CN109815210B - Track voltage abnormal data monitoring method and system and terminal equipment - Google Patents
Track voltage abnormal data monitoring method and system and terminal equipment Download PDFInfo
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Abstract
The invention provides a track voltage abnormal data monitoring method, a system and terminal equipment, wherein the method comprises the following steps: acquiring a track voltage data set; splitting the rail voltage data set into a first preset number of candidate rail voltage data subsets; removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets; and acquiring the target track voltage data subsets meeting a second preset condition from the plurality of target track voltage data subsets to acquire a plurality of track voltage abnormal data subsets. The track voltage data in the obtained track voltage data set are automatically screened to obtain the track voltage abnormal data subset, so that the efficiency of detecting the track voltage abnormal data is improved.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a track voltage abnormal data monitoring method, a system and terminal equipment.
Background
The track circuit is a circuit formed by taking a section of railway line steel rail as a conductor, is used for automatically and continuously detecting whether the section of railway line is occupied by rolling stock or not, and is also used for controlling a signal device or a switch device so as to ensure equipment for driving safety. The track circuit is a key infrastructure in a railway signal control system, and plays an important role in meeting the technical requirements of bulk locomotive signals and train overspeed protection.
The rail circuit can have faults such as poor shunting, rail breakage, abnormal curve fluctuation and the like under the influence of rolling impact of the locomotive, temperature, humidity, rain, snow and other external factors. If the track circuit is in fault, the corresponding track voltage changes, and whether the track circuit is in fault can be found by monitoring the track voltage. At present, whether track voltage data are abnormal or not is often judged through human experience, and track voltage abnormal data are screened out when the track voltage data are abnormal, however, the track voltage abnormal data cannot be detected quickly through the method.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, a system, and a terminal device for monitoring abnormal rail voltage data, so as to solve the problem of low efficiency in detecting the abnormal rail voltage data.
A first aspect of an embodiment of the present invention provides a track voltage anomaly data monitoring method, including:
acquiring a track voltage data set;
splitting the rail voltage data set into a first preset number of candidate rail voltage data subsets;
removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets;
and acquiring the target track voltage data subsets meeting a second preset condition from the plurality of target track voltage data subsets to acquire a plurality of track voltage abnormal data subsets.
A second aspect of an embodiment of the present invention provides a track voltage anomaly data monitoring system, including:
the data acquisition module is used for acquiring a track voltage data set;
a data splitting module for splitting the rail voltage data set into a first preset number of candidate rail voltage data subsets;
the data deleting module is used for removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets;
and the data extraction module is used for acquiring the target track voltage data subsets meeting a second preset condition from the target track voltage data subsets to acquire a plurality of track voltage abnormal data subsets.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the track voltage abnormal data monitoring method when executing the computer program.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the track voltage abnormal data monitoring method as described above.
According to the track voltage abnormal data detection method and device, track voltage data in the obtained track voltage data set are automatically screened to obtain the track voltage abnormal data subset, and the track voltage abnormal data detection efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a track voltage anomaly data monitoring method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a track voltage anomaly data monitoring method according to another embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a rail voltage anomaly data monitoring system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The terms "comprises" and "comprising," as well as any other variations, in the description and claims of this invention and the drawings described above, are intended to mean "including but not limited to," and are intended to cover non-exclusive inclusions. For example, a process, method, or system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third," etc. are used to distinguish between different objects and are not used to describe a particular order.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Example 1:
fig. 1 shows a flowchart of an implementation of a track voltage abnormal data monitoring method according to an embodiment of the present invention, and for convenience of description, only the relevant portions of the track voltage abnormal data monitoring method according to the embodiment of the present invention are shown, and the detailed description is as follows:
as shown in fig. 1, the track voltage abnormal data monitoring method provided in the embodiment of the present invention includes:
s101, acquiring a track voltage data set.
S102, splitting the track voltage data set into a first preset number of candidate track voltage data subsets.
S103, removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets.
S104, obtaining the target track voltage data subsets meeting second preset conditions from the plurality of target track voltage data subsets, and obtaining a plurality of track voltage abnormal data subsets.
In this embodiment, before acquiring the track voltage data set, the target dimension and the number of parallel threads of the target track voltage abnormal data set need to be set.
In the present embodiment, the track voltage data set may be voltage data for 1 hour, voltage data for 30 minutes, or the like.
In one embodiment of the present invention, S102 includes:
the track voltage data set is sequentially split into a first preset number of candidate track voltage data subsets.
In this embodiment, the first predetermined number refers to the number of parallel threads on which the subset of candidate rail voltage data is processed.
As an example, the track voltage data set is divided into a plurality of parts according to the time sequence of acquisition and the like, assuming that the track voltage data set has data corresponding to 1000 moments, and the first preset number is 10, the data corresponding to the first 100 moments may be used as the 1 st candidate track voltage data subset P1, the data corresponding to the 101 st to 200 th moments may be used as the 2 nd candidate track voltage data subsets P2, … …, and so on, to obtain 10 candidate track voltage data subsets, and the 10 candidate track voltage data subsets have a precedence order. In one embodiment of the present invention, S103 includes:
s301, calculating the mean value and the mean square error of each candidate track voltage data subset.
S302, calculating an upper limit value and a lower limit value of each candidate track voltage data subset, wherein,
MAX＝Avg(x)+α*Std(x) (α∈(1,3))，
MIX＝Avg(x)-α*Std(x) (α∈(1,3))，
wherein MAX is an upper limit value; MIX is the lower limit value; avg (x) is the mean; std (x) is mean square error.
And S303, recording the maximum value of the upper limit values of all the candidate track voltage data subsets as an original upper limit value, and recording the minimum value of the lower limit values of all the candidate track voltage data subsets as an original lower limit value.
S304, deleting the data which are larger than the original upper limit value and the data which are smaller than the original lower limit value in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets.
In this embodiment, the value of α is any value between 1 and 3, and the larger the value of α, the larger the error.
In this embodiment, the number of target track voltage data subsets is less than or equal to the number of candidate track voltage data subsets.
In one embodiment of the present invention, S104 includes:
s401, calculating the mean value and the mean square error of each target track voltage data subset.
S402, calculating an upper limit value and a lower limit value of each target track voltage data subset, wherein,
MAX1＝Avg1(x)+α*Std1(x) (α∈(1,3))，
MIX1＝Avg1(x)-α*Std1(x) (α∈(1,3))，
wherein, MAX1 is an upper limit value; MIX1 is the lower limit value; avg1(x) as a mean; std1(x) is the mean square error.
And S403, recording the maximum value of the upper limit values of all the target track voltage data subsets as a final upper limit value, and recording the minimum value of the lower limit values of all the target track voltage data subsets as a final lower limit value.
S404, if all the data in the target track voltage data subset are between the final upper limit value and the final lower limit value, marking the target track voltage data subset as a track voltage normal data subset, otherwise, marking the target track voltage data subset as a track voltage abnormal data subset.
In this embodiment, if one or several data in all the data in the target track voltage data subset is not between the final upper limit value and the final lower limit value, the target track voltage data subset is recorded as a track voltage abnormal data subset.
As shown in fig. 2, in an embodiment of the present invention, after S104, the method further includes:
and S105, merging the adjacent track voltage abnormal data subsets to obtain a candidate track voltage abnormal data set.
In this embodiment, the merging of the adjacent track voltage abnormal data subsets refers to two track voltage abnormal data subsets finally obtained from the adjacent candidate track voltage data subsets, and the two track voltage abnormal data subsets are merged into one candidate track voltage abnormal data set. And marking the track voltage abnormal data subset as a candidate track voltage abnormal data set when no adjacent track voltage abnormal data subset exists.
For example, if the obtained track voltage abnormal data subsets AP2, AP3, AP4, and AP7 correspond to the candidate track voltage data subsets P2, P3, P4, and P7, then the AP2, AP3, and AP4 are adjacent track voltage abnormal data subsets, which indicates that the AP2, the AP3, and the AP4 correspond to the same segment of fault, and the AP2, the AP3, and the AP4 need to be merged into a candidate track voltage abnormal data set, and similarly, the track voltage abnormal data subset AP7 that has not been merged is renamed to the candidate track voltage abnormal data set.
As shown in fig. 2, in an embodiment of the present invention, after S105, the method further includes:
and S106, obtaining a track voltage abnormity function corresponding to each candidate track voltage abnormity data set based on each candidate track voltage abnormity data set.
S107, acquiring a second preset number of track voltage abnormal data based on the track voltage abnormal function to obtain a target track voltage abnormal data set.
In one embodiment of the present invention, S106 includes:
and respectively fitting each candidate track voltage abnormal data set into an RBF neural network function corresponding to each candidate track voltage abnormal data set.
In one embodiment of the present invention, S107 includes:
and drawing an track voltage abnormal curve based on the track voltage abnormal function, and collecting a second preset number of track voltage abnormal data at equal intervals from the track voltage abnormal curve to obtain a target track voltage abnormal data set.
In this embodiment, the second preset number is a preset target dimension of the target rail voltage abnormal data set.
In this embodiment, a second preset number of track voltage abnormal data are collected at equal intervals from each track voltage abnormal curve, and finally a target track voltage abnormal data set corresponding to a target dimension is formed, where the target track voltage abnormal data set is an abnormal data set with a fixed dimension detected from an original track voltage data set.
As described above, after the adjacent track voltage anomaly data subsets are merged, the curve obtained by fitting the merged candidate track voltage anomaly data set is equivalent to the same fault curve. In practical application, the acquired track voltage data set can be used for detecting track voltage abnormal data within a relatively short time (for example, 10 minutes) or a relatively long time (for example, 2 hours), manual screening is not needed, and the efficiency of detecting the abnormal data is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Example 2:
as shown in fig. 3, a track voltage anomaly data monitoring system 100 according to an embodiment of the present invention is used for executing the method steps in the embodiment corresponding to fig. 1, and includes:
a data acquisition module 110 for acquiring a track voltage data set;
a data splitting module 120 configured to split the rail voltage data set into a first preset number of candidate rail voltage data subsets;
the data deleting module 130 is configured to remove the track voltage data that does not satisfy the first preset condition in each candidate track voltage data subset, so as to obtain a plurality of target track voltage data subsets;
the data extraction module 140 is configured to obtain a target track voltage data subset satisfying a second preset condition from the plurality of target track voltage data subsets, and obtain a plurality of track voltage abnormal data subsets.
In an embodiment of the present invention, the data splitting module 120 in the embodiment corresponding to fig. 3 is further configured to:
splitting the track voltage data set into a first preset number of candidate track voltage data subsets in sequence;
in an embodiment of the present invention, the data deleting module 130 in the embodiment corresponding to fig. 3 includes:
a first calculating unit, configured to calculate a mean and a mean square error of each of the candidate track voltage data subsets.
A second calculation unit for calculating an upper limit value and a lower limit value for each of the candidate rail voltage data subsets, wherein,
MAX＝Avg(x)+α*Std(x) (α∈(1,3))，
MIX＝Avg(x)-α*Std(x) (α∈(1,3))，
wherein MAX is an upper limit value; MIX is the lower limit value; avg (x) is the mean; std (x) is mean square error.
And the first screening unit is used for recording the maximum value of the upper limit values of all the candidate track voltage data subsets as an original upper limit value and recording the minimum value of the lower limit values of all the candidate track voltage data subsets as an original lower limit value.
And the second screening unit is used for deleting the data which are larger than the original upper limit value and the data which are smaller than the original lower limit value in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets.
In an embodiment of the present invention, the data extraction module 140 in the embodiment corresponding to fig. 3 includes:
and the third calculation unit is used for calculating the mean value and the mean square error of each target track voltage data subset.
A fourth calculation unit for calculating an upper limit value and a lower limit value of each of the target track voltage data subsets, wherein,
MAX1＝Avg1(x)+α*Std1(x) (α∈(1,3))，
MIX1＝Avg1(x)-α*Std1(x) (α∈(1,3))，
wherein, MAX1 is an upper limit value; MIX1 is the lower limit value; avg1(x) as a mean; std1(x) is the mean square error.
And the third screening unit is used for recording the maximum value of the upper limit values of all the target track voltage data subsets as a final upper limit value and recording the minimum value of the lower limit values of all the target track voltage data subsets as a final lower limit value.
And the fourth screening unit is used for recording the target track voltage data subset as a track voltage normal data subset if all data in the target track voltage data subset are between the final upper limit value and the final lower limit value, and recording the target track voltage data subset as a track voltage abnormal data subset if not.
In an embodiment of the present invention, the data extraction module 140 further comprises:
and the data merging module is used for merging the adjacent track voltage abnormal data subsets to obtain a candidate track voltage abnormal data set.
In an embodiment of the present invention, the data merging module further includes:
and the data model fitting module is used for obtaining a track voltage abnormity function corresponding to each candidate track voltage abnormity data set based on each candidate track voltage abnormity data set.
And the target data acquisition module is used for acquiring a second preset number of track voltage abnormal data based on the track voltage abnormal function to obtain a target track voltage abnormal data set.
In one embodiment of the invention, the data model fitting module is configured to:
and respectively fitting each candidate track voltage abnormal data set into an RBF neural network function corresponding to each candidate track voltage abnormal data set.
In one embodiment of the invention, the target data obtaining module comprises:
and drawing an track voltage abnormal curve based on the track voltage abnormal function, and collecting a second preset number of track voltage abnormal data at equal intervals from the track voltage abnormal curve to obtain a target track voltage abnormal data set.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the foregoing function distribution may be completed by different functional modules according to needs, that is, the internal structure of the track voltage abnormal data monitoring system is divided into different functional modules to complete all or part of the above-described functions. Each functional module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated module may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional modules are only used for distinguishing one functional module from another, and are not used for limiting the protection scope of the application. The specific working process of the module in the track voltage abnormal data monitoring system may refer to the corresponding process in embodiment 1, and is not described herein again.
Example 3:
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40, when executing the computer program 42, implements the steps in the embodiments as described in embodiment 1, such as steps S101 to S104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the system embodiments as described in embodiment 2, such as the functions of the modules 110 to 140 shown in fig. 3.
The terminal device 4 refers to a terminal with data processing capability, and includes but is not limited to a computer, a workstation, a server, and even some Smart phones, palmtop computers, tablet computers, Personal Digital Assistants (PDAs), Smart televisions (Smart TVs), and the like with excellent performance. The terminal device is generally installed with an operating system, including but not limited to: windows operating system, LINUX operating system, Android (Android) operating system, Symbian operating system, Windows mobile operating system, and iOS operating system, among others. While specific examples of the terminal device 4 have been listed in detail above, those skilled in the art will appreciate that the terminal device is not limited to the listed examples.
The terminal device may include, but is not limited to, a processor 40, a memory 41. It will be understood by those skilled in the art that fig. 4 is only an example of the terminal device 4, and does not constitute a limitation to the terminal device 4, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device 4 may further include an input-output device, a network access device, a bus, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal device 4. The memory 41 may also be used to temporarily store data that has been output or is to be output.
Example 4:
an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps in the embodiments described in embodiment 1, for example, step S101 to step S104 shown in fig. 1. Alternatively, the computer program, when executed by a processor, implements the functions of the respective modules/units in the respective system embodiments as described in embodiment 2, for example, the functions of the modules 110 to 140 shown in fig. 3.
The computer program may be stored in a computer readable storage medium, which when executed by a processor, may implement the steps of the various method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
In the above embodiments, the description of each embodiment has a respective emphasis, and embodiments 1 to 4 may be combined arbitrarily, and a new embodiment formed by combining is also within the scope of the present application. For parts which are not described or illustrated in a certain embodiment, reference may be made to the description of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described system/terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; 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; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (8)
1. The track voltage abnormal data monitoring method is characterized by comprising the following steps:
acquiring a track voltage data set;
splitting the rail voltage data set into a first preset number of candidate rail voltage data subsets;
removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets; removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets, wherein the removing comprises the following steps:
calculating the mean and mean square error of each of the candidate track voltage data subsets;
calculating an upper limit value and a lower limit value for each of the candidate track voltage data subsets,
MAX＝Avg(x)+α*Std(x)(α∈(1,3))
MIX＝Avg(x)-α*Std(x)(α∈(1,3))
wherein MAX is an upper limit value; MIX is the lower limit value; avg (x) is the mean; std (x) is mean square error;
recording the maximum value of the upper limit values of all the candidate track voltage data subsets as an original upper limit value, and recording the minimum value of the lower limit values of all the candidate track voltage data subsets as an original lower limit value;
deleting data larger than an original upper limit value and data smaller than an original lower limit value in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets;
acquiring a target track voltage data subset meeting a second preset condition from the plurality of target track voltage data subsets to acquire a plurality of track voltage abnormal data subsets; the obtaining of the target track voltage data subsets satisfying a second preset condition from the plurality of target track voltage data subsets to obtain a plurality of track voltage abnormal data subsets includes:
calculating the mean and mean square error of each target track voltage data subset;
calculating an upper limit value and a lower limit value for each of the target track voltage data subsets, wherein,
MAX1＝Avg1(x)+α*Std1(x)(α∈(1,3))
MIX1＝Avg1(x)-α*Std1(x)(α∈(1,3))
wherein, MAX1 is an upper limit value; MIX1 is the lower limit value; avg1(x) as a mean; std1(x) is mean square error;
recording the maximum value of the upper limit values of all the target track voltage data subsets as a final upper limit value, and recording the minimum value of the lower limit values of all the target track voltage data subsets as a final lower limit value;
and if all the data in the target track voltage data subset are between the final upper limit value and the final lower limit value, recording the target track voltage data subset as a track voltage normal data subset, otherwise, recording the target track voltage data subset as a track voltage abnormal data subset.
2. The rail voltage anomaly data monitoring method according to claim 1, wherein the splitting of the rail voltage data set into a first preset number of candidate rail voltage data subsets comprises:
splitting the track voltage data set into a first preset number of candidate track voltage data subsets in sequence;
correspondingly, after obtaining the plurality of track voltage anomaly data subsets, the method further comprises:
and merging the adjacent track voltage abnormal data subsets to obtain a candidate track voltage abnormal data set.
3. The rail voltage anomaly data monitoring method according to claim 2, further comprising, after obtaining the candidate rail voltage anomaly data set:
obtaining a track voltage abnormity function corresponding to each candidate track voltage abnormity data set based on each candidate track voltage abnormity data set;
and acquiring a second preset number of track voltage abnormal data based on the track voltage abnormal function to obtain a target track voltage abnormal data set.
4. The rail voltage anomaly data monitoring method according to claim 3, wherein the obtaining a rail voltage anomaly function corresponding to each candidate rail voltage anomaly data set based on each candidate rail voltage anomaly data set comprises:
and respectively fitting each candidate track voltage abnormal data set into an RBF neural network function corresponding to each candidate track voltage abnormal data set.
5. The method for monitoring abnormal rail voltage data according to claim 3, wherein the acquiring a second preset number of abnormal rail voltage data based on the abnormal rail voltage function to obtain a target abnormal rail voltage data set comprises:
and drawing an track voltage abnormal curve based on the track voltage abnormal function, and collecting a second preset number of track voltage abnormal data at equal intervals from the track voltage abnormal curve to obtain a target track voltage abnormal data set.
6. Track voltage anomaly data monitoring system characterized by, includes:
the data acquisition module is used for acquiring a track voltage data set;
a data splitting module for splitting the rail voltage data set into a first preset number of candidate rail voltage data subsets;
the data deleting module is used for removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets; removing the track voltage data which do not meet the first preset condition in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets, wherein the removing comprises the following steps:
calculating the mean and mean square error of each of the candidate track voltage data subsets;
calculating an upper limit value and a lower limit value for each of the candidate track voltage data subsets,
MAX＝Avg(x)+α*Std(x)(α∈(1,3))
MIX＝Avg(x)-α*Std(x)(α∈(1,3))
wherein MAX is an upper limit value; MIX is the lower limit value; avg (x) is the mean; std (x) is mean square error;
recording the maximum value of the upper limit values of all the candidate track voltage data subsets as an original upper limit value, and recording the minimum value of the lower limit values of all the candidate track voltage data subsets as an original lower limit value;
deleting data larger than an original upper limit value and data smaller than an original lower limit value in each candidate track voltage data subset to obtain a plurality of target track voltage data subsets;
the data extraction module is used for acquiring a target track voltage data subset meeting a second preset condition from the target track voltage data subsets to acquire a plurality of track voltage abnormal data subsets; the obtaining of the target track voltage data subsets satisfying a second preset condition from the plurality of target track voltage data subsets to obtain a plurality of track voltage abnormal data subsets includes:
calculating the mean and mean square error of each target track voltage data subset;
calculating an upper limit value and a lower limit value for each of the target track voltage data subsets, wherein,
MAX1＝Avg1(x)+α*Std1(x)(α∈(1,3))
MIX1＝Avg1(x)-α*Std1(x)(α∈(1,3))
wherein, MAX1 is an upper limit value; MIX1 is the lower limit value; avg1(x) as a mean; std1(x) is mean square error;
recording the maximum value of the upper limit values of all the target track voltage data subsets as a final upper limit value, and recording the minimum value of the lower limit values of all the target track voltage data subsets as a final lower limit value;
and if all the data in the target track voltage data subset are between the final upper limit value and the final lower limit value, recording the target track voltage data subset as a track voltage normal data subset, otherwise, recording the target track voltage data subset as a track voltage abnormal data subset.
7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the rail voltage anomaly data monitoring method according to any one of claims 1 to 5 when executing the computer program.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which when executed by a processor implements the steps of the track voltage anomaly data monitoring method according to any one of claims 1 to 5.
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| CN201811633433.5A CN109815210B (en) | 2018-12-29 | 2018-12-29 | Track voltage abnormal data monitoring method and system and terminal equipment |
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