Railway trunk early warning method and device, electronic equipment and storage medium

Abstract

The invention discloses a railway trunk early warning method and device, electronic equipment and a storage medium, and the method is characterized in that the method comprises the steps: collecting target operation road condition data, and the target operation road condition data comprises target operation road condition image data and target operation road condition point cloud data; judging whether a preset abnormal road condition exists in the target operation road condition data or not; if yes, early warning information is sent out, and the early warning information comprises abnormal road condition information. The problem that the abnormal condition of the railway trunk cannot be accurately and rapidly identified in the prior art is solved, and the railway line management and emergency efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of rail transit safety warning, and in particular to a railway trunk line warning method, device, electronic equipment and storage medium. Background Art

[0002] Railway transportation is one of the most important forms of transportation in the world. The safety of railway trunk lines is crucial to maintaining social stability and promoting economic development. At present, railway trunk line safety warnings are mainly achieved through visual methods of train drivers or inspectors inspecting trains or video monitoring methods set up on trains. However, this warning method has certain limitations and passivity. The manual inspection and observation process is cumbersome and may be affected by weather, time, and the status of inspectors. The video monitoring method only uses cameras for monitoring, and the monitoring accuracy is greatly affected by natural conditions such as light, dust, and fog. The above two warning methods are prone to misjudgment and omission of abnormal situations, and cannot timely realize the warning of abnormal situations on railway trunk lines. .

[0003] Therefore, it is necessary to provide a railway trunk line early warning method to solve the problem that the existing technology cannot accurately and quickly identify abnormal conditions of railway trunk lines and improve the railway line management and emergency efficiency. Summary of the invention

[0004] The main purpose of the present invention is to provide a railway trunk line early warning method, device, electronic equipment and storage medium to solve the problem that the prior art cannot accurately and quickly identify abnormal conditions of railway trunk lines, and improve railway line management and emergency efficiency.

[0005] In a first aspect, the present invention provides a railway trunk line early warning method, comprising:

[0006] Collecting target running road condition data, wherein the target running road condition data includes target running road condition image data and target running road condition point cloud data;

[0007] Determine whether the target running road condition data has a preset abnormal road condition;

[0008] If so, a warning message is issued, which includes abnormal road condition information.

[0009] Optionally, the step of collecting target running road condition data includes:

[0010] Collect target running road condition image data through target camera;

[0011] The target road condition point cloud data is collected through the target laser radar.

[0012] Optionally, the step of determining whether the target running road condition data has a preset abnormal road condition includes:

[0013] Perform image recognition on the target running road condition image data to determine whether there is a preset abnormal image in the target running road condition image data.

[0014] Optionally, the step of determining whether the target running road condition data has a preset abnormal road condition includes:

[0015] Determine whether there is a preset abnormal point cloud in the target running road condition point cloud data.

[0016] Optionally, the warning information includes an abnormality code, and the abnormality code includes the locomotive number, train number, locomotive position, abnormality type, and abnormal relative locomotive position.

[0017] Optionally, the step of issuing warning information includes:

[0018] Sent to the ground monitoring center or driver monitoring station.

[0019] According to a second aspect of an embodiment of the present invention, the present invention provides a railway trunk line early warning device, comprising:

[0020] Acquisition module: used for acquiring target running road condition data, wherein the target running road condition data includes target running road condition image data and target running road condition point cloud data;

[0021] A judgment module: used to judge whether the target running road condition data has a preset abnormal road condition;

[0022] Warning module: used to issue warning information, which includes abnormal road condition information.

[0023] Optionally, the judgment module further includes an image judgment unit: configured to perform image recognition on the target running road condition image data to judge whether there is a preset abnormal image in the target running road condition image data.

[0024] According to a third aspect of an embodiment of the present invention, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the railway trunk line early warning method described in any one of the first aspects above.

[0025] According to a fourth aspect of an embodiment of the present invention, the present invention provides a storage medium storing a program, wherein the program, when executed by a computer, implements the railway trunk line early warning method described in any one of the first aspects above.

[0026] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0027] The present invention solves the problem that the prior art cannot accurately and quickly identify abnormal conditions of railway trunk lines by fusing and judging road condition image data and road condition point cloud data, thereby improving railway line management and emergency response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, for non-specialized technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0029] Figure 1 A schematic diagram of a flow chart of a railway trunk line early warning method provided by an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of a railway trunk line early warning device provided by an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of a network device management computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present invention and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the protection scope of the present invention.

[0033] Embodiment 1

[0034] like Figure 1 As shown, an embodiment of the present invention provides a railway trunk line early warning method, comprising the following steps S101 to S103:

[0035] Step S101: collecting target running road condition data, wherein the target running road condition data includes target running road condition image data and target running road condition point cloud data;

[0036] For example, the method of collecting target running road condition data is, for example, through a camera and a lidar, respectively performing real-time image collection and point cloud data collection on the locomotive running road condition.

[0037] Step S102: determining whether the target running road condition data has a preset abnormal road condition;

[0038] For example, after collecting the target operating road condition image data, the abnormal conditions in the target operating road condition image data are identified, and when the image recognition detects an abnormality, the abnormal image data is cached. After collecting the target operating road condition point cloud data, the abnormal conditions in the point cloud are identified, and when the abnormality is identified, the abnormal point cloud data is cached. The preset abnormal road conditions include situations where people, animals, stones, etc. within the railway trunk line limit interfere with the operation of the train or other abnormal conditions that the user is concerned about, which can be set by the target user. By combining the abnormal image data and the abnormal point cloud data, it is determined whether the target operating road condition data has a preset abnormal road condition. For example, the abnormal image data includes abnormal animals, and there is an abnormal vector set in the abnormal point cloud data, it can be determined that the target operating road condition data has a preset abnormal road condition. The preset abnormal image is such as an abnormal animal image, and the preset abnormal point cloud data is such as a preset abnormal vector set.

[0039] Step S103: issuing a warning message, wherein the warning message includes abnormal road condition information.

[0040] For example, if there are abnormal road conditions, the abnormal image data and abnormal point cloud data are fused and an abnormal code is generated. The abnormal code includes the locomotive number, train number, locomotive position, abnormal type, and abnormal relative locomotive position. The abnormal code is sent to the ground monitoring center or the driver monitoring station for early warning.

[0041] At present, railway trunk line safety warning is mainly achieved through the visual method of train drivers or patrol train inspectors or the video monitoring method set up on the train. However, this application method has certain limitations and passivity. Manual inspection is cumbersome and may be affected by weather, time, and inspector status. The video monitoring method only uses a single sensor, and the monitoring accuracy is greatly affected by natural conditions such as light. The above two detection methods are prone to misjudgment and missed judgment, and cannot timely realize the warning of abnormal conditions on railway trunk lines. Compared with the existing warning technology, the present invention provides a railway trunk line warning method driven by multi-source information fusion, which uses multiple sensor devices such as cameras and laser radars to realize the perception of railway trunk lines, and fuses these sensor data to realize the detection of abnormal conditions and safety warnings, thereby improving the management and emergency efficiency of railway lines.

[0042] Embodiment 2

[0043] like Figure 2 As shown, an embodiment of the present invention provides a railway trunk line early warning device, comprising:

[0044] Acquisition module: used for acquiring target running road condition data, wherein the target running road condition data includes target running road condition image data and target running road condition point cloud data;

[0045] A judgment module: used to judge whether the target running road condition data has a preset abnormal road condition;

[0046] Warning module: used to issue warning information, which includes abnormal road condition information.

[0047] Optionally, the judgment module further includes an image judgment unit: configured to perform image recognition on the target running road condition image data to judge whether there is a preset abnormal image in the target running road condition image data.

[0048] Optionally, the judgment module also includes a point cloud judgment unit: used to judge whether there is a preset abnormal point cloud in the target running road condition point cloud data.

[0049] Optionally, the exception code includes locomotive number, train number, locomotive position, exception type, and exception relative locomotive position.

[0050] Optionally, the warning module is also used to send warning information to a ground monitoring center or a driver monitoring station.

[0051] Embodiment 3

[0052] Figure 3 A schematic diagram of the structure of a computer device provided by an exemplary embodiment of the present application is shown, and the computer device includes:

[0053] The processor 301 includes one or more processing cores. The processor 301 executes various functional applications and data processing by running software programs and modules.

[0054] The receiver 302 and the transmitter 303 can be implemented as a communication component, and the communication component can be a communication chip. Optionally, the communication component can be implemented to include a signal transmission function. That is, the transmitter 303 can be used to transmit a control signal to the image acquisition device and the scanning device, and the receiver 302 can be used to receive the corresponding feedback instruction.

[0055] The memory 304 is connected to the processor 301 via a bus 305 .

[0056] The memory 304 may be used to store at least one instruction, and the processor 301 may be used to execute the at least one instruction to implement steps S101 to S104 in the above control method embodiment.

[0057] Those skilled in the art will understand that Figure 3 It is only an example of a computer device and does not constitute a limitation of the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include a network access device, etc.

[0058] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0059] The memory 304 may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. The memory 304 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the computer device. Further, the memory 304 may also include both an internal storage unit of the computer device and an external storage device. The memory 304 is used to store the computer program and other programs and data required by the terminal device. The memory 304 may also be used to temporarily store data that has been output or is to be output.

[0060] Embodiment 4

[0061] An embodiment of the present application also provides a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set, which is loaded and executed by a processor to implement the above-mentioned railway trunk line early warning method.

[0062] Optionally, the computer readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid state drive (SSD) or an optical disk, etc. Among them, the random access memory may include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0063] Embodiment 5

[0064] The present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the railway trunk line early warning method described in any of the above embodiments.

[0065] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the pros and cons of the implementation.

[0066] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned embodiments can be completed by hardware, or by instructing the relevant hardware to complete the steps through a program. The program can be stored in a computer-readable storage medium, and the storage medium mentioned above can be a read-only memory, a disk or an optical disk, etc. Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0067] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0068] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0069] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0070] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A railway trunk line early warning method, characterized in that: include: Collecting target running road condition data, wherein the target running road condition data includes target running road condition image data and target running road condition point cloud data; Determine whether the target running road condition data has a preset abnormal road condition; If so, a warning message is issued, which includes abnormal road condition information.

2. The method according to claim 1, characterized in that The step of collecting target running road condition data includes: Collect target running road condition image data through target camera; The target road condition point cloud data is collected through the target laser radar.

3. The method according to claim 1, characterized in that The step of determining whether the target running road condition data has a preset abnormal road condition includes: Perform image recognition on the target running road condition image data to determine whether there is a preset abnormal image in the target running road condition image data.

4. The method according to claim 1, characterized in that: The step of determining whether the target running road condition data has a preset abnormal road condition includes: Determine whether there is a preset abnormal point cloud in the target running road condition point cloud data.

5. The method according to claim 1, characterized in that: The warning information includes an abnormality code, and the abnormality code includes the locomotive number, train number, locomotive position, abnormality type, and abnormal relative locomotive position.

6. The method according to claim 1, characterized in that The step of issuing the warning information includes: Sent to the ground monitoring center or driver monitoring station.

7. A railway trunk line early warning device, characterized in that: include: Acquisition module: used for acquiring target running road condition data, wherein the target running road condition data includes target running road condition image data and target running road condition point cloud data; A judgment module: used to judge whether the target running road condition data has a preset abnormal road condition; Warning module: used to issue warning information, which includes abnormal road condition information.

8. The device according to claim 7, characterized in that The judgment module further includes an image judgment unit: configured to perform image recognition on the target running road condition image data, and judge whether there is a preset abnormal image in the target running road condition image data.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the railway trunk line early warning method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a computer, the railway trunk line early warning method according to any one of claims 1 to 6 is implemented.

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