Railway overhead line system carrier cable tension abnormity detection system and method

Abstract

The invention relates to the technical field of tension anomaly detection, and discloses a railway contact network carrier cable tension anomaly detection system and method, and the system comprises a threshold value determination module which is configured to determine an initial anomaly threshold value of a carrier cable to be detected based on an analysis result; the judgment module is configured to judge whether to optimize the initial abnormal threshold value or not according to the temperature characteristic value; the optimization module is configured to determine an optimization coefficient of the initial abnormal threshold value based on the actual wind load and the actual train operation load and obtain an optimization abnormal threshold value; and the abnormity early warning module is configured to compare the real-time tension value with the optimized abnormity threshold value, perform early warning on the to-be-detected carrier cable according to a comparison result, and obtain an early warning level. According to the invention, real-time monitoring and early warning of the tension state of the carrier cable of the railway contact network can be realized, and the detection efficiency and accuracy are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tension anomaly detection, and in particular to a system and method for detecting tension anomaly in a railway contact network catenary cable. Background Art

[0002] With the rapid development of high-speed railways and urban rail transit, the reliability and safety of railway catenary systems are becoming increasingly important. As a crucial component of the railway catenary system, the tension of catenary cables directly impacts their stability and the proper operation of trains. To ensure safe and efficient railway transportation, the maintenance and inspection of these cables are crucial. This involves not only routine inspections and regular maintenance, but also precise measurement and adjustment of cable tension to ensure they are always in optimal working condition.

[0003] Existing tension detection methods mostly rely on manual inspections and measurements, which are not only inefficient but also subject to the risk of human error. Furthermore, because railway contact networks typically cover extensive areas and operate in complex environments, manual inspections make it difficult to achieve real-time monitoring and early warning of catenary cable tension.

[0004] Therefore, it is necessary to design a railway contact network catenary cable tension anomaly detection system and method to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a system and method for detecting abnormal tension of the supporting cables of the railway contact network, aiming to solve the problem that most tension detection methods in current technology rely on manual inspections, which is not only inefficient but also difficult to accurately reflect the tension status of the supporting cables in real time.

[0006] In one aspect, the present invention provides a railway contact network catenary cable tension anomaly detection system, comprising:

[0007] a threshold determination module configured to collect structural parameters of the to-be-detected catenary cable, analyze the structural parameters, and determine an initial abnormality threshold of the to-be-detected catenary cable based on the analysis results;

[0008] a judgment module configured to collect real-time temperature data of the area where the to-be-detected catenary cable is located, perform feature extraction on the real-time temperature data to obtain a temperature characteristic value, and determine whether to optimize the initial abnormality threshold value based on the temperature characteristic value;

[0009] an optimization module configured to, when determining to optimize the initial abnormality threshold, collect actual wind loads and actual train operating loads in the area where the catenary cable to be inspected is located within a preset time interval, determine an optimization coefficient of the initial abnormality threshold based on the actual wind loads and actual train operating loads, and obtain an optimized abnormality threshold;

[0010] The abnormality warning module is configured to collect the real-time tension value of the load-bearing cable to be detected, and compare the real-time tension value with the optimized abnormality threshold value, and whether to issue an early warning for the load-bearing cable to be detected according to the comparison result, and obtain the early warning level.

[0011] Furthermore, the threshold determination module analyzes the structural parameters and determines the initial abnormality threshold of the to-be-detected catenary based on the analysis result, including:

[0012] Analyzing the structural parameters to obtain the type, length, effective diameter, and effective setting height of the load-bearing cable to be tested;

[0013] Comparing the structural parameters with historical data, and determining an initial abnormality threshold of the to-be-detected catenary according to the comparison result;

[0014] When there is a historical structural parameter identical to the structural parameter in the historical data, a historical abnormality threshold corresponding to the historical structural parameter is used as the initial abnormality threshold of the load-bearing cable to be detected;

[0015] When there are no historical structural parameters identical to the structural parameters in the historical data, the historical data are screened to obtain historical structural parameters of the same type as the load-bearing cable to be detected, the overlap between the structural parameters and each of the historical structural parameters is calculated, and the maximum overlap is extracted; and the initial abnormality threshold of the load-bearing cable to be detected is determined based on the maximum overlap.

[0016] Furthermore, when the threshold determination module determines the initial abnormality threshold of the to-be-detected catenary according to the maximum overlap, it includes:

[0017] Comparing the maximum overlap with a first maximum overlap and a second maximum overlap, and determining an initial abnormality threshold of the catenary to be detected according to the comparison result; wherein the first maximum overlap is smaller than the second maximum overlap;

[0018] When the maximum overlap is less than or equal to a first maximum overlap, determining the initial abnormality threshold as a first abnormality threshold;

[0019] When the maximum overlap is greater than the first maximum overlap and less than or equal to the second maximum overlap, determining the initial abnormality threshold as the second abnormality threshold;

[0020] When the maximum overlap is greater than the second maximum overlap, the initial abnormality threshold is determined to be a third abnormality threshold.

[0021] Furthermore, when the judgment module judges whether to optimize the initial abnormality threshold according to the temperature characteristic value, it includes:

[0022] Obtaining a temperature standard value corresponding to the temperature characteristic value;

[0023] Calculating the ratio of the temperature characteristic value to the temperature standard value and recording it as the temperature ratio;

[0024] Comparing the temperature ratio with a temperature ratio threshold, and determining whether to optimize the initial abnormality threshold according to the comparison result;

[0025] When the temperature ratio is within the temperature ratio threshold, determining to optimize the initial abnormality threshold;

[0026] Otherwise, the initial abnormality threshold is not optimized.

[0027] Furthermore, the optimization module determines the optimization coefficient of the initial abnormality threshold based on the actual wind load and the actual train operating load, and obtains the optimized abnormality threshold, including:

[0028] Analyzing the actual wind load to obtain a maximum wind load, a minimum wind load, and an average wind load;

[0029] Analyzing the actual train running load to obtain the train weight and running speed;

[0030] Obtaining the standard maximum wind load, standard minimum wind load, and standard average wind load corresponding to the maximum wind load, minimum wind load, and average wind load, and the standard train weight and standard running speed corresponding to the train weight and running speed;

[0031] Calculating a wind load offset value according to the maximum wind load, the minimum wind load, the average wind load, the standard maximum wind load, the standard minimum wind load, and the standard average wind load;

[0032] Calculating an operating load offset value according to the train weight, operating speed, standard train weight and standard operating speed;

[0033] Performing weighted calculation on the wind load offset value and the operating load offset value to obtain a comprehensive load offset value;

[0034] An optimization coefficient of the initial abnormality threshold is determined according to the comprehensive load offset value, and a product value of the optimization coefficient and the initial abnormality threshold is used as the optimized abnormality threshold.

[0035] Furthermore, the optimization module performs weighted calculation on the wind load offset value and the operating load offset value to obtain a comprehensive load offset value, including:

[0036] When performing weighted calculation, respectively calculating the wind load confidence and the operating load confidence according to the actual wind load and the actual train operating load;

[0037] Using the ratio of the wind load confidence level to the operational load confidence level as the ratio of the weight coefficients corresponding to the wind load offset value and the operational load offset value;

[0038] The wind load offset value and the operating load offset value are weightedly calculated according to the ratio of the weight coefficients to obtain the comprehensive load offset value.

[0039] Furthermore, when the optimization module determines the optimization coefficient of the initial abnormality threshold according to the comprehensive load offset value, it includes:

[0040] comparing the integrated load offset value with a first integrated load offset value and a second integrated load offset value, and determining the optimization coefficient according to the comparison result; wherein the first integrated load offset value is smaller than the second integrated load offset value;

[0041] When the comprehensive load offset value is less than or equal to the first comprehensive load offset value, determining the optimization coefficient to be the first optimization coefficient;

[0042] When the comprehensive load offset value is greater than the first comprehensive load offset value and less than or equal to the second comprehensive load offset value, determining the optimization coefficient to be the second optimization coefficient;

[0043] When the comprehensive load offset value is greater than the second comprehensive load offset value, the optimization coefficient is determined to be a third optimization coefficient.

[0044] Furthermore, the abnormal warning module determines whether to issue a warning to the to-be-detected catenary according to the comparison result and obtains a warning level, including:

[0045] When the real-time tension value is greater than the optimized abnormal threshold, it is determined that the tension of the to-be-detected catenary is abnormal, and an early warning signal is issued;

[0046] Calculating the difference between the real-time tension value and the optimized abnormal threshold value, and recording it as the tension difference;

[0047] A warning level is determined according to the tension difference.

[0048] Furthermore, when the abnormal warning module determines the warning level according to the tension difference, it includes:

[0049] Comparing the tension difference with a tension difference threshold, and determining a warning level according to the comparison result; wherein the tension difference threshold includes a first tension difference threshold and a second tension difference threshold, and the first tension difference threshold is smaller than the second tension difference threshold;

[0050] When the tension difference is greater than a second tension difference threshold, determining the warning level to be a first-level warning;

[0051] When the tension difference is greater than the first tension difference threshold and less than the second tension difference threshold, determining the warning level to be a level 2 warning;

[0052] When the tension difference is less than or equal to the first tension difference threshold, the warning level is determined to be a level three warning.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows: the railway contact network catenary cable tension anomaly detection system provided by the present invention can realize real-time monitoring and early warning of the tension status of the railway contact network catenary cables, greatly improving the detection efficiency and accuracy. Specifically, the threshold determination module can intelligently determine the initial anomaly threshold based on the structural parameters of the catenary cables, providing a reliable basis for subsequent judgment. The judgment module uses real-time temperature data to dynamically adjust the initial anomaly threshold, ensuring that the system can maintain a high detection accuracy under various temperature conditions. The optimization module further considers actual factors such as wind load and train running load, and optimizes the initial anomaly threshold, making the system more in line with the needs of actual application scenarios. Finally, the anomaly warning module can collect the tension value of the catenary cables in real time, and compare it with the optimized anomaly threshold, and issue an early warning signal in time, effectively avoiding safety accidents caused by tension anomalies.

[0054] In another aspect, the present invention further provides a method for detecting abnormal tension of a railway contact network catenary cable, comprising the following steps:

[0055] collecting structural parameters of the load-bearing cable to be detected, analyzing the structural parameters, and determining an initial abnormality threshold of the load-bearing cable to be detected based on the analysis results;

[0056] collecting real-time temperature data of the area where the to-be-detected catenary cable is located, performing feature extraction on the real-time temperature data to obtain a temperature characteristic value, and determining whether to optimize the initial abnormality threshold value based on the temperature characteristic value;

[0057] When it is determined that the initial abnormality threshold is to be optimized, collecting the actual wind load and the actual train operating load of the area where the catenary cable to be inspected is located within a preset time interval, determining the optimization coefficient of the initial abnormality threshold based on the actual wind load and the actual train operating load, and obtaining the optimized abnormality threshold;

[0058] The real-time tension value of the load-bearing cable to be detected is collected, and the real-time tension value is compared with the optimized abnormal threshold value. According to the comparison result, whether the load-bearing cable to be detected is warned or not is determined, and the warning level is obtained.

[0059] It is understandable that the above-mentioned railway contact network catenary cable tension anomaly detection system and method have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0061] Figure 1 A structural block diagram of a railway contact network catenary cable tension anomaly detection system provided by an embodiment of the present invention;

[0062] Figure 2 A flow chart of a method for detecting abnormal tension in a railway contact network catenary cable provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features described in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0064] See Figure 1 As shown, in some embodiments of the present application, this embodiment provides a railway contact network catenary cable tension anomaly detection system, comprising:

[0065] a threshold determination module configured to collect structural parameters of the catenary cable to be detected, analyze the structural parameters, and determine an initial abnormality threshold of the catenary cable to be detected based on the analysis results;

[0066] a judgment module configured to collect real-time temperature data of the area where the load-bearing cable to be detected is located, perform feature extraction on the real-time temperature data to obtain a temperature characteristic value, and determine whether to optimize the initial abnormality threshold value based on the temperature characteristic value;

[0067] an optimization module configured to, when determining to optimize the initial abnormality threshold, collect actual wind loads and actual train operating loads in the area where the catenary cables to be inspected are located within a preset time interval, determine an optimization coefficient of the initial abnormality threshold based on the actual wind loads and the actual train operating loads, and obtain an optimized abnormality threshold;

[0068] The abnormal warning module is configured to collect the real-time tension value of the load-bearing cable to be detected, and compare the real-time tension value with the optimized abnormal threshold value, and whether to issue an early warning for the load-bearing cable to be detected based on the comparison result and obtain the early warning level.

[0069] It is understandable that the railway contact network catenary cable tension anomaly detection system provided by this embodiment can realize real-time monitoring and early warning of the tension status of the railway contact network catenary cables, greatly improving the detection efficiency and accuracy. Specifically, the threshold determination module can intelligently determine the initial anomaly threshold based on the structural parameters of the catenary cables, providing a reliable basis for subsequent judgment. The judgment module uses real-time temperature data to dynamically adjust the initial anomaly threshold, ensuring that the system can maintain a high detection accuracy under various temperature conditions. The optimization module further considers actual factors such as wind load and train running load, and optimizes the initial anomaly threshold, making the system more in line with the needs of actual application scenarios. Finally, the anomaly warning module can collect the tension value of the catenary cables in real time, compare it with the optimized anomaly threshold, and issue an early warning signal in a timely manner, effectively avoiding safety accidents caused by tension anomalies.

[0070] Specifically, the threshold determination module analyzes the structural parameters and determines the initial abnormality threshold of the load-bearing cable to be detected based on the analysis results, including:

[0071] Analyze the structural parameters to obtain the type, length, effective diameter and effective setting height of the load-bearing cable to be tested;

[0072] Compare the structural parameters with historical data and determine the initial abnormal threshold of the load-bearing cable to be tested based on the comparison results;

[0073] When there are historical structural parameters identical to the structural parameters in the historical data, the historical abnormality threshold corresponding to the historical structural parameters is used as the initial abnormality threshold of the load-bearing cable to be detected;

[0074] When there are no historical structural parameters identical to the structural parameters in the historical data, the historical data are screened to obtain historical structural parameters of the same type as the load-bearing cable to be detected, the overlap between the structural parameters and each historical structural parameter is calculated, and the maximum overlap is extracted; the initial abnormal threshold of the load-bearing cable to be detected is determined based on the maximum overlap.

[0075] In this embodiment, the historical data includes several historical structural parameters.

[0076] In this embodiment, the effective diameter and the effective setting height refer to the diameter of the catenary cable in a normal working state and the height from the ground, respectively.

[0077] In this embodiment, the overlap is obtained by the following formula:

[0078]

[0079] Among them, Ri represents the overlap between the i-th historical structural parameter and the structural parameter; ωL, ωD, and ωH represent the important indices of length, effective diameter, and effective setting height, respectively, and ωL+ωD+ωH=1; Lc, Dc, and Hc represent the length, effective diameter, and effective setting height of the load-bearing cable to be tested, respectively; Li, Di, and Hi represent the length, effective diameter, and effective setting height of the i-th historical structural parameter, respectively; Lmax, Dmax, and Hmax represent the maximum values ​​of length, effective diameter, and effective setting height, respectively.

[0080] It's understandable that through detailed analysis of structural parameters and comparison with historical data, the system can intelligently determine the initial anomaly threshold for the catenary. This step not only improves the system's automation level but also ensures the accuracy and rationality of the initial threshold. When historical records exist that match the structural parameters of the catenary to be tested, the system directly uses the anomaly threshold corresponding to that historical record. This not only simplifies the calculation process but also ensures the reliability of the threshold. If the historical data doesn't contain identical structural parameters, the system calculates the overlap between the structural parameters and the historical data, selects the maximum overlap as a reference, and determines the initial anomaly threshold based on this reference. This approach fully accounts for the similarities between catenary cables, making the determined initial threshold closer to reality.

[0081] Specifically, when the threshold determination module determines the initial abnormal threshold of the to-be-detected catenary according to the maximum overlap, it includes:

[0082] Comparing the maximum overlap with the first maximum overlap and the second maximum overlap, and determining an initial abnormality threshold of the catenary to be detected according to the comparison results; wherein the first maximum overlap is less than the second maximum overlap;

[0083] When the maximum overlap is less than or equal to the first maximum overlap, determining the initial abnormality threshold as the first abnormality threshold;

[0084] When the maximum overlap is greater than the first maximum overlap and less than or equal to the second maximum overlap, determining the initial abnormality threshold to be the second abnormality threshold;

[0085] When the maximum overlap is greater than the second maximum overlap, the initial abnormality threshold is determined to be the third abnormality threshold.

[0086] In this embodiment, the first maximum overlap and the second maximum overlap are preset thresholds based on the overlap distribution in historical data.

[0087] In this embodiment, the magnitude relationship of the abnormality thresholds is that the first abnormality threshold is greater than the second abnormality threshold, and the second abnormality threshold is greater than the third abnormality threshold.

[0088] As you can see, by comparing the maximum overlap with the first and second maximum overlaps, the system can intelligently select the most appropriate initial anomaly threshold, providing a reliable basis for subsequent judgment and early warning. This step further enhances the system's automation and intelligence, making the entire detection system more efficient and accurate.

[0089] Specifically, when the judgment module judges whether to optimize the initial abnormal threshold value based on the temperature characteristic value, it includes:

[0090] Obtain the temperature standard value corresponding to the temperature characteristic value;

[0091] Calculate the ratio of the temperature characteristic value to the temperature standard value and record it as the temperature ratio;

[0092] Compare the temperature ratio with the temperature ratio threshold, and determine whether to optimize the initial abnormal threshold based on the comparison result;

[0093] When the temperature ratio is within the temperature ratio threshold, it is determined that the initial abnormal threshold is optimized;

[0094] Otherwise, no optimization is performed on the initial anomaly threshold.

[0095] In this embodiment, the temperature characteristic values ​​refer to the maximum temperature value, the minimum temperature value, the temperature standard deviation, and the temperature change rate.

[0096] In this embodiment, the temperature characteristic value is preferably the temperature change rate, because the temperature change rate can more intuitively reflect the impact of temperature on the tension of the catenary cable, thereby improving the accuracy and reliability of the judgment.

[0097] It's understandable that when the temperature ratio is within the threshold, it indicates that the current temperature conditions may have a significant impact on the tension of the catenary cables, so the system needs to optimize the initial anomaly threshold to accommodate this change. However, when the temperature ratio exceeds the threshold, it's assumed that the current temperature conditions have a minimal impact on the cable tension, and no initial anomaly threshold optimization is required, thus maintaining system stability and detection efficiency.

[0098] Specifically, the optimization module determines the optimization coefficient of the initial abnormality threshold based on the actual wind load and the actual train operating load, and obtains the optimized abnormality threshold, including:

[0099] Analyze the actual wind load to obtain the maximum wind load, minimum wind load and average wind load;

[0100] Analyze the actual train running load to obtain the train weight and running speed;

[0101] Obtaining the standard maximum wind load, standard minimum wind load, and standard average wind load corresponding to the maximum wind load, minimum wind load, and average wind load, as well as the standard train weight and standard running speed corresponding to the train weight and running speed;

[0102] Calculate the wind load offset value based on the maximum wind load, minimum wind load, average wind load and standard maximum wind load, standard minimum wind load and standard average wind load;

[0103] Calculate the running load offset value based on the train weight, running speed, standard train weight and standard running speed;

[0104] Perform weighted calculation on the wind load offset value and the operating load offset value to obtain the comprehensive load offset value;

[0105] The optimization coefficient of the initial abnormal threshold is determined according to the comprehensive load offset value, and the product of the optimization coefficient and the initial abnormal threshold is used as the optimized abnormal threshold.

[0106] In this embodiment, the calculation formula of the wind load offset value is:

[0107]

[0108] Among them, ΔF represents the wind load offset value; α1, α2, and α3 represent the correlation coefficients of the maximum wind load, minimum wind load, and average wind load, respectively, and α1+α2+α3=1; Wmax, Wmin, and Wavg represent the actual maximum wind load, actual minimum wind load, and actual average wind load, respectively; Wa, Wb, and Wc represent the standard maximum wind load, standard minimum wind load, and standard average wind load, respectively.

[0109] In this embodiment, the calculation formula of the operating load offset value is:

[0110]

[0111] Among them, ΔY represents the operating load offset value; β1 and β2 represent the weight indices of train weight and operating speed, respectively, and β1+β2=1; M and V represent the actual train weight and actual operating speed, respectively; Ms and Vs represent the standard train weight and standard operating speed, respectively.

[0112] It is understood that the optimization module fully considers the impact of actual wind loads and train operating loads on the tension of the catenary cables. By calculating the wind load offset and operating load offset values ​​and performing a weighted calculation, a comprehensive load offset value is obtained. This step not only improves the system's adaptability to complex environmental factors, but also ensures that the optimized anomaly threshold is more consistent with actual conditions. Based on the optimization coefficient determined by the comprehensive load offset value, the system can intelligently adjust the initial anomaly threshold, so that the optimized anomaly threshold maintains high detection accuracy under different wind load and train operating load conditions.

[0113] Specifically, the optimization module performs weighted calculation on the wind load offset value and the operating load offset value to obtain the comprehensive load offset value, including:

[0114] When performing weighted calculations, the wind load confidence level and the operating load confidence level are calculated based on the actual wind load and the actual train operating load respectively;

[0115] The ratio of the wind load confidence level to the operational load confidence level is used as the ratio of the weight coefficients corresponding to the wind load offset value and the operational load offset value;

[0116] The wind load offset value and the operating load offset value are weightedly calculated according to the ratio of the weight coefficients to obtain the comprehensive load offset value.

[0117] In this embodiment, the wind load confidence level is:

[0118]

[0119] Where CF represents the confidence factor of wind load; δF represents the integrity factor of wind load; σF represents the standard deviation of wind load; Wavg represents the actual average wind load; and ε represents the correction factor of wind load.

[0120] In this embodiment, the wind load integrity factor refers to the completeness of the wind load data and is used to assess whether the actually collected wind load data fully reflects the wind load characteristics in the area where the catenary cables are located. The wind load correction factor is used to correct the wind load data to eliminate outliers and noise, thereby improving the accuracy and reliability of the data.

[0121] In this embodiment, the operating load confidence is:

[0122]

[0123] Among them, CY represents the confidence level of the operating load; δY represents the integrity factor of the operating load; σY represents the standard deviation of the operating load; Yavg represents the actual average operating load; and θ represents the correction factor of the operating load.

[0124] In this embodiment, the actual train operating load is calculated by the following formula:

[0125] Y=kMV 2 ;

[0126] Among them, Y represents the train operating load; k is the train operating load coefficient; M is the train weight; and V is the train operating speed.

[0127] In this embodiment, the value of k is preferably 0.8. This value is obtained based on statistical analysis of a large amount of experimental data and actual operating experience, and can more accurately reflect the impact of train weight and operating speed on the tension of the catenary cables.

[0128] In this embodiment, the operating load integrity coefficient refers to the completeness of the actual train operating load data, and is used to evaluate whether the actually collected train operating load data fully and accurately reflects the train operating conditions in the area where the catenary cables are located.

[0129] As you can see, by calculating confidence levels for both wind load offset and operating load offset, and determining weighting coefficients based on the confidence level ratio, the system can more accurately assess the impact of wind load and train operating load on catenary cable tension. This approach not only improves the rationality of the weighted calculation but also ensures that the combined load offset value better reflects actual conditions.

[0130] Specifically, when the optimization module determines the optimization coefficient of the initial abnormality threshold according to the comprehensive load offset value, it includes:

[0131] Comparing the integrated load offset value with the first integrated load offset value and the second integrated load offset value, and determining an optimization coefficient according to the comparison result; wherein the first integrated load offset value is less than the second integrated load offset value;

[0132] When the comprehensive load offset value is less than or equal to the first comprehensive load offset value, determining the optimization coefficient to be the first optimization coefficient;

[0133] When the comprehensive load offset value is greater than the first comprehensive load offset value and less than or equal to the second comprehensive load offset value, determining the optimization coefficient to be the second optimization coefficient;

[0134] When the comprehensive load offset value is greater than the second comprehensive load offset value, the optimization coefficient is determined to be the third optimization coefficient.

[0135] In this embodiment, the first integrated load offset value and the second integrated load offset value are preset thresholds based on the distribution of integrated load offset values ​​in historical data.

[0136] In this embodiment, the magnitude relationship of the optimization coefficients is that the first optimization coefficient is smaller than the second optimization coefficient, and the second optimization coefficient is smaller than the third optimization coefficient.

[0137] It can be understood that by comparing the comprehensive load offset value with the preset first comprehensive load offset value and the second comprehensive load offset value, the system can intelligently select the most appropriate optimization coefficient and adjust the initial abnormality threshold to obtain a more accurate optimized abnormality threshold.

[0138] Specifically, the abnormal warning module determines whether to issue a warning for the load-bearing cable to be tested based on the comparison results and obtains the warning level, including:

[0139] When the real-time tension value is greater than the optimized abnormal threshold, it is determined that the tension of the load-bearing cable to be tested is abnormal, and an early warning signal is issued;

[0140] Calculate the difference between the real-time tension value and the optimized abnormal threshold value and record it as the tension difference;

[0141] The warning level is determined based on the tension difference.

[0142] Specifically, when the abnormal warning module determines the warning level based on the tension difference, it includes:

[0143] Comparing the tension difference with a tension difference threshold, and determining a warning level according to the comparison result; wherein the tension difference threshold includes a first tension difference threshold and a second tension difference threshold, and the first tension difference threshold is smaller than the second tension difference threshold;

[0144] When the tension difference is greater than the second tension difference threshold, the warning level is determined to be a level one warning;

[0145] When the tension difference is greater than the first tension difference threshold and less than the second tension difference threshold, the warning level is determined to be a level 2 warning;

[0146] When the tension difference is less than or equal to the first tension difference threshold, the warning level is determined to be a level three warning.

[0147] In this embodiment, the first tension difference threshold and the second tension difference threshold are preset thresholds based on the distribution of tension differences in historical data.

[0148] Understandably, the purpose of setting warning levels is to help personnel quickly understand the severity of abnormal cable tension and take appropriate measures. A Level 1 warning indicates severe abnormal cable tension, requiring immediate action to prevent accidents. A Level 2 warning indicates a more severe abnormal cable tension, requiring prompt inspection and maintenance. A Level 3 warning indicates a mild abnormal cable tension, requiring attention during routine inspections and appropriate maintenance. By setting different warning levels, the system can more accurately reflect the tension status of the cable, providing stronger protection for the safety and stability of railway transportation.

[0149] See Figure 2 As shown, in some embodiments of the present application, this embodiment provides a method for detecting abnormal tension of a catenary cable of a railway contact network, comprising the following steps:

[0150] S100: collecting structural parameters of the to-be-detected catenary cable, analyzing the structural parameters, and determining an initial abnormality threshold of the to-be-detected catenary cable based on the analysis results;

[0151] S200: collecting real-time temperature data of the area where the catenary to be inspected is located, performing feature extraction on the real-time temperature data to obtain a temperature characteristic value, and determining whether to optimize the initial abnormality threshold value based on the temperature characteristic value;

[0152] S300: When it is determined that the initial abnormality threshold is to be optimized, the actual wind load and the actual train operating load of the area where the catenary to be inspected are located within a preset time interval are collected, and an optimization coefficient of the initial abnormality threshold is determined based on the actual wind load and the actual train operating load, thereby obtaining an optimized abnormality threshold.

[0153] S400: collecting the real-time tension value of the load-bearing cable to be tested, and comparing the real-time tension value with the optimized abnormal threshold value, and determining whether to issue an early warning for the load-bearing cable to be tested based on the comparison result, and obtaining an early warning level.

[0154] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0156] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A railway contact network catenary cable tension anomaly detection system, characterized in that: include: a threshold determination module configured to collect structural parameters of the to-be-detected catenary cable, analyze the structural parameters, and determine an initial abnormality threshold of the to-be-detected catenary cable based on the analysis results; a judgment module configured to collect real-time temperature data of the area where the to-be-detected catenary cable is located, perform feature extraction on the real-time temperature data to obtain a temperature characteristic value, and determine whether to optimize the initial abnormality threshold value based on the temperature characteristic value; an optimization module configured to, when determining to optimize the initial abnormality threshold, collect actual wind loads and actual train operating loads in the area where the catenary cable to be inspected is located within a preset time interval, determine an optimization coefficient of the initial abnormality threshold based on the actual wind loads and actual train operating loads, and obtain an optimized abnormality threshold; The abnormality warning module is configured to collect the real-time tension value of the load-bearing cable to be detected, and compare the real-time tension value with the optimized abnormality threshold value, and whether to issue an early warning for the load-bearing cable to be detected according to the comparison result, and obtain the early warning level.

2. The railway contact network catenary cable tension anomaly detection system according to claim 1, characterized in that: The threshold determination module analyzes the structural parameters and determines the initial abnormality threshold of the to-be-detected catenary based on the analysis result, including: Analyzing the structural parameters to obtain the type, length, effective diameter, and effective setting height of the load-bearing cable to be tested; Comparing the structural parameters with historical data, and determining an initial abnormality threshold of the to-be-detected catenary according to the comparison result; When there is a historical structural parameter identical to the structural parameter in the historical data, the historical abnormality threshold corresponding to the historical structural parameter is used as the initial abnormality threshold of the load-bearing cable to be detected; When there are no historical structural parameters identical to the structural parameters in the historical data, the historical data are screened to obtain historical structural parameters of the same type as the load-bearing cable to be detected, the overlap between the structural parameters and each of the historical structural parameters is calculated, and the maximum overlap is extracted; and the initial abnormality threshold of the load-bearing cable to be detected is determined based on the maximum overlap.

3. The railway contact network catenary cable tension anomaly detection system according to claim 2, characterized in that: When the threshold determination module determines the initial abnormality threshold of the to-be-detected catenary according to the maximum overlap, it includes: Comparing the maximum overlap with a first maximum overlap and a second maximum overlap, and determining an initial abnormality threshold of the catenary to be detected according to the comparison result; wherein the first maximum overlap is smaller than the second maximum overlap; When the maximum overlap is less than or equal to a first maximum overlap, determining the initial abnormality threshold as a first abnormality threshold; When the maximum overlap is greater than the first maximum overlap and less than or equal to the second maximum overlap, determining the initial abnormality threshold as the second abnormality threshold; When the maximum overlap is greater than the second maximum overlap, the initial abnormality threshold is determined to be a third abnormality threshold.

4. The railway contact network catenary cable tension anomaly detection system according to claim 3, characterized in that: When the judgment module judges whether to optimize the initial abnormality threshold according to the temperature characteristic value, it includes: Obtaining a temperature standard value corresponding to the temperature characteristic value; Calculating the ratio of the temperature characteristic value to the temperature standard value and recording it as the temperature ratio; Comparing the temperature ratio with a temperature ratio threshold, and determining whether to optimize the initial abnormality threshold according to the comparison result; When the temperature ratio is within the temperature ratio threshold, determining to optimize the initial abnormality threshold; Otherwise, the initial abnormality threshold is not optimized.

5. The railway contact network catenary cable tension anomaly detection system according to claim 4, characterized in that: The optimization module determines the optimization coefficient of the initial abnormality threshold based on the actual wind load and the actual train operating load, and obtains the optimized abnormality threshold, including: Analyzing the actual wind load to obtain a maximum wind load, a minimum wind load, and an average wind load; Analyzing the actual train running load to obtain the train weight and running speed; Obtaining the standard maximum wind load, standard minimum wind load, and standard average wind load corresponding to the maximum wind load, minimum wind load, and average wind load, and the standard train weight and standard running speed corresponding to the train weight and running speed; Calculating a wind load offset value according to the maximum wind load, the minimum wind load, the average wind load, the standard maximum wind load, the standard minimum wind load, and the standard average wind load; Calculating an operating load offset value according to the train weight, operating speed, standard train weight and standard operating speed; Performing weighted calculation on the wind load offset value and the operating load offset value to obtain a comprehensive load offset value; An optimization coefficient of the initial abnormality threshold is determined according to the comprehensive load offset value, and a product value of the optimization coefficient and the initial abnormality threshold is used as the optimized abnormality threshold.

6. The railway contact network catenary cable tension anomaly detection system according to claim 5, characterized in that: The optimization module performs weighted calculation on the wind load offset value and the operating load offset value to obtain a comprehensive load offset value, including: When performing weighted calculation, respectively calculating the wind load confidence and the operating load confidence according to the actual wind load and the actual train operating load; Using the ratio of the wind load confidence level to the operational load confidence level as the ratio of the weight coefficients corresponding to the wind load offset value and the operational load offset value; The wind load offset value and the operating load offset value are weightedly calculated according to the ratio of the weight coefficients to obtain the comprehensive load offset value.

7. The railway contact network catenary cable tension anomaly detection system according to claim 6, characterized in that: When the optimization module determines the optimization coefficient of the initial abnormality threshold according to the comprehensive load offset value, it includes: comparing the integrated load offset value with a first integrated load offset value and a second integrated load offset value, and determining the optimization coefficient according to the comparison result; wherein the first integrated load offset value is smaller than the second integrated load offset value; When the comprehensive load offset value is less than or equal to the first comprehensive load offset value, determining the optimization coefficient to be the first optimization coefficient; When the comprehensive load offset value is greater than the first comprehensive load offset value and less than or equal to the second comprehensive load offset value, determining the optimization coefficient to be the second optimization coefficient; When the comprehensive load offset value is greater than the second comprehensive load offset value, the optimization coefficient is determined to be a third optimization coefficient.

8. The railway contact network catenary cable tension anomaly detection system according to claim 1, characterized in that: The abnormal warning module determines whether to issue a warning to the load-bearing cable to be detected according to the comparison result and obtains the warning level, including: When the real-time tension value is greater than the optimized abnormal threshold, it is determined that the tension of the to-be-detected catenary is abnormal, and an early warning signal is issued; Calculating the difference between the real-time tension value and the optimized abnormal threshold value, and recording it as the tension difference; A warning level is determined according to the tension difference.

9. The railway contact network catenary cable tension anomaly detection system according to claim 8, characterized in that: When the abnormal warning module determines the warning level according to the tension difference, it includes: Comparing the tension difference with a tension difference threshold, and determining a warning level according to the comparison result; wherein the tension difference threshold includes a first tension difference threshold and a second tension difference threshold, and the first tension difference threshold is smaller than the second tension difference threshold; When the tension difference is greater than a second tension difference threshold, determining the warning level to be a first-level warning; When the tension difference is greater than the first tension difference threshold and less than the second tension difference threshold, determining the warning level to be a level 2 warning; When the tension difference is less than or equal to the first tension difference threshold, the warning level is determined to be a level three warning.

10. A method for detecting abnormal tension of catenary cables of a railway overhead contact network, applied to the abnormal tension detection system of a railway overhead contact network catenary cables according to any one of claims 1 to 9, characterized in that: include: collecting structural parameters of the load-bearing cable to be detected, analyzing the structural parameters, and determining an initial abnormality threshold of the load-bearing cable to be detected based on the analysis results; collecting real-time temperature data of the area where the to-be-detected catenary cable is located, performing feature extraction on the real-time temperature data to obtain a temperature characteristic value, and determining whether to optimize the initial abnormality threshold value based on the temperature characteristic value; When it is determined that the initial abnormality threshold is to be optimized, collecting the actual wind load and the actual train operating load of the area where the catenary cable to be inspected is located within a preset time interval, determining the optimization coefficient of the initial abnormality threshold based on the actual wind load and the actual train operating load, and obtaining the optimized abnormality threshold; The real-time tension value of the load-bearing cable to be detected is collected, and the real-time tension value is compared with the optimized abnormal threshold value. According to the comparison result, whether the load-bearing cable to be detected is warned or not is determined, and the warning level is obtained.

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