Method and device for judging levitation fault of maglev train and readable storage medium

Abstract

The invention discloses a maglev train suspension fault judgment method and device and a readable storage medium, and relates to the field of maglev train fault judgment, and the method comprises the steps that firstly, a vehicle network control system receives data sent by a suspension system at a fixed period; preprocessing the received data according to the suspension points of the maglev train, and storing the processed data; key data values of all suspension points are obtained, and standard deviation processing is carried out; and finally, according to the obtained standard deviation, comparing the data of each suspension point, and judging whether a fault index is exceeded or not. And when the suspension point is abnormal, the vehicle display screen displays the state information and fault alarm of the suspension system. According to the invention, through comprehensive analysis of data, equipment abnormity and potential faults can be accurately identified, and a powerful guarantee is provided for safe operation of a train.

Description

Technical Field

[0001] The invention relates to the field of maglev train fault judgment, and in particular to a maglev train suspension fault judgment method, device and readable storage medium. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] With the advancement and widespread application of maglev train technology, its high efficiency, safety and environmental protection characteristics have become increasingly prominent, and the demand for real-time monitoring and control systems has also increased sharply.

[0004] Traditional train monitoring interfaces often have single functions and the information display is not intuitive enough, which makes it difficult to meet the needs of modern maglev trains in complex operation scenarios. Therefore, it is particularly urgent to develop an innovative method for maglev train suspension fault judgment. Summary of the invention

[0005] The present invention aims to display the key operating parameters of the train in real time and accurately through highly integrated technical means, providing an intuitive and comprehensive train status monitoring platform that can quickly capture subtle changes in train operation, timely and accurately judge the health status of the suspension system, and then implement precise and effective control measures, thereby greatly improving the safety and reliability of train operation and providing strong guarantees for the stable operation of maglev trains.

[0006] The technical solution of the present invention is as follows:

[0007] A method for judging suspension failure of a maglev train, comprising:

[0008] Step S1: The vehicle network control system receives data sent by the suspension system at a fixed period;

[0009] Step S2: pre-processing the received data according to the maglev train suspension point, and saving the processed data;

[0010] Step S3: Obtain key data values ​​of each suspension point and perform standard deviation processing;

[0011] Step S4: Compare the data of each suspension point according to the obtained standard deviation to determine whether it exceeds the fault index; when an abnormality occurs in the suspension point, the vehicle display screen displays the status information and fault alarm of the suspension system.

[0012] Furthermore, the step S2 comprises:

[0013] The received data is cleaned according to the suspension point of the maglev train, residual values ​​and abnormal data are processed, and the processed data is saved; wherein, it is determined whether the data are all 0 or the verification fails, and if so, it is determined to be an abnormal value.

[0014] Furthermore, the residual value is determined by the following formula:

[0015]

[0016] Among them, the number of suspension points is 1 to n, and the data range is 1 to i.

[0017] Furthermore, the standard deviation processing in step S3 is performed by the following formula:

[0018]

[0019] Among them, μ is the calculated mean and σ is the standard deviation.

[0020] Furthermore, the calculated mean μ is calculated by the following formula:

[0021]

[0022] Furthermore, the step S4 comprises:

[0023] Step S41: judging whether the data is abnormal according to the standard deviation σ and the mean μ;

[0024] Step S42: for the abnormal data, based on the fault threshold coefficient, determine whether it exceeds the fault index.

[0025] Furthermore, the step S41 includes:

[0026]

[0027] Among them, k is the abnormal threshold coefficient.

[0028] Furthermore, the step S42 includes:

[0029]

[0030] Wherein, M is the fault threshold coefficient.

[0031] The present invention also proposes a device for judging suspension faults of maglev trains, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for judging suspension faults of maglev trains are implemented.

[0032] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for determining suspension failure of a maglev train are implemented.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention significantly enhances the operational safety and efficiency of maglev trains through finely designed data transmission, intelligent communication determination, logic processing and status display processes; at the same time, it can perform in-depth modeling analysis and fine processing on the received data, further improving the accuracy and reliability of monitoring and ensuring efficient management of maglev train operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flow chart of a method for judging suspension failure of a maglev train. DETAILED DESCRIPTION

[0036] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0037] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0038] Embodiment 1

[0039] See also Figure 1 A method for judging suspension failure of a maglev train comprises the following steps:

[0040] Step S1: The vehicle network control system receives data sent by the suspension system at a fixed period;

[0041] Step S2: pre-processing the received data according to the maglev train suspension point, and saving the processed data;

[0042] Step S3: Obtain key data values ​​of each suspension point and perform standard deviation processing;

[0043] Step S4: Compare the data of each suspension point according to the obtained standard deviation to determine whether it exceeds the fault index; when an abnormality occurs in the suspension point, the vehicle display screen displays the status information and fault alarm of the suspension system.

[0044] In this embodiment, specifically, step S2 includes:

[0045] The received data is cleaned according to the suspension point of the maglev train, residual values ​​and abnormal data are processed, and the processed data is saved; wherein, it is determined whether the data are all 0 or the verification fails, and if so, it is determined to be an abnormal value.

[0046] In this embodiment, specifically, the residual value is determined by the following formula:

[0047]

[0048] Among them, the number of floating points is 1 to n, and the data range is 1 to i.

[0049] In this embodiment, specifically, the standard deviation processing in step S3 is performed by the following formula:

[0050]

[0051] Among them, μ is the calculated mean and σ is the standard deviation.

[0052] In this embodiment, specifically, the calculated mean μ is calculated by the following formula:

[0053]

[0054] In this embodiment, specifically, step S4 includes:

[0055] Step S41: judging whether the data is abnormal based on the standard deviation σ and the mean μ, i.e., performing outlier monitoring;

[0056] Step S42: for the abnormal data, based on the fault threshold coefficient, determine whether it exceeds the fault index.

[0057] In this embodiment, specifically, step S41 includes:

[0058]

[0059] Among them, k is the abnormal threshold coefficient.

[0060] In this embodiment, specifically, step S42 includes:

[0061]

[0062] Wherein, M is the fault threshold coefficient.

[0063] This embodiment also proposes a maglev train suspension fault judgment device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned maglev train suspension fault judgment method when executing the computer program; preferably, the computer program can be executed on a terminal device, such as a personal computer.

[0064] This embodiment also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned method for determining the suspension fault of a maglev train; however, the device of the present invention is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, device or device.

[0065] The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0066] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0067] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0068] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

[0069] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A method for judging suspension failure of a maglev train, characterized in that: include: Step S1: The vehicle network control system receives data sent by the suspension system at a fixed period; Step S2: pre-processing the received data according to the maglev train suspension point, and saving the processed data; Step S3: Obtain key data values ​​of each suspension point and perform standard deviation processing; Step S4: Compare the data of each suspension point according to the obtained standard deviation to determine whether it exceeds the fault index; when an abnormality occurs in the suspension point, the vehicle display screen displays the status information and fault alarm of the suspension system.

2. A method for determining suspension failure of a maglev train according to claim 1, characterized in that: The step S2 comprises: The received data is cleaned according to the suspension point of the maglev train, residual values ​​and abnormal data are processed, and the processed data is saved; wherein, it is determined whether the data are all 0 or the verification fails, and if so, it is determined to be an abnormal value.

3. A method for determining suspension failure of a maglev train according to claim 2, characterized in that: The residual value is determined by the following formula: Among them, the number of floating points is 1 to n, and the data range is 1 to i.

4. A method for determining suspension failure of a maglev train according to claim 1, characterized in that: The standard deviation processing in step S3 is performed by the following formula: Among them, μ is the calculated mean and σ is the standard deviation.

5. A method for determining suspension failure of a maglev train according to claim 4, characterized in that: The calculated mean μ is calculated by the following formula:

6. A method for determining suspension failure of a maglev train according to claim 5, characterized in that: The step S4 comprises: Step S41: judging whether the data is abnormal according to the standard deviation σ and the mean μ; Step S42: for the abnormal data, based on the fault threshold coefficient, determine whether it exceeds the fault index.

7. A method for determining suspension failure of a maglev train according to claim 6, characterized in that: The step S41 comprises: Among them, k is the abnormal threshold coefficient.

8. A method for determining suspension failure of a maglev train according to claim 7, characterized in that: The step S42 comprises: Wherein, M is the fault threshold coefficient.

9. A maglev train suspension fault judgment device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for determining suspension faults of a maglev train as described in any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for determining suspension failure of a maglev train as described in any one of claims 1 to 8 are implemented.

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