Intelligent detection method and device for railway overhead line system and computer equipment

Abstract

The invention relates to an intelligent detection method and device for a railway overhead line system and computer equipment. The method comprises the steps that when a train travels to a target area, pressure change information of a pantograph, track information of the train and a wind power data sequence in the target area are collected, and track wind power information of the train in the target area is recognized based on the track information of the train and the wind power data sequence in the target area; based on the track wind power information, identifying a pressure change standard comparison table of the pantograph corresponding to the track wind power information through a pressure standard identification strategy, and based on the pressure change information of the pantograph, identifying contact network state information of the pantograph through the pressure change standard comparison table; and on the basis of the state information of the contact network of the pantograph, abnormal detection information of the contact network is identified through an abnormal analysis strategy, and a contact network detection result of the contact network is generated on the basis of the abnormal detection information of the contact network. By adopting the method, the detection accuracy of the overhead line system can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart cards, in particular to a smart detection method and device for a railway overhead contact system and a computer device. BACKGROUND

[0002] Generally, the power source of high-speed trains and motor trains comes from the pantograph above the locomotive and the overhead contact system. The overhead contact system is a circuit structure for supplying power to electric locomotives on a railway line, and the contact wire is an important component of the overhead contact system. Therefore, in order to supply power, the pantograph must be in continuous contact with the overhead contact system. The overhead contact system will be affected by long-term contact wear, atmospheric oxidation, rain erosion, and even changes in the terrain along the railway line (for example, slight concave), which will affect the normal running and safe operation of the train. Therefore, how to detect the state of the overhead contact system to ensure continuous and stable contact between the overhead contact system and the pantograph and improve the safety and stability of train running is a technical problem that needs to be solved.

[0003] The existing detection method for the overhead contact system mainly measures the parameters of the overhead contact system and performs real-time monitoring through measuring instruments or detection vehicles, so as to detect the overhead contact system. However, this detection method has low efficiency, high difficulty in manual detection, and low accuracy, resulting in low detection accuracy of the overhead contact system. SUMMARY

[0004] Therefore, it is necessary to provide a smart detection method and device for a railway overhead contact system, a computer device, a computer readable storage medium, and a computer program product in view of the above technical problems.

[0005] In a first aspect, the present application provides a smart detection method for a railway overhead contact system, comprising:

[0006] When the train travels to a target area, the pressure change information of the pantograph, the trajectory information of the train, and the wind data sequence in the target area are collected, and based on the trajectory information of the train and the wind data sequence in the target area, the trajectory wind information of the train in the target area is identified;

[0007] Based on the trajectory wind information, a pressure standard identification strategy is used to identify a pressure change standard reference table corresponding to the trajectory wind information, and based on the pressure change information of the pantograph, the pressure change standard reference table is used to identify the overhead contact system state information of the pantograph;

[0008] Based on the overhead contact system state information of the pantograph, an abnormal analysis strategy is used to identify abnormal detection information of the overhead contact system, and based on the abnormal detection information of the overhead contact system, an overhead contact system detection result of the overhead contact system is generated.

[0009] Optionally, the trajectory wind information of the train in the target area is identified based on the trajectory information of the train and the wind data sequence in the target area, and the trajectory wind information of the train in the target area comprises:

[0010] The trajectory information of the train is split into time points corresponding to each trajectory point and position information of each trajectory point, and each detected trajectory point is screened in the target area based on the position information of each trajectory point.

[0011] The wind data sequence is split into wind data corresponding to each time point, and the trajectory wind data corresponding to each detected trajectory point is identified based on the time point corresponding to each trajectory point and the wind data corresponding to each trajectory point.

[0012] The trajectory wind data corresponding to each detected trajectory point is arranged in chronological order of the time point corresponding to each detected trajectory point to obtain the trajectory wind information of the train.

[0013] Optionally, the pressure change standard reference table of the pantograph corresponding to the trajectory wind information is identified based on the trajectory wind information and a pressure standard identification strategy, and the pressure change standard reference table of the pantograph corresponding to the trajectory wind information comprises:

[0014] Each historical wind data of the catenary in a normal state and historical pressure data of the pantograph corresponding to each historical wind data are screened in a historical detection database, and the historical pressure data distribution range corresponding to the wind data of each detected trajectory point is identified based on the wind data of each detected trajectory point and the historical pressure data corresponding to each historical wind data.

[0015] The standard historical pressure data corresponding to the wind data of each detected trajectory point is calculated based on the historical pressure data distribution range corresponding to the wind data of each detected trajectory point, and the standard historical pressure data corresponding to the wind data of each detected trajectory point is arranged in the arrangement order of each detected trajectory point in the trajectory wind information to obtain the pressure change standard reference table of the pantograph corresponding to the trajectory wind information.

[0016] Optionally, the catenary state information of the pantograph is identified based on the pressure change information of the pantograph and the pressure change standard reference table, and the catenary state information of the pantograph comprises:

[0017] The pressure data change sequence of the pantograph is identified based on the pressure change information of the pantograph, and the pressure difference distribution information of the pantograph is identified based on the pressure data change sequence of the pantograph and the pressure change standard reference table.

[0018] calculating a pressure difference value percentage of each of the detection trajectory points based on the pressure difference value distribution information of the pantograph and the pressure change standard table, and taking the pressure difference value percentage of each of the detection trajectory points as a pressure difference value percentage of a catenary position point of each of the detection trajectory points in the target area;

[0019] taking the pressure difference value percentages of all of the catenary position points as catenary state information of the pantograph.

[0020] Optionally, the catenary state information of the pantograph is used to identify abnormal detection information of the catenary through an abnormality analysis strategy, including:

[0021] arranging the pressure difference value percentages of each of the catenary position points in the order of the train passing through each of the catenary position points to obtain a pressure difference value percentage sequence of the catenary;

[0022] calculating a percentage change difference value between each of the catenary position points based on the pressure difference value percentage sequence;

[0023] identifying an abnormal type between each of the catenary position points and an abnormal degree value between each of the catenary position points based on the percentage change difference value between each of the catenary position points through an abnormality analysis strategy;

[0024] taking the abnormal type between each of the catenary position points and the abnormal degree value between each of the catenary position points as abnormal detection information of the catenary.

[0025] Optionally, the abnormal detection information of the catenary is used to generate catenary detection results of the catenary, including:

[0026] obtaining a catenary model diagram of the catenary, and marking each of the catenary position points in the catenary model diagram to obtain a catenary detection model diagram;

[0027] performing abnormal marking processing in the catenary detection model diagram based on the abnormal type between each of the catenary position points and the abnormal degree value between each of the catenary position points to obtain a catenary abnormal marking diagram;

[0028] performing distribution fitting processing on the catenary abnormal marking diagram to obtain an abnormal distribution diagram of the catenary, and taking the abnormal distribution diagram of the catenary as the catenary detection results of the catenary.

[0029] In a second aspect, the present application further provides an intelligent detection device for a railway catenary, including:

[0030] The collection module is configured to collect pressure change information of a pantograph, track information of a train, and a wind force data sequence in a target area when the train travels to the target area, and identify track wind force information of the train in the target area based on the track information of the train and the wind force data sequence in the target area.

[0031] The identification module is configured to identify a pressure change standard correspondence table of the pantograph corresponding to the track wind force information based on the track wind force information through a pressure standard identification strategy, and identify catenary state information of the pantograph based on the pressure change information of the pantograph through the pressure change standard correspondence table.

[0032] The generation module is configured to identify abnormal detection information of a catenary based on the catenary state information of the pantograph through an abnormal analysis strategy, and generate catenary detection results of the catenary based on the abnormal detection information of the catenary.

[0033] Optionally, the collection module is specifically configured to:

[0034] The track information of the train is split into time points corresponding to each track point and position information of each track point, and each detection track point is screened in the target area based on the position information of each track point.

[0035] The wind force data sequence is split into wind force data corresponding to each time point, and track wind force data corresponding to each detection track point is identified based on the time points corresponding to each track point and the wind force data corresponding to each track point.

[0036] The track wind force data corresponding to each detection track point is arranged in time sequence of the time points corresponding to each detection track point to obtain the track wind force information of the train.

[0037] Optionally, the identification module is specifically configured to:

[0038] Each historical wind force data of a catenary in a normal state and historical pressure data of the pantograph corresponding to each historical wind force data are screened in a historical detection database, and historical pressure data distribution ranges corresponding to the wind force data of each detection track point are identified based on the wind force data of each detection track point and the historical pressure data corresponding to each historical wind force data.

[0039] The standard historical pressure data corresponding to the wind data of each detection trajectory point is calculated based on a historical pressure data distribution range corresponding to the wind data of each detection trajectory point, and the standard historical pressure data corresponding to the wind data of each detection trajectory point is arranged according to the arrangement order of each detection trajectory point in the trajectory wind force information, so as to obtain a pressure change standard reference table of the pantograph corresponding to the trajectory wind force information.

[0040] Optionally, the identification module is specifically used for:

[0041] Based on the pressure change information of the pantograph, a pressure data change sequence of the pantograph is identified, and based on the pressure data change sequence of the pantograph and the pressure change standard reference table, pressure difference value distribution information of the pantograph is identified.

[0042] Based on the pressure difference value distribution information of the pantograph and the pressure change standard reference table, a pressure difference value percentage of each detection trajectory point is calculated, and the pressure difference value percentage of each detection trajectory point is taken as a pressure difference value percentage of a catenary position point of each detection trajectory point in the target area.

[0043] The pressure difference value percentages of all catenary position points are taken as catenary state information of the pantograph.

[0044] Optionally, the generation module is specifically used for:

[0045] The pressure difference value percentages of each catenary position point are arranged according to the sequence of the train passing through each catenary position point, so as to obtain a pressure difference value percentage sequence of the catenary.

[0046] Based on the pressure difference value percentage sequence, a percentage change difference value between each catenary position point of the catenary is calculated.

[0047] Based on the percentage change difference value between each catenary position point, an abnormal type between each catenary position point and an abnormal degree value between each catenary position point are identified through an abnormal analysis strategy.

[0048] The abnormal type between each catenary position point and the abnormal degree value between each catenary position point are taken as abnormal detection information of the catenary.

[0049] Optionally, the generation module is specifically used for:

[0050] A catenary model diagram of the catenary is obtained, and each catenary position point is marked in the catenary model diagram, so as to obtain a catenary detection model diagram.

[0051] Based on the abnormal type between each contact net position point and the abnormal degree value between each contact net position point, an abnormal marking process is performed in the contact net detection model graph to obtain a contact net abnormal marking graph.

[0052] A distribution fitting process is performed on the contact net abnormal marking graph to obtain an abnormal distribution graph of the contact net, and the abnormal distribution graph of the contact net is taken as the contact net detection result of the contact net.

[0053] In a third aspect, a computer device is provided. The computer device includes a memory and a processor. The memory stores a computer program. The processor implements the steps of the method in any one of the first aspect when executing the computer program.

[0054] In a fourth aspect, a computer readable storage medium is provided. The computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of the method in any one of the first aspect.

[0055] In a fifth aspect, a computer program product is provided. The computer program product includes a computer program. The computer program is executed by a processor to implement the steps of the method in any one of the first aspect.

[0056] The intelligent detection method, device and computer device of the railway contact net, by collecting the pressure change information of the pantograph, the trajectory information of the train and the wind force data sequence in the target area when the train travels to the target area, and based on the trajectory information of the train and the wind force data sequence in the target area, identifying the trajectory wind force information of the train in the target area, based on the trajectory wind force information, identifying the pressure change standard reference table corresponding to the trajectory wind force information through the pressure standard identification strategy, and based on the pressure change information of the pantograph, identifying the contact net state information of the pantograph through the pressure change standard reference table, based on the contact net state information of the pantograph, identifying the abnormal detection information of the contact net through the abnormal analysis strategy, and based on the abnormal detection information of the contact net, generating the contact net detection result of the contact net. The present scheme obtains the actual value of the pantograph pressure of the train at each trajectory point by collecting the trajectory information of the train, and judges the contact net state of each trajectory point in the target area based on the actual value of the pantograph pressure and the standard value of the pantograph pressure, solves the technical problems that the current contact net is easily affected by wear and external environmental factors, resulting in that the contact net and the pantograph cannot be in continuous and stable contact and the detection is difficult and the accuracy is not high, and thus the detection accuracy of the contact net is comprehensively improved. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or the related art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0058] Figure 1 Flowchart of the intelligent detection method of the railway overhead line system in one embodiment;

[0059] Figure 2 Flowchart of the intelligent detection example of the railway overhead line system in one embodiment;

[0060] Figure 3 Block diagram of the intelligent detection device of the railway overhead line system in one embodiment;

[0061] Figure 4 Internal structure diagram of the computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0063] The intelligent detection method of the railway overhead line system provided by the embodiments of the present application can be applied to, for example, Figure 1The intelligent detection system for railway overhead contact lines shown includes a processor 1001 (e.g., CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 enables communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (e.g., a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may be a storage device independent of the processor 1001. The memory 1005 may include an operating system, a network communication module, a user interface module, and an overhead contact line detection program. The network interface 1004 is mainly used to connect to the backend server and communicate with it. The user interface 1003 is mainly used to connect to the client (user terminal) and communicate with it. The processor 1001 can be used to call the catenary detection program stored in the memory 1005. The processor 1001 can be applied to a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. The terminal collects the train's trajectory information to obtain the actual value of the pantograph pressure at each trajectory point. Based on the actual pantograph pressure value and the standard pantograph pressure value, it determines the catenary status at each trajectory point in the target area. This solves the current technical problems of catenary being easily worn and affected by external environmental factors, resulting in unstable and difficult detection between the catenary and the pantograph, and thus improving the overall accuracy of catenary detection.

[0064] In one exemplary embodiment, such as Figure 1 As shown, an intelligent detection method for railway overhead contact lines is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0065] Step S101: When the train travels to the target area, collect the pantograph pressure change information, the train trajectory information, and the wind force data sequence in the target area, and identify the train trajectory wind force information in the target area based on the train trajectory information and the wind force data sequence in the target area.

[0066] In this embodiment, the terminal uses pressure sensors installed in the target area to collect pressure data from various points on the overhead contact line in real time. This data is then arranged chronologically to obtain pressure change information. The terminal then acquires trajectory information composed of various trajectory points as the train passes through the target area. This trajectory information includes the time and location information corresponding to each trajectory point. The pressure data from each point on the overhead contact line is the pressure value detected by the pressure sensor when the pantograph contacts the contact line. Next, the terminal acquires wind data from wind sensors collected at various time points within the target area and sorts it chronologically to obtain a wind data sequence within the target area. The target area is the area where the overhead contact line is located. This wind data includes wind direction and wind speed data. The terminal then establishes a correspondence between the wind data and the pressure data corresponding to the trajectory information to obtain the train's trajectory wind information. This trajectory wind information includes the pressure data and wind data corresponding to each trajectory point. The specific establishment process will be explained in detail later.

[0067] Step S102: Based on the trajectory wind information, identify the pantograph pressure change standard reference table corresponding to the trajectory wind information through the pressure standard identification strategy, and identify the pantograph contact network status information based on the pantograph pressure change information through the pressure change standard reference table.

[0068] In this embodiment, the terminal, based on the trajectory wind information, identifies a pressure change standard reference table for the pantograph corresponding to the trajectory wind information using a pressure standard identification strategy. Based on the pantograph's pressure change information, it then identifies the pantograph's contact wire status information using this pressure change standard reference table. The pressure standard identification strategy identifies the standard pressure data corresponding to each wind data point in the estimated wind information. This results in a pantograph pressure change standard reference table arranged in the order of contact points, with the standard pressure data corresponding to each wind data point as its specific value. The pantograph's contact wire status information includes the percentage of pressure difference between each trajectory point and the contact wire location in the target area. This percentage represents the difference between the pantograph's pressure data and its standard pressure data, and accounts for a percentage of the standard pressure data. The specific identification process will be explained in detail later.

[0069] Step S103: Based on the contact network status information of the pantograph, anomaly detection information of the contact network is identified through anomaly analysis strategy, and contact network detection results are generated based on the anomaly detection information of the contact network.

[0070] In this embodiment, the terminal, based on the pantograph's contact network status information, identifies anomaly detection information in the contact network through an anomaly analysis strategy, and generates contact network detection results based on this information. The anomaly analysis strategy analyzes the anomaly types and anomaly severity values ​​between different contact network locations; the specific analysis process will be explained in detail later. The contact network detection results include the distribution information of anomaly types and anomaly severity values ​​at different locations within the contact network.

[0071] Based on the above scheme, by collecting the train's trajectory information, the actual value of the pantograph pressure at each trajectory point is obtained. Based on the actual value of the pantograph pressure and the standard value of the pantograph pressure, the condition of the contact wire at each trajectory point in the target area is determined. This solves the technical problems of the contact wire being easily worn and the inability to maintain stable contact between the contact wire and the pantograph due to the influence of external environmental factors, as well as the difficulty and low accuracy of detection. This comprehensively improves the detection accuracy of the contact wire.

[0072] Optionally, based on the train's trajectory information and the wind data sequence within the target area, the train's trajectory wind information within the target area is identified, including: splitting the train's trajectory information into the time points corresponding to each trajectory point and the location information of each trajectory point, and filtering each detection trajectory point within the target area based on the location information of each trajectory point; splitting the wind data sequence into wind data corresponding to each time point, and identifying the trajectory wind data corresponding to each detection trajectory point based on the time points corresponding to each trajectory point and the wind data corresponding to each trajectory point; and arranging the trajectory wind data corresponding to each detection trajectory point according to the time order of the time points corresponding to each detection trajectory point to obtain the train's trajectory wind information.

[0073] In this embodiment, the terminal breaks down the train's trajectory information into the time point corresponding to each trajectory point and the location information of each trajectory point. Based on the location information of each trajectory point, it filters each detected trajectory point within the target area. Each trajectory point is the location point where the train is traveling, recorded in the trajectory information.

[0074] Then, the terminal breaks down the wind data sequence into wind data corresponding to each time point, and identifies the trajectory wind data corresponding to each detected trajectory point based on the time point and the corresponding wind data. The terminal then arranges the trajectory wind data corresponding to each detected trajectory point according to the chronological order of the time points corresponding to each detected trajectory point to obtain the train's trajectory wind information.

[0075] Based on the above scheme, by using the order of time points, the wind force data and the detection trajectory points are linked to construct information, thereby improving the accuracy of wind force data identification for each trajectory point.

[0076] Optionally, based on the trajectory wind information, a pressure standard identification strategy is used to identify a standard reference table of pantograph pressure changes corresponding to the trajectory wind information. This includes: filtering historical wind data of the overhead contact line under normal conditions and historical pressure data of the pantograph corresponding to each historical wind data in the historical monitoring database; identifying the distribution range of historical pressure data corresponding to the wind data of each monitoring trajectory point based on the wind data of each monitoring trajectory point and the historical pressure data corresponding to each historical wind data; calculating the standard historical pressure data corresponding to the wind data of each monitoring trajectory point based on the distribution range of historical pressure data corresponding to the wind data of each monitoring trajectory point; and arranging the standard historical pressure data corresponding to the wind data of each monitoring trajectory point according to the arrangement order of each monitoring trajectory point in the trajectory wind information to obtain a standard reference table of pantograph pressure changes corresponding to the trajectory wind information.

[0077] In this embodiment, the terminal filters historical wind data of the overhead contact line under normal conditions and historical pressure data of the pantograph corresponding to each historical wind data in the historical detection database, and identifies the distribution range of historical pressure data corresponding to the wind data of each detection trajectory point based on the wind data of each detection trajectory point and the historical pressure data corresponding to each historical wind data.

[0078] Based on the historical pressure data distribution range corresponding to the wind force data of each detection trajectory point, the terminal calculates the standard historical pressure data corresponding to the wind force data of each detection trajectory point. Then, according to the arrangement order of the detection trajectory points in the trajectory wind force information, the standard historical pressure data corresponding to the wind force data of each detection trajectory point are sorted and processed to obtain a standard reference table of pantograph pressure changes corresponding to the trajectory wind force information. Specifically, the method for calculating the standard historical pressure data corresponding to the wind force data of each detection trajectory point is as follows: if the historical pressure data corresponding to the same wind force data is a single instance, then the standard historical pressure data is determined accordingly. If the historical pressure data corresponding to the same wind force data is not a single instance, this scheme identifies the weight value of each historical pressure data through a normalization principle and obtains the standard historical pressure data by weighted summation of the historical pressure data.

[0079] In this process, during regular railway line inspections and maintenance, the overhead contact line is typically replaced with a new one after reaching its service life, and the offset of the overhead contact line fixing poles is checked. For a period after replacement and pole offset checks, the new contact line is unaffected by contact wear, atmospheric oxidation, rain erosion, or changes in the railway's terrain (such as slight depressions). During this period, the pantograph pressure generated by wind force data is used as standard historical pressure data. This standard historical pressure data is used to establish a comparison table with wind force data. Therefore, after obtaining wind force data, the corresponding standard historical pressure data can be obtained by matching the wind force data with this table. The condition of the overhead contact line in the target area can then be determined based on the actual pantograph pressure value and the standard historical pressure data. Since each regular railway line inspection and maintenance clears the pantograph pressure values ​​collected and stored in the database, the standard historical pressure data is the pantograph pressure value collected earliest among the historical pantograph pressure values ​​corresponding to the same wind force data. The standard historical pressure data is obtained by calling the earliest pantograph pressure value from the database.

[0080] Based on the above scheme, by determining standard historical pressure data, a corresponding comparison table of wind data is constructed. This allows for the avoidance of environmental impacts caused by wind data when analyzing the contact network status, thereby improving the accuracy of UI contact network status identification.

[0081] Optionally, based on the pressure change information of the pantograph, the contact network status information of the pantograph is identified through a pressure change standard comparison table. This includes: identifying the pressure data change sequence of the pantograph based on the pressure change information of the pantograph, and identifying the pressure difference distribution information of the pantograph based on the pressure data change sequence of the pantograph and the pressure change standard comparison table; calculating the pressure difference percentage of each detection trajectory point based on the pressure difference distribution information of the pantograph and the pressure change standard comparison table, and using the pressure difference percentage of each detection trajectory point as the pressure difference percentage of the contact network location points in the target area of ​​each detection trajectory point; and using the pressure difference percentage of all contact network location points as the contact network status information of the pantograph.

[0082] In this embodiment, the terminal identifies the pantograph pressure data change sequence based on the pantograph pressure change information, and identifies the pantograph pressure difference distribution information based on the pantograph pressure data change sequence and a pressure change standard comparison table. This pressure difference distribution information is a sequence obtained by arranging the pressure data difference for each detection trajectory point according to the trajectory order of each detection trajectory point.

[0083] Based on the pressure difference distribution information of the pantograph and the pressure change standard comparison table, the terminal calculates the percentage of pressure difference at each detection trajectory point and uses the percentage of pressure difference at each detection trajectory point as the percentage of pressure difference at the contact wire location point in the target area of ​​each detection trajectory point.

[0084] Finally, the terminal uses the percentage of pressure difference at all contact wire locations as the contact wire status information for the pantograph.

[0085] Based on the above scheme, by calculating the percentage of pressure difference between each detection trajectory point, it is possible to effectively analyze abnormal information such as the structure and shape of the contact network, avoiding the inefficiency and low accuracy of manual inspection.

[0086] Optionally, based on the pantograph's overhead contact line status information, an anomaly analysis strategy is used to identify anomaly detection information of the overhead contact line, including: arranging the percentage pressure difference values ​​at each overhead contact line location point according to the order in which the train passes each location point to obtain a sequence of pressure difference percentage values; calculating the percentage change difference of the overhead contact line between each location point based on the pressure difference percentage sequence; identifying the anomaly type and degree of anomaly between each location point based on the percentage change difference; and using the anomaly type and degree of anomaly between each location point as anomaly detection information of the overhead contact line.

[0087] In this embodiment, the terminal arranges the percentage pressure difference values ​​at each contact network location according to the order in which the train passes through each contact network location, obtaining a sequence of contact network pressure difference percentages. Then, based on this sequence, the terminal calculates the percentage change difference of the contact network between each contact network location. Next, the aggregation section, based on the percentage change difference between each contact network location, uses anomaly analysis strategies to identify the anomaly type and degree of anomaly between each contact network location. The terminal stores a first preset threshold and a second preset threshold. The first preset threshold can be appropriately selected based on the safety level requirements or power stability requirements of different regions, such as preferred values ​​like 4% or 6%. The second preset threshold can be appropriately selected based on factors such as train frequency and air pollution levels in different regions, such as preferred values ​​like 20% or 40%. When the percentage change difference between each contact wire location point does not exceed the second preset threshold, it is determined that the contact wire at the trajectory point corresponding to the actual pantograph pressure value in the target area has abnormal contact wire wear; wherein, when the percentage change difference between each contact wire location point exceeds the second preset threshold, it is determined that the contact wire fixing rod at the trajectory point corresponding to the actual pantograph pressure value in the target area has abnormality.

[0088] Specifically, catenary anomalies typically include two types: catenary wear caused by contact abrasion, atmospheric oxidation, and rainwater erosion; and catenary pole tilting caused by changes in the terrain along the railway line (e.g., slight depressions). This embodiment determines the type of catenary anomaly by calculating the percentage difference between the actual pantograph pressure value and the standard pantograph pressure value over a continuous time period, relative to the standard pantograph pressure value. Specifically, if the percentage difference over a continuous time period exceeds a second preset threshold, it indicates a rapid rate of change, usually caused by catenary pole tilting. In this case, it is determined that the catenary pole at the trajectory point corresponding to the actual pantograph pressure value in the target area has a catenary pole anomaly. If the percentage difference over a continuous time period does not exceed the second preset threshold, it indicates a slow rate of change, usually caused by slow factors such as contact abrasion, atmospheric oxidation, and rainwater erosion. In this case, it is determined that the catenary at the trajectory point corresponding to the actual pantograph pressure value in the target area has a catenary wear anomaly. Each anomaly severity value corresponding to each anomaly type corresponds to a percentage difference range. The terminal identifies the anomaly severity values ​​between different catenary locations through range adaptation.

[0089] Finally, the terminal uses the anomaly type and the anomaly degree value between each contact wire location point as the anomaly detection information of the contact wire.

[0090] Based on the above scheme, by identifying the anomaly type through threshold and by adapting the anomaly degree values ​​between each contact wire location point through range adaptation, the accuracy and efficiency of identifying anomaly detection information of the contact wire are improved.

[0091] Optionally, based on the anomaly detection information of the overhead contact system, an overhead contact system detection result is generated, including: obtaining an overhead contact system model diagram and marking each overhead contact system location point in the overhead contact system model diagram to obtain an overhead contact system detection model diagram; performing anomaly marking processing on the overhead contact system detection model diagram based on the anomaly type and anomaly degree value between each overhead contact system location point to obtain an overhead contact system anomaly marking map; performing distribution fitting processing on the overhead contact system anomaly marking map to obtain an anomaly distribution map of the overhead contact system, and using the anomaly distribution map of the overhead contact system as the overhead contact system detection result.

[0092] In this embodiment, the terminal acquires a catenary model diagram of the overhead contact system and marks each catenary location point in the catenary model diagram to obtain a catenary detection model diagram. This catenary detection model diagram is a two-dimensional image with the catenary's planar structure as its plane.

[0093] Then, based on the anomaly type and anomaly severity values ​​between each contact wire location point, the terminal performs anomaly marking processing on the contact wire detection model diagram to obtain a contact wire anomaly marking map. This contact wire anomaly marking map is a planar image of the contact wire with the anomaly type and severity values ​​marked at each contact wire location point.

[0094] Finally, the terminal performs distribution fitting processing on the catenary anomaly marking map to obtain the catenary anomaly distribution map, and uses this map as the catenary inspection result. This distribution fitting processing can be achieved using the planar distribution fitting program in MATLAB software to distribute and mark the anomaly types and degrees corresponding to the connection points between each catenary location, thus obtaining the catenary range corresponding to the anomaly degree of different anomaly types.

[0095] Based on the above scheme, by establishing a catenary detection model diagram and then generating an anomaly distribution diagram of the catenary through a planar distribution method, the comprehensiveness and accuracy of the anomaly analysis of the catenary are improved.

[0096] This application also provides an example of intelligent detection for railway overhead contact lines, such as... Figure 2 As shown, the specific processing procedure includes the following steps:

[0097] Step S201: When the train travels to the target area, collect information on the pressure change of the pantograph, the train's trajectory information, and the wind data sequence within the target area.

[0098] Step S202: The train's trajectory information is broken down into the time point corresponding to each trajectory point and the location information of each trajectory point. Based on the location information of each trajectory point, each detection trajectory point is selected within the target area.

[0099] Step S203: The wind data sequence is split into wind data corresponding to each time point, and the trajectory wind data corresponding to each detection trajectory point is identified based on the time point corresponding to each trajectory point and the wind data corresponding to each trajectory point.

[0100] Step S204: Arrange the wind force data corresponding to each detection trajectory point according to the time sequence of the time points corresponding to each detection trajectory point to obtain the wind force information of the train trajectory.

[0101] Step S205: In the historical detection database, filter the historical wind force data of the contact network under normal conditions and the historical pressure data of the pantograph corresponding to each historical wind force data, and identify the distribution range of the historical pressure data corresponding to the wind force data of each detection trajectory point based on the wind force data of each detection trajectory point and the historical pressure data corresponding to each historical wind force data.

[0102] Step S206: Based on the distribution range of historical pressure data corresponding to the wind force data of each detection trajectory point, calculate the standard historical pressure data corresponding to the wind force data of each detection trajectory point, and arrange the standard historical pressure data corresponding to the wind force data of each detection trajectory point according to the arrangement order of each detection trajectory point in the trajectory wind force information to obtain the standard comparison table of pantograph pressure change corresponding to the trajectory wind force information.

[0103] Step S207: Based on the pressure change information of the pantograph, identify the pressure data change sequence of the pantograph, and based on the pressure data change sequence of the pantograph and the pressure change standard comparison table, identify the pressure difference distribution information of the pantograph.

[0104] Step S208: Based on the pressure difference distribution information of the pantograph and the pressure change standard comparison table, calculate the pressure difference percentage of each detection trajectory point, and use the pressure difference percentage of each detection trajectory point as the pressure difference percentage of each detection trajectory point in the contact wire location point in the target area.

[0105] Step S209: Use the percentage of pressure difference at all contact wire locations as the contact wire status information for the pantograph.

[0106] Step S210: Arrange the percentage of pressure difference at each contact wire location point according to the order in which the train passes through each contact wire location point to obtain a sequence of percentage pressure difference of the contact wire.

[0107] Step S211: Based on the percentage sequence of pressure difference, calculate the percentage change difference of the contact wire between each contact wire location point.

[0108] Step S212: Based on the percentage change difference between each contact wire location point, an anomaly analysis strategy is used to identify the anomaly type and the anomaly degree value between each contact wire location point.

[0109] Step S213: The anomaly type and the anomaly degree value between each contact wire location point are used as the anomaly detection information of the contact wire.

[0110] Step S214: Obtain the catenary model diagram and mark each catenary location point in the catenary model diagram to obtain the catenary detection model diagram.

[0111] Step S215: Based on the anomaly type and anomaly degree value between each contact wire location point, perform anomaly marking processing on the contact wire detection model diagram to obtain the contact wire anomaly marking diagram.

[0112] Step S216: Perform distribution fitting processing on the catenary anomaly marking map to obtain the catenary anomaly distribution map, and use the catenary anomaly distribution map as the catenary inspection result.

[0113] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0114] Based on the same inventive concept, this application also provides an intelligent detection device for railway catenary systems to implement the intelligent detection method for railway catenary systems described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more intelligent detection device embodiments for railway catenary systems provided below can be found in the limitations of the intelligent detection method for railway catenary systems described above, and will not be repeated here.

[0115] In one exemplary embodiment, such as Figure 3 As shown, an intelligent detection device for railway overhead contact lines is provided, comprising: a data acquisition module 310, an identification module 320, and a generation module 330, wherein:

[0116] The acquisition module 310 is used to acquire pantograph pressure change information, train trajectory information, and wind data sequence in the target area when the train travels to the target area, and to identify the train trajectory wind information in the target area based on the train trajectory information and the wind data sequence in the target area.

[0117] The identification module 320 is used to identify the pantograph pressure change standard reference table corresponding to the track wind information based on the track wind information and through the pressure standard identification strategy, and to identify the contact wire status information of the pantograph based on the pantograph pressure change information and through the pressure change standard reference table.

[0118] The generation module 330 is used to identify abnormal detection information of the contact network based on the contact network status information of the pantograph through an anomaly analysis strategy, and generate the contact network detection result of the contact network based on the abnormal detection information of the contact network.

[0119] Optionally, the acquisition module 310 is specifically used for:

[0120] The train's trajectory information is broken down into the time point corresponding to each trajectory point and the location information of each trajectory point. Based on the location information of each trajectory point, each detected trajectory point is filtered within the target area.

[0121] The wind data sequence is split into wind data corresponding to each time point, and based on the time point corresponding to each trajectory point and the wind data corresponding to each trajectory point, the trajectory wind data corresponding to each detection trajectory point is identified.

[0122] The wind force data corresponding to each of the detection trajectory points are arranged and processed according to the time sequence of the time points corresponding to each detection trajectory point to obtain the wind force information of the train's trajectory.

[0123] Optionally, the identification module 320 is specifically used for:

[0124] In the historical detection database, the historical wind force data of the overhead contact line under normal conditions and the historical pressure data of the pantograph corresponding to each historical wind force data are screened. Based on the wind force data of each detection trajectory point and the historical pressure data corresponding to each historical wind force data, the distribution range of the historical pressure data corresponding to the wind force data of each detection trajectory point is identified.

[0125] Based on the distribution range of historical pressure data corresponding to the wind force data of each of the detection trajectory points, the standard historical pressure data corresponding to the wind force data of each of the detection trajectory points is calculated, and the standard historical pressure data corresponding to the wind force data of each of the detection trajectory points is arranged according to the arrangement order of the detection trajectory points in the trajectory wind force information to obtain the standard comparison table of pantograph pressure change corresponding to the trajectory wind force information.

[0126] Optionally, the identification module 320 is specifically used for:

[0127] Based on the pressure change information of the pantograph, the pressure data change sequence of the pantograph is identified, and based on the pressure data change sequence of the pantograph and the pressure change standard comparison table, the pressure difference distribution information of the pantograph is identified.

[0128] Based on the pressure difference distribution information of the pantograph and the pressure change standard comparison table, the pressure difference percentage of each detection trajectory point is calculated, and the pressure difference percentage of each detection trajectory point is used as the pressure difference percentage of the contact wire position point of each detection trajectory point in the target area.

[0129] The percentage of pressure difference at all contact wire locations is used as the contact wire status information for the pantograph.

[0130] Optionally, the generation module 330 is specifically used for:

[0131] The percentage of pressure difference at each contact wire location is arranged according to the order in which the train passes through each contact wire location to obtain a sequence of percentage pressure difference of the contact wire.

[0132] Based on the pressure difference percentage sequence, calculate the percentage change difference of the contact wire between each contact wire location point;

[0133] Based on the percentage change difference between each of the contact wire location points, an anomaly analysis strategy is used to identify the anomaly type and the anomaly degree value between each of the contact wire location points.

[0134] The anomaly type and the anomaly degree value between each of the aforementioned contact wire locations are used as the anomaly detection information of the contact wire.

[0135] Optionally, the generation module 330 is specifically used for:

[0136] Obtain the contact network model diagram and mark each contact network position point in the contact network model diagram to obtain the contact network detection model diagram;

[0137] Based on the anomaly type and the anomaly degree value between each of the contact wire location points, anomaly marking processing is performed on the contact wire detection model diagram to obtain a contact wire anomaly marking diagram.

[0138] The abnormal marking map of the overhead contact line is subjected to distribution fitting processing to obtain the abnormal distribution map of the overhead contact line, and the abnormal distribution map of the overhead contact line is used as the detection result of the overhead contact line.

[0139] Each module in the aforementioned intelligent detection device for railway overhead contact lines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0140] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an intelligent detection method for railway catenary. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0141] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of an intelligent detection method for railway catenary.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being executed by a processor to implement the steps of an intelligent detection method for railway overhead contact lines.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of an intelligent detection method for railway overhead contact lines.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0146] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A smart detection method for railway overhead contact lines, characterized in that, The method includes: When the train travels to the target area, it collects information on the pressure change of the pantograph, the trajectory information of the train, and the wind data sequence in the target area. Based on the trajectory information of the train and the wind data sequence in the target area, it identifies the trajectory wind information of the train in the target area. Based on the trajectory wind information, a pressure change standard reference table for the pantograph corresponding to the trajectory wind information is identified through a pressure standard identification strategy. Based on the pressure change information of the pantograph, the contact wire status information of the pantograph is identified through the pressure change standard reference table. Based on the contact wire status information of the pantograph, an anomaly analysis strategy is used to identify anomaly detection information of the contact wire, and based on the anomaly detection information of the contact wire, the contact wire detection result is generated.

2. The method according to claim 1, characterized in that, The step of identifying the train's trajectory wind information within the target area based on the train's trajectory information and the wind data sequence within the target area includes: The train's trajectory information is broken down into the time point corresponding to each trajectory point and the location information of each trajectory point. Based on the location information of each trajectory point, each detected trajectory point is filtered within the target area. The wind data sequence is split into wind data corresponding to each time point, and based on the time point corresponding to each trajectory point and the wind data corresponding to each trajectory point, the trajectory wind data corresponding to each detection trajectory point is identified. The wind force data corresponding to each of the detection trajectory points are arranged and processed according to the time sequence of the time points corresponding to each detection trajectory point to obtain the wind force information of the train's trajectory.

3. The method according to claim 2, characterized in that, The step of identifying the pantograph pressure change standard reference table corresponding to the trajectory wind information using a pressure standard identification strategy includes: In the historical detection database, the historical wind force data of the overhead contact line under normal conditions and the historical pressure data of the pantograph corresponding to each historical wind force data are screened. Based on the wind force data of each detection trajectory point and the historical pressure data corresponding to each historical wind force data, the distribution range of the historical pressure data corresponding to the wind force data of each detection trajectory point is identified. Based on the distribution range of historical pressure data corresponding to the wind force data of each of the detection trajectory points, the standard historical pressure data corresponding to the wind force data of each of the detection trajectory points is calculated, and the standard historical pressure data corresponding to the wind force data of each of the detection trajectory points is arranged according to the arrangement order of the detection trajectory points in the trajectory wind force information to obtain the standard comparison table of pantograph pressure change corresponding to the trajectory wind force information.

4. The method according to claim 2, characterized in that, The step of identifying the contact wire status information of the pantograph based on the pressure change information of the pantograph and through the pressure change standard lookup table includes: Based on the pressure change information of the pantograph, the pressure data change sequence of the pantograph is identified, and based on the pressure data change sequence of the pantograph and the pressure change standard comparison table, the pressure difference distribution information of the pantograph is identified. Based on the pressure difference distribution information of the pantograph and the pressure change standard comparison table, the pressure difference percentage of each detection trajectory point is calculated, and the pressure difference percentage of each detection trajectory point is used as the pressure difference percentage of the contact wire position point of each detection trajectory point in the target area. The percentage of pressure difference at all contact wire locations is used as the contact wire status information for the pantograph.

5. The method according to claim 4, characterized in that, Based on the contact network status information of the pantograph, the anomaly analysis strategy is used to identify anomaly detection information of the contact network, including: The percentage of pressure difference at each contact wire location is arranged according to the order in which the train passes through each contact wire location to obtain a sequence of percentage pressure difference of the contact wire. Based on the pressure difference percentage sequence, calculate the percentage change difference of the contact wire between each contact wire location point; Based on the percentage change difference between each of the contact wire location points, an anomaly analysis strategy is used to identify the anomaly type and the anomaly degree value between each of the contact wire location points. The anomaly type and the anomaly degree value between each of the aforementioned contact wire locations are used as the anomaly detection information of the contact wire.

6. The method according to claim 5, characterized in that, The step of generating the contact network inspection result based on the anomaly detection information of the contact network includes: Obtain the contact network model diagram and mark each contact network position point in the contact network model diagram to obtain the contact network detection model diagram; Based on the anomaly type and the anomaly degree value between each of the contact wire location points, anomaly marking processing is performed on the contact wire detection model diagram to obtain a contact wire anomaly marking diagram. The abnormal marking map of the overhead contact line is subjected to distribution fitting processing to obtain the abnormal distribution map of the overhead contact line, and the abnormal distribution map of the overhead contact line is used as the detection result of the overhead contact line.

7. An intelligent detection device for railway overhead contact lines, characterized in that, The device includes: The data acquisition module is used to acquire pantograph pressure change information, train trajectory information, and wind data sequence within the target area when the train travels to the target area, and to identify the train's trajectory wind information within the target area based on the train's trajectory information and the wind data sequence within the target area. The identification module is used to identify the pantograph pressure change standard reference table corresponding to the track wind information based on the pressure standard identification strategy, and to identify the contact wire status information of the pantograph based on the pantograph pressure change information through the pressure change standard reference table. The generation module is used to identify abnormal detection information of the contact network based on the contact network status information of the pantograph through an anomaly analysis strategy, and generate the contact network detection result of the contact network based on the abnormal detection information of the contact network.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

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