Rail transit power supply intelligent operation and maintenance system based on digital twinning

Abstract

The invention discloses a rail transit power supply intelligent operation and maintenance system based on digital twinning, and relates to the technical field of rail transit operation and maintenance. The system comprises a data acquisition unit, an interactive transmission unit, a twin monitoring unit and remote equipment, wherein the data acquisition unit is used for acquiring operation data of a rail transit power supply system in real time; the interactive transmission unit is used for caching the operation data in a set time period and performing communication transmission on each piece of cached data according to a popping rule; the twinborn monitoring unit is used for constructing a digital twinborn model according to data received in a set time period, performing fault prediction and maintenance scheduling based on the digital twinborn model, and determining an operation and maintenance scheme; the operation and maintenance scheme comprises an output fault early warning signal and a resource scheduling signal; and the remote device is used for visually displaying each operation data, giving an alarm according to the fault early warning signal, and carrying out scheduling prompt to a corresponding supervisor according to the resource scheduling signal. The operation and maintenance efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of rail transit operation and maintenance technology, and in particular to a rail transit power supply intelligent operation and maintenance system based on digital twins. Background Art

[0002] With the rapid development of urban rail transit systems, the stability and safety of power supply systems have become key factors in ensuring operational efficiency and passenger safety. Traditional power supply system monitoring and operation and maintenance models often rely on manual inspections and regular maintenance, which is not only inefficient, but also difficult to detect and prevent potential faults in a timely manner, and the response speed to sudden faults is also limited. Therefore, it is particularly important to develop an intelligent rail transit power supply system monitoring and operation and maintenance system. Summary of the invention

[0003] The purpose of the present invention is to provide a rail transit power supply intelligent operation and maintenance system based on digital twin, which can improve the operation and maintenance efficiency.

[0004] To achieve the above object, the present invention provides the following solutions: A rail transit power supply intelligent operation and maintenance system based on digital twin, including: Data acquisition unit, used to collect real-time operation data of rail transit power supply system; An interactive transmission unit, used to cache the operation data within a set period of time, and to communicate and transmit each cached data according to a stacking rule; A twin monitoring unit is used to build a digital twin model based on the data received within a set period of time, and to perform fault prediction and maintenance scheduling based on the digital twin model to determine an operation and maintenance plan; the operation and maintenance plan includes outputting a fault warning signal and a resource scheduling signal; The remote device is used to visualize various operating data, issue an alarm according to the fault warning signal, and issue a scheduling prompt to the corresponding supervisor according to the resource scheduling signal.

[0005] Optionally, the data acquisition unit includes a plurality of sensors arranged in a set area; the sensors are used to collect current, voltage and temperature data of the rail transit power supply system in real time.

[0006] Optionally, a relay node is provided between every two sensors.

[0007] Optionally, the interactive transmission unit specifically includes: a buffer and a communication module; the input end of the buffer is connected to the output end of the data acquisition unit; the output end of the buffer is connected to the input end of the communication module; the output end of the communication module is connected to the twin monitoring unit; The buffer is used to add the latest data to the top of the data at the previous moment in chronological order as the top of the stack for caching, and when the set time period requirements are met, the data is removed from the top of the stack to the communication module for transmission.

[0008] Optionally, the communication module adopts an I / O interface or a wireless communication module.

[0009] Optionally, the twin monitoring unit specifically includes: a fault warning module and a maintenance scheduling module connected in sequence; the fault warning module is also connected to the interactive transmission unit; the maintenance scheduling module is also connected to the remote device; Among them, the fault warning module is used to preprocess the data received within a set time period, and extract the characteristics of the preprocessed data, construct and optimize the digital twin model according to the characteristics, input the data transmitted in real time at the next moment into the optimized digital twin model for fault prediction, and output the corresponding fault warning signal according to the abnormal state of the data; the maintenance scheduling module is used to schedule maintenance resources according to the abnormal state of the data, and output the corresponding resource scheduling signal.

[0010] Optionally, the fault warning module includes a model building submodule and a model optimization submodule connected in sequence; the model building submodule is also connected to the interactive transmission unit; the model optimization submodule is also connected to the maintenance scheduling module; Among them, the model construction submodule is used to preprocess the data received within a set time period, extract the features of the preprocessed data, and construct a digital twin model based on the features; the model optimization submodule is used to optimize the training of the digital twin model using a gradient descent strategy, and use the trained digital twin model for fault prediction at the next moment to obtain a fault warning signal corresponding to the abnormal data state.

[0011] Optionally, the remote device includes at least one of a personal computer, a laptop, a smart phone, a tablet computer and a portable wearable device.

[0012] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention discloses a rail transit power supply intelligent operation and maintenance system based on digital twins, the system includes a data acquisition unit, an interactive transmission unit, a twin monitoring unit and a remote device; wherein the data acquisition unit is used to collect the operation data of the rail transit power supply system in real time; the interactive transmission unit is used to cache the operation data within a set period, and communicate and transmit each cached data according to the stacking rule; the twin monitoring unit is used to build a digital twin model according to the data received within the set period, and perform fault prediction and maintenance scheduling based on the digital twin model, and determine the operation and maintenance plan; the operation and maintenance plan includes outputting fault warning signals and resource scheduling signals; the remote device is used to visualize the operation data, and give an alarm according to the fault warning signal, and give a scheduling prompt to the corresponding supervisor according to the resource scheduling signal. The present invention can improve the operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0014] Figure 1 This is a structural schematic diagram of the rail transit power supply intelligent operation and maintenance system based on digital twins of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] The purpose of the present invention is to provide a rail transit power supply intelligent operation and maintenance system based on digital twin, which can improve the operation and maintenance efficiency.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the present invention provides a rail transit power supply intelligent operation and maintenance system based on digital twin, including: a data acquisition unit, an interactive transmission unit, a twin monitoring unit and a remote device.

[0019] Among them, the data acquisition unit is used to collect the operating data of the rail transit power supply system in real time; the interactive transmission unit is used to cache the operating data within a set time period, and communicate and transmit each cached data according to the stacking rules; the twin monitoring unit is used to build a digital twin model according to the data received within the set time period, and perform fault prediction and maintenance scheduling based on the digital twin model to determine the operation and maintenance plan; the operation and maintenance plan includes outputting fault warning signals and resource scheduling signals; the remote device is used to visualize the various operating data, and to issue alarms based on the fault warning signals, and to issue scheduling prompts to the corresponding supervisors based on the resource scheduling signals.

[0020] As a specific implementation, the data acquisition unit includes a plurality of sensors arranged in a set area; the sensors are used to collect the current, voltage and temperature data of the rail transit power supply system in real time, wherein a relay node is arranged between every two sensors.

[0021] As a specific implementation, the interactive transmission unit specifically includes: a buffer and a communication module; the input end of the buffer is connected to the output end of the data acquisition unit; the output end of the buffer is connected to the input end of the communication module; the output end of the communication module is connected to the twin monitoring unit.

[0022] The buffer is used to add the latest data to the top of the data at the previous moment in chronological order as the top of the stack for caching, and when the set time period requirements are met, the data is removed from the top of the stack to the communication module for transmission. The communication module uses an I / O interface or a wireless communication module.

[0023] As a specific implementation, the twin monitoring unit specifically includes: a fault warning module and a maintenance scheduling module connected in sequence; the fault warning module is also connected to the interactive transmission unit; the maintenance scheduling module is also connected to the remote device.

[0024] Among them, the fault warning module is used to preprocess the data received within a set time period, and extract the characteristics of the preprocessed data, construct and optimize the digital twin model according to the characteristics, input the data transmitted in real time at the next moment into the optimized digital twin model for fault prediction, and output the corresponding fault warning signal according to the abnormal state of the data; the maintenance scheduling module is used to schedule maintenance resources according to the abnormal state of the data, and output the corresponding resource scheduling signal.

[0025] As a further optimization scheme, the fault warning module includes a model building submodule and a model optimization submodule which are connected in sequence; the model building submodule is also connected to the interactive transmission unit; and the model optimization submodule is also connected to the maintenance scheduling module.

[0026] Among them, the model construction submodule is used to preprocess the data received within a set time period, extract the features of the preprocessed data, and construct a digital twin model based on the features; the model optimization submodule is used to optimize the training of the digital twin model using a gradient descent strategy, and use the trained digital twin model for fault prediction at the next moment to obtain a fault warning signal corresponding to the abnormal data state.

[0027] As a specific implementation, the remote device includes at least one of a personal computer, a laptop computer, a smart phone, a tablet computer, and a portable wearable device.

[0028] Based on the above technical solution, the following embodiments are provided.

[0029] This embodiment describes in detail the specific implementation of the rail transit power supply intelligent operation and maintenance system based on digital twins. The system integrates functions such as data acquisition, interactive transmission, twin monitoring and remote visualization to achieve real-time monitoring, fault prediction and intelligent operation and maintenance of the rail transit power supply system.

[0030] Data acquisition unit: Multiple high-precision sensors are deployed at key locations of the rail transit power supply system, such as substations and overhead lines. These sensors are responsible for real-time collection of key operating data such as current, voltage, and temperature of the power supply system. In order to ensure the reliability and stability of data transmission, a relay node is also set between every two sensors for relay transmission of data. Sensors and relay nodes together constitute a data acquisition unit to ensure comprehensive coverage and real-time data.

[0031] Interactive transmission unit: The interactive transmission unit consists of a buffer and a communication module. The buffer adopts a first-in-first-out (FIFO) stack structure, adding the latest collected data to the top of the stack in chronological order, and when the amount of cached data reaches the requirement of the set time period (such as every hour), the data is removed from the top of the stack to the communication module in sequence. The communication module supports two modes: I / O interface and wireless communication. Flexible selection according to actual needs ensures that data can be efficiently and stably transmitted to the twin monitoring unit.

[0032] Twin monitoring unit: The twin monitoring unit is the core part of the system, which consists of a fault warning module and a maintenance scheduling module.

[0033] Fault warning module: This module first receives data from the interactive transmission unit and performs data preprocessing, including data cleaning, feature extraction and other steps. Subsequently, these features are used to build the initial digital twin model. In order to improve the prediction accuracy of the model, the module has a built-in model optimization submodule, which uses a gradient descent strategy to iteratively optimize the model. After the training is completed, the real-time transmitted data is input into the optimized digital twin model for fault prediction. Once an abnormal data state is detected, the corresponding fault warning signal is immediately output.

[0034] The process of iterative optimization training of the model is: The preprocessed data and the manually annotated fault labels are used as training data to construct an AlexNet model, the training data is input into the AlexNet model for gradient descent training, and the trained AlexNet model is determined as an optimized model.

[0035] Among them, the preprocessing process includes: determining a pseudo two-dimensional image of the operating data based on the received data, decomposing the pseudo two-dimensional image using a two-dimensional variational mode decomposition algorithm to obtain an intrinsic mode function image; further extracting image texture features from the intrinsic mode function image to obtain image texture features; and determining the fault label of the training battery based on the image texture using a manual labeling method.

[0036] As a further processing process, the method for determining the pseudo two-dimensional image is: using a recursive correlation coefficient algorithm, calculating the recursive correlation coefficient of the data at every two adjacent moments, obtaining multiple recursive correlation coefficients, and generating a coefficient sequence image based on the recursive correlation coefficients; truncating the coefficient sequence image according to a set time period to obtain multiple image data segments; constructing a recursive correlation coefficient matrix according to the spatial distribution order of the image data segments; and using the recursive correlation coefficient matrix as the image grayscale value to generate a pseudo two-dimensional image of the target battery.

[0037] As a further processing process, the image texture feature is extracted from the intrinsic mode function image, and the specific process of obtaining the image texture feature includes: The grayscale co-occurrence matrix algorithm is used to calculate the grayscale statistical feature quantity between two pixels at a set distance in the intrinsic mode function image; the grayscale statistical feature quantity includes energy, inverse variance, entropy and contrast; and the image texture feature is determined according to the grayscale statistical feature quantity.

[0038] Maintenance scheduling module: After receiving the fault warning signal, the maintenance scheduling module automatically evaluates the required maintenance resources according to the abnormal data status and generates corresponding resource scheduling signals. These signals include dispatch instructions for maintenance personnel, deployment information for maintenance tools, etc., which are sent to the corresponding supervisors through remote devices.

[0039] Remote Device: Remote devices include personal computers, laptops, smartphones, tablets, and portable wearable devices. Users can choose appropriate devices to access the system according to actual needs. These devices are connected to the twin monitoring unit through dedicated software or APP to achieve visual display of operating data, real-time reception of fault warning signals, and scheduling prompts for resource scheduling signals. For example, when a smartphone receives a fault warning signal, it will immediately notify the user in the form of a pop-up window and display the specific location and nature of the fault; at the same time, according to the resource scheduling signal, a text message or push notification will be sent to the designated supervisor to remind him to perform the corresponding maintenance scheduling work.

[0040] This embodiment has the following beneficial effects: This embodiment builds an efficient and intelligent rail transit power supply intelligent operation and maintenance system by integrating functional modules such as data acquisition, interactive transmission, twin monitoring and remote visualization. The system can realize real-time monitoring, accurate fault prediction and intelligent operation and maintenance scheduling of the power supply system, effectively improving the safety and reliability of the rail transit power supply system.

[0041] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0042] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A rail transit power supply intelligent operation and maintenance system based on digital twins, It is characterized in that include: Data acquisition unit, used to collect real-time operation data of rail transit power supply system; An interactive transmission unit, used to cache the operation data within a set period of time, and to communicate and transmit each cached data according to a stacking rule; A twin monitoring unit is used to build a digital twin model based on the data received within a set period of time, and to perform fault prediction and maintenance scheduling based on the digital twin model to determine an operation and maintenance plan; the operation and maintenance plan includes outputting a fault warning signal and a resource scheduling signal; The remote device is used to visualize various operating data, issue an alarm according to the fault warning signal, and issue a scheduling prompt to the corresponding supervisor according to the resource scheduling signal.

2. According to the digital twin-based rail transit power supply intelligent operation and maintenance system of claim 1, It is characterized in that The data acquisition unit includes a plurality of sensors arranged in a set area; the sensors are used to collect current, voltage and temperature data of the rail transit power supply system in real time.

3. According to the rail transit power supply intelligent operation and maintenance system based on digital twin according to claim 2, It is characterized in that A relay node is also arranged between every two of the sensors.

4. According to the digital twin-based rail transit power supply intelligent operation and maintenance system of claim 1, It is characterized in that The interactive transmission unit specifically includes: a buffer and a communication module; the input end of the buffer is connected to the output end of the data acquisition unit; the output end of the buffer is connected to the input end of the communication module; the output end of the communication module is connected to the twin monitoring unit; The buffer is used to add the latest data to the top of the data at the previous moment in chronological order as the top of the stack for caching, and when the set time period requirements are met, the data is removed from the top of the stack to the communication module for transmission.

5. According to the rail transit power supply intelligent operation and maintenance system based on digital twin according to claim 4, It is characterized in that The communication module adopts an I / O interface or a wireless communication module.

6. According to the digital twin-based rail transit power supply intelligent operation and maintenance system of claim 1, It is characterized in that The twin monitoring unit specifically includes: a fault warning module and a maintenance scheduling module connected in sequence; the fault warning module is also connected to the interactive transmission unit; the maintenance scheduling module is also connected to the remote device; Among them, the fault warning module is used to preprocess the data received within a set time period, and extract the characteristics of the preprocessed data, construct and optimize the digital twin model according to the characteristics, input the data transmitted in real time at the next moment into the optimized digital twin model for fault prediction, and output the corresponding fault warning signal according to the abnormal state of the data; the maintenance scheduling module is used to schedule maintenance resources according to the abnormal state of the data, and output the corresponding resource scheduling signal.

7. According to the rail transit power supply intelligent operation and maintenance system based on digital twin according to claim 6, It is characterized in that The fault warning module includes a model building submodule and a model optimization submodule connected in sequence; the model building submodule is also connected to the interactive transmission unit; the model optimization submodule is also connected to the maintenance scheduling module; Among them, the model construction submodule is used to preprocess the data received within a set time period, extract the features of the preprocessed data, and construct a digital twin model based on the features; the model optimization submodule is used to optimize the training of the digital twin model using a gradient descent strategy, and use the trained digital twin model for fault prediction at the next moment to obtain a fault warning signal corresponding to the abnormal data state.

8. According to the digital twin-based rail transit power supply intelligent operation and maintenance system of claim 1, It is characterized in that The remote device includes at least one of a personal computer, a laptop, a smart phone, a tablet computer, and a portable wearable device.

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