CN114368409A - Method and device for analyzing health condition of track traffic turnout - Google Patents
Method and device for analyzing health condition of track traffic turnout Download PDFInfo
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- CN114368409A CN114368409A CN202111563507.4A CN202111563507A CN114368409A CN 114368409 A CN114368409 A CN 114368409A CN 202111563507 A CN202111563507 A CN 202111563507A CN 114368409 A CN114368409 A CN 114368409A
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- 238000010219 correlation analysis Methods 0.000 claims description 5
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
- B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
- B61K9/08—Measuring installations for surveying permanent way
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
- B61L23/042—Track changes detection
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
- B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
- B61L23/042—Track changes detection
- B61L23/047—Track or rail movements
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Abstract
The invention provides a method and a device for analyzing the health condition of a rail transit turnout, and relates to the technical field of rail transit. The method comprises the following steps: obtaining rail transit turnout data according to the action process of the rail transit turnout; obtaining the change trend of turnout data according to the turnout data of the rail transit; comparing the track traffic turnout data with turnout normal condition data to obtain turnout data deviation degree; and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout. The invention can know the health condition of the rail transit turnout on the whole, not only can maintain the rail transit turnout with hidden trouble in advance and reduce the possibility of turnout fault, but also can provide multi-directional data support for tracing the fault reason when the turnout fault occurs, avoid the recurrence of the turnout fault and ensure the stability and safety of the traffic track operation.
Description
Technical Field
The present invention relates to the field of rail transit technologies, and in particular, to a method and an apparatus for analyzing a health status of a rail transit turnout, an electronic device, a non-transitory computer-readable storage medium, and a computer program product.
Background
The rail transit turnout is one of weak links of a rail as important basic equipment of a railway signal system, and has decisive significance for the running safety and the transportation efficiency of a railway train. At present, each railway operation unit can only set alarm upper and lower limit values through microcomputer monitoring, and report fault information if the alarm upper and lower limit values exceed the range so as to inform technicians to eliminate turnout faults, but the health condition of a rail transit turnout is not comprehensively evaluated, so that the fault is maintained every time the turnout fault occurs, the fault reason cannot be radically treated, and the turnout fault is repeatedly reproduced.
Disclosure of Invention
The invention provides a method and a device for analyzing the health condition of a track traffic turnout, which are used for solving the defect that the turnout fault is repeatedly reproduced due to the lack of comprehensive evaluation on the health condition of the track traffic turnout in the prior art.
The invention provides a health condition analysis method of a rail transit turnout, which comprises the following steps:
obtaining rail transit turnout data according to the action process of the rail transit turnout;
obtaining the change trend of turnout data according to the rail transit turnout data;
comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
According to the method for analyzing the health condition of the rail transit turnout, provided by the invention, the normal condition data of the turnout is obtained according to the three-phase electric data of the rail transit turnout under the normal condition.
According to the health condition analysis method of the rail transit turnout provided by the invention, the evaluation result of the health condition of the rail transit turnout is any one of the following items:
the evaluation result of the health condition of the rail transit turnout is the health condition;
the evaluation result of the health condition of the rail transit turnout is a sub-health condition;
and the evaluation result of the health condition of the rail transit turnout is a fault condition.
The health condition analysis method for the rail transit turnout provided by the invention further comprises the following steps:
and when the evaluation result of the health condition of the rail transit turnout is a fault condition, analyzing the fault type of the rail transit turnout.
According to the health condition analysis method of the rail transit turnout provided by the invention, the analysis of the fault type of the rail transit turnout comprises the following steps:
obtaining a turnout fault tree according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout;
and performing correlation analysis on the turnout fault tree to obtain fault type information of the rail transit turnout.
According to the health condition analysis method of the rail transit turnout provided by the invention, the turnout data change trend is obtained according to the rail transit turnout data, and the method comprises the following steps:
obtaining a plurality of data key points according to the rail transit turnout data;
obtaining a turnout data curve according to the plurality of data key points;
and obtaining the change trend of the turnout data according to the turnout data curve.
According to the method for analyzing the health condition of the rail transit turnout, the evaluation result of the health condition of the rail transit turnout is obtained according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout, and the method specifically comprises the following steps:
according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout, evaluating the health condition of the rail transit turnout through a turnout condition evaluation model to obtain an evaluation result of the health condition of the rail transit turnout;
and the turnout condition evaluation model is obtained by training based on turnout sample data.
The invention also provides a health condition analysis device of the rail transit turnout, which comprises:
the rail transit turnout data obtaining module is used for: obtaining rail transit turnout data according to the action process of the rail transit turnout;
the turnout data change trend obtaining module is used for: obtaining the change trend of turnout data according to the rail transit turnout data;
the turnout data deviation degree obtaining module is used for: comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
the turnout health condition evaluation module is used for: and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method for analyzing the health condition of the rail transit turnout.
The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for analyzing the health of a rail transit switch as described in any one of the above.
The invention also provides a computer program product comprising a computer program which, when being executed by a processor, realizes the steps of the method for analyzing the health condition of the rail transit turnout.
The method and the device for analyzing the health condition of the track traffic turnout comprehensively evaluate the health condition of the track traffic turnout from three aspects of the action process of the track traffic turnout, the data change trend of the turnout and the data deviation degree of the turnout, can integrally know the health condition of the track traffic turnout, can not only maintain the track traffic turnout with hidden trouble in advance and reduce the possibility of turnout fault, but also provide multi-directional data support for tracing the fault reason when the turnout fault occurs, and avoid the recurrence of the turnout fault so as to ensure the stability and the safety of the traffic track operation.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for analyzing the health condition of a rail transit turnout provided by the invention;
fig. 2 shows a schematic diagram in which three-phase electric curves are placed on the same coordinate axis, wherein the ordinate represents voltage (V/volt), the abscissa represents time (s/sec), a represents an unlocking stage, B represents an action stage, C represents a slow-release stage, and cureveA, cureveB, and cureveC represent three electric curves of the three-phase electric curves.
FIG. 3 is a schematic structural diagram of a health status analysis device for rail transit turnouts provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The health condition analysis method, the health condition analysis device and the health condition analysis electronic equipment for the rail transit turnout provided by the invention are described in the following with reference to fig. 1 to 4.
Referring to fig. 1, the method for analyzing the health condition of a rail transit turnout provided by the invention may include:
s110, obtaining rail transit turnout data according to the action process of the rail transit turnout;
s120, obtaining a turnout data change trend according to the rail transit turnout data;
s130, comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
s140, obtaining an evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
It should be noted that the executing body of the method for analyzing the health condition of the track traffic turnout provided by the invention can be any terminal side device, such as a track traffic operation system and the like.
Before the terminal-side device performs steps S110 to S140, step S100 is performed:
and setting the normal condition data of the turnout.
In one embodiment, the terminal side device can obtain the turnout normal condition data according to the three-phase electric data of the rail transit turnout under the normal condition.
It should be noted that the normal condition includes a state where the rail transit switch is not faulty and a state where the rail transit switch has a negligible fault.
It should be noted that the three-phase electric data may be obtained from a three-phase electric curve (for example, referring to fig. 2, the three-phase electric curve is placed on a coordinate axis, an ordinate represents voltage (V), an abscissa represents time(s), and key features are extracted), and the three-phase electric curve may be divided into three feature extraction intervals when analyzed: an unlocking stage, an action stage and a slow release stage, and the starting point of each interval is set as a0,b0,c0Then, thenThe unlocking stage is [ a ]0,b0) The action phase is (b)0,c0) The slow release stage is (c)0,len(curve)-a0) And len (curve) is a three-phase electric data sampling point, and extraction can be performed according to the current size, the frequency size and the phase of a three-phase electric curve when key features are extracted. In addition, the three-phase power curve can be divided into a plurality of characteristic extraction intervals according to actual conditions, such as an unlocking stage, a conversion stage, a locking stage and a slow release stage.
In step S110, the terminal side device may obtain the track traffic switch data according to the operation process of the track traffic switch.
It should be noted that the operation process of the rail transit turnout refers to the operation process of the rail transit turnout. In the operation process of the rail transit turnout, the terminal side equipment can obtain the rail transit turnout data in real time through equipment for controlling and monitoring the rail transit turnout, for example, the rail transit turnout data in the states of excitation, falling, transition and the like of the relay can be obtained according to the unlocking stage, the conversion stage, the locking stage and the slow release stage of the corresponding three-phase power curve in the operation process of the rail transit turnout.
According to the action process of the rail transit turnout, real-time rail transit turnout data are obtained, and the accuracy and the authenticity of the rail transit turnout data can be guaranteed.
In step S120, the terminal side device may obtain a change trend of the switch data according to the rail transit switch data.
In one embodiment, the obtaining of the change trend of the switch data according to the rail transit switch data includes:
obtaining a plurality of data key points according to the rail transit turnout data;
obtaining a turnout data curve according to the plurality of data key points;
and obtaining the change trend of the turnout data according to the turnout data curve.
It should be noted that the terminal side device may extract data key points, such as a full conversion duration value key point, a conversion duration value key point, an unlocking current peak value key point, a conversion current average value key point, a conversion current mathematical contrast key point, a slow discharge trial production key point, a slow discharge current average value key point, and the like, according to the track traffic turnout data, and then draw a plurality of data key points into curves to obtain a turnout data curve, and then analyze the turnout data curve to obtain a turnout data variation trend, which may be a gentle variation trend, an increasing variation trend, a decreasing variation trend, a wave variation trend, and the like.
The turnout data change trend can be obtained more intuitively through the turnout data curve, and the subsequent evaluation of the health condition of the rail transit turnout is facilitated.
In step S130, the terminal side device compares the track traffic turnout data with the turnout normal condition data to obtain the turnout data deviation degree.
It should be noted that the turnout normal condition data already includes all turnout data belonging to the turnout normal condition, and the turnout normal condition data is used as a comparison reference to compare the rail transit turnout data with the turnout normal condition data, and the turnout data deviation degree may be, for example, a deviation degree of the rail transit turnout data exceeding the turnout normal condition data, or may be, for example, a deviation degree of the rail transit turnout data not reaching the turnout normal condition data, and the like.
In step S140, the terminal side device obtains an evaluation result of the health condition of the track traffic turnout according to the operation process of the track traffic turnout, the data change trend of the turnout, and the degree of deviation of the turnout data.
In one embodiment, the obtaining of the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the change trend of the turnout data, and the deviation degree of the turnout data specifically includes:
and evaluating the health condition of the rail transit turnout through a turnout condition evaluation model according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout to obtain the evaluation result of the health condition of the rail transit turnout.
It should be noted that the switch condition evaluation model may be obtained by training based on switch sample data in advance, for example, obtaining switch sample data of the rail transit switch in a normal state, and then learning the characteristics of the switch sample data by using the deep learning model, so that the switch condition evaluation model can realize the function of evaluating the health condition of the rail transit switch according to the action process of the rail transit switch, the change trend of the switch data, and the deviation degree of the switch data.
The terminal side equipment obtains the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout through the turnout condition evaluation model, and the accuracy and precision of the evaluation result of the health condition of the rail transit turnout can be ensured. Meanwhile, the switch condition evaluation model can be used for predicting the service life of the rail transit switch according to the evaluation result of the health condition of the rail transit switch, and the fault risk of the rail transit switch can be predicted.
In addition, the health condition of the rail transit turnout can be further evaluated by manually analyzing the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout, so that the evaluation result of the health condition of the rail transit turnout can be obtained. Or the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout are manually analyzed, and the analyzed data are input into the turnout condition evaluation model to obtain the evaluation result of the health condition of the rail transit turnout.
The method for analyzing the health condition of the track traffic turnout comprehensively evaluates the health condition of the track traffic turnout from three aspects of the action process of the track traffic turnout, the data change trend of the turnout and the data deviation degree of the turnout, can integrally know the health condition of the track traffic turnout, can not only maintain the track traffic turnout with hidden trouble in advance and reduce the possibility of turnout fault, but also provide multi-directional data support for tracing the fault reason when the turnout fault occurs, avoid the recurrence of turnout fault and ensure the stability and safety of traffic track operation.
In addition, the rail transit turnout health condition analysis method provided by the invention is not limited to be used for analyzing the health condition of the rail transit turnout beyond the range of the upper limit value and the lower limit value of the alarm, but is used for comprehensively analyzing the health condition of the whole life cycle of the rail transit turnout, so that the situation that the rail transit turnout is possible to have sudden change in the whole life cycle can be deeply excavated, and the health conditions of the rail transit turnout, such as abrasion, blockage and the like, can be analyzed and judged according to the obtained evaluation result of the health conditions, so that the running safety of an urban rail train is improved, and the safe travel of passengers is effectively guaranteed.
In one embodiment, the evaluation of the health of the rail transit turnout results in any one of:
the evaluation result of the health condition of the rail transit turnout is the health condition;
the evaluation result of the health condition of the rail transit turnout is a sub-health condition;
and the evaluation result of the health condition of the rail transit turnout is a fault condition.
It should be noted that the health condition indicates that the track traffic turnout is completely faultless and excellent in condition, the sub-health state indicates that the track traffic turnout is in good condition, but there are problems that can be improved (for example, the track traffic turnout has slight wear or the track traffic turnout has jamming and the like), and these problems have no or little influence on the operation of the turnout, and the fault condition indicates that the track traffic turnout is in poor condition and is in urgent need of maintenance.
It should be noted that a calculation formula, such as a weight ratio of various data and the like, for analyzing the health condition evaluation score of the rail transit turnout according to the action process of the rail transit turnout, the change trend of the turnout data and the deviation degree of the turnout data can be set in the turnout condition evaluation model, so as to obtain the health condition evaluation score of the rail transit turnout, and then classify the health condition of the rail transit turnout.
For example, the health condition evaluation score can be output through a switch condition evaluation model, and then the health condition of the rail transit switch is obtained through classification, specifically, the score is divided into three ranges: 0 score, (0, 90), [90, 100], when the health condition evaluation score is 0 score, the evaluation result of the health condition of the rail transit turnout can be judged as a fault condition, when the health condition evaluation score is in the range (0, 90), the evaluation result of the health condition of the rail transit turnout can be judged as a sub-health condition, and when the health condition evaluation score is in the range [90, 100], the evaluation result of the health condition of the rail transit turnout can be judged as a health condition.
Different health conditions of the rail transit turnout are represented by different evaluation results, and the rail transit turnout maintenance method is beneficial to technicians to maintain the rail transit turnout in a targeted mode.
In an embodiment, the method for analyzing the health condition of the rail transit turnout provided by the invention may further include:
and when the evaluation result of the health condition of the rail transit turnout is a fault condition, analyzing the fault type of the rail transit turnout.
When the evaluation result of the health condition of the rail transit turnout is a fault condition, namely the rail transit turnout is in urgent need of maintenance, the maintenance efficiency can be improved by analyzing the fault type of the rail transit turnout, and technicians can conveniently and accurately deal with turnout faults.
In one embodiment, the analyzing the fault type of the rail transit turnout comprises:
obtaining a turnout fault tree according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout;
and performing correlation analysis on the turnout fault tree to obtain fault type information of the rail transit turnout.
It should be noted that the turnout fault tree obtained according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout is a special inverted tree-shaped logical causal relationship diagram, and can describe the causal relationship among various events in the action process of the rail transit turnout through an event symbol, a logic gate symbol and a transition symbol, perform correlation analysis on the branches, nodes and the like of the turnout fault tree, quickly determine the fault type information and the fault reason of the rail transit turnout, and facilitate the quick first-aid repair of technicians.
In an embodiment, the method for analyzing the health condition of the rail transit turnout provided by the invention may further include:
and when the evaluation result of the health condition of the rail transit turnout is a fault condition, sending fault alarm information.
It should be noted that the terminal side device can warn maintenance personnel to overhaul the rail transit turnout in time by sending out fault alarm information.
The health condition analysis device for the rail transit turnout provided by the invention is described below, and the health condition analysis device for the rail transit turnout described below and the health condition analysis method for the rail transit turnout described above can be referred to correspondingly.
Referring to fig. 3, the health condition analysis apparatus for a rail transit switch according to the present invention may include:
the rail transit turnout data obtaining module 310 is configured to: obtaining rail transit turnout data according to the action process of the rail transit turnout;
the switch data trend obtaining module 320 is configured to: obtaining the change trend of turnout data according to the rail transit turnout data;
the switch data deviation degree obtaining module 330 is configured to: comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
a switch health assessment module 340 for: and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
In one embodiment, the turnout normal condition data is obtained according to three-phase electric data of the rail transit turnout under the normal condition.
In one embodiment, the evaluation of the health of the rail transit turnout results in any one of:
the evaluation result of the health condition of the rail transit turnout is the health condition;
the evaluation result of the health condition of the rail transit turnout is a sub-health condition;
and the evaluation result of the health condition of the rail transit turnout is a fault condition.
In one embodiment, further comprising:
the turnout fault type identification module is used for: and when the evaluation result of the health condition of the rail transit turnout is a fault condition, analyzing the fault type of the rail transit turnout.
In one embodiment, the switch fault type identification module includes:
the turnout fault tree obtaining submodule is used for: obtaining a turnout fault tree according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout;
a location information obtaining submodule for: and performing correlation analysis on the turnout fault tree to obtain fault type information of the rail transit turnout.
In one embodiment, the switch data trend obtaining module 320 includes:
a data keypoint obtaining submodule for: obtaining a plurality of data key points according to the rail transit turnout data;
the turnout data curve obtaining submodule is used for: obtaining a turnout data curve according to the plurality of data key points;
the turnout data change trend obtaining submodule is used for: and obtaining the change trend of the turnout data according to the turnout data curve.
In one embodiment, the switch health assessment module 340 is specifically configured to:
according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout, evaluating the health condition of the rail transit turnout through a turnout condition evaluation model to obtain an evaluation result of the health condition of the rail transit turnout;
and the turnout condition evaluation model is obtained by training based on turnout sample data.
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 call logic instructions in the memory 830 to perform a method of health analysis of rail transit switches, the method comprising:
obtaining rail transit turnout data according to the action process of the rail transit turnout;
obtaining the change trend of turnout data according to the rail transit turnout data;
comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
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 another aspect, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, when the computer program is executed by a processor, a computer can execute the method for analyzing the health condition of a rail transit turnout provided by the above methods, and the method includes:
obtaining rail transit turnout data according to the action process of the rail transit turnout;
obtaining the change trend of turnout data according to the rail transit turnout data;
comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the method for analyzing the health condition of a rail transit turnout provided by the above methods, the method comprising:
obtaining rail transit turnout data according to the action process of the rail transit turnout;
obtaining the change trend of turnout data according to the rail transit turnout data;
comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
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 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 described in the 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 (11)
1. A health condition analysis method for rail transit turnouts is characterized by comprising the following steps:
obtaining rail transit turnout data according to the action process of the rail transit turnout;
obtaining the change trend of turnout data according to the rail transit turnout data;
comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
2. The rail transit turnout health condition analysis method according to claim 1, wherein the turnout normal condition data is obtained according to three-phase electric data of the rail transit turnout under a normal condition.
3. The method for analyzing the health condition of the rail transit turnout according to claim 1, wherein the evaluation result of the health condition of the rail transit turnout is any one of the following items:
the evaluation result of the health condition of the rail transit turnout is the health condition;
the evaluation result of the health condition of the rail transit turnout is a sub-health condition;
and the evaluation result of the health condition of the rail transit turnout is a fault condition.
4. The method for analyzing the health of a rail transit switch as claimed in claim 3, further comprising:
and when the evaluation result of the health condition of the rail transit turnout is a fault condition, analyzing the fault type of the rail transit turnout.
5. The method for analyzing the health condition of the rail transit turnout according to claim 4, wherein the analyzing the fault type of the rail transit turnout comprises:
obtaining a turnout fault tree according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout;
and performing correlation analysis on the turnout fault tree to obtain fault type information of the rail transit turnout.
6. The method for analyzing the health condition of the rail transit turnout according to any one of claims 1-5, wherein the obtaining of the change trend of the turnout data according to the rail transit turnout data comprises:
obtaining a plurality of data key points according to the rail transit turnout data;
obtaining a turnout data curve according to the plurality of data key points;
and obtaining the change trend of the turnout data according to the turnout data curve.
7. The method for analyzing the health condition of the rail transit turnout according to any one of claims 1-5, wherein the evaluation result of the health condition of the rail transit turnout is obtained according to the action process of the rail transit turnout, the change trend of the turnout data and the deviation degree of the turnout data, and specifically comprises the following steps:
according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout, evaluating the health condition of the rail transit turnout through a turnout condition evaluation model to obtain an evaluation result of the health condition of the rail transit turnout;
and the turnout condition evaluation model is obtained by training based on turnout sample data.
8. A health condition analysis device of a rail transit turnout is characterized by comprising:
the rail transit turnout data obtaining module is used for: obtaining rail transit turnout data according to the action process of the rail transit turnout;
the turnout data change trend obtaining module is used for: obtaining the change trend of turnout data according to the rail transit turnout data;
the turnout data deviation degree obtaining module is used for: comparing the rail transit turnout data with turnout normal condition data to obtain turnout data deviation degree;
the turnout health condition evaluation module is used for: and obtaining the evaluation result of the health condition of the rail transit turnout according to the action process of the rail transit turnout, the data change trend of the turnout and the data deviation degree of the turnout.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for analyzing the health of a rail transit switch as claimed in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method for analyzing the health of a rail transit switch as claimed in any one of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method for analyzing the health of a rail transit switch as claimed in any one of claims 1 to 7.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260595A (en) * | 2015-04-02 | 2016-01-20 | 北京交通大学 | Feature extraction method for switch action current curve and switch fault diagnosis method |
| WO2018104040A1 (en) * | 2016-12-09 | 2018-06-14 | Siemens Aktiengesellschaft | Method and device for diagnosing railway switches under load |
| CN108416362A (en) * | 2018-01-29 | 2018-08-17 | 同济大学 | A kind of track switch abnormity early warning and method for diagnosing faults |
| CN109492784A (en) * | 2018-12-08 | 2019-03-19 | 深圳科安达电子科技股份有限公司 | The health state evaluation system of acclerating road switch |
| CN209938618U (en) * | 2018-12-17 | 2020-01-14 | 深圳科安达电子科技股份有限公司 | Railway switch conversion process detection system |
| CN110723166A (en) * | 2019-11-07 | 2020-01-24 | 交控科技股份有限公司 | Turnout monitoring method and system |
| CN110930119A (en) * | 2019-11-25 | 2020-03-27 | 交控科技股份有限公司 | Switch management system and method |
| CN112001532A (en) * | 2020-08-04 | 2020-11-27 | 交控科技股份有限公司 | Turnout fault prediction method and device, electronic equipment and storage medium |
| CN112036505A (en) * | 2020-09-17 | 2020-12-04 | 广西交控智维科技发展有限公司 | Method and device for determining equipment state of turnout switch machine and electronic equipment |
| CN112477917A (en) * | 2020-11-10 | 2021-03-12 | 交控科技股份有限公司 | Turnout fault detection method and device, electronic equipment and storage medium |
-
2021
- 2021-12-20 CN CN202111563507.4A patent/CN114368409A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260595A (en) * | 2015-04-02 | 2016-01-20 | 北京交通大学 | Feature extraction method for switch action current curve and switch fault diagnosis method |
| WO2018104040A1 (en) * | 2016-12-09 | 2018-06-14 | Siemens Aktiengesellschaft | Method and device for diagnosing railway switches under load |
| CN108416362A (en) * | 2018-01-29 | 2018-08-17 | 同济大学 | A kind of track switch abnormity early warning and method for diagnosing faults |
| CN109492784A (en) * | 2018-12-08 | 2019-03-19 | 深圳科安达电子科技股份有限公司 | The health state evaluation system of acclerating road switch |
| CN209938618U (en) * | 2018-12-17 | 2020-01-14 | 深圳科安达电子科技股份有限公司 | Railway switch conversion process detection system |
| CN110723166A (en) * | 2019-11-07 | 2020-01-24 | 交控科技股份有限公司 | Turnout monitoring method and system |
| CN110930119A (en) * | 2019-11-25 | 2020-03-27 | 交控科技股份有限公司 | Switch management system and method |
| CN112001532A (en) * | 2020-08-04 | 2020-11-27 | 交控科技股份有限公司 | Turnout fault prediction method and device, electronic equipment and storage medium |
| CN112036505A (en) * | 2020-09-17 | 2020-12-04 | 广西交控智维科技发展有限公司 | Method and device for determining equipment state of turnout switch machine and electronic equipment |
| CN112477917A (en) * | 2020-11-10 | 2021-03-12 | 交控科技股份有限公司 | Turnout fault detection method and device, electronic equipment and storage medium |
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