CN110991487A - Integrated coupling model generation method for multi-source monitoring detection data - Google Patents

Integrated coupling model generation method for multi-source monitoring detection data Download PDF

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CN110991487A
CN110991487A CN201911084101.0A CN201911084101A CN110991487A CN 110991487 A CN110991487 A CN 110991487A CN 201911084101 A CN201911084101 A CN 201911084101A CN 110991487 A CN110991487 A CN 110991487A
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CN110991487B (en
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王超
张社荣
郭宝航
刘婷
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Tianjin University
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Abstract

The invention discloses an integrated coupling model generation method of multi-source monitoring detection data, which processes and analyzes safety monitoring data and manual detection information by using data level fusion and feature level fusion in an information fusion technology, provides a reliable basic data source for long-distance diversion project structure safety risk evaluation, enables an evaluation result to truly reflect the structure safety state of a project operation period, ensures long-term stable operation of the project, and effectively solves the problem that the evaluation result deviates from the reality due to the fact that the long-distance diversion project structure safety state is evaluated by a single data source in the prior art.

Description

Integrated coupling model generation method for multi-source monitoring detection data
Technical Field
The invention relates to the technical field of safety detection data processing, in particular to an integrated coupling model generation method of multi-source monitoring detection data.
Background
The long-distance water diversion project is an important means for solving the problem of uneven distribution of water resource space in China and realizing the optimal allocation of water resources. Therefore, it is very important to ensure that the diversion engineering structure is in a safe and stable state. However, the long-distance water diversion project is generally a linear project, and has the characteristics of long water delivery line, wide cross-domain range, various building species and quantity, complex geographic environment and the like, and the characteristics bring great challenges to the accuracy of the structural safety risk assessment of the long-distance water diversion project. Therefore, how to effectively process and analyze the original measured engineering data and accurately evaluate the structure safety state of the diversion engineering during operation according to the processed and analyzed data to ensure the safe and stable operation of the engineering is an important problem to be considered during the operation of the long-distance diversion engineering.
The structural safety state evaluation in the long-distance diversion project operation period is still based on the safety monitoring data mainly monitored by a sensor or on the single data source such as manual detection information mainly detected by inspection tour, so that the evaluation result is inevitably deviated from the actual condition, and the evaluation method is mainly embodied in the following aspects:
firstly, the reliability of the monitoring data is not high: due to the complexity of the long-distance water diversion project and the environment in which the long-distance water diversion project is located, and the limitation of factors such as the accuracy, the quality and the service life of the sensor, the monitoring data have inevitable errors, namely, the acquired data are often incomplete, noisy or inconsistent. The errors include single point errors of the data and continuous errors of the data, so that the authenticity and the reliability of the monitored data need to be improved.
Second, poor detection information availability: the long-distance water diversion project is complex in environment, various in diseases, complex and diverse in content of manual inspection information, detection information comprises qualitative indexes (such as subsidence, collapse, water seepage and the like) and quantitative indexes (such as crack length and width, carbonization depth, corrosion area and the like) with detection results of specific numerical values, and the detection information cannot be used as an ideal data source for evaluating the safety risk of the project structure without carrying out structural treatment on the detection information.
Third, the data source is single: safety monitoring data or manual detection information is simply adopted to evaluate the safety state of the long-distance water diversion project structure, so that basic data resources are lost, and the evaluation result deviates from the reality.
In summary, the problem that how to solve the problem that the evaluation result deviates from the actual result when the safety state of the long-distance diversion engineering structure is evaluated mainly by a single data source at present is a problem that needs to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In view of the above, the invention provides an integrated coupling model generation method for multi-source monitoring detection data, which processes and analyzes safety monitoring data and manual detection information by using data-level fusion and feature-level fusion in an information fusion technology, and provides a reliable basic data source for long-distance diversion project structure safety risk evaluation, so that an evaluation result can truly reflect the structure safety state of a project during operation, and long-term stable operation of the project is ensured.
In order to achieve the purpose, the invention adopts the following technical scheme:
an integrated coupling model generation method for multi-source monitoring detection data comprises the following steps:
the method comprises the following steps: and (3) data level fusion:
s11: self-processing of raw monitoring data: eliminating single-point errors in the original monitoring data sequence;
s12: and (3) mutual processing of information combined with manual detection: combining with manual detection information, processing multipoint continuous errors in a monitoring data sequence after processing single-point errors to obtain structured monitoring information;
step two: and (3) feature level fusion:
s21: grading the manual detection information, and formulating a detection index evaluation standard which meets the actual safe operation condition of the long-distance diversion project;
s22: determining the evaluation result of the detection item by adopting the most dangerous principle based on the evaluation standard of the detection index to obtain structured detection information;
step three: constructing a monitoring and detecting fusion model: and fusing the structured monitoring information and the structured detection information, and constructing a multi-level index system to obtain a monitoring and detecting fusion model.
Preferably, step S11 specifically includes:
processing the missing value in the original monitoring data sequence by adopting a method of ignoring the missing value;
and eliminating the outliers by adopting a Laplace criterion.
Preferably, step S12 specifically includes:
calculating the change rate of the index monitoring value of a certain part and the change rate of the artificial detection value;
taking the change rate of the manual detection value as a threshold value, and when the change rate of the index monitoring value is greater than the threshold value, judging that the current monitoring value is abnormal and rejecting; for a measurement point without an artificial detection value, a threshold value is defined as the maximum value of the rate of change in the artificial detection value detected at the site.
Preferably, in the third step, the index system is divided into four layers, and the interlayer relations are that the upper layer comprises the lower layer, wherein the first layer is a target layer and represents the whole engineering; the second floor is an engineering part floor, namely, the second floor is divided into a plurality of parts according to the integral structure of the building; the third layer is a sub-part layer and comprises monitoring items and detection items under each engineering part; the fourth layer is an index layer and comprises a plurality of monitoring detection indexes.
Compared with the prior art, the integrated coupling model generation method for the multi-source monitoring detection data has the following advantages compared with the traditional method for evaluating the structural safety state of the diversion project by adopting a single data source:
1. aiming at the safety monitoring data processing, a self-processing mode is adopted to eliminate single-point errors, and a mutual processing mode combining manual detection information is adopted to eliminate multi-point continuous errors, so that the authenticity and the reliability of a monitoring data source are greatly improved.
2. The method aims at the manual detection information processing, carries out grading processing and combination on qualitative indexes and quantitative indexes in the detection information, solves the problem that the manual detection information cannot be effectively utilized due to complexity and diversity of the manual detection information, and provides a uniform evaluation standard for detection items and detection indexes.
3. The characteristics of safety monitoring information and manual inspection information are fully considered, and the two processed information sources are used for building an index system of a long-distance diversion engineering building. Compared with an index system constructed by applying a single data source, the index system constructed based on multiple data sources is more consistent with the actual situation, and the obtained evaluation result is more accurate.
In conclusion, the method provided by the invention is in accordance with engineering practice, has strong practicability and is convenient to realize, the accuracy of the water diversion engineering structure safety state evaluation result is greatly improved, and the safe and stable operation of the long-distance water diversion engineering is ensured.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for generating an integrated coupling model of multi-source monitoring detection data according to the present invention;
FIG. 2 is a safety evaluation index system of a certain engineering example constructed based on multi-source information provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention discloses an integrated coupling model generation method for multi-source monitoring detection data, including:
the method comprises the following steps: the data level fuses, and this processing procedure is used for eliminating the error, improves monitoring data authenticity and reliability, mainly contains two steps: the first step is self-processing of original monitoring data aiming at single-point errors; the second step is mutual processing of combined manual detection information aiming at multipoint continuous errors, and specifically comprises the following steps:
s11: self-processing of raw monitoring data: eliminating single point errors in the original monitoring data sequence in the following specific mode:
processing the missing value in the original monitoring data sequence by adopting a method of ignoring the missing value;
because the operation period of the diversion project is long, the data acquisition frequency of each monitoring item is high, the basic data quantity is large, and the trend change of the data cannot be influenced by the missing of individual values, the missing value is ignored.
Eliminating outliers by adopting a Lauda criterion;
adopting Laviand criterion, namely rejecting when the monitored value and the sample mean value exceed 3 times of mean square error, and obtaining formula 1:
Figure BDA0002264856170000041
wherein x isiIn order to obtain the value to be checked,
Figure BDA0002264856170000042
the statistical mean of the historical monitoring data and sigma is the mean square error.
S12: and (3) mutual processing of information combined with manual detection: combining with manual detection information, processing multipoint continuous errors in a monitoring data sequence after processing single-point errors to obtain structured monitoring information;
after processing the single point error, further processing is required to be performed on the multiple point continuous errors in the monitoring data sequence. The steel bar stress monitoring item is taken as an example for explanation, other monitoring values with similar various measuring means can refer to the algorithm, and the specific mode is as follows:
respectively calculating the monitoring stress and the manual detection stress change rate of the steel bar at the position, and obtaining a formula 2 and a formula 3:
equation 2: m isi＝(di-d0i)/d0i
Equation 3: c. Ci＝(Di-D0i)/D0i
Wherein: diAnd DiRespectively a reinforcement stress monitoring value and an artificial detection value, d, of the ith measurement point of the part at the same period0iAnd D0iFor monitoring and manual detection of initial values, miAnd ciAnd monitoring the change rate and manually detecting the change rate for the corresponding steel bar stress.
Using the stress change rate of the artificially detected steel bar as a threshold value, when mi|＞|ciIf l, then d is determinediIf the abnormal value is found, it should be eliminated. For some measuring points without steel bar stress artificial detection values, the threshold value is defined as the maximum value of the steel bar stress change rate detected by the important part, so that the threshold value determination method of all steel bar stresses is shown in a formula 4:
equation 4:
Figure BDA0002264856170000051
wherein: and N represents the number of the stress detection measuring points of the steel bar at a certain important part.
Step two: feature level fusion, namely, manual detection information structuring: the processing process is used for extracting the characteristics of the detection indexes, formulating the evaluation standard of the detection indexes, and obtaining the usable and easily-evaluated data source after the complex and various detection information is subjected to structuralization processing, and mainly comprises grading processing and merging.
S21: grading treatment: grading the manual detection information, and formulating a detection index evaluation standard which meets the actual safe operation condition of the long-distance diversion project;
on the basis of other industry index evaluation standards, the detection evaluation standard meeting the actual safe operation condition of the long-distance water diversion project is formulated. Each detection index is divided into 5 assessment grades, which are respectively as follows: good, bad, dangerous, each rating level gives a corresponding qualitative or quantitative description of the severity level according to the difference of the detection index category. The qualitative indicators are described in table 1. The rating criteria of the quantitative indicators are determined according to thresholds set for different severity levels.
Table 1 detection index evaluation keyword
Figure BDA0002264856170000061
S22: merging: determining the evaluation result of the detection item by adopting the most dangerous principle based on the evaluation standard of the detection index to obtain structured detection information;
in order to reflect the sensitivity and importance of the most unfavorable detection indexes, the safety state of a detection item is determined by adopting the most dangerous principle, namely when a certain detection item is evaluated, the highest evaluation scale in all the detection indexes is used as the evaluation result of the detection item.
Step three: constructing a monitoring and detecting fusion model: and fusing the structured monitoring information and the structured detection information to construct a multi-level index system to obtain a monitoring and detecting fusion model, which is shown in the attached figure 2.
The construction of the monitoring and detecting fusion model depends on the construction of a safety evaluation index system of various building structures of the long-distance diversion project. The index system is divided into four layers, the interlayer relations are that the upper layer comprises a lower layer, and the first layer is a target layer and represents the whole engineering; the second floor is an engineering part floor, namely, the second floor is divided into a plurality of parts according to the integral structure of the building; the third layer is a sub-part layer and comprises monitoring items (engineering monitoring sections) and detection items under each engineering part; the fourth layer is an index layer and comprises a plurality of monitoring detection indexes.
Compared with the traditional method for evaluating the structural safety state of the diversion project by adopting a single data source, the method provided by the invention has the following advantages:
1. aiming at the safety monitoring data processing, a self-processing mode is adopted to eliminate single-point errors, and a mutual processing mode combining manual detection information is adopted to eliminate multi-point continuous errors, so that the authenticity and the reliability of a monitoring data source are greatly improved.
2. The method aims at the manual detection information processing, carries out grading processing and combination on qualitative indexes and quantitative indexes in the detection information, solves the problem that the manual detection information cannot be effectively utilized due to complexity and diversity of the manual detection information, and provides a uniform evaluation standard for detection items and detection indexes.
3. The characteristics of safety monitoring information and manual inspection information are fully considered, and the two processed information sources are used for building an index system of a long-distance diversion engineering building. Compared with an index system constructed by applying a single data source, the index system constructed based on multiple data sources is more consistent with the actual situation, and the obtained evaluation result is more accurate.
In conclusion, the method provided by the invention is in accordance with engineering practice, has strong practicability and is convenient to realize, the accuracy of the water diversion engineering structure safety state evaluation result is greatly improved, and the safe and stable operation of the long-distance water diversion engineering is ensured.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (4)

1. An integrated coupling model generation method for multi-source monitoring detection data is characterized by comprising the following steps:
the method comprises the following steps: and (3) data level fusion:
s11: self-processing of raw monitoring data: eliminating single-point errors in the original monitoring data sequence;
s12: and (3) mutual processing of information combined with manual detection: combining with manual detection information, processing multipoint continuous errors in a monitoring data sequence after processing single-point errors to obtain structured monitoring information;
step two: and (3) feature level fusion:
s21: grading the manual detection information, and formulating a detection index evaluation standard which meets the actual safe operation condition of the long-distance diversion project;
s22: determining the evaluation result of the detection item by adopting the most dangerous principle based on the evaluation standard of the detection index to obtain structured detection information;
step three: constructing a monitoring and detecting fusion model: and fusing the structured monitoring information and the structured detection information, and constructing a multi-level index system to obtain a monitoring and detecting fusion model.
2. The method for generating the integrated coupling model of the multi-source monitoring detection data according to claim 1, wherein step S11 specifically includes:
processing the missing value in the original monitoring data sequence by adopting a method of ignoring the missing value;
and eliminating the outliers by adopting a Laplace criterion.
3. The method for generating the integrated coupling model of the multi-source monitoring detection data according to claim 1, wherein step S12 specifically includes:
calculating the change rate of the index monitoring value of a certain part and the change rate of the artificial detection value;
taking the change rate of the manual detection value as a threshold value, and when the change rate of the index monitoring value is greater than the threshold value, judging that the current monitoring value is abnormal and rejecting; for a measurement point without an artificial detection value, a threshold value is defined as the maximum value of the rate of change in the artificial detection value detected at the site.
4. The method for generating the integrated coupling model of the multi-source monitoring and detecting data according to claim 1, wherein in step three, the index system is divided into four layers, and the interlayer relations are that the upper layer contains the lower layer, wherein the first layer is a target layer and represents the whole engineering; the second floor is an engineering part floor, namely, the second floor is divided into a plurality of parts according to the integral structure of the building; the third layer is a sub-part layer and comprises monitoring items and detection items under each engineering part; the fourth layer is an index layer and comprises a plurality of monitoring detection indexes.
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Citations (5)

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Publication number Priority date Publication date Assignee Title
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CN107764963A (en) * 2017-10-12 2018-03-06 水利部交通运输部国家能源局南京水利科学研究院 A kind of diversion works lake ecological influences monitoring and assessment technology method
CN107829452A (en) * 2017-11-12 2018-03-23 湖南科技大学 It is a kind of to merge multisensor and ground SAR deep foundation pit construction monitoring and warning technology
CN109656793A (en) * 2018-11-22 2019-04-19 安徽继远软件有限公司 A kind of information system performance stereoscopic monitoring method based on multi-source heterogeneous data fusion
US20190205477A1 (en) * 2017-12-29 2019-07-04 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Method for Processing Fusion Data and Information Recommendation System

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107679717A (en) * 2017-09-15 2018-02-09 西安博兴自动化科技有限公司 A kind of management system based on water amount information collection and Group of Pumping Station Optimized Operation
CN107764963A (en) * 2017-10-12 2018-03-06 水利部交通运输部国家能源局南京水利科学研究院 A kind of diversion works lake ecological influences monitoring and assessment technology method
CN107829452A (en) * 2017-11-12 2018-03-23 湖南科技大学 It is a kind of to merge multisensor and ground SAR deep foundation pit construction monitoring and warning technology
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