CN116030431A

CN116030431A - Train positioning method and system, electronic equipment and storage medium

Info

Publication number: CN116030431A
Application number: CN202310076592.4A
Authority: CN
Inventors: 罗静; 邢世阳; 朱强; 唐潇; 徐亚萍; 唐浩程
Original assignee: CRSC Communication and Information Group Co Ltd CRSCIC
Current assignee: CRSC Communication and Information Group Co Ltd CRSCIC
Priority date: 2023-01-16
Filing date: 2023-01-16
Publication date: 2023-04-28

Abstract

The application discloses a train positioning method and system, electronic equipment and a storage medium, and relates to the technical field of video monitoring. The method comprises the following steps: inputting the acquired information of the train to be tracked into a train operation model for identification, and determining an operation interval of the train to be tracked in a preset period, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning; invoking target image acquisition equipment in an operation interval to detect a train to be tracked; in the case where it is determined that the train to be tracked is detected, real-time position information of the train to be tracked is determined based on the image information of the train to be tracked extracted from the target image acquisition device. The real-time running condition of the train to be tracked and the running environment around the train to be tracked are conveniently monitored, and the running safety of the train is improved.

Description

Train positioning method and system, electronic equipment and storage medium

Technical Field

The application relates to the technical field of video monitoring, in particular to a train positioning method and system, electronic equipment and a storage medium.

Background

At present, in the railway transportation industry, video information corresponding to the train position pushed by a dynamic monitoring system (Dynamic Monitoring System, DMS) of train control equipment is mainly adopted, so that the real-time position of the train is tracked. The method can simultaneously acquire the position data of the multi-path trains and realize the positioning of the train positions.

However, because the video monitoring system and the DMS belong to different local area networks, coordination, docking, deployment of network devices and the like of the dual systems are required in the use process, the complexity of data processing is increased, and meanwhile, the security of data is reduced.

Disclosure of Invention

Therefore, the application provides a train positioning method and system, electronic equipment and a storage medium, which solve the problems of how to track a train and accurately position the real-time running position of the train.

To achieve the above object, a first aspect of the present application provides a train positioning method, including: inputting the acquired information of the train to be tracked into a train operation model for identification, and determining an operation interval of the train to be tracked in a preset period, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning; invoking target image acquisition equipment in an operation interval to detect a train to be tracked; in the case where it is determined that the train to be tracked is detected, real-time position information of the train to be tracked is determined based on the image information of the train to be tracked extracted from the target image acquisition device.

To achieve the above object, a second aspect of the present application provides a train positioning system, comprising: the system comprises a train tracking device, a user terminal, a video monitoring server and a plurality of image acquisition devices which are in communication connection; a train tracking device configured to perform any of the train positioning methods of the embodiments of the present application; the user terminal is configured to send a positioning request to the train tracking device, wherein the positioning request comprises information of a train to be tracked, so that the train tracking device inputs the acquired information of the train to be tracked into a train operation model for identification, and an operation interval of the train to be tracked in a preset period is determined; the video monitoring server is configured to manage the plurality of image acquisition devices and acquire image information required by the train tracking device; the image acquisition device is configured to acquire images of the trains in the monitoring range and send the acquired vehicles to the video monitoring server.

To achieve the above object, according to a third aspect of the present application, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, one or more of the computer programs being executable by the at least one processor to enable the at least one processor to perform the train positioning method described above.

In order to achieve the above object, the present application provides, in a fourth aspect, a computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor/processing core, implements the above-mentioned train positioning method.

The train positioning method, the system, the electronic equipment and the storage medium are characterized in that the acquired information of the train to be tracked is input into a train operation model for identification, and an operation interval of the train to be tracked in a preset period is determined, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning, the specific operation range of the train to be tracked is defined, and the monitoring range of the train to be tracked is reduced; and calling target image acquisition equipment in the operation interval to detect the train to be tracked so as to determine whether the target image acquisition equipment can detect the train to be tracked, and determining real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image acquisition equipment under the condition that the train to be tracked is determined to be detected, so that the real-time operation condition of the train to be tracked and the surrounding operation environment of the train to be tracked are conveniently monitored, and the operation safety of the train is improved.

Drawings

The accompanying drawings are included to provide a further understanding of embodiments of the disclosure, and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure, without limitation to the disclosure. The above and other features and advantages will become more readily apparent to those skilled in the art by describing in detail exemplary embodiments with reference to the attached drawings, in which:

fig. 1 is a schematic flow chart of a train positioning method according to an embodiment of the present application.

Fig. 2 is a schematic distribution diagram of an image acquisition device along a train tracking line according to an embodiment of the present application.

Fig. 3 is a flow chart of a training method of a train operation model according to an embodiment of the present application.

Fig. 4 is a schematic diagram of a process of using a machine learning linear regression algorithm to identify train time and kilometers along a train running line according to an embodiment of the present application.

Fig. 5 is a block diagram of a train positioning device according to an embodiment of the present application.

Fig. 6 is a block diagram of a train positioning system according to an embodiment of the present application.

Fig. 7 is a block diagram of a train positioning system according to an embodiment of the present application.

Fig. 8 is a flow chart of a working method of the train positioning system according to the embodiment of the present application.

Fig. 9 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The following detailed description of specific embodiments of the present application refers to the accompanying drawings. It should be understood that the detailed description is presented herein for purposes of illustration and explanation only and is not intended to limit the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

With the development of railway transportation, the video monitoring system has increasingly remarkable functions in the aspects of transportation command, public security, production operation, safety monitoring and the like. The comprehensive video monitoring system has the characteristics of wide coverage in space, all-weather service in time and the like, and can provide large-scale networked coverage for areas such as railway work areas, station sections, road bureaus/companies and the like along the railway. In general, video information corresponding to the train position pushed by the DMS system is mainly adopted, so that the real-time position of the train is tracked.

However, since the integrated video monitoring system generally works in a video private network and the DMS works in an office private network, coordination, docking, deployment of network devices, etc. of the dual systems are required in the use process, which increases complexity of data processing and reduces security of data. And the comprehensive video monitoring system adopts a three-level deployment mode of line level, region level and core level, which is completely different from the deployment mode of the DMS system, and cannot comprehensively use the two systems.

In view of the above, the application provides a train positioning method and system, electronic equipment and storage medium, so as to solve the problem that accurate positioning and tracking of a train can still be realized through a comprehensive video monitoring system under the condition that a DMS system is not used, and ensure the running safety of the train.

The first aspect of the embodiment of the application provides a train positioning method. Fig. 1 is a schematic flow chart of a train positioning method according to an embodiment of the present application. The train positioning method can be applied to a train positioning device. As shown in fig. 1, the train positioning method includes, but is not limited to, the following steps.

Step S101, inputting the acquired information of the train to be tracked into a train operation model for identification, and determining an operation interval of the train to be tracked in a preset period.

The train operation model is obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning.

For example, the information of the train to be tracked can be obtained by retrieving the historical video data from the integrated video system, so as to obtain the image information corresponding to the train to be tracked.

And step S102, calling target image acquisition equipment in an operation interval to detect the train to be tracked.

The historical image information acquired by the target image acquisition equipment in the operation interval has the highest possibility of including the train to be tracked, so that the train to be tracked can be detected by calling the historical image data stored in the target image acquisition equipment in the operation interval, and the detection accuracy of the train to be tracked is improved.

Step S103, in the case where it is determined that the train to be tracked is detected, real-time position information of the train to be tracked is determined based on the image information of the train to be tracked extracted from the target image capturing device.

If the train to be tracked is detected from the historical image data stored in the target image acquisition equipment, the historical image data can be further analyzed, and the stored image information of the train to be tracked can be extracted so as to analyze the image information, determine the real-time position information of the train to be tracked, the surrounding environment information of the train to be tracked in the running process and the like, and ensure the running safety of the train to be tracked.

In the embodiment, the acquired information of the train to be tracked is input into a train operation model for identification, and an operation interval of the train to be tracked in a preset period is determined, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning, so that a specific operation range of the train to be tracked is defined, and a monitoring range of the train to be tracked is reduced; and calling target image acquisition equipment in the operation interval to detect the train to be tracked so as to determine whether the target image acquisition equipment can detect the train to be tracked, and determining real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image acquisition equipment under the condition that the train to be tracked is determined to be detected, so that the real-time operation condition of the train to be tracked and the surrounding operation environment of the train to be tracked are conveniently monitored, and the operation safety of the train is improved.

Fig. 2 is a schematic distribution diagram of an image acquisition device along a train tracking line according to an embodiment of the present application. As shown in fig. 2, a train to be tracked starts from a station 1, passes through a station 2 and runs to a station 3, and in the running process, the train to be tracked passes through image acquisition equipment 1, … … and image acquisition equipment k arranged between the station 1 and the station 2; and image acquisition devices m and … … and image acquisition devices m+p arranged between the station 2 and the station 3. Wherein k, m and p are integers greater than or equal to 1.

The plurality of image acquisition devices can monitor the train to be tracked so as to acquire the image information of the train to be tracked in the running process, thereby accurately acquiring the surrounding environment information of the train to be tracked in the running process, monitoring whether the train to be tracked normally runs or not, accurately positioning the train to be tracked and improving the running safety of the train.

In some alternative embodiments, the information of the sample train includes: and the information of the plurality of sample image acquisition devices is the image acquisition devices between the starting station and the ending station corresponding to the sample train.

Before the step S101 of inputting the acquired information of the train to be tracked into the train operation model for identification, determining an operation interval of the train to be tracked within a preset period of time, the method further includes:

determining kilometer identification information corresponding to each sample image acquisition device according to the acquired identification of each sample image acquisition device; and sequencing the plurality of sample image acquisition devices according to kilometer identification information corresponding to each sample image acquisition device to obtain a device sequencing result.

By extracting information from the obtained identifier of each sample image acquisition device (for example, the identifier of the sample image acquisition device includes kilometers corresponding to the sample acquisition device), kilometer identifier information corresponding to each sample image acquisition device can be obtained, so that the sample image acquisition device can be conveniently positioned.

And the plurality of sample image acquisition devices are ordered based on kilometer identification information corresponding to the sample image acquisition devices, so that the sequence of the plurality of sample image acquisition devices in the running process of the train can be clarified, the train is positioned based on serial numbers corresponding to different sample image acquisition devices in the device ordering result, and the positioning accuracy is improved.

The embodiment of the application provides another possible implementation manner, wherein the information of the train to be tracked comprises train number information of the train to be tracked, the operation interval comprises a plurality of stations to be detected, and image acquisition equipment to be confirmed is arranged between two adjacent stations to be detected and inside the stations to be detected.

The detection of the train to be tracked by the target image acquisition device in the calling operation interval in the step S102 can be realized by adopting the following steps: screening a plurality of image acquisition devices to be confirmed according to the current time period and train number information of the train to be tracked, and determining target image acquisition devices; calling target image acquisition equipment to detect a train in a monitoring range of the target image acquisition equipment to obtain detection image information; analyzing the detected image information to determine whether the detected image information comprises a train to be tracked or not; and determining that the train to be tracked is detected under the condition that the train to be tracked is included in the detected image information.

By matching the current time period with train number information (such as the departure time of a train at a start station, the arrival time of the train at a terminal station, and the like) of the train to be tracked, whether the train to be tracked is running in the current time period can be clarified, further, under the condition that the train to be tracked is determined to run in the current time period, target image acquisition devices (such as camera devices arranged between the start station of the train to be tracked and the terminal station of the train to be tracked) corresponding to the current time period and the train number information of the train to be tracked are screened out from a plurality of image acquisition devices to be confirmed, the target image acquisition devices through which the train to be tracked passes can be accurately determined, and the positioning range is shortened.

Further, calling target image acquisition equipment to detect the train in the monitoring range to obtain detection image information; analyzing the detected image information by using a target recognition algorithm of deep learning, and determining whether the detected image information comprises a train to be tracked or not; and under the condition that the detected image information comprises the train to be tracked, the train to be tracked is detected, and the positioning accuracy of the train to be tracked is improved.

In some alternative embodiments, the information of the train to be tracked includes: the running speed of the train to be tracked.

The determining of the real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image capturing device in step S103 may be implemented as follows:

extracting image information of a train to be tracked from target image acquisition equipment; analyzing the extracted image information by adopting a target detection algorithm, and determining the position information of the train to be tracked at the detected moment; and determining the real-time position information of the train to be tracked according to the running speed of the train to be tracked, the current time period information, the time when the train to be tracked is detected and the corresponding position information.

The running speed of the train to be tracked and the current time period information can be used for calculating the running kilometer number of the train to be tracked; then, from the detected position information of the train to be tracked, the kilometer identifier corresponding to the detected time of the train to be tracked is extracted, and based on the kilometer identifier corresponding to the detected time of the train to be tracked and the travelling kilometer number of the train to be tracked, the real-time position information of the train to be tracked (such as the kilometer identifier of the train to be tracked which travels in the current time period) can be calculated and obtained, so that the positioning error is reduced, and the positioning accuracy of the train to be tracked is improved.

In some alternative embodiments, the information of the sample train includes: the running route information corresponding to the sample train;

before the step S101 of inputting the acquired information of the train to be tracked into the train operation model for identification, determining an operation interval of the train to be tracked within a preset period of time, the method further includes: and processing the training data by using a linear regression algorithm of machine learning to obtain a train operation model.

Training data is extracted from historical image information stored in a plurality of sample image acquisition devices in the corresponding operation route information of the sample train, and the training data is trained through a linear regression algorithm of machine learning until the obtained train operation model can identify different trains, so that the train operation model can be used for identifying the trains required to be tracked by a user, and the accuracy of identifying the trains is improved.

For example, fig. 3 is a schematic flow chart of a training method of a train operation model according to an embodiment of the present application. As shown in fig. 3, the training method of the train operation model includes, but is not limited to, the following steps.

Step S301, respectively extracting historical image information stored in each sample image acquisition device according to the running route information corresponding to the sample train.

The running route information corresponding to the sample train can comprise train number information, train schedule, kilometer identification information of the running route, kilometer identification information of each station along the line and the like.

Wherein, the name of the sample image acquisition device can comprise kilometer identification information so as to position each sample image acquisition device.

Step S302, a sample period of a sample image acquisition device through which a sample train passes is acquired.

Wherein, according to the time information of each train operation in the train schedule, the sample period of the sample image acquisition device passed by each sample train can be determined.

For example, in the running process of the train a, the information (such as the time information of the train a passing through the sample image acquisition device, the corresponding kilometer identifier and the like) of the train a is recorded in the sample image acquisition device (such as the monitoring camera or the photographing device and the like), so that the sample time period (such as 10:00-10:15, 11:00-11:15, 12:00-12:15 and the like) corresponding to the train a can be extracted.

Step S303, determining training data according to a plurality of sample periods, historical image information stored in a plurality of sample image acquisition devices and device ordering results.

The device ordering result is a result obtained by ordering each sample image acquisition device based on kilometer identification information included in the name of the sample image acquisition device. The running sequence of the sample train and the sequence of the sample image acquisition equipment corresponding to the running sequence can be embodied.

Further, based on the device sorting result, a plurality of sample image acquisition devices, through which the sample train passes in a plurality of sample time periods, can be clarified, so that historical image information corresponding to each sample time period, which is stored in the sample image acquisition devices, is extracted, and analysis is conducted on the historical image information, and training data is determined.

And step S304, processing the training data by using a linear regression algorithm of machine learning to obtain a train operation model.

The train time in the training data and the line kilometer mark can be linearly corresponding to obtain information with operation rules, so that a train operation model is obtained.

For example, fig. 4 is a schematic diagram of a process of using a linear regression algorithm of machine learning to identify a train time and kilometer along a train running line according to an embodiment of the present application. As shown in fig. 4, a coordinate system is established with the train time as a vertical axis and the line kilometer sign as a horizontal axis, wherein at least two dashed lines (such as a dashed line 401 and a dashed line 402 in fig. 4) with a linear rule can be obtained by plotting points in the coordinate system by a corresponding relationship between a plurality of groups of train time of training data and the line kilometer sign. As can be seen from the law presented in fig. 4, the train operation model varies linearly and ranges between the dashed line 401 and the dashed line 402.

Through the processing, the obtained train operation model can embody which line kilometer identifier the sample train runs to at different train moments, so that the sample train is positioned rapidly and accurately, and the positioning accuracy is improved.

In some alternative embodiments, determining training data from a plurality of sample periods, historical image information stored in a plurality of sample image acquisition devices, and device ordering results includes: screening the historical image information stored in the sample image acquisition equipment according to the sample time periods, and determining sample image information to be processed, which is matched with the sample time periods; according to the device ordering result and the identification of the image acquisition device corresponding to the sample image information to be processed, ordering the sample image information to be processed to obtain image ordering information; and determining training data according to the image ordering information.

For example, taking N sample periods and the history image information stored in the M sample image capturing devices, and obtaining N pieces of sample image information to be processed corresponding to the N sample periods by extracting images including the sample periods in the history image information; further, the identifiers of the image acquisition devices corresponding to the N pieces of sample image information to be processed are ordered, and because the N sample time periods have time continuity, the corresponding N pieces of sample image information to be processed also have time continuity, so that the image acquisition devices corresponding to the sample image information to be processed are ordered based on the time continuity, and the obtained image ordering information can reflect the sequence of the region through which the sample train passes. Wherein N, M is an integer greater than or equal to 1, and M is greater than N.

And, the training data includes a plurality of training images, the training images including: the image information which corresponds to the sample train and has the running time sequence is processed by adopting a linear regression algorithm which uses machine learning, so that the obtained train running model can embody the running sequence of the train in the running time sequence, the running rule of the sample train and the running rule of the train can be embodied, and the subsequent positioning of the train is convenient.

In some optional embodiments, after determining the real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image capturing device in step S103, the method further includes: and feeding back the real-time position information of the train to be tracked and the image information of the train to be tracked to the user terminal so as to monitor the train to be tracked by the user terminal.

The image information of the train to be tracked comprises: an image with run-time information and/or with a run kilometer identification.

For example, through automatic screen cutting and playing technology of videos, real-time position information of a train to be tracked, image information related to the real-time position information and the like are displayed on a user terminal, and further, an automatic staring control function of the train to be tracked can be achieved by switching other camera equipment adjacent to the real-time position information of the train to be tracked, the user terminal can be guaranteed to observe the running condition of the train to be tracked in real time, the real-time running environment of the train to be tracked is monitored conveniently, and running safety of the train is improved.

A second aspect of the embodiments of the present application provides a train positioning device. Fig. 5 is a block diagram of a train positioning device according to an embodiment of the present application. As shown in fig. 5, the train positioning device 500 includes, but is not limited to, the following modules.

The identifying module 501 is configured to input the acquired information of the train to be tracked into a train operation model for identification, and determine an operation interval of the train to be tracked within a preset period, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning.

The detection module 502 is configured to call a target image acquisition device in the operation interval to detect the train to be tracked.

The determining module 503 is configured to determine real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image capturing device in a case where it is determined that the train to be tracked is detected.

In the embodiment, the acquired information of the train to be tracked is input into a train operation model through an identification module to be identified, and an operation interval of the train to be tracked in a preset period is determined, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning, so that a specific operation range of the train to be tracked is defined, and a monitoring range of the train to be tracked is reduced; the detection module is used for calling the target image acquisition equipment in the operation interval to detect the train to be tracked so as to determine whether the target image acquisition equipment can detect the train to be tracked, and the determination module is used for determining the real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image acquisition equipment under the condition that the train to be tracked is determined to be detected, so that the real-time operation condition of the train to be tracked and the surrounding operation environment of the train to be tracked are conveniently monitored, and the operation safety of the train is improved.

It should be noted that each module in this embodiment is a logic module, and in practical application, one logic unit may be one physical unit, or may be a part of one physical unit, or may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present application, elements that are not so close to solving the technical problem presented in the present application are not introduced in the present embodiment, but it does not indicate that other elements are not present in the present embodiment.

A third aspect of the embodiments of the present application provides a train positioning system. Fig. 6 is a block diagram of a train positioning system according to an embodiment of the present application. As shown in fig. 6, the train positioning system includes, but is not limited to, the following:

the train tracking device 610, the user terminal 620, the video monitoring server 630 and the plurality of image capturing devices 640 (e.g., the first image capturing device 641, the second image capturing devices 642, … …, the nth image capturing device 64N) are in communication connection, wherein N represents the number of image capturing devices, and N is an integer greater than or equal to 1.

Wherein the train tracking device 610 is configured to perform any of the train positioning methods of the embodiments of the present application.

The user terminal 620 is configured to send a positioning request to the train tracking device, where the positioning request includes information of the train to be tracked, so that the train tracking device inputs the acquired information of the train to be tracked into the train operation model for identification, and determines an operation interval of the train to be tracked within a preset period;

the video monitoring server 630 is configured to manage a plurality of image acquisition devices and acquire image information required by the train tracking device;

the image acquisition device 640 is configured to acquire images of the trains within the monitoring range thereof, and send the acquired vehicles to the video monitoring server.

Fig. 7 is a block diagram of a train positioning system according to an embodiment of the present application. As shown in fig. 7, the train positioning system includes a train tracking system 700 and a rail integrated video monitoring system 710. The train tracking system 700 includes, but is not limited to, the following modules: a user interface module 701, an integrated video access module 702, a video detection module 703 and a train data management module 704.

The integrated video surveillance system 710 communicates with the train tracking system 700 via the integrated video access module 702.

The integrated video access module 702 is configured to obtain information of a tracking line camera (for example, an identifier of a camera along a railway, a name and position information of the camera, etc.) through an interface with the integrated video monitoring system 710 of a railway, and question kilometer identification information (for example, the number of kilometers, the position of kilometers, etc.) corresponding to the camera from the name of the camera; and provides a unified video access interface for other modules.

The video detection module 703 is configured to detect video information based on a target detection algorithm of the train, where the video information is information obtained from the integrated video access module 702. When a train to be tracked is detected, the information of the train to be tracked is sent to the train data management module 704.

The train data management module 704 is configured to import a train schedule of related train numbers according to a user's requirement, and send a train detection task to the video detection module 703 according to the train schedule; determining a train operation model based on the information of the received multiple sample trains and a linear regression algorithm based on machine learning; in the case where it is determined that the train to be tracked is detected, real-time position information of the train to be tracked is determined based on the image information of the train to be tracked extracted from the target image acquisition device.

The user interface module 701 automatically retrieves video information of the train to be tracked from the integrated video access module 702 according to the user's requirements and the information fed back by the train data management module 704, and pushes the video information to other display devices if necessary.

Fig. 8 is a flow chart of a working method of the train positioning system according to the embodiment of the present application. As shown in fig. 8, the method of operation of the train positioning system includes, but is not limited to, the following steps.

Step S801, information of a train to be tracked selected by a user terminal is acquired.

The user terminal is a terminal which is expected to track or position the train to be tracked.

Step S802, inputting information of the train to be tracked into a train operation model for identification, and determining an operation interval of the train to be tracked in a preset period.

And step 803, calling target image acquisition equipment in the operation interval to detect the train to be tracked.

In step S804, in the case where it is determined that the train to be tracked is detected, real-time position information of the train to be tracked is determined based on the image information of the train to be tracked extracted from the target image capturing device.

Step S805, feeding back the determined real-time position information of the train to be tracked to the user terminal, so that the user terminal can determine the running state of the train to be tracked based on the real-time position information of the train to be tracked.

The user terminal can monitor and manage the train to be tracked conveniently through the determined real-time position information of the train to be tracked, and the running safety of the train is improved.

In the embodiment, the positioning and tracking of the train to be tracked can be realized without using a DMS system, so that the positioning of the train to be tracked can be realized through a railway integrated video monitoring system. The method comprises the steps of inputting the acquired information of a train to be tracked into a train operation model for identification, and determining an operation interval of the train to be tracked in a preset period, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning, so that a specific operation range of the train to be tracked is defined, and a monitoring range of the train to be tracked is reduced; and calling target image acquisition equipment in the operation interval to detect the train to be tracked so as to determine whether the target image acquisition equipment can detect the train to be tracked, and determining real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image acquisition equipment under the condition that the train to be tracked is determined to be detected, so that the real-time operation condition of the train to be tracked and the surrounding operation environment of the train to be tracked are conveniently monitored, and the operation safety of the train is improved.

The fourth aspect of the embodiments of the present application provides an electronic device and a computer readable storage medium, where the foregoing may be used to implement any one of the train positioning methods in the embodiments of the present application, and corresponding technical solutions and descriptions and corresponding descriptions referring to method parts are not repeated.

Fig. 9 is a block diagram of an electronic device according to an embodiment of the present application. As shown in fig. 9, an embodiment of the present application provides an electronic device, including: at least one processing module 901; at least one memory 902, and one or more I/O interfaces 903, connected between the processing module 901 and the memory 902; the memory 902 stores one or more computer programs executable by the at least one processing module 901, and the one or more computer programs are executed by the at least one processing module 901 to enable the at least one processing module 901 to perform the train positioning method described above.

The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program realizes the train positioning method when being executed by a processor/processing core. The computer readable storage medium may be a volatile or nonvolatile computer readable storage medium.

Embodiments of the present application also provide a computer program product comprising computer readable code, or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the train positioning method described above.

Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer-readable storage media, which may include computer storage media (or non-transitory media) and communication media (or transitory media).

The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable program instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, random Access Memory (RAM), read Only Memory (ROM), erasable Programmable Read Only Memory (EPROM), static Random Access Memory (SRAM), flash memory or other memory technology, portable compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and may include any information delivery media.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for performing the operations of the present application may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present application are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which may execute the computer readable program instructions.

The computer program product described herein may be embodied in hardware, software, or a combination thereof. In an alternative embodiment, the computer program product is embodied as a computer storage medium, and in another alternative embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), or the like.

Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted in a generic and descriptive sense only and not for purpose of limitation. In some instances, it will be apparent to one skilled in the art that features, characteristics, and/or elements described in connection with a particular embodiment may be used alone or in combination with other embodiments unless explicitly stated otherwise. It will therefore be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the scope of the present application as set forth in the following claims.

Claims

1. A method of locating a train, the method comprising:

inputting the acquired information of the train to be tracked into a train operation model for identification, and determining an operation interval of the train to be tracked in a preset period, wherein the train operation model is a model obtained by processing information of a plurality of sample trains by using a linear regression algorithm of machine learning;

invoking target image acquisition equipment in the operation interval to detect the train to be tracked;

And determining real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image acquisition equipment under the condition that the train to be tracked is determined to be detected.

2. The method according to claim 1, wherein the information of the train to be tracked includes train number information of the train to be tracked, the operation section includes a plurality of stations to be detected, and image acquisition devices to be confirmed are arranged between two adjacent stations to be detected and inside the stations to be detected;

the step of calling the target image acquisition equipment in the operation interval to detect the train to be tracked comprises the following steps:

screening a plurality of image acquisition devices to be confirmed according to the current time period and the train number information of the train to be tracked, and determining the target image acquisition device;

calling the target image acquisition equipment to detect the train in the monitoring range to obtain detection image information;

analyzing the detection image information to determine whether the detection image information comprises the train to be tracked or not;

and determining that the train to be tracked is detected under the condition that the train to be tracked is included in the detection image information.

3. The method of claim 1, wherein the information of the train to be tracked comprises: the running speed of the train to be tracked;

the determining real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image acquisition equipment comprises the following steps:

extracting the image information of the train to be tracked from the target image acquisition equipment;

analyzing the extracted image information by adopting a target detection algorithm, and determining the position information of the train to be tracked at the detected moment;

and determining the real-time position information of the train to be tracked according to the running speed of the train to be tracked, the current time period information, the time when the train to be tracked is detected and the corresponding position information of the train to be tracked.

4. The method of claim 1, wherein the information of the sample train comprises: information of a plurality of sample image acquisition devices, wherein the sample image acquisition devices are image acquisition devices between a starting station and a stopping station corresponding to the sample train;

the step of inputting the acquired information of the train to be tracked into a train operation model for identification, and before determining the operation interval of the train to be tracked in a preset period, the method further comprises the steps of:

Determining kilometer identification information corresponding to each sample image acquisition device according to the obtained identification of each sample image acquisition device;

and sequencing the plurality of sample image acquisition devices according to kilometer identification information corresponding to each sample image acquisition device to obtain a device sequencing result.

5. The method of claim 4, wherein the information of the sample train comprises: running route information corresponding to the sample train;

respectively extracting historical image information stored in each sample image acquisition device according to the running route information corresponding to the sample train;

acquiring a sample period of the sample image acquisition device through which the sample train passes;

determining training data according to the plurality of sample periods, the historical image information stored in the plurality of sample image acquisition devices and the device ordering result;

and processing the training data by using a linear regression algorithm of machine learning to obtain the train operation model.

6. The method of claim 5, wherein said determining training data based on a plurality of said sample periods, historical image information stored in a plurality of said sample image capturing devices, and said device ordering results comprises:

screening the historical image information stored in the sample image acquisition equipment according to the sample time periods to determine sample image information to be processed, which is matched with the sample time periods;

sorting the plurality of sample image information to be processed according to the device sorting result and the identification of the image acquisition device corresponding to the sample image information to be processed, so as to obtain image sorting information;

determining the training data according to the image ordering information;

wherein the training data comprises a plurality of training images, the training images comprising: image information corresponding to the sample train having a run-time order.

7. The method according to any one of claims 1 to 6, wherein after the determining of the real-time position information of the train to be tracked based on the image information of the train to be tracked extracted from the target image capturing device, further comprises:

Feeding back the real-time position information of the train to be tracked and the image information of the train to be tracked to a user terminal so that the user terminal can monitor the train to be tracked;

8. A train positioning system, comprising: the system comprises a train tracking device, a user terminal, a video monitoring server and a plurality of image acquisition devices which are in communication connection;

the train tracking device configured to perform the train positioning method of any one of claims 1 to 7;

the user terminal is configured to send a positioning request to the train tracking device, wherein the positioning request comprises information of a train to be tracked, so that the train tracking device can input the acquired information of the train to be tracked into a train operation model for identification, and an operation interval of the train to be tracked in a preset period is determined;

the video monitoring server is configured to manage a plurality of image acquisition devices and acquire image information required by the train tracking device;

the image acquisition device is configured to acquire images of the trains within the monitoring range and send the acquired vehicles to the video monitoring server.

9. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores one or more computer programs executable by the at least one processor to enable the at least one processor to perform the train positioning method of any of claims 1-7.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the train positioning method according to any of claims 1-7.