CN113945253B - Water level measuring method for rail traffic track area - Google Patents
Water level measuring method for rail traffic track area Download PDFInfo
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- CN113945253B CN113945253B CN202111212759.2A CN202111212759A CN113945253B CN 113945253 B CN113945253 B CN 113945253B CN 202111212759 A CN202111212759 A CN 202111212759A CN 113945253 B CN113945253 B CN 113945253B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
- G01F23/14—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measurement of pressure
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
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Abstract
The invention discloses a water level measuring method of a rail traffic track area, which comprises the following steps: s1: acquiring first water level data in a preset time period of a track area by using first water level acquisition equipment; s2: acquiring second water level data in a preset time period of the track area by using second water level acquisition equipment; s3: according to the first water level data and the second water level data, utilizing an information fusion network to obtain final water level data of the track area; s4: and obtaining a water level change curve in a preset time period according to the final water level data of the track area, and finishing water level measurement.
Description
Technical Field
The invention relates to the technical field of traffic water level measurement, in particular to a water level measurement method for a rail traffic track area.
Background
In recent years, the rainwater volume is obviously increased compared with the past year, so that partial subway ponding is excessive due to construction planning. The subway ponding can cause the shutdown of a subway line, and even endanger personal safety and train equipment loss when serious, so the method is particularly important for the measurement of the subway ponding.
Disclosure of Invention
The invention aims to provide a water level measuring method for a rail traffic track area, which aims to solve the problem that accumulated water in the existing subway cannot be accurately measured.
The technical scheme for solving the technical problems is as follows:
the invention provides a water level measuring method of a rail traffic track area, which comprises the following steps:
s1: acquiring first water level data in a preset time period of a track area by using first water level acquisition equipment;
s2: acquiring second water level data in a preset time period of the track area by using second water level acquisition equipment;
s3: according to the first water level data and the second water level data, utilizing an information fusion network to obtain final water level data of the track area;
s4: and obtaining a water level change curve in a preset time period according to the final water level data of the track area, and finishing water level measurement.
Alternatively, one of the first water level collecting device and the second water level collecting device is configured as a pressure type liquid level sensor, and the other is configured as a camera.
Optionally, the pressure type liquid level sensor and the camera are all a plurality of, and a plurality of pressure type liquid level sensor and a plurality of the camera equipartition is in the both sides of track district.
Optionally, in step S3, the information fusion network includes a first data input layer, a linear transformation layer, a comparison layer, a second data input layer, a classification layer and an output layer, where the first data input layer is connected to an input end of the linear transformation layer, the linear transformation layer includes a first output end and a second output end, the first output end is connected to the first input end of the comparison layer, the second data input layer is connected to the second input end of the comparison layer, an output end of the comparison layer is connected to an input end of the classification layer, and an output end of the classification layer and a second output end of the linear transformation layer are simultaneously connected to the output layer.
Optionally, the step S3 includes:
s31: acquiring time sequence data of the first water level data in a preset time period;
s32: performing linear transformation operation on the time sequence data of the first water level data to obtain information to be matched and information to be extracted;
s33: acquiring time sequence data of second water level data in the preset time period;
s34: performing data comparison operation on the time series data of the information to be matched and the second water level data to obtain a data comparison result;
s35: classifying and normalizing the data comparison result to obtain a classification and normalization result;
s36: and obtaining final water level data of the track area according to the classification and normalization results and the information to be extracted.
Optionally, in the step S32, the linear transformation operation is:
k i ＝W k x i
v i ＝W v x i
wherein x is i Is a vector of d dimension, W k And W is v Is a d x d transform matrix, k i Is the information to be matched, v i Is the information to be extracted.
Optionally, in the step S34, the data comparing operation is:
wherein a is 1,i Representing the similarity value of the 1 st sensor data to the i-th sensor data,
the j-th component representing the first sensor data,>
the j-th component of the i-th sensor data is represented, i represents the j-th sensor, j represents the j-th component, and d represents the dimension of the data.
Optionally, the normalizing operation is:
wherein c 1,i Representing a similarity value representing the 1 st sensor data and the i-th sensor data after normalization, a 1,i A represents a similarity value between the 1 st sensor data and the i-th sensor data, a 1,j Representing the 1 st and the j th componentsI represents the jth sensor, j represents the jth component, and n represents the number of sensors.
Optionally, in step S4, the final water level data of the track area is fitted by using a decision tree and/or a linear ridge regression, so as to obtain a water level change curve in a preset time period.
The invention has the following beneficial effects:
according to the invention, by fusing the first water level data acquired by the first water level acquisition equipment and the second water level data acquired by the second water level acquisition equipment, the water level of a track traffic track area can be obtained more accurately, so that the investigation of the water volume in the track traffic is facilitated, maintenance personnel can clean the water in time, the occurrence of subway operation accidents is further avoided, and the subway operation is smooth.
Drawings
FIG. 1 is a flow chart of a water level measuring method for a rail transit track area provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a setting mode of a water level acquisition device in a water level measurement method of a rail transit track area according to an embodiment of the present invention;
FIG. 3 is a partial flow chart of step S3 in FIG. 1;
fig. 4 is a schematic structural diagram of an information fusion network provided by the present invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Examples
The technical scheme for solving the technical problems is as follows:
the invention provides a water level measuring method of a track traffic track area, which is shown by referring to fig. 1, and comprises the following steps:
s1: acquiring first water level data in a preset time period of a track area by using first water level acquisition equipment;
s2: acquiring second water level data in a preset time period of the track area by using second water level acquisition equipment;
here, the first water level collecting device may be a pressure type liquid level sensor or a camera. As the pressure type liquid level sensor adopts the diffused silicon piezoresistive pressure sensor with the stainless steel isolating film as the signal measuring element, the pressure type liquid level sensor can accurately measure hydrostatic pressure proportional to liquid level depth, and establishes the linear corresponding relation between an output signal and the liquid depth, thereby realizing accurate measurement of the liquid depth; the camera projects the optical signal obtained by the optical component onto the image sensor, and the optical signal is converted into an electric signal and then into a digital signal. Therefore, in the invention, one of the pressure type liquid level sensor and the camera is adopted as the first water level acquisition device, and the other one is adopted as the second water level acquisition device, so that the data acquired by the two water level acquisition devices can be fused at a later stage, and more accurate water level measurement data can be obtained.
The person skilled in the art can set the first water level acquisition device as a pressure type liquid level sensor, and then the second water level acquisition device as a camera; if the second water level collecting device is a pressure type liquid level sensor, the first water level collecting device can be a camera, and the invention is not particularly limited.
In addition, referring to fig. 2, in the specific arrangement mode of the first water level collecting device and the second water level collecting device, since the collected water levels are collected in the track area, a situation that measurement is inaccurate can not be avoided when a group of water level collecting devices are independently arranged, so that a plurality of water level collecting devices are arranged, each of the plurality of water level collecting devices comprises a plurality of pressure type liquid level sensors and a plurality of cameras, and the pressure type liquid level sensors and the cameras are uniformly distributed on two sides of the track area.
Specifically, a graduated scale is arranged above the pressure type liquid level sensor, and the reading on the ruler is required to be calibrated with the output of the liquid level sensor so as to ensure that the water level position above the output of the sensor is consistent with the reading of the graduated scale. On the other hand, the visual field of the camera can shoot the graduated scale, and the position of the water level and the reading on the graduated scale can be identified through an image identification algorithm (the image identification algorithm can be any identification algorithm, and the invention is not limited). Given the time series data generated by a set of multiple sensors, the purpose of fusing them is to find the most similar data from the sequences, so that the interference of outliers is reduced when the subsequent data are simulated, and therefore, the calculation of the similarity between the sequences is a key step of fusing.
S3: according to the first water level data and the second water level data, utilizing an information fusion network to obtain final water level data of the track area;
optionally, in step S3, the information fusion network includes a first data input layer, a linear transformation layer, a comparison layer, a second data input layer, a classification layer and an output layer, where the first data input layer is connected to an input end of the linear transformation layer, the linear transformation layer includes a first output end and a second output end, the first output end is connected to the first input end of the comparison layer, the second data input layer is connected to the second input end of the comparison layer, an output end of the comparison layer is connected to an input end of the classification layer, and an output end of the classification layer and a second output end of the linear transformation layer are simultaneously connected to the output layer.
Optionally, referring to fig. 3, the step S3 includes:
s31: acquiring time sequence data of the first water level data in a preset time period;
if the first water level acquisition device is a pressure type liquid level sensor, the first water level data are acquired through the pressure type liquid level sensor, and similarly, if the first water level acquisition device is a camera, the first water level data are acquired through the camera. In the specific embodiment provided by the invention, the first water level acquisition device is set as the pressure type liquid level sensor, so that the time series data of the first water level data are a plurality of time series generated by a plurality of sensors in a preset time period, and the second water level data are a plurality of time series generated by a plurality of cameras in the preset time period.
S32: performing linear transformation operation on the time sequence data of the first water level data to obtain information to be matched and information to be extracted;
optionally, in the step S32, the linear transformation operation is:
k i ＝W k x i
v i ＝W v x i
wherein x is i Is a vector of d dimension, W k And W is v Is a d x d transform matrix, k i Is the information to be matched, v i Is the information to be extracted.
S33: acquiring time sequence data of second water level data in the preset time period;
s34: performing data comparison operation on the time series data of the information to be matched and the second water level data to obtain a data comparison result;
wherein a is 1,i Representing the similarity value of the 1 st sensor data to the i-th sensor data,
the j-th component representing the first sensor data,>
the j-th component of the i-th sensor data is represented, i represents the j-th sensor, j represents the j-th component, and d represents the dimension of the data.
S35: classifying and normalizing the data comparison result to obtain a classification and normalization result; optionally, the normalizing operation is:
wherein c 1,i The representation is normalized 1 stSimilarity value of sensor data and i-th sensor data, a 1,i A represents a similarity value between the 1 st sensor data and the i-th sensor data, a 1,j The similarity value between the 1 st component and the j th component is represented, i represents the j th sensor, j represents the j th component, and n represents the number of sensors.
S36: and obtaining final water level data of the track area according to the classification and normalization results and the information to be extracted.
Assume that there are n pairs of cameras and hydraulic sensors, wherein the time series generated by one pair is (x i ,y i ) I e (0, n), then sequence x i Fusion is performed through a transducer information fusion network and other sequences as shown in fig. 4. The N sequences are linearly transformed to obtain N pairs (k i ,v i ) I.e. (0, n), where k i Is the vector to be matched, v i Is a vector of information to be extracted, their length is the same as x and y.
The calculation of the information fusion process is shown in fig. 4. Sequence vector y 1 And each k i Respectively calculating vector dot products to obtain similarity values a between every two 1,i And (2) and
these values are normalized by the classification layer (i.e. soft-max layer in the figure), i.e +.>
These values range from [0,1]And their sum is 1. Finally, a sequence vector b is obtained 1 ＝∑ i c 1,i v i It is based on y 1 And x i Weighted summation of similarity, which represents y 1 And x i Features most similar to each other and some dissimilar parts are excluded.
Other y can be calculated in the same way i And x i And x i And y i Such that there are a total of 2n fusion vectors b, and finally fitting these vectors using decision tree or linear ridge regression to obtain a certain vectorA variation profile of the water level over a period of time.
S4: and obtaining a water level change curve in a preset time period according to the final water level data of the track area, and finishing water level measurement.
Optionally, in step S4, the final water level data of the track area is fitted by using a decision tree and/or a linear ridge regression, so as to obtain a water level change curve in a preset time period.
The invention has the following beneficial effects:
according to the invention, by fusing the first water level data acquired by the first water level acquisition equipment and the second water level data acquired by the second water level acquisition equipment, the water level of a track traffic track area can be obtained more accurately, so that the investigation of the water volume in the track traffic is facilitated, maintenance personnel can clean the water in time, the occurrence of subway operation accidents is further avoided, and the subway operation is smooth.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (4)
1. The water level measuring method for the rail transit rail-mounted area is characterized by comprising the following steps of:
s1: acquiring first water level data in a preset time period of a track area by using first water level acquisition equipment;
s2: acquiring second water level data in a preset time period of the track area by using second water level acquisition equipment;
s3: according to the first water level data and the second water level data, utilizing an information fusion network to obtain final water level data of the track area;
s4: according to the final water level data of the track area, a water level change curve in a preset time period is obtained, and water level measurement is completed;
in the step S3, the information fusion network includes a first data input layer, a linear transformation layer, a comparison layer, a second data input layer, a classification layer and an output layer, where the first data input layer is connected to an input end of the linear transformation layer, the linear transformation layer includes a first output end and a second output end, the first output end is connected to the first input end of the comparison layer, the second data input layer is connected to the second input end of the comparison layer, the output end of the comparison layer is connected to the input end of the classification layer, and the output end of the classification layer and the second output end of the linear transformation layer are simultaneously connected to the output layer;
the step S3 includes:
s31: acquiring time sequence data of the first water level data in a preset time period;
s32: performing linear transformation operation on the time sequence data of the first water level data to obtain information to be matched and information to be extracted;
s33: acquiring time sequence data of second water level data in the preset time period;
s34: performing data comparison operation on the time series data of the information to be matched and the second water level data to obtain a data comparison result;
s35: classifying and normalizing the data comparison result to obtain a classification and normalization result;
s36: obtaining final water level data of the track area according to the classification and normalization results and the information to be extracted; in the step S32, the linear transformation operation is as follows:
k i ＝W k x i
v i ＝W v x i
wherein x is i Is a vector of d dimension, W k And W is v Is a d x d transform matrix, k i Is the information to be matched, v i Is information to be extracted;
in the step S34, the data comparing operation is as follows:
wherein a is 1,i Representing the similarity value of the 1 st sensor data to the i-th sensor data,
the j-th component representing the first sensor data,>
a j-th component representing i-th sensor data, i representing i-th sensor, j representing j-th component, d representing a dimension of the data;
the normalization operation is as follows:
wherein c 1,i Representing the similarity value of the 1 st sensor data and the i th sensor data after normalization, a 1,i A represents a similarity value between the 1 st sensor data and the i-th sensor data, a 1,j The similarity value between the 1 st component and the j th component is represented, i represents the i th sensor, j represents the j th component, and n represents the number of sensors.
2. The method of water level measurement in a rail transit zone of claim 1, wherein one of the first water level acquisition device and the second water level acquisition device is configured as a pressure type liquid level sensor and the other is configured as a camera.
3. The method for measuring the water level of the rail transit area according to claim 2, wherein the pressure type liquid level sensor and the cameras are all multiple, and the pressure type liquid level sensor and the cameras are uniformly distributed on two sides of the rail transit area.
4. The method according to claim 1, wherein in step S4, the final water level data of the track area is fitted by using decision tree and/or linear ridge regression to obtain a water level change curve in a preset time period.
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