CN116308621A - Method and device for detecting and correcting gross errors of contact line height detection data - Google Patents
Method and device for detecting and correcting gross errors of contact line height detection data Download PDFInfo
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Abstract
The invention discloses a contact line height detection data rough difference detection and correction method and device, and relates to the technical field of electrified railway power supply. Wherein the method comprises the following steps: sampling the height of the contact line to obtain contact line height detection data; according to the contact line arrangement characteristics, a continuous and stable system is constructed by utilizing contact line height detection data, and a system state measurement value is obtained; carrying out regression prediction by using the contact line height detection data to obtain a system state prediction value; comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value; and when the difference exceeds a given threshold value, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value. The method can accurately identify and correct the rough difference existing in the contact line height detection data, and is beneficial to improving the use efficiency of the contact line height detection data.
Description
Technical Field
The invention relates to the technical field of electrified railway power supply, in particular to a method and a device for detecting and correcting the gross error of contact line height detection data.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
According to long-term experience and statistical theory, dynamic detection often comprises that 1% -5% of data deviate from a true value seriously, even 10% -20% in extreme cases, and the measurement result of the serious deviation from the true value is called a gross error or an abnormal value.
The contact net geometric parameter detection device is usually installed on the roof and is influenced by factors such as strong light, rain and snow, high-voltage electromagnetic interference and the like, and even if a well-designed data sampling system is designed, measurement errors can not be avoided. The contact line height smoothness is used as an important index for evaluating the operation quality of contact net equipment, and when coarse difference exists in detection data, the objectivity and the accuracy of the comprehensive evaluation result are affected. Therefore, the effective recognition and correction of the rough differences existing in the contact line height detection data are a basic premise for accurately evaluating the state of the contact line equipment.
The gross differences present in contact line height measurement data mainly include two types: the first type is that the contact line height detection data at a single sampling point deviate from a true value to form a single-point rough difference, and the nearby positions are normal; the second type is that the contact line height detection data at a plurality of continuous sampling points deviate from the true value to form continuous rough differences. Because two contact wires exist at the contact wire equal-height point, normal jump points exist in the contact wire height detection data, and the influence on the normal jump of the data should be avoided when detecting and correcting rough differences.
Because of the existence of continuous gross errors, some conventional outlier diagnosis techniques based on statistical rules, such as PauTa criterion, grubbs criterion, dixon criterion and the like, are difficult to effectively apply in contact line height gross error detection. In contrast, some mature coarse difference detection and correction methods exist in other engineering fields, and mainly include a hypothesis testing method, a robust estimation method, a machine learning method and the like. The methods have strong pertinence generally, have severe requirements on engineering background and calculation conditions, are difficult to process normal jump points existing near the high points of the contact line and the like, and can not achieve good effects when processing the gross error of the height detection data of the contact line.
Currently, it is difficult in the prior art to effectively identify and correct the gross errors present in contact line height measurement data.
Disclosure of Invention
The embodiment of the invention provides a method for detecting and correcting the gross error of contact line height detection data, which is used for accurately identifying and correcting the gross error existing in the contact line height detection data and is beneficial to improving the use efficiency of the contact line height detection data, and the method comprises the following steps:
sampling the height of the contact line to obtain contact line height detection data comprising a plurality of continuous sampling points;
according to the contact line arrangement characteristics, carrying out characteristic modeling on the contact line height detection data, and constructing a continuous stable system to obtain a system state measurement value containing a plurality of continuous sampling points;
carrying out regression prediction on the system state of the continuous and stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points;
comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value;
when the difference exceeds a given threshold, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value;
and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
The embodiment of the invention also provides a device for detecting and correcting the gross error of the contact line height detection data, which is used for accurately identifying and correcting the gross error existing in the contact line height detection data and is beneficial to improving the use efficiency of the contact line height detection data, and the device comprises:
the sampling module is used for sampling the height of the contact line to obtain contact line height detection data;
the modeling module is used for performing feature modeling on the contact line height detection data according to the contact line arrangement features, constructing a continuous stable system and obtaining a system state measured value comprising a plurality of continuous sampling points;
the prediction module is used for carrying out regression prediction on the system state of the continuous stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points;
the comparison module is used for comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value;
the correction module is used for determining that the system state measured value comprises a coarse difference sampling point when the difference exceeds a given threshold value, and correcting the system state measured value into a system state predicted value; and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the contact line height detection data rough difference detection and correction method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the contact line height detection data rough difference detection and correction method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the contact line height detection data rough difference detection and correction method when being executed by a processor.
In the embodiment of the invention, the height of the contact line is sampled to obtain contact line height detection data comprising a plurality of continuous sampling points; according to the contact line arrangement characteristics, carrying out characteristic modeling on the contact line height detection data, and constructing a continuous stable system to obtain a system state measurement value containing a plurality of continuous sampling points; carrying out regression prediction on the system state of the continuous and stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points; comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value; when the difference exceeds a given threshold, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value; and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value. Compared with the technical scheme that the rough difference existing in the contact line height detection data is difficult to effectively identify and correct in the prior art, the method and the device can accurately identify and correct the rough difference existing in the contact line height detection data, and are beneficial to improving the use efficiency of the contact line height detection data.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic diagram of two kinds of coarse differences existing in contact line height detection data in an embodiment of the present invention;
FIG. 2 is a schematic diagram of contact line placement and test data storage near medium high points in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for detecting and correcting a rough difference of contact line height detection data according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a piecewise linearization scheme in accordance with an embodiment of the invention;
FIG. 5 is a schematic diagram of a rough error identification and correction scheme according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an overcorrection phenomenon according to an embodiment of the present invention;
FIG. 7 is a diagram showing an exemplary method for detecting and correcting the gross error of contact line height measurement data according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a sliding window backtracking mechanism according to an embodiment of the present invention;
FIG. 9 is a flow chart of a method for detecting and correcting the gross error of contact line height detection data according to the embodiment of the invention;
fig. 10 is a schematic structural diagram of a device for detecting and correcting a gross error of contact line height detection data according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of a device for detecting and correcting a rough difference of contact line height detection data according to another embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are open-ended terms, meaning including, but not limited to. Reference to the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is used to schematically illustrate the practice of the present application, and is not limited thereto and may be appropriately adjusted as desired.
The gross differences present in contact line height measurement data mainly include two types: the first type is that the contact line height detection data at a single sampling point deviate from a true value to form a single-point rough difference, and the nearby positions are normal; the second type is that the contact line height detection data at a plurality of continuous sampling points deviate from the true value to form continuous rough differences. Fig. 1 is a schematic diagram of two kinds of gross errors existing in contact line height detection data, and the processing effects of the two kinds of gross errors should be considered simultaneously in detection and correction.
In the bow net comprehensive detection device (1C), non-contact geometric parameter detection equipment uses an equidistant sampling principle, in order to consider the double-branch contact line arrangement characteristic of the anchor segment joint or line fork position, the collected geometric parameter data are usually stored in two fields, namely height1 and height2, and the data stored in other positions are the same. FIG. 2 is a schematic diagram of a contact line arrangement and test data storage near a contour point, wherein a) is a contact line arrangement near a contour point, and it can be seen that there are two contact line intersection sections I and II near the contour point; b) For the data characteristics of the height1 near the contour point, it can be seen that a jump point exists in the height1, the height data of the contact line I is stored before jump, and the height data of the contact line II is stored after jump; c) For the data feature of height2 near the contour point, it can be seen that there is also a jump point in height2, the height data of contact line i is stored before the jump, and the height data of contact line ii is stored after the jump. Thus, the contact line height detection data stored in a single field may have a trip point that should be avoided from affecting such trips when detecting and correcting gross errors.
The inventor finds that some traditional outlier diagnosis technologies based on statistical rules, such as PauTa rule, grubbs rule, dixon rule and the like, are difficult to effectively apply in contact line height coarse difference detection due to continuous coarse differences. In contrast, some mature coarse difference detection and correction methods exist in other engineering fields, and mainly include a hypothesis testing method, a robust estimation method, a machine learning method and the like. The methods have strong pertinence generally, have severe requirements on engineering background and calculation conditions, are difficult to process normal jump points existing near the high points of the contact line and the like, and can not achieve good effects when processing the gross error of the height detection data of the contact line.
In order to solve the above problems, in the embodiment of the present invention, according to the Kalman filtering concept and the spatial arrangement characteristics of the contact line, a method for detecting and correcting the rough difference of the contact line height detection data is provided.
Fig. 3 is a schematic diagram of a method for detecting and correcting a gross error of contact line height detection data according to an embodiment of the present invention, as shown in fig. 3, the method includes:
step 301: sampling the height of the contact line to obtain contact line height detection data comprising a plurality of continuous sampling points;
step 302: according to the contact line arrangement characteristics, carrying out characteristic modeling on the contact line height detection data, and constructing a continuous stable system to obtain a system state measurement value containing a plurality of continuous sampling points;
step 303: carrying out regression prediction on the system state of the continuous and stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points;
step 304: comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value;
step 305: when the difference exceeds a given threshold, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value;
step 306: and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
As can be seen from the flow shown in fig. 3, the embodiment of the invention can solve the problem of filtering contact line height detection data including single-point coarse difference and continuous coarse difference, can more scientifically and accurately identify and correct coarse difference existing in the contact line height detection data, is beneficial to improving the use efficiency of contact line geometric parameter detection data, and improves the accuracy of comprehensive quality evaluation of the contact line, so as to better help railway infrastructure managers evaluate the state of contact line equipment.
In the specific implementation, feature modeling is carried out on the contact line height detection data, so that a continuous and stable system is constructed, namely, the contact line height detection data in a short distance is regarded as a system state value, and then a linear relation exists between two adjacent system state values, and regression prediction can be carried out on the system state at the current moment by the system state value at the previous moment.
In the embodiment of the invention, the height of the contact line is sampled firstly to obtain the contact line height detection data; according to the contact line arrangement characteristics, carrying out characteristic modeling on the contact line height detection data, and constructing a continuous stable system to obtain a system state measurement value containing a plurality of continuous sampling points; and carrying out regression prediction on the system state of the continuous stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points.
In one embodiment, performing regression prediction on the system state of the continuous stable system according to the contact line height detection data to obtain a system state prediction value including a plurality of continuous sampling points may include: constructing a unitary linear regression model for the system state of the continuous and stable system according to the contact line height detection data; and carrying out regression prediction by using the unitary linear regression model to obtain a system state predicted value comprising a plurality of continuous sampling points.
In one embodiment, let the contact line height detection data be h (k), introduce the parameter δ, let the contact line height be approximately linearized over δ sampling points, define the system state value at time t (i.e. sliding window) as:
wherein: k is the sampling point sequence number; h (k) is contact line height detection data; f (f) δ (k, t) is a rectangular window function, expressed as:
FIG. 4 is the presentIn the embodiment of the invention, the schematic diagram of the piecewise linearization scheme is shown in fig. 4, and the system state value at time t is shown
A rectangular sliding window containing delta contact line height measurements is shown. According to the definition, predicting the system state value at the time t is equivalent to constructing a unitary linear regression model by using the system state value at the time t-1. Is provided with->
The sequence of sampling points in (k, h (k))|k=t-1, t, …, t+δ -2, then the contact line height prediction value +.>
The regression equation for sample number k, the unitary linear regression model, is as follows:
wherein:
and->
Is a regression equation coefficient; k is the sampling sequence number of the sampling point; />
A contact line height predicted value corresponding to k;
the system state predictors are as follows:
wherein:
system state pre-determination for time tMeasuring a value; h (t), h (t+1), …, h (t+delta-2) are +.>
The contact line height measurement values corresponding to the middle sampling points t, t+1, … and t+delta-2 respectively; />
And calculating a contact line height predicted value obtained by a unitary linear regression model for the sampling point t+delta-1.
According to
In (2), h (t+1), …, h (t+delta-2) and the corresponding sampling points t, t+1, …, t+delta-2 and the unitary linear regression model, determining ∈ ->
And->
By->
Calculating the contact line height predicted value of the sampling point t+delta-1
The system state measurements are:
wherein:
a system state measured value at the time t; h (t), h (t+1), h (t+delta-2) is +.>
The contact line height measurements corresponding to the middle sampling points t, t+1,..；/>
Is a contact line height measurement of the sampling point t + delta-1.
After the system state measured value and the system state predicted value are obtained, the system state measured value is compared with the system state predicted value, and the difference between the system state measured value and the system state predicted value is obtained.
In one embodiment, comparing the system state measurement value with the system state prediction value to obtain a difference between the system state measurement value and the system state prediction value may include:
comparing the system state measured value with the system state predicted value to obtain the Euclidean distance between the system state measured value and the system state predicted value;
and determining the Euclidean distance between the system state measured value and the system state predicted value as the difference between the system state measured value and the system state predicted value.
The core of the coarse detection and correction is to measure the system state at the time t
And system state prediction value->
Comparison was performed. FIG. 5 is a schematic diagram of the rough difference recognition and correction scheme, wherein the points A, B respectively represent the predicted value +_for the contact line height when the sampling point number is t+delta-1>
And measurement value->
In FIG. 5 +.>
And->
Only A, B, the European distance between A, B points can be used to measure +.>
And->
Will be->
And->
The difference of (2) is marked as->
Then
Wherein:
a system state measured value at the time t; />
The system state predicted value at the time t; />
A contact line height measurement for the sampling point t+delta-1; />
And calculating a contact line height predicted value obtained by a unitary linear regression model for the sampling point t+delta-1.
When the difference exceeds a given threshold, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value; and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
In the examples, a given threshold ε is introduced if
And->
If the difference exceeds a given threshold epsilon, then the measured value is considered
For the coarse sampling point, it is corrected to the predicted value +.>
Otherwise, consider the measured value
For normal sampling points, the values are kept and the predicted values are discarded +.>
The contact line height detection data gross error detection and correction scheme is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
a system state value for final determination; />
A system state measured value at the time t; />
The system state predicted value at the time t; epsilon is a threshold parameter that affects the determination of the final system state value.
When jump points exist in the contact line height detection data, the rough correction is directly carried out according to the scheme, so that the overcorrection phenomenon is caused. Fig. 6 is a schematic diagram of an overcorrection phenomenon, which is caused by the fact that the difference between the heights of the contact lines at both sides of the jump point is greater than a given threshold epsilon, so that the measured values after jump are all corrected as rough differences to be predicted values, and after a plurality of iterations, the corrected values gradually deviate from the true values.
In order to avoid overcorrection, a backtracking mechanism is introduced, fig. 7 is a specific example diagram of a method for detecting and correcting a gross error of contact line height detection data according to an embodiment of the present invention, and as shown in fig. 7, the method for detecting and correcting a gross error of contact line height detection data according to an embodiment of the present invention may further include:
step 701: monitoring the number of the system state predicted values in real time, if the system state measured values at a certain moment are all corrected to be the system state predicted values, starting from a first sampling point of the system state measured values at the moment, obtaining the system state measured values containing a plurality of continuous sampling points and the system state predicted values containing a plurality of continuous sampling points again to obtain the difference between the system state measured values and the system state predicted values, and if the difference exceeds a given threshold value, correcting the system state measured values to be the system state predicted values; if the difference does not exceed the given threshold, the system state measurement value is reserved, and the system state prediction value is abandoned.
The backtracking mechanism is that if all sampling points in the system state value at a certain moment are corrected to be predicted values, the system state value at the moment is reconstructed from the measured value at the first sampling position of the system state value at the moment, and the subsequent rough difference identification and correction are restarted with the newly constructed system state value. FIG. 8 is a schematic diagram of a sliding window backtracking mechanism, in which the system state value at time s
All points in (a) are predicted values, trace back to get +.>
From +.o.d.moment s-delta->
System state value +.>
And thus complete subsequent iterations.
In order to facilitate the understanding of how the present invention may be practiced, the method for detecting and correcting the gross error of the contact line height measurement data will be described in detail with reference to fig. 9.
In one embodiment, as shown in fig. 9, δ is a construction parameter of a system state value, and represents the number of sampling points included in the system state value, and ε is a predetermined threshold. In FIG. 9, the updated system state values are divided into two cases, when the Euclidean distance is smaller than ε, the contact line height measurement corresponding to the largest sampling number is measured
And merging the tail part of the current system state value, deleting the contact line height measured value corresponding to the minimum sampling sequence number in the current system state value, and obtaining the updated system state value. When the Euclidean distance is larger than epsilon, the contact line height predictive value corresponding to the largest sampling sequence number is +.>
And merging the tail part of the current system state value, deleting the contact line height measured value corresponding to the minimum sampling sequence number in the current system state value, and obtaining the updated system state value. The detailed steps are as follows:
s1: input contact line height measurement
Wherein i represents the contact line height measurement +.>
Is shifted to step S2;
s2: if the number n of elements in the current system state value is less than delta, then n is added with 1, and then
Adding the sampling sequence number into the current system state value according to the position of the sampling sequence number, and returning to the step S1; otherwise, the initial system state value is constructed, and the step S3 is carried out;
s3: calculating the contact line height predicted value in the current system state value
Contact line height measurement->
If the Euclidean distance is larger than epsilon, the step S4 is carried out, otherwise, the step S5 is carried out;
s4: counting w and adding 1, and switching to step S6 when w is greater than or equal to delta, otherwise, predicting the contact line height corresponding to the maximum sampling sequence number
Merging the tail part of the current system state value, deleting the contact line height measurement value corresponding to the minimum sampling sequence number in the current system state value to obtain an updated system state value, and returning to the step S1;
s5: resetting w to 0, and measuring the contact line height corresponding to the largest sampling serial number
Merging the tail part of the current system state value, deleting the contact line height measurement value corresponding to the minimum sampling sequence number in the current system state value to obtain an updated system state value, and returning to the step S1;
s6: before backtracking to delta sampling points, resetting i to i-delta, clearing all elements in the current system state value, resetting n to 0, returning to the step S1, and reconstructing the system state value.
The embodiment of the invention also provides a contact line height detection data rough difference detection and correction device, as described in the following embodiment. Because the principle of the device for solving the problems is similar to that of the contact line height detection data coarse difference detection and correction method, the implementation of the device can be referred to the implementation of the contact line height detection data coarse difference detection and correction method, and the repetition is omitted.
Fig. 10 is a schematic structural diagram of a device for detecting and correcting a gross error of contact line height detection data according to an embodiment of the present invention, as shown in fig. 10, the device includes:
the sampling module 01 is used for sampling the height of the contact line to obtain contact line height detection data;
the modeling module 02 is used for performing feature modeling on the contact line height detection data according to the contact line arrangement features, and constructing a continuous stable system to obtain a system state measured value containing a plurality of continuous sampling points;
the prediction module 03 is configured to perform regression prediction on the system state of the continuous and stable system according to the contact line height detection data, so as to obtain a system state predicted value including a plurality of continuous sampling points;
the comparison module 04 is used for comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value;
the correction module 05 is configured to determine that the system state measurement value includes a coarse difference sampling point when the difference exceeds a given threshold value, and correct the system state measurement value to a system state prediction value; and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
In one embodiment, the prediction module 03 is specifically configured to: constructing a unitary linear regression model for the system state of the continuous and stable system according to the contact line height detection data; and carrying out regression prediction by using the unitary linear regression model to obtain a system state predicted value comprising a plurality of continuous sampling points.
In an embodiment, the unitary linear regression model is as follows:
wherein:
and->
Is a regression equation coefficient; k is the sampling sequence number of the sampling point; />
A contact line height predicted value corresponding to k;
the system state predictors are as follows:
wherein:
the system state predicted value at the time t; h (t), h (t+1), …, h (t+delta-2) are +.>
The contact line height measurement values corresponding to the middle sampling points t, t+1, … and t+delta-2 respectively; />
And calculating a contact line height predicted value obtained by a unitary linear regression model for the sampling point t+delta-1.
According to
H (t), h (t+1),. H (t+delta-2) and corresponding sampling points t, t+1, …, t+delta-2 and a unitary linear regression model, determining +.>
And->
By->
Calculating the contact line height predicted value of the sampling point t+delta-1
In one embodiment, the comparison module 04 is specifically configured to: comparing the system state measured value with the system state predicted value to obtain the Euclidean distance between the system state measured value and the system state predicted value; and determining the Euclidean distance between the system state measured value and the system state predicted value as the difference between the system state measured value and the system state predicted value.
In one embodiment, as shown in fig. 11, the contact line height detection data coarse detection and correction device may further include:
and the backtracking module 06 is configured to monitor the number of the system state predicted values in real time, and if the system state measured values at a certain moment are all corrected to the system state predicted values, execute the modeling module, the predicting module, the comparing module and the correcting module again from the first sampling point of the system state measured values at the moment.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the contact line height detection data rough difference detection and correction method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the contact line height detection data rough difference detection and correction method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the contact line height detection data rough difference detection and correction method when being executed by a processor.
Compared with the technical scheme that the rough difference in the contact line height detection data is difficult to effectively identify and correct in the prior art, the contact line height detection data comprising a plurality of continuous sampling points is obtained by sampling the contact line height; according to the contact line arrangement characteristics, carrying out characteristic modeling on the contact line height detection data, and constructing a continuous stable system to obtain a system state measurement value containing a plurality of continuous sampling points; carrying out regression prediction on the system state of the continuous and stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points; comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value; when the difference exceeds a given threshold, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value; and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value. The method can accurately identify and correct the rough difference existing in the contact line height detection data, can stably process the overcorrection phenomenon caused by normal jump, and is beneficial to improving the use efficiency of the contact line height detection data.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (13)
1. The method for detecting and correcting the gross error of the contact line height detection data is characterized by comprising the following steps:
sampling the height of the contact line to obtain contact line height detection data comprising a plurality of continuous sampling points;
according to the contact line arrangement characteristics, carrying out characteristic modeling on the contact line height detection data, and constructing a continuous stable system to obtain a system state measurement value containing a plurality of continuous sampling points;
carrying out regression prediction on the system state of the continuous and stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points;
comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value;
when the difference exceeds a given threshold, determining that the system state measured value comprises a coarse difference sampling point, and correcting the system state measured value into a system state predicted value;
and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
2. The method of claim 1, wherein performing regression prediction on the system state of the continuous stationary system based on the contact line height detection data to obtain a system state prediction value including a plurality of continuous sampling points, comprises:
constructing a unitary linear regression model for the system state of the continuous and stable system according to the contact line height detection data;
and carrying out regression prediction by using the unitary linear regression model to obtain a system state predicted value comprising a plurality of continuous sampling points.
3. The method of claim 2, wherein the unitary linear regression model is as follows:
wherein:
and->
Is a regression equation coefficient; k is the sampling sequence number of the sampling point; />
A contact line height predicted value corresponding to k;
the system state predictors are as follows:
wherein:
the system state predicted value at the time t; h (t), h (t+1), …, h (t+delta-2) are +.>
The contact line height measurement values corresponding to the middle sampling points t, t+1, … and t+delta-2 respectively; />
And calculating a contact line height predicted value obtained by a unitary linear regression model for the sampling point t+delta-1.
4. The method of claim 1, wherein comparing the system state measurement value with the system state prediction value to obtain a difference between the system state measurement value and the system state prediction value comprises:
comparing the system state measured value with the system state predicted value to obtain the Euclidean distance between the system state measured value and the system state predicted value;
and determining the Euclidean distance between the system state measured value and the system state predicted value as the difference between the system state measured value and the system state predicted value.
5. The method as recited in claim 1, further comprising:
monitoring the number of the system state predicted values in real time, if the system state measured values at a certain moment are all corrected to be the system state predicted values, starting from a first sampling point of the system state measured values at the moment, obtaining the system state measured values containing a plurality of continuous sampling points and the system state predicted values containing a plurality of continuous sampling points again to obtain the difference between the system state measured values and the system state predicted values, and if the difference exceeds a given threshold value, correcting the system state measured values to be the system state predicted values; if the difference does not exceed the given threshold, the system state measurement value is reserved, and the system state prediction value is abandoned.
6. A contact line height detection data gross error detection and correction device, characterized by comprising:
the sampling module is used for sampling the height of the contact line to obtain contact line height detection data;
the modeling module is used for performing feature modeling on the contact line height detection data according to the contact line arrangement features, constructing a continuous stable system and obtaining a system state measured value comprising a plurality of continuous sampling points;
the prediction module is used for carrying out regression prediction on the system state of the continuous stable system according to the contact line height detection data to obtain a system state predicted value comprising a plurality of continuous sampling points;
the comparison module is used for comparing the system state measured value with the system state predicted value to obtain the difference between the system state measured value and the system state predicted value;
the correction module is used for determining that the system state measured value comprises a coarse difference sampling point when the difference exceeds a given threshold value, and correcting the system state measured value into a system state predicted value; and when the difference does not exceed a given threshold, determining that the system state measured value does not contain a coarse difference sampling point, reserving the system state measured value, and discarding the system state predicted value.
7. The apparatus of claim 6, wherein the prediction module is specifically configured to:
constructing a unitary linear regression model for the system state of the continuous and stable system according to the contact line height detection data;
and carrying out regression prediction by using the unitary linear regression model to obtain a system state predicted value comprising a plurality of continuous sampling points.
8. The apparatus of claim 7, wherein the unitary linear regression model is as follows:
wherein:
and->
Is a regression equation coefficient; k is the sampling sequence number of the sampling point; />
A contact line height predicted value corresponding to k;
the system state predictors are as follows:
wherein:
the system state predicted value at the time t; h (t), h (t+1), …, h (t+delta-2) are +.>
The contact line height measurement values corresponding to the middle sampling points t, t+1, … and t+delta-2 respectively; />
And calculating a contact line height predicted value obtained by a unitary linear regression model for the sampling point t+delta-1.
9. The apparatus of claim 6, wherein the comparison module is specifically configured to:
comparing the system state measured value with the system state predicted value to obtain the Euclidean distance between the system state measured value and the system state predicted value;
and determining the Euclidean distance between the system state measured value and the system state predicted value as the difference between the system state measured value and the system state predicted value.
10. The apparatus as recited in claim 6, further comprising:
and the backtracking module is used for monitoring the number of the system state predicted values in real time, and if the system state measured values at a certain moment are corrected to the system state predicted values, the modeling module, the predicting module, the comparing module and the correcting module are re-executed from the first sampling point of the system state measured values at the moment.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 5 when executing the computer program.
12. 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 method of any of claims 1 to 5.
13. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 5.
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