Railway equipment detection method, device, equipment, medium and program product

Abstract

The invention relates to a railway equipment detection method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring detection data for target railway equipment, wherein the detection data comprises dynamic response data and static sensing data; under the condition that the dynamic response data meets a preset condition, determining a static data constraint interval matched with the dynamic response data from a preset matching table; and under the condition that the static sensing data is not matched with the static data constraint interval, outputting static data exception information. By adopting the method, the detection accuracy of the railway equipment can be improved.

Description

Technical Field

[0001] This application relates to the field of testing technology, and in particular to a testing method, apparatus, equipment, medium, and procedure for railway equipment. Background Technology

[0002] With the rapid development of railway transportation, train operation density and speed are continuously increasing, and the reliability of railway equipment is directly related to transportation safety and efficiency. Traditional inspection methods mainly rely on regular manual inspections and planned maintenance, which involve visual inspections, functional tests, and component replacements of equipment at preset intervals.

[0003] However, while this approach can cover basic maintenance needs, the fixed cycle of periodic testing is difficult to adapt to the actual degradation patterns of equipment, and the analysis of abnormal parameters often requires manual work based on experience or manuals, which affects the accuracy of equipment testing. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, medium, and procedure for testing railway equipment to address the aforementioned technical problems and improve the accuracy of railway equipment testing.

[0005] Firstly, this application provides a method for testing railway equipment, including:

[0006] Acquire detection data for the target railway equipment, including dynamic response data and static sensor data;

[0007] If the dynamic response data meets the preset conditions, determine the static data constraint range that matches the dynamic response data from the preset matching table;

[0008] When the static sensing data does not match the static data constraint range, output static data anomaly information.

[0009] In one embodiment, the dynamic response data includes at least one type; correspondingly, determining the static data constraint interval that matches the dynamic response data from a preset matching table includes: for each dynamic response data, determining the dynamic data constraint interval to which the dynamic response data belongs; querying the preset matching table for target condition items that meet each dynamic data constraint interval; and determining the static data constraint interval that matches the target condition items from the preset matching table.

[0010] In one embodiment, the static sensing data includes at least one type; correspondingly, in the event of a mismatch between the static sensing data and the static data constraint range, static data anomaly information is output, including:

[0011] For each static sensing data, obtain the target data constraint interval that matches the target type of the static sensing data from the static data constraint interval;

[0012] When the static sensing data does not match the target data constraint range, output static data anomaly information for the static sensing data.

[0013] In one embodiment, outputting static data anomaly information for static sensing data includes: determining the terminal information of the receiving terminal corresponding to the target type based on preset matching conditions; and sending the static data anomaly information for static sensing data to the receiving terminal based on the terminal information.

[0014] In one embodiment, the target railway equipment includes a target track; the target track is connected to a signal transmitter for transmitting locomotive signals to a signal receiver on a target locomotive via the target track; a signal acquisition device is also provided on the target track; correspondingly, the above method further includes: acquiring a first locomotive signal received by the signal receiver and a second locomotive signal acquired by the signal acquisition device; extracting first frequency information from the first locomotive signal and extracting second frequency information from the second locomotive signal; and determining the anomaly type of the target track based on the first frequency information and the second frequency information.

[0015] In one embodiment, determining the anomaly type of the target orbit based on the first frequency information and the second frequency information includes: determining frequency distribution data based on the first frequency information and the second frequency information; and inputting the frequency distribution data into a pre-trained anomaly recognition model to obtain the anomaly type of the target orbit.

[0016] Secondly, this application also provides a testing device for railway equipment, comprising:

[0017] The first acquisition module is used to acquire detection data for the target railway equipment. The detection data includes dynamic response data and static sensor data.

[0018] The first determining module is used to determine the static data constraint interval that matches the dynamic response data from a preset matching table when the dynamic response data meets the preset conditions.

[0019] The output module is used to output static data anomaly information when the static sensing data does not match the static data constraint range.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0021] Acquire detection data for the target railway equipment, including dynamic response data and static sensor data;

[0022] If the dynamic response data meets the preset conditions, determine the static data constraint range that matches the dynamic response data from the preset matching table;

[0023] When the static sensing data does not match the static data constraint range, output static data anomaly information.

[0024] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0025] Acquire detection data for the target railway equipment, including dynamic response data and static sensor data;

[0026] If the dynamic response data meets the preset conditions, determine the static data constraint range that matches the dynamic response data from the preset matching table;

[0027] When the static sensing data does not match the static data constraint range, output static data anomaly information.

[0028] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0029] Acquire detection data for the target railway equipment, including dynamic response data and static sensor data;

[0030] If the dynamic response data meets the preset conditions, determine the static data constraint range that matches the dynamic response data from the preset matching table;

[0031] When the static sensing data does not match the static data constraint range, output static data anomaly information.

[0032] The aforementioned railway equipment detection methods, devices, equipment, media, and program products acquire detection data for the target railway equipment, including dynamic response data and static sensor data. This provides a joint observation basis for assessing the operational status of the target railway equipment, combining dynamic operating characteristics with static fundamental parameters, thus achieving a multi-dimensional quantitative characterization of the equipment's operational status. By determining a static data constraint interval matching the dynamic response data from a preset matching table when the dynamic response data meets preset conditions, a matching relationship between the dynamic operating status and static parameter requirements is established, realizing an adaptive detection mechanism that dynamically adjusts the acceptable range of static sensor data based on real-time operating conditions. Furthermore, by outputting static data anomaly information when the static sensor data does not match the static data constraint interval, timely warnings are provided for deviations of static sensor data from allowable ranges, improving the detection accuracy of the target railway equipment. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating a method for detecting railway equipment in one embodiment;

[0035] Figure 2 This is a flowchart illustrating the steps for determining the anomaly type of a target orbit in one embodiment;

[0036] Figure 3 This is a schematic diagram of the data acquisition system in one embodiment;

[0037] Figure 4 This is a flowchart illustrating a method for detecting railway equipment in another embodiment;

[0038] Figure 5 This is a structural block diagram of a detection device for railway equipment in one embodiment;

[0039] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] In one embodiment, such as Figure 1 As shown, a method for detecting railway equipment is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0042] S110. Obtain detection data for the target railway equipment, including dynamic response data and static sensor data.

[0043] The target railway equipment may include at least one of the following: the target track and a switch machine installed on the target track. This application does not limit the specific type of equipment for the target railway equipment.

[0044] Dynamic response data can be understood as the material quantity or state parameters generated in real time during the operation of the target railway equipment, which change with time or operating conditions.

[0045] For example, the dynamic response data may include at least one of vibration data and pressure data of the target track when the target locomotive passes over the target track. Optionally, the vibration data can be collected by a vibration sensor installed on the target track; the pressure data can be collected by a pressure sensor installed on the target track.

[0046] Among them, railway equipment can be understood as the physical quantity or geometric parameter of the target railway equipment measured under static or stable operating conditions, reflecting its inherent properties or basic state.

[0047] For example, static sensing parameters may include at least one of the following: installation error between the switch machine and the target track, position data of the target track, and locking arm force of the switch machine. Optionally, the installation error can be obtained by at least one of the following: a distance sensor, a displacement sensor, and an angle sensor; the position data can be obtained by a displacement sensor; and the locking arm force can be obtained by a force sensor.

[0048] S120. If the dynamic response data meets the preset conditions, determine the static data constraint interval that matches the dynamic response data from the preset matching table.

[0049] Different static data constraint intervals correspond to different degrees of interval subdivision.

[0050] To facilitate understanding, the following examples illustrate different levels of interval subdivision. For instance, the first constraint interval is [a1, a...]. 10The second constraint interval is [a3, a7], and the third constraint interval is [a4, a5], where a1 < a3 < a4 < a5 < a7 < a 10 It can be seen that the first constraint interval has the lowest degree of interval subdivision, meaning it corresponds to the widest acceptable range; the third constraint interval has the highest degree of interval subdivision, meaning it corresponds to the narrowest acceptable range. It is worth noting that this application does not impose any limitations on the specific number, specific division method, or specific degree of interval subdivision of static data constraint intervals.

[0051] For example, the better the dynamic operating state corresponding to the dynamic response data, the higher the degree of interval subdivision of the corresponding static data constraint interval. That is, the closer the dynamic response data is to the standard value, the higher the degree of interval subdivision of the corresponding static data constraint interval.

[0052] For example, different static data constraint intervals can be obtained by acquiring a standard constraint interval for static sensing data; and different static data constraint intervals can be obtained based on different degrees of subdivision of the standard constraint interval.

[0053] It is understandable that by setting corresponding static data constraint intervals for different dynamic response data, and with different static data constraint intervals corresponding to different interval subdivisions, an adaptive correlation mechanism between dynamic operating status (i.e., dynamic response data) and the acceptable range of static sensing data (i.e., static data constraint interval) is established. This enables dynamic adjustment of the acceptable range of static sensing data based on real-time operating conditions, which is beneficial to improving the accuracy of subsequent anomaly assessment.

[0054] In an optional embodiment, a standard data constraint range can be pre-defined for the dynamic response data. Accordingly, if the dynamic response data conforms to the standard data constraint range, a static data constraint range that matches the dynamic response data is determined from a preset matching table.

[0055] Optionally, the dynamic response data may include at least one type, each corresponding to a standard data constraint interval. If all dynamic response data conforms to the corresponding standard data constraint interval, a static data constraint interval matching the dynamic response data is determined from a preset matching table.

[0056] In an optional embodiment, if the dynamic response data does not meet preset conditions, dynamic data anomaly information can be output.

[0057] In an optional embodiment, the dynamic response data may include at least one type; correspondingly, for each dynamic response data, the dynamic data constraint interval to which the dynamic response data belongs may be determined; target condition items that meet each dynamic data constraint interval may be queried from a preset matching table; and static data constraint intervals that match the target condition items may be determined from the preset matching table.

[0058] Optionally, the dynamic response data may include vibration and pressure data of the target track as the target locomotive passes over it. Correspondingly, target condition items that simultaneously meet the dynamic data constraint intervals of both the vibration and pressure data can be retrieved from a preset matching table.

[0059] S130. When the static sensing data does not match the static data constraint range, output static data anomaly information.

[0060] In an optional embodiment, if the static sensing data is outside the static data constraint range, it indicates that the static sensing data does not match the static data constraint range, and static data anomaly information can be output.

[0061] In another optional embodiment, if the static sensing data is within the static data constraint range, it indicates that the static sensing data matches the static data constraint range, and normal static data information can be output.

[0062] In an optional embodiment, the static sensing data may include at least one type; correspondingly, for each static sensing data, a target data constraint interval matching the target type of the static sensing data can be obtained from the static data constraint interval; if the static sensing data does not match the target data constraint interval, static data anomaly information for the static sensing data is output.

[0063] For example, static sensing data may include the installation error between the switch machine and the target track, the position data of the target track, and the locking arm force of the switch machine. Accordingly, target types may include installation error types, position data types, and locking arm force types.

[0064] In an optional embodiment, different target types can be categorized into different functional categories. For example, functional categories may include at least one of electrical control, engineering, and comprehensive categories. It is understood that maintenance or repair of target railway equipment typically involves different departments. By categorizing different target types into different functional categories, it facilitates the rapid acquisition of anomaly information by anomaly receiving terminals of different functions.

[0065] For example, installation errors can be categorized into a comprehensive category, location data into an engineering category, and locking arm force into an electrical control category. It should be noted that this application does not impose any limitations on the specific method of classifying functional categories.

[0066] Optionally, based on preset matching conditions, the terminal information of the abnormal receiving terminal corresponding to the target type can be determined; based on the terminal information, static data anomaly information for static sensing data can be sent to the abnormal receiving terminal. The terminal information may include the target address of the abnormal receiving terminal.

[0067] For example, the anomaly receiving terminal may include a first terminal and a second terminal. Accordingly, when the target type is the electronic control category, static data anomaly information for static sensing data can be sent to the first terminal; when the target type is the engineering category, static data anomaly information for static sensing data can be sent to the second terminal; when the target type is the comprehensive category, static data anomaly information for static sensing data is sent to both the first terminal and the second terminal.

[0068] The aforementioned railway equipment detection method acquires detection data for the target railway equipment, including dynamic response data and static sensor data. This provides a joint observation basis for assessing the operational status of the target railway equipment, combining dynamic operating characteristics with static fundamental parameters, thus achieving a multi-dimensional quantitative characterization of the equipment's operational status. By determining a static data constraint interval matching the dynamic response data from a pre-defined matching table when the dynamic response data meets preset conditions, a matching relationship between the dynamic operating status and static parameter requirements is established. This enables an adaptive detection mechanism that dynamically adjusts the acceptable range of static sensor data based on real-time operating conditions. Furthermore, by outputting static data anomaly information when the static sensor data does not match the static data constraint interval, timely warnings are provided for deviations of static sensor data from allowable ranges, improving the detection accuracy of the target railway equipment.

[0069] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment, in which a step of determining the anomaly type of the target track is added. The target railway equipment includes a target track; a signal transmitter is connected to the target track, and the signal transmitter is used to send locomotive signals to a signal receiver on the target locomotive via the target track; signal acquisition equipment is also provided on the target track.

[0070] refer to Figure 2 The steps for determining the anomaly type of the target orbit are shown, including:

[0071] S210. Acquire the first locomotive signal received by the signal receiver and the second locomotive signal acquired by the signal acquisition device.

[0072] S220. Extract first frequency information from the first locomotive signal and extract second frequency information from the second locomotive signal.

[0073] S230. Determine the anomaly type of the target orbit based on the first frequency information and the second frequency information.

[0074] It is understandable that the signal transmitter sends locomotive signals to the signal receiver on the target locomotive via the target track. At this time, the first locomotive signal acquired by the signal acquisition equipment and the second locomotive signal received by the signal receiver may not match due to data transmission issues or anomalies in the target track, or the first and second locomotive signals themselves may be abnormal. By extracting first frequency information from the first locomotive signal and second frequency information from the second locomotive signal, and based on the first and second frequency information, the anomaly type of the target track can be determined, thus facilitating accurate identification of the target track's anomaly type.

[0075] For example, the signal receiver can receive the first locomotive signal through electromagnetic induction with the target track.

[0076] For example, the anomaly type of the target track may include at least one of the following: target track breakage, aging of equipment on the target track, and target track misalignment. This application does not limit the specific anomaly type of the target track.

[0077] In an optional embodiment, frequency distribution data can be determined based on first frequency information and second frequency information; the frequency distribution data can be input into a pre-trained anomaly recognition model to obtain the anomaly type of the target orbit.

[0078] Optionally, a frequency distribution map can be generated based on the first and second frequency information, and frequency distribution data can be determined from the frequency distribution map. In the frequency distribution map, the horizontal axis can represent time, and the vertical axis can represent frequency.

[0079] In an optional embodiment, after determining the anomaly type of the target orbit, verification can be performed on the determined anomaly type.

[0080] For example, in the case of an anomaly type of target track breakage, monitoring data of the target track can be retrieved, and an anomaly investigation can be carried out if the target track breakage is confirmed.

[0081] For example, in the case of an anomaly type of equipment aging on the target track, environmental data within a preset historical period can be obtained to verify whether conditions for equipment aging exist. Environmental data may include at least one of temperature data, humidity data, and rainfall data.

[0082] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment, in which a data acquisition system is provided to acquire at least one of the detection data of the target railway equipment and the above-mentioned environmental data.

[0083] refer to Figure 3 The diagram shows the structure of the data acquisition system, which includes: signal acquisition module 1, electromagnetic shielding module 2, data processing module 3, environmental sensing module 4, data storage module 5, data transmission module 6, and data display module 7.

[0084] Optionally, the signal acquisition module 1 may include multiple sensors. The sensors may be high-precision sensors 101. A high-precision sensor can be understood as a sensor whose acquisition accuracy is greater than a preset accuracy threshold.

[0085] For example, the sensor may include at least one of pressure sensor 101a, displacement sensor 101b, acceleration sensor 101c, vibration sensor 101d, and optical sensor 101e.

[0086] For example, the pressure sensor may include a first pressure sensor and a second pressure sensor. The first pressure sensor may be installed on the target track and can be used to collect pressure data of the target track. The second pressure sensor may be installed on the switch machine and can be used to collect the switching hydraulic pressure of the switch machine. The displacement sensor 101b can be used to collect at least one of the following: position data of the target track and turnout switch rail crawl data. The acceleration sensor 101c can be used to collect acceleration data of the target locomotive. The vibration sensor 101d can be used to collect vibration data of the target track. The optical sensor 101e can be used to collect at least one of the following: status of railway signal lights and track occupancy. Optionally, the sensor may also include a signal acquisition sensor for collecting second locomotive signals.

[0087] Optionally, the electromagnetic shielding module 2 can be based on electromagnetic shielding materials to isolate external electromagnetic interference and reduce its impact on the signal acquisition module 1. For example, the electromagnetic shielding module can adaptively adjust its shielding effect according to the intensity of electromagnetic interference in different environments to achieve optimal isolation.

[0088] Optionally, the data processing module 3 may include a data preprocessing module 301 and a data analysis module 302. The data preprocessing module 301 can preprocess the data acquired by the signal acquisition module 1 based on intelligent signal processing methods to improve the signal-to-noise ratio and data quality. Preprocessing may include filtering and noise reduction. The data analysis module 302 can extract key information or key features from the acquired data to provide data support for railway monitoring, scheduling, and equipment testing.

[0089] Optionally, the environmental sensing module 4 can be used to sense the external environment in order to monitor weather changes in real time. For example, the environmental sensing module 4 may include a temperature sensor 401, a humidity sensor 402, and a wind speed sensor 403. The temperature sensor can be used to collect ambient temperature, the humidity sensor can be used to collect ambient humidity, and the wind speed sensor can be used to collect ambient wind speed. For example, the focus of data collection can be automatically adjusted according to different weather conditions; for example, during rain, the focus can be on collecting parameters such as humidity within the device, ensuring the accuracy and relevance of the data collection.

[0090] Optionally, the data storage module 5 can be used to store the signal data processed by the data processing module 3 for subsequent data analysis and processing. The data storage module 5 can use a large-capacity, high-performance storage device to ensure data integrity and security. At the same time, the data storage module 5 also has data backup and recovery functions to cope with data loss or damage caused by unexpected situations.

[0091] For example, the data storage module 5 may include a database 501, a data backup module 502, and a data recovery module 503. The data backup module 502 and the data recovery module 503 can be used for data backup and recovery to address data loss or corruption caused by unforeseen circumstances. The data backup module 502 may include a physical backup module 502a and a logical backup module 502b. The physical backup module 502a is used to copy the physical files of the database 501, which include data files, control files, and log files.

[0092] Optionally, the data transmission module 6 may include a communication module 601, a data encryption module 602, and a data anti-tampering module 603. The communication module 601 is responsible for transmitting data to the railway monitoring center or other devices requiring data via wired or wireless means. The communication module 601 can employ a high-speed, stable transmission protocol to ensure the real-time performance and reliability of the data. The data encryption module 602 can use various encryption algorithms to ensure the security of data during transmission and storage; encryption algorithms can be broadly classified into symmetric and asymmetric encryption algorithms. The data anti-tampering module 603 is used to ensure the integrity and authenticity of the data, prevent data from being tampered with during transmission and storage, and guarantee the security of the data during transmission.

[0093] Optionally, the data display module 7 is used to process and display the received data so that users can view the data information more intuitively. For example, the data display module 7 can receive data transmitted by the data transmission module 6, and perform necessary processing on the data, such as decoding, conversion, data cleaning, and data analysis, and finally display it to the user in the form of tables, charts, images, and text so that users can view the data information more intuitively.

[0094] Based on the technical solutions of the above embodiments, the testing method for railway equipment is described in detail.

[0095] refer to Figure 4 The diagram shown is a flowchart of a railway equipment testing method in another embodiment, including:

[0096] S410. Obtain detection data for the target railway equipment, including dynamic response data and static sensor data.

[0097] S420. When the dynamic response data meets the preset conditions, determine the dynamic data constraint interval to which the dynamic response data belongs for each dynamic response data.

[0098] S430. Query the target condition items that meet each dynamic data constraint interval from the preset matching table.

[0099] S440. Determine the static data constraint interval that matches the target condition item from the preset matching table.

[0100] S450. For each static sensing data, obtain the target data constraint interval that matches the target type of the static sensing data from the static data constraint interval.

[0101] S460. In the case where the static sensing data and the target data constraint range do not match, determine the terminal information of the abnormal receiving terminal corresponding to the target type according to the preset matching conditions.

[0102] S470. Based on the terminal information, send static data anomaly information for static sensor data to the anomaly receiving terminal.

[0103] S480: Acquire the first locomotive signal received by the signal receiver and the second locomotive signal acquired by the signal acquisition device.

[0104] S490. Extract first frequency information from the first locomotive signal and extract second frequency information from the second locomotive signal; and determine the anomaly type of the target track based on the first frequency information and the second frequency information.

[0105] 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.

[0106] Based on the same inventive concept, this application also provides a railway equipment testing device for implementing the railway equipment testing method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more railway equipment testing device embodiments provided below can be found in the limitations of the railway equipment testing method described above, and will not be repeated here.

[0107] In one exemplary embodiment, such as Figure 5 As shown, a detection device for railway equipment is provided, comprising: a first acquisition module 510, a first determination module 520, and an output module 530, wherein:

[0108] The first acquisition module 510 is used to acquire detection data for the target railway equipment, including dynamic response data and static sensing data.

[0109] The first determining module 520 is used to determine the static data constraint interval that matches the dynamic response data from a preset matching table when the dynamic response data meets preset conditions.

[0110] The output module 530 is used to output static data anomaly information when the static sensing data does not match the static data constraint range.

[0111] In one embodiment, the dynamic response data includes at least one type; correspondingly, the first determining module includes: a first determining unit, configured to determine the dynamic data constraint interval to which the dynamic response data belongs for each dynamic response data; a querying unit, configured to query a target condition item that conforms to each dynamic data constraint interval from a preset matching table; and a second determining unit, configured to determine a static data constraint interval that matches the target condition item from the preset matching table.

[0112] In one embodiment, the static sensing data includes at least one type; correspondingly, the output module 530 includes: an acquisition unit, configured to acquire, for each static sensing data, a target data constraint interval matching the target type of the static sensing data from the static data constraint interval; and an output unit, configured to output static data anomaly information for the static sensing data when the static sensing data does not match the target data constraint interval.

[0113] In one embodiment, the output unit includes: a first determining subunit, configured to determine the terminal information of the abnormal receiving terminal corresponding to the target type according to a preset matching condition; and a sending subunit, configured to send static data abnormal information for static sensing data to the abnormal receiving terminal according to the terminal information.

[0114] In one embodiment, the target railway equipment includes a target track; the target track is connected to a signal transmitter, which is used to send locomotive signals to a signal receiver on a target locomotive via the target track; a signal acquisition device is also provided on the target track; the device further includes: a second acquisition module, used to acquire a first locomotive signal received by the signal receiver and a second locomotive signal acquired by the signal acquisition device; an extraction module, used to extract first frequency information from the first locomotive signal and extract second frequency information from the second locomotive signal; and a second determination module, used to determine the anomaly type of the target track based on the first frequency information and the second frequency information.

[0115] In one embodiment, the second determining module includes: a third determining unit, configured to determine frequency distribution data based on first frequency information and second frequency information; and an input unit, configured to input the frequency distribution data into a pre-trained anomaly recognition model to obtain the anomaly type of the target orbit.

[0116] Each module in the aforementioned railway equipment detection device 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 computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0117] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 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 computational 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, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for detecting railway equipment. 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.

[0118] Those skilled in the art will understand that Figure 6 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.

[0119] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0120] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0121] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of 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, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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, artificial intelligence (AI) processors, etc., and are not limited to these.

[0123] 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 application.

[0124] 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 method for testing railway equipment, characterized in that, include: Acquire detection data for the target railway equipment, the detection data including dynamic response data and static sensor data; If the dynamic response data meets the preset conditions, a static data constraint interval that matches the dynamic response data is determined from a preset matching table; If the static sensing data does not match the static data constraint range, static data anomaly information is output.

2. The method according to claim 1, characterized in that, The dynamic response data includes at least one type; correspondingly, determining the static data constraint interval that matches the dynamic response data from the preset matching table includes: For each dynamic response data, determine the dynamic data constraint interval to which the dynamic response data belongs; Query the target condition items that meet each of the dynamic data constraint intervals from the preset matching table; Determine the static data constraint interval that matches the target condition from the preset matching table.

3. The method according to claim 1, characterized in that, The static sensing data includes at least one type; correspondingly, the step of outputting static data anomaly information when the static sensing data does not match the static data constraint range includes: For each static sensing data, a target data constraint interval matching the target type of the static sensing data is obtained from the static data constraint interval; If the static sensing data does not match the target data constraint range, static data anomaly information is output for the static sensing data.

4. The method according to claim 3, characterized in that, The output of static data anomaly information for the static sensing data includes: Based on preset matching conditions, determine the terminal information of the abnormal receiving terminal corresponding to the target type; Based on the terminal information, static data anomaly information for the static sensing data is sent to the anomaly receiving terminal.

5. The method according to any one of claims 1-4, characterized in that, The target railway equipment includes a target track; the target track is connected to a signal transmitter, which is used to send locomotive signals to a signal receiver on the target locomotive via the target track. The target orbit is also equipped with signal acquisition equipment; correspondingly, the method further includes: Acquire the first locomotive signal received by the signal receiver and the second locomotive signal acquired by the signal acquisition device; Extract first frequency information from the first locomotive signal and extract second frequency information from the second locomotive signal; Based on the first frequency information and the second frequency information, the anomaly type of the target orbit is determined.

6. The method according to claim 5, characterized in that, Determining the anomaly type of the target orbit based on the first frequency information and the second frequency information includes: Based on the first frequency information and the second frequency information, determine the frequency distribution data; The frequency distribution data is input into a pre-trained anomaly recognition model to obtain the anomaly type of the target orbit.

7. A testing device for railway equipment, characterized in that, The device includes: The first acquisition module is used to acquire detection data for the target railway equipment, the detection data including dynamic response data and static sensor data; The first determining module is used to determine a static data constraint interval that matches the dynamic response data from a preset matching table when the dynamic response data meets preset conditions. The output module is used to output static data anomaly information when the static sensing data does not match the static data constraint range.

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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