A rail transit train underbody detection device and detection system

Abstract

The present invention discloses a rail transit train underbody detection device and detection system, which relate to the field of train detection technology. The data of the train is collected by a data acquisition module and sent to a data processing module. The data processing module calculates the real-time vibration frequency coefficient of the train, and then the data analysis module analyzes it. If it is greater than the maximum value of the train vibration frequency, the data analysis module sends a deceleration signal to the execution module, and then collects the train data again and analyzes it again. If it is within the range, the train is running normally. If it is still greater than the maximum value of the train vibration frequency, the data analysis module sends the collected sound signal to the data acquisition module. The data acquisition module collects the noise data coefficient of the underbody of the train. The data analysis module compares the noise data coefficient with the standard noise coefficient. If it is greater than, an alarm signal is sent to the alarm module. The alarm module prompts the staff of the abnormal train situation through the alarm.

Description

Technical Field

[0001] The present invention relates to the technical field of train detection, and in particular to a rail transit train undercarriage detection device and a detection system. Background Art

[0002] Trains are the most important mechanical means of transportation in human history. Early steam locomotives, also known as trains, traveled on separate tracks. Railway trains can be categorized by their load: freight wagons for transporting goods and passenger coaches; some also combine both. A train generally refers to a series of vehicles, particularly a train consisting of a traction locomotive and freight or passenger coaches. The definition of a train is: a train is a series of vehicles connected for a specific purpose. Unlike a train, the vehicles that make up a train are divided into locomotives and rolling stock. The locomotive provides propulsion, while the rolling stock performs its functions. EMUs are also trains, but within a train, no distinction is made between locomotives and rolling stock; the smallest complete functional unit is considered a complete train. Different types of railways correspond to different train types: land rail, subway, air rail, and Pakistan rail. Land rail is categorized as heavy rail. Land rail and heavy rail are the original and most prevalent types of railways. In China, these trains are categorized into conventional, express, and high-speed trains. These concepts have a narrow and broad meaning and do not correspond to the three levels of Chinese railways.

[0003] Existing rail trains may have some problems during use. There may be certain safety hazards under the train when the track is released. However, when detecting these hazards, existing trains usually use manual inspection after arriving at the station, resulting in the inability to diagnose the situation under the train during the train's operation, which may cause certain safety problems to be not solved in time. For this reason, a rail transit train undercarriage detection device and detection system are now proposed. Summary of the Invention

[0004] In order to solve the deficiencies mentioned in the above-mentioned background technology, the purpose of the present invention is to provide a rail transit train underbody detection device and detection system, which is used to solve the technical problem that the existing method of manually detecting the safety of the underbody of a rail transit train after stopping at a station usually leads to certain safety problems that cannot be solved during the train's travel.

[0005] The object of the present invention can be achieved by the following technical solution: A rail transit train undercarriage detection system includes a data acquisition module, a data processing module, a data analysis module, an alarm module and an execution module, wherein the data acquisition module is used to collect train data and send the train data to the data processing module;

[0006] The data processing module is used to process the received train data, and obtain the real-time vibration frequency coefficient of the train by calculation, and send the real-time vibration frequency coefficient of the train to the data analysis module;

[0007] The data analysis module is used to analyze the received real-time vibration frequency coefficient of the train, set the standard vibration frequency range of the train, and compare the real-time vibration frequency coefficient of the train with the standard vibration frequency range of the train. If the real-time vibration frequency coefficient of the train is within the standard vibration frequency range of the train, the train runs normally. If the real-time vibration frequency coefficient of the train is less than the minimum value of the train vibration frequency, the data analysis module sends an acceleration signal to the execution module; if the real-time vibration frequency coefficient of the train is greater than the maximum value of the train vibration frequency, the data analysis module sends a deceleration signal to the execution module.

[0008] The data analysis module sends a re-collection signal to the data acquisition module, and then performs the above operations again. If it is within the standard vibration frequency range of the train, the train is running normally. If the real-time vibration frequency coefficient of the train is greater than the maximum value of the train vibration frequency, the data analysis module sends a collected sound signal to the data acquisition module. The data acquisition module collects the noise data coefficient of the bottom of the train and sends it to the data analysis module. The data analysis module compares the noise data coefficient with the set standard noise coefficient. If the noise data coefficient is less than the standard noise coefficient, the data analysis module sends a deceleration signal to the execution module to decelerate again. If the noise data coefficient is greater than or equal to the standard noise coefficient, the data analysis module sends an alarm signal to the alarm module. The alarm module prompts the train staff to check the location of the bottom of the train for high friction after the train arrives at the station, and then perform corresponding processing.

[0009] Furthermore, the train data includes the real-time speed of the train and the friction coefficient of the train bottom.

[0010] Furthermore, the processing process of the data processing module includes the following steps:

[0011] Step 1: Mark the real-time speed of the train as V i and the real-time friction coefficient of the train bottom is marked as M i , and set the real-time vibration frequency coefficient of the train to Zd i , where i is the number of acquisitions by the data acquisition module, and i=1, 2, 3, ..., n, and n is the total number of acquisitions;

[0012] Step 2: Use the formula Calculate the real-time vibration frequency coefficient Zd of the train i , where V p is the average speed of the train, M pis the normal friction coefficient of the train bottom, α is the influence speed coefficient of the train, and β is the influence coefficient of the friction coefficient of the train bottom;

[0013] Step 3: The calculated real-time vibration frequency coefficient Zd of the train i Send to the data analysis module for analysis.

[0014] Furthermore, the analysis process of the data analysis module includes the following steps:

[0015] Step S1: Set the standard vibration frequency range of the train to [Zd mi , Zd ma ], and the real-time vibration frequency coefficient Zd of the train i Compared with the standard vibration frequency range of the train [Zd mi , Zd ma ] for comparison;

[0016] Step S2: If Zd mi ≤Zd i ≤Zd ma , it means that the real-time vibration frequency coefficient of the train is within the normal range;

[0017] Step S3: If Zd i <Zd mi , it means that the real-time vibration frequency coefficient of the train is less than the minimum value of the train vibration frequency, which means that the train speed is too slow at this time. The data analysis module sends an acceleration signal to the execution module, and the execution module accelerates the train;

[0018] Step S4: If Zd i >Zd ma , it means that the real-time vibration frequency coefficient of the train is greater than the maximum value of the train's vibration frequency. At this time, the train needs to slow down. The data analysis module sends a deceleration signal to the execution module, and the execution module slows down the train.

[0019] Furthermore, the data acquisition module includes a speed meter, a friction coefficient meter and a noise meter.

[0020] Furthermore, a rail transit train undercarriage detection device includes a memory and one or more processors, wherein the memory is used to store one or more programs, and the one or more programs are executed by one or more processors, so that the processors implement a rail transit train undercarriage detection system as described above.

[0021] Beneficial effects of the present invention:

[0022] When the present invention is in use, the data of the train is collected by the data acquisition module and sent to the data processing module. The data processing module calculates the real-time vibration frequency coefficient of the train and sends it to the data analysis module for analysis. The data analysis module then analyzes again. If it is within the standard vibration frequency range of the train, the train is running normally. If it is greater than the maximum vibration frequency of the train, the data analysis module sends a deceleration signal to the execution module. Then the data acquisition module collects the data of the train again and analyzes it again. If it is within the standard vibration frequency range of the train, the train is running normally. If it is still greater than the maximum vibration frequency of the train, the data analysis module sends the collected sound signal to the data acquisition module. The data acquisition module collects the noise data coefficient of the bottom of the train and sends it to the data analysis module. The noise data coefficient is compared with the standard noise coefficient. If it is greater, an alarm signal is sent to the alarm module. The alarm module prompts the staff of the abnormal train situation through the alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0024] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] like Figure 1 As shown, a rail transit train undercarriage detection system includes a data acquisition module, a data processing module, a data analysis module, an alarm module and an execution module. The data acquisition module is used to collect train data;

[0027] It should be further explained that, in a specific implementation process, the train data includes: the real-time speed of the train and the friction coefficient of the train bottom; it should be explained that the data acquisition module includes a speed meter, a friction coefficient meter and a noise meter;

[0028] The data acquisition module sends the collected train data to the data processing module, which is used to process the received train data. Specifically, the processing process of the data processing module includes the following steps:

[0029] Step 1: Process the train data and mark the real-time speed of the train as V i and the real-time friction coefficient of the train bottom is marked as M i , and set the real-time vibration frequency coefficient of the train to Zd i , where i is the number of acquisitions by the data acquisition module, and i=1, 2, 3, ..., n, and n is the total number of acquisitions;

[0030] Step 2: Use the formula Calculate the real-time vibration frequency coefficient Zd of the train i , where V p is the average speed of the train, M p is the normal friction coefficient of the train bottom, α is the influence speed coefficient of the train, and β is the influence coefficient of the friction coefficient of the train bottom;

[0031] It should be further explained that, in the specific implementation process, the average speed of the train V p The average speed V of the train is obtained by collecting the train speed at ten different times during the train's travel and removing the maximum and minimum values to obtain the eight speed values in the middle. The average of these eight speeds is then calculated to obtain the average speed V of the train. p , the normal friction coefficient of the train bottom is obtained by measuring the train bottom before the train runs.

[0032] Step 3: The calculated real-time vibration frequency coefficient Zd of the train i Send to the data analysis module for analysis.

[0033] The data analysis module receives the real-time vibration frequency coefficient Zd of the train sent by the data processing module. i Then, the real-time vibration frequency coefficient Zd of the train is calculated. i To perform analysis, the analysis process of the data analysis module includes the following steps:

[0034] Step S1: Set the standard vibration frequency range of the train to [Zd mi , Zd ma ], and the real-time vibration frequency coefficient Zd of the train i Compared with the standard vibration frequency range of the train [Zd mi , Zd ma ] for comparison;

[0035] It should be further explained that, in the specific implementation process, the standard vibration frequency range of the train [Zd mi , Zd ma ] is the normal vibration range of the train during operation. If it is not within this range, it means that there is a certain abnormality in the train driving process, and further analysis is required;

[0036] Step S2: If Zd mi ≤Zd i ≤Zd ma , it means that the real-time vibration frequency coefficient of the train is within the normal range;

[0037] Step S3: If Zd i <Zd mi , it means that the real-time vibration frequency coefficient of the train is less than the minimum value of the train vibration frequency, which means that the train speed is too slow at this time. The data analysis module sends an acceleration signal to the execution module, and the execution module accelerates the train;

[0038] Step S4: If Zd i >Zd ma , it means that the real-time vibration frequency coefficient of the train is greater than the maximum value of the train's vibration frequency. At this time, the train needs to slow down. The data analysis module sends a deceleration signal to the execution module, and the execution module decelerates the train.

[0039] It should be further explained that, in the specific implementation process, after the train decelerates, the data analysis module sends a re-collection signal to the data acquisition module to collect the train data again, and sends the train data to the data processing module, which recalculates the real-time vibration frequency coefficient Zd of the train. i , and send Zd i The standard vibration frequency range between the data analysis module and the train is [Zd mi , Zd ma ], if it is within the standard vibration frequency range of the train, the train is running normally. If Zd i >Zd ma , then the real-time vibration frequency coefficient Zd of the train i The real-time friction coefficient M of the train bottom i the impact of;

[0040] At this time, the data analysis module sends the collected sound signal to the data acquisition module, and the data acquisition module collects the noise data coefficient S of the train bottom and sends it to the data analysis module. The data analysis module compares the received noise data coefficient S with the set standard noise coefficient S1. If S<S1, it means that the noise of the train bottom is within the normal range, and the data analysis module sends a deceleration signal to the execution module to decelerate again. If S≥S1, it means that the noise of the train bottom exceeds the normal range, which means that the real-time friction coefficient M of the train bottom is i If the friction is too large, the data analysis module sends an alarm signal to the alarm module, which prompts the train staff to check the position on the bottom of the train where the friction is large after the train arrives at the station and take corresponding measures.

[0041] A rail transit train undercarriage detection device includes a memory and one or more processors. The memory is used to store one or more programs. The one or more programs are executed by one or more processors, so that the processors implement a rail transit train undercarriage detection system as described above.

[0042] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A rail transit train undercarriage detection system, characterized in that: It includes a data acquisition module, a data processing module, a data analysis module, an alarm module and an execution module. The data acquisition module is used to collect train data and send the train data to the data processing module; The data processing module is used to process the received train data, and obtain the real-time vibration frequency coefficient of the train by calculation, and send the real-time vibration frequency coefficient of the train to the data analysis module; The processing process of the data processing module includes the following steps: Step 1: Mark the real-time speed of the train as V i and the real-time friction coefficient of the train bottom is marked as M i , and set the real-time vibration frequency coefficient of the train to Zd i , where i is the number of acquisitions by the data acquisition module, and i=1, 2, 3, ..., n, and n is the total number of acquisitions; Step 2: Use the formula Calculate the real-time vibration frequency coefficient Zd of the train i , where V p is the average speed of the train, M p is the normal friction coefficient of the train bottom, α is the influence speed coefficient of the train, and β is the influence coefficient of the friction coefficient of the train bottom; Step 3: The calculated real-time vibration frequency coefficient Zd of the train i Send to the data analysis module for analysis; The data analysis module is used to analyze the received real-time vibration frequency coefficient of the train, set the standard vibration frequency range of the train, and compare the real-time vibration frequency coefficient of the train with the standard vibration frequency range of the train. If the real-time vibration frequency coefficient of the train is within the standard vibration frequency range of the train, the train runs normally. If the real-time vibration frequency coefficient of the train is less than the minimum value of the train vibration frequency, the data analysis module sends an acceleration signal to the execution module; if the real-time vibration frequency coefficient of the train is greater than the maximum value of the train vibration frequency, the data analysis module sends a deceleration signal to the execution module. The analysis process of the data analysis module includes the following steps: Step S1: Set the standard vibration frequency range of the train to [Zd mi , Zd ma ], and the real-time vibration frequency coefficient Zd of the train i Compared with the standard vibration frequency range of the train [Zd mi , Zd ma ] for comparison; Step S2: If Zd mi ≤Zd i ≤Zd ma , it means that the real-time vibration frequency coefficient of the train is within the normal range; Step S3: If Zd i <Zd mi , it means that the real-time vibration frequency coefficient of the train is less than the minimum value of the train vibration frequency, which means that the train speed is too slow at this time. The data analysis module sends an acceleration signal to the execution module, and the execution module accelerates the train; Step S4: If Zd i >Zd ma , it means that the real-time vibration frequency coefficient of the train is greater than the maximum value of the train's vibration frequency. At this time, the train needs to slow down. The data analysis module sends a deceleration signal to the execution module, and the execution module decelerates the train. The data analysis module sends a re-collection signal to the data acquisition module, and then performs the above operations again. If it is within the standard vibration frequency range of the train, the train is running normally. If the real-time vibration frequency coefficient of the train is greater than the maximum value of the train vibration frequency, the data analysis module sends a collected sound signal to the data acquisition module. The data acquisition module collects the noise data coefficient of the bottom of the train and sends it to the data analysis module. The data analysis module compares the noise data coefficient with the set standard noise coefficient. If the noise data coefficient is less than the standard noise coefficient, the data analysis module sends a deceleration signal to the execution module to decelerate again. If the noise data coefficient is greater than or equal to the standard noise coefficient, the data analysis module sends an alarm signal to the alarm module. The alarm module prompts the train staff to check the location of the bottom of the train for high friction after the train arrives at the station, and then perform corresponding processing.

2. A rail transit train undercarriage detection system according to claim 1, characterized in that: The train data includes the real-time speed of the train and the friction coefficient of the train bottom.

3. A rail transit train undercarriage detection system according to claim 1, characterized in that: The data acquisition module includes a speed meter, a friction coefficient meter and a noise meter.

4. A rail transit train undercarriage detection device, characterized in that: It includes a memory and one or more processors, the memory is used to store one or more programs, and the one or more programs are executed by one or more processors, so that the processors implement a rail transit train underbody detection system as described in any one of claims 1 to 3.

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