Railway line intelligent inspection method

Abstract

The invention relates to an intelligent inspection method for a railway line, and the method comprises the steps: constructing a digital twinborn model of the railway line, and constructing a digital twinborn model of the railway line; voice information of a user is obtained, and a voice instruction carried in the voice information is recognized; according to the voice instruction, controlling environment monitoring equipment corresponding to the voice instruction to collect environment information of the railway line; and synchronously updating the digital twin model according to the environment information collected by the environment monitoring equipment so as to be used for railway line maintenance decision making. Railway line inspection is carried out based on the digital twin data and the voice recognition technology, and the timeliness of fault discovery and the efficiency of fault processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of railway operation and maintenance, and in particular to an intelligent railway line inspection method. Background Art

[0002] With the rapid development of the railway transportation industry, the operation and maintenance management of railway lines has become increasingly complex, and the requirements for inspection work have become higher and higher.

[0003] Traditional railway line inspections mainly rely on manual work, which is not only time-consuming and labor-intensive, but also poses certain safety risks.

[0004] Therefore, how to improve inspection efficiency and safety has become the focus of railway operation and maintenance technology research. Summary of the invention

[0005] In response to the above problems, the purpose of the present invention is to provide an intelligent railway line inspection method. Compared with the existing technology, the railway line inspection can be carried out based on digital twin data and speech recognition technology, thereby improving the timeliness of fault discovery and the efficiency of fault handling.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present application provides a railway line intelligent inspection method, the method comprising:

[0008] Constructing a digital twin model of the railway line;

[0009] Obtain the user's voice information and recognize the voice commands contained therein;

[0010] According to the voice command, controlling the environment monitoring device corresponding to the voice command to collect the environment information of the railway line;

[0011] The digital twin model is synchronously updated based on the environmental information collected by the environmental monitoring equipment for use in railway line maintenance decision-making.

[0012] In one implementation, the digital twin model establishes a virtual mapping with the real railway line, including station information, tunnel information and running direction of the railway line.

[0013] In one implementation, the railway line corresponds to a bidirectional coordinate system; each station corresponds to a name, a station code and a unique location coordinate; each tunnel corresponds to a tunnel code, a tunnel starting location coordinate and a tunnel ending location coordinate; the running direction includes up, down and random.

[0014] In one implementation, each environment monitoring device has a corresponding device code.

[0015] In one implementation, a correspondence between a device code and a railway line location coordinate is established; and a correspondence between a device code and a station code and / or a tunnel code is established.

[0016] In one implementation, controlling the environment monitoring device corresponding to the voice instruction to collect the environment information of the railway line according to the voice instruction includes:

[0017] Identify the railway line location coordinates, or station code, or tunnel code, and running direction corresponding to the voice command;

[0018] Controlling the environmental monitoring equipment corresponding to the railway line location coordinates, or the station code, or the tunnel code to collect environmental information; and

[0019] According to the running direction, the running sequence of the environment monitoring devices and the running time intervals between the devices are controlled.

[0020] In one implementation, the environment monitoring device at least includes an image acquisition device.

[0021] In one implementation, the method further includes: synchronously displaying the environmental information collected by the environmental monitoring device.

[0022] In one implementation, the method further includes: analyzing the environmental information collected by the environmental monitoring device based on a preset deep learning algorithm to identify fault signals of the railway line.

[0023] In one implementation, the method further includes: performing a preset alarm according to the fault signal.

[0024] The present invention adopts the above technical solution, which has the following advantages:

[0025] 1. Improve inspection efficiency: By creating a virtual copy of the railway line through digital twin technology, the line conditions in the real world can be simulated and analyzed in a digital environment, so as to quickly identify problem areas and reduce the time and cost of on-site inspections.

[0026] 2. Real-time monitoring and early warning: Digital twin technology can synchronize the status data of railway lines in real time. Combined with the instant feedback of the voice recognition system, managers can quickly obtain key information and take preventive measures before problems occur.

[0027] 3. Reduce human errors: The voice recognition system can automatically record the information reported by the inspectors, reduce errors in the manual input process, and improve the accuracy of data records.

[0028] 4. Improved safety: Through the intelligent inspection system, inspectors can complete most inspection work in a safe environment, reducing the risk of on-site operations, especially in bad weather or dangerous areas.

[0029] 5. Data analysis and optimization: The large amount of data collected can be used for in-depth analysis to help railway management departments optimize maintenance strategies and workflows and improve overall operational efficiency.

[0030] 6. Resource conservation: Reducing the frequency of on-site inspections and manpower requirements can save resources and reduce the impact on the environment.

[0031] 7. Improve the scientific nature of maintenance decisions: Through comprehensive analysis and evaluation of the railway line status, maintenance plans can be arranged more scientifically to ensure the reliability and safety of railway operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a method flow chart of the method according to an embodiment of the present invention;

[0033] Figure 2 is a schematic diagram of partial information of a model constructed based on the digital twin technology according to an embodiment of the present invention;

[0034] Figure 3 It is a schematic diagram of partial control information of an image acquisition device according to an embodiment of the present invention;

[0035] Figure 4 and Figure 5 It is the name of the decision support algorithm and a flowchart of the embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0037] In response to the problems of low efficiency and poor safety of manual railway inspection in the prior art, the present application provides a method for intelligent railway line inspection, which includes: constructing a digital twin model of the railway line; obtaining the user's voice information and identifying the voice instructions carried therein; according to the voice instructions, controlling the environmental monitoring equipment corresponding to the voice instructions to collect the environmental information of the railway line; according to the environmental information collected by the environmental monitoring equipment, synchronously updating the digital twin model for railway line maintenance decision-making.

[0038] The method provided in the present application is described in more detail below in conjunction with the accompanying drawings of the present application to illustrate its process and technical advantages.

[0039] like Figure 1 , providing an overall method flow chart.

[0040] Among them, in the embodiment of the present application, computer vision and modeling software are used to generate a 3D model of the railway line based on the collected data, and it is updated synchronously with the physical entity.

[0041] Specific implementation methods include:

[0042] 1) Line data collection

[0043] According to the current statistics of existing resources, there are 18 railway bureaus in China. Each of the 18 railway bureaus is coded. Each railway bureau is responsible for several lines. Each line is coded and the line length (unit: km) is counted. Due to the complex environment around the railway line, we adopt simplified modeling when modeling, and only model the line, stations and tunnels along the line. In the subsequent solution description, the Beijing-Shanghai line section under the jurisdiction of the Beijing Bureau will be used as an example for detailed description. The data required for modeling are as follows Figure 2 :

[0044] Figure 2 The data is based on the comprehensive video surveillance system, and all subsequent operations must rely on Figure 2 Therefore, in the process of digital twin modeling, the current modeling line (per kilometer) must be uniquely coded according to the road bureau code and line code. The coding rules are as follows:

[0045] Railway Bureau Code: Non-repeating English letters from AZ

[0046] Line code: L001-L999 non-repeating L + three digits

[0047] Station code: S0001-S9999, non-repeating S+four digits

[0048] Tunnel code: T00001-T99999, non-repeating T+5 digits

[0049] like:

[0050] Beijing Bureau, Beijing-Shanghai Line, 100 km, code: A_L001_K100

[0051] Beijing Bureau Beijing-Shanghai Line Langfang Station, code: A_L001_S0002_K56

[0052] Beijing Bureau Beijing-Shanghai Line Tunnel 1, code: A_L001_T00001_K101-K106

[0053] 2) Camera deployment along the route

[0054] According to the current camera inventory of the China Railway Corporation, there are about 150,000 cameras, which can fully cover the tracks along the high-speed railway. In the integrated video surveillance system, the naming rules of each camera are fixed, including the line name + line kilometer mark + up / down line, which can be accurately marked in the model, such as Figure 3 As shown:

[0055] Figure 3 The data is based on the integrated video surveillance system, and subsequent inspection operations rely on Figure 3 Therefore, in the process of modeling, all cameras must be uniquely coded according to the bureau code and line code. The coding rules are as follows:

[0056] Railway Bureau Code: Non-repeating English letters from AZ

[0057] Line code: L001-L999 non-repeating L + three digits

[0058] For example: Beijing Bureau Beijing-Shanghai Line K4+522BJN-LF01 outdoor fixed focus uplink, code: A_L001_U_K4522

[0059] Beijing Bureau Beijing-Shanghai Line K14+713BJN-LF04 indoor hemisphere downlink, code: A_L001_D_K14713.

[0060] Furthermore, there are two ways to conduct inspections. The first is to conduct scheduled inspections based on historical inspection records or to select a task in the history list by voice to start inspections. The other is to start inspections by voice inputting inspection tasks.

[0061] For example: a. Inspection of Beijing Railway Station to Tianjin Station on Beijing-Shanghai Railway of Beijing Bureau

[0062] b. Inspection of Beijing-Shanghai Line Tunnel 1 of Beijing Bureau

[0063] c. Inspection of Beijing-Shanghai Line of Beijing Bureau from 100km to 300km

[0064] d. Inspection of the entire Beijing-Shanghai line of Beijing Bureau (up / down / random)

[0065] Among them, inspection history tasks:

[0066] You can view historical inspection tasks in the system, and set a scheduled inspection cycle for historical inspection tasks. You can also directly say in the system: "Open the inspection task list", "Inspect task No. 5"

[0067] This application integrates advanced speech recognition technology, which can accurately understand and execute the instructions of inspectors. During the line inspection process, inspectors can speak out the inspection requirements, and then use speech recognition technology to convert the speech into text information, extract effective key information, and match it in the digital twin model.

[0068] The specific cases are as follows:

[0069] Case A: The inspector said "Inspection of Beijing Railway Station to Tianjin Railway Station on Beijing-Shanghai Railway of Beijing Bureau";

[0070] Specific solutions for speech recognition technology include:

[0071] (1) Voice command recognition

[0072] When the system receives the above command, it converts the voice into text and extracts the keywords: Beijing Bureau, Beijing-Shanghai Line, Beijing Station, Tianjin Station. If no valid keywords can be extracted, it will prompt "Invalid command";

[0073] (2) Effective voice commands are matched in the database to find the inspection range

[0074] After extracting the keywords in the previous step, match the keywords in the database to find the unique code: A_L001_K0 and A_L001_K103, as well as the uplink and downlink identifiers all / U / D / random

[0075] Case b: The inspector said "Inspection of Beijing Bureau Beijing-Shanghai Line Tunnel 1";

[0076] (1) Voice command recognition

[0077] When the system receives the above command, it converts the voice into text and extracts the keywords: Beijing Bureau, Beijing-Shanghai Line, Tunnel 1. If no valid keywords can be extracted, it will prompt "Invalid command";

[0078] (2) Effective voice commands are matched in the database to find the inspection range

[0079] After extracting the keywords in the previous step, match the keywords in the database to find the unique code: A_L001_K101 and A_L001_K106, as well as the uplink and downlink identifiers all / U / D / random

[0080] Case C: The inspector said "inspection of the Beijing-Shanghai line from 100km to 300km";

[0081] (1) Voice command recognition

[0082] When the system receives the above command, it converts the voice into text and extracts the keywords: Beijing Bureau, Beijing-Shanghai Line, 100km, 300km. If no valid keywords can be extracted, it will prompt "Invalid command";

[0083] (2) Effective voice commands are matched in the database to find the inspection range

[0084] After extracting the keywords in the previous step, match the keywords in the database to find the unique code: A_L001_K100 and A_L001_K300, as well as the uplink and downlink identifiers all / U / D / random

[0085] Case d: The inspector said "Inspection of the entire Beijing-Shanghai line of Beijing Bureau (up / down / random)";

[0086] (1) Voice command recognition

[0087] When the system receives the above command, it converts the voice into text and extracts the keywords: Beijing Bureau, Beijing-Shanghai Line, All Lines, Upward. If no valid keywords can be extracted, it will prompt "Invalid Command";

[0088] (2) Effective voice commands are matched in the database to find the inspection range

[0089] After extracting the keywords in the previous step, match the keywords in the database to find the unique code: A_L001_K0 and A_L001_K1318, as well as the uplink and downlink identifiers all / U / D / random

[0090] In the embodiment of the present application: sensors, cameras and other detection equipment are installed on inspection robots or drones to collect data. This patent mainly describes the data collection of cameras along the route.

[0091] The specific implementation includes:

[0092] 1) Call the interface to find the camera that meets the conditions

[0093] In the case in step 3, find the unique code range in the instruction information, call the interface " / videoResource / getVideoByCode", and pass the parameters: {startCode: A_L001_K0, endCode: A_L001_ K1318, isAll: true / U / D / random} Find all cameras in the range in the database,Start the data collection task. startCode refers to the starting code of the inspection line returned in step 3, endCode refers to the ending code of the inspection line returned in step 3, isAll value (default: all): return all cameras, U: return uplink cameras, D: return downlink cameras, random: return random cameras).

[0094] 2) Open the cameras one by one to start collecting data

[0095] According to the order of camera kilometer marks from small to large (default) or from large to small (when isAll is D), open one camera every three seconds, merge the camera image with the digital twin line, and combine virtual and real. You can also choose to suspend the inspection or restart the inspection. When the camera is turned on, perform data analysis and decision making in step 5 on the current image.

[0096] The data analysis and decision support in the embodiment of this application use machine learning algorithms to deeply analyze the data, identify abnormal patterns, and propose corresponding maintenance measures through decision support judgment. The algorithm used in this module is derived from the image diagnosis algorithm in the comprehensive video surveillance system, including 12 algorithms such as connection failure, video freeze, intrusion, etc. Figure 4 .

[0097] The specific implementation includes:

[0098] 1) In-depth analysis of data

[0099] The data collected in step 4 is used to conduct an in-depth analysis of the video images using 12 analysis algorithms in the integrated video surveillance system. When a problem occurs, the problem is recorded.

[0100] 2) Decision support

[0101] The results of the above problems are classified and summarized according to the severity, and the recorded problems are analyzed again to avoid false alarms. After the secondary analysis, if there are still problems, the problems will be confirmed and fed back, and the corresponding camera points in the digital twin model will be marked in red.

[0102] This application develops a graphical user interface accordingly, allowing inspection personnel to monitor the inspection status in real time, view analysis reports and maintenance recommendations.

[0103] The specific implementation includes:

[0104] Develop a graphical user interface, including but not limited to the following functions: digital twin line management, common voice inspection prompts, visual large screen, analysis report query and export, etc.

[0105] 1) Digital Twin Line Management

[0106] The interface should first display a list of routes, where you can add, delete, and modify routes. When adding a new route, it should include: selecting a railway bureau, route name, route code that automatically increases according to rules, route length, stations along the way, and how many tunnels along the way. Figure 2 As shown;

[0107] 2) Common voice inspection prompts

[0108] When using the system for the first time, you may not understand how to issue inspection instructions. This interface can provide help to users who are not familiar with the system.

[0109] 3) Visualization large screen

[0110] This interface can display real-time and historical analysis reports in a chart display interface that is classified and summarized according to different dimensions, allowing a more intuitive understanding of the operation of the entire system.

[0111] 4) Analysis report query and export

[0112] This interface can query and export the details of the problem data, making it easier for operation and maintenance personnel to troubleshoot problems.

[0113] The present invention adopts the above technical solution, which has the following advantages:

[0114] 1. Improve inspection efficiency: By creating a virtual copy of the railway line through digital twin technology, the line conditions in the real world can be simulated and analyzed in a digital environment, so as to quickly identify problem areas and reduce the time and cost of on-site inspections.

[0115] 2. Real-time monitoring and early warning: Digital twin technology can synchronize the status data of railway lines in real time. Combined with the instant feedback of the voice recognition system, managers can quickly obtain key information and take preventive measures before problems occur.

[0116] 3. Reduce human errors: The voice recognition system can automatically record the information reported by the inspectors, reduce errors in the manual input process, and improve the accuracy of data records.

[0117] 4. Improved safety: Through the intelligent inspection system, inspectors can complete most inspection work in a safe environment, reducing the risk of on-site operations, especially in bad weather or dangerous areas.

[0118] 5. Data analysis and optimization: The large amount of data collected can be used for in-depth analysis to help railway management departments optimize maintenance strategies and workflows and improve overall operational efficiency.

[0119] 6. Resource conservation: Reducing the frequency of on-site inspections and manpower requirements can save resources and reduce the impact on the environment.

[0120] 7. Improve the scientific nature of maintenance decisions: Through comprehensive analysis and evaluation of the railway line status, maintenance plans can be arranged more scientifically to ensure the reliability and safety of railway operations.

[0121] These benefits can help railway operators improve the quality and efficiency of maintenance work and reduce operation and maintenance costs. However, the realization of these benefits depends on the maturity of the technology used, the degree of system integration and the training level of operators.

[0122] In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0123] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (Processor) to perform some steps of the above-mentioned method of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0124] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A railway line intelligent inspection method, characterized in that: The method comprises: Constructing a digital twin model of the railway line; Obtain the user's voice information and recognize the voice commands contained therein; According to the voice command, controlling the environment monitoring device corresponding to the voice command to collect the environment information of the railway line; The digital twin model is synchronously updated based on the environmental information collected by the environmental monitoring equipment for use in railway line maintenance decision-making.

2. The railway line intelligent inspection method according to claim 1, characterized in that: The digital twin model establishes a virtual mapping with the real railway line, including the station information, tunnel information and running direction of the railway line.

3. The railway line intelligent inspection method according to claim 2, characterized in that: The railway line corresponds to a bidirectional coordinate system; each station corresponds to a name, a station code and a unique position coordinate; each tunnel corresponds to a tunnel code, a tunnel starting position coordinate and a tunnel ending position coordinate; the running direction includes up, down and random.

4. The railway line intelligent inspection method according to claim 3, characterized in that: Each environmental monitoring device has a corresponding device code.

5. The railway line intelligent inspection method according to claim 4, characterized in that: Constructing a correspondence between the equipment code and the railway line location coordinates; and constructing a correspondence between the equipment code and the station code and / or tunnel code.

6. The railway line intelligent inspection method according to claim 5, characterized in that: The step of controlling the environment monitoring device corresponding to the voice command to collect the environment information of the railway line according to the voice command includes: Identify the railway line location coordinates, or station code, or tunnel code, and running direction corresponding to the voice command; Controlling the environmental monitoring equipment corresponding to the railway line location coordinates, or the station code, or the tunnel code to collect environmental information; and According to the running direction, the running sequence of the environment monitoring devices and the running time intervals between the devices are controlled.

7. The railway line intelligent inspection method according to claim 6, characterized in that: The environment monitoring device at least includes an image acquisition device.

8. The railway line intelligent inspection method according to claim 1, characterized in that: The method further includes: synchronously displaying the environmental information collected by the environmental monitoring device.

9. The railway line intelligent inspection method according to claim 8, characterized in that: The method further includes: analyzing the environmental information collected by the environmental monitoring equipment based on a preset deep learning algorithm to identify fault signals of the railway line.

10. The railway line intelligent inspection method according to claim 9, characterized in that: The method further comprises: issuing an alarm in a preset form according to the fault signal.

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