CN118865265B - A method and system for quality control of side-scan flow radar data - Google Patents
A method and system for quality control of side-scan flow radar data Download PDFInfo
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Abstract
本发明提供一种侧扫测流雷达数据质量控制方法及系统，其中，所述方法包括以下步骤：对待监测水域的每一监测区域进行数据采集，得到当前点云数据、当前图像数据以及当前流速数据；基于当前点云数据、当前图像数据确定对应监测区域的水域的水域状态，并确定对应处于异常状态下的异常时间；进行与异常时间具有相同时长的数据采集，得补充点云数据、补充图像数据以及补充流速数据；响应于基于补充点云数据、补充图像数据确定对应监测区域处于正常状态，将补充流速数据对对应所述异常时间的异常流速部分进行替换；基于各当前流速数据确定与所述待监测水域对应的水域流速。本发明至少提高了对于水域流速的测量精准性。
The present invention provides a side-scanning current radar data quality control method and system, wherein the method comprises the following steps: data collection is performed on each monitoring area of the monitored water area to obtain current point cloud data, current image data and current flow rate data; based on the current point cloud data and current image data, the water state of the water area corresponding to the monitored area is determined, and the abnormal time corresponding to the abnormal state is determined; data collection with the same duration as the abnormal time is performed to obtain supplementary point cloud data, supplementary image data and supplementary flow rate data; in response to determining that the corresponding monitoring area is in a normal state based on the supplementary point cloud data and supplementary image data, the abnormal flow rate portion corresponding to the abnormal time is replaced by the supplementary flow rate data; based on each current flow rate data, the water flow rate corresponding to the monitored water area is determined. The present invention at least improves the measurement accuracy of the water flow rate.
Description
Technical Field
The invention relates to a data processing technology, in particular to a data quality control method and system for a side-sweep flow radar.
Background
The water flow rate is generally referred to as the velocity of the water flow and can be expressed in different units, such as meters per second (m/s) or centimeters per second (cm/s). The flow rate may vary depending on the nature of the body of water flow and the environmental conditions, such as the flow rate in rivers, streams, oceans.
In the prior art, a professional scanning radar is often used for measuring the flow rate of the water area to finish the corresponding measuring process, but the water area is always in different water area states due to different natural factors or human factors, for example, when a large vortex area, a large ripple area and a large noise area exist in the water area, the measuring accuracy of the flow rate of the water area can be influenced, the accurate measurement of the flow rate of the water area can not be realized, and the corresponding measuring efficiency is reduced.
Disclosure of Invention
The present invention has been made in view of the above problems, and it is an object of the present invention to provide a method and system for controlling data quality of a side-scan flow radar that overcomes or at least partially solves the above problems.
According to one aspect of the present invention, there is provided a method for controlling the quality of data of a side-scan flow radar, comprising the steps of:
carrying out grating treatment on a water area to be monitored, and respectively determining the obtained grating point positions as monitoring areas;
data acquisition is carried out on each monitoring area for preset time, and current point cloud data, current image data and current flow rate data of the corresponding monitoring area are obtained;
determining a water area state of a water area corresponding to the monitoring area within the preset time based on the current point cloud data and the current image data, and determining an abnormal time corresponding to the abnormal state;
Performing data acquisition of the supplementary time with the same duration as the abnormal time to obtain supplementary point cloud data, supplementary image data and supplementary flow rate data corresponding to the monitoring area;
In response to determining that the water area corresponding to the monitoring area is in a normal state within the replenishment time based on the replenishment point cloud data and the replenishment image data, replacing an abnormal flow rate portion corresponding to the abnormal time in the current flow rate data with the replenishment flow rate data;
And determining the water area flow rate corresponding to the water area to be monitored based on the current flow rate data corresponding to each monitoring area.
Optionally, in the method according to the present invention, data acquisition is performed for a preset time for each monitoring area to obtain current point cloud data, current image data, and current flow rate data of the corresponding monitoring area, including:
Triggering a point cloud radar, an image radar and a flow rate radar to acquire data for maintaining preset time in each monitoring area based on preset acquisition frequency;
Determining acquisition times based on the preset acquisition frequency and the preset time, and acquiring secondary point cloud data, secondary image data and secondary flow rate data corresponding to each acquisition time respectively;
Grouping each secondary point cloud data, each secondary image data and each secondary flow rate data based on each time node corresponding to each acquisition time respectively to obtain each node data group of the secondary point cloud data, the secondary image data and the secondary flow rate data corresponding to the same time node respectively;
and determining each node data set as current point cloud data, current image data and current flow rate data corresponding to the monitoring area.
Optionally, in the method according to the present invention, determining a water area state of the water area corresponding to the monitoring area within the preset time based on the current point cloud data and the current image data, and determining an abnormal time corresponding to the abnormal state includes:
Establishing a two-dimensional plane based on a monitoring area, and establishing point clouds of each area corresponding to each point cloud height on the two-dimensional plane based on secondary point cloud data in any node data set;
Acquiring the area height corresponding to the monitoring area, respectively comparing the cloud height of each point with the area height, and determining a first morphological attribute of the monitoring area under a time node corresponding to the node data set based on a comparison result;
retrieving secondary image data in the same node data set as the secondary point cloud data, and determining a second shape attribute of the monitoring area under a time node corresponding to the node data set based on the secondary image data;
Determining a water area state of the monitoring area under the time node based on the first morphological attribute and the second morphological attribute corresponding to the monitoring area;
And summing all the time nodes corresponding to the abnormal state to obtain the abnormal time corresponding to the abnormal state.
Optionally, in the method according to the present invention, determining a first morphological attribute of the monitoring area under a time node corresponding to the node data set based on a comparison result includes:
Determining all the regional point clouds with the corresponding point cloud heights smaller than the regional heights as vortex point cloud groups according to the comparison result, and determining all the regional point clouds with the corresponding point cloud heights larger than the regional heights as ripple point cloud groups according to the comparison result;
performing point cloud connection on each area point cloud in adjacent position relation in the vortex point cloud group in the two-dimensional plane to obtain each vortex area in the two-dimensional plane;
performing point cloud connection on each area point cloud in adjacent position relation in the corrugated point cloud group in the two-dimensional plane to obtain each corrugated area in the two-dimensional plane;
Carrying out merging processing based on surrounding relation on each vortex area based on a preset merging strategy;
After the merging processing, determining the first area size and the first area number corresponding to each vortex area, and the second area size and the second area number corresponding to each ripple area as first morphological attributes of the monitoring area under the time node corresponding to the node data set.
Optionally, in the method according to the present invention, the merging process based on the surrounding relation is performed on each vortex area based on a preset merging policy, including:
carrying out coordinated processing on the two-dimensional plane to obtain vortex coordinate points respectively positioned in each vortex area, and obtaining vortex coordinate groups respectively corresponding to the vortex areas;
And comparing the vortex coordinate sets in pairs, determining whether the vortex coordinate sets have a surrounding relation or not based on a comparison result, and merging the vortex areas corresponding to the vortex coordinate sets with the surrounding relation into one vortex area.
Optionally, in the method according to the present invention, determining a second shape attribute of the monitoring area under a time node corresponding to the node data set based on the secondary image data comprises:
Performing binarization processing on the secondary image data to obtain a binarized image, wherein the binarized image comprises all water area pixel points corresponding to first pixel values and all noise pixel points corresponding to second pixel values respectively;
Pixel point connection is carried out on each noise pixel point in adjacent position relation in the secondary image data, so that each noise region in the secondary image data is obtained;
And determining the size and the number of the third areas corresponding to the noise areas as second shape attributes of the monitoring areas under the time nodes corresponding to the node data sets.
Optionally, in the method according to the present invention, determining the water status of the monitoring area under the time node based on the first morphological attribute and the second morphological attribute corresponding to the monitoring area includes:
summing calculation is carried out based on the sizes of all the first areas to obtain a first size total value, and ratio calculation is carried out based on the first size total value and the monitoring size of the corresponding monitoring area to obtain a first size ratio;
Respectively carrying out normalization processing on the first size ratio and the first area number to obtain a first size coefficient and a first quantity coefficient, and carrying out summation calculation on the basis of the first size coefficient and the first quantity coefficient to obtain a vortex coefficient;
Summing calculation is carried out based on the sizes of the second areas to obtain a second size total value, and ratio calculation is carried out based on the second size total value and the monitoring sizes of the corresponding monitoring areas to obtain a second size ratio;
respectively carrying out normalization processing on the second size ratio and the second area number to obtain a second size coefficient and a second number coefficient, and carrying out summation calculation on the basis of the second size coefficient and the second number coefficient to obtain a ripple coefficient;
summing calculation is carried out based on the sizes of all the third areas to obtain a third size total value, and ratio calculation is carried out based on the third size total value and the monitoring size of the corresponding monitoring area to obtain a third size ratio;
Respectively carrying out normalization processing on the third size ratio and the third area number to obtain a third size coefficient and a third number coefficient, and carrying out summation calculation based on the third size coefficient and the third number coefficient to obtain a noise coefficient;
And determining the water area state of the monitoring area under the time node based on the vortex coefficient, the ripple coefficient and the noise coefficient.
Optionally, in the method according to the present invention, determining the water state of the monitoring area under the time node based on the swirl coefficient, the ripple coefficient and the noise coefficient includes:
invoking vortex weight, ripple weight and noise weight;
performing product calculation on the vortex coefficient and the vortex weight to obtain a vortex evaluation value;
performing product calculation on the ripple coefficient and the ripple weight to obtain a ripple evaluation value;
performing product calculation on the noise coefficient and the noise weight to obtain a noise evaluation value;
When the vortex evaluation value is larger than a first preset value, determining that the water area state of the monitoring area under the time node is an abnormal state;
when the ripple evaluation value is larger than a second preset value, determining that the water area state of the monitoring area under the time node is an abnormal state;
When the noise evaluation value is larger than a third preset value, determining that the water area state of the monitoring area under the time node is an abnormal state;
When the vortex evaluation value is smaller than or equal to a first preset value, the ripple evaluation value is smaller than or equal to a second preset value and the noise evaluation value is smaller than or equal to a third preset value, summing the vortex evaluation value, the ripple evaluation value and the noise evaluation value to obtain a comprehensive evaluation value;
And when the comprehensive evaluation value is larger than a fourth preset value, determining that the water area state of the monitoring area under the time node is an abnormal state.
Optionally, in the method according to the present invention, the acquiring management end obtains the actual determining data based on the state of any node corresponding to any time sent by any monitoring area, and compares the water area state corresponding to the monitoring area with the actual determining data of the state;
If the state actually determines that the data is in a normal state and the water area state is in an abnormal state, performing reduction training on the vortex weight, the ripple weight and the noise weight;
if the state actually determines that the data is in an abnormal state and the water area state is in a normal state, training the vortex weight, the ripple weight and the noise weight in an increasing way;
The trained vortex weight, ripple weight, and noise weight are obtained by the following formula:
Wherein, Weighting eddiesThe number of times of training is increased,Weighting eddiesIs used for the training of the constant value of (a),Weighting eddiesThe number of times of training is reduced and,In order for the trained vortex weights to be applied,Is the ripple weightThe number of times of training is increased,Is the ripple weightIs used for the training of the constant value of (a),Is weighted by ripple coefficientThe number of times of training is reduced and,For the trained ripple weight,Weighting noiseThe number of times of training is increased,Weighting noiseIs used for the training of the constant value of (a),Weighting noiseThe number of times of training is reduced and,Is the weight of the vortex after training.
According to yet another aspect of the present invention, there is provided a side sweep radar data quality control system comprising:
The grid processing module is configured to perform grid processing on a water area to be monitored, and each obtained grid point position is respectively determined to be each monitoring area;
the data acquisition module is configured to acquire data of each monitoring area for a preset time to obtain current point cloud data, current image data and current flow rate data of the corresponding monitoring area;
The state determining module is configured to determine a water area state of the water area corresponding to the monitoring area within the preset time based on the current point cloud data and the current image data, and determine an abnormal time corresponding to the abnormal state;
The supplementary acquisition module is configured to acquire data of supplementary time with the same duration as the abnormal time, and obtain supplementary point cloud data, supplementary image data and supplementary flow rate data corresponding to the monitoring area;
A data replacement module configured to replace the supplementary flow rate data with an abnormal flow rate portion corresponding to the abnormal time in the current flow rate data in response to determining that the water area corresponding to the monitoring area is in a normal state for the supplementary time based on the supplementary point cloud data, the supplementary image data;
And the flow rate determining module is configured to determine the water area flow rate corresponding to the water area to be monitored based on each current flow rate data corresponding to each monitoring area.
According to the scheme of the invention, through carrying out grating treatment on the water area to be monitored, respectively carrying out corresponding data acquisition based on the obtained monitoring areas, carrying out statistical calculation on the current flow velocity data corresponding to each monitoring area, obtaining the water area flow velocity corresponding to the water area to be monitored, finely realizing flow velocity measurement of the water area to be monitored, improving corresponding measurement accuracy, and simultaneously, when carrying out flow velocity measurement on each monitoring area, carrying out corresponding determination on the water area state of each monitoring area based on vortex dimension, ripple dimension and noise dimension, and when the water area state of the corresponding monitoring area is in an abnormal state, carrying out corresponding supplementary acquisition on the monitoring area, thereby eliminating interference on flow velocity measurement due to natural factors or artificial factors and further improving corresponding measurement accuracy.
Drawings
FIG. 1 illustrates a flow chart of a method of side sweep radar data quality control according to one embodiment of the present invention;
Fig. 2 shows a schematic diagram of flow rate measurement of a monitored area by the scanning radar in the present embodiment;
fig. 3 shows a system block diagram of a side-scan flow radar data quality control system according to another embodiment of the present invention.
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.
The water flow rate is generally referred to as the velocity of the water flow and can be expressed in different units, such as meters per second (m/s) or centimeters per second (cm/s). The flow rate may vary depending on the nature of the body of water flow and the environmental conditions, such as the flow rate in rivers, streams, oceans.
In the prior art, a professional scanning radar is often used for measuring the flow rate of the water area to finish the corresponding measuring process, but the water area is always in different water area states due to different natural factors or human factors, for example, when a large vortex area, a large ripple area and a large noise area exist in the water area, the measuring accuracy of the flow rate of the water area can be influenced, the accurate measurement of the flow rate of the water area can not be realized, and the corresponding measuring efficiency is reduced.
The proposal of the invention is provided for solving the problems in the prior art. One embodiment of the present invention provides a method for controlling the quality of data of a side-scan flow radar, which can be performed in a computing device, wherein the computing device can be understood as a terminal having a data processing function, such as a mobile phone or a computer.
As shown in fig. 1, the objective of the present embodiment is to implement a method for controlling the quality of data of a side-scan radar, which starts in step S102, and includes the following steps:
And carrying out rasterization treatment on the water area to be monitored, and respectively determining each obtained grille area as each monitoring area.
For example, in this embodiment, the flow rate measurement of the water area is generally performed by using a corresponding scanning radar, as shown in fig. 2, the scanning radar forms a corresponding scanning surface when scanning, and the scanning radar is controlled to move the scanning surface to the water surface of the water area to be monitored, so as to perform the flow rate scanning for a preset time on the water surface area overlapped with the scanning surface, so as to determine the water area flow rate of the water surface area; in a real situation, since the water area to be monitored generally has a larger water area, that is, the water area may be far larger than the area corresponding to the scanning surface, the water area to be monitored is subjected to corresponding grating treatment to obtain a plurality of corresponding grating areas, and the plurality of grating areas are respectively determined as each monitoring area, so that in a subsequent step, corresponding flow velocity measurement can be performed based on each monitoring area, and then the water area flow velocity corresponding to the water area to be monitored is determined according to the measurement result of each monitoring area, thereby improving the corresponding measurement accuracy.
In step S104, the following are included:
And acquiring data of each monitoring area for a preset time to obtain current point cloud data, current image data and current flow rate data of the corresponding monitoring area.
It should be noted that, in the embodiment, when the scanning radar is used to measure the flow rate of the water area to be monitored, the bad water surface condition of the water area to be monitored may affect the corresponding measurement accuracy, for example, when the corresponding large ripple, large vortex and large floater exist on the water surface, the measurement accuracy of the water area to be monitored is correspondingly reduced, and in order to reduce the influence of the bad water surface conditions on the flow rate measurement, the bad water surface conditions need to be identified correspondingly; it is known that when no wave or vortex appears on the water surface in any monitoring area, the area height corresponding to the monitoring area should be kept uniform, i.e. in a horizontal state, and when the corresponding wave or vortex appears on the water surface, the area height corresponding to the monitoring area may change correspondingly, i.e. when the wave appears, the area height corresponding to the position of the wave increases, and when the vortex appears, the area height corresponding to the position of the vortex decreases, based on the characteristic, the current point position data can be correspondingly acquired while the flow rate of the monitoring area is measured to determine whether the corresponding wave or vortex exists, and for the floating object, the current image data can be correspondingly acquired to further determine whether the corresponding monitoring area has larger floating object according to the current image data.
Further, in this embodiment, the foregoing "performing data acquisition for a preset time for each monitoring area to obtain current point cloud data, current image data, and current flow rate data of the corresponding monitoring area" may further include the following steps:
Triggering a point cloud radar, an image radar and a flow rate radar to acquire data for maintaining preset time in each monitoring area based on preset acquisition frequency;
Determining acquisition times based on the preset acquisition frequency and the preset time, and acquiring secondary point cloud data, secondary image data and secondary flow rate data corresponding to each acquisition time respectively;
Grouping each secondary point cloud data, each secondary image data and each secondary flow rate data based on each time node corresponding to each acquisition time respectively to obtain each node data group of the secondary point cloud data, the secondary image data and the secondary flow rate data corresponding to the same time node respectively;
and determining each node data set as current point cloud data, current image data and current flow rate data corresponding to the monitoring area.
For example, in this embodiment, the current point cloud data may be obtained based on the corresponding point cloud radar, the current image data may be obtained based on the corresponding image radar, the current flow rate data may be obtained based on the corresponding flow rate radar, and since the accuracy of the corresponding flow rate measurement is guaranteed when the data is collected in the water area to be monitored for a corresponding preset time, and the water generally has the corresponding fluidity, for the same monitoring area, different water surface conditions may be corresponding at different time instants, for example, when the preset time is 30s, only the corresponding larger ripple appears in 15s, and the corresponding larger vortex and float appear in 16s, and only the smaller float appears in 17s, in this case, it may be seen that the accuracy of the flow rate measurement for 17s is higher, and in order to be able to divide the point cloud data, the image data and the flow rate data at the same time instant, so as to determine the corresponding water surface state at any area may be realized by:
firstly, triggering a point cloud radar, an image radar and a flow rate radar to respectively acquire data for maintaining preset time for each monitoring area based on the same preset acquisition frequency;
Then, the acquisition times corresponding to the preset time can be determined based on the preset acquisition frequency, for example, when the preset time is 30s and the preset acquisition frequency is 2 s/time, the corresponding acquisition times are 15 times, and after the corresponding acquisition times are determined, the point cloud radar, the image radar and the flow rate radar can be controlled to acquire data of the monitoring area in the corresponding acquisition times at the same time so as to obtain secondary point cloud data, secondary image data and secondary flow rate data corresponding to the same acquisition times;
Then, the secondary point cloud data, the secondary image data and the secondary flow rate data corresponding to the same acquisition times can be divided into the same node data set to obtain each node data set corresponding to the monitoring area;
and finally, determining each node data set as current point cloud data, current image data and current flow rate data obtained after data acquisition of the corresponding monitoring area in a preset time.
In step S106, the following are included:
And determining the water area state of the water area corresponding to the monitoring area within the preset time based on the current point cloud data and the current image data, and determining the abnormal time corresponding to the abnormal state.
For example, in this embodiment, after the corresponding current point cloud data and the corresponding current image data are obtained, the water area state of the monitoring area within the preset time may be corresponding to the two data, and according to the above, when at least one of the larger ripples, the eddies or the floaters occurs in the water surface condition of any monitoring water area, the measurement accuracy of the flow rate measurement corresponding to the time node is lower, that is, the water area state corresponding to the time node may be determined as the abnormal state, and in order to improve the corresponding measurement accuracy, it is necessary to locate the time nodes and determine the abnormal time of the corresponding monitoring area in the abnormal state, so as to perform the supplementary monitoring on the monitoring area with the same duration as the abnormal time in the subsequent steps.
Further, in this embodiment, the above-mentioned "determining the water area state of the water area corresponding to the monitoring area within the preset time based on the current point cloud data and the current image data, and determining the abnormal time corresponding to the abnormal state" may further include the following steps:
Establishing a two-dimensional plane based on a monitoring area, and establishing point clouds of each area corresponding to each point cloud height on the two-dimensional plane based on secondary point cloud data in any node data set;
Acquiring the area height corresponding to the monitoring area, respectively comparing the cloud height of each point with the area height, and determining a first morphological attribute of the monitoring area under a time node corresponding to the node data set based on a comparison result;
retrieving secondary image data in the same node data set as the secondary point cloud data, and determining a second shape attribute of the monitoring area under a time node corresponding to the node data set based on the secondary image data;
Determining a water area state of the monitoring area under the time node based on the first morphological attribute and the second morphological attribute corresponding to the monitoring area;
And summing all the time nodes corresponding to the abnormal state to obtain the abnormal time corresponding to the abnormal state.
For example, in the present embodiment, the acquisition of the abnormal time may be specifically realized by the following scheme:
Firstly, a corresponding two-dimensional plane can be established based on a horizontal plane of a monitoring area in a static state without external influence, and point clouds of each area corresponding to each point cloud height are established on the two-dimensional plane based on secondary point cloud data in a node data set; it should be noted that, because the point cloud radar may acquire height information of the corresponding point cloud, that is, acquire the height information based on the generated regional point cloud in the corresponding monitoring region, so as to obtain corresponding secondary point cloud data, and further generate each regional point cloud corresponding to the height of each point cloud on the two-dimensional plane according to the secondary point cloud data;
Then, in order to determine the waves and/or eddies located in the monitoring area based on each area point cloud, it is necessary to acquire an area height corresponding to the monitoring area, where the area height may be understood as a height of a horizontal plane of the monitoring area in a stationary state without external influence; because the point cloud heights at the areas corresponding to the corrugated areas are larger than the area heights and the point cloud heights at the areas corresponding to the vortexes are smaller than the area heights, based on the characteristics, the point cloud heights of the point clouds of each area in the same monitoring area can be respectively compared with the area heights, and the first morphological attribute of the monitoring area under the time node of the corresponding node data set can be determined based on the corresponding comparison result;
Then, in order to be able to determine the float located in the monitoring area, secondary image data located in the same node data set as the secondary point cloud data may be retrieved, and a second shape attribute of the monitoring area under the time node of the corresponding node data set may be determined based on the corresponding secondary image data;
then, after determining the corresponding first morphological attribute and second morphological attribute, determining the water area state of the monitoring area under the time node based on the first morphological attribute and the second morphological attribute;
And finally, summing the time nodes corresponding to the abnormal states, thereby obtaining the abnormal time corresponding to the abnormal states.
It should be noted that, in this embodiment, the first shape attribute is determined based on whether there is a corresponding ripple or vortex in the monitoring area, that is, by the point cloud height, and the second shape attribute is determined based on whether there is a corresponding float in the monitoring area, that is, by image recognition.
Further, in this embodiment, the above-mentioned "determining the first morphological attribute of the monitoring area under the time node corresponding to the node data set based on the comparison result" may further include the following steps:
Determining all the regional point clouds with the corresponding point cloud heights smaller than the regional heights as vortex point cloud groups according to the comparison result, and determining all the regional point clouds with the corresponding point cloud heights larger than the regional heights as ripple point cloud groups according to the comparison result;
performing point cloud connection on each area point cloud in adjacent position relation in the vortex point cloud group in the two-dimensional plane to obtain each vortex area in the two-dimensional plane;
performing point cloud connection on each area point cloud in adjacent position relation in the corrugated point cloud group in the two-dimensional plane to obtain each corrugated area in the two-dimensional plane;
Carrying out merging processing based on surrounding relation on each vortex area based on a preset merging strategy;
After the merging processing, determining the first area size and the first area number corresponding to each vortex area, and the second area size and the second area number corresponding to each ripple area as first morphological attributes of the monitoring area under the time node corresponding to the node data set.
For example, in this embodiment, in the monitoring area, all area point clouds with a corresponding point cloud height smaller than the area height may be determined as a vortex point cloud group, and all area point clouds with a corresponding point cloud height greater than the area height may be determined as a vortex point cloud group, by performing point cloud connection on each area point cloud located in the vortex point cloud group and in an adjacent position relationship, each vortex area located in the two-dimensional plane may be obtained, and similarly, each area point cloud located in the corrugated point cloud group and in an adjacent position relationship may be performed, each corrugated area located in the two-dimensional plane may be obtained, where, since the corrugated areas and the vortex areas may have an array state, that is, there may be a corresponding surrounding relationship between each corrugated area, so, in order to be able to determine the number of corrugated areas and vortex areas respectively, it is necessary to determine whether each corrugated area and each area may be combined respectively based on a preset combining strategy, after the corresponding combining process is completed, each corrugated area may be obtained, and the first size of each area and the corresponding number of vortex areas and the second size of each corrugated area may be further determined as the first node size and the second size of the corresponding corrugated area.
Here, the above-mentioned "merging processing based on the surrounding relation for each vortex region based on the preset merging policy" may further include the following steps:
carrying out coordinated processing on the two-dimensional plane to obtain vortex coordinate points respectively positioned in each vortex area, and obtaining vortex coordinate groups respectively corresponding to the vortex areas;
And comparing the vortex coordinate sets in pairs, determining whether the vortex coordinate sets have a surrounding relation or not based on a comparison result, and merging the vortex areas corresponding to the vortex coordinate sets with the surrounding relation into one vortex area.
For example, in the present embodiment, in order to determine whether or not there is a corresponding surrounding relationship between the vortex regions, this may be achieved by:
Firstly, carrying out coordinated processing on two-dimensional coordinates so as to obtain vortex coordinate points forming each vortex region, and respectively forming vortex coordinate groups corresponding to the vortex regions;
Then, based on the characteristics of the vortexes, the vortexes are generally spiral water vortexes, namely, the vortexes are radiated outwards in an array mode through the center points of the vortexes, so that the vortexes can be compared in pairs, whether the vortexes have an enclosing relation or not is determined based on the comparison result, when at least two vortexes have the enclosing relation, at least two vortexes which have the same vortexes can be determined, and at the moment, the vortexes with the enclosing relation can be combined into one vortexes, and therefore the combined screening of the vortexes is completed, and therefore the first area number and the first area size of the vortexes can be accurately obtained in the subsequent method steps.
Here, after the determination of the first morphological attribute of the monitoring area within the preset time is completed based on the corresponding current point cloud data, the second morphological attribute of the monitoring area within the preset time may be determined based on the corresponding current image data, that is, in this embodiment, "determining the second morphological attribute of the monitoring area under the time node corresponding to the node data set based on the secondary image data" may further include the following steps:
Performing binarization processing on the secondary image data to obtain a binarized image, wherein the binarized image comprises all water area pixel points corresponding to first pixel values and all noise pixel points corresponding to second pixel values respectively;
Pixel point connection is carried out on each noise pixel point in adjacent position relation in the secondary image data, so that each noise region in the secondary image data is obtained;
And determining the size and the number of the third areas corresponding to the noise areas as second shape attributes of the monitoring areas under the time nodes corresponding to the node data sets.
For example, in this embodiment, in order to determine the second shape attribute under the time node of the corresponding node data set, the floaters located in the monitored area may be identified based on the image identification method, where the area formed by the floaters may be referred to as a noise area, and the floaters may include leaves, branches, or other objects;
In order to quickly identify the floating objects floating on the monitoring area, binarization processing can be performed on the acquired secondary image data to obtain corresponding binarized images; because the pixel values of all the water area pixel points corresponding to the monitoring area are the same, other pixel points with different pixel values with the water area pixel points are noise pixel points corresponding to floaters, the corresponding water area pixel points can be respectively converted into a first pixel value and the corresponding noise pixel points can be respectively converted into a second pixel value through the binarization processing of the secondary image data, so that the identification of floaters is completed, compared with the existing mode of adopting an image identification model for identification, the method has lower data processing amount, and the corresponding identification precision can be ensured;
After the determination of each noise pixel point is completed, further connecting the noise pixel points in adjacent position relation to obtain each noise region in the secondary image data, and determining the size of each third region and the number of the third regions corresponding to each noise region as a second shape attribute of the monitoring region under the time node of the corresponding node data set.
For example, in this embodiment, after the determination of the first morphological attribute and the second morphological attribute is completed, the water area state of the corresponding time node may be determined based on the first morphological attribute and the second morphological attribute of the corresponding monitoring area, where specific method steps may include:
summing calculation is carried out based on the sizes of all the first areas to obtain a first size total value, and ratio calculation is carried out based on the first size total value and the monitoring size of the corresponding monitoring area to obtain a first size ratio;
Respectively carrying out normalization processing on the first size ratio and the first area number to obtain a first size coefficient and a first quantity coefficient, and carrying out summation calculation on the basis of the first size coefficient and the first quantity coefficient to obtain a vortex coefficient;
Summing calculation is carried out based on the sizes of the second areas to obtain a second size total value, and ratio calculation is carried out based on the second size total value and the monitoring sizes of the corresponding monitoring areas to obtain a second size ratio;
respectively carrying out normalization processing on the second size ratio and the second area number to obtain a second size coefficient and a second number coefficient, and carrying out summation calculation on the basis of the second size coefficient and the second number coefficient to obtain a ripple coefficient;
summing calculation is carried out based on the sizes of all the third areas to obtain a third size total value, and ratio calculation is carried out based on the third size total value and the monitoring size of the corresponding monitoring area to obtain a third size ratio;
Respectively carrying out normalization processing on the third size ratio and the third area number to obtain a third size coefficient and a third number coefficient, and carrying out summation calculation based on the third size coefficient and the third number coefficient to obtain a noise coefficient;
And determining the water area state of the monitoring area under the time node based on the vortex coefficient, the ripple coefficient and the noise coefficient.
For example, in the present embodiment, the corresponding swirl coefficient, ripple coefficient, and noise coefficient can be determined based on the obtained first morphology attribute, respectively, where the determination of the swirl coefficient includes the following processes:
Firstly, acquiring the monitoring size of a corresponding monitoring area, and simultaneously carrying out summation calculation on the sizes of the first areas corresponding to the vortex areas to obtain a corresponding first size total value;
Then, calculating the ratio of the first dimension total value to the monitoring dimension to obtain a corresponding first dimension ratio;
and finally, carrying out normalization processing on the first size proportion and the first area number to obtain a corresponding first size coefficient and a first number coefficient, and obtaining the vortex coefficient of the corresponding vortex area through summation calculation.
Further, the determination of the ripple factor includes the following process:
Firstly, acquiring the monitoring size of a corresponding monitoring area, and simultaneously carrying out summation calculation on the sizes of the second areas corresponding to the corrugated areas to obtain a corresponding total value of the second sizes;
Then, calculating the ratio of the second dimension total value to the monitoring dimension to obtain a corresponding second dimension ratio;
And finally, carrying out normalization processing on the second size proportion and the second area number to obtain a corresponding second size coefficient and a second number coefficient, and obtaining the ripple coefficient of the corresponding ripple area through summation calculation.
Further, the determination of the noise figure includes the following process:
Firstly, acquiring the monitoring size of a corresponding monitoring area, and simultaneously carrying out summation calculation on the sizes of all third areas corresponding to all noise areas to obtain a corresponding third size total value;
Then, calculating the ratio of the third dimension total value to the monitoring dimension to obtain a corresponding third dimension ratio;
And finally, carrying out normalization processing on the third size proportion and the third region number to obtain a corresponding third size coefficient and a third number coefficient, and obtaining the noise coefficient of the corresponding noise region through summation calculation.
For example, in this embodiment, since when a large ripple, a vortex or a floater appears in the monitoring area at a certain time node, the corresponding measurement accuracy is affected, in this case, the water area state of the monitoring area under the corresponding time node needs to be determined based on the obtained vortex coefficient, the obtained ripple coefficient and the obtained noise coefficient, and the specific method process may include the following steps:
invoking vortex weight, ripple weight and noise weight;
performing product calculation on the vortex coefficient and the vortex weight to obtain a vortex evaluation value;
performing product calculation on the ripple coefficient and the ripple weight to obtain a ripple evaluation value;
performing product calculation on the noise coefficient and the noise weight to obtain a noise evaluation value;
When the vortex evaluation value is larger than a first preset value, determining that the water area state of the monitoring area under the time node is an abnormal state;
when the ripple evaluation value is larger than a second preset value, determining that the water area state of the monitoring area under the time node is an abnormal state;
When the noise evaluation value is larger than a third preset value, determining that the water area state of the monitoring area under the time node is an abnormal state;
When the vortex evaluation value is smaller than or equal to a first preset value, the ripple evaluation value is smaller than or equal to a second preset value and the noise evaluation value is smaller than or equal to a third preset value, summing the vortex evaluation value, the ripple evaluation value and the noise evaluation value to obtain a comprehensive evaluation value;
And when the comprehensive evaluation value is larger than a fourth preset value, determining that the water area state of the monitoring area under the time node is an abnormal state.
For example, in this embodiment, by taking the corresponding vortex weight, ripple weight, and noise weight, and multiplying each weight by each corresponding coefficient to obtain the corresponding vortex evaluation value, ripple evaluation value, and noise evaluation value, it can be known through the above calculation process that the larger the vortex evaluation value, the more the corresponding vortex size and number of vortices, the larger the corresponding ripple size and number of ripples, the larger the noise evaluation value, the more the corresponding noise size and number of noises, based on which it can be determined that when any evaluation value is greater than the corresponding preset value, the water area state under the time node of the monitoring area is determined as an abnormal state, and when all evaluation values are less than or equal to the corresponding preset value, in order to further determine the water area state, it is necessary to determine the comparison result between the integrated evaluation value obtained by summing the vortex evaluation value, the ripple evaluation value, and the noise evaluation value and the corresponding preset value, and when the integrated evaluation value is greater than the corresponding preset value, the water area state can also be indicated as the abnormal state under the time node of the monitoring area.
In this embodiment, the weights and the preset values may be set by the corresponding management end, and the specific numerical values are not limited in this embodiment.
Further, in this embodiment, since the determination of the water area state is determined based on the corresponding numerical comparison result, there may be a certain deviation between the water area state determined by the numerical comparison result and the actual situation, and in order to improve the corresponding determination accuracy, the corresponding weights may be updated and trained in real time, so as to implement the process of updating and iterating the weights, and further, the corresponding determination accuracy may be continuously improved.
Here, the update iteration for each weight can be achieved by the following scheme steps:
Acquiring actual state determination data of a management end under any time node corresponding to any monitoring area, and comparing the water area state corresponding to the monitoring area with the actual state determination data;
If the state actually determines that the data is in a normal state and the water area state is in an abnormal state, performing reduction training on the vortex weight, the ripple weight and the noise weight;
if the state actually determines that the data is in an abnormal state and the water area state is in a normal state, training the vortex weight, the ripple weight and the noise weight in an increasing way;
The trained vortex weight, ripple weight, and noise weight are obtained by the following formula:
Wherein, Weighting eddiesThe number of times of training is increased,Weighting eddiesIs used for the training of the constant value of (a),Weighting eddiesThe number of times of training is reduced and,In order for the trained vortex weights to be applied,Is the ripple weightThe number of times of training is increased,Is the ripple weightIs used for the training of the constant value of (a),Is weighted by ripple coefficientThe number of times of training is reduced and,For the trained ripple weight,Weighting noiseThe number of times of training is increased,Weighting noiseIs used for the training of the constant value of (a),Weighting noiseThe number of times of training is reduced and,Is the weight of the vortex after training.
In step S108, the following are included:
And acquiring data of the supplementing time with the same duration as the abnormal time, and obtaining supplementing point cloud data, supplementing image data and supplementing flow rate data corresponding to the monitoring area.
For example, in this embodiment, by counting the time nodes in the abnormal state, it is possible to determine the abnormal time existing in the preset time, and since the abnormal time corresponds to each time node affecting the measurement accuracy, it is necessary to remove each corresponding time node and perform data acquisition of the complementary time having the same duration as the abnormal time, thereby obtaining the complementary point cloud data, the complementary image data, and the complementary flow rate data corresponding to the monitoring area.
In step S110, the following are included:
and replacing the abnormal flow rate part corresponding to the abnormal time in the current flow rate data in response to the fact that the water area corresponding to the monitoring area is determined to be in a normal state in the supplementing time based on the supplementing point cloud data and the supplementing image data.
For example, since the supplementary point cloud data and the supplementary image data are obtained based on the supplementation, the corresponding supplementary flow rate data needs to be ensured to be in a normal state so as to replace the abnormal flow rate part corresponding to the abnormal time in the current flow rate data, thereby improving the corresponding measurement accuracy, and if the corresponding supplementary point cloud data and the supplementary image data correspond to the abnormal state, the corresponding supplementation needs to be performed again so as to ensure that the current flow rate data after the supplementation has higher measurement accuracy.
In step S112, the following are included:
And determining the water area flow rate corresponding to the water area to be monitored based on the current flow rate data corresponding to each monitoring area.
For example, after the data supplementation for each monitoring area is completed, the water area flow rate corresponding to the water area to be monitored can be determined according to the current flow rate data corresponding to each monitoring area, wherein the water area flow rate obtaining mode can be obtained based on mean value calculation, that is, when the monitoring areas comprise three monitoring areas, the current flow rate data corresponding to the three monitoring areas can be subjected to mean value calculation, and the corresponding mean value result can be determined to be the water area flow rate.
According to the scheme of the embodiment, through carrying out grating treatment on the water area to be monitored, respectively carrying out corresponding data acquisition on the basis of the obtained monitoring areas, carrying out statistical calculation on the current flow velocity data corresponding to each monitoring area, the water area flow velocity corresponding to the water area to be monitored can be obtained, flow velocity measurement of the water area to be monitored is realized in a refined mode, corresponding measurement accuracy is improved, meanwhile, when the flow velocity measurement of each monitoring area is carried out, the water area state of each monitoring area is correspondingly determined on the basis of vortex dimension, ripple dimension and noise dimension, when the water area state of the corresponding monitoring area is in an abnormal state, corresponding supplementary acquisition is needed on the monitoring area, and therefore interference of natural factors or artificial factors on flow velocity measurement can be eliminated, and corresponding measurement accuracy is further improved.
Another embodiment of the present invention provides a system for controlling data quality of a side-scan flow radar, and fig. 3 is a corresponding system block diagram, where the system includes:
The grid processing module is configured to perform grid processing on a water area to be monitored, and each obtained grid point position is respectively determined to be each monitoring area;
the data acquisition module is configured to acquire data of each monitoring area for a preset time to obtain current point cloud data, current image data and current flow rate data of the corresponding monitoring area;
The state determining module is configured to determine a water area state of the water area corresponding to the monitoring area within the preset time based on the current point cloud data and the current image data, and determine an abnormal time corresponding to the abnormal state;
The supplementary acquisition module is configured to acquire data of supplementary time with the same duration as the abnormal time, and obtain supplementary point cloud data, supplementary image data and supplementary flow rate data corresponding to the monitoring area;
A data replacement module configured to replace the supplementary flow rate data with an abnormal flow rate portion corresponding to the abnormal time in the current flow rate data in response to determining that the water area corresponding to the monitoring area is in a normal state for the supplementary time based on the supplementary point cloud data, the supplementary image data;
And the flow rate determining module is configured to determine the water area flow rate corresponding to the water area to be monitored based on each current flow rate data corresponding to each monitoring area.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with examples of the invention. The required structure for a construction of such a system is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It should be appreciated that the teachings of the present invention as described herein may be implemented in a variety of programming languages and that the foregoing description of specific languages is provided for disclosure of preferred embodiments of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention 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 invention, various features of the invention 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.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.
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.
Furthermore, those skilled in the art will appreciate that while some embodiments described 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 invention and form different embodiments.
Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of apparatus for performing the functions performed by the elements for the purpose of practicing the invention.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter.
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| CN116125459A (en) * | 2023-02-03 | 2023-05-16 | 水利部南京水利水文自动化研究所 | An Effective Velocity Unit Determination Method for Side Scan Radar |
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