Railway axle counter monitoring system and method for rail transit equipment industry

Abstract

The invention relates to the technical field of railway operation management, in particular to a railway axle counter monitoring system and method for the rail transit equipment industry, and the method comprises the steps: analyzing the matching degree of an axle counter counting mode and train operation characteristics through an operation characteristic matching degree analysis model; analyzing the interference degree of the counting mode of the axle counter in the section operation environment through a counting environment interference degree analysis model; constructing a counting mode adaptation risk assessment model, and assessing the adaptation risk of the counting mode of the axle counter in the railway operation section; according to the adaptation risk assessment result of the axle counter counting mode in the railway operation section, adjusting and early warning are carried out on the axle counter counting mode; the running state of the axle counter can be tracked in real time, the problem of axle counting deviation caused by train axle load difference and environmental interference can be found and solved in time, the counting mode of the axle counter can be adjusted in time, and it is ensured that counting of the axle counter is always accurate and reliable.

Description

Technical Field

[0001] This invention relates to the field of railway operation management technology, and in particular to a railway axle counter monitoring system and method for use in the rail transit equipment industry. Background Technology

[0002] Axle counters, as key equipment in railway signaling systems, are used to accurately count the number of axles on trains and dynamically monitor train trajectories. Their stable and reliable operation is crucial for ensuring the safety, efficiency, and order of railway transportation. In the high-density, high-speed operation of modern railways, train operations are becoming increasingly complex, and environmental interference factors are becoming more diverse, posing more stringent challenges to the axle counter's counting process. The axle loads vary significantly between different train models. Freight trains, carrying heavy loads, often have larger axle loads, while passenger trains, prioritizing high speed and comfort, have relatively lighter axle loads. Therefore, axle counters must accurately identify these differences to ensure that the counting mode is adapted to the train's operational characteristics. When heavy-haul freight trains pass by, the larger axle load will generate a stronger impact force on the track. This impact force will be transmitted to the axle counter, resulting in increased vibration of the axle counting equipment and affecting the accuracy of axle counting. When high-speed passenger trains pass by, the rapid movement of the train will cause the pulse signal of the axle counter to change more rapidly. Therefore, the requirements for the signal acquisition and processing speed of the axle counter are higher. If the axle counter does not respond in time, counting errors may occur, which will affect the normal operation of functions such as train route control.

[0003] However, existing technologies for monitoring axle counters do not fully consider the impact of inherent train characteristics such as axle load and pantograph features on the counting pulses, nor do they take into account the actual operating status of the axle counter under complex conditions. Furthermore, existing technologies judge the axle counter status only through basic parameter matching or a single interference metric, ignoring the combined effects of time and axle count matching, as well as the differentiated impacts of vibration and electromagnetic interference. This leads to a disconnect between risk assessment and actual operational needs, making it easy to over-maintain and increase operating costs. It also lacks a comprehensive analysis of the nonlinear effects of axle load deviation and pantograph area differences, and cannot accurately reflect the interference effect of train characteristics on the axle counter counting mode.

[0004] To address these issues, this application presents a railway axle counter monitoring system and method for the rail transit equipment industry. Summary of the Invention

[0005] The purpose of this invention is to provide a railway axle counter monitoring system and method for the rail transit equipment industry. This system comprehensively analyzes the matching degree between the axle counter counting mode and train operation characteristics, as well as the degree of interference experienced by the axle counter counting mode within the operating environment of the railway section. This enables a comprehensive assessment of the adaptation risk of the axle counter counting mode within the railway operating section. Based on the assessment results, the system provides early warning adjustments to the axle counter counting mode. It can track the operating status of the axle counter in real time, promptly detect and resolve axle counting deviations caused by differences in train axle load and environmental interference, and achieve timely adjustments to the axle counter counting mode, ensuring that the axle counter counting is always accurate and reliable.

[0006] This invention is implemented as follows: In a first aspect, the present invention provides a railway axle counter monitoring method for the rail transit equipment industry, comprising the following steps: S1. Obtain section operating environment data and axle counter count data within the railway operating section, and simultaneously obtain train operation plan data and train characteristic data; S2. Import the axle counter counting data, train operation plan data, and train characteristic data into the operation characteristic matching degree analysis model to analyze the matching degree between the axle counter counting mode and the train operation characteristics; S3. Import the axle counter counting data, section operating environment data, and train characteristic data into the counting environment interference analysis model to analyze the degree of interference experienced by the axle counter counting mode in the section operating environment. S4. Construct a counting mode adaptation risk assessment model. Import the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment into the counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operation section. S5. Based on the risk assessment results of the axle counter counting mode adaptation in the railway operating section, adjust and issue an early warning for the axle counter counting mode.

[0007] As a preferred technical solution of the present invention, step S2 analyzes the degree of matching between the axle counter counting mode and the train operation characteristics, specifically including: S21. Extract axle counter counting data, train operation plan data, and train characteristic data; S22. Construct an analysis model for the degree of matching of operating characteristics. Import axle counter counting data, train operation plan data, and train characteristic data into the analysis model for the degree of matching of operating characteristics. Analyze the degree of matching between the axle counter counting mode and the train operation characteristics to obtain the analysis results of the degree of matching between the axle counter counting mode and the train operation characteristics.

[0008] As a preferred technical solution of the present invention, the construction process of the feature matching degree analysis model in step S22 specifically includes: S221. Based on axle counter counting data and train operation plan data, analyze the time matching degree between axle counter counting mode and train operation plan, and obtain the analysis results of the time matching degree between axle counter counting mode and train operation plan. S222. Based on axle counter counting data, train operation plan data, and train characteristic data, the degree of axle number matching between the axle counter counting mode and the train operation characteristics is analyzed to obtain the analysis results of the degree of axle number matching between the axle counter counting mode and the train operation characteristics. S223. Based on the analysis results of the time matching degree between the axle counter counting mode and the train operation plan, and the analysis results of the axle number matching degree between the axle counter counting mode and the train operation characteristics, analyze the matching degree between the axle counter counting mode and the train operation characteristics. The formula for calculating the degree of matching between the axle counter counting mode and the train operation characteristics is as follows: ; In the formula, Sp represents the degree of matching between the axle counter counting mode and the train operation characteristics, st represents the analysis result of the time matching between the axle counter counting mode and the train operation plan, and sz represents the analysis result of the axle count matching between the axle counter counting mode and the train operation characteristics.

[0009] As a preferred technical solution of the present invention, step S3 analyzes the degree of interference experienced by the axle counter counting mode in the section operating environment, specifically including the following steps: S31. Extract axle counter counting data, section operating environment data, and train characteristic data; S32. Construct a counting environment interference analysis model. Import axle counter counting data, section operating environment data, and train characteristic data into the counting environment interference analysis model to analyze the interference level of the axle counter counting mode in the section operating environment and obtain the analysis results of the interference level of the axle counter counting mode in the section operating environment.

[0010] As a preferred technical solution of the present invention, the construction process of the counting environment interference level analysis model in step S32 includes the following specific steps: S321. Based on axle counter counting data, section operating environment data, and train characteristic data, analyze the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment, and obtain the analysis results of the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment. S322. Based on axle counter counting data, section operating environment data, and train characteristic data, analyze the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment, and obtain the analysis results of the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment. S323. Based on the analysis results of the degree of environmental vibration interference and environmental electromagnetic interference obtained from the analysis of the axle counter counting mode in the section operating environment, analyze the degree of interference of the axle counter counting mode in the section operating environment. The formula for calculating the degree of interference experienced by the axle counter counting mode in the section operating environment is as follows: ; In the formula, Gr represents the degree of interference experienced by the axle counter counting mode in the section operating environment, zd represents the analysis result of the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment, and dc represents the analysis result of the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment.

[0011] As a preferred technical solution of the present invention, step S4, which involves constructing a counting pattern adaptation risk assessment model, includes the following specific steps: S41. Obtain the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment. S42. Based on the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment, assess the adaptation risk of the axle counter counting mode in the railway operation section. The formula for calculating the adaptation risk of the axle counter counting mode within the railway operating section is as follows: ; In the formula, SF represents the adaptation risk of the axle counter counting mode within the railway operating section.

[0012] As a preferred technical solution of the present invention, step S5 involves adjusting and issuing an early warning for the axle counter counting mode based on the risk assessment results of the axle counter counting mode adaptation within the railway operating section. Specifically, this includes: S51. Obtain the risk assessment results of the axle counter counting mode obtained from the assessment within the railway operating section; S52. Preset adaptation risk threshold. When the adaptation risk assessment result of the axle counter counting mode in the railway operating section is greater than the adaptation risk threshold, the axle counter counting mode is adjusted and an early warning is issued; when the adaptation risk assessment result of the axle counter counting mode in the railway operating section is less than or equal to the adaptation risk threshold, the current axle counter counting mode is maintained.

[0013] Secondly, the present invention provides a railway axle counter monitoring system for the rail transit equipment industry, comprising: The data acquisition module is used to acquire section operating environment data and axle counter count data within the railway operating section, as well as train operation plan data and train characteristic data. The operation feature matching degree analysis module is used to import axle counter counting data, train operation plan data and train feature data into the operation feature matching degree analysis model to analyze the matching degree between axle counter counting mode and train operation features; The counting environment interference analysis module is used to import axle counter counting data, section operating environment data and train characteristic data into the counting environment interference analysis model to analyze the degree of interference experienced by the axle counter counting mode in the section operating environment. The counting mode adaptation risk assessment module is used to construct a counting mode adaptation risk assessment model. It imports the matching degree analysis results of the axle counter counting mode and train operation characteristics, and the interference degree analysis results of the axle counter counting mode in the section operation environment into the counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operation section. The counting mode adjustment early warning module is used to provide early warning of axle counter counting mode adjustment based on the risk assessment results of the axle counter counting mode adaptation in the railway operating section. The control module is used to control the operation of the data acquisition module, the running feature matching degree analysis module, the counting environment interference degree analysis module, the counting mode adaptation risk assessment module, and the counting mode adjustment early warning module.

[0014] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a railway axle counter monitoring method for the rail transit equipment industry by calling the computer program stored in the memory.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: This invention analyzes the matching degree between the axle counter counting mode and train operation characteristics using an operational feature matching degree analysis model; it analyzes the interference degree of the axle counter counting mode in the section operation environment using a counting environment interference degree analysis model; it constructs a counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operating section; based on the adaptation risk assessment results of the axle counter counting mode in the railway operating section, it provides early warning for adjusting the axle counter counting mode; it can track the operating status of the axle counter in real time, promptly detect and resolve axle counting deviation problems caused by differences in train axle weight and environmental interference, realize timely adjustment of the axle counter counting mode, and ensure that the axle counter counting is always accurate and reliable. Attached Figure Description

[0016] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of the railway axle counter monitoring method for the rail transit equipment industry according to the present invention. Figure 2 This is a schematic diagram of the railway axle counter monitoring system for the rail transit equipment industry according to the present invention. Figure 3 This is an analytical flowchart of step S2 of the railway axle counter monitoring method for the rail transit equipment industry of the present invention. Figure 4 This is an analysis flowchart of step S3 of the railway axle counter monitoring method for the rail transit equipment industry of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0018] Example 1 like Figure 1 As shown, this embodiment provides a railway axle counter monitoring method for the rail transit equipment industry, specifically including the following steps: S1. Obtain section operating environment data and axle counter count data within the railway operating section, and simultaneously obtain train operation plan data and train characteristic data; S2. Import the axle counter counting data, train operation plan data, and train characteristic data into the operation characteristic matching degree analysis model to analyze the matching degree between the axle counter counting mode and the train operation characteristics; S3. Import the axle counter counting data, section operating environment data, and train characteristic data into the counting environment interference analysis model to analyze the degree of interference experienced by the axle counter counting mode in the section operating environment. S4. Construct a counting mode adaptation risk assessment model. Import the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment into the counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operation section. S5. Based on the risk assessment results of the axle counter counting mode adaptation in the railway operating section, adjust and issue an early warning for the axle counter counting mode.

[0019] In this embodiment, as Figure 3 As shown, step S2 analyzes the degree of matching between the axle counter counting mode and the train operation characteristics, specifically including: S21. Extract axle counter counting data, train operation plan data, and train characteristic data; S22. Construct an analysis model for the degree of matching of operating characteristics. Import axle counter counting data, train operation plan data, and train characteristic data into the analysis model for the degree of matching of operating characteristics. Analyze the degree of matching between the axle counter counting mode and the train operation characteristics to obtain the analysis results of the degree of matching between the axle counter counting mode and the train operation characteristics.

[0020] In this embodiment, the process of constructing the feature matching degree analysis model in step S22 specifically includes: S221. Based on axle counter counting data and train operation plan data, analyze the time matching degree between axle counter counting mode and train operation plan, and obtain the analysis results of the time matching degree between axle counter counting mode and train operation plan. The formula for calculating the time matching degree between the axle counter counting mode and the train operation plan is as follows: ; In the formula, st represents the time matching degree between the axle counter counting mode and the train operation plan, Psit represents the normal pulse change time sequence corresponding to the train operation plan of the i-th column of trains passing through the axle counter section in the train operation plan data, Pit represents the actual pulse change time sequence of the i-th column of trains passing through the axle counter section detected during the actual counting process of the axle counter in the axle counter counting data, Tp represents the actual passing time of the i-th column of trains passing through the axle counter section in the axle counter counting data, Ts represents the planned passing time of the i-th column of trains passing through the axle counter section in the train operation plan data, min() is the minimum value function, n represents the number of trains passing through the axle counter section in the train operation plan data, and i is any term from 1 to n; For example, this embodiment is used to analyze the time matching degree between the axle counter counting mode and the train operation plan. The calculation formula is constructed from the perspective of signal matching, and the correlation between the two signal sequences is calculated through the similarity measurement principle in signal processing.

[0021] The actual pulse sequence Pit of the axle counter and the normal pulse sequence Psit of the train operation plan represent the actual and planned pulse change time sequences, respectively, used to reflect the pulse characteristics when the train passes through the axle counting section. This embodiment uses integral calculations. The collaborative contribution of two pulse sequences within a common time interval (where the common time interval is the minimum of the actual passage time Tp and the planned passage time Ts) is calculated to measure their synchronicity in the time dimension; in the denominator... Then calculate the energy accumulation of the actual and planned pulse sequences respectively.

[0022] Based on the aforementioned fractional structure, this embodiment effectively measures the similarity between two sequences and normalizes the matching degree to a reasonable range by using the calculation logic of cosine similarity. Furthermore, by averaging across n trains, this embodiment eliminates the randomness of a single train, thereby reflecting the overall time matching level of multiple trains within a section. It can accurately capture the degree of fit between the actual axle counter count and the train operation plan in terms of time pulse characteristics.

[0023] Among them, the normal pulse change time series Where t is the current time, j is the sequence number of the train axle of the i-th train passing through the axle counting section, m is the number of train axles of the i-th train passing through the axle counting section, k is used to represent the k-th axle before the j-th axle, dk is the axle distance between the k-th axle before the j-th axle and the (k-1)-th axle, vst is the planned running speed of the i-th train passing through the axle counting section at the current time t, D() is the Dirac function, j is any term from 1 to m, and k is any term from 1 to j; S222. Based on axle counter counting data, train operation plan data, and train characteristic data, the degree of axle number matching between the axle counter counting mode and the train operation characteristics is analyzed to obtain the analysis results of the degree of axle number matching between the axle counter counting mode and the train operation characteristics. The formula for calculating the degree of axle count matching between the axle counter counting mode and the train operation characteristics is as follows: ; In the formula, sz represents the degree of axle number matching between the axle counter counting mode and the train operation characteristics, Ni represents the number of axles of the i-th column of trains passing through the axle counting section detected by the axle counter during the actual counting process in the axle counter counting data, Nsi represents the actual number of axles of the i-th column of trains passing through the axle counting section in the train characteristic data, and max() is the maximum value function. For example, this embodiment is used to analyze the degree of matching between the axle counter counting mode and the axle count characteristics of the train operation, through... The overlap ratio between the actual number of axles and the characteristic number of a single train is calculated. In this embodiment, the ratio of the minimum to the maximum value is used to intuitively reflect the degree of closeness between the two. For example, when the number of axles matches perfectly, the ratio is 1; the greater the difference in the number of axles, the smaller the ratio. Furthermore, this embodiment takes the average for n trains, which can extend the axle number matching of a single train to the overall level of multiple trains within a section, thereby eliminating the influence of individual differences and comprehensively evaluating the fit between the axle counter and the inherent characteristics of the train in terms of axle count.

[0024] S223. Based on the analysis results of the time matching degree between the axle counter counting mode and the train operation plan, and the analysis results of the axle number matching degree between the axle counter counting mode and the train operation characteristics, analyze the matching degree between the axle counter counting mode and the train operation characteristics. The formula for calculating the degree of matching between the axle counter counting mode and the train operation characteristics is as follows: ; In the formula, Sp represents the degree of matching between the axle counter counting mode and the train operation characteristics, st represents the analysis result of the time matching between the axle counter counting mode and the train operation plan, and sz represents the analysis result of the axle count matching between the axle counter counting mode and the train operation characteristics.

[0025] For example, this embodiment integrates information from two dimensions—time matching and axle count matching—to comprehensively analyze the degree of matching between the axle counter counting pattern and train operation characteristics. The formula provided in this embodiment consists of two parts, the first part... Drawing on an extended form of cosine similarity in vector space, it can calculate the collaborative metric of two matching vectors, st and sz, thus emphasizing their combined matching contribution; Part Two The focus is on analyzing the difference between st and sz, and penalizing cases where the difference is too large by using an exponential function; for example, the smaller the difference between st and sz, the closer the function value is to 1, and vice versa, the function value will decay rapidly. In this embodiment, `st` and `sz` serve as matching indices for the time and axle number dimensions, respectively, participating in two computational parts. In the first part, `st` and `sz` are multiplied and squared to obtain the root, simulating the ratio of the inner product to the modulus of vectors, reflecting their collaborative matching ability. In the second part, the difference is normalized by the ratio of the difference to the sum, and then compressed to the (0,1] interval using an exponential function, highlighting the impact of the difference on the overall matching. This achieves a comprehensive consideration of both collaborative matching and dimensional differences. Furthermore, this embodiment emphasizes both the combined effect of time and axle number matching and their balance. Specifically, by introducing an exponential function, its smoothness and sensitivity to differences are utilized to reasonably penalize mismatches between the time and axle number dimensions. The combination of fractions and square roots ensures a reasonable measurement of the collaborative effect, ultimately integrating the matching information of the time and axle number dimensions into a comprehensive matching degree index, which can fully reflect the overall matching degree between the axle counter counting mode and the train operation characteristics, laying the foundation for subsequent assessment of adaptation risks.

[0026] In this embodiment, as Figure 4 As shown, step S3 analyzes the degree of interference experienced by the axle counter counting mode in the section's operating environment, specifically including the following steps: S31. Extract axle counter counting data, section operating environment data, and train characteristic data; S32. Construct a counting environment interference analysis model. Import axle counter counting data, section operating environment data, and train characteristic data into the counting environment interference analysis model to analyze the interference level of the axle counter counting mode in the section operating environment and obtain the analysis results of the interference level of the axle counter counting mode in the section operating environment.

[0027] In this embodiment, the construction process of the counting environment interference level analysis model in step S32 includes the following specific steps: S321. Based on axle counter counting data, section operating environment data, and train characteristic data, analyze the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment, and obtain the analysis results of the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment. The formula for calculating the degree of environmental vibration interference experienced by the axle counter counting mode within the section's operating environment is as follows: ; In the formula, zd represents the degree of environmental vibration interference experienced by the axle counter in the section operation environment, Ai represents the average vibration acceleration detected by the axle counter position in the i-th column during the actual passage time of the train passing through the axle counting section in the section operation environment data, Ao represents the nominal vibration resistance acceleration of the axle counter, Amax represents the maximum vibration acceleration detected by the axle counter position in the section operation environment data during the actual passage time of all trains passing through the axle counting section, wi represents the average axle load of the i-th column of the train passing through the axle counting section in the train characteristic data, and w represents the average axle load of all trains passing through the axle counting section in the train characteristic data. For example, this embodiment analyzes the degree of interference of the axle counter counting mode with environmental vibration by focusing on the influence of vibration acceleration and train axle load. The actual vibration acceleration reflects the real-time vibration intensity of the section environment, the axle counter's nominal vibration resistance acceleration is the inherent disturbance rejection threshold of the axle counter, the maximum vibration acceleration of the section is used for normalization, and the train average axle load and the section average train axle load are used to reflect the influence of train characteristics on vibration interference. Specifically, this embodiment uses... The relative deviation between the actual vibration of a single train and the vibration resistance of the axle counter, as well as the environmental conditions of the section, is calculated to measure the interference potential of the vibration itself; by introducing... This embodiment utilizes an exponential function to amplify the impact of train axle load deviation from the average axle load of the section. The larger the axle load deviation, the larger the exponential function value, indicating a more significant amplification effect of vibration interference caused by axle load. By introducing the exponential function, this embodiment further achieves nonlinear amplification of the impact of axle load deviation, reflecting that the greater the axle load difference, the more significant the vibration transmission and interference. This embodiment constructs the relative deviation by dividing the absolute value by the average value, further achieving dimensionless formula and ensuring that parameters of different orders of magnitude can reasonably participate in the calculation. From the basic measurement of vibration deviation to the amplification of the train axle load impact, and then to the averaging of multiple trains, this embodiment comprehensively constructs the analysis process of the degree of interference to the axle counter counting mode under the combined effect of environmental vibration and train characteristics, accurately capturing the interference mechanism of the vibration environment and the train's own characteristics on the axle counter counting mode.

[0028] S322. Based on axle counter counting data, section operating environment data, and train characteristic data, analyze the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment, and obtain the analysis results of the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment. The formula for calculating the degree of environmental electromagnetic interference experienced by the axle counter in the section's operating environment is as follows: ; In the formula, dc represents the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operation environment, Dif represents the electromagnetic interference intensity spectrum when the i-th column of the section operation environment data actually passes through the axle counter section, Df represents the maximum electromagnetic interference intensity spectrum when all columns of the section operation environment data actually pass through the axle counter section, f1 and f2 represent the minimum and maximum values ​​of the frequency range of the axle counter working signal in the axle counter counting data, si represents the pantograph area of ​​the i-th column of the train passing through the axle counter section in the train characteristic data, s represents the average pantograph area of ​​all columns of the train passing through the axle counter section in the train characteristic data, and erf() represents the error function; For example, this embodiment combines the influence of the electromagnetic spectrum and the pantograph area of ​​the train to comprehensively analyze the degree of environmental electromagnetic interference on the axle counter. The actual electromagnetic interference spectrum of the train and the maximum electromagnetic interference spectrum of the section are used to measure the relative intensity of electromagnetic interference, which is then integrated. Calculate the relative levels of actual electromagnetic interference for a single train and the environmental conditions of the section; through... Introducing an error function allows for the measurement of the impact of the train's pantograph area deviating from the average pantograph area of ​​the section. In this embodiment, the error function provides a continuous and sensitive measurement of area differences; the greater the area deviation, the larger the error function value, and the more significant the amplification effect of electromagnetic interference. This effect is then linearly superimposed onto the measurement of electromagnetic interference intensity using 1+erf(). This embodiment employs an integral function to ensure a complete consideration of the electromagnetic spectrum within the axle counter's operating frequency range, capturing interference characteristics in the frequency dimension. The introduction of the error function erf() reflects the law of electromagnetic induction and interference transmission influenced by pantograph area differences in electromagnetic coupling, achieving a quantitative impact of train characteristics on electromagnetic interference. Furthermore, this embodiment averages the results for n trains, considering multiple trains to reflect the overall degree of environmental electromagnetic interference affecting the axle counter within the section. This embodiment analyzes the interference under the synergistic effect of environmental electromagnetic fields and train characteristics by measuring the relative intensity of the electromagnetic spectrum and superimposing the impact of the train's pantograph area, accurately reflecting the interference of the electromagnetic environment and the train's own characteristics on the axle counter's counting mode.

[0029] S323. Based on the analysis results of the degree of environmental vibration interference and environmental electromagnetic interference obtained from the analysis of the axle counter counting mode in the section operating environment, analyze the degree of interference of the axle counter counting mode in the section operating environment. The formula for calculating the degree of interference experienced by the axle counter counting mode in the section operating environment is as follows: ; In the formula, Gr represents the degree of interference experienced by the axle counter counting mode in the section operating environment, zd represents the analysis result of the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment, and dc represents the analysis result of the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment.

[0030] For example, this embodiment comprehensively analyzes the total degree of vibration and electromagnetic interference experienced by the axle counter within the section's operating environment by integrating two dimensions: vibration interference and electromagnetic interference. Through... A vector-like magnitude is constructed to comprehensively quantify the intensity of vibration and electromagnetic interference; [the following is introduced] Focusing on the differences between the two, the influence of these differences is amplified through a square ratio. The larger the difference between zd and dc, the larger this value, emphasizing the impact of the imbalance between interference dimensions on the overall interference level. Specifically, zd and dc serve as interference indicators for the vibration and electromagnetic dimensions, respectively, participating in two calculation parts: In the first part, the square root of the sum of squares simulates the magnitude of a vector in two-dimensional space, reflecting the synergistic strength of the two types of interference; in the second part, the difference is normalized and amplified through the square ratio of the difference to the sum, highlighting the impact of dimensional differences on the overall interference. This comprehensively considers both the total interference intensity and dimensional differences, ensuring that the calculation formula provided in this embodiment emphasizes both the combined effect of vibration and electromagnetic interference and their balance. Specifically, in this embodiment, This ensured a reasonable balance of strength. This effectively amplifies the impact of differences and ultimately integrates the interference information from the two dimensions into a comprehensive quantitative indicator, fully reflecting the overall degree of interference to the axle counter counting mode in the section's operating environment, and providing a key basis for subsequent adaptation risk assessment.

[0031] In this embodiment, step S4, which involves constructing a counting pattern adaptation risk assessment model, includes the following specific steps: S41. Obtain the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment. S42. Based on the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment, assess the adaptation risk of the axle counter counting mode in the railway operation section. The formula for calculating the adaptation risk of the axle counter counting mode within the railway operating section is as follows: ; In the formula, SF represents the adaptation risk of the axle counter counting mode within the railway operating section.

[0032] For example, this embodiment is used to assess the adaptability risk of the axle counter counting mode within a railway operating section. The calculation logic used to reflect that the lower the matching degree, the higher the risk is directly based on the reverse value of the matching degree. This value is used to emphasize the impact of the level of interference. The higher the level of interference Gr, the larger this value is, and the risk is further amplified.

[0033] In this embodiment, the degree of matching and the degree of interference are calculated from the perspectives of adaptability and environmental influence, respectively: This is used to convert the matching degree into the risk of mismatch; the lower the matching degree, the higher the risk of mismatch. By leveraging the effect of squared amplification of disturbances, the larger the disturbance, the more significant the risk multiplier effect. Multiplying the two together allows for the quantification of the synergistic effect between mismatch risk and disturbance-amplified risk.

[0034] In this embodiment, step S5 involves adjusting and issuing an early warning for the axle counter counting mode based on the risk assessment results of the axle counter counting mode's adaptation within the railway operating section. Specifically, this includes: S51. Obtain the risk assessment results of the axle counter counting mode obtained from the assessment within the railway operating section; S52. Preset adaptation risk threshold. When the adaptation risk assessment result of the axle counter counting mode in the railway operating section is greater than the adaptation risk threshold, the axle counter counting mode is adjusted and an early warning is issued; when the adaptation risk assessment result of the axle counter counting mode in the railway operating section is less than or equal to the adaptation risk threshold, the current axle counter counting mode is maintained. It should be noted that the setting parameters (e.g., weights and thresholds) in this embodiment are obtained experimentally by those skilled in the art. The specific experimental method is as follows: acquire section operation environment data and axle counter counting data within the railway operating section where multiple historical axle counters are located, and simultaneously acquire the corresponding train operation plan data and train characteristic data. Substitute these data into each step of this embodiment to assess the adaptation risk of multiple historical axle counter counting modes within the railway operating section, and simultaneously acquire the judgment result of whether multiple historical axle counter counting modes can be adapted within the railway operating section. Import the judgment result of whether multiple historical axle counter counting modes can be adapted within the railway operating section and the adaptation risk assessment result of multiple historical axle counter counting modes within the railway operating section obtained from each step into the fitting software for continuous fitting to obtain the values ​​of the setting parameters (e.g., weights and thresholds) that meet the maximum accuracy of adaptation risk judgment.

[0035] Example 2 like Figure 2 As shown, this embodiment provides a railway axle counter monitoring system for the rail transit equipment industry, including: The data acquisition module is used to acquire section operating environment data and axle counter count data within the railway operating section, as well as train operation plan data and train characteristic data. The operation feature matching degree analysis module is used to import axle counter counting data, train operation plan data and train feature data into the operation feature matching degree analysis model to analyze the matching degree between axle counter counting mode and train operation features; The counting environment interference analysis module is used to import axle counter counting data, section operating environment data and train characteristic data into the counting environment interference analysis model to analyze the degree of interference experienced by the axle counter counting mode in the section operating environment. The counting mode adaptation risk assessment module is used to construct a counting mode adaptation risk assessment model. It imports the matching degree analysis results of the axle counter counting mode and train operation characteristics, and the interference degree analysis results of the axle counter counting mode in the section operation environment into the counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operation section. The counting mode adjustment early warning module is used to provide early warning of axle counter counting mode adjustment based on the risk assessment results of the axle counter counting mode adaptation in the railway operating section. The control module is used to control the operation of the data acquisition module, the running feature matching degree analysis module, the counting environment interference degree analysis module, the counting mode adaptation risk assessment module, and the counting mode adjustment early warning module.

[0036] The parameters and steps for implementing the corresponding functions of each unit module in the railway axle counter monitoring system for the rail transit equipment industry described above can be referred to the parameters and steps in the embodiments of the railway axle counter monitoring method for the rail transit equipment industry above, and will not be repeated here.

[0037] Example 3 An electronic device according to an embodiment of the present invention includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes a railway axle counter monitoring method for the rail transit equipment industry by calling the computer program stored in the memory. It should be noted that all computer programs for the railway axle counter monitoring method for the rail transit equipment industry are implemented using C language. The data acquisition module, the operating characteristic matching degree analysis module, the counting environment interference degree analysis module, the counting mode adaptation risk assessment module, the counting mode adjustment early warning module, and the control module are all controlled by a remote server.

[0038] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0039] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A railway axle counter monitoring method for the rail transit equipment industry, characterized in that, Includes the following steps: S1. Obtain section operating environment data and axle counter count data within the railway operating section, and simultaneously obtain train operation plan data and train characteristic data; S2. Import the axle counter counting data, train operation plan data, and train characteristic data into the operation characteristic matching degree analysis model to analyze the matching degree between the axle counter counting mode and the train operation characteristics; S3. Import the axle counter counting data, section operating environment data, and train characteristic data into the counting environment interference analysis model to analyze the degree of interference experienced by the axle counter counting mode in the section operating environment. S4. Construct a counting mode adaptation risk assessment model. Import the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment into the counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operation section. S5. Based on the risk assessment results of the axle counter counting mode adaptation in the railway operating section, adjust and issue an early warning for the axle counter counting mode.

2. The railway axle counter monitoring method for the rail transit equipment industry according to claim 1, characterized in that, Step S2 involves analyzing the degree of matching between the axle counter counting mode and the train operation characteristics, specifically including: S21. Extract axle counter counting data, train operation plan data, and train characteristic data; S22. Construct an analysis model for the degree of matching of operating characteristics. Import axle counter counting data, train operation plan data, and train characteristic data into the analysis model for the degree of matching of operating characteristics. Analyze the degree of matching between the axle counter counting mode and the train operation characteristics to obtain the analysis results of the degree of matching between the axle counter counting mode and the train operation characteristics.

3. The railway axle counter monitoring method for the rail transit equipment industry according to claim 2, characterized in that, The construction process of the feature matching degree analysis model in step S22 specifically includes: S221. Based on axle counter counting data and train operation plan data, analyze the time matching degree between axle counter counting mode and train operation plan, and obtain the analysis results of the time matching degree between axle counter counting mode and train operation plan. S222. Based on axle counter counting data, train operation plan data, and train characteristic data, the degree of axle number matching between the axle counter counting mode and the train operation characteristics is analyzed to obtain the analysis results of the degree of axle number matching between the axle counter counting mode and the train operation characteristics. S223. Based on the analysis results of the time matching degree between the axle counter counting mode and the train operation plan, and the analysis results of the axle number matching degree between the axle counter counting mode and the train operation characteristics, analyze the matching degree between the axle counter counting mode and the train operation characteristics. The formula for calculating the degree of matching between the axle counter counting mode and the train operation characteristics is as follows: ； In the formula, Sp represents the degree of matching between the axle counter counting mode and the train operation characteristics, st represents the analysis result of the time matching between the axle counter counting mode and the train operation plan, and sz represents the analysis result of the axle count matching between the axle counter counting mode and the train operation characteristics.

4. The railway axle counter monitoring method for the rail transit equipment industry according to claim 3, characterized in that, Step S3 analyzes the degree of interference experienced by the axle counter counting mode in the section's operating environment, specifically including the following steps: S31. Extract axle counter counting data, section operating environment data, and train characteristic data; S32. Construct a counting environment interference analysis model. Import axle counter counting data, section operating environment data, and train characteristic data into the counting environment interference analysis model to analyze the interference level of the axle counter counting mode in the section operating environment and obtain the analysis results of the interference level of the axle counter counting mode in the section operating environment.

5. The railway axle counter monitoring method for the rail transit equipment industry according to claim 4, characterized in that, The construction process of the counting environment interference level analysis model in step S32 includes the following specific steps: S321. Based on axle counter counting data, section operating environment data, and train characteristic data, analyze the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment, and obtain the analysis results of the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment. S322. Based on axle counter counting data, section operating environment data, and train characteristic data, analyze the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment, and obtain the analysis results of the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment. S323. Based on the analysis results of the degree of environmental vibration interference and environmental electromagnetic interference obtained from the analysis of the axle counter counting mode in the section operating environment, analyze the degree of interference of the axle counter counting mode in the section operating environment. The formula for calculating the degree of interference experienced by the axle counter counting mode in the section operating environment is as follows: ； In the formula, Gr represents the degree of interference experienced by the axle counter counting mode in the section operating environment, zd represents the analysis result of the degree of environmental vibration interference experienced by the axle counter counting mode in the section operating environment, and dc represents the analysis result of the degree of environmental electromagnetic interference experienced by the axle counter counting mode in the section operating environment.

6. The railway axle counter monitoring method for the rail transit equipment industry according to claim 5, characterized in that, The step S4, which involves constructing a counting pattern-adaptive risk assessment model, includes the following specific steps: S41. Obtain the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment. S42. Based on the analysis results of the matching degree between the axle counter counting mode and the train operation characteristics, and the analysis results of the interference degree of the axle counter counting mode in the section operation environment, assess the adaptation risk of the axle counter counting mode in the railway operation section. The formula for calculating the adaptation risk of the axle counter counting mode within the railway operating section is as follows: ； In the formula, SF represents the adaptation risk of the axle counter counting mode within the railway operating section.

7. The railway axle counter monitoring method for the rail transit equipment industry according to claim 6, characterized in that, In step S5, based on the risk assessment results of the axle counter counting mode's adaptability within the railway operating section, an adjustment warning is issued for the axle counter counting mode. Specifically, this includes: S51. Obtain the risk assessment results of the axle counter counting mode obtained from the assessment within the railway operating section; S52. Preset adaptation risk threshold. When the adaptation risk assessment result of the axle counter counting mode in the railway operating section is greater than the adaptation risk threshold, the axle counter counting mode is adjusted and an early warning is issued; when the adaptation risk assessment result of the axle counter counting mode in the railway operating section is less than or equal to the adaptation risk threshold, the current axle counter counting mode is maintained.

8. A railway axle counter monitoring system for the rail transit equipment industry, used to implement the railway axle counter monitoring method for the rail transit equipment industry as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire section operating environment data and axle counter count data within the railway operating section, as well as train operation plan data and train characteristic data. The operation feature matching degree analysis module is used to import axle counter counting data, train operation plan data and train feature data into the operation feature matching degree analysis model to analyze the matching degree between axle counter counting mode and train operation features; The counting environment interference analysis module is used to import axle counter counting data, section operating environment data and train characteristic data into the counting environment interference analysis model to analyze the degree of interference experienced by the axle counter counting mode in the section operating environment. The counting mode adaptation risk assessment module is used to construct a counting mode adaptation risk assessment model. It imports the matching degree analysis results of the axle counter counting mode and train operation characteristics, and the interference degree analysis results of the axle counter counting mode in the section operation environment into the counting mode adaptation risk assessment model to assess the adaptation risk of the axle counter counting mode in the railway operation section. The counting mode adjustment early warning module is used to provide early warning of axle counter counting mode adjustment based on the risk assessment results of the axle counter counting mode adaptation in the railway operating section. The control module is used to control the operation of the data acquisition module, the running feature matching degree analysis module, the counting environment interference degree analysis module, the counting mode adaptation risk assessment module, and the counting mode adjustment early warning module.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the railway axle counter monitoring method for the rail transit equipment industry as described in any one of claims 1-7 by calling the computer program stored in the memory.

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