CN114954586A - Intelligent operation system, method, device, equipment, product and rail vehicle - Google Patents
Intelligent operation system, method, device, equipment, product and rail vehicle Download PDFInfo
- Publication number
- CN114954586A CN114954586A CN202210689508.1A CN202210689508A CN114954586A CN 114954586 A CN114954586 A CN 114954586A CN 202210689508 A CN202210689508 A CN 202210689508A CN 114954586 A CN114954586 A CN 114954586A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- data
- module
- rail
- abnormal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000002159 abnormal effect Effects 0.000 claims abstract description 113
- 238000001514 detection method Methods 0.000 claims abstract description 16
- 239000013598 vector Substances 0.000 claims description 90
- 238000004891 communication Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 12
- 238000013486 operation strategy Methods 0.000 claims description 9
- 230000005855 radiation Effects 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 2
- 238000003745 diagnosis Methods 0.000 abstract description 4
- 238000012423 maintenance Methods 0.000 description 39
- 230000005856 abnormality Effects 0.000 description 13
- 230000003993 interaction Effects 0.000 description 7
- 238000012360 testing method Methods 0.000 description 7
- 239000000463 material Substances 0.000 description 6
- 230000008447 perception Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000008595 infiltration Effects 0.000 description 1
- 238000001764 infiltration Methods 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/40—Handling position reports or trackside vehicle data
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention provides an intelligent operation system, a method, a device, equipment, a product and a rail vehicle, wherein the system comprises the following components: the system comprises a vehicle-end data module, a ground-end data module and a server; the vehicle end data module is arranged on the rail vehicle; the ground end data module is connected with ground end equipment on the running line of the railway vehicle; the server is respectively connected with the vehicle end data module and the ground end data module so as to generate an operation decision according to data fed back by the vehicle end data module and the ground end data module; wherein the rail vehicle is an unmanned vehicle. According to the invention, by acquiring the external environment of the unmanned rail vehicle and the vehicle operation data, the simultaneous domain data sharing of the unmanned rail vehicle is realized, the sensing and detection of the abnormal conditions of the unmanned rail vehicle are improved, the operation reliability of the unmanned rail vehicle is ensured, and the time and cost for fault diagnosis are also reduced.
Description
Technical Field
The invention relates to the technical field of railway vehicles, in particular to an intelligent operation system, method, device, equipment, product and railway vehicle.
Background
With the development and maturity of the full-automatic unmanned technology, the practical application is more and more extensive, the unmanned line for opening the rail transit is more and more, and the new challenge and the new problem are gradually shown. The system installed by the vehicle is more and more complicated, each system works independently, data is managed independently, information sharing and data interaction are not facilitated, a systematized and integrated comprehensive system is difficult to form, the maximum value of various data cannot be effectively played, and the technology improvement of vehicle digital informationized intelligence is not facilitated.
Disclosure of Invention
The invention provides an intelligent operation system of a railway vehicle, which is used for solving the defects that in the prior art, the system for installing the vehicle is more and more complicated, each system works independently, data is managed independently, information sharing is not facilitated, and data interaction is not facilitated.
The invention also provides an operation method of the intelligent test system.
The invention also provides an operation device for the intelligent test.
The invention also provides electronic equipment.
The invention also provides a computer program product.
The invention further provides the railway vehicle.
According to a first aspect of the present invention, an intelligent operation system for a rail vehicle is provided, comprising: the system comprises a vehicle-end data module, a ground-end data module and a server;
the vehicle end data module is arranged on the rail vehicle;
the ground end data module is connected with ground end equipment on the running line of the railway vehicle;
the server is respectively connected with the vehicle end data module and the ground end data module so as to generate an operation decision according to data fed back by the vehicle end data module and the ground end data module;
wherein the rail vehicle is an unmanned vehicle.
It should be noted that, through setting up the intelligent fortune dimension system that vehicle end data module, ground end data module and server formed, the information of intelligent perception and analysis is more comprehensive, can be more high-efficient, accurate support vehicle emergency, overhaul and maintain, not only promote intelligent operation system ability, need not ground and arrange operating personnel and dedicated check out test set alone, arrange the activity duration alone, developed the detection operation, saved manpower and materials.
Furthermore, abnormal conditions of the train end and the ground end are checked through multiple rows of rail vehicles running on the line, so that the abnormality detection and the accurate positioning are more accurate and reliable, the server can identify the abnormality and automatically generate a work order, and related teams can be timely sent to carry out maintenance operation through the operation and maintenance terminal.
According to one embodiment of the invention, a plurality of the rail vehicles are connected with the server through the vehicle-end data module, so that simultaneous domain data sharing is realized.
Specifically, the embodiment provides an implementation mode of the simultaneous domain data, and by connecting a plurality of vehicle-end data modules with the server, simultaneous domain data sharing is established among a plurality of rows of railway vehicles, and the efficiency of operation planning is improved.
It should be noted that by constructing the simultaneous domain data sharing among the plurality of unmanned rail vehicles, the accurate detection, the abnormal early warning and the fault alarm of the abnormal condition are realized, the normal operation of the unmanned rail vehicles running on the whole line is ensured, the perception data is shared and utilized, the data isolated island is avoided, the utilization value of data is favorable for deeply excavating, meanwhile, the problems that the existing abnormal condition needs to be subjected to field inspection by maintenance personnel, the problem that the abnormal position is not timely and the abnormal position is inaccurately positioned are avoided, the investment of a large amount of manpower and material resources is reduced, and the working efficiency is improved.
Furthermore, abnormal conditions do not influence the operation of the rail vehicle and the ground equipment, the maintenance team is maintained at night, when the detection result of the abnormal conditions has the possibility of influencing the operation, the maintenance team can be guaranteed to take corresponding emergency measures in the first time, and data interaction sharing is realized in the network, so that the comprehensive monitoring of the operation on the ground is greatly improved, the abnormality is timely mastered, the operation and maintenance efficiency is improved, the state repair and accurate maintenance operation are realized, the dependence on manpower is reduced, and the operation and maintenance intelligence level is improved.
According to one embodiment of the invention, the operational decision comprises at least an operational strategy of the rail vehicle and/or the end-of-site installation.
Particularly, the embodiment provides an implementation mode of operation decision, and through the proposition to rail vehicle and/or ground terminal equipment operation strategy, the operation and maintenance of rail vehicle and ground terminal equipment have been guaranteed, the state of realizing team's operation is repaiied and is repaiied with accurate, improves maintenance efficiency, reduces manpower and materials resource and occupies, reduces to overhaul and maintain life cycle cost.
According to a second aspect of the present invention, an operation method based on the above intelligent operation system is provided, and is applied to a server, and the method includes:
responding to an abnormal signal, and acquiring vehicle end parameters of an abnormal area corresponding to a first railway vehicle, wherein the abnormal area is a road area in the running process of the first railway vehicle, and the vehicle end parameters are running data of the first railway vehicle;
judging according to the vehicle end parameters;
determining that the vehicle end parameters meet a normal operation threshold, and judging that the rail vehicle meets the operation requirement;
and if the vehicle end parameters are determined not to meet the normal operation threshold, generating an operation decision.
According to an embodiment of the present invention, the step of determining that the vehicle-end parameter does not meet the normal operation threshold specifically includes:
acquiring a first vehicle end characteristic vector and a second vehicle end characteristic vector of the first railway vehicle, wherein the first vehicle end characteristic vector points to normal vehicle end parameters of the first railway vehicle corresponding to the abnormal area, and the second vehicle end characteristic vector points to abnormal vehicle end parameters of the first railway vehicle corresponding to the abnormal area;
acquiring an operation list of the abnormal area, and extracting operation characteristic vectors in the operation list, wherein the operation characteristic vectors point to N rows of second rail vehicles which pass through the abnormal area recently, and N is a positive integer greater than or equal to one;
generating an operation strategy for the second railway vehicle to pass through the abnormal area according to the first vehicle end characteristic vector and the operation characteristic vector;
and judging according to the operation strategy, and generating an operation decision according to a judgment result.
Specifically, this embodiment provides an implementation manner for determining that the vehicle-end parameter does not satisfy the normal operation threshold, by obtaining a first vehicle-end feature vector and a second vehicle-end feature vector of the first rail vehicle, marking the normal vehicle-end parameter and the abnormal vehicle-end parameter of the first rail vehicle, obtaining an operation list of an abnormal area at the same time, extracting N rows of second rail vehicles in the operation list, and generating an operation policy that the second rail vehicle passes through the abnormal area according to the normal vehicle-end parameter pointed by the first vehicle-end feature vector, thereby implementing corresponding judgment according to the operation policy, and judging and generating the operation decision.
According to an embodiment of the present invention, the step of performing the judgment according to the operation policy and generating the operation decision according to the judgment result specifically includes:
acquiring a third vehicle end characteristic vector of the second railway vehicle, and judging, wherein the third vehicle end characteristic vector of the second railway vehicle and the second vehicle end characteristic vector of the first railway vehicle point to the same group of running data;
and if the third vehicle-end feature vector meets a normal operation threshold value, generating the operation decision according to the second vehicle-end feature vector.
Specifically, the embodiment provides an implementation manner of performing judgment according to the operation policy and generating an operation decision according to a judgment result, and the generation of the operation and maintenance decision is realized by acquiring a third vehicle-end feature vector of the second rail vehicle in the running process in the abnormal area and performing judgment according to the third vehicle-end feature vector.
It should be noted that the third vehicle-end feature vector and the second vehicle-end feature vector point to the same set of driving data, and whether the first rail vehicle is abnormal or the ground-end equipment in the corresponding abnormal area is abnormal can be determined according to the third vehicle-end feature vector.
According to an embodiment of the present invention, the step of obtaining and determining the third vehicle-end feature vector of the second rail vehicle specifically includes:
determining that the third vehicle-end feature vector does not meet a normal operation threshold;
acquiring all driving data pointed by the second vehicle-end characteristic vector, and generating a first vehicle-end data cluster;
acquiring all the running data pointed by the third vehicle-end feature vector, and generating a second vehicle-end data cluster;
and judging according to the first vehicle-end data cluster and the second vehicle-end data cluster, and generating the operation decision according to a judgment result.
Specifically, this embodiment provides an implementation manner for obtaining and judging a third end feature vector of the second rail vehicle, where the third end feature vector does not meet a normal operation threshold, and if the third end feature vector does not meet the normal operation threshold, the third end feature vector is obtained and all pieces of driving data pointed by the third end feature vector are obtained, and a judgment is performed according to all pieces of driving data, and an operation decision is generated according to a judgment result.
According to an embodiment of the present invention, the step of determining according to the first vehicle-end data cluster and the second vehicle-end data cluster specifically includes:
generating a data pointer according to the second vehicle-end data cluster, wherein the data pointer points to an abnormal vehicle-end parameter in the second vehicle-end data cluster;
traversing the first vehicle-end data cluster according to the data pointer, and judging;
determining that each data value in the first vehicle-end data cluster corresponds to the data pointer one to one, and generating the operation decision of the ground-end equipment corresponding to the abnormal area;
if it is determined that at least one data value in the first vehicle-end data cluster is not matched with the data pointer, generating the operation decision according to the second vehicle-end feature vector and the ground-end equipment corresponding to the abnormal area;
and a step of reacquiring the operation list corresponding to the abnormal area if at least one of the data pointers is determined not to be matched with a corresponding data value in the first vehicle-end data cluster.
Specifically, the embodiment provides an implementation manner of performing the determination according to the first vehicle-end data cluster and the second vehicle-end data cluster, where a data pointer is generated through the second vehicle-end data cluster, and the first vehicle-end data cluster is traversed according to the data pointer, so as to generate a corresponding operation decision.
According to one embodiment of the present invention, the travel data includes: the method comprises the following steps that one or more of bow net data, bridge and tunnel data, obstacle data, track detection data, speed data, noise data, radiation data and vibration data of the railway vehicle in the process of the area along the way are combined.
Specifically, the embodiment provides an implementation mode of running data, by acquiring running data of a rail vehicle in a running process in a region along a road, state detection, abnormality early warning and fault diagnosis of ground-end equipment and surrounding environment where the rail vehicle is connected with the outside are achieved, data are analyzed, operated and analyzed through simultaneous domain data sharing, specific states are identified finally, abnormal position location is labeled, intelligent judgment is carried out, and operation and maintenance information such as abnormality early warning and fault warning is output.
According to a third aspect of the present invention, an operation device of an intelligent operation system is provided, including: the device comprises a parameter acquisition module, a parameter judgment module, a first determination module and a second determination module;
the parameter acquisition module is used for responding to an abnormal signal and acquiring vehicle end parameters of an abnormal area corresponding to a first railway vehicle, wherein the abnormal area is a road area in the running process of the first railway vehicle, and the vehicle end parameters are running data of the first railway vehicle;
the parameter judgment module is used for judging according to the vehicle end parameters;
the first determining module is used for determining that the vehicle end parameters meet a normal operation threshold value, and then judging that the rail vehicle meets the operation requirement;
and the second determining module is used for determining that the vehicle end parameters do not meet the normal operation threshold value, and generating an operation decision.
According to a fourth aspect of the present invention, there is provided an electronic apparatus comprising: a memory and a processor;
the memory and the processor complete mutual communication through a bus;
the memory stores computer instructions executable on the processor;
and when the processor calls the computer instruction, the operation method of the intelligent operation system can be executed.
According to a fifth aspect of the invention, there is provided a computer program product comprising a non-transitory machine-readable medium storing instructions which, when executed by a processor, implement the steps of the method of operating an intelligent operating system as described above.
According to a sixth aspect of the present invention, there is provided a rail vehicle having the above-mentioned intelligent operation system, or an operation method using the above-mentioned intelligent operation system when operating a rail vehicle, or an operation device having the above-mentioned intelligent operation system, or the above-mentioned electronic device, or the above-mentioned computer program product.
One or more technical solutions in the present invention have at least one of the following technical effects: according to the intelligent operation system, the method, the device, the equipment, the product and the rail vehicle, the simultaneous domain data sharing of the unmanned rail vehicle is realized by acquiring the external environment of the unmanned rail vehicle and the vehicle operation data, the sensing and the detection of the abnormal conditions of the unmanned rail vehicle are improved, the operation reliability of the unmanned rail vehicle is ensured, and the time and the cost of fault diagnosis are also reduced.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic layout of an intelligent operation system for a rail vehicle provided by the present invention;
FIG. 2 is a flow chart of an operation method of the intelligent operation system provided by the invention;
fig. 3 is a schematic structural diagram of an operation device of the intelligent operation system provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Reference numerals:
10. a vehicle end data module; 20. a ground data module; 30. a server;
40. a parameter acquisition module; 50. a parameter judgment module; 60. a first determination module; 70. a second determination module;
810. a processor; 820. a communication interface; 830. a memory; 840. a communication bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments. In the description of the present invention, "at least one" includes one or more unless otherwise specified. "plurality" means two or more. For example, at least one of A, B and C, comprising: a alone, B alone, a and B in combination, a and C in combination, B and C in combination, and A, B and C in combination. In the present invention, "/" indicates "or" means, for example, A/B may indicate A or B; "and/or" herein is merely an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The present invention will be described in detail with reference to the following embodiments.
In some embodiments of the present invention, as shown in fig. 1, the present invention provides an intelligent operation system for a rail vehicle, comprising: the system comprises a vehicle-end data module 10, a ground-end data module 20 and a server 30; the vehicle end data module 10 is arranged on a rail vehicle; the ground end data module 20 is connected with ground end equipment on a running line of the railway vehicle; the server 30 is respectively connected with the vehicle-end data module 10 and the ground-end data module 20 so as to generate an operation decision according to data fed back by the vehicle-end data module 10 and the ground-end data module 20; wherein, the rail vehicle is unmanned vehicle.
In detail, the invention provides an intelligent operation system of a railway vehicle, which is used for solving the defects that in the prior art, vehicle installation systems are more and more complicated, each system works independently, data are managed independently, information sharing is not facilitated, and data interaction is not facilitated.
It should be noted that, through setting up the intelligent operation and maintenance system that vehicle end data module 10, ground end data module 20 and server 30 formed, the information of intelligence perception and analysis is more comprehensive, can be more high-efficient, accurate support vehicle emergency, overhaul and maintain, not only promote intelligent operation system ability, need not ground and arrange operating personnel and dedicated check out test set alone, arrange the activity duration alone, developed the detection operation, saved manpower and materials.
Furthermore, the abnormal conditions of the train end and the ground end are checked through the multiple rows of rail vehicles running on the line, so that the abnormality detection and the accurate positioning are more accurate and reliable, the server 30 can identify the abnormality and automatically generate a work order, and relevant teams can be timely sent to carry out maintenance operation through the operation and maintenance terminal.
In some possible embodiments of the present invention, a plurality of rail vehicles are connected to the server 30 through the vehicle-end data module 10, so as to implement the same time domain data sharing.
Specifically, the embodiment provides an implementation mode of the simultaneous domain data, and by connecting the plurality of vehicle-end data modules 10 with the server 30, simultaneous domain data sharing is established among multiple rows of railway vehicles, so that the operation planning efficiency is improved.
It should be noted that by constructing the simultaneous domain data sharing among the plurality of unmanned rail vehicles, the accurate detection, the abnormal early warning and the fault alarm of the abnormal condition are realized, the normal operation of the unmanned rail vehicles running on the whole line is ensured, the perception data is shared and utilized, the data isolated island is avoided, the utilization value of data is favorable for deeply excavating, meanwhile, the problems that the existing abnormal condition needs to be subjected to field inspection by maintenance personnel, the problem that the abnormal position is not timely and the abnormal position is inaccurately positioned are avoided, the investment of a large amount of manpower and material resources is reduced, and the working efficiency is improved.
Furthermore, abnormal conditions do not influence the operation of rail vehicles and ground terminal equipment, maintain the team and maintain at night, when the detection result of abnormal conditions has the possibility of influencing the operation, then can guarantee to maintain the team and take corresponding emergency measures the very first time, and realize data interaction sharing in the network, thereby promote the all-round control of ground to the operation by a wide margin, in time master the anomaly, improve the operation and maintenance efficiency, realize the operation of state repair and accurate maintenance, reduce the reliance to the manual work, improve the intelligent level of operation and maintenance.
In a possible embodiment, the possible abnormal conditions of the rail vehicle include: speed anomalies, acceleration anomalies, noise anomalies, vibration anomalies, radiation anomalies, communication anomalies, temperature anomalies, and so forth.
In a possible embodiment, the possible abnormal conditions of the end-to-end device include: contact net foreign matter, contact net fracture, bridge tunnel infiltration, bridge tunnel collapse, track foreign matter invasion, track fracture, hasp drop etc..
In an application scene, an unmanned rail vehicle detects the water seepage of a tunnel, positions the water seepage position of the tunnel, sends information to the server 30 in real time, automatically generates a work order after the server 30 analyzes and identifies the work order and sends the work order to an operation and maintenance terminal of a tunnel maintenance team, and the operation and maintenance team immediately starts emergency disposal of a water inlet area according to the information of the work order.
In an application scene, an unmanned rail vehicle detects rail damage, positions the rail damage, sends information to the server 30 in real time, automatically generates a work order after the server 30 analyzes and identifies, sends the work order to an operation and maintenance terminal of a rail maintenance team, informs the team that the team needs to be polished and repaired, and immediately prepares to conduct online polishing on the organization according to the work order information.
In an application scene, the former unmanned rail vehicle detects that the contact network is abnormal, and the ground terminal equipment corresponding to the contact network also detects that the front contact network is abnormal, corresponding abnormal information is sent to the subsequent unmanned rail vehicle through the server 30, more time is strived for the subsequent unmanned rail vehicle to take emergency response, the influence of the abnormality on the vehicle operation is reduced to the maximum extent, meanwhile, the server 30 sends the work order to the operation and maintenance terminal of the contact network maintenance team, and the contact network maintenance team maintains the contact network according to the work order.
In some possible embodiments of the invention, the operational decision comprises at least an operational strategy of the rail vehicle and/or the ground end equipment.
Particularly, the embodiment provides an implementation mode of operation decision, and through the proposition to rail vehicle and/or ground terminal equipment operation strategy, the operation and maintenance of rail vehicle and ground terminal equipment have been guaranteed, the state of realizing team's operation is repaiied and is repaiied with accurate, improves maintenance efficiency, reduces manpower and materials resource and occupies, reduces to overhaul and maintain life cycle cost.
In a possible implementation mode, the line abnormal information can be subjected to fusion management, other running unmanned railway vehicles are informed to carry out emergency treatment in advance, more emergency time is strived for, and the influence of the abnormality on the vehicle operation is reduced.
In a possible implementation mode, a 5G module is further arranged on the test vehicle, and vehicle-mounted equipment and ground equipment of the test vehicle realize network transmission of data through the 5G module so as to realize automatic downloading and processing of test data; the system can also remotely log in through a mobile phone or a PC terminal, and is convenient and quick.
In some embodiments of the present invention, as shown in fig. 2, the present solution provides an operation method based on the above-mentioned intelligent operation system, which is applied to the server 30, and the method includes:
responding to the abnormal signal, and acquiring vehicle end parameters of the first rail vehicle corresponding to an abnormal area, wherein the abnormal area is a road area in the running process of the first rail vehicle, and the vehicle end parameters are running data of the first rail vehicle;
judging according to the vehicle end parameters;
determining that the vehicle end parameters meet a normal operation threshold, and judging that the rail vehicle meets the operation requirement;
and if the vehicle end parameters are determined not to meet the normal operation threshold, generating an operation decision.
In some possible embodiments of the present invention, the step of determining that the vehicle-end parameter does not satisfy the normal operation threshold specifically includes:
acquiring a first vehicle end characteristic vector and a second vehicle end characteristic vector of a first railway vehicle, wherein the first vehicle end characteristic vector points to normal vehicle end parameters of the first railway vehicle corresponding to an abnormal area, and the second vehicle end characteristic vector points to abnormal vehicle end parameters of the first railway vehicle corresponding to the abnormal area;
acquiring an operation list of the abnormal area, and extracting operation characteristic vectors in the operation list, wherein the operation characteristic vectors point to N rows of second rail vehicles which pass through the abnormal area recently, and N is a positive integer greater than or equal to one;
generating an operation strategy for the second railway vehicle to pass through the abnormal area according to the first vehicle end characteristic vector and the operation characteristic vector;
and judging according to the operation strategy, and generating an operation decision according to a judgment result.
Specifically, this embodiment provides an implementation manner for determining that a vehicle-end parameter does not satisfy a normal operation threshold, where a first vehicle-end feature vector and a second vehicle-end feature vector of a first rail vehicle are obtained, a normal vehicle-end parameter and an abnormal vehicle-end parameter of the first rail vehicle are marked, an operation list of an abnormal area is obtained at the same time, N rows of second rail vehicles in the operation list are extracted, and an operation policy that the second rail vehicle passes through the abnormal area according to the normal vehicle-end parameter pointed by the first vehicle-list feature vector is generated, so that corresponding judgment according to the operation policy is implemented, and an operation decision is judged and generated.
In a possible implementation manner, in the traveling process of a first rail vehicle on a section of line, if the noise parameter is detected to be too large, the line is marked as an abnormal area, the noise parameter is marked as an abnormal vehicle end parameter, namely a second vehicle end characteristic vector, and the rest of the traveling parameters are marked as normal vehicle end parameters, such as a speed parameter, an acceleration parameter, comfort, stability and the like, namely a first vehicle end characteristic vector; obtaining an operation list of the abnormal area, determining N rows of second rail vehicles which pass through the abnormal area recently through operation characteristic vectors, comparing the first rail vehicles with the second rail vehicles, determining the second rail vehicles which are matched with parameters of the first rail vehicles in various aspects such as model, driving parameters, passenger capacity and the like, driving the matched second rail vehicles through the corresponding abnormal area through the first vehicle end characteristic vectors, obtaining corresponding data for judgment, and generating operation decisions according to judgment results.
In a possible implementation manner, when a first rail vehicle runs on a section of a line, if the vibration parameter is detected to be too large, the line is marked as an abnormal area, the vibration parameter is marked as an abnormal vehicle-end parameter, namely a second vehicle-end feature vector, and the rest of the running parameters are marked as normal vehicle-end parameters, such as a speed parameter, an acceleration parameter, comfort, stability and the like, namely a first vehicle-end feature vector; obtaining an operation list of the abnormal area, determining N rows of second rail vehicles which pass through the abnormal area recently through operation characteristic vectors, comparing the first rail vehicles with the second rail vehicles, determining the second rail vehicles which are matched with parameters of the first rail vehicles in various aspects such as model, driving parameters, passenger capacity and the like, driving the matched second rail vehicles through the corresponding abnormal area through the first vehicle end characteristic vectors, obtaining corresponding data for judgment, and generating operation decisions according to judgment results.
In some possible embodiments of the present invention, the step of performing a judgment according to the operation policy and generating an operation decision according to a judgment result specifically includes:
acquiring a third vehicle end characteristic vector of the second railway vehicle, and judging, wherein the third vehicle end characteristic vector of the second railway vehicle and the second vehicle end characteristic vector of the first railway vehicle point to the same group of running data;
and if the third vehicle-end feature vector meets the normal operation threshold, generating an operation decision according to the second vehicle-end feature vector.
Specifically, the embodiment provides an implementation manner that the operation decision is determined according to the operation policy and is generated according to the determination result, and the generation of the operation and maintenance decision is realized by obtaining a third vehicle-end feature vector of the second rail vehicle in the driving process in the abnormal area and performing the determination according to the third vehicle-end feature vector.
It should be noted that the third vehicle-end feature vector and the second vehicle-end feature vector point to the same set of driving data, and whether the first rail vehicle is abnormal or the ground-end equipment in the corresponding abnormal area is abnormal can be determined according to the third vehicle-end feature vector.
In some possible embodiments of the present invention, the step of obtaining a third vehicle-end feature vector of the second rail vehicle and performing the determination specifically includes:
determining that the third vehicle-end feature vector does not meet a normal operation threshold;
acquiring all driving data pointed by the second vehicle-end characteristic vector, and generating a first vehicle-end data cluster;
acquiring all running data pointed by the third vehicle-end feature vector, and generating a second vehicle-end data cluster;
and judging according to the first vehicle-end data cluster and the second vehicle-end data cluster, and generating an operation decision according to a judgment result.
Specifically, this embodiment provides an implementation manner of obtaining a third end feature vector of the second rail vehicle and performing the determination, where if the third end feature vector does not meet the normal operation threshold, the operation decision is generated according to the determination result by obtaining all the driving data pointed by the second end feature vector and the third end feature vector, and performing the determination according to all the driving data.
In a possible embodiment, the second vehicle-end feature vector points to the noise parameter, the vibration parameter and the radiation parameter during the operation of the first rail vehicle, i.e. the abnormal vehicle-end parameter of the first rail vehicle includes the noise parameter, the vibration parameter and the radiation parameter.
In a possible embodiment, the third vehicle-end feature vector points to a current parameter, a voltage parameter, a noise parameter, a vibration parameter and a radiation parameter during the operation of the second rail vehicle, i.e. the abnormal vehicle-end parameter of the first rail vehicle includes the current parameter, the voltage parameter, the noise parameter, the vibration parameter and the radiation parameter.
In some possible embodiments of the present invention, the step of determining according to the first vehicle-end data cluster and the second vehicle-end data cluster specifically includes:
generating a data pointer according to the second vehicle-end data cluster, wherein the data pointer points to abnormal vehicle-end parameters in the second vehicle-end data cluster;
traversing the first vehicle-end data cluster according to the data pointer, and judging;
determining that each data value in the first vehicle-end data cluster corresponds to a data pointer one by one, and generating an operation decision of the ground-end equipment corresponding to the abnormal area;
determining that at least one data value in the first vehicle-end data cluster is not matched with the data pointer, and generating an operation decision according to the second vehicle-end feature vector and the ground-end equipment corresponding to the abnormal area;
and a step of re-acquiring the operation list corresponding to the abnormal area if at least one of the data pointers is determined not to be matched with the corresponding data value in the first vehicle-end data cluster.
Specifically, the embodiment provides an implementation manner of performing the determination according to the first vehicle-end data cluster and the second vehicle-end data cluster, where a data pointer is generated through the second vehicle-end data cluster, and the first vehicle-end data cluster is traversed according to the data pointer, so as to generate a corresponding operation decision.
In a possible implementation manner, each data value in the first vehicle-end data cluster corresponds to a data pointer one to one, which indicates that corresponding abnormal problems exist when the first rail vehicle and the second rail vehicle run in an abnormal area, and a source of the abnormal problem may be a ground-end device, so that an operation decision is preferentially generated according to the ground-end device, and the operation decision includes maintenance of the ground-end device by an operation and maintenance person, and setting of an operation path and an operation parameter of a subsequent rail vehicle.
In a possible implementation manner, if at least one data value in the first vehicle-end data cluster is not matched with the data pointer, it is indicated that corresponding abnormal problems exist when the first rail vehicle and the second rail vehicle run in an abnormal area, and the first rail vehicle is also likely to have a vehicle-end abnormal condition, so that an operation decision is generated according to the second vehicle-end feature vector and the ground-end equipment corresponding to the abnormal area, and the operation decision includes the maintenance of the rail vehicle and the ground-end equipment by an operation and maintenance person, and the setting of an operation path and an operation parameter of a subsequent rail vehicle.
In a possible implementation manner, if at least one of the data pointers does not match a corresponding data value in the first vehicle-end data cluster, it indicates that corresponding abnormal problems exist when the first rail vehicle and the second rail vehicle operate in the abnormal region, and the source of the abnormal problem cannot be determined, so that the step of performing data acquisition on the subsequent rail vehicle through the abnormal region needs to be performed again, and the data are acquired for multiple times to obtain more accurate abnormal data, so as to judge the direction of the abnormal condition and generate a corresponding operation decision, where the operation decision includes maintenance of the rail vehicle and/or the end-to-end equipment by operation and maintenance personnel, and setting of an operation path and an operation parameter of the subsequent rail vehicle.
In some possible embodiments of the invention, the driving data comprises: the method comprises the following steps of combining one or more of bow net data, bridge and tunnel data, obstacle data, track detection data, speed data, noise data, radiation data and vibration data of the railway vehicle in the process of the area along the way.
Specifically, the embodiment provides an implementation mode of running data, by acquiring running data of a rail vehicle in a running process in a region along a road, state detection, abnormality early warning and fault diagnosis of ground-end equipment and surrounding environment where the rail vehicle is connected with the outside are achieved, data are analyzed, operated and analyzed through simultaneous domain data sharing, specific states are identified finally, abnormal position location is labeled, intelligent judgment is carried out, and operation and maintenance information such as abnormality early warning and fault warning is output.
In a possible implementation mode, multiple trains running on line detect the same abnormality at the same position, so that the abnormality detection efficiency is improved, the manual operation time and the cost are reduced, the reliability and the accuracy are greatly improved, and the purposes of state maintenance and accurate maintenance in the true sense are realized.
In some embodiments of the present invention, as shown in fig. 3, the present solution provides an operation device of an intelligent operation system, including: a parameter obtaining module 40, a parameter judging module 50, a first determining module 60 and a second determining module 70;
the parameter obtaining module 40 is configured to, in response to the abnormal signal, obtain a vehicle-end parameter of an abnormal area corresponding to the first rail vehicle, where the abnormal area is a region along the way in the running process of the first rail vehicle, and the vehicle-end parameter is running data of the first rail vehicle;
the parameter judgment module 50 is used for judging according to the vehicle end parameters;
the first determining module 60 is configured to determine that the vehicle-end parameter meets a normal operation threshold, and determine that the rail vehicle meets an operation requirement;
the second determining module 70 is configured to determine that the vehicle-end parameter does not meet the normal operation threshold, and generate an operation decision.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform an operation method of the intelligent operation system.
It should be noted that, when being implemented specifically, the electronic device in this embodiment may be a server, a PC, or other devices, as long as the structure includes the processor 810, the communication interface 820, the memory 830, and the communication bus 840 shown in fig. 4, where the processor 810, the communication interface 820, and the memory 830 complete mutual communication through the communication bus 840, and the processor 810 may call the logic instructions in the memory 830 to execute the above method. The embodiment does not limit the specific implementation form of the electronic device.
The server may be a single server or a server group. The set of servers can be centralized or distributed (e.g., the servers can be a distributed system). In some embodiments, the server may be local or remote to the terminal. For example, the server may access information stored in the user terminal, a database, or any combination thereof via a network. As another example, the server may be directly connected to at least one of the user terminal and the database to access information and/or data stored therein. In some embodiments, the server may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof. In some embodiments, the server and the user terminal may be implemented on an electronic device having one or more components in embodiments of the invention.
Further, the network may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., servers, user terminals, and databases) in an interaction scenario may send information and/or data to other components. In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, the Network may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, or a Near Field Communication (NFC) Network, among others, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the interaction scenario may connect to the network to exchange data and/or information.
In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In a possible implementation manner, the embodiment of the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the operation method of the intelligent operation system provided by the foregoing embodiments.
In a possible implementation, the embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the method provided by the above-mentioned method embodiments.
In some embodiments of the present invention, the present invention provides a rail vehicle, which has the above-mentioned intelligent operation system, or when operating a rail vehicle, an operation method using the above-mentioned intelligent operation system, or an operation device having the above-mentioned intelligent operation system, or the above-mentioned electronic device, or the above-mentioned computer program product is used.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (13)
1. An intelligent operation system of a rail vehicle, comprising: the system comprises a vehicle-end data module, a ground-end data module and a server;
the vehicle end data module is arranged on the rail vehicle;
the ground end data module is connected with ground end equipment on the running line of the railway vehicle;
the server is respectively connected with the vehicle end data module and the ground end data module so as to generate an operation decision according to data fed back by the vehicle end data module and the ground end data module;
wherein the rail vehicle is an unmanned vehicle.
2. The intelligent operation system of rail vehicles according to claim 1, wherein a plurality of rail vehicles are connected with the server through the vehicle-end data module, so as to realize simultaneous domain data sharing.
3. The intelligent operation system of rail vehicles according to claim 1 or 2, characterized in that the operation decision comprises at least an operation strategy of the rail vehicle and/or the ground end equipment.
4. An operation method based on the intelligent operation system of any one of the above claims 1 to 3, applied to a server, characterized in that the method comprises:
responding to an abnormal signal, and acquiring vehicle end parameters of an abnormal area corresponding to a first railway vehicle, wherein the abnormal area is a road area in the running process of the first railway vehicle, and the vehicle end parameters are running data of the first railway vehicle;
judging according to the vehicle end parameters;
determining that the vehicle end parameters meet a normal operation threshold, and judging that the rail vehicle meets the operation requirement;
and if the vehicle end parameters are determined not to meet the normal operation threshold, generating an operation decision.
5. The operation method of the intelligent operation system according to claim 4, wherein the step of determining that the vehicle-end parameter does not satisfy the normal operation threshold specifically includes:
acquiring a first vehicle end characteristic vector and a second vehicle end characteristic vector of the first railway vehicle, wherein the first vehicle end characteristic vector points to a normal vehicle end parameter of the first railway vehicle corresponding to the abnormal area, and the second vehicle end characteristic vector points to an abnormal vehicle end parameter of the first railway vehicle corresponding to the abnormal area;
acquiring an operation list of the abnormal area, and extracting operation characteristic vectors in the operation list, wherein the operation characteristic vectors point to N rows of second rail vehicles which pass through the abnormal area recently, and N is a positive integer greater than or equal to one;
generating an operation strategy for the second railway vehicle to pass through the abnormal area according to the first vehicle end characteristic vector and the operation characteristic vector;
and judging according to the operation strategy, and generating an operation decision according to a judgment result.
6. The operation method of the intelligent operation system according to claim 5, wherein the step of performing the judgment according to the operation policy and generating the operation decision according to the judgment result specifically comprises:
acquiring a third vehicle end characteristic vector of the second railway vehicle, and judging, wherein the third vehicle end characteristic vector of the second railway vehicle and the second vehicle end characteristic vector of the first railway vehicle point to the same group of running data;
and if the third vehicle-end feature vector meets a normal operation threshold value, generating the operation decision according to the second vehicle-end feature vector.
7. The operation method of the intelligent operation system according to claim 6, wherein the step of obtaining the third vehicle-end feature vector of the second rail vehicle and performing the judgment specifically further comprises:
determining that the third vehicle-end feature vector does not meet a normal operation threshold;
acquiring all driving data pointed by the second vehicle-end feature vector, and generating a first vehicle-end data cluster;
acquiring all the running data pointed by the third vehicle-end feature vector, and generating a second vehicle-end data cluster;
and judging according to the first vehicle-end data cluster and the second vehicle-end data cluster, and generating the operation decision according to a judgment result.
8. The operation method of the intelligent operation system according to claim 7, wherein the step of determining according to the first vehicle-end data cluster and the second vehicle-end data cluster specifically includes:
generating a data pointer according to the second vehicle-end data cluster, wherein the data pointer points to abnormal vehicle-end parameters in the second vehicle-end data cluster;
traversing the first vehicle-end data cluster according to the data pointer, and judging;
determining that each data value in the first vehicle-end data cluster corresponds to the data pointer one by one, and generating the operation decision of the ground-end equipment corresponding to the abnormal area;
if at least one data value in the first vehicle-end data cluster is determined not to be matched with the data pointer, generating the operation decision according to the second vehicle-end feature vector and the ground-end equipment corresponding to the abnormal area;
and a step of reacquiring the operation list corresponding to the abnormal area if at least one of the data pointers is determined not to be matched with a corresponding data value in the first vehicle-end data cluster.
9. The operation method of the intelligent operation system according to any one of claims 4 to 8, wherein the travel data includes: the method comprises the following steps of acquiring and processing a plurality of data of the railway vehicle, wherein the data of the railway vehicle comprises one or more of bow net data, bridge and tunnel data, obstacle data, track detection data, speed data, noise data, radiation data and vibration data in the process of the railway vehicle in a region along the way.
10. An operation device of an intelligent operation system, comprising: the device comprises a parameter acquisition module, a parameter judgment module, a first determination module and a second determination module;
the parameter acquisition module is used for responding to an abnormal signal and acquiring vehicle end parameters of an abnormal area corresponding to a first railway vehicle, wherein the abnormal area is a road area in the running process of the first railway vehicle, and the vehicle end parameters are running data of the first railway vehicle;
the parameter judgment module is used for judging according to the vehicle end parameters;
the first determining module is used for determining that the vehicle end parameters meet a normal operation threshold value, and then judging that the rail vehicle meets the operation requirement;
and the second determining module is used for determining that the vehicle end parameters do not meet the normal operation threshold value, and generating an operation decision.
11. An electronic device, comprising: a memory and a processor;
the memory and the processor complete mutual communication through a bus;
the memory stores computer instructions executable on the processor;
the processor, when invoking the computer instructions, is capable of executing the operation method of the intelligent operation system of any one of claims 4 to 9.
12. A computer program product comprising a non-transitory machine readable medium storing instructions, wherein the instructions, when executed by a processor, implement the steps of the method for operating an intelligent operating system according to any one of claims 4 to 9.
13. A rail vehicle, characterized in that, the intelligent operation system of any one of the above claims 1 to 3 is provided, or when operating a rail vehicle, the operation method of the intelligent operation system of any one of the above claims 4 to 9 is adopted, or the operation device of the intelligent operation system of the above claim 10 is provided, or the electronic device of the above claim 11 is provided, or the computer program product of the above claim 12 is provided.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210689508.1A CN114954586B (en) | 2022-06-16 | 2022-06-16 | Intelligent operation system, method, device, equipment, product and railway vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210689508.1A CN114954586B (en) | 2022-06-16 | 2022-06-16 | Intelligent operation system, method, device, equipment, product and railway vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114954586A true CN114954586A (en) | 2022-08-30 |
| CN114954586B CN114954586B (en) | 2023-12-26 |
Family
ID=82963110
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210689508.1A Active CN114954586B (en) | 2022-06-16 | 2022-06-16 | Intelligent operation system, method, device, equipment, product and railway vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114954586B (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0966833A (en) * | 1995-09-04 | 1997-03-11 | Toshiba Transport Eng Kk | Train operation managing device |
| US5987979A (en) * | 1996-04-01 | 1999-11-23 | Cairo Systems, Inc. | Method and apparatus for detecting railtrack failures by comparing data from a plurality of railcars |
| JP2008148466A (en) * | 2006-12-11 | 2008-06-26 | Mitsubishi Heavy Ind Ltd | Fault diagnosis method and fault diagnosis system of orbital system transportation system |
| KR20100061011A (en) * | 2008-11-28 | 2010-06-07 | 한국철도기술연구원 | System and method for terror monitoring of train station |
| WO2016113724A1 (en) * | 2015-01-15 | 2016-07-21 | 株式会社日立製作所 | State monitoring device, state monitoring system, and train |
| GB201710761D0 (en) * | 2015-01-15 | 2017-08-16 | Hitachi Ltd | State monitoring device, state monitoring system and train |
| JP2018054567A (en) * | 2016-09-30 | 2018-04-05 | 日本車輌製造株式会社 | Railway vehicle abnormality detection method |
| US20180222504A1 (en) * | 2017-02-08 | 2018-08-09 | Intel Corporation | Location based railway anomaly detection |
| CN109413614A (en) * | 2018-09-27 | 2019-03-01 | 北京全路通信信号研究设计院集团有限公司 | Integrated vehicle-ground comprehensive analysis platform |
| CN109760718A (en) * | 2017-11-09 | 2019-05-17 | 许昌许继软件技术有限公司 | A kind of city underground alarm centralized processing method |
| CN110378493A (en) * | 2019-08-07 | 2019-10-25 | 唐智科技湖南发展有限公司 | A kind of rail traffic vehicles O&M detection device, method and mobile terminal |
| CN111746598A (en) * | 2019-03-28 | 2020-10-09 | 上海申通地铁集团有限公司 | Fault detection system of train-ground communication system |
| CN112498416A (en) * | 2020-12-23 | 2021-03-16 | 成都运达科技股份有限公司 | Integrated intelligent management and control system for vehicle servicing |
| CN112660211A (en) * | 2021-01-16 | 2021-04-16 | 湖南科技大学 | Intelligent operation and maintenance management system for railway locomotive |
| US20210349997A1 (en) * | 2019-08-30 | 2021-11-11 | Panasonic Intellectual Property Corporation Of America | Anomalous vehicle detection server and anomalous vehicle detection method |
| CN114120472A (en) * | 2021-11-23 | 2022-03-01 | 安雪斌 | Automatic driving vehicle safety management system |
| CN114245407A (en) * | 2021-12-30 | 2022-03-25 | 中国联合网络通信集团有限公司 | Network optimization method, device, equipment and storage medium |
| CN114506365A (en) * | 2020-11-17 | 2022-05-17 | 比亚迪股份有限公司 | Overhead rail operation abnormity processing method and system |
-
2022
- 2022-06-16 CN CN202210689508.1A patent/CN114954586B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0966833A (en) * | 1995-09-04 | 1997-03-11 | Toshiba Transport Eng Kk | Train operation managing device |
| US5987979A (en) * | 1996-04-01 | 1999-11-23 | Cairo Systems, Inc. | Method and apparatus for detecting railtrack failures by comparing data from a plurality of railcars |
| JP2008148466A (en) * | 2006-12-11 | 2008-06-26 | Mitsubishi Heavy Ind Ltd | Fault diagnosis method and fault diagnosis system of orbital system transportation system |
| KR20100061011A (en) * | 2008-11-28 | 2010-06-07 | 한국철도기술연구원 | System and method for terror monitoring of train station |
| WO2016113724A1 (en) * | 2015-01-15 | 2016-07-21 | 株式会社日立製作所 | State monitoring device, state monitoring system, and train |
| GB201710761D0 (en) * | 2015-01-15 | 2017-08-16 | Hitachi Ltd | State monitoring device, state monitoring system and train |
| JP2018054567A (en) * | 2016-09-30 | 2018-04-05 | 日本車輌製造株式会社 | Railway vehicle abnormality detection method |
| US20180222504A1 (en) * | 2017-02-08 | 2018-08-09 | Intel Corporation | Location based railway anomaly detection |
| CN109760718A (en) * | 2017-11-09 | 2019-05-17 | 许昌许继软件技术有限公司 | A kind of city underground alarm centralized processing method |
| CN109413614A (en) * | 2018-09-27 | 2019-03-01 | 北京全路通信信号研究设计院集团有限公司 | Integrated vehicle-ground comprehensive analysis platform |
| WO2020063280A1 (en) * | 2018-09-27 | 2020-04-02 | 北京全路通信信号研究设计院集团有限公司 | Train-ground integrated comprehensive analysis platform |
| CN111746598A (en) * | 2019-03-28 | 2020-10-09 | 上海申通地铁集团有限公司 | Fault detection system of train-ground communication system |
| CN110378493A (en) * | 2019-08-07 | 2019-10-25 | 唐智科技湖南发展有限公司 | A kind of rail traffic vehicles O&M detection device, method and mobile terminal |
| US20210349997A1 (en) * | 2019-08-30 | 2021-11-11 | Panasonic Intellectual Property Corporation Of America | Anomalous vehicle detection server and anomalous vehicle detection method |
| CN114506365A (en) * | 2020-11-17 | 2022-05-17 | 比亚迪股份有限公司 | Overhead rail operation abnormity processing method and system |
| CN112498416A (en) * | 2020-12-23 | 2021-03-16 | 成都运达科技股份有限公司 | Integrated intelligent management and control system for vehicle servicing |
| CN112660211A (en) * | 2021-01-16 | 2021-04-16 | 湖南科技大学 | Intelligent operation and maintenance management system for railway locomotive |
| CN114120472A (en) * | 2021-11-23 | 2022-03-01 | 安雪斌 | Automatic driving vehicle safety management system |
| CN114245407A (en) * | 2021-12-30 | 2022-03-25 | 中国联合网络通信集团有限公司 | Network optimization method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 张梦双;伍尚志;贺翔;黄硕;: "广州地铁3号线北延段受电弓碳滑板异常磨耗原因分析", 价值工程, no. 05 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114954586B (en) | 2023-12-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109413614B (en) | Integrated vehicle-ground comprehensive analysis platform | |
| CN107123295A (en) | Congested link Forecasting Methodology, device, server and storage medium | |
| CN105591784A (en) | Warning processing method and apparatus | |
| CN112000557A (en) | Automatic testing arrangement of track traffic signal system | |
| CN109387376A (en) | The diagnostic method of vehicle trouble, the diagnostic device of vehicle trouble and server | |
| CN114707401A (en) | Fault early warning method and device for signal system equipment | |
| CN112000558A (en) | Method for generating automatic test case of rail transit signal system | |
| CN110533794A (en) | Vehicle risk management method, computer installation and computer readable storage medium | |
| CN110501989B (en) | Train operation fault filtering method and device | |
| CN113635947B (en) | Interlocking simulation model design method applied to urban rail and interlocking simulation model | |
| CN110728612B (en) | Rail transit emergency simulation evaluation method and system | |
| CN112172878A (en) | Train speed limit curve calculation method and system | |
| CN111348535A (en) | Health state monitoring system and method for escalator used in rail transit station | |
| CN113590468A (en) | Automatic testing method and device for ATS interface of automatic train monitoring system | |
| CN114954586A (en) | Intelligent operation system, method, device, equipment, product and rail vehicle | |
| CN111597637A (en) | Fault detection method, device, server and system for field parts of bullet train | |
| CN110969338A (en) | Visual comprehensive display method for power distribution network production | |
| CN115571196A (en) | Intelligent handling method and system for positive line fault based on rule engine | |
| CN114997440A (en) | Intelligent operation and maintenance system, method, device, equipment and product of railway vehicle | |
| CN106896799B (en) | A kind of locomotive inspection and repair management method | |
| CN114662290A (en) | Full-electronic interlocking monitoring data processing method and device | |
| CN114709923A (en) | Intelligent fault judgment method for power Internet of things | |
| CN112306039B (en) | Debugging method and system for full-electronic interlocking system | |
| CN112637783B (en) | Rail transit safety management method and device based on visible light positioning technology | |
| CN113550992B (en) | Method, device and equipment for evaluating braking operation of railway vehicle and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |