CN111695595B - Method and device for identifying abnormal data of track scale - Google Patents
Method and device for identifying abnormal data of track scale Download PDFInfo
- Publication number
- CN111695595B CN111695595B CN202010362235.0A CN202010362235A CN111695595B CN 111695595 B CN111695595 B CN 111695595B CN 202010362235 A CN202010362235 A CN 202010362235A CN 111695595 B CN111695595 B CN 111695595B
- Authority
- CN
- China
- Prior art keywords
- data
- metering data
- historical
- control limit
- real
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Factory Administration (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The application discloses a method and a device for identifying abnormal data of a railroad track scale, wherein the method comprises the following steps: acquiring first historical metering data of the track scale; determining a first centerline, a first upper control limit, and a first lower control limit of the control map based on the first historical metering data; identifying abnormal data in the first real-time metering data of the rail scale measurement based on the first center line, the first upper control limit and the first lower control limit; when the identification process reaches a preset updating threshold value, second historical metering data of the track scale are obtained; the second historical metering data comprises first real-time metering data; updating the control diagram to obtain a second central line, a second upper control limit and a second lower control limit of the control diagram; abnormal data in the second real-time metering data of the rail scale measurement is identified based on the second centerline, the second upper control limit, and the second lower control limit. The method and the device can detect and identify the abnormal data in the metering data of the track scale.
Description
Technical Field
The application relates to the technical field of ferrous metallurgy, in particular to a method and a device for identifying abnormal data of a railroad track scale.
Background
Along with the development of informatization technology and continuous improvement of intelligence, the equipment structure is more complex and changeable, the association of each part is more and more intimate, the flexibility degree is also improved rapidly, but the accuracy of metering data or equipment parameters can not meet the requirement of detecting small changes of certain parameters. It may result in catastrophic destruction of the entire device and even the environment in which the device is concerned, as a result of a small change in certain parameters in an explosive chain reaction. This not only causes great economic loss, but also endangers personal safety in serious cases, and the consequences are not considered. In the field of ferrous metallurgy, the rail weighbridge is adopted for metering, fluctuation and abnormality of metering data of the rail weighbridge are easy to occur, and no method for identifying and controlling the abnormal metering data of the rail weighbridge exists at present.
Disclosure of Invention
In view of the above problems, the present application provides a method and an apparatus for identifying abnormal data of a railroad track scale, which can effectively identify abnormal metering data of the railroad track scale.
In a first aspect, the present application provides, according to an embodiment of the present application, the following technical solutions:
an abnormal data identification method of a railroad track scale, comprising:
acquiring first historical metering data of the track scale;
determining a first centerline, a first upper control limit, and a first lower control limit of a control map based on the first historical metering data;
identifying anomalous data in the first real-time metrology data of the rail scale measurement based on the first centerline, the first upper control limit, and the first lower control limit;
when the identification process reaches a preset updating threshold value, second historical metering data of the track scale are obtained; wherein the second historical metering data comprises the first real-time metering data;
updating the control chart based on the second historical metering data to obtain a second central line, a second upper control limit and a second lower control limit of the control chart;
and identifying abnormal data in second real-time metering data of the rail scale measurement based on the second center line, the second upper control limit and the second lower control limit.
Preferably, the first historical metering data, the second historical metering data, the first real-time metering data and the second real-time metering data are all tare data.
Preferably, the obtaining of the first upper control limit and the first lower control limit includes:
and determining that the first upper control limit is the average value of the first historical metering data plus 4 times of standard deviation based on the abnormal data duty ratio in the first historical metering data, and the first lower control limit is the average value of the first historical metering data minus 4 times of standard deviation.
Preferably, the acquiring the second historical metering data of the track scale includes:
the first real-time metering data within one month is taken as the second historical metering data.
Preferably, the anomaly data includes first anomaly data and second anomaly data; identifying anomalous data in the first real-time metrology data of the rail scale measurement based on the first centerline, the first upper control limit, and the first lower control limit, comprising:
identifying data which is located outside the first control upper limit in the first real-time metering data, and obtaining the first abnormal data;
and identifying data which is positioned outside the first control lower limit in the first real-time metering data, and obtaining the second abnormal data.
According to the second aspect, based on the same inventive concept, the present application provides the following technical solutions according to an embodiment of the present application:
an abnormal data identification device of a railroad track scale, comprising:
the first data acquisition module is used for acquiring first historical metering data of the track scale;
the control diagram construction module is used for determining a first central line, a first upper control limit and a first lower control limit of the control diagram based on the first historical metering data;
a first identifying module configured to identify abnormal data in first real-time metering data of the rail scale measurement based on the first center line, the first upper control limit, and the first lower control limit;
the second data acquisition module is used for acquiring second historical metering data of the track scale when the identification process reaches a preset updating threshold value; wherein the second historical metering data comprises the first real-time metering data;
the control diagram updating module is used for updating the control diagram based on the second historical metering data to obtain a second central line, a second upper control limit and a second lower control limit of the control diagram;
and the second identifying module is used for identifying abnormal data in the second real-time metering data of the rail scale measurement based on the second central line, the second upper control limit and the second lower control limit.
Preferably, the first historical metering data, the second historical metering data, the first real-time metering data and the second real-time metering data are all tare data.
Preferably, the control diagram construction module is specifically configured to:
and determining that the first upper control limit is the average value of the first historical metering data plus 4 times of standard deviation based on the abnormal data duty ratio in the first historical metering data, and the first lower control limit is the average value of the first historical metering data minus 4 times of standard deviation.
Preferably, the second data acquisition module is configured to:
the first real-time metering data within one month is taken as the second historical metering data.
In a third aspect, based on the same inventive concept, the present application provides, through an embodiment of the present application, the following technical solutions:
a computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method according to any of the second aspects.
According to the method and the device for identifying the abnormal data of the track scale, the first historical metering data of the track scale are obtained, the control diagram is constructed based on the first historical metering data, and then the first real-time metering data are screened through the control diagram to identify the abnormal data. Further, the control diagram is updated by acquiring the second historical data, and the updated control diagram is the control diagram which is most suitable for the current track scale and the metering object and avoids recognition distortion caused by system errors due to external factor changes because the second historical data comprises the first real-time metering data; finally, the continuous iterative updating of the control diagram is carried out, and the updated control diagram is used for identifying the real-time metering data, so that the identification and control of the complete track scale metering data can be accurately carried out, the data is ensured to be normal, and the fault chain reaction of equipment is avoided.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a flowchart of a method for identifying abnormal data of a railroad track scale according to a first embodiment of the present application;
FIG. 2 is a schematic diagram showing real-time metering data identification for 9 months for an 800t railroad track scale in accordance with a first embodiment of the present application;
fig. 3 is a functional block diagram of an abnormal data recognition device of a railroad track scale according to a second embodiment of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
First embodiment
Referring to fig. 1, fig. 1 shows a flowchart of a method for identifying abnormal data of a railroad track scale according to a first embodiment of the present application. The method for identifying the abnormal data of the track scale specifically comprises the following steps:
step S10: acquiring first historical metering data of the track scale;
step S20: determining a first centerline, a first upper control limit, and a first lower control limit of a control map based on the first historical metering data;
step S30: identifying anomalous data in the first real-time metrology data of the rail scale measurement based on the first centerline, the first upper control limit, and the first lower control limit;
step S40: when the identification process reaches a preset updating threshold value, second historical metering data of the track scale are obtained; wherein the second historical metering data comprises the first real-time metering data;
step S50: updating the control chart based on the second historical metering data to obtain a second central line, a second upper control limit and a second lower control limit of the control chart;
step S60: and identifying abnormal data in second real-time metering data of the rail scale measurement based on the second center line, the second upper control limit and the second lower control limit.
In this embodiment, the first real-time metering data is screened by acquiring the first historical metering data of the railroad track scale, constructing a control chart based on the first historical metering data, and then screening the first real-time metering data by the control chart to identify the abnormal data. Further, the control diagram is updated by acquiring the second historical data, and the updated control diagram is the control diagram which is most suitable for the current track scale and the metering object and avoids recognition distortion caused by system errors due to external factor changes because the second historical data comprises the first real-time metering data; finally, the continuous iterative updating of the control diagram is carried out, and the updated control diagram is used for identifying the real-time metering data, so that the identification and control of the complete track scale metering data can be accurately carried out, the data is ensured to be normal, and the fault chain reaction of equipment is avoided.
In this embodiment, the first historical measurement data may be gross weight or tare weight; specifically, the first history metric data represents all history data over a period of time, such as data obtained by measurement within 1 week (7 days), data obtained by measurement within 1 month (or 30 days), data obtained by measurement within one quarter, and data obtained by measurement within 1 year (or 365 days). In step S10, the first historical measurement data may be read or obtained in a system connected to the track scale, and the obtaining process is a common data read-write operation, which is not described herein.
In step S20, a dynamic field of the data constructed by using the first history metering data in combination with the control map is expressed, and the dynamic field expresses a fluctuation range of the first history data. Specifically, by constructing data induction arrangement for the first historical metering data, a six sigma tool is adopted to calculate a first central line, a first upper control limit and a first lower control limit of the control chart. Wherein there areUpper control limit->Lower control limit-> N represents a multiple and σ represents a standard deviation of the first historical metering data; the control limit is typically 3 times the standard deviation of the mean, but in this embodiment the first historical metering data is tare data based on the first calendar of the tare dataThe analysis of the proportion of abnormal data in the history metering data can determine that the optimal value of N is 4, that is, the control limit is 4 times the standard deviation of the mean value (which will be described in detail later), that is, the first upper control limit is the mean value of the first history metering data plus 4 times the standard deviation, and the first lower control limit is the mean value of the first history metering data minus 4 times the standard deviation.
In step S30, when the track scale measures, the first real-time measurement data obtained by measurement can be input into the control chart to identify abnormal data, the identified abnormal data is marked, the identification result is output, the output processing result can be in reverse order arrangement, and the data with latest overload time can be ensured to be arranged at the forefront end. In this embodiment, the first real-time measurement data may be detected and identified in real time online, or may be input into the control chart in a unified manner for identification after the first real-time measurement data is collected in a certain period of time.
Further, after the step S30 is performed for a period of time, the measurement data is fluctuated and changed due to the change of external factors, and at this time, the original control chart is still used to identify the abnormal data, which may cause the error to become larger gradually. Therefore, step S40 is performed to update the control map, and the first real-time metering data is included in the second historical metering data. In particular, the second historical metering data may be entirely composed of the first real-time metering data, or may be composed of a part of the first historical metering data and the first real-time metering data. Preferably, the second historical metering data is composed of a part of the first historical metering data and the first real-time metering data; for example, when the first historical measurement data and the second historical measurement data are both 30 days of measurement data, the preset updating threshold is 15 days, and the second historical measurement data is composed of 15 days of first historical measurement data and 15 days of first real-time measurement data, so that the characteristics of the first historical measurement data can be reserved, the characteristics of the first real-time measurement data are contained, continuity of updating of the control diagram is guaranteed, and accuracy of identification is improved.
The updating threshold in step S40 may be the measurement duration of the first historical measurement data, or may be 1/2, 1/3, 1/4, etc. of the measurement duration of the first historical measurement data, which is not limited; at this time, the recognition process reaching the preset update threshold value means that the recognition time reaches the preset update threshold value. In addition, the updated threshold value may also be the amount of real-time metering data, for example, the updated threshold value is 10 pieces of real-time metering data, 100 pieces of real-time metering data, 1000 pieces of real-time metering data, and so on; at this time, the recognition process reaching the preset update threshold means that the recognition number reaches the preset update threshold.
In step S50, the second center line, the second upper control limit, and the second lower control limit of the control chart may be referred to the description of the first center line, the first upper control limit, and the first lower control limit in step S20, and the description is omitted.
In the present embodiment, for an understanding of step S60, reference may be made to step S30. Through steps S10-S60, the control map may be updated iteratively according to the historical metering data and the real-time metering data to ensure accuracy of the recognition model (control map).
In this embodiment, an 800t railroad track scale is used for illustration, and the tare metering data is collected for the construction of a control chart by collecting the tare historical metering data every month, and the control chart is updated continuously. After the accuracy rate of the dynamic model identification reaches 100%, the method can be used for identifying the gross weight and the net weight and displaying and controlling the gross weight and the net weight in an 800t rail balance metering system. Specifically, the input real-time metering data is identified. Firstly, the average value and standard deviation of the tare weight of each month are calculated, the limit of a control chart is determined, tare weight measurement data (real-time measurement data) of each month are input into the control chart (points are marked in the control chart according to the tare weight data), abnormal points are searched according to the judgment criteria of the control chart, and real-time monitoring and control are carried out on the measurement data. And meanwhile, determining a conventional tare range according to the historical data of each month and the calculated result adjustment limit, and updating the control chart. Step S30 is performed, and specific recognition results are shown in table 1 below.
TABLE 1 tare data distribution case
In table 1, the underlined bold data is the identified abnormal data.
Further, the study was conducted with 1 month of tare weight historical metering data (i.e., first historical metering data), and the average value and standard deviation of the tare weight data were calculated from the obtained tare weight historical metering data, so as to obtain the limits (including the first center line, the first upper control limit and the first lower control limit) of the control chart. The limits are adjusted based on historical data and calculated mean and standard deviation results, for example, based on 9 months of data. The final determination is preferably that the weight ranges are all within 4 standard deviations of the mean line. And then, continuously identifying new metering data, updating the limit of the control chart by taking the new metering data as historical metering data, and identifying tare data (second real-time metering data) through the updated control chart. The adjustment limit and the identification of abnormal data are schematically shown in fig. 2, wherein the abnormal rate is 0.56% in the first adjustment as shown in fig. 2 (a), and the abnormal rate is high; the abnormality rate at the time of the second adjustment was reduced to 0.23%, as shown in fig. 2 (B); the anomaly rate is 0.21% during the third adjustment, as shown in fig. 2 (C), the anomaly rate of the upper and lower limits of the control chart is hardly changed in the third adjustment, the control limit range is finally determined to be within 4 standard deviations, anomaly identification is continuously performed on the tare metering data of 11 months and 12 months, the anomaly rate of the tare metering data of 10 months can be obtained to be 0.20%, the anomaly rate of the tare metering data of 11 months is 0.17%, as shown in fig. 2 (D), the anomaly rate is almost completely screened out, and the anomaly rate is 0.21%, so that the actual application situation is met, and in the embodiment, the control limit range is maintained within 4 standard deviations.
In summary, in the method for identifying abnormal data of the track scale provided in the embodiment, the first historical measurement data of the track scale is obtained, the control chart is constructed based on the first historical measurement data, and then the first real-time measurement data is screened through the control chart to identify the abnormal data. Further, the control diagram is updated by acquiring the second historical data, and the updated control diagram is the control diagram which is most suitable for the current track scale and the metering object and avoids recognition distortion caused by system errors due to external factor changes because the second historical data comprises the first real-time metering data; finally, the continuous iterative updating of the control diagram is carried out, and the updated control diagram is used for identifying the real-time metering data, so that the identification and control of the complete track scale metering data can be accurately carried out, the data is ensured to be normal, and the fault chain reaction of equipment is avoided.
Second embodiment
Based on the same inventive concept, a second embodiment of the present application provides an abnormal data recognition apparatus 300 of a railroad track scale. Fig. 3 is a functional block diagram of an abnormal data recognition apparatus 300 of a railroad track scale according to a second embodiment of the present application.
The device 300 for identifying abnormal data of the railroad track scale comprises:
a first data acquisition module 301, configured to acquire first historical measurement data of the track scale;
a control diagram construction module 302, configured to determine a first center line, a first upper control limit, and a first lower control limit of a control diagram based on the first historical metering data;
a first identifying module 303, configured to identify abnormal data in first real-time metering data of the rail scale measurement based on the first center line, the first upper control limit, and the first lower control limit;
a second data obtaining module 304, configured to obtain second historical measurement data of the railroad track scale when the identification process reaches a preset update threshold; wherein the second historical metering data comprises the first real-time metering data;
a control diagram updating module 305, configured to update the control diagram based on the second historical measurement data, and obtain a second center line, a second upper control limit, and a second lower control limit of the control diagram;
a second identifying module 306, configured to identify abnormal data in second real-time metering data of the rail scale measurement based on the second center line, the second upper control limit, and the second lower control limit.
As an alternative embodiment, the first historical metering data, the second historical metering data, the first real-time metering data and the second real-time metering data are all tare data.
As an alternative embodiment, the control diagram construction module 302 is specifically configured to:
and determining that the first upper control limit is the average value of the first historical metering data plus 4 times of standard deviation based on the abnormal data duty ratio in the first historical metering data, and the first lower control limit is the average value of the first historical metering data minus 4 times of standard deviation.
As an alternative embodiment, the second data obtaining module 304 is configured to:
the first real-time metering data within one month is taken as the second historical metering data.
It should be noted that, in the embodiment of the present application, the specific implementation and the technical effects of the abnormal data identification apparatus 300 for a railroad track scale are the same as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding contents in the foregoing method embodiment where the apparatus embodiment portion is not mentioned.
The functional modules integrated with the apparatus provided by the present application may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the present application may implement all or part of the flow of the method of the above-described embodiments, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the above-described method embodiments when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that the teachings of the present application described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed application requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in an apparatus according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (8)
1. The method for identifying the abnormal data of the track scale is characterized by comprising the following steps of:
acquiring first historical metering data of the track scale;
determining a first centerline, a first upper control limit, and a first lower control limit of a control map based on the first historical metering data;
identifying anomalous data in the first real-time metrology data of the rail scale measurement based on the first centerline, the first upper control limit, and the first lower control limit;
when the identification process reaches a preset updating threshold value, second historical metering data of the track scale are obtained; wherein the second historical metering data comprises the first real-time metering data;
updating the control chart based on the second historical metering data to obtain a second central line, a second upper control limit and a second lower control limit of the control chart;
identifying anomalous data in second real-time metrology data of the rail scale measurement based on the second centerline, the second upper control limit, and the second lower control limit;
the second historical metering data consists of a part of first historical metering data and first real-time metering data;
the obtaining of the first upper control limit and the first lower control limit includes:
and determining that the first upper control limit is the average value of the first historical metering data plus 4 times of standard deviation based on the abnormal data duty ratio in the first historical metering data, and the first lower control limit is the average value of the first historical metering data minus 4 times of standard deviation.
2. The method of claim 1, wherein the first historical metering data, the second historical metering data, the first real-time metering data, and the second real-time metering data are each tare data.
3. The method of claim 1, wherein the obtaining second historical metrology data for the railroad track scale comprises:
the first real-time metering data within one month is taken as the second historical metering data.
4. The method of claim 1, wherein the exception data comprises first exception data and second exception data; identifying anomalous data in the first real-time metrology data of the rail scale measurement based on the first centerline, the first upper control limit, and the first lower control limit, comprising:
identifying data which is located outside the first control upper limit in the first real-time metering data, and obtaining the first abnormal data;
and identifying data which is positioned outside the first control lower limit in the first real-time metering data, and obtaining the second abnormal data.
5. An abnormal data identification device for a railroad track scale, comprising:
the first data acquisition module is used for acquiring first historical metering data of the track scale;
the control diagram construction module is used for determining a first central line, a first upper control limit and a first lower control limit of the control diagram based on the first historical metering data;
a first identifying module configured to identify abnormal data in first real-time metering data of the rail scale measurement based on the first center line, the first upper control limit, and the first lower control limit;
the second data acquisition module is used for acquiring second historical metering data of the track scale when the identification process reaches a preset updating threshold value; wherein the second historical metering data comprises the first real-time metering data;
the control diagram updating module is used for updating the control diagram based on the second historical metering data to obtain a second central line, a second upper control limit and a second lower control limit of the control diagram;
a second identifying module configured to identify abnormal data in second real-time metering data of the rail scale measurement based on the second center line, the second upper control limit, and the second lower control limit;
the second historical metering data consists of a part of first historical metering data and first real-time metering data;
the control diagram construction module is specifically configured to:
and determining that the first upper control limit is the average value of the first historical metering data plus 4 times of standard deviation based on the abnormal data duty ratio in the first historical metering data, and the first lower control limit is the average value of the first historical metering data minus 4 times of standard deviation.
6. The apparatus of claim 5, wherein the first historical metering data, the second historical metering data, the first real-time metering data, and the second real-time metering data are each tare data.
7. The apparatus of claim 5, wherein the second data acquisition module is configured to:
the first real-time metering data within one month is taken as the second historical metering data.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010362235.0A CN111695595B (en) | 2020-04-30 | 2020-04-30 | Method and device for identifying abnormal data of track scale |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010362235.0A CN111695595B (en) | 2020-04-30 | 2020-04-30 | Method and device for identifying abnormal data of track scale |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111695595A CN111695595A (en) | 2020-09-22 |
| CN111695595B true CN111695595B (en) | 2023-09-15 |
Family
ID=72476935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010362235.0A Active CN111695595B (en) | 2020-04-30 | 2020-04-30 | Method and device for identifying abnormal data of track scale |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111695595B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106249728A (en) * | 2016-09-28 | 2016-12-21 | 清华大学 | A kind of thermal power generation unit on-line performance monitoring method based on characteristics of components |
| CN106325227A (en) * | 2015-06-24 | 2017-01-11 | 中芯国际集成电路制造(上海)有限公司 | Manufacturing process management and control line generating method and device, and manufacturing process management and control system |
| CN107578104A (en) * | 2017-08-31 | 2018-01-12 | 江苏康缘药业股份有限公司 | A kind of Chinese Traditional Medicine knowledge system |
| CN110187679A (en) * | 2019-04-29 | 2019-08-30 | 新奥数能科技有限公司 | A kind of alarm method and device of SCADA system |
| CN110849404A (en) * | 2019-11-18 | 2020-02-28 | 中国华能集团清洁能源技术研究院有限公司 | Continuous discrimination method for sensor data abnormity |
| CN110907883A (en) * | 2019-10-25 | 2020-03-24 | 湖北省计量测试技术研究院 | Metering supervision method and system for automatic verification system of electric energy meter |
-
2020
- 2020-04-30 CN CN202010362235.0A patent/CN111695595B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106325227A (en) * | 2015-06-24 | 2017-01-11 | 中芯国际集成电路制造(上海)有限公司 | Manufacturing process management and control line generating method and device, and manufacturing process management and control system |
| CN106249728A (en) * | 2016-09-28 | 2016-12-21 | 清华大学 | A kind of thermal power generation unit on-line performance monitoring method based on characteristics of components |
| CN107578104A (en) * | 2017-08-31 | 2018-01-12 | 江苏康缘药业股份有限公司 | A kind of Chinese Traditional Medicine knowledge system |
| CN110187679A (en) * | 2019-04-29 | 2019-08-30 | 新奥数能科技有限公司 | A kind of alarm method and device of SCADA system |
| CN110907883A (en) * | 2019-10-25 | 2020-03-24 | 湖北省计量测试技术研究院 | Metering supervision method and system for automatic verification system of electric energy meter |
| CN110849404A (en) * | 2019-11-18 | 2020-02-28 | 中国华能集团清洁能源技术研究院有限公司 | Continuous discrimination method for sensor data abnormity |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111695595A (en) | 2020-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Westgard et al. | Establishing evidence-based statistical quality control practices | |
| CN111242793A (en) | Method and device for detecting medical insurance data abnormity | |
| CN116720881A (en) | Agricultural product sales supervision early warning method, system and medium based on positioning information | |
| Okamura et al. | Ridge virtual population analysis to reduce the instability of fishing mortalities in the terminal year | |
| Andreeski et al. | Modelling, forecasting and testing decisions for seasonal time series in tourism | |
| CN111695595B (en) | Method and device for identifying abnormal data of track scale | |
| CN116879831A (en) | Intelligent ammeter monitoring method and system based on Internet of things | |
| CN117422450B (en) | Digital maintenance system for metering equipment | |
| CN113128797A (en) | Method and device for monitoring abnormal business indexes | |
| CN114996257A (en) | Data amount abnormality detection method, device, medium, and program product | |
| CN114066089A (en) | Batch job operation time-consuming interval determining method and device | |
| CN116756199A (en) | Wind measurement data processing recommendation method and system | |
| CN110910061A (en) | Material management method, material management system, storage medium and electronic equipment | |
| US20100332907A1 (en) | System and method for testing different computer types | |
| CN114529181A (en) | Machine learning-based industrial chain optimal enterprise feature optimization method and system | |
| CN111008150B (en) | Test report generation method, device and equipment | |
| JP4681574B2 (en) | Sampling inspection system | |
| CN116302774A (en) | Scheduling task monitoring method and device, electronic equipment and readable storage medium | |
| CN112149833B (en) | Prediction method, device, equipment and storage medium based on machine learning | |
| Morozova et al. | Homogenization of the historical series from the Coimbra Magnetic Observatory, Portugal | |
| CN112580981A (en) | Crop climate risk identification method and device and computer equipment | |
| JP2008204116A (en) | Quality control system and method, and program and recording medium | |
| JP2003029825A (en) | Quality management system, quality management method, quality management program and recording medium with its program recorded | |
| US7343352B2 (en) | Method and system for assessing the quality and cost of inspection | |
| CN117057488B (en) | Method and system for predicting workload of radiation medical equipment based on artificial intelligent model |
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 |