CN115709748A - Train safety protection method based on dynamic parameters - Google Patents
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Abstract
The embodiment of the disclosure provides a train safety protection method based on dynamic parameters. The method comprises the following steps: predicting traction braking parameters of the train according to the operation data of the train; updating the traction braking parameters in the safety protection model by using the predicted traction braking parameters; calculating the safety protection speed of the train according to the updated safety protection model; and according to the safety protection speed, carrying out safety protection on the train. In this way, the traction braking parameters in the safety protection model can be dynamically updated according to the operation data of the train, so that the traction braking capability of the train is truly reflected, the safety protection speed of the train is accurately calculated, the overall driving efficiency is improved, and the dynamic safety protection of the train is realized.
Description
Technical Field
The disclosure relates to the technical field of rail transit, in particular to a train safety protection method based on dynamic parameters.
Background
With the continuous improvement of the running speed of the train, the safety of the train is more and more emphasized. Currently, most rail transit lines are Based on a Communication Based Train operation Control System (CBTC) System, and a moving block tracking mode Based on an absolute brake safety protection model is realized. The protection model considers that at any time, the front vehicle is like a hard wall, and the rear vehicle cannot cross the position under the condition of emergency braking. In fact, the front train is still normally running in most cases, so the "hard wall" model limits the tracking interval of two trains to some extent. In contrast, more is currently mentioned in the research field based on a relative brake safeguard model. After the protection model artificially considers more dynamic information such as the position, the speed and even the acceleration of the front vehicle, the position which can be reached by the rear vehicle furthest extends forwards along with the movement of the front vehicle, and the protection model is called as a soft wall collision model. Due to the fact that train movement authorization is extended, the 'soft wall collision' model can further reduce the operation interval and improve the system transport capacity.
However, whether the model is a 'hard wall collision' model based on absolute braking distance or a 'soft wall collision' model based on relative braking distance, the model is essentially based on artificially selected traction braking parameters to model the running process of the train and then solve the safety protection speed. It can be known that these security models can be said to be static security models. Actually, due to safety considerations, traction braking parameters are often conservative in the selection process, so that the safety protection speed of theoretical calculation is low, the train tracking interval is increased, and the running efficiency is reduced.
Disclosure of Invention
The present disclosure provides a train safety protection method, device, equipment and storage medium based on dynamic parameters, which can improve the driving efficiency.
In a first aspect, an embodiment of the present disclosure provides a train safety protection method based on dynamic parameters, where the method includes:
predicting traction braking parameters of the train according to the operation data of the train;
updating the traction braking parameters in the safety protection model by using the predicted traction braking parameters;
calculating the safety protection speed of the train according to the updated safety protection model;
and carrying out safety protection on the train according to the safety protection speed.
In some implementations of the first aspect, the operational data includes: train operation data and line environment data.
In some implementations of the first aspect, predicting a traction braking parameter of the train from operational data of the train comprises:
and if the change of the running scene of the train is detected, predicting the traction braking parameters of the train according to the operation data of the train.
In some implementations of the first aspect, the change in the operational scenario includes at least one of:
the weather changes;
the traction braking system fails but does not influence the continuous running;
after the traction braking system, the wheels and the steel rails are overhauled;
the line gradient and the line curve change;
the train full load rate changes.
In some implementations of the first aspect, predicting a traction braking parameter of the train from operational data of the train comprises:
inputting the operation data into a pre-trained traction brake prediction model to obtain traction brake parameters of the train;
the traction braking prediction model is obtained by training a preset neural network by utilizing a traction braking training data set, wherein the traction braking training data set is generated according to the operation data sample and the traction braking parameter label corresponding to the operation data sample.
In some implementations of the first aspect, the traction braking parameter of the train comprises: traction acceleration, service braking acceleration, and emergency braking acceleration;
inputting the operation data into a pre-trained traction brake prediction model to obtain traction brake parameters of the train, wherein the traction brake parameters comprise:
and inputting the operation data into a traction braking prediction model, and respectively calculating and processing the operation data by a traction capacity prediction model, a common braking capacity prediction model and an emergency braking capacity prediction model in the traction braking prediction model to obtain the traction acceleration, the common braking acceleration and the emergency braking acceleration of the train.
In some implementations of the first aspect, before, after, or while updating the traction braking parameters in the safeguard model, the method further comprises:
and updating the gradient parameters in the safety protection model by using the calculated dynamic average gradient parameters.
In some implementations of the first aspect, the calculating of the dynamic average grade parameter includes:
according to the position of the train, inquiring the gradient information in the future running range of the train;
a dynamic average grade parameter is calculated based on the length of the future operating range and the grade information.
In some implementations of the first aspect, the grade information includes: the number, size, length of gradients present in the future operating range;
calculating a dynamic average grade parameter based on the length of the future operating range and the grade information, comprising:
inputting the length and gradient information of the future operating range into a gradient dynamic average calculation formula, and calculating a dynamic average gradient parameter;
the gradient dynamic average calculation formula is as follows:
G da representing a dynamic average grade parameter, S representing a length of a future operating range, n representing a number of grades present in the future operating range, G i Indicating magnitude of slope in future operating range, S i Indicating the length of the grade in the future operating range.
In some implementations of the first aspect, the safety protection of the train according to the safety protection speed includes:
and if the speed of the train is detected to be greater than or equal to the safety protection speed, emergency braking is carried out on the train.
In a second aspect, an embodiment of the present disclosure provides a train safety protection device based on dynamic parameters, where the train safety protection device includes:
the prediction module is used for predicting the traction braking parameters of the train according to the operation data of the train;
the updating module is used for updating the traction braking parameters in the safety protection model by utilizing the predicted traction braking parameters;
the calculation module is used for calculating the safety protection speed of the train according to the updated safety protection model;
and the protection module is used for carrying out safety protection on the train according to the safety protection speed.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
In a fourth aspect, the disclosed embodiments provide a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described above.
According to the method and the device, the traction braking parameters in the safety protection model can be dynamically updated according to the operation data of the train, so that the traction braking capability of the train is truly reflected, the safety protection speed of the train is accurately calculated, the overall driving efficiency is improved, and the dynamic safety protection of the train is realized.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. The accompanying drawings are included to provide a further understanding of the present disclosure, and are not intended to limit the disclosure thereto, and the same or similar reference numerals will be used to indicate the same or similar elements, where:
FIG. 1 illustrates a schematic diagram of a conventional three-stage safety protection model;
FIG. 2 is a flowchart illustrating a train safety protection method based on dynamic parameters according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of a train safety protection method based on dynamic parameters according to an embodiment of the present disclosure;
FIG. 4 is a block diagram illustrating a dynamic parameter based train safety device provided by an embodiment of the present disclosure;
FIG. 5 sets forth a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without inventive step, are intended to be within the scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
Before introducing the embodiments of the present disclosure, it is necessary to know the safety protection model of the lower train. The different types of security models can be summarized as follows:
S r (t)＝S l (t)+S 0 -S f (t)≥S t t∈[t 0 ,t n ] (1)
wherein S is r (t) is the distance from the rear vehicle head to the front vehicle tail at the time t;
S l (t) is the distance traveled by the leading vehicle at time t compared to the initial time;
S f (t) is the distance traveled by the rear vehicle at time t compared with the initial time;
S 0 the distance from the rear vehicle head to the front vehicle tail at the initial time;
S t is a safety margin;
t 0 is the initial time;
t n the moment when the speed is 0 after the rear vehicle is braked emergently.
That is, the intuitive meaning of the formula means that the rear vehicle and the front vehicle do not overlap in displacement at any time from when the rear vehicle triggers the emergency brake until the speed is 0.
For the "hard wall crash" model (based on the absolute braking safety protection model), the front vehicle is considered not to move, i.e. S l (t) is constantly equal to 0, and only the emergency braking distance of the rear vehicle is required to be ensured to be not more than the actual distance between the two vehicles minus a safety margin, so that the formula (1) can be simplified as follows:
S f ≤S 0 -S t (2)
for S l (t) and S f The 'soft wall collision' model (based on the relative braking safety protection model) in (t) mostly adopts a staged model, such as the traditional three-stage safety protection model shown in fig. 1, and divides the emergency braking process of the train into three stages, namely traction cutting, emergency braking establishment and emergency braking. It can be seen that the train running distance is influenced by the traction acceleration of the train in the first stage, the emergency braking acceleration (namely, the emergency braking rate) in the third stage and the slope acceleration in the whole process, and particularly the emergency braking acceleration has the greatest influence on the running distance.
Therefore, for the safety protection model of the train, the traction braking parameter (i.e. traction braking capability) of the train is a very critical parameter, especially the emergency braking acceleration, which plays a decisive role in the calculation of the final safety protection speed.
However, whether the model is a hard wall collision model or a soft wall collision model, the safety protection speed is solved essentially based on artificially selected traction braking parameters. Due to safety consideration, traction brake parameters are often conservative in the selection process, so that the safety protection speed of theoretical calculation is low, the train tracking interval is increased, and the running efficiency is reduced.
In view of the above, the embodiments of the present disclosure provide a train safety protection method, apparatus, device and storage medium based on dynamic parameters. Specifically, according to the operation data of the train, the traction brake parameters of the train can be predicted, the traction brake parameters in the safety protection model are updated by using the predicted traction brake parameters, then the safety protection speed of the train is calculated according to the updated safety protection model, and further the safety protection of the train is performed according to the safety protection speed.
In this way, the traction braking parameters in the safety protection model can be dynamically updated according to the operation data of the train, so that the traction braking capability of the train is truly reflected, the safety protection speed of the train is accurately calculated, the overall driving efficiency is improved, and the dynamic safety protection of the train is realized.
The train safety protection method, device, equipment and storage medium based on dynamic parameters provided by the embodiments of the present disclosure are described in detail below with reference to fig. 2 to 5.
Fig. 2 shows a flowchart of a train safety protection method based On dynamic parameters according to an embodiment of the present disclosure, and as shown in fig. 2, the train safety protection method 200 may be applied to a Vehicle On-Board Controller (VOBC), and includes the following steps:
and S210, predicting traction braking parameters of the train according to the operation data of the train.
Wherein, the operation data of the train can include: train operation data (e.g., train position, train speed, train acceleration, control commands, traction brake characteristics, etc.) and line environment data (e.g., line grade, line profile, etc.), and further, train equipment data (e.g., air compressor data, brake disc data, etc.) may also be included.
Therefore, the operation data of the train covers the relevant data of the train traction brake, is very representative, and can effectively predict the traction brake parameters (such as traction acceleration, service brake acceleration and emergency brake acceleration) of the train.
For example, the operation data of the train can be input into a pre-trained traction braking prediction model, and the traction braking prediction model performs calculation processing on the operation data to obtain the traction braking parameters of the train quickly.
The traction braking prediction model is obtained by training a preset neural network (such as a BP neural network, a convolutional neural network, a fully-connected neural network and the like) by utilizing a traction braking training data set, and has strong prediction capability. Here, the traction brake training data set is generated from the operational data samples and their corresponding traction brake parameter tags.
Meanwhile, the traction braking prediction model can be continuously updated by using an online learning method according to real-time data, so that the prediction accuracy is improved. Namely, the operation data is used as an operation data sample, and the corresponding actual traction brake parameters are used as corresponding labels to be added into a traction brake training data set for model training.
It should be noted that, under normal conditions, the braking capability of the train does not change greatly in a short time, so that the traction braking parameters in the safety protection model are not suitable for being updated in real time, the operation scene of the train can be detected, and if the change of the operation scene of the train is detected, the traction braking parameters of the train are predicted according to the operation data of the train and are used for subsequently updating the traction braking parameters in the safety protection model, so that occupation of real-time updating on computing resources is avoided.
The operation scene change may include, but is not limited to, at least one of the following options:
(1) The weather changes, such as rain and snow weather, influence the relationship between the train wheel and the rail;
(2) Partial failure of the traction braking system without affecting continued service, such as a truck failure;
(3) After the traction braking system, the wheels and the steel rails are overhauled;
(4) The gradient and curve of the line are obviously changed;
(5) The train fullness varies significantly.
And S220, updating the traction braking parameters in the safety protection model by using the predicted traction braking parameters.
And replacing the traction braking parameters in the safety protection model by using the predicted traction braking parameters to realize the dynamic update of the traction braking parameters.
And S230, calculating the safety protection speed of the train according to the updated safety protection model.
And S240, performing safety protection on the train according to the safety protection speed.
Illustratively, the speed of the train can be detected, and if the detected speed of the train is greater than or equal to the safety protection speed, the train is emergently braked, so that the collision between a front train and a rear train is avoided, and the safety of the train is ensured.
According to the embodiment of the disclosure, the traction braking parameters in the safety protection model can be dynamically updated according to the operation data of the train, so that the traction braking capability of the train is truly reflected, the safety protection speed of the train is accurately calculated, the overall driving efficiency is improved, and the dynamic safety protection of the train is realized.
It should be noted that the gradient parameter in the conventional safety protection model is generally the most unfavorable gradient within the length range of the train or the operation range of the train, i.e. the gradient with the minimum gradient value. It can be known that this processing method is conservative and cannot reflect the change trend of the true gradient.
In some embodiments, to reflect the trend of the change of the true gradient, the gradient parameter in the safety protection model may also be updated using the calculated dynamic average gradient parameter before, after, or simultaneously with the update of the traction brake parameter in the safety protection model.
For example, the calculation of the dynamic average grade parameter may include the steps of:
and inquiring the gradient information in the future running range of the train according to the real-time position of the train.
The future operating range can be a fixed length defined manually or an emergency braking running distance calculated according to the current speed of the train; the gradient information may include: the number, size, length of gradients present in the future operating range.
And accurately calculating the dynamic average gradient parameter according to the length and gradient information of the future operating range.
For example, the length of the future operating range and the grade information may be input into a grade dynamic average calculation formula to calculate a dynamic average grade parameter.
The gradient dynamic average calculation formula can be as follows:
wherein G is da Representing a dynamic average grade parameter, S representing a length of a future operating range, n representing a number of grades present in the future operating range, G i Indicating the magnitude of the grade in the future operating range, S i Indicating the length of the grade in the future operating range.
The train safety protection method based on dynamic parameters, which is publicly provided, is explained in detail below with reference to a specific embodiment, specifically as follows:
as shown in fig. 3, the preset neural network includes a traction capability prediction network, a common braking capability prediction network, and an emergency braking capability prediction network. And respectively training a traction capacity prediction network, a common braking capacity prediction network and an emergency braking capacity prediction network in a preset neural network by using a traction braking training data set, and taking the trained preset neural network as a traction braking prediction model, wherein the traction braking prediction model comprises a traction capacity prediction model, a common braking capacity prediction model and an emergency braking capacity prediction model.
Detecting the running scene of the train, if the running scene of the train is detected to be changed, triggering a traction brake parameter updating mechanism, inputting the operation data of the train into a pre-trained traction brake prediction model, and respectively calculating and processing the operation data by a traction capacity prediction model, a common brake capacity prediction model and an emergency brake capacity prediction model in the traction brake prediction model to quickly obtain traction brake parameters (traction acceleration, common brake acceleration and emergency brake acceleration) of the train.
And updating the traction braking parameters in the safety protection model by using the obtained traction braking parameters.
And calculating the safety protection speed of the train according to the updated safety protection model.
According to the safety protection speed, the train is subjected to safety protection, and the safe and efficient operation of the train is guaranteed.
Meanwhile, the current operation data is used as an operation data sample, and the corresponding actual traction brake parameters are used as corresponding labels to be added into a traction brake training data set for traction brake prediction model training to continuously optimize the traction brake prediction model.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 4 shows a block diagram of a dynamic parameter-based train safety device according to an embodiment of the present disclosure, and as shown in fig. 4, the train safety device 400 may include:
and the prediction module 410 is used for predicting the traction braking parameters of the train according to the operation data of the train.
And the updating module 420 is used for updating the traction braking parameters in the safety protection model by using the predicted traction braking parameters.
And the calculating module 430 is used for calculating the safety protection speed of the train according to the updated safety protection model.
And the protection module 440 is used for performing safety protection on the train according to the safety protection speed.
In some embodiments, the operational data includes: train operation data and line environment data.
In some embodiments, the prediction module 410 is specifically configured to:
and if the change of the running scene of the train is detected, predicting the traction braking parameters of the train according to the operation data of the train.
In some embodiments, the change in the operational scenario includes at least one of the following options:
the weather changes;
the traction braking system fails but does not influence the continuous running;
after the traction braking system, the wheels and the steel rails are overhauled;
the line gradient and the line curve change;
the train full load rate changes.
In some embodiments, the prediction module 410 is specifically configured to:
inputting the operation data into a pre-trained traction brake prediction model to obtain traction brake parameters of the train;
the traction braking prediction model is obtained by training a preset neural network by utilizing a traction braking training data set, wherein the traction braking training data set is generated according to the operation data sample and the traction braking parameter label corresponding to the operation data sample.
In some embodiments, the traction braking parameters of the train include: traction acceleration, service braking acceleration, and emergency braking acceleration;
the prediction module 410 is specifically configured to:
and inputting the operation data into a traction braking prediction model, and respectively calculating and processing the operation data by a traction capacity prediction model, a common braking capacity prediction model and an emergency braking capacity prediction model in the traction braking prediction model to obtain the traction acceleration, the common braking acceleration and the emergency braking acceleration of the train.
In some embodiments, the update module 420 is further configured to update the grade parameter in the safety protection model using the calculated dynamic average grade parameter before, after, or simultaneously with the update of the traction braking parameter in the safety protection model.
In some embodiments, the calculation of the dynamic average grade parameter comprises the steps of:
according to the position of the train, inquiring the gradient information in the future running range of the train;
a dynamic average grade parameter is calculated based on the length of the future operating range and the grade information.
In some embodiments, the grade information comprises: the number, size, length of the grade that exists within the future operating range;
calculating a dynamic average grade parameter based on the length of the future operating range and the grade information, comprising:
inputting the length and gradient information of the future operation range into a gradient dynamic average calculation formula to calculate a dynamic average gradient parameter;
the gradient dynamic average calculation formula is as follows:
G da representing a dynamic average grade parameter, S representing a length of a future operating range, n representing a number of grades present in the future operating range, G i Indicating the magnitude of the grade in the future operating range, S i Indicating the length of the grade in the future operating range.
In some embodiments, the guard module 440 is specifically configured to:
and if the speed of the train is detected to be greater than or equal to the safety protection speed, emergency braking is carried out on the train.
It can be understood that each module/unit in the train safety protection device 400 shown in fig. 4 has a function of implementing each step in the train safety protection method 200 provided by the embodiment of the disclosure, and can achieve the corresponding technical effect, and for brevity, no further description is provided herein.
FIG. 5 illustrates a block diagram of an electronic device that may be used to implement embodiments of the present disclosure. The electronic device 500 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device 500 may also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the electronic device 500 may include a computing unit 501 that may perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the electronic apparatus 500 can also be stored. The calculation unit 501, the ROM502, and the RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the electronic device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 501 performs the various methods and processes described above, such as the method 200. For example, in some embodiments, the method 200 may be implemented as a computer program product, including a computer program, tangibly embodied in a computer-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 500 via ROM502 and/or communications unit 509. When the computer program is loaded into RAM503 and executed by the computing unit 501, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 200 by any other suitable means (e.g., by means of firmware).
The various embodiments described herein above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a computer-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the present disclosure also provides a non-transitory computer readable storage medium storing computer instructions, where the computer instructions are used to enable a computer to execute the method 200 and achieve the corresponding technical effects achieved by the method according to the embodiments of the present disclosure, and for brevity, the detailed description is omitted here.
Additionally, the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method 200.
To provide for interaction with a user, the above-described embodiments may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The embodiments described above may be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user may interact with an implementation of the systems and techniques described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (10)
1. A train safety protection method based on dynamic parameters is characterized by comprising the following steps:
predicting traction braking parameters of the train according to operation data of the train;
updating the traction braking parameters in the safety protection model by using the predicted traction braking parameters;
calculating the safety protection speed of the train according to the updated safety protection model;
and carrying out safety protection on the train according to the safety protection speed.
2. The method of claim 1, wherein the operational data comprises: train operation data and line environment data.
3. The method of claim 1, wherein predicting a traction braking parameter of the train from operational data of the train comprises:
and if the change of the running scene of the train is detected, predicting the traction braking parameters of the train according to the operation data of the train.
4. The method of claim 3, wherein the operational scenario change comprises at least one of:
the weather changes;
the traction braking system fails but does not influence the continuous running;
after the traction braking system, the wheels and the steel rails are overhauled;
the line gradient and the line curve change;
the train full load rate changes.
5. The method of claim 1, wherein predicting a traction braking parameter of the train from operational data of the train comprises:
inputting the operation data into a pre-trained traction brake prediction model to obtain traction brake parameters of the train;
the traction braking prediction model is obtained by training a preset neural network by utilizing a traction braking training data set, wherein the traction braking training data set is generated according to an operation data sample and a traction braking parameter label corresponding to the operation data sample.
6. The method of claim 5, wherein the train's traction braking parameters comprise: traction acceleration, service braking acceleration, and emergency braking acceleration;
inputting the operation data into a pre-trained traction brake prediction model to obtain traction brake parameters of the train, wherein the method comprises the following steps:
and inputting the operation data into the traction braking prediction model, and respectively calculating and processing the operation data by a traction capacity prediction model, a common braking capacity prediction model and an emergency braking capacity prediction model in the traction braking prediction model to obtain traction acceleration, common braking acceleration and emergency braking acceleration of the train.
7. The method according to any one of claims 1-6, wherein before, after or simultaneously with said updating the traction brake parameters in the safety-protected model, the method further comprises:
and updating the gradient parameters in the safety protection model by using the calculated dynamic average gradient parameters.
8. The method of claim 7, wherein the calculation of the dynamic average grade parameter comprises the steps of:
according to the position of the train, inquiring gradient information in a future running range of the train;
a dynamic average grade parameter is calculated based on the length of the future operating range and the grade information.
9. The method of claim 8, wherein the grade information comprises: the number, size, length of gradients present in the future operating range;
calculating a dynamic average grade parameter based on the length of the future operating range and the grade information, comprising:
inputting the length of the future operating range and the gradient information into a gradient dynamic average calculation formula, and calculating a dynamic average gradient parameter;
the gradient dynamic average calculation formula is as follows:
G da representing a dynamic average grade parameter, S representing a length of a future operating range, n representing a number of grades present in the future operating range, G i Indicating magnitude of slope in future operating range, S i Indicating the length of the grade in the future operating range.
10. The method of claim 1, wherein said safeguarding the train in accordance with the safeguarding speed comprises:
and if the speed of the train is detected to be greater than or equal to the safety protection speed, carrying out emergency braking on the train.
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| CN116923488A (en) * | 2023-07-21 | 2023-10-24 | 西南交通大学 | Improved car control method for high-speed railway, algorithm storage medium and equipment |
| CN116923488B (en) * | 2023-07-21 | 2024-04-16 | 西南交通大学 | Improved car control method for high-speed railway, algorithm storage medium and equipment |
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