Heavy haul railway track structure risk level detection method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a heavy haul railway track structure risk level detection method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of heavy haul railway ballast track engineering, and can provide more comprehensive and reliable risk detection for a heavy haul railway. The method comprises the following steps: acquiring a plurality of detection indexes of a track; obtaining a measurable quantized value of each detection index in each detection section of the track; performing data standardization processing on the measurable quantized value according to the forward or reverse performance of each detection index to obtain a detection matrix; determining the weight of each detection index by adopting an analytic hierarchy process; acquiring a risk detection value of each detection section based on the detection matrix and the weight; and determining the risk level of each detection section according to the risk detection value.

Description

Technical Field

[0001] The present application relates to the technical field of heavy-load railway ballasted track engineering, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for detecting the risk level of heavy-load railway track structures. Background Art

[0002] The stability of heavy-haul railway track structures is a prerequisite for ensuring operational safety, making risk assessment crucial. Existing assessment methods primarily utilize data such as the track quality index, evaluating only the physical condition of the track structure itself. However, these methods provide incomplete assessment results and fail to truly reflect the overall risk profile. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for detecting the risk level of heavy-load railway track structure in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for detecting the risk level of a heavy-load railway track structure, comprising:

[0005] Acquiring multiple detection indicators of the track, the multiple detection indicators including track structural characteristic indicators related to the track structural state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management;

[0006] Obtaining a measurable quantitative value of each of the detection indicators in each detection section of the track;

[0007] According to the positivity or negativity of each of the detection indicators, data normalization processing is performed on the measurable quantitative values ​​to obtain a detection matrix;

[0008] Determining the weight of each of the detection indicators using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative values;

[0009] Based on the detection matrix and the weight, obtaining a risk detection value for each of the detection sections;

[0010] The risk level of each detection section is determined according to the risk detection value.

[0011] In one embodiment, performing data normalization processing on the measurable quantitative values ​​according to the positivity or negativity of each of the detection indicators to obtain a detection matrix includes:

[0012] determining the maximum value of each of the detection indicators within the plurality of detection sections;

[0013] For a positivity detection indicator, determining a ratio of a current measurable quantified value corresponding to the detection indicator to the maximum value as a normalized value corresponding to the positivity detection indicator;

[0014] For a detection indicator of reversibility, determining a standardized value corresponding to the detection indicator of reversibility based on a difference between a current measurable quantified value corresponding to the detection indicator and the maximum value;

[0015] The detection matrix is ​​determined based on the normalized value corresponding to the positive detection index and the normalized value corresponding to the negative detection index.

[0016] In one embodiment, the step of obtaining a measurable quantitative value of at least one type of indicator among the track structure characteristic indicators includes:

[0017] Obtaining inspection records of the detection section;

[0018] Determining, based on the track damage information recorded in the inspection, damage events in the inspection section that meet a preset damage definition, and obtaining the number of damage events of the damage events;

[0019] According to the weighted processing results of the numbers of each of the damage events, a measurable quantitative value of the at least one type of indicator is obtained.

[0020] In one embodiment, the operation and maintenance management indicators include one or more of the degree of automation of track maintenance, the capability of maintenance objects, and the completeness of emergency plans;

[0021] The steps of obtaining the measurable quantitative value of the operation and maintenance management indicator include:

[0022] Determining the actual degree of automation of maintenance for each of the detection sections, and determining a measurable quantitative value of the degree of automation of maintenance for the track based on the scores corresponding to the actual degrees of automation; wherein different actual degrees of automation correspond to different scores; and / or,

[0023] Determining the actual maintenance object capability of each of the detection sections, and determining a measurable quantifiable value of the maintenance object capability of the track based on the scores corresponding to each of the actual maintenance object capabilities; wherein different actual maintenance object capabilities correspond to different scores; and / or,

[0024] Determine the completeness of the emergency plan for each of the detection sections, and determine a measurable quantified value of the completeness of the emergency plan for the track based on the scores corresponding to the completeness of each of the emergency plans; wherein different completeness of the emergency plans corresponds to different scores.

[0025] In one embodiment, the step of obtaining a measurable quantitative value of at least one type of indicator among the environmental characteristic indicators includes:

[0026] Performing difference calculation on the environmental parameter value of the detection section of the track and the preset parameter reference value, and determining the measurable quantitative value of the at least one type of indicator based on the interpolation result of the difference calculation; or,

[0027] The environment of the detection section of the track is mapped to a level value corresponding to the environment according to a preset intensity level standard of the environmental phenomenon, and the level value is used as a measurable quantitative value of at least one type of indicator.

[0028] In one embodiment, the judgment matrix is ​​determined by the following steps:

[0029] Calculating the mean and standard deviation of the measurable quantitative values ​​of each of the detection indicators in each of the detection sections;

[0030] Based on the mean and standard deviation, and in combination with a preset importance parameter, determining matrix elements in an initial judgment matrix;

[0031] According to the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order, the consistency index is determined;

[0032] Comparing the consistency index with an average random consistency index corresponding to a matrix of the same order to calculate a consistency ratio;

[0033] If the consistency ratio is not less than the preset threshold, the matrix element with the largest deviation in the initial judgment matrix is ​​determined and adjusted, and the process returns to execute the relationship between the maximum value of the matrix element in the initial judgment matrix and the matrix order to determine the consistency index until the preset stop condition is met to obtain the judgment matrix.

[0034] In a second aspect, the present application also provides a heavy-load railway track structure risk level detection device, comprising:

[0035] A detection index acquisition module is used to obtain multiple detection indicators of the track, wherein the multiple detection indicators include track structure characteristic indicators related to the track structure state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management;

[0036] a detection index quantification module, configured to obtain a measurable quantified value of each detection index in each detection section of the track;

[0037] A detection matrix acquisition module, configured to perform data normalization processing on the measurable quantitative values ​​according to the positivity or negativity of each detection indicator to obtain a detection matrix;

[0038] a detection indicator weight determination module, configured to determine the weight of each detection indicator using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative value;

[0039] a risk detection value acquisition module, configured to acquire a risk detection value of each detection section based on the detection matrix and the weight;

[0040] The risk level determination module is used to determine the risk level of each detection section according to the risk detection value.

[0041] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] Acquiring multiple detection indicators of the track, the multiple detection indicators including track structural characteristic indicators related to the track structural state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management;

[0043] Obtaining a measurable quantitative value of each of the detection indicators in each detection section of the track;

[0044] According to the positivity or negativity of each of the detection indicators, data normalization processing is performed on the measurable quantitative values ​​to obtain a detection matrix;

[0045] Determining the weight of each of the detection indicators using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative values;

[0046] Based on the detection matrix and the weight, obtaining a risk detection value for each detection section;

[0047] The risk level of each detection section is determined according to the risk detection value.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0049] Acquiring multiple detection indicators of the track, the multiple detection indicators including track structural characteristic indicators related to the track structural state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management;

[0050] Obtaining a measurable quantitative value of each of the detection indicators in each detection section of the track;

[0051] According to the positivity or negativity of each of the detection indicators, data normalization processing is performed on the measurable quantitative values ​​to obtain a detection matrix;

[0052] Determining the weight of each of the detection indicators using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative values;

[0053] Based on the detection matrix and the weight, obtaining a risk detection value for each detection section;

[0054] The risk level of each detection section is determined according to the risk detection value.

[0055] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0056] Acquiring multiple detection indicators of the track, the multiple detection indicators including track structural characteristic indicators related to the track structural state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management;

[0057] Obtaining a measurable quantitative value of each of the detection indicators in each detection section of the track;

[0058] According to the positivity or negativity of each of the detection indicators, data normalization processing is performed on the measurable quantitative values ​​to obtain a detection matrix;

[0059] Determining the weight of each of the detection indicators using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative values;

[0060] Based on the detection matrix and the weight, obtaining a risk detection value for each detection section;

[0061] The risk level of each detection section is determined according to the risk detection value.

[0062] The above-mentioned heavy-load railway track structure risk level detection method, device, computer equipment, computer-readable storage medium and computer program product obtain multiple detection indicators of the track, including track structure characteristic indicators related to the track structure status, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance and management; obtain the measurable quantitative value of each detection indicator in each detection section of the track; perform data standardization processing on the measurable quantitative value according to the positivity or reversibility of each detection indicator to obtain a detection matrix; use the hierarchical analysis method to determine the weight of each detection indicator, wherein the judgment matrix used for calculating the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative value; based on the detection matrix and the weight, obtain the risk detection value of each detection section; and determine the risk level of each detection section based on the risk detection value. This application overcomes the problem of the prior art that the detection results are out of touch with the actual risk due to the single detection dimension, by comprehensively detecting the track structure, environment and operation and maintenance management. Secondly, unlike the drawbacks of relying on subjective experience and expert scoring during the detection process, by quantifying all indicators and adopting a method to determine weights based on the statistical characteristics of the data, the objectivity of the detection results is significantly improved, thereby providing more comprehensive and reliable risk detection for heavy-load railways. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 1 is a flow chart of a method for detecting risk levels of heavy-load railway track structures in one embodiment;

[0065] Figure 2 A schematic diagram of the hierarchy of detection indicators of a method for detecting risk levels of heavy-load railway track structures in one embodiment;

[0066] Figure 3 A schematic flow chart of a method for detecting the risk level of a heavy-load railway track structure in another embodiment;

[0067] Figure 4 This is a structural block diagram of a device for detecting risk levels of heavy-load railway track structures in one embodiment;

[0068] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] In one embodiment, Figure 1 As shown, a method for detecting the risk level of heavy-load railway track structure is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0071] Step S102: Acquire multiple detection indicators of the track.

[0072] The detection indicators can be a structured set of parameters. Their function is to systematically decompose the various risk sources affecting heavy-haul railway operational safety into quantifiable basic units, thus serving as the data input basis for the entire risk detection model. Operationally, this set is specifically divided into track structural characteristic indicators related to the track structure state, environmental characteristic indicators related to the track environment, and operation and maintenance management indicators related to track maintenance and management. This ensures comprehensive coverage of risks from multiple dimensions, including the physical state of equipment, external natural conditions, and institutional factors.

[0073] For example, multiple detection indicators of heavy-load railway track structure risks can be obtained hierarchically, such as Figure 2 The model shown in the figure first decomposes the top-level total risk assessment target A into three main criterion-level indicators, namely the track structure characteristic indicator B1, the environmental characteristic indicator B2, and the operation and maintenance management indicator B3. Among them, the track structure characteristic indicator B1 is further subdivided into four sub-criteria layers: rail C1, connecting parts C2, sleepers C3, and roadbed C4. Under these sub-criteria layers, there are corresponding specific inspection indicators at the bottom level. For example, rail C1 includes rail damage D1, track irregularity D2, rail creep D3, and rail weld sagittal D4; connecting parts C2 includes fastener damage number D5, guardrail fastener torque D6, and insulation joint number D7; sleeper C3 includes uneven spacing D8, sleeper hanging D9, and sleeper failure D10. 10 Indicator; the sub-section of track bed C4 includes the number of mud overflowing sections D 11 , longitudinal resistance of track bed D 12 , lateral resistance of track bed D 13 、Ballast size 1D 14 , Track bed size 2D 15At the same time, the environmental characteristic index B2 and the operation and maintenance management index B3 are directly decomposed into multiple specific detection indicators. For example, the environmental characteristic index B2 includes wind speed level C5, extreme high temperature C6, rainfall C7, air salt spray concentration C8, earthquake intensity C9, total weight C10, etc. 10 The operation and maintenance management indicator B3 includes the maintenance object capability C 11 、Number of track maintenance personnel C 12 , Maintenance automation level C 13 , Monitoring system indicators C 14 , detection cycle C 15 、Emergency Plan C 16 .

[0074] Step S104 : obtaining a measurable quantitative value of each detection indicator in each detection section of the track.

[0075] A test section can be a pre-defined, standard-length track unit designed for refined management. A measurable value can be a specific numerical value representing the current state of a test indicator within a specific test section. This is used to convert objective physical conditions or abstract management concepts into standardized data that can be directly recognized and processed by computers.

[0076] For example, when determining the detection section, the length of the risk detection section of the heavy-load railway track structure with strip-like characteristics can be determined; if the detection section is too short, the detection workload will be too large, which is not conducive to actual on-site application; if the detection section is too long, the detection will be too rough, which is not conducive to the selection and determination of risk reduction measures. In an optional embodiment, taking into account factors such as the track quality index (TQI) indicator unit section length, the length of the seamless line in the ordinary section, the curve length, and the ramp length, 2.0 km can be used as the length of the heavy-load railway track risk detection section.

[0077] For example, the terminal performs an operation to obtain measurable quantitative values ​​for a specific inspection section. This operation is not a single data collection operation, but rather a multi-modal data processing process. Optionally, for the "Track Irregularity Quality Index" metric, the terminal receives multiple geometric parameter data streams covering the inspection section from the track inspection vehicle, analyzes and statistically processes these data streams, and extracts the maximum value as the quantitative value.

[0078] Step S106 , performing data normalization processing on the measurable quantized values ​​according to the positivity or inverseness of each detection index to obtain a detection matrix.

[0079] Data standardization is a mathematical process that transforms heterogeneous quantitative values ​​into dimensionless values. It must adhere to the principles of constant relative disparity within the same indicator, uncertainty between different indicators, and equality of maximum values ​​after standardization. Positive and negative indicators can be determined based on the relationship between the quantitative value of the detection indicator and the level of risk. Optionally, the larger the quantitative value of the positive indicator, the greater the track risk; the opposite is true for the negative indicator.

[0080] Exemplarily, after obtaining all measurable quantitative values, the terminal performs data standardization processing. The terminal can match and call the corresponding standardization processing rules for each detection indicator based on the pre-configured attribute information of the indicator. The attribute information can at least define the positivity or inverseness of the indicator. Exemplarily, when the terminal processes positive indicators such as "number of rail damage", the standardization rule it applies can be a linear normalization transformation. Optionally, in another embodiment, when the terminal recognizes that there are extreme outliers in the data of certain indicators, in order to enhance the robustness of subsequent calculations, it can switch to a nonlinear transformation rule, such as a logarithmic function transformation, to effectively compress the high-end range of the data. After completing the corresponding transformation processing on the quantitative values ​​of all indicators, the terminal organizes the obtained standardized values ​​into a detection matrix.

[0081] Step S108: using the analytic hierarchy process to determine the weight of each detection indicator.

[0082] Among them, the judgment matrix used to calculate the weights in the hierarchical analysis method is determined based on the statistical characteristics of measurable quantitative values.

[0083] In some embodiments, the traditional AHP method uses expert scoring to determine weight coefficients; another method uses an improved AHP method to determine weight coefficients. The traditional AHP method of constructing a judgment matrix to determine weight coefficients is significantly influenced by expert subjective factors. This can be reduced by using multiple industry experts to determine weight coefficients. In this embodiment, the terminal's weight determination process does not obtain expert scores through a human-computer interaction interface, but rather automatically performs a determination based on historical data.

[0084] For example, the terminal can batch read historical measurable quantitative value data covering, for example, hundreds of detection segments. It then performs statistical operations on this historical data set for each detection indicator to obtain its statistical characteristics. For example, these characteristics may include the mean and standard deviation, where the mean reflects the typical risk level of the indicator, while the standard deviation reflects the volatility and dispersion of its risk. The terminal then automatically generates all matrix elements of the judgment matrix based on a pre-set mathematical model that converts the mean and standard deviation into a significance scale.

[0085] Optionally, in another embodiment, statistical features are not limited to mean and standard deviation, but may also include statistical indicators such as coefficient of variation or information entropy that better reflect the degree of data dispersion and information content, to adapt to weight calculation scenarios with different data distribution characteristics. After constructing the judgment matrix, the terminal further determines the weight coefficients of each detection indicator by solving the eigenvector of the matrix.

[0086] Step S110 : obtaining the risk detection value of each detection section based on the detection matrix and the weight.

[0087] For example, the terminal retrieves two key data structures from its internal storage: a weight vector, calculated in the previous step, containing the weight coefficients for all detection indicators; and a state vector, extracted from the detection matrix and specific to the segment to be detected. This state vector consists of the normalized values ​​of all indicators for that segment. The normalized values ​​of the state vectors for all detection indicators within a specific detection segment are weighted and aggregated according to the weight coefficients of their respective weight vectors. The resulting single composite value representing the overall risk level of the segment is the risk measurement.

[0088] In some embodiments, the aggregation operation is not limited to linear weighted summation. To highlight the impact of certain extreme indicators, nonlinear aggregation functions can also be used. For example, a method of raising the normalized value to a power and then performing a weighted summation can be used to amplify the contribution of high-risk indicators to the final score.

[0089] Step S112: determining the risk level of each detection section according to the risk detection value.

[0090] For example, after obtaining a risk detection value for a detection section, the terminal determines the risk level of each assessment section based on the correspondence between risk assessment values ​​and risk levels. This correspondence can be stored as a risk level mapping table, as shown in Table 1. Risk levels can be divided into four levels, arranged from low to high: low risk, general risk, significant risk, and major risk, and displayed in blue, yellow, orange, and red, respectively.

[0091] For example, after obtaining a risk detection value of 0.65 for a certain detection segment, the terminal invokes its internally configured risk level determination rule. This rule can be stored as a risk level mapping table, which exemplarily defines that the risk detection value interval (0.6, 0.8) corresponds to a higher risk level, and the visual indicator associated with this level is orange. The terminal compares the calculated value of 0.65 with the threshold interval in the table and determines that it falls within the higher risk range.

[0092] Table 1

[0093] Risk Level Significant risks Greater risk General Risks Low risk Risk Assessment Value (0.8,1.0] (0.6,0.8] (0.4,0.6] (0,0.4]

[0094] This embodiment overcomes the disconnect between test results and actual risks, a problem inherent in existing technologies, caused by a single testing dimension, by comprehensively testing track structure, environment, and operations and maintenance management. Furthermore, unlike the drawbacks of relying on subjective experience and expert scoring during the testing process, this method significantly improves the objectivity of test results by quantifying all indicators and employing a method that determines weights based on the statistical characteristics of the data, thereby providing more comprehensive and reliable risk detection for heavy-haul railways.

[0095] In an exemplary embodiment, data normalization is performed on the measurable quantitative values ​​according to the positivity or negativity of each detection indicator to obtain a detection matrix, including:

[0096] Determine the maximum value of each detection indicator in multiple detection sections; for the positive detection indicator, determine the ratio of the measurable quantized value corresponding to the current detection indicator to the maximum value as the normalized value corresponding to the positive detection indicator; for the negative detection indicator, determine the difference between the measurable quantized value corresponding to the current detection indicator and the maximum value as the normalized value corresponding to the negative detection indicator; based on the normalized value corresponding to the positive detection indicator and the normalized value corresponding to the negative detection indicator, determine the detection matrix.

[0097] Specifically, the data normalization method defined in this step is a specific data transformation implementation that calibrates and normalizes data based on the extreme values ​​of each test indicator. The technical concept is to utilize the predetermined maximum value of each indicator within multiple test segments as a stable reference anchor point. By performing a ratio or difference calculation between the current measurable quantitative value and this reference anchor point, the raw data is converted into a relative value that intuitively reflects the degree to which the current state deviates from the most unfavorable state. This achieves dimensionless processing of different indicator data in a computationally simple and physically clear manner.

[0098] Exemplarily, the positivity indicator data is normalized, as shown in formula (1), by determining the ratio of the measurable quantized value corresponding to the current detection indicator to the maximum value as the normalized value corresponding to the positivity detection indicator.

[0099]

[0100] Where r ij is the element in the i-th row and j-th column of the fuzzy evaluation matrix R; x max (i) is the maximum value of the i-th evaluation index.

[0101] For the reversibility detection indicator, when testing multiple segments, the terminal first calculates the difference between its preset maximum value and the current measurable quantized value. This difference reflects the gap between the current state and the optimal state in a physical sense. Then, the difference is compared with the maximum value to perform a ratio operation. This operation aims to normalize the degree of improvement into a relative proportional value, as shown in formula (2).

[0102] Normalization of reverse index data (applicable to multiple segment evaluations):

[0103]

[0104] Optionally, in another embodiment, a more complete linear normalization rule may be used for the reversibility indicator, that is, by first calculating the difference between its preset maximum value and the current measurable quantized value, as well as the difference between the preset maximum value and the preset minimum value, and then comparing the difference between the two to obtain a standardized value, so that the standardized value can more accurately reflect its relative position in the entire possible value range, as shown in formula (3).

[0105] Standardization of reverse index data (applicable to individual segment evaluation):

[0106]

[0107] Among them, x min (i) is the minimum value of the i-th evaluation index.

[0108] In this embodiment, by using the maximum value of a fixed engineering boundary as a unified evaluation benchmark for multiple / at least part of the detection segments, it is ensured that the standardized results do not drift with the changes in data samples, and the vertical and horizontal comparability of the detection results at different times and in different segments is guaranteed, thereby improving the transparency and engineering practical value of the entire detection model results.

[0109] In an exemplary embodiment, the step of obtaining a measurable quantitative value of at least one type of track structure characteristic index includes:

[0110] Obtain inspection records of the inspection section; determine damage events in the inspection section that meet a preset damage definition based on the track damage information in the inspection records, and obtain the number of damage events; and obtain a measurable quantitative value for at least one type of indicator based on a weighted processing result of the number of each damage event.

[0111] Specifically, the terminal is configured to process multi-source electronic inspection records and test data from manual or automated equipment to determine measurable quantitative values ​​for multiple track structure characteristic indicators within a specific inspection section. The terminal's processing logic is divided into multiple sub-processes, corresponding to different components of the track structure: rails, connecting parts, sleepers, and trackbed. These sub-processes exemplarily include:

[0112] In the processing sub-process of rail indicators: Regarding rail damage (positive indicator), the terminal first classifies the damage events in the inspection records based on the preset defect definition. The classification can be divided into internal damage (specifically including rail head damage, rail waist damage, rail bottom transverse cracks, etc.), surface damage (specifically including tread abrasions, wave wear, rail head wear, fish scale pattern, peeling, etc.) and welding damage (specifically including gray spots, grinding burns, electrode burns, etc.). The terminal further determines the damage events into light and heavy injuries based on the judgment rules stored internally and the judgment indicators such as rail head wear, rail end or rail top surface peeling, rail top surface abrasion, rail head droop, wave wear, rail surface cracks, rail internal cracks, rail deformation and rail rust. The terminal then performs a weighted count of these damage events, assigning a weight of 0.5 to each minor event and a weight of 1 to a major event, and summing up the weighted number of all events, with the weighted total serving as the quantitative value of the indicator. This weighted processing method is based on the basic principles of material mechanics and fracture mechanics. That is, compared with minor events (such as surface wear), major damage events (such as internal cracks) have an exponentially higher probability of causing brittle fracture of the track structure and threatening driving safety. Therefore, assigning a higher weight value is an objective quantification of the physical risk level corresponding to different damage types, rather than a subjective setting.

[0113] Regarding track irregularities (positive indicators), the terminal receives and processes track irregularity quality index data derived from dynamic data such as height, track direction, gauge, level, and triangular pits. The track irregularity quality index is a neutral indicator and evaluation method that uses mathematical statistics to describe the overall quality status of the track section. For a detection section containing multiple (e.g., 10) 200-meter TQI units, the terminal uses the maximum value in this group of TQI data as the final quantitative value. The selection of the maximum TQI value as the quantification basis is based on the principle of vehicle-track coupling dynamics. This is because for high-speed and heavy-load trains, it is often the most severe geometric irregularity on the track (i.e., the maximum TQI point) that triggers the most violent wheel-rail interaction and creates the greatest instantaneous derailment risk. Therefore, capturing this maximum value directly quantifies the most dangerous "short board effect" in the physical system. Compared with using an average value, it can more truly reflect the extreme risk level of the section.

[0114] Regarding rail locking temperature / rail creep (positive indicator), the terminal can directly obtain and use the rail creep measurement value obtained from the creeping observation pile as a quantitative value. This indicator is used to comprehensively characterize the risk of actual locking rail temperature changes caused by rail creep, and indirectly monitor the longitudinal stress accumulation inside the rail caused by temperature changes and train braking force.

[0115] Regarding rail welds (a positive indicator), the terminal captures and uses a one-meter ruler to measure the sagittal value of the weld top surface, indicating concavity or saddle wear. Weld concavity physically constitutes a high-frequency impact source for the wheels. The greater the sagittal value, the more severe the impact force, which not only accelerates fatigue damage to the weld itself but also threatens driving stability. The maximum sagittal value for all welds within the inspection section is used as a quantitative value.

[0116] In the sub-process for processing indicators related to connecting parts, regarding fastener damage (a positive indicator), the terminal counts the total number of damaged fasteners mentioned in inspection records, which are caused by insufficient or excessive fastener bolt torque (e.g., not within the range of 80 to 150 N·m), broken or floating spikes, damaged nuts or screw threads, malfunctioning washers, damaged spring bars that cannot maintain the required clamping pressure, severe wear of the clamp plate or gauge plate with the front-to-back distance between the two exceeding 2.0 mm, and crushing or protrusion of the rail pad. This is used as a quantitative value. The technical purpose of counting damaged fasteners is to quantify the degree of degradation of the track frame stiffness and gauge retention capacity provided by the fastener system. Each damaged fastener represents a point of loss of the track's lateral restraint force, and the total number of damaged fasteners is directly related to the probability of lateral track instability.

[0117] Regarding guardrail fastener resistance (a positive indicator), the terminal calculates the average value of guardrail fastener bolt torque from a sample inspection of inspection records (primarily the overall average value, as the maximum or minimum values ​​of a single group have little impact on the longitudinal force of the rail). This value serves as a quantitative representation of the guardrail fastener's longitudinal resistance to assess its impact on the additional force on the CWR base rail on the bridge. If there is no guardrail in the inspection section, this quantitative value is set to zero.

[0118] Regarding the glued insulation joints (positive indicator), the terminal counts the total number of glued insulation joints that are weak links in the line within the detection section as its quantitative value.

[0119] In the sleeper indicator processing sub-process: For uneven sleeper spacing (a positive indicator), the terminal counts the number of sleeper spacing unevenness events due to position, spacing deviation, or skew greater than 50mm based on inspection records, as a measurable quantitative value. For empty sleeper / hanging board sleepers (a positive indicator), the terminal counts the number of empty sleeper / hanging board sleeper events based on inspection records, as a measurable quantitative value. For sleeper failure / serious damage (a positive indicator), the terminal counts the number of serious sleeper damage events based on the load preset serious damage rule based on inspection records, as a measurable quantitative value.

[0120] In the sub-process for processing roadbed indicators, regarding roadbed contamination (a positive indicator), the terminal counts the number of mud and mud spills caused by roadbed contamination, as clearly observed in inspection records, as a quantitative value. The number of mud and mud spills quantifies the extent to which the roadbed's two core physical functions, drainage and load transfer, are compromised.

[0121] Regarding the longitudinal resistance of the track bed (positive indicator), the terminal performs conditional difference calculation: for the seamless line section on the bridge, the quantitative value is determined by calculating the difference between the actual value of the longitudinal resistance of the track bed and the preset benchmark value (such as 18kN / sleeper). When the actual value is less than the benchmark value, the quantitative value is zero; at the same time, the quantitative value is determined by calculating the difference between the actual value of the lateral resistance of the track bed and the stability benchmark value (such as 12kN / sleeper). When the actual value is greater than the benchmark value, the quantitative value is zero.

[0122] Regarding the trackbed geometry (positive indicators), it primarily consists of three dimensions: trackbed thickness, top surface width, and side slope gradient. Top surface width and side slope gradient are closely related to the trackbed's lateral resistance and can be characterized by the trackbed's lateral resistance index. Changes in thickness affect the vertical stiffness of the track and the stability of the trackbed, characterized by the trackbed thickness change rate. The terminal obtains the unbalanced superelevation value of the curve section as a quantitative value, and further determines the quantitative value of the trackbed thickness change rate by calculating the ratio of the difference between the maximum and normal trackbed thickness, Δh, to the corresponding mileage, L (Δh / L).

[0123] In this embodiment, by obtaining inspection records and weighting the damage events that meet preset definitions, the actual health status and potential risk level of the track structure can be more accurately reflected compared to the traditional method of simply counting all defects.

[0124] In an exemplary embodiment, the step of obtaining a measurable quantitative value of an operation and maintenance management indicator includes:

[0125] Determine the actual maintenance automation level of each detection section, and determine a measurable quantitative value of the maintenance automation level of the track based on the scores corresponding to each actual maintenance automation level; wherein different actual maintenance automation levels correspond to different scores; and / or, determine the actual maintenance object capability of each detection section, and determine a measurable quantitative value of the maintenance object capability of the track based on the scores corresponding to each actual maintenance object capability; wherein different actual maintenance object capabilities correspond to different scores; and / or, determine the completeness of the emergency plan of each detection section, and determine a measurable quantitative value of the completeness of the emergency plan of the track based on the scores corresponding to the completeness of each emergency plan; wherein different emergency plan completenesses correspond to different scores.

[0126] Among them, operation and maintenance management indicators include one or more of the degree of automation of track maintenance, the capabilities of maintenance objects, and the completeness of emergency plans.

[0127] Specifically, in order to determine the measurable quantitative values ​​of multiple operation and maintenance management indicators within a certain detection section, the terminal is configured to be able to process various types of input information representing the management status from the line management department.

[0128] The terminal's processing logic is divided into multiple sub-processes, corresponding to different management dimensions, exemplarily including:

[0129] Regarding the maintenance object capability (reverse indicator), the terminal evaluates the quality level of maintenance personnel by processing their qualification information. Its processing logic is based on a preset scoring rule, scoring 1 point for personnel with junior professional titles, 2 points for intermediate professional titles, and 3 points for senior professional titles. The scores of all personnel in the detection section are then accumulated, and the total score is used as a quantitative value to characterize the maintenance object capability. The terminal can also calculate the average number of people involved in maintenance (reverse indicator). It first calculates the average maintenance mileage per person by receiving the total length of the line under the jurisdiction of the engineering section and the total number of maintenance personnel, and then multiplies this value by the length of the detection section to determine a quantitative value.

[0130] Regarding the degree of automation in track maintenance (a positive indicator), the terminal determines its quantitative value based on a nine-level mapping table, as shown in Table 2. This mapping table categorizes maintenance methods from manual (relying entirely on manual labor and simple tools) to semi-manual and semi-mechanized (relying partially on machines) (relying 3) to mechanized (relying largely on machines but requiring human interaction) (relying 5) to automated (relying on some human intervention) (relying 7) to intelligent (relying completely on no human interaction) (relying 9). Intermediate scores are set for transitional states between these levels. Manual refers to the fact that all maintenance and repair tasks are primarily performed by manual labor and simple tools; semi-manual and semi-mechanized refers to the fact that some maintenance tasks are performed by maintenance machines and some by manual labor and simple tools; mechanized refers to the fact that all maintenance tasks are essentially performed by machines but require human interaction; automated refers to the fact that all maintenance tasks are replaced by machines but require some human interaction; and intelligent refers to the fact that all maintenance tasks are completely replaced by machines and no human interaction is required. This scoring system aims to quantify the impact of different maintenance modes on the accuracy and durability of track geometry restoration. Intelligent, automated maintenance (higher scores) allows for more precise control of operational parameters such as trackbed tamping and rail grinding compared to manual maintenance (lower scores). This allows the track to achieve and maintain optimal engineering standards after repair, directly impacting the physical performance and degradation rate of the track structure.

[0131] Table 2

[0132] Grade / Rating describe Remark 1 Artificial 2 Between manual and semi-manual semi-mechanized states 3 Semi-manual and semi-mechanized 4 Between semi-manual and semi-mechanized and mechanized states 5 mechanization 6 Between mechanization and automation 7 automation 8 Between automation and intelligence 9 Intelligent

[0133] Regarding the completeness of the emergency plan (reverse indicator), the terminal maps it to a four-level scoring system based on the degree of detail, perfection and feasibility of the plan. For example, a complete and feasible multi-level emergency plan corresponds to a value of 1, a relatively complete but not detailed emergency plan corresponds to a value of 2, only a single emergency plan corresponds to a value of 3, and the lack of an emergency plan corresponds to a value of 4.

[0134] Optionally, the terminal can also process other operation and maintenance management indicators. For example, for the monitoring system (reverse indicator), the terminal adopts a multi-factor product model (k=k1×k2×k3×k4×k5). The terminal determines the values ​​of the five characterization coefficients respectively: k1 (whether there is a monitoring system, 1 for yes, 0 for no), k2 (whether there is an early warning function, 2 for yes, 1 for no), k3 (the number of monitoring sections in the detection section), k4 (the number of monitoring points) and k5 (the reliability of the monitoring method), where the value of k5 is further obtained from a preset reliability coefficient table based on a combination of sensor accuracy, acquisition instrument advancement and data processing principle assumptions, and finally the five coefficient values ​​are multiplied to obtain a quantitative value. Among them, the reliability coefficient table of k5 is shown in Table 3.

[0135] Table 3

[0136]

[0137] For the detection period (positive indicator), the terminal obtains the maximum detection period of various detection devices used in the detection section as its quantitative value.

[0138] In this embodiment, by setting specific quantitative acquisition steps for operation and maintenance management indicators, the assessment of the two risk sources, human and management, can be scientifically integrated into a unified quantitative risk model, so that the final risk detection results can more comprehensively and deeply reflect the actual comprehensive risk level of the line.

[0139] In an exemplary embodiment, the step of obtaining a measurable quantitative value of at least one type of environmental characteristic indicators includes:

[0140] The difference between the environmental parameter value of the detection section of the track and the preset parameter reference value is calculated, and based on the interpolation result of the difference calculation, a measurable quantitative value of at least one category of indicators is determined; or, the environment of the detection section of the track is mapped to a grade value corresponding to the environment based on a preset intensity level standard of the environmental phenomenon, and the grade value is used as the measurable quantitative value of at least one category of indicators.

[0141] Specifically, the terminal is configured to process information from external data sources such as meteorological, geological, and line asset management systems to determine measurable, quantitative values ​​for multiple environmental characteristic indicators within a detection section. The terminal's processing logic may employ different quantification rules depending on the nature of the indicators being processed, including, for example:

[0142] For some indicators, the terminal performs a difference calculation based on the preset parameter baseline value or directly uses the monitoring value. For example:

[0143] Regarding extreme high temperatures (positive indicators), they are used to characterize the risk of instability of seamless lines. The terminal obtains the current maximum rail temperature measured value, and compares it with a preset standard maximum rail temperature reference value determined based on historical statistical data, and uses the difference between the two as a quantified value. When the measured value is lower than the reference value, the quantified value is determined to be zero, otherwise it is the difference between the two. This difference quantification method directly applies the physical law of thermal expansion and contraction of materials and the stress calculation formula in structural mechanics. In this principle, the temperature difference between the rail temperature and the locked rail temperature is the core variable that determines the magnitude of the thermal stress inside the rail. When the stress exceeds the lateral resistance that the track frame can provide, physical buckling instability occurs. Therefore, the present invention achieves a direct measurement of the fundamental physical driving force of track instability by quantifying the temperature difference, rather than a simple numerical comparison.

[0144] Regarding rainfall (a positive indicator), it is used to characterize the risks such as red light bands that may be caused by the limited drainage capacity of the roadbed. The terminal can directly receive and use the corresponding physical monitoring values ​​as its quantitative value. The direct use of rainfall as an indicator is based on the fact that the amount of rainfall directly determines the amount of water infiltrating into the roadbed and roadbed. When the amount of infiltrating water exceeds the drainage capacity of the roadbed, it will cause the roadbed to be saturated and the pore water pressure to increase, resulting in a sharp drop in its physical shear strength. This deterioration of mechanical properties will directly cause structural diseases such as "mud slurry" and track unevenness. Therefore, rainfall is a key external physical load input for evaluating the stability of the roadbed structure.

[0145] Regarding the air salt spray concentration (positive indicator), it is used to characterize the corrosion risk of metal components such as rails and fasteners. The terminal can directly receive and use the corresponding air salt spray concentration parameters as its quantitative value, which is especially important for coastal lines. The theoretical basis for using air salt spray concentration as an indicator is that the chloride ions in the salt spray are strong catalysts that accelerate the oxidation of metals (especially steel), which will destroy the passivation film on the metal surface and form a corrosion cell. The higher the salt spray concentration, the faster the electrochemical reaction rate, and the faster the effective cross-section of key load-bearing structures such as rails and fasteners will decrease, directly weakening their mechanical strength and fatigue life. Therefore, this indicator is a direct technical quantification of the corrosiveness of the chemical environment in which the track structure is located.

[0146] Regarding the total passing weight (positive indicator), it is used to characterize the impact of loads on track structure fatigue. The terminal can directly receive and use the cumulative total weight of the assessment section as its quantitative value. Using the cumulative passing total weight as a quantitative indicator is a direct application of the material fatigue cumulative damage theory in railway engineering. Each component of the track structure is subject to the cyclic stress brought by the train load. The cumulative total weight is the most direct physical parameter to measure the total amount of these cyclic loads. It is directly related to the initiation and expansion of microcracks inside the structural material, that is, the degree of fatigue life consumption. This indicator allows risk assessment to be upgraded from static strength verification to the dimension of dynamic fatigue life prediction.

[0147] For other indicators, the terminal performs a mapping based on a pre-set severity level of environmental phenomena. For example:

[0148] Wind speed (a positive indicator) is used to indicate the risk of train overturning caused by excessive wind loads. Since wind speed sensors may not be installed in some locations, the terminal can receive or determine a wind speed level based on wind phenomena (for example, this level can be adaptively increased for special conditions such as canyon winds) and use this level as a quantitative value.

[0149] Seismic intensity (a positive indicator) is used to characterize the impact of earthquake motion on the ballast bed accumulation state or plastic deformation of the track structure. Based on the relevant provisions of the seismic design code, the terminal directly receives or determines an intensity index representing the impact of earthquake motion as its quantified value.

[0150] In addition, mapping phenomena such as wind speed and earthquakes into intensity levels is not a simple non-technical classification, but a technical means of engineering and standardizing complex nonlinear physical effects. For example, the overturning moment of wind on a train is proportional to the square of the wind speed, and the classification of wind force levels itself contains this nonlinear aerodynamic relationship. Similarly, earthquake intensity is a professional technical indicator in earthquake engineering that comprehensively reflects the degree of macroscopic damage to the surface. It can better characterize the actual physical destructive capacity of specific ground structures than a single earthquake magnitude. The use of these mature engineering levels allows the model to quantify the risks of these extreme natural disasters in a more scientific and engineering-oriented way.

[0151] In this embodiment, by converting external environmental influences of varying nature into unified numerical values ​​that can participate in comprehensive calculations, the entire risk detection model is no longer limited to the static properties of the track structure itself, but has the ability to perceive and quantify dynamic changes in the external environment, thereby making the risk detection results more comprehensive.

[0152] In an exemplary embodiment, the judgment matrix is ​​determined by the following steps:

[0153] Calculate the mean and standard deviation of the measurable quantitative values ​​of each detection indicator in each detection section; based on the mean and standard deviation, and in combination with the preset importance parameter, determine the matrix elements in the initial judgment matrix; determine the consistency index according to the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order; compare the consistency index with an average random consistency index corresponding to a matrix of the same order, and calculate the consistency ratio; if the consistency ratio is not less than the preset threshold, determine and adjust the matrix element with the largest deviation in the initial judgment matrix, and return to execute according to the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order, determine the consistency index, until the preset stop condition is met to obtain the judgment matrix.

[0154] Specifically, the terminal first calculates the mean and standard deviation of the measurable quantitative values ​​of each detection indicator in multiple detection sections. Then, based on these statistical features and in combination with a preset importance parameter calculated from the maximum and minimum values ​​of the standard deviation, the terminal determines the elements of the initial judgment matrix, which can be implemented by formulas (4) to (6). For example, the importance parameter is shown in Table 4. Thereafter, the terminal performs a consistency test on the initial judgment matrix. If the test fails, the terminal iteratively corrects the element with the largest deviation in the matrix according to the preset optimization correction formula until the consistency requirement is met, and finally obtains a judgment matrix that can be used to calculate the weight. The correction process can be implemented by formula (7).

[0155]

[0156]

[0157] In the formula is the mean of the i-th evaluation index value; s(i) is the standard deviation of the i-th evaluation index value; s max 、s min are the maximum and minimum values ​​of the standard deviation of the evaluation index; b m is the importance parameter value, Among them, min and int are cancellation and rounding functions respectively; b ij is the element in the i-th row and j-th column of the judgment matrix R;

[0158] If the judgment matrix b ij If the consistency check is not satisfied, it can be corrected as follows:

[0159]

[0160] Where y ij is the element in the i-th row and j-th column of the optimized judgment matrix.

[0161] Table 4

[0162] Indicator layer Column vector value Possibly the best value After standardization <![CDATA[Number of rail damages D1]]> 5 10 0.5 <![CDATA[TQI D2]]> 8.21 10 0.821 <![CDATA[Rail creepage D3]]> 2 5 0.4 <![CDATA[Verticality D4 of rail weld]]> 0.56 1 0.56 <![CDATA[Number of damaged fasteners D5]]> 10 50 0.2 <![CDATA[Guard rail fastener torque D6]]> 0 200 0 <![CDATA[Number of insulating joints D7]]> 2 8 0.25 <![CDATA[Uneven pitch D8]]> 10 50 0.2 <![CDATA[Sleeper Hollow Lift D9]]> 10 20 0.5 <![CDATA[Sleeper failure D 10 > 1 10 0.1 <![CDATA[Number D of ballast bed dirt spots 11 > 2 5 0.4 <![CDATA[Longitudinal resistance D of the ballast bed 12 > 0 12 0 <![CDATA[Lateral track resistance D 13 > 3 6 0.5 <![CDATA[Unbalanced superelevation D 14 > 60 150 0.4 <![CDATA[Thickness change rate D 15 > 0 50 0 <![CDATA[Wind speed level C5]]> 4 8 0.5 <![CDATA[Extreme high temperature C6]]> 2 10 0.2 <![CDATA[Rainfall C7]]> 150 300 0.5 <![CDATA[Salt spray concentration C8]]> 2 38.3 0.052 <![CDATA[Seismic intensity C9]]> 5.5 8 0.6875 <![CDATA[Through gross weight C 10 > 4 10 0.4 <![CDATA[Composition of Personnel Titles C 11 > 20 10 / 60 0.8 <![CDATA[Number of track maintenance personnel C 12 > 16 5 / 30 0.56 <![CDATA[Maintenance Advance C 13 > 5 1 / 9 0.5 <![CDATA[Monitoring System C 14 > 0 1.0 <![CDATA[Detection period C 15 > 1 4 0.25 <![CDATA[Emergency Response Plan C 16 > 1 4 0.25

[0163] Alternatively, when evaluating only individual detection segments without sufficient historical data, the judgment matrix can be constructed using a traditional expert-based scoring approach. In this approach, the terminal receives a judgment matrix from one or more industry experts, which represents the relative importance of indicators.

[0164] Table 5 C1-D judgment matrix

[0165] <![CDATA[C1]]> <![CDATA[D1]]> <![CDATA[D2]]> <![CDATA[D3]]> <![CDATA[D4]]> <![CDATA[D1]]> 1 1 / 2 2 3 <![CDATA[D2]]> 2 1 4 6 <![CDATA[D3]]> 1 / 2 1 / 4 1 2 <![CDATA[D4]]> 1 / 3 1 / 6 1 / 2 1

[0166] Table 6 C2-D judgment matrix

[0167] <![CDATA[C2]]> <![CDATA[D5]]> <![CDATA[D6]]> <![CDATA[D7]]> <![CDATA[D5]]> 1 1 / 3 1 / 6 <![CDATA[D6]]> 3 1 1 / 2 <![CDATA[D7]]> 6 2 1

[0168] Table 7 C3-D judgment matrix

[0169] <![CDATA[C3]]> <![CDATA[D8]]> <![CDATA[D9]]> <![CDATA[D 10 ]]> <![CDATA[D8]]> 1 1 / 5 1 / 2 <![CDATA[D9]]> 5 1 3 <![CDATA[D 10 ]]> 2 1 / 3 1

[0170] Table 8 C4-D judgment matrix

[0171] <![CDATA[C4]]> <![CDATA[D 11 ]]> <![CDATA[D 12 ]]> <![CDATA[D 13 ]]> <![CDATA[D 14 ]]> <![CDATA[D 15 ]]> <![CDATA[D 11 ]]> 1 2 1 / 2 1 / 3 1 / 2 <![CDATA[D 12 ]]> 1 / 2 1 1 / 4 1 / 6 1 / 3 <![CDATA[D 13 ]]> 2 4 1 1 / 2 2 <![CDATA[D 14 ]]> 3 6 2 1 2 <![CDATA[D 15 ]]> 2 3 1 / 2 1 / 2 1

[0172] Table 9 B1-C judgment matrix

[0173] <![CDATA[B1]]> <![CDATA[C1]]> <![CDATA[C2]]> <![CDATA[C3]]> <![CDATA[C4]]> <![CDATA[C1]]> 1 6 3 2 <![CDATA[C2]]> 1 / 6 1 1 / 2 1 / 4 <![CDATA[C3]]> 1 / 3 2 1 1 / 2 <![CDATA[C4]]> 1 / 2 4 2 1

[0174] Table 10 B2-C judgment matrix

[0175] <![CDATA[B2]]> <![CDATA[C5]]> <![CDATA[C6]]> <![CDATA[C7]]> <![CDATA[C8]]> <![CDATA[C9]]> <![CDATA[C 10 ]]> <![CDATA[C5]]> 1 1 / 2 1 / 2 1 1 / 4 1 / 3 <![CDATA[C6]]> 2 1 1 2 1 / 2 1 / 2 <![CDATA[C7]]> 2 1 1 2 1 / 2 1 / 2 <![CDATA[C8]]> 1 1 / 2 1 / 2 1 1 / 4 1 / 3 <![CDATA[C9]]> 4 2 2 4 1 2 <![CDATA[C 10 ]]> 3 2 2 3 1 / 2 1

[0176] Table 11 B3-C judgment matrix

[0177] <![CDATA[B3]]> <![CDATA[C 11 ]]> <![CDATA[C 12 ]]> <![CDATA[C 13 ]]> <![CDATA[C 14 ]]> <![CDATA[C 15 ]]> <![CDATA[C 16 ]]> <![CDATA[C 11 ]]> 1 6 2 1 / 2 2 3 <![CDATA[C 12 ]]> 1 / 6 1 1 / 3 1 / 8 1 / 4 1 / 2 <![CDATA[C 13 ]]> 1 / 2 3 1 1 / 2 1 2 <![CDATA[C 14 ]]> 2 8 2 1 2 4 <![CDATA[C 15 ]]> 1 / 2 4 1 1 / 2 1 2 <![CDATA[C 16 ]]> 1 / 3 2 1 / 2 1 / 4 1 / 2 1

[0178] Table 12 AB judgment matrix

[0179] A <![CDATA[B1]]> <![CDATA[B2]]> <![CDATA[B3]]> <![CDATA[B1]]> 1 1 / 2 1 <![CDATA[B2]]> 2 1 2 <![CDATA[B3]]> 1 1 / 2 1

[0180] Table 13 shows the consistency test results and weight vectors

[0181] matrix CI CR Passed Weight vector <![CDATA[C1-D]]> 0.0035 0.0038 yes <![CDATA[W=[0.260,0.520,0.140,0.080] T ]]> <![CDATA[C2-D]]> 0 0 yes <![CDATA[W=[0.100,0.300,0.600] T ]]> <![CDATA[C3-D]]> 0.0018 0.0032 yes <![CDATA[W=[0.122,0.648,0.230] T ]]> <![CDATA[C4-D]]> 0.0147 0.0132 yes <![CDATA[W=[0.115,0.061,0.254,0.389,0.181] T ]]> <![CDATA[B1-C]]> 0.0035 0.0038 yes <![CDATA[W=[0.490,0.076,0.152,0.282] T ]]> <![CDATA[B2-C]]> 0.0091 0.0074 yes <![CDATA[W=[0.075,0.144,0.144,0.075,0.326,0.236] T ]]> <![CDATA[B3-C]]> 0.01 0.008 yes <![CDATA[W=[0.246,0.041,0.143,0.341,0.150,0.079] T ]]> AB 0 0 yes W=[0.250,0.500,0.250]T

[0182] Optionally, according to formula (8), a risk detection vector consisting of risk detection values ​​corresponding to each detection section is calculated to obtain the results shown in Table 14.

[0183] F=RW, formula (8).

[0184] Among them, R is the detection matrix, W is the weight vector, and F is the risk detection vector.

[0185] Table 14

[0186]

[0187] In an exemplary embodiment, in a heavy-duty railway system, the track structure is the basic part that carries the train load, and the stability and reliability of its service status are prerequisites for ensuring the safe operation of the train. Therefore, scientifically evaluating the risk level of the heavy-duty railway track structure has important technical value for guiding the maintenance and repair operations of the line, optimizing the allocation of maintenance resources, and formulating operating instructions under special working conditions. However, the track structure risk assessment methods in the existing technology are mainly limited to the detection and evaluation of the physical service status of the track structure itself. This type of method uses on-site monitoring / detection data such as the track quality index TQI, vehicle dynamic response, and track structure response to determine whether the current physical state of the track meets the basic requirements for train operation.

[0188] This type of assessment method has significant technical flaws: its assessment dimensions are relatively single, and it fails to incorporate key risk sources such as external environmental factors (such as extreme weather and geological conditions), human factors (such as the skills of maintenance personnel), and management process factors (such as maintenance systems and the completeness of emergency plans) that affect operational safety into a unified assessment model. This results in safety accidents such as derailment and operational delays that may still occur due to the above-mentioned unevaluated factors, even if the physical indicators of the track structure itself are assessed to meet operational needs. In summary, the existing technology lacks comprehensive consideration of various risk factors, resulting in a lack of systematic and comprehensive assessment process. The assessment results obtained are not accurate and complete, and cannot provide line managers with reliable decision-making support for pre-emptive prevention.

[0189] In order to solve the above problems, this application provides a method for detecting the risk level of heavy-load railway track structure, such as Figure 3 As shown, the method includes:

[0190] Step S301, determine the length L of the heavy-load railway detection section as a detection unit. When the terminal starts the detection task, it is first necessary to unitize the detection object. The terminal virtually divides the heavy-load railway line to be detected according to a preset length L to form a plurality of detection sections connected end to end. For example, the determination of the length L can comprehensively consider technical specifications such as the statistical unit length of the track quality index (TQI) and the length of the seamless line in the ordinary section, and finally determine it as a standard value, such as 2.0 kilometers. The terminal assigns a unique identifier to each generated detection section, and uses it as the basic unit address for data storage and calculation of the quantitative values ​​of all subsequent detection indicators.

[0191] Step S302: Comprehensively consider the four factors of man-machine-environment-management and construct a detection index set V including structural characteristics, environmental characteristics, and operation and maintenance management. Based on the service characteristics of heavy-haul railway tracks, a detection index set V = {v1, v2, ..., v n}, v i Represents the i-th detection index, with a total of n detection indicators.

[0192] Step S303 determines the value method and standard for each detection indicator based on the detection unit length and the detection indicator set. Its characteristic is that the determined detection indicators can be quantitatively characterized without the need for a fuzzy membership judgment method, making the quantitative results more credible and objective. For example, for the track irregularity indicator, the rule is set to receive all TQI measurement values ​​within the specified detection section and extract the maximum value; for the maintenance method advancement index, the rule is set to receive a status code representing the current method and consult the built-in status code-score mapping table to obtain the score; for the extreme high temperature index, the rule is set to receive the current rail temperature and the standard rail temperature and perform a conditional difference calculation. In subsequent steps, the terminal will strictly process the input data according to these preset rules to obtain measurable quantitative values ​​for each indicator.

[0193] Step S304: Combine the structural characteristics, environmental characteristics and operation and maintenance management content of the heavy-duty railway in the detection section to form a detection matrix X. Based on the measurable quantitative value results of step S304, a preliminary detection matrix is ​​constructed for the detection line (which can be the entire line or a part of the line). x ij Represents the value of the i-th detection index of the j-th detection segment (the element in the i-th row and j-th column of the detection matrix), with a total of N detection segments.

[0194] Step S305, construct the fuzzy detection matrix R by normalizing the detection matrix X data. Based on the positive and negative properties of each indicator in the detection index set in step S302 (the positive index indicates that the larger the index value, the greater the track risk; the negative index is just the opposite), the data of the detection matrix is ​​standardized. The terminal first identifies the positive or negative attribute of the i-th indicator and calls the corresponding processing rule. Exemplarily, for the positive indicator, the terminal applies a linear normalization rule to convert it into a standardized value. Optionally, in another embodiment, in order to deal with the problem that some indicator data may have extreme outliers, the terminal can switch to a nonlinear transformation rule, such as a logarithmic function transformation, to enhance the robustness of the calculation. After the terminal performs the corresponding transformation processing on all elements of the matrix X, the final fuzzy detection matrix R is obtained.

[0195] Step S306: Determine the weight vector W. This embodiment provides two methods for determining weights: one is the traditional AHP method, which uses expert scoring to determine weight coefficients; the other is an improved AHP method for determining weight coefficients. The traditional AHP method for constructing a judgment matrix to determine weight coefficients is significantly influenced by expert subjective factors, so it can be determined by multiple industry experts to reduce the influence of expert subjective factors. The improved AHP method uses indicator data to construct a judgment matrix, eliminating the need for expert opinion and is therefore more objective.

[0196] The improved hierarchical analysis method mainly improves objectivity by improving the determination of the judgment matrix. For example, the judgment matrix is ​​determined by calculating the mean and standard deviation of the measurable quantitative values ​​of each detection indicator in each detection section; based on the mean and standard deviation, and combined with a preset importance parameter, the matrix elements in the initial judgment matrix are determined; according to the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order, the consistency index is determined; the consistency index is compared with an average random consistency index corresponding to a matrix of the same order to calculate the consistency ratio; if the consistency ratio is not less than a preset threshold, the matrix element with the largest deviation in the initial judgment matrix is ​​determined and adjusted, and the execution is returned to determine the consistency index based on the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order, until the preset stop condition is met to obtain the judgment matrix.

[0197] After constructing the judgment matrix, the terminal further determines the weight coefficients of each detection indicator by solving the eigenvector of the matrix to form a weight vector W.

[0198] Step S307, determine the risk detection value vector. The standardized detection matrix R is weighted and aggregated with the determined weight vector W to calculate the final single composite value for each detection segment that can represent the overall risk level of the segment, namely the risk detection value. Specifically, for each column in the detection matrix (representing the state vector of a detection segment), the terminal multiplies it with the corresponding element of the weight vector pair by pair, and then accumulates and sums all the product results. After performing this operation on the column vectors of all detection segments, a vector containing the final risk detection values ​​of all segments is obtained.

[0199] Step S308: Determine the risk level of the heavy-load railway track structure in the detection section. Based on the risk detection value calculated in step S307, the risk level of each detection section is finally determined by comparing it with a set of preset threshold intervals.

[0200] In this embodiment, an evaluation index set is constructed by comprehensively considering various influencing factors, and the values ​​of each index in the detection section are quantitatively determined. A judgment matrix is ​​constructed in combination with field data to avoid the influence of subjective judgment. The risk status level of the heavy-load railway track structure can be detected more objectively, comprehensively and scientifically.

[0201] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0202] Based on the same inventive concept, embodiments of the present application also provide a device for detecting the risk level of a heavy-haul railway track structure, for implementing the aforementioned method for detecting the risk level of a heavy-haul railway track structure. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for detecting the risk level of a heavy-haul railway track structure provided below can be found in the aforementioned limitations of the method for detecting the risk level of a heavy-haul railway track structure, and will not be further elaborated here.

[0203] In an exemplary embodiment, Figure 4 As shown, a device for detecting the risk level of a heavy-load railway track structure is provided, comprising: a detection index acquisition module 410, a detection index quantification module 420, a detection matrix acquisition module 430, a detection index weight determination module 440, a risk detection value acquisition module 450, and a risk level determination module 460, wherein:

[0204] A detection index acquisition module 410 is used to acquire multiple detection indicators of the track, wherein the multiple detection indicators include track structural characteristic indicators related to the track structural state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management;

[0205] A detection indicator quantification module 420 is configured to obtain a measurable quantified value of each detection indicator in each detection section of the track;

[0206] A detection matrix acquisition module 430 is configured to perform data normalization processing on the measurable quantitative values ​​according to the positivity or negativity of each detection indicator to obtain a detection matrix;

[0207] A detection indicator weight determination module 440 is configured to determine the weight of each detection indicator using an analytic hierarchy process (AHP), wherein a judgment matrix used to calculate the weight in the AHP is determined based on the statistical characteristics of the measurable quantitative values;

[0208] a risk detection value acquisition module 450, configured to acquire a risk detection value of each detection section based on the detection matrix and the weight;

[0209] The risk level determination module 460 is configured to determine the risk level of each detection section according to the risk detection value.

[0210] In one embodiment, the detection matrix acquisition module 430 is further used to determine the maximum value of each of the detection indicators within the multiple detection segments; for the positive detection indicator, the ratio of the measurable quantized value corresponding to the current detection indicator to the maximum value is determined as the standardized value corresponding to the positive detection indicator; for the negative detection indicator, based on the difference between the measurable quantized value corresponding to the current detection indicator and the maximum value, the standardized value corresponding to the negative detection indicator is determined; based on the standardized value corresponding to the positive detection indicator and the standardized value corresponding to the negative detection indicator, the detection matrix is ​​determined.

[0211] In one embodiment, the detection index quantification module 420 is further configured to obtain inspection records of the detection section;

[0212] Determining, based on the track damage information recorded in the inspection, damage events in the inspection section that meet a preset damage definition, and obtaining the number of damage events of the damage events;

[0213] According to the weighted processing results of the numbers of each of the damage events, a measurable quantitative value of the at least one type of indicator is obtained.

[0214] In one embodiment, the operation and maintenance management indicators include one or more of the degree of automation of track maintenance, the capability of maintenance objects, and the completeness of emergency plans. The detection indicator quantification module 420 is further used to determine the actual degree of automation of maintenance of each detection section, and determine the measurable quantitative value of the degree of automation of maintenance of the track according to the score corresponding to each actual degree of automation of maintenance; wherein different actual degrees of automation of maintenance correspond to different scores; and / or, determine the actual capability of maintenance objects of each detection section, and determine the measurable quantitative value of the capability of maintenance objects of the track according to the score corresponding to each actual capability of maintenance objects; wherein different actual capability of maintenance objects corresponds to different scores; and / or, determine the completeness of emergency plans of each detection section, and determine the measurable quantitative value of the completeness of emergency plans of the track according to the score corresponding to each completeness of emergency plans; wherein different completeness of emergency plans corresponds to different scores.

[0215] In one embodiment, the detection index quantification module 420 is further used to calculate the difference between the environmental parameter value of the detection section of the track and the preset parameter reference value, and determine the measurable quantified value of the at least one type of indicator based on the interpolation result of the difference calculation; or, map the environment of the detection section of the track to a level value corresponding to the environment based on a preset intensity level standard of the environmental phenomenon, and use the level value as the measurable quantified value of the at least one type of indicator.

[0216] In one embodiment, the detection index weight determination module 440 is also used to calculate the mean and standard deviation of the measurable quantitative values ​​of each of the detection indicators in each of the detection sections; based on the mean and standard deviation, and in combination with a preset importance parameter, determine the matrix elements in the initial judgment matrix; determine the consistency index according to the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order; compare the consistency index with an average random consistency index corresponding to a matrix of the same order to calculate the consistency ratio; if the consistency ratio is not less than a preset threshold, determine and adjust the matrix element with the largest deviation in the initial judgment matrix, and return to execute the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order to determine the consistency index until the preset stop condition is met to obtain the judgment matrix.

[0217] Each module in the aforementioned heavy-load railway track structure risk level detection device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0218] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for detecting the risk level of a heavy-load railway track structure is implemented. The display unit of the computer device is used to form a visually visible image, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0219] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0220] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0221] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0222] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-mentioned method embodiments. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0223] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic unit (PLC), a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0224] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0225] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for detecting the risk level of heavy-load railway track structure, characterized in that: The method comprises: Acquiring multiple detection indicators of the track, the multiple detection indicators including track structural characteristic indicators related to the track structural state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management; Obtaining a measurable quantitative value of each of the detection indicators in each detection section of the track; According to the positivity or negativity of each of the detection indicators, data normalization processing is performed on the measurable quantitative values ​​to obtain a detection matrix; Determining the weight of each of the detection indicators using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative values; Based on the detection matrix and the weight, obtaining a risk detection value for each of the detection sections; The risk level of each detection section is determined according to the risk detection value.

2. The method according to claim 1, characterized in that The method of performing data normalization processing on the measurable quantitative values ​​according to the positivity or negativity of each of the detection indicators to obtain a detection matrix includes: determining the maximum value of each of the detection indicators within the plurality of detection sections; For a positivity detection indicator, determining a ratio of a current measurable quantified value corresponding to the detection indicator to the maximum value as a normalized value corresponding to the positivity detection indicator; For a detection indicator of reversibility, determining a standardized value corresponding to the detection indicator of reversibility based on a difference between a current measurable quantified value corresponding to the detection indicator and the maximum value; The detection matrix is ​​determined based on the normalized value corresponding to the positive detection index and the normalized value corresponding to the negative detection index.

3. The method according to claim 1, characterized in that The step of obtaining a measurable quantitative value of at least one type of indicator among the track structure characteristic indicators comprises: Obtaining inspection records of the detection section; Determining, based on the track damage information recorded in the inspection, damage events in the inspection section that meet a preset damage definition, and obtaining the number of damage events of the damage events; According to the weighted processing results of the numbers of each of the damage events, a measurable quantitative value of the at least one type of indicator is obtained.

4. The method according to claim 1, wherein The operation and maintenance management indicators include one or more of the degree of automation of track maintenance, the capability of maintenance objects, and the completeness of emergency plans; The steps of obtaining the measurable quantitative value of the operation and maintenance management indicator include: Determining the actual degree of automation of maintenance for each of the detection sections, and determining a measurable quantitative value of the degree of automation of maintenance for the track based on the scores corresponding to the actual degrees of automation; wherein different actual degrees of automation correspond to different scores; and / or, Determining the actual maintenance object capability of each of the detection sections, and determining a measurable quantifiable value of the maintenance object capability of the track based on the scores corresponding to each of the actual maintenance object capabilities; wherein different actual maintenance object capabilities correspond to different scores; and / or, Determine the completeness of the emergency plan for each of the detection sections, and determine a measurable quantified value of the completeness of the emergency plan for the track based on the scores corresponding to the completeness of each of the emergency plans; wherein different completeness of the emergency plans corresponds to different scores.

5. The method according to claim 1, wherein The step of obtaining a measurable quantitative value of at least one type of indicator among the environmental characteristic indicators comprises: Performing difference calculation on the environmental parameter value of the detection section of the track and the preset parameter reference value, and determining the measurable quantitative value of the at least one type of indicator based on the interpolation result of the difference calculation; or, The environment of the detection section of the track is mapped to a level value corresponding to the environment according to a preset intensity level standard of the environmental phenomenon, and the level value is used as a measurable quantitative value of at least one type of indicator.

6. The method according to any one of claims 1 to 5, characterized in that The judgment matrix is ​​determined by the following steps: Calculating the mean and standard deviation of the measurable quantitative values ​​of each of the detection indicators in each of the detection sections; Based on the mean and standard deviation, and in combination with a preset importance parameter, determining matrix elements in an initial judgment matrix; According to the relationship between the maximum value of the matrix elements in the initial judgment matrix and the matrix order, the consistency index is determined; Comparing the consistency index with an average random consistency index corresponding to a matrix of the same order to calculate a consistency ratio; If the consistency ratio is not less than the preset threshold, the matrix element with the largest deviation in the initial judgment matrix is ​​determined and adjusted, and the process returns to execute the relationship between the maximum value of the matrix element in the initial judgment matrix and the matrix order to determine the consistency index until the preset stop condition is met to obtain the judgment matrix.

7. A heavy-load railway track structure risk level detection device, characterized in that: The device comprises: A detection index acquisition module is used to obtain multiple detection indicators of the track, wherein the multiple detection indicators include track structure characteristic indicators related to the track structure state, environmental characteristic indicators related to the environment in which the track is located, and operation and maintenance management indicators related to track maintenance management; a detection index quantification module, configured to obtain a measurable quantified value of each detection index in each detection section of the track; A detection matrix acquisition module, configured to perform data normalization processing on the measurable quantitative values ​​according to the positivity or negativity of each detection indicator to obtain a detection matrix; a detection indicator weight determination module, configured to determine the weight of each detection indicator using a hierarchical analysis method, wherein a judgment matrix used to calculate the weight in the hierarchical analysis method is determined based on the statistical characteristics of the measurable quantitative value; a risk detection value acquisition module, configured to acquire a risk detection value of each detection section based on the detection matrix and the weight; The risk level determination module is used to determine the risk level of each detection section according to the risk detection value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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