CN118274786B - Buried pipeline settlement monitoring method and system based on Beidou coordinates - Google Patents
Buried pipeline settlement monitoring method and system based on Beidou coordinates Download PDFInfo
- Publication number
- CN118274786B CN118274786B CN202410694879.8A CN202410694879A CN118274786B CN 118274786 B CN118274786 B CN 118274786B CN 202410694879 A CN202410694879 A CN 202410694879A CN 118274786 B CN118274786 B CN 118274786B
- Authority
- CN
- China
- Prior art keywords
- buried pipeline
- coordinate prediction
- sample data
- representation
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C5/00—Measuring height; Measuring distances transverse to line of sight; Levelling between separated points; Surveyors' levels
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/004—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring coordinates of points
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Bioethics (AREA)
- Evolutionary Biology (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Software Systems (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Geophysics And Detection Of Objects (AREA)
- Image Processing (AREA)
Abstract
The invention provides a buried pipeline settlement monitoring method and system based on Beidou coordinates, and relates to the technical field of pipeline monitoring. Performing error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data, and forming a training data set; analyzing the training data set by using a coordinate prediction model to obtain a coordinate prediction result; and (5) carrying out encryption treatment on the coordinate prediction result to obtain a buried pipeline sedimentation monitoring result. The invention solves the problem that the sedimentation of the buried pipeline is difficult to monitor.
Description
Technical Field
The specification relates to the technical field of pipeline monitoring, in particular to a buried pipeline settlement monitoring method and system based on Beidou coordinates.
Background
The buried pipeline is used as a common carrier of important resources such as oil gas, and the like, and the utilization rate is higher and higher along with the economic development. And geological disasters such as landslide, collapse, mud-rock flow, sedimentation, potential unstable slopes, water damage and the like which are easy to exist along the pipeline easily cause damage and position change to the buried pipeline. The existing common buried pipeline monitoring method is to monitor by using a surrounding positioning device, but the problems of insufficient precision, high price, delay in time and the like still exist.
Disclosure of Invention
Aiming at the defects in the prior art, the buried pipeline settlement monitoring method and system based on Beidou coordinates solve the problem that the settlement of the buried pipeline is difficult to monitor.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a buried pipeline settlement monitoring method based on Beidou coordinates comprises the following steps:
s1: marking the buried pipeline to obtain Beidou coordinates of the marking position of the buried pipeline;
S2: performing error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data, and forming a training data set;
S3: analyzing the training data set by using a coordinate prediction model to obtain a coordinate prediction result; the coordinate prediction model is obtained through training;
S4: and carrying out encryption treatment on the coordinate prediction result to obtain a buried pipeline settlement monitoring result.
The beneficial effects of the invention are as follows: the processor performs error correction on the Beidou coordinates of the obtained buried pipeline marking position, analyzes the Beidou coordinates by utilizing a coordinate prediction model to obtain a coordinate prediction result, and obtains a buried pipeline sedimentation monitoring result by an encryption method. (1) By the error correction method, errors generated by time-dependent changes of the coordinates of the buried pipeline can be reduced, and the accuracy of position monitoring is improved; (2) By utilizing the coordinate prediction model, the real-time performance and accuracy of the position monitoring of the buried pipeline can be greatly improved, the method is applicable to the monitoring of the buried pipeline in a deeper layer, and meanwhile, a small amount of training data can be used for obtaining a high-performance coordinate prediction model in the model training mode; (3) By encrypting the coordinate prediction result, the safety of the buried pipeline settlement monitoring result in the transmission process can be improved.
Further, the step S2 includes:
analyzing and correcting errors of Beidou coordinates of the buried pipeline marking position by utilizing a discrete filtering algorithm to obtain sample data, and forming a training data set, wherein the expression of the sample data is as follows:
；
；
wherein, Representation ofThe sample data of the time instant is obtained,Representation ofThe Beidou coordinates of the moment,Representation ofRelative time of dayThe coordinate correction parameters of the moment of time,Representation ofAn observation matrix of the moment of time,Representation ofA state estimate of the time of day,Representation ofThe noise is measured randomly at the moment in time,Representation ofRelative time of dayThe angle change data of the time of day,Representation ofA state estimate of the time of day,Representation ofThe noise input matrix of the moment in time,Representation ofRandom process noise at time.
The method has the further beneficial effects that the processor can reduce errors generated by the change of the coordinates of the buried pipeline along with the time through error correction, and the accuracy of position monitoring is improved.
Further, the step S3 includes:
S310: performing feature extraction on the training data set through a feature extraction layer to construct image feature data;
s320: analyzing the image characteristic data through the viewpoint generating layer to obtain adjustment parameters;
S330: fusing the image characteristic data and the adjustment parameters through a fusion layer to obtain a coordinate prediction result; the feature extraction layer, the viewpoint generation layer and the fusion layer belong to a coordinate prediction model.
Further, the expression of the image characteristic data is specifically:
；
wherein, Represent the firstImage characteristic data of the individual sample data,Represent the firstThe rotational component of the individual sample data,Represent the firstThe translational component of the individual sample data,Representing the transposed matrix of the zero matrix.
Further, the S320 includes:
s321: based on the translation components, the distance between the translation components is obtained by calculation:
；
wherein, Representing the distance between the translational components,Representing the euclidean distance and,Represent the firstThe translational component of the individual sample data,Represent the firstThe translational component of the individual sample data,Representing the number of sample data;
S322: based on the distance between the translation components, uniformly sampling the translation components with the distance smaller than a threshold value to obtain translation transformation parameters:
；
wherein, The translation transformation parameters are represented as such,A number of sample data representing a distance of the translation component less than a threshold;
S323: based on the rotation component, rotation transformation parameters are obtained through calculation:
；
；
wherein, The rotation transformation parameters are represented by a set of values,The weight parameter is represented by a number of weight parameters,Represent the firstThe rotation parameters corresponding to the data of the individual samples,Represent the firstThe rotation parameters corresponding to the data of the individual samples,Representing the rotation parameters derived based on the rotation components,The norms of the rotation parameters are represented,、、AndFour parameter values corresponding to the rotation parameters are respectively represented;
s324: combining the translational transformation parameters and the rotational transformation parameters to obtain the adjustment parameters 。
Further, the expression of the coordinate prediction result is specifically:
；
wherein, Represent the firstThe result of the coordinate prediction of the individual sample data,The rendering function is represented as a function of the rendering,Indicating that the adjustment parameters are to be used,Represent the firstImage characteristic data of the individual sample data.
Further, the concrete expression of the buried pipeline sedimentation monitoring result is as follows:
；
；
；
wherein, The result of the buried pipeline settlement monitoring is shown,Representation ofIs used for the encryption result of (a),Representation ofIs used for the encryption result of (a),The average coefficient representing the result of the coordinate prediction,Representing the encryption key(s),And a difference coefficient representing the coordinate prediction result.
The method has the further beneficial effects that the processor uses the encryption key to encrypt the coordinate prediction result to obtain the buried pipeline settlement monitoring result. By the method, the encryption efficiency and the security of the sedimentation monitoring result of the buried pipeline can be greatly improved.
One or more embodiments of the present specification provide a buried pipeline settlement monitoring system based on Beidou coordinates, including:
the acquisition module is used for marking the buried pipeline and acquiring Beidou coordinates of the marking position of the buried pipeline;
The correction module is used for carrying out error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data and form a training data set;
the analysis module is used for analyzing the training data set by utilizing a coordinate prediction model to obtain a coordinate prediction result; the coordinate prediction model is obtained through training;
And the encryption module is used for carrying out encryption processing on the coordinate prediction result to obtain a buried pipeline settlement monitoring result.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic block diagram of a buried pipeline settlement monitoring system based on Beidou coordinates according to some embodiments of the present description;
fig. 2 is an exemplary flow chart of a buried pipeline settlement monitoring method based on Beidou coordinates, according to some embodiments of the present description.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
Example 1
Fig. 1 is a schematic block diagram of a buried pipeline settlement monitoring system based on beidou coordinates according to some embodiments of the present description.
In some embodiments, the Beidou coordinate-based buried pipeline settlement monitoring system can include an acquisition module, a correction module, an analysis module and an encryption module.
The acquisition module is used for marking the buried pipeline and acquiring Beidou coordinates of the marking position of the buried pipeline; for more details on Beidou coordinates of buried pipe marking locations, see FIG. 2 and its associated description.
The correction module is used for carrying out error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data and form a training data set; for more details on the training data set, see fig. 2 and its associated description.
The analysis module is used for analyzing the training data set by utilizing a coordinate prediction model to obtain a coordinate prediction result; for more details on the coordinate prediction results, see fig. 2 and its associated description.
The encryption module is used for carrying out encryption processing on the coordinate prediction result to obtain a buried pipeline settlement monitoring result; for more details on buried pipeline settlement monitoring results see figure 2 and its associated description.
In some embodiments, the Beidou coordinate-based buried pipeline settlement monitoring system may be used to perform a Beidou coordinate-based buried pipeline settlement monitoring method comprising: s1: marking the buried pipeline to obtain Beidou coordinates of the marking position of the buried pipeline; s2: performing error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data, and forming a training data set; s3: analyzing the training data set by using a coordinate prediction model to obtain a coordinate prediction result; the coordinate prediction model is obtained through training; s4: and carrying out encryption treatment on the coordinate prediction result to obtain a buried pipeline settlement monitoring result.
In some embodiments of the present description, the processor performs the Beidou coordinate-based buried pipeline settlement monitoring method using a Beidou coordinate-based buried pipeline settlement monitoring system. By the method, errors generated by time change of coordinates of the buried pipeline can be reduced by an error correction method, and the accuracy of position monitoring is improved; (2) By utilizing the coordinate prediction model, the real-time performance and accuracy of the position monitoring of the buried pipeline can be greatly improved, the method is applicable to the monitoring of the buried pipeline in a deeper layer, and meanwhile, a small amount of training data can be used for obtaining a high-performance coordinate prediction model in the model training mode; (3) By encrypting the coordinate prediction result, the safety of the buried pipeline settlement monitoring result in the transmission process can be improved.
Example two
Fig. 2 is an exemplary flow chart of a buried pipeline settlement monitoring method based on Beidou coordinates, according to some embodiments of the present description. As shown in fig. 2, the flow includes the following steps. In some embodiments, the flow may be performed by a processor.
S1: and marking the buried pipeline to obtain Beidou coordinates of the marking position of the buried pipeline.
Buried pipelines are pipelines buried underground as transport carriers for oil and gas and other resources.
In some embodiments, the processor may mark the surface of the buried pipeline with pipeline displacement monitoring markers such as buoys, coordinate sensors, etc., to facilitate obtaining positional information of the buried pipeline.
In some embodiments, the processor may acquire the coordinates of the buried pipeline marking location using a Beidou satellite to acquire the Beidou coordinates of the buried pipeline marking location.
S2: and carrying out error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data, and forming a training data set.
The sample data is buried pipeline coordinate related data. For example, the sample data may include data of the position coordinates of each marker of the buried pipeline over time, and the like.
In some embodiments, the processor may analyze and error correct the beidou coordinates of the buried pipeline marker position using a discrete filtering algorithm to obtain sample data:
；
；
wherein, Representation ofThe sample data of the time instant is obtained,Representation ofThe Beidou coordinates of the moment,Representation ofRelative time of dayThe coordinate correction parameters of the moment of time,Representation ofAn observation matrix of the moment of time,Representation ofA state estimate of the time of day,Representation ofThe noise is measured randomly at the moment in time,Representation ofRelative time of dayThe angle change data of the time of day,Representation ofA state estimate of the time of day,Representation ofThe noise input matrix of the moment in time,Representation ofRandom process noise at time.
In some embodiments of the present disclosure, the processor may reduce errors in the time-dependent changes in the coordinates of the buried pipeline through error correction, thereby improving the accuracy of the position monitoring.
The training dataset is a dataset for training a coordinate prediction model.
In some embodiments, the processor may pass through the sample data, forming a training data set.
S3: and analyzing the training data set by using a coordinate prediction model to obtain a coordinate prediction result.
The coordinate prediction results are the prediction results of the current position of the buried pipeline and the future time position. For example, the coordinate prediction results may include the current position coordinates of each marker of the buried pipeline, the current predicted global coordinates, the position coordinates of each marker for a future period of time, the global coordinates for a future period of time, and the like.
In some embodiments, the processor may implement S3 using the following steps: s310: performing feature extraction on the training data set through a feature extraction layer to construct image feature data; s320: analyzing the image characteristic data through the viewpoint generating layer to obtain adjustment parameters; s330: fusing the image characteristic data and the adjustment parameters through a fusion layer to obtain a coordinate prediction result; the feature extraction layer, the viewpoint generation layer and the fusion layer belong to a coordinate prediction model.
The image feature data is feature data reflecting rotation and displacement of each sample data in the training data set with time.
In some embodiments, the expression of the image feature data may be:
；
wherein, Represent the firstImage characteristic data of the individual sample data,Represent the firstThe rotational component of the individual sample data,Represent the firstThe translational component of the individual sample data,Representing the transposed matrix of the zero matrix.
The adjustment parameter is a parameter for adjusting the image feature data.
In some embodiments, the processor may implement S320 using the following steps: s321: based on the translation components, obtaining the distance between the translation components through calculation; s322: based on the distance between the translation components, uniformly sampling the translation components with the distance smaller than a threshold value to obtain translation transformation parameters; s323: based on the rotation component, obtaining a rotation transformation parameter through calculation; s324: and combining the translation transformation parameter and the rotation transformation parameter to obtain the adjustment parameter.
In some embodiments, the expression for the distance between the translational components may be:
；
wherein, Representing the distance between the translational components,Representing the euclidean distance and,Represent the firstThe translational component of the individual sample data,Represent the firstThe translational component of the individual sample data,Representing the number of sample data.
In some embodiments, the translational transformation parameter expression may be:
；
wherein, The translation transformation parameters are represented as such,A number of sample data representing a distance of the translation component less than a threshold value.
In some embodiments, the rotation transformation parameter expression may be:
；
；
wherein, The rotation transformation parameters are represented by a set of values,The weight parameter is represented by a number of weight parameters,Represent the firstThe rotation parameters corresponding to the data of the individual samples,Represent the firstThe rotation parameters corresponding to the data of the individual samples,Representing the rotation parameters derived based on the rotation components,The norms of the rotation parameters are represented,、、AndAnd respectively represent four parameter values corresponding to the rotation parameters.
In some embodiments, the four parameter values corresponding to the rotation parameters may be calculated by:
；
；
；
；
wherein, 、、、、、、、AndData representing positions corresponding to the 3×3 matrix of rotation components, respectively.
In some embodiments, the weight parameters may be calculated by:
；
wherein, Represent the firstThe data of the individual samples are taken,Representing the number of sample data.
In some embodiments, the adjustment parameters may be expressed as。
In some embodiments, the expression of the coordinate prediction result may be:
；
wherein, Represent the firstThe result of the coordinate prediction of the individual sample data,The rendering function is represented as a function of the rendering,Indicating that the adjustment parameters are to be used,Represent the firstImage characteristic data of the individual sample data.
The coordinate prediction model is a model for predicting the current and future time positions of the buried pipeline.
In some embodiments, the input of the coordinate prediction model may be a training data set and the output may be a coordinate prediction result.
In some embodiments, the coordinate prediction model may include a feature extraction layer, a view generation layer, and a fusion layer, an output of the feature extraction layer being an input of the view generation layer, an output of the view generation layer and an output of the feature extraction layer being inputs of the fusion layer, an output of the fusion layer being an output of the coordinate prediction model.
In some embodiments, the input of the feature extraction layer may be a training data set and the output may be image feature data.
In some embodiments, the input of the viewpoint-generating layer may be image feature data and the output may be an adjustment parameter.
In some embodiments, the inputs to the fusion layer may be image feature data and adjustment parameters, and the outputs may be coordinate predictors.
In some embodiments, the coordinate prediction model may be trained from a plurality of labeled training samples. For example, a plurality of labeled training samples may be input into an initial coordinate prediction model, a loss function may be constructed from the label and the results of the initial coordinate prediction model, and parameters of the initial coordinate prediction model may be iteratively updated by gradient descent or other methods based on the loss function. And when the preset conditions are met, model training is completed, and a trained coordinate prediction model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.
In some embodiments, the training samples may be training data sets. The labels may be coordinates corresponding to each sample data in the training data set. The labels may be manually marked.
S4: and carrying out encryption treatment on the coordinate prediction result to obtain a buried pipeline settlement monitoring result.
The buried pipeline settlement monitoring result is a monitoring result reflecting the current and future time coordinates of the buried pipeline.
In some embodiments, the processor may analyze coordinate transformations of various portions of the buried pipeline based on the buried pipeline settlement monitoring results to determine whether the buried pipeline is damaged or is subject to misalignment.
In some embodiments, the concrete expression of the buried pipeline settlement monitoring result may be:
；
；
；
wherein, Represent the firstThe buried pipeline sedimentation monitoring results corresponding to the coordinate prediction results,Representation ofIs used for the encryption result of (a),Representation ofIs used for the encryption result of (a),The average coefficient representing the result of the coordinate prediction,Representing the encryption key(s),And a difference coefficient representing the coordinate prediction result.
In some embodiments, the encryption key may be calculated using the following:
；
wherein, Representing the length of the hash key,A value representing the hash key is provided,Represent the firstGaussian random number of each coordinate prediction result.
In some embodiments, the processor may reverse process the buried pipeline settlement monitoring result based on the encryption key to generate a decrypted buried pipeline settlement monitoring result.
In some embodiments of the present disclosure, the processor encrypts the coordinate prediction results using an encryption key to obtain buried pipeline settlement monitoring results. By the method, the encryption efficiency and the security of the sedimentation monitoring result of the buried pipeline can be greatly improved.
In some embodiments of the present disclosure, the processor performs error correction on the obtained Beidou coordinates of the marking position of the buried pipeline, analyzes the Beidou coordinates by using a coordinate prediction model to obtain a coordinate prediction result, and obtains a buried pipeline settlement monitoring result by an encryption method. (1) By the error correction method, errors generated by time-dependent changes of the coordinates of the buried pipeline can be reduced, and the accuracy of position monitoring is improved; (2) By utilizing the coordinate prediction model, the real-time performance and accuracy of the position monitoring of the buried pipeline can be greatly improved, the method is applicable to the monitoring of the buried pipeline in a deeper layer, and meanwhile, a small amount of training data can be used for obtaining a high-performance coordinate prediction model in the model training mode; (3) By encrypting the coordinate prediction result, the safety of the buried pipeline settlement monitoring result in the transmission process can be improved.
Claims (5)
1. The buried pipeline settlement monitoring method based on Beidou coordinates is characterized by comprising the following steps of:
s1: marking the buried pipeline to obtain Beidou coordinates of the marking position of the buried pipeline;
S2: performing error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data, and forming a training data set;
s3: analyzing the training data set by using a coordinate prediction model to obtain a coordinate prediction result; wherein, the coordinate prediction model is obtained through training, and the step S3 comprises:
s310: performing feature extraction on the training data set through a feature extraction layer to construct image feature data; the expression of the image characteristic data is specifically:
；
wherein, Represent the firstImage characteristic data of the individual sample data,Represent the firstThe rotational component of the individual sample data,Represent the firstThe translational component of the individual sample data,Representing a transpose of the zero matrix;
S320: analyzing the image characteristic data through the viewpoint generating layer to obtain adjustment parameters; wherein, the S320 includes:
S321: based on the translation components, the distance between the translation components is obtained through calculation:
；
wherein, Representing the distance between the translational components,Representing the euclidean distance and,Represent the firstThe translational component of the individual sample data,Represent the firstThe translational component of the individual sample data,Representing the number of sample data;
S322: based on the distance between the translation components, uniformly sampling the translation components with the distance smaller than a threshold value to obtain translation transformation parameters:
；
wherein, The translation transformation parameters are represented as such,A number of sample data representing a distance of the translation component less than a threshold;
S323: based on the rotation component, obtaining a rotation transformation parameter by calculation:
；
；
wherein, The rotation transformation parameters are represented by a set of values,The weight parameter is represented by a number of weight parameters,Represent the firstThe rotation parameters corresponding to the data of the individual samples,Represent the firstThe rotation parameters corresponding to the data of the individual samples,Representing the rotation parameters derived based on the rotation components,The norms of the rotation parameters are represented,、、AndFour parameter values corresponding to the rotation parameters are respectively represented;
s324: combining the translational transformation parameters and the rotational transformation parameters to obtain the adjustment parameters ；
S330: fusing the image characteristic data and the adjustment parameters through a fusion layer to obtain a coordinate prediction result; wherein the feature extraction layer, the viewpoint generation layer and the fusion layer belong to a coordinate prediction model;
S4: and carrying out encryption treatment on the coordinate prediction result to obtain a buried pipeline settlement monitoring result.
2. The method for monitoring sedimentation of a buried pipeline based on beidou coordinates according to claim 1, wherein the step S2 comprises:
Analyzing and correcting errors of Beidou coordinates of the buried pipeline marking position by utilizing a discrete filtering algorithm to obtain sample data, and forming a training data set, wherein the expression of the sample data is as follows:
；
；
wherein, Representation ofThe sample data of the time instant is obtained,Representation ofThe Beidou coordinates of the moment,Representation ofRelative time of dayThe coordinate correction parameters of the moment of time,Representation ofAn observation matrix of the moment of time,Representation ofA state estimate of the time of day,Representation ofThe noise is measured randomly at the moment in time,Representation ofRelative time of dayThe angle change data of the time of day,Representation ofA state estimate of the time of day,Representation ofThe noise input matrix of the moment in time,Representation ofRandom process noise at time.
3. The method for monitoring sedimentation of buried pipelines based on Beidou coordinates according to claim 1, wherein the expression of the coordinate prediction result is specifically:
；
wherein, Represent the firstThe result of the coordinate prediction of the individual sample data,The rendering function is represented as a function of the rendering,Indicating that the adjustment parameters are to be used,Represent the firstImage characteristic data of the individual sample data.
4. The method for monitoring sedimentation of buried pipelines based on Beidou coordinates according to claim 1, wherein the concrete expression of the sedimentation monitoring result of the buried pipelines is:
；
；
；
wherein, The result of the buried pipeline settlement monitoring is shown,Representation ofIs used for the encryption result of (a),Representation ofIs used for the encryption result of (a),The average coefficient representing the result of the coordinate prediction,Representing the encryption key(s),And a difference coefficient representing the coordinate prediction result.
5. A buried pipeline settlement monitoring system based on beidou coordinates for executing the buried pipeline settlement monitoring method based on beidou coordinates as set forth in any one of claims 1 to 4, and is characterized by comprising:
the acquisition module is used for marking the buried pipeline and acquiring Beidou coordinates of the marking position of the buried pipeline;
The correction module is used for carrying out error correction on the Beidou coordinates of the buried pipeline marking position based on the change condition of the Beidou coordinates of the buried pipeline marking position along with time to obtain sample data and form a training data set;
the analysis module is used for analyzing the training data set by utilizing a coordinate prediction model to obtain a coordinate prediction result; the coordinate prediction model is obtained through training;
And the encryption module is used for carrying out encryption processing on the coordinate prediction result to obtain a buried pipeline settlement monitoring result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410694879.8A CN118274786B (en) | 2024-05-31 | 2024-05-31 | Buried pipeline settlement monitoring method and system based on Beidou coordinates |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410694879.8A CN118274786B (en) | 2024-05-31 | 2024-05-31 | Buried pipeline settlement monitoring method and system based on Beidou coordinates |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118274786A CN118274786A (en) | 2024-07-02 |
| CN118274786B true CN118274786B (en) | 2024-08-13 |
Family
ID=91634215
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410694879.8A Active CN118274786B (en) | 2024-05-31 | 2024-05-31 | Buried pipeline settlement monitoring method and system based on Beidou coordinates |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118274786B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127690A (en) * | 2016-07-06 | 2016-11-16 | 李长春 | A kind of quick joining method of unmanned aerial vehicle remote sensing image |
| CN107968916A (en) * | 2017-12-04 | 2018-04-27 | 国网山东省电力公司电力科学研究院 | A kind of fast video digital image stabilization method suitable for on-fixed scene |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8238648B2 (en) * | 2006-12-04 | 2012-08-07 | Electronics And Telecommunications Research Institute | Apparatus and method for estimating focal length of camera |
| CN101915571B (en) * | 2010-07-20 | 2011-08-10 | 桂林理工大学 | Full-automatic acquisition method for image matching initial parallax based on phase correlation |
| CN103345642B (en) * | 2013-06-28 | 2016-05-25 | 华中科技大学 | A kind of image matching method based on dotted line antithesis |
| CN106296598B (en) * | 2016-07-29 | 2019-11-26 | 厦门美图之家科技有限公司 | 3 d pose processing method, system and camera terminal |
| US10989563B2 (en) * | 2018-06-25 | 2021-04-27 | CloudNav Inc. | Automatic calibration of rate gyroscope sensitivity |
| US11341676B2 (en) * | 2019-02-05 | 2022-05-24 | Google Llc | Calibration-free instant motion tracking for augmented reality |
| CN111795639B (en) * | 2020-05-29 | 2021-12-03 | 湖南联智科技股份有限公司 | Infrastructure structure deformation monitoring method based on Beidou high-precision positioning |
| CN111522006B (en) * | 2020-06-29 | 2020-10-09 | 航天宏图信息技术股份有限公司 | Earth surface settlement monitoring method and device fusing Beidou and InSAR data |
| CN111783986B (en) * | 2020-07-02 | 2024-06-14 | 清华大学 | Network training method and device, and gesture prediction method and device |
| CN116048032B (en) * | 2023-04-03 | 2023-06-13 | 四川宏大安全技术服务有限公司 | Petrochemical production safety monitoring method and system based on Internet |
-
2024
- 2024-05-31 CN CN202410694879.8A patent/CN118274786B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127690A (en) * | 2016-07-06 | 2016-11-16 | 李长春 | A kind of quick joining method of unmanned aerial vehicle remote sensing image |
| CN107968916A (en) * | 2017-12-04 | 2018-04-27 | 国网山东省电力公司电力科学研究院 | A kind of fast video digital image stabilization method suitable for on-fixed scene |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118274786A (en) | 2024-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115265523B (en) | Robot simultaneous positioning and mapping method, device and readable medium | |
| CN111507982B (en) | A point cloud semantic segmentation method based on deep learning | |
| Quach | Optimal cover estimation methods and steganographic payload location | |
| CN117173579B (en) | Image change detection method based on fusion of inherent features and multistage features | |
| CN106547724A (en) | Theorem in Euclid space coordinate transformation parameter acquisition methods based on minimum point set | |
| CN110874591A (en) | Image positioning method, device, equipment and storage medium | |
| CN113838058A (en) | Automatic medical image labeling method and system based on small sample segmentation | |
| CN115510578A (en) | Landslide instability time probability prediction method based on InSAR near real-time monitoring and product | |
| Hongbin et al. | Feature preserving holes filling of scattered point cloud based on tensor voting | |
| US20130124147A1 (en) | Random Sample Consensus for Groups of Data | |
| CN118276036A (en) | Laser radar positioning method, device and readable medium based on diffusion model | |
| CN118274786B (en) | Buried pipeline settlement monitoring method and system based on Beidou coordinates | |
| CN102254296B (en) | Method for calculating similarity of Geographic Information System (GIS) vector data image watermarks | |
| US20130121558A1 (en) | Point Selection in Bundle Adjustment | |
| Wang et al. | RST invariant fragile watermarking for 2D vector map authentication | |
| CN102314798A (en) | Method and device for automatically simplifying shape points of polygonal surface elements of electronic map | |
| CN117521916A (en) | Carbon emission estimation method, apparatus, computer device, and storage medium | |
| CN102663673B (en) | A Fragile Watermarking Method Based on Chaotic Mapping for Shp Line-Surface Layer | |
| CN117150456A (en) | Vector geographic data exchange password watermarking method, device and medium | |
| CN116817920A (en) | Visual positioning method and device for plane mobile robot without three-dimensional map model | |
| CN115482445A (en) | Coal mine well wall water seepage identification method based on deep neural network | |
| CN104866737B (en) | A kind of DEM fragile watermark completeness certification methods for taking features of terrain into account | |
| CN111950433B (en) | Automatic construction method of deep learning training sample set for optical satellite image feature matching | |
| CN105550970B (en) | A kind of map vector reversible information hidden method | |
| Koparde et al. | Single Device-Based Deep Learning Approach for Geographical Position Spoofing Detection on an Instant Messaging Platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |