Early warning method and device for abnormal acceleration of train

Abstract

The invention provides a train acceleration abnormity early warning method and device, and is applied to the technical field of rail transit. The method comprises the following steps: acquiring train operation information, the train operation information comprises at least one of train speed transmission data, level information, whole train passenger carrying rate, a vehicle-mounted controller mode, acceleration sensor installation gradient, train control and management system information, bogie idling information, bogie braking information and train automatic protection system idle sliding information; jumping information and longitude and latitude information of a full-automatic driving mode; inputting the train operation information into a long short-term memory (LSTM) network model to obtain an acceleration value of the train in the current period; and if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in a plurality of continuous periods meets a preset early warning level, generating early warning information of a corresponding level.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a method and device for warning abnormal train acceleration. Background Art

[0002] During the operation of the urban rail transit system, if the acceleration increases abnormally, the train speed may be out of control, and it may be difficult to brake according to the established signal system and operation plan during driving, thereby increasing the risk of collision with the train or obstacle in front. On the contrary, if the acceleration decreases abnormally or even becomes negative acceleration (sudden deceleration), the rear train may not have time to react and a rear-end collision may occur. Especially in rainy and snowy weather, when the track surface is slippery, the adhesion between the wheel and the rail surface decreases, and idling may occur during traction, and slipping may occur during braking. In this case, the available braking rate of the train decreases, and monitoring and early warning of acceleration can determine whether the braking rate requirements are met. Therefore, it is crucial to monitor the abnormal acceleration of the train.

[0003] In the prior art, the train acceleration can be calculated based on the relationship between the level and the traction force and by applying Newton's second law (the acceleration of an object is proportional to the resultant force it receives and inversely proportional to the mass of the object). Then, the train acceleration can be compared with the standard value to determine whether there is any abnormality.

[0004] However, since this monitoring method relies on the calculation of traction, train mass and resistance, and each calculation process will lead to errors in the calculation results due to various objective reasons, the final calculation results of train acceleration often also have large errors, resulting in low reliability of abnormal train acceleration warning. Summary of the invention

[0005] The present invention provides a train acceleration abnormality early warning method and device, which are used to solve the problem of low reliability of train acceleration abnormality early warning.

[0006] The present invention provides a train acceleration abnormality warning method, comprising: obtaining train operation information, the train operation information comprising at least one of the following: train speed transmission data, level information, vehicle passenger load factor, vehicle-mounted controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, full automatic driving mode jump information and longitude and latitude information; inputting the train operation information into a long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets a preset warning level, then generating warning information of the corresponding level.

[0007] According to a train acceleration abnormality warning method provided by the present invention, before obtaining the train operation information, the method also includes: obtaining the acceleration value of the train in a historical period and the corresponding train operation information; training the LSTM model based on the historical acceleration value and the corresponding train operation information.

[0008] According to a train acceleration abnormality warning method provided by the present invention, the acquisition of the acceleration value of the train in a historical period and the corresponding train operation information includes: detecting the acceleration value in the historical period by installing an acceleration sensor.

[0009] According to a train acceleration abnormality warning method provided by the present invention, the LSTM model adopts a double hidden layer structure.

[0010] The present invention also provides a train acceleration abnormality warning device, comprising the following modules: an acquisition module and a processing module; the acquisition module is used to acquire train operation information, and the train operation information includes at least one of the following: train speed transmission data, level information, vehicle passenger capacity, on-board controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, fully automatic driving mode jump information and longitude and latitude information; the processing module is used to input the train operation information into a long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, a warning information of the corresponding level is generated.

[0011] According to a train acceleration abnormality warning device provided by the present invention, the acquisition module is used to obtain the acceleration value of the train in a historical period and the corresponding train operation information; the processing module is used to train the LSTM model based on the historical acceleration value and the corresponding train operation information.

[0012] According to a train acceleration abnormality warning device provided by the present invention, the acquisition module is used to detect the acceleration value in the historical period by installing an acceleration sensor.

[0013] According to a train acceleration abnormality warning device provided by the present invention, the LSTM model adopts a double hidden layer structure.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the train acceleration abnormality warning method as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the train acceleration abnormality warning method as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned train acceleration abnormality warning methods.

[0017] The train acceleration abnormality warning method and device provided by the present invention can predict the acceleration value of the train in the current cycle through the LSTM model, and calculate the difference between the acceleration value and the acceleration value detected in real time. On the one hand, since the acceleration value predicted in multiple consecutive cycles is compared with the acceleration value detected in real time, the change trend of the acceleration value can be obtained, thereby obtaining a more accurate judgment result; on the other hand, since the early warning judgment is based on the difference of the acceleration values ​​obtained by different channels, the problem of large errors caused by using a single acceleration acquisition method can be avoided, thereby further improving the accuracy of the early warning judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the train acceleration abnormality early warning method provided by the present invention; Figure 2 It is a structural schematic diagram of the abnormal train acceleration warning device provided by the present invention; Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0022] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes 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 one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0023] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and order of execution.

[0024] The embodiments of the present application describe some exemplary embodiments for the purpose of explanation. It should be understood that the present application can be implemented in other ways that are not specifically shown in the drawings.

[0025] like Figure 1 As shown, the embodiment of the present application provides a train acceleration abnormality warning method, which can be applied to a train acceleration abnormality warning device. The train acceleration abnormality warning device can be an on-board intelligent computing unit. The train acceleration abnormality warning method can include S101-S103: S101. A train acceleration abnormality warning device obtains train operation information.

[0026] Among them, the above-mentioned train operation information includes at least one of the following: train speed transmission data, level information, vehicle passenger capacity, on-board controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system slip information, fully automatic driving mode jump information and longitude and latitude information.

[0027] It should be noted that the automatic train protection system air slip information refers to whether the automatic train protection system (Automatic Train Protection, ATP) has air slipped, and the full automatic driving mode jump information refers to whether the full automatic driving mode (Full Automatic Mode, FAM) jump has occurred.

[0028] Specifically, the train acceleration abnormality warning device can collect the longitude and latitude information of the train through the satellite positioning combined navigation sensor, and obtain the train speed data, level information, vehicle passenger rate, on-board controller mode and other information from the data stored in the Automatic Train Protection (ATP) and Train Control and Management System (TCMS).

[0029] It should be noted that after obtaining the train operation information, each feature in the train operation information can be normalized to the interval [0, 1] by the following formula (1): ; (1)

[0030] in, x max and x min Represent the maximum and minimum value of a feature respectively.

[0031] S102. The train acceleration abnormality warning device inputs the train operation information into a long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle.

[0032] The Long Short-Term Memory (LSTM) model is a special recurrent neural network (RNN) structure, which is mainly designed to solve the gradient vanishing and gradient exploding problems that may occur in traditional RNNs when processing long sequence data, as well as the long-term dependency problem. LSTM has memory cells and gating mechanisms, in which memory cells can store and transmit long-term information. This memory cell can be regarded as an internal state that can be updated over time and can be selectively read and written by the control gate. The existence of memory cells enables LSTM to effectively handle long-term dependency problems, that is, when processing sequence data, it can remember the information of earlier time steps and combine it with the information of the current time step for prediction. The gating mechanism involves three gating units, namely the input gate, the forget gate, and the output gate. These gating units control the flow of information by learning parameters, thereby realizing the read and write operations of the memory unit. The input gate determines how much of the input information of the current time step can be stored in the memory unit; the forget gate determines which information in the memory unit should be forgotten; the output gate determines how much information in the memory unit can be output to the next layer of the network or as the prediction result of the current time step.

[0033] Optionally, the LSTM model in the present application adopts a double hidden layer structure. The structure is F-U1-U2-1, where F represents the number of input neurons, U1 represents the number of LSTM units in the first hidden layer, U2 represents the number of LSTM units in the second hidden layer, and the number of output neurons is 1. The use of a double hidden layer structure can enhance the expressiveness of the model, improve the generalization ability and robustness of the model, and accelerate the training speed of the model.

[0034] Optionally, the train acceleration abnormality warning device can obtain the acceleration value of the train in a historical period and the corresponding train operation information; and train the LSTM model based on the historical acceleration value and the corresponding train operation information.

[0035] Specifically, the train acceleration abnormality warning device can detect the acceleration value in the historical period by installing an acceleration sensor, and then first process the acquired train acceleration value in the historical period and the corresponding train operation information into a data format suitable for LSTM model input, and then input it into the LSTM model for training. The input of the LSTM model is the train operation information of the current period and the previous period, and the output is the acceleration value of the current period.

[0036] Optionally, the objective function is set to a root mean square error function. After the train operation information of the current cycle and the previous cycle is input into the LSTM model, the LSTM model can perform forward calculation according to the following formula: ; (2)

[0037] ; (3)

[0038] ; (4)

[0039] ; (5)

[0040] ; (6)

[0041] ; (7)

[0042] ; (8)

[0043] ; (9)

[0044] in, is the weight parameter.

[0045] The objective function can be expressed as: ; (10)

[0046] Afterwards, the adam optimizer can be used to update the weight parameters in the LSTM model. The update rules are as follows: ; (11)

[0047] ; (12)

[0048] ; (13)

[0049] ; (14)

[0050] ; (15)

[0051] in, g t is the gradient, obtained by taking the partial derivative of the objective function with respect to the weight parameter. β 1 and β 2 is the decay coefficient of the exponentially weighted average, and is the bias-corrected moving average of the gradient, is the updated parameter, is the learning rate, To avoid division by zero.

[0052] The weight parameters of the entire model are updated according to the above formula (15). After the learning process is completed, the weight parameters of the model are obtained and the training is completed.

[0053] S103. The train acceleration abnormality warning device generates warning information of the corresponding level if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level.

[0054] When the train is running, the train acceleration abnormality warning device can be based on the above-mentioned LSTM model, according to the train operation information of the current cycle and the previous cycle, and output the acceleration value of the current cycle considered by the LSTM model through weight parameters and the corresponding forward calculation formula, that is, the acceleration value predicted by the LSTM model.

[0055] When the difference between the acceleration value given by the LSTM model inference in multiple consecutive cycles and the acceleration value at the current level meets the preset warning level, a warning message of the corresponding level can be generated. Among them, the warning level can be freely set according to the range of the difference, and this application does not limit this.

[0056] In the embodiment of the present application, the acceleration value of the train in the current cycle can be predicted by the LSTM model, and the difference between the acceleration value and the acceleration value detected in real time can be calculated. On the one hand, since the acceleration value predicted in multiple consecutive cycles is compared with the acceleration value detected in real time, the change trend of the acceleration value can be obtained, thereby obtaining a more accurate judgment result; on the other hand, since the early warning judgment is based on the difference of the acceleration values ​​obtained by different means, the problem of large errors caused by using a single acceleration acquisition method can be avoided, thereby further improving the accuracy of the early warning judgment.

[0057] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment disclosed herein, the embodiment of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0058] It should be noted that the device in the embodiment of the present application includes a virtual device and a physical device. The virtual device can be a train acceleration abnormality warning device, and the physical device can include an electronic device, a computer storage medium, and a computer program product.

[0059] The train acceleration abnormality warning method provided in the embodiment of the present application can be executed by a train acceleration abnormality warning device, or a control module for train acceleration abnormality warning in the train acceleration abnormality warning device. In the embodiment of the present application, the train acceleration abnormality warning method is executed by a train acceleration abnormality warning device as an example to illustrate the train acceleration abnormality warning device provided in the embodiment of the present application.

[0060] It should be noted that the embodiment of the present application can divide the train acceleration abnormality warning device into functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0061] like Figure 2 As shown, an embodiment of the present application provides a train acceleration abnormality warning device 200. The train acceleration abnormality warning device 200 includes: an acquisition module 201 and a processing module 202. The acquisition module 201 can be used to obtain train operation information, and the train operation information includes at least one of the following: train speed transmission data, level information, vehicle passenger load, on-board controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, full automatic driving mode jump information and longitude and latitude information; the processing module 202 is used to input the train operation information into the long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, a warning information of the corresponding level is generated.

[0062] Optionally, the acquisition module 201 is used to obtain the acceleration value of the train in a historical period and the corresponding train operation information; the processing module 202 is used to train the LSTM model based on the historical acceleration value and the corresponding train operation information.

[0063] Optionally, the acquisition module 201 is used to detect the acceleration value in the historical period by installing an acceleration sensor.

[0064] Optionally, the LSTM model adopts a double hidden layer structure.

[0065] In the embodiment of the present application, the acceleration value of the train in the current cycle can be predicted by the LSTM model, and the difference between the acceleration value and the acceleration value detected in real time can be calculated. On the one hand, since the acceleration value predicted in multiple consecutive cycles is compared with the acceleration value detected in real time, the change trend of the acceleration value can be obtained, thereby obtaining a more accurate judgment result; on the other hand, since the early warning judgment is based on the difference of the acceleration values ​​obtained by different means, the problem of large errors caused by using a single acceleration acquisition method can be avoided, thereby further improving the accuracy of the early warning judgment.

[0066] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the train acceleration abnormality warning method, the method comprising: obtaining train operation information, the train operation information including at least one of the following: train speed transmission data, level information, vehicle passenger rate, vehicle controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, full automatic driving mode jump information and longitude and latitude information; inputting the train operation information into the long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, then generating a warning information of the corresponding level.

[0067] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0068] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the train acceleration abnormal warning method provided by the above methods, and the method includes: obtaining train operation information, and the train operation information includes at least one of the following: train speed transmission data, grade information, vehicle passenger load, on-board controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, fully automatic driving mode jump information and longitude and latitude information; inputting the train operation information into the long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, then a warning information of the corresponding level is generated.

[0069] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the train acceleration abnormality warning method provided by the above-mentioned methods, the method comprising: obtaining train operation information, the train operation information comprising at least one of the following: train speed transmission data, grade information, vehicle passenger capacity, on-board controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, fully automatic driving mode jump information and longitude and latitude information; inputting the train operation information into a long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; if the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, a warning information of the corresponding level is generated.

[0070] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0071] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A train acceleration abnormality warning method, characterized in that: include: Acquire train operation information, the train operation information including at least one of the following: train speed transmission data, level information, vehicle passenger load factor, vehicle controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system slip information, fully automatic driving mode jump information, and longitude and latitude information; The train operation information is input into a long short-term memory network (LSTM) model to obtain the acceleration value of the train in the current cycle; If the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, a warning information of the corresponding level is generated.

2. The train acceleration abnormality warning method according to claim 1, characterized in that: Before obtaining the train operation information, the method further includes: Obtain the acceleration value of the train in the historical cycle and the corresponding train operation information; The LSTM model is trained based on the historical acceleration values ​​and corresponding train operation information.

3. The train acceleration abnormality warning method according to claim 2 is characterized in that: The obtaining of the acceleration value of the train in the historical period and the corresponding train operation information includes: The acceleration value in the historical period is detected by installing an acceleration sensor.

4. The train acceleration abnormality warning method according to any one of claims 1 to 3, characterized in that: The LSTM model adopts a double hidden layer structure.

5. A train acceleration abnormality warning device, characterized in that: include: Acquisition module and processing module; The acquisition module is used to acquire train operation information, and the train operation information includes at least one of the following: train speed transmission data, level information, vehicle passenger load factor, vehicle controller mode, acceleration sensor installation slope, train control and management system information, bogie idling information, bogie braking information, train automatic protection system air slip information, full automatic driving mode jump information, and longitude and latitude information; The processing module is used to input the train operation information into a long short-term memory network LSTM model to obtain the acceleration value of the train in the current cycle; If the difference between the acceleration value predicted by the LSTM model and the acceleration value detected in real time in multiple consecutive cycles meets the preset warning level, a warning information of the corresponding level is generated.

6. The train acceleration abnormality warning device according to claim 5, characterized in that: The acquisition module is used to acquire the acceleration value of the train in the historical period and the corresponding train operation information; The processing module is used to train the LSTM model based on the historical acceleration values ​​and corresponding train operation information.

7. The train acceleration abnormality warning device according to claim 6, characterized in that: The acquisition module is used to detect the acceleration value in the historical period by installing an acceleration sensor.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the train acceleration abnormality warning method as described in any one of claims 1 to 4 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the train acceleration abnormality warning method as described in any one of claims 1 to 4 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the train acceleration abnormality warning method as described in any one of claims 1 to 4 is implemented.

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