard section based on the control point and the control point data to determine the control evaluation result of the heavy-haul train in the target operating section includes: Based on a multi-objective optimization model, the braking position and braking speed in the control point data are calculated, as well as the release position and release speed of the control point, to determine the actual range of the control point. The similarity of the heavy-haul train in the target travel section is determined by superimposing and comparing the standard interval and the actual interval of the control point. The similarity is used as the evaluation result of the manipulation.

5. The method according to claim 1, characterized in that, The step of determining the degree of execution of control items by the heavy-haul train in the target operating section based on the control item data and the preset control item specification range includes: The manipulation item data is traversed based on the normed interval of the manipulation item to determine the manipulation item data that satisfies the normed interval; Based on the control item data and corresponding control item weights that satisfy the specified interval, the degree of control item execution of the heavy-haul train in the target operating interval is determined.

6. The method according to claim 1, characterized in that, The acquisition of the control point data for the target operating section of the heavy-haul train includes: Collect train operation data of the heavy-haul train in the target operating section; The train operation data is cleaned to obtain the first train operation data; Traction calculations are performed on the first train's operating data to determine the control point data.

7. An evaluation device for heavy-haul train operation, characterized in that, include: The acquisition module is used to acquire the control point data of the target operating section of the heavy-haul train; The first determining module is used to determine the degree of execution of the control items by the heavy-haul train in the target travel section based on the control item data and the preset control item specification range; The second determining module is used to determine the operation evaluation result of the heavy-haul train in the target travel section based on the operation item data and the preset standard range of the operation item, when it is determined that the execution degree of the operation item meets the operation specifications. The range of the specification range of the operation item is greater than the range of the standard range of the operation item.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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