CN117833460A - Intelligent rail transit power supply operation and maintenance system based on digital twinning - Google Patents
Intelligent rail transit power supply operation and maintenance system based on digital twinning Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 103
- 238000012544 monitoring process Methods 0.000 claims abstract description 45
- 238000012545 processing Methods 0.000 claims abstract description 18
- 230000010354 integration Effects 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims abstract description 8
- 238000005457 optimization Methods 0.000 claims description 18
- 238000007405 data analysis Methods 0.000 claims description 17
- 238000010801 machine learning Methods 0.000 claims description 17
- 238000004458 analytical method Methods 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000003745 diagnosis Methods 0.000 claims description 6
- 238000012417 linear regression Methods 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 5
- 230000000007 visual effect Effects 0.000 claims description 3
- 230000008439 repair process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000013154 diagnostic monitoring Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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- 238000003062 neural network model Methods 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00036—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers
- H02J13/0004—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers involved in a protection system
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Human Computer Interaction (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a digital twin-based intelligent operation and maintenance system for rail transit power supply, which relates to the field of rail transit operation and maintenance, and comprises a data acquisition unit, a digital twin unit, a fault prediction unit, a maintenance scheduling unit, a system integration unit and a remote monitoring unit; the data acquisition unit is used for acquiring the operation data of the power supply system; the digital twin unit is used for establishing a digital twin model according to the collected operation data and monitoring the power supply system in real time; the fault prediction unit is used for predicting faults of the power supply system and sending out early warning; the maintenance scheduling unit is used for processing faults; the system integration unit is used for integrating with the urban rail transit power supply system; and the remote monitoring unit is used for remotely monitoring the power supply system. The invention can rapidly discover and process faults in the power supply system through real-time monitoring and fault prediction, and improves the operation and maintenance efficiency.
Description
Technical Field
The invention relates to the field of rail transit operation and maintenance, in particular to a digital twin-based intelligent rail transit power supply operation and maintenance system.
Background
Along with the rapid development of urban rail transit, the stable operation of the power supply system has important significance for guaranteeing the safe and efficient operation of the urban rail transit, and the urban rail transit is taken as an important public transportation mode and bears the travel demands of a large number of passengers, so the reliability and stability of the power supply system are key factors for guaranteeing the operation safety and the comfort of the passengers, the power supply system is mainly responsible for providing electric energy for the rail transit and comprises equipment such as overhead lines, power substations, traction converters and the like, and the normal operation of the equipment directly influences the traction and the braking of a train, so a reliable power supply operation and maintenance mode is required to be designed for guaranteeing the normal operation of the power supply system.
In the prior art, the power supply equipment is regularly overhauled and maintained to ensure the stable operation of a power supply system, for example, the damaged parts are timely replaced by regularly checking the states of the insulators, the wires, the contact net and other parts of the overhead line; inspecting the substation, checking the running state and temperature of equipment, and finding and repairing faults in time; checking and maintaining the traction converter to ensure that the traction converter stably outputs electric energy; these maintenance tasks typically rely on manual inspection and repair to discover potential faults by visual or simple physical measurement means, maintaining proper operation of the equipment.
The existing power supply operation and maintenance mode plays an important role in ensuring the normal operation and the safety of a rail transit system, but has some limitations: firstly, because the power supply equipment is widely distributed and numerous, manual inspection is difficult to cover all equipment, so that faults are not found timely, for example, in a large-scale rail transit network, hidden fault points possibly exist, and a great deal of time and manpower are consumed for searching and repairing; secondly, low maintenance efficiency is also a challenge of the existing power supply operation and maintenance mode, and manual maintenance needs to be completed within a limited time between train operations, which brings huge pressure to maintenance personnel; in addition, since the power supply device is usually installed in a complex environment such as a tunnel and a viaduct, the difficulty of maintenance operation is high, and a large number of auxiliary devices and personnel may be needed, it is highly desirable to develop a digital twin-based intelligent operation and maintenance system for rail transit power supply so as to improve the operation and maintenance level and efficiency of the rail transit power supply system.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a digital twin-based intelligent operation and maintenance system for rail transit power supply, which has the advantages of realizing real-time monitoring, fault prediction and rapid processing of a power supply system, and further solves the problems of untimely fault discovery and low maintenance efficiency in the traditional operation and maintenance mode in the prior art.
For this purpose, the invention adopts the following specific technical scheme:
the intelligent operation and maintenance system based on digital twin track traffic power supply comprises a data acquisition unit, a digital twin unit, a fault prediction unit, a maintenance scheduling unit, a system integration unit and a remote monitoring unit;
the data acquisition unit is used for acquiring the operation data of the power supply system in real time;
the digital twin unit is used for establishing a digital twin model according to the collected operation data and monitoring the power supply system in real time;
the fault prediction unit is used for predicting the faults of the power supply system based on the digital twin model by utilizing big data analysis and machine learning technology and sending out early warning;
the maintenance scheduling unit is used for scheduling maintenance resources to process faults according to the fault prediction result so as to restore power supply;
the system integration unit is used for integrating with the urban rail transit power supply system;
and the remote monitoring unit is used for remotely monitoring the running state of the power supply system.
Further, the data acquisition unit comprises a sensor module and a data receiving module;
the sensor module is used for collecting current, voltage and temperature data of the rail transit power supply system in real time;
the data receiving module is used for receiving the operation data acquired by the sensor module and storing and managing the operation data.
Further, the digital twin unit comprises a data processing module, a model building module and a diagnosis monitoring module;
the data processing module is used for cleaning and normalizing the collected operation data;
the model construction module is used for extracting features from the processed data and constructing a digital twin model;
and the diagnosis monitoring module is used for inputting the data acquired in real time into the digital twin model and monitoring the running state of the power supply system in real time.
Further, the fault prediction unit comprises a data analysis module, a machine learning module and a fault early warning module;
the data analysis module is used for carrying out predictive analysis on the operation data of the power supply system by utilizing a big data analysis technology;
the machine learning module is used for optimizing the digital twin model by utilizing a machine learning algorithm and predicting faults of the power supply system;
and the fault early warning module is used for sending out fault early warning according to the prediction result.
Further, the predictive analysis of the power supply system operation data using the big data analysis technique includes:
fitting power supply system operation data by using a linear regression model algorithm based on a digital twin model, and carrying out predictive analysis on input data;
the calculation formula for carrying out predictive analysis on the input data is as follows:
yc(x n )＝v 0 +v 1 ×x 1 +v 2 ×x 2 +...+v n ×x n
in the method, in the process of the invention,yc(x n ) Representing a digital twin model versus input data x n Is a predicted value of (2);
v 0 ,v 1 ,v 2 ,...,v n parameters representing a linear regression model algorithm;
x 1 ,x 2 ,...,x n representing the entered power system operational data.
Further, optimizing the digital twin model by using a machine learning algorithm, and predicting the power supply system fault includes:
initializing parameters of the digital twin model;
carrying out iterative optimization on the digital twin model by adopting a gradient descent optimization algorithm so as to minimize a loss function;
and inputting the real-time operation data into the optimized digital twin model to predict the faults of the power supply system.
Further, the digital twin model is subjected to iterative optimization by adopting a gradient descent optimization algorithm, so that a calculation formula for minimizing a loss function is as follows:
w＝w-α×▽T(w)
wherein w represents a loss function of the digital twin model and is used for measuring the difference between a predicted value and an actual value of the model;
alpha represents a learning rate;
t (w) represents the gradient of the loss function w;
v represents the number of samples of the input data;
z n representing the true value of the input data sample.
Further, the maintenance scheduling unit comprises a resource management module, a scheduling optimization module and a maintenance recording module;
the resource management module is used for managing maintenance resources, including maintenance personnel, maintenance equipment and spare part management;
the scheduling optimization module is used for scheduling maintenance resources according to the emergency degree of the faults;
the maintenance recording module is used for recording operation steps of maintenance tasks and maintenance results.
Further, the system integration unit comprises an interface management module, a data conversion module and a system test module;
the interface management module is used for interfacing with an interface of the urban rail transit power supply system to realize data transmission and interaction;
the data conversion and synchronization module is used for adapting and adjusting transmission data according to the data structure of the urban rail transit power supply system, so as to ensure the consistency of the data structure;
and the system testing module is used for testing the integrated system and verifying the compatibility of the integrated system.
Further, the remote monitoring unit comprises a network communication module, a monitoring interface module and a safety guarantee module;
the network communication module is used for carrying out remote communication with the power supply system and transmitting the operation data acquired by the data acquisition unit to the remote monitoring center;
the monitoring interface module is used for carrying out visual processing on the transmitted data and displaying the data on a monitoring interface;
and the safety guarantee module is used for encrypting the transmitted data by using an encryption algorithm and managing the access right of the monitoring interface module.
The beneficial effects of the invention are as follows:
(1) The operation and maintenance efficiency is improved: through real-time monitoring and fault prediction, faults in a power supply system can be rapidly found and processed, and the operation and maintenance efficiency is improved.
(2) The labor cost is reduced: through remote monitoring and fault prediction, the power supply system does not need to be checked manually frequently, and labor cost is reduced.
(3) The safety is improved: potential faults and risks can be found in advance through real-time monitoring and fault prediction, measures are taken in time to treat, and the safety of a power supply system is improved.
(4) Optimizing resource scheduling: the maintenance scheduling unit can reasonably schedule maintenance resources, so that the resource is optimally configured, and the maintenance efficiency is improved.
(5) The expansibility is strong: the system can be integrated with the existing rail transit power supply system, does not need to carry out large-scale reconstruction on the existing system, and has stronger expansibility.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic block diagram of a digital twin-based rail transit power supply intelligent operation and maintenance system according to an embodiment of the present invention.
Detailed Description
For the purpose of further illustrating the various embodiments, the present invention provides the accompanying drawings, which are a part of the disclosure of the present invention, and which are mainly used to illustrate the embodiments and, together with the description, serve to explain the principles of the embodiments, and with reference to these descriptions, one skilled in the art will recognize other possible implementations and advantages of the present invention, wherein elements are not drawn to scale, and like reference numerals are generally used to designate like elements.
According to the embodiment of the invention, a digital twin-based intelligent operation and maintenance system for rail transit power supply is provided.
The invention is further described with reference to the accompanying drawings and the specific embodiments, as shown in fig. 1, the intelligent operation and maintenance system based on digital twin rail transit power supply according to the embodiment of the invention comprises a data acquisition unit 1, a digital twin unit 2, a fault prediction unit 3, a maintenance scheduling unit 4, a system integration unit 5 and a remote monitoring unit 6;
the data acquisition unit 1 is used for acquiring the operation data of the power supply system in real time;
the digital twin unit 2 is used for establishing a digital twin model according to the collected operation data and monitoring the power supply system in real time;
the fault prediction unit 3 is used for predicting the faults of the power supply system based on the digital twin model by utilizing big data analysis and machine learning technology and sending out early warning;
a maintenance scheduling unit 4, configured to schedule maintenance resources to process the fault to restore power supply according to the result of the fault prediction;
the system integration unit 5 is used for integrating with the urban rail transit power supply system;
and the remote monitoring unit 6 is used for remotely monitoring the operation state of the power supply system.
In one embodiment, the data acquisition unit 1 includes a sensor module 101 and a data receiving module 102;
the sensor module 101 is used for collecting current, voltage and temperature data of the rail transit power supply system in real time;
the data receiving module 102 is configured to receive the operation data collected by the sensor module, and store and manage the operation data.
In one embodiment, the digital twin unit 2 includes a data processing module 201, a model building module 202, and a diagnostic monitoring module 203;
the data processing module 201 is used for cleaning and normalizing the collected operation data;
specifically, the data processing module 201 firstly performs cleaning and denoising processing on the collected operation data, and removes abnormal values and noise so as to ensure the accuracy and reliability of the data; and then carrying out normalization and standardization processing on the cleaned data, and converting the data in different ranges and units into uniform data ranges and distributions so as to facilitate subsequent model construction.
The model construction module 202 is configured to extract features from the processed data and construct a digital twin model;
the diagnosis monitoring module 203 is configured to input the data collected in real time into the digital twin model, and monitor the operation state of the power supply system in real time.
Specifically, when the digital twin model is built, historical power supply system data are required to be used for training and building the model, the data comprise operation data, fault data and maintenance records of the power supply system, firstly, the data are subjected to cleaning and normalization processing through a data processing module 201, then the most representative and relevant characteristics are extracted from the processed data through a model building module 202 and are used as input of a neural network model, so that the digital twin model of the power supply system is built, finally, real-time collected data comprising current, voltage and temperature data and parameter data collected by other sensors are input into the digital twin model through a diagnosis monitoring module 203, and further real-time monitoring of the power supply system is achieved.
In one embodiment, the fault prediction unit 3 includes a data analysis module 301, a machine learning module 302, and a fault early warning module 303;
the data analysis module 301 is configured to perform predictive analysis on operation data of the power supply system by using a big data analysis technology;
the machine learning module 302 is configured to optimize the digital twin model by using a machine learning algorithm, and predict a fault of the power supply system;
and the fault early warning module 303 is used for sending out fault early warning according to the prediction result.
Specifically, the fault early warning module 303 determines whether the power supply system has potential faults and problems by analyzing the result predicted by the machine learning module 302, and notifies the operation and maintenance personnel to take corresponding maintenance and adjustment measures so as to avoid occurrence of the faults or reduce the influence of the faults on the power supply system.
In one embodiment, predictive analysis of power system operational data using big data analysis techniques includes:
fitting power supply system operation data by using a linear regression model algorithm based on a digital twin model, and carrying out predictive analysis on input data;
the calculation formula for carrying out predictive analysis on the input data is as follows:
yc(x n )＝v 0 +v 1 ×x 1 +v 2 ×x 2 +...+v n ×x n
in the formula, yc (x) n ) Representing a digital twin model versus input data x n Is a predicted value of (2);
v 0 ,v 1 ,v 2 ,...,v n parameters representing a linear regression model algorithm;
x 1 ,x 2 ,...,x n representing the entered power system operational data.
In one embodiment, optimizing the digital twin model using a machine learning algorithm and predicting a failure of the power supply system includes:
initializing parameters of the digital twin model;
carrying out iterative optimization on the digital twin model by adopting a gradient descent optimization algorithm so as to minimize a loss function;
and inputting the real-time operation data into the optimized digital twin model to predict the faults of the power supply system.
In one embodiment, a gradient descent optimization algorithm is used to iteratively optimize the digital twin model to minimize the loss function according to the calculation formula:
w＝w-α×▽T(w)
wherein w represents a loss function of the digital twin model and is used for measuring the difference between a predicted value and an actual value of the model;
alpha represents a learning rate;
t (w) represents the gradient of the loss function w;
v represents the number of samples of the input data;
z n representing the true value of the input data sample.
In one embodiment, the maintenance scheduling unit 4 includes a resource management module 401, a scheduling optimization module 402, and a maintenance recording module 403;
the resource management module 401 is used for managing maintenance resources, including maintenance personnel, maintenance equipment and spare part management;
specifically, the resource management module 401 ensures that there are enough suitable personnel to perform the maintenance task by managing the information, skills, and work arrangement of the maintenance personnel; the availability and proper operation of the equipment are ensured by managing equipment and tools required for maintenance, including maintenance, service and scheduling of the equipment, and the timely supply and sufficiency of the spare parts are ensured by managing spare parts and supplies required for maintenance, including procurement, inventory management and use of the spare parts.
The scheduling optimization module 402 is configured to schedule maintenance resources according to the emergency degree of the fault;
specifically, the scheduling optimization module 402 firstly performs priority analysis on maintenance tasks according to the emergency degree and the influence range of the fault, and determines the emergency degree and the importance of maintenance; and then analyzing the availability and the workload of the maintenance resources, determining the quantity and the capability of the available resources by considering the skills of personnel, the states of equipment and the supply condition of spare parts, and then performing intelligent scheduling of the maintenance resources by applying a scheduling algorithm based on the failure priority and the resource availability so as to realize reasonable allocation of the resources and efficient execution of tasks.
The maintenance recording module 403 is used for recording the operation steps of maintenance tasks and maintenance results.
Specifically, the maintenance recording module 403 records basic information of maintenance tasks, including fault description, maintenance personnel, maintenance time, etc., so as to facilitate subsequent tracking and analysis; by recording key steps, operations and results in the maintenance process, including maintenance schemes, problems and solutions in the maintenance process, and the like, subsequent experience summarization and knowledge sharing are facilitated; by evaluating the effect and quality of the repair, the effect of the repair and the effect of the improvement are recorded, and references are provided for subsequent repair decisions and improvements.
In one embodiment, the system integration unit 5 includes an interface management module 501, a data conversion module 502, and a system test module 503;
the interface management module 501 is used for interfacing with an interface of the urban rail transit power supply system to realize data transmission and interaction;
specifically, the interface management module 501 defines interfaces required for integration with the existing system according to the characteristics and requirements of the existing system, including data format, communication protocol, and interface parameters; then, the stability and the reliability of the interface are ensured by managing and maintaining the interface of the existing urban rail transit power supply system, including registration, configuration, updating, version control and the like of the interface; and then interfaces with the existing system and provides corresponding calling methods and interface documents so that other modules can interact and integrate with the existing system.
The data conversion and synchronization module 502 is configured to adapt and adjust transmission data according to a data structure of the urban rail transit power supply system, so as to ensure consistency of the data structure;
specifically, the data conversion and synchronization module 502 firstly converts the data required by the system of the present invention into a data format compatible with the existing system, then adapts and adjusts the data of the system of the present invention according to the data structure requirement of the existing urban rail transit power supply system, and finally realizes the data synchronization and sharing between the system of the present invention and the existing system through data conversion and adaptation, so that the data synchronization and sharing is matched with the data structure of the existing urban rail transit power supply system, and the data consistency and instantaneity between the two systems are ensured.
And the system testing module 503 is used for testing the integrated system and verifying the compatibility of the integrated system.
Specifically, the system test module 503 verifies the functional integrity and stability of the system by performing overall test on the integrated system, ensures normal cooperative work and data interaction between each module, and verifies whether the interface and data interaction of the system meet the expectations or not by testing the compatibility of the system of the invention and the existing urban rail transit power supply system, thereby ensuring the reliability and stability of the integration; and meanwhile, performance testing is carried out on the integrated system, indexes such as response speed, throughput and the like of the system are evaluated, and optimization is carried out so as to improve the performance and efficiency of the system.
In one embodiment, the remote monitoring unit 6 includes a network communication module 601, a monitoring interface module 602, and a security module 603;
the network communication module 601 is configured to perform remote communication with the power supply system, and transmit the operation data acquired by the data acquisition unit 1 to a remote monitoring center;
the monitoring interface module 602 is configured to perform visualization processing on the transmitted data, and display the data to a monitoring interface;
the security module 603 is configured to encrypt the transmitted data using an encryption algorithm and manage access rights of the monitoring interface module 602.
Specifically, the real-time data of the power supply system is transmitted to the remote monitoring center through the network communication module 601, so that the power supply system is remotely connected, the data of the power supply system can be obtained in real time, and the data can be remotely regulated and controlled to ensure the normal operation of the power supply system;
in order to facilitate understanding of the above technical solutions of the present invention, the following describes in detail the working principle or operation manner of the present invention in the actual process.
When the invention is actually applied, the operation and maintenance of a certain power supply system are carried out, firstly, the operation data of the power supply system are collected through the data collection unit 1, the collected data are input into the digital twin unit 2 for data processing and feature extraction, a digital twin model is built, then the operation data of the power supply system are predicted and analyzed through the data analysis module 301 in the fault prediction unit 3 by utilizing a big data analysis technology, the digital twin model is optimized through the machine learning module 302, the fault prediction of the power supply system is realized, the fault early warning module 303 sends out timely fault early warning according to the prediction result, and then the maintenance scheduling unit 4 intelligently schedules maintenance resources according to the emergency degree of the fault and the availability of resources, so that maintenance personnel can know the possible fault of the power supply system in advance according to the fault early warning information, quickly respond and take corresponding maintenance measures to the site, and finally the quick positioning and maintenance of the fault are realized; meanwhile, through the system integration unit 5, the prior rail transit power supply system is not required to be modified on a large scale, early warning and rapid processing can be performed on faults, the time and the influence range of power interruption are effectively reduced, and the labor cost is reduced while the operation and maintenance efficiency is improved.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The intelligent operation and maintenance system for the rail transit power supply based on the digital twin is characterized by comprising a data acquisition unit (1), a digital twin unit (2), a fault prediction unit (3), a maintenance scheduling unit (4), a system integration unit (5) and a remote monitoring unit (6);
the data acquisition unit (1) is used for acquiring the operation data of the power supply system in real time;
the digital twin unit (2) is used for establishing a digital twin model according to the collected operation data and monitoring the power supply system in real time;
the fault prediction unit (3) is used for predicting faults of the power supply system based on a digital twin model by utilizing big data analysis and machine learning technology and sending out early warning;
the maintenance scheduling unit (4) is used for scheduling maintenance resources to process faults according to the fault prediction result so as to restore power supply;
the system integration unit (5) is used for integrating with an urban rail transit power supply system;
the remote monitoring unit (6) is used for remotely monitoring the running state of the power supply system.
2. The intelligent operation and maintenance system based on digital twin rail transit power supply according to claim 1, wherein the data acquisition unit (1) comprises a sensor module (101) and a data receiving module (102);
the sensor module (101) is used for collecting current, voltage and temperature data of the rail transit power supply system in real time;
the data receiving module (102) is used for receiving the operation data acquired by the sensor module and storing and managing the operation data.
3. The intelligent operation and maintenance system based on digital twin rail transit power supply according to claim 1, wherein the digital twin unit (2) comprises a data processing module (201), a model construction module (202) and a diagnosis monitoring module (203);
the data processing module (201) is used for cleaning and normalizing the collected operation data;
the model construction module (202) is used for extracting features from the processed data and constructing a digital twin model;
the diagnosis monitoring module (203) is used for inputting data acquired in real time into the digital twin model and monitoring the running state of the power supply system in real time.
4. The intelligent operation and maintenance system based on digital twin rail transit power supply according to claim 1, wherein the fault prediction unit (3) comprises a data analysis module (301), a machine learning module (302) and a fault early warning module (303);
the data analysis module (301) is used for performing predictive analysis on the operation data of the power supply system by utilizing a big data analysis technology;
the machine learning module (302) is used for optimizing the digital twin model by utilizing a machine learning algorithm and predicting faults of the power supply system;
and the fault early warning module (303) is used for sending out fault early warning according to the prediction result.
5. The intelligent operation and maintenance system for power supply of rail transit based on digital twinning according to claim 4, wherein the predictive analysis of the operation data of the power supply system by using big data analysis technology comprises:
fitting power supply system operation data by using a linear regression model algorithm based on a digital twin model, and carrying out predictive analysis on input data;
the calculation formula for carrying out predictive analysis on the input data is as follows:
yc(x n )＝v 0 +v 1 ×x 1 +v 2 ×x 2 +...+v n ×x n
in the formula, yc (x) n ) Representing a digital twin model versus input data x n Is a predicted value of (2);
v 0 ,v 1 ,v 2 ,...,v n parameters representing a linear regression model algorithm;
x 1 ,x 2 ,...,x n representing the entered power system operational data.
6. The intelligent operation and maintenance system for power supply of rail transit based on digital twinning according to claim 4, wherein the optimizing the digital twinning model by using a machine learning algorithm and predicting the fault of the power supply system comprise:
initializing parameters of the digital twin model;
carrying out iterative optimization on the digital twin model by adopting a gradient descent optimization algorithm so as to minimize a loss function;
and inputting the real-time operation data into the optimized digital twin model to predict the faults of the power supply system.
7. The intelligent operation and maintenance system based on digital twin rail transit power supply of claim 6, wherein the calculation formula for iteratively optimizing the digital twin model by adopting a gradient descent optimization algorithm to minimize the loss function is as follows:
wherein w represents a loss function of the digital twin model and is used for measuring the difference between a predicted value and an actual value of the model;
alpha represents a learning rate;
t (w) represents the gradient of the loss function w;
v represents the number of samples of the input data;
z n representing the true value of the input data sample.
8. The intelligent operation and maintenance system based on digital twin rail transit power supply according to claim 1, wherein the maintenance scheduling unit (4) comprises a resource management module (401), a scheduling optimization module (402) and a maintenance recording module (403);
the resource management module (401) is used for managing maintenance resources, including maintenance personnel, maintenance equipment and spare part management;
the scheduling optimization module (402) is used for scheduling maintenance resources according to the emergency degree of faults;
the maintenance recording module (403) is used for recording operation steps of maintenance tasks and maintenance results.
9. The intelligent operation and maintenance system based on digital twin rail transit power supply according to claim 1, wherein the system integration unit (5) comprises an interface management module (501), a data conversion module (502) and a system test module (503);
the interface management module (501) is used for interfacing with an interface of the urban rail transit power supply system to realize data transmission and interaction;
the data conversion and synchronization module (502) is used for adapting and adjusting transmission data according to the data structure of the urban rail transit power supply system, so as to ensure the consistency of the data structure;
the system testing module (503) is used for testing the integrated system and verifying the compatibility of the integrated system.
10. The intelligent operation and maintenance system based on digital twin rail transit power supply according to claim 1, wherein the remote monitoring unit (6) comprises a network communication module (601), a monitoring interface module (602) and a security assurance module (603);
the network communication module (601) is used for carrying out remote communication with the power supply system and transmitting the operation data acquired by the data acquisition unit (1) to a remote monitoring center;
the monitoring interface module (602) is used for carrying out visual processing on the transmitted data and displaying the data on a monitoring interface;
the security module (603) is used for encrypting the transmitted data by using an encryption algorithm and managing the access right of the monitoring interface module (602).
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