CN113610274A - Related relation-based method for mining long-time monitoring abnormal information of subway construction - Google Patents
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Abstract
The invention provides a method for mining long-time monitoring abnormal information of subway construction based on correlation, which comprises the steps of collecting monitoring data of a construction site and establishing a data warehouse; data selection is carried out on the data warehouse, and accumulated deformation data of the underground continuous wall are determined as research objects; calculating correlation coefficients of accumulated deformation of the underground continuous walls at different depths of the same measuring point, determining an empirical reference threshold of the correlation coefficients, comparing the correlation coefficients of the underground continuous walls at the depths with the empirical reference threshold, and searching and judging abnormal data existing in monitoring data of the underground continuous walls at each depth; dividing time windows of the underground continuous wall monitoring period at a certain depth, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall under each time window, and positioning abnormal data; and obtaining a data mining result of the long-term monitoring abnormal information based on the correlation based on the abnormal data positioned at each depth, and early warning the actual engineering risk.
Description
Technical Field
The invention relates to the field of subway construction monitoring and processing, in particular to a method for mining long-time monitoring abnormal information of subway construction based on a correlation relation.
Background
The geological and hydrological conditions faced by subway engineering construction are various, a large number of sensitive urban construction facilities and lifeline systems need to be penetrated through the interior of a city, the uncertainty of the underground and overground existing structures under the action of complex environments is high, the underground and overground existing structures belong to high-risk production activities at home and abroad, the engineering construction risk is prominent, and serious economic loss, adverse social influence and even casualties are easily caused.
CN112982503A provides based on subway foundation ditch construction monitoring system, method, equipment and storage medium, through the relevant data of various sensors acquisition job site, but lacks corresponding effective data processing means.
CN112069225A provides a data mining method for correlation relations of multisource heterogeneous monitoring data in subway construction, which sets up a data mining target, determines a multisource heterogeneous monitoring data sample set, and calculates an average value of the correlation relations, thereby determining the correlation among multisource heterogeneous monitoring data. The rationality of this approach is to be further investigated.
Data mining is applied to the industrial field at the earliest, is widely applied to fault diagnosis and accurate position positioning in a standard industrial process, and mainly comprises the steps of data acquisition, data analysis, data application and data feedback, wherein the data application is the most critical, and certain correlation among data is found by analyzing the data and performing exploratory analysis or hypothesis verification analysis on reasons behind the data. However, the current research on abnormal data in the deformation data of the underground diaphragm wall lacks quantitative basis, and no mature method exists, which brings difficulty for further prediction and control of engineering risks.
Therefore, it is necessary to develop a method for mining abnormal information based on long-term monitoring, which can make full use of the correlation to predict the abnormality.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a long-time monitoring abnormal information mining method based on correlation, which can be started from actual monitoring data, is combined with the correlation and improved, and automatically positions the spatial position and the time position of abnormal data.
The technical scheme of the invention provides a method for mining long-time monitoring abnormal information of subway construction based on a correlation relationship, which comprises the following steps:
1) collecting monitoring data of a construction site and establishing a data warehouse;
2) data selection is carried out on the data warehouse, and accumulated deformation data of the underground continuous wall are determined as research objects;
3) calculating correlation coefficients of accumulated deformation of the underground continuous walls at different depths of the same measuring point, determining an empirical reference threshold of the correlation coefficients, comparing the correlation coefficients of the underground continuous walls at the depths with the empirical reference threshold, and searching and judging abnormal data existing in monitoring data of the underground continuous walls at each depth;
4) according to the judgment result of the step 3), carrying out time window division on the underground continuous wall monitoring period at a certain depth, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall under each time window, and positioning abnormal data;
5) and obtaining a data mining result of the long-term monitoring abnormal information based on the correlation based on the abnormal data positioned at each depth, and early warning the actual engineering risk.
Moreover, the data warehouse is a set of monitoring data of a construction site.
And the underground continuous wall body deformation data is acquired in a mode that a plurality of groups of underground continuous wall monitoring points are arranged along the periphery of the foundation pit, and each monitoring point is provided with a deformation monitoring point at a fixed distance from the top of the wall body to the bottommost end.
And in the step 3), calculating correlation coefficients of the accumulated deformation of the underground continuous wall at different depths and a certain specific depth of the same measuring point, and if the correlation coefficients of the accumulated deformation of other depths and the accumulated deformation of the underground continuous wall at the specific depth are far smaller than an empirical reference threshold, prejudging that abnormal data exists in the accumulated deformation data of the underground continuous wall at the depth.
And in the step 4), according to the pre-judgment result, time window division is carried out on the underground diaphragm wall monitoring period at the depth where the abnormal data exist and the specific depth, correlation coefficients under each time window are calculated and contrastively analyzed, and the abnormal data are positioned.
Moreover, the duration of dividing the time window can be determined according to engineering characteristics.
And the abnormal data positioning is realized by judging whether the correlation coefficient of a certain time window is far smaller than the correlation coefficients of other time windows, and if so, determining that the abnormal data is positioned in the monitoring data of the underground continuous wall in the time window.
Furthermore, the correlation coefficient calculation uses the pearson correlation coefficient.
According to the method, a data warehouse is established based on construction site monitoring, research objects are selected, correlation relations are applied, correlation coefficients of the research objects in a full monitoring period are calculated, an experience reference threshold S of the correlation coefficients in the full monitoring period is determined, the spatial positions of abnormal data are located through comparative analysis, time windows are divided in the full monitoring period, the sizes of the correlation coefficients under all the time windows are calculated and analyzed, the experience reference threshold T of the correlation coefficients in the time windows is determined, the time positions of the abnormal data are located automatically, and early warning is conducted on actual engineering risks. The method is suitable for automatically monitoring the engineering risks of multiple subway construction sites in the city at the same time, and has the advantages of strong real-time performance and high precision.
The scheme of the invention is simple and convenient to implement, has strong practicability, solves the problems of low practicability and inconvenient practical application of the related technology, can improve the user experience, and has important market value.
Drawings
Fig. 1 is a flowchart of a data mining method for long-term monitoring abnormal information based on a correlation in an embodiment of the present invention.
FIG. 2 is a graph of correlation coefficients for a set of CX06 points at depths greater than-5 m and the cumulative deformation of a diaphragm wall over the full monitoring period at a depth of-0.5 m, according to one embodiment of the present invention.
FIG. 3 is a graph of correlation coefficients for a set of CX20 points at depths greater than-5 m and the cumulative deformation of a diaphragm wall over the full monitoring period at a depth of-0.5 m, in accordance with one embodiment of the present invention.
FIG. 4 is a graph of correlation coefficients for each time window for the cumulative change of the underground diaphragm wall at-1.5 m depth and-0.5 m depth for a set of CX20 test points, according to one embodiment of the present invention.
Detailed Description
The technical solution of the present invention is specifically described below with reference to the accompanying drawings and examples.
Referring to fig. 1, the method for mining long-term monitoring abnormal information of subway construction based on a correlation provided by the embodiment of the present invention may include:
1) collecting monitoring data of a construction site and establishing a data warehouse;
in the invention, the data warehouse is a set of monitoring data of a construction site.
2) Data selection is carried out on the data warehouse, and accumulated deformation data of the underground continuous wall are determined as research objects;
in order to identify abnormal monitoring data of the diaphragm wall in subway construction, data is selected to determine the accumulated deformation data of the diaphragm wall as a research object. (the method is mainly applied to identifying abnormal monitoring data of the diaphragm wall in subway construction).
In the foundation pit construction process, the construction site monitoring data comprise underground continuous wall body deformation data, surface subsidence deformation data and peripheral building subsidence deformation data, wherein the underground continuous wall body deformation data are collected and mainly 32 groups of underground continuous wall monitoring points are arranged along the periphery of the foundation pit, and each monitoring point is provided with a deformation monitoring point at an interval of 0.5m from the top of the wall body to the bottommost end.
3) Calculating the correlation coefficient of the accumulated deformation of the underground continuous walls at different depths of the same measuring point, determining an empirical reference threshold S of the correlation coefficient in the full monitoring period, judging whether the correlation coefficient of the underground continuous walls at each depth is smaller than the empirical reference threshold, and if so, judging that abnormal data exists in the monitoring data of the underground continuous walls at the depth.
Calculating correlation coefficients into monitoring data of all time periods, judging the spatial position of abnormal data by comparing the correlation coefficients of the underground continuous walls at all depths with an experience reference threshold S, dividing time windows in the subsequent step 4) based on the judgment result in the step 3), dividing the monitoring data of all time periods of the underground continuous walls with the abnormal data and at certain depth into time windows, calculating the correlation coefficients under different time windows, determining the experience reference threshold T of the correlation coefficients of the time windows, and carrying out contrastive analysis to locate the time position of the abnormal data.
Preferably, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall at different depths and the depth of-0.5 m at the same measuring point: an empirical reference threshold S for the correlation coefficient for the full monitoring period is determined.
In specific implementation, the value of S can be obtained by analyzing according to a correlation coefficient calculation principle and a deformation mechanism, the correlation coefficient calculation value is-1, the correlation coefficient above 0.95 is a strong correlation relation, and the empirical reference threshold value S is determined by combining a diaphragm wall deformation mechanism, wherein the diaphragm wall at the position of-0.5 m and the diaphragm wall above-5 m are both strong correlation relations.
In the embodiment, the correlation coefficient of the accumulated deformation of the underground continuous wall at the depth of more than-5 m and-0.5 m at the same measuring point is calculated: an empirical reference threshold S for the correlation coefficient for the full monitoring period is determined.
The empirical reference threshold S for the correlation coefficient of the full-time surveillance data is 0.95.
If the correlation coefficient of the accumulated deformation of the underground continuous wall at a certain depth and the depth of-0.5 m is smaller than the empirical reference threshold S, judging that abnormal data exists in the accumulated deformation data of the underground continuous wall at the depth in advance.
In an embodiment, the calculation method of the correlation coefficient preferably adopts a calculation formula of the pearson correlation coefficient, which is specifically as follows:
in an embodiment, R in the above calculation formula is a correlation coefficient of the full-time monitoring data, XiAccumulated deformation data of the underground continuous wall at the depth of-0.5 m,
is XIAverage value of (A), YIAccumulating deformation data (Y) for underground diaphragm wall at other depthsIThe accumulated deformation data of the underground continuous wall at the depths of-1 m, -1.5m, -2m, -2.5m, -3m, -3.5m, -4m, -4.5m and-5 m respectively,
is YITaking I as 1, 2, 3 and 4 … N, wherein N is XIOr YINumber of data of (1), XIAnd YIEqual in number).
4) And according to the judgment result, dividing time windows of the underground continuous wall monitoring period at a certain depth, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall under each time window to determine the empirical reference threshold T of the correlation coefficient of the time window, comparing and analyzing, positioning the time position of abnormal data, and realizing the early warning of the actual engineering risk.
The correlation coefficient calculation method in this step is the same as that in step 3), and is also a pearson correlation coefficient calculation formula, and the correlation coefficient in the 3 rd time window of the CX20 measurement point group is the correlation coefficient r calculated from the monitoring data of the diaphragm wall at the depth of-0.5 m and-1.5 m between the 21 st day and the 30 th day of the monitoring period, and the specific calculation formula is as follows:
In the embodiment, according to the pre-judgment result, time window division is carried out on the underground continuous wall monitoring period at a certain depth and the position of-0.5 m, correlation coefficients under each time window are calculated and contrastively analyzed, an empirical reference threshold T of the correlation coefficients of the time windows is determined, and the time position of abnormal data is located through contrastive analysis.
In an embodiment, the time duration for dividing the time window is preferably set to 10 days, and the time duration for implementing the time window can be set by a user. In an embodiment, the correlation coefficient for the time window is empirically referenced to a threshold T of 0.75.
In specific implementation, according to the strong correlation pre-judgment result in the step 3, the strong correlation is that the variation trends of two deep underground walls are similar, so that after time windows are divided, the correlation coefficient of each time window is greater than the empirical reference threshold T.
In an embodiment, the positioning exception data is specifically implemented as: and if the correlation coefficient of a certain time window is smaller than the empirical reference threshold T of the correlation coefficient of the time window, the abnormal data is regarded as being located in the monitoring data of the underground continuous wall in the time window.
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Collecting monitoring data of a construction site and establishing a data warehouse;
data selection is carried out on the data warehouse, and accumulated deformation data of the underground continuous wall are determined as research objects;
calculating a correlation coefficient of accumulated deformation of the underground continuous wall at a depth of more than-5 m and-0.5 m at the same measuring point, determining an empirical reference threshold S of the correlation coefficient in the full monitoring period, comparing whether the correlation coefficient of the underground continuous wall at each depth is smaller than the empirical reference threshold S of the correlation coefficient in the full monitoring period, and if so, judging that abnormal data exist in the monitoring data of the underground continuous wall at the depth, namely determining the spatial positioning of the abnormal data;
and according to the judgment result, dividing a time window of the underground continuous wall at a certain depth in the full monitoring period, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall under each time window, determining an empirical reference threshold T of the correlation coefficient of the time window, and performing comparative analysis on the empirical reference threshold T to determine the time positioning of the abnormal data.
FIG. 2 is a graph of correlation coefficients for a set of CX06 points at depths greater than-5 m and the cumulative deformation of a diaphragm wall over the full monitoring period at a depth of-0.5 m, according to one embodiment of the present invention.
As shown in fig. 2, in the correlation coefficient graph of the CX06 measurement point group, correlation coefficients of the cumulative deformation of the underground continuous wall at the positions of-1 m, -1.5m, -2m, -2.5m, -3m, -3.5m, -4m, -4.5m, -5m and the underground continuous wall at the depth of-0.5 m are respectively 0.999, 0.998, 0.996, 0.995 and 0.990 at the minimum monitoring depth of 0.5m, and the empirical reference threshold value S of the correlation coefficients at all time periods is 0.950, and the correlation coefficients are all greater than 0.95, and if large fluctuation does not occur, it is determined that abnormal data does not occur in the monitoring data of the underground continuous wall between-1 m and-5 m.
FIG. 3 is a graph of correlation coefficients for a set of CX20 points at depths greater than-5 m and the cumulative deformation of a diaphragm wall over the full monitoring period at a depth of-0.5 m, in accordance with one embodiment of the present invention.
As shown in fig. 3, in the correlation coefficient graph of the CX20 measurement point group, correlation coefficients of the cumulative deformation of the underground continuous wall at the minimum monitoring depth of 0.5m and at the positions of-1 m, -1.5m, -2m, -2.5m, -3m, -3.5m, -4m, -4.5m and-5 m and the underground continuous wall at the depth of-0.5 m are respectively 0.999, 0.882, 0.999, 0.998, 0.994, 0.991, 0.980 and 0.968, the empirical reference threshold value S of the correlation coefficient at the whole time is 0.950, the second correlation coefficient is less than 0.950, and abnormal data is predicted from the monitoring data of the underground continuous wall at the depth of-1.5 m.
FIG. 4 is a graph of correlation coefficients for each time window for the cumulative change of the underground diaphragm wall at-1.5 m depth and-0.5 m depth for a set of CX20 test points, according to one embodiment of the present invention.
As shown in fig. 4, the effective monitoring period of the CX20 measuring point group is 120 days, the monitoring period is divided by taking 10 as a time window, the time windows from day 1 to day 10 are one time window, and the like obtain 12 time windows, the correlation coefficients of the cumulative changes of the diaphragm wall at the depth of-1.5 m and the underground diaphragm wall at the depth of-0.5 m in each time window are respectively 0.917, 0.997, 0.945, 0.800, 0.993, 0.956, 1.000, 0.997, 0.121 and 1.000, the empirical reference threshold T of the correlation coefficient of the time window is 0.750, and the correlation coefficient of the 11 th time window is less than 0.75, it is determined that abnormal data exists in the two days of the 11 th time window (i.e. days 100 to 110 of the monitoring period), the monitoring data is observed, the cumulative deformation values of the diaphragm walls at days 109, 110 and 11 th day are respectively-21.98 mm, -21.06 mm, and thus the difference between the monitoring data and the day before and after the monitoring data is observed, therefore, the data monitored at day 110, 21.61mm, was judged as abnormal data.
In summary, in the invention, by establishing a data warehouse to select study objects and applying a correlation method, correlation coefficients of the study objects in a full monitoring period are calculated, an empirical reference threshold S of the correlation coefficients in the full monitoring period is determined, comparison and analysis are performed, spatial positions of abnormal data are located, time windows are divided for the full monitoring period, the magnitude of the correlation coefficients in each time window is calculated and analyzed, an empirical reference threshold T of the correlation coefficients in the time windows is determined, and time positions of the abnormal data are located.
For ease of reference, the effects are now provided as follows:
the following table is a correlation coefficient matrix of a full monitoring period with the depth of more than-5 m of a CX22 underground continuous wall measuring point group in an engineering example, and the correlation coefficient matrix is compared with a correlation coefficient empirical reference threshold value of the full monitoring period for analysis to judge that abnormal data exist in monitoring data at the depth of-1.5 m.
In specific implementation, a person skilled in the art can implement the automatic operation process by using a computer software technology, and a system device for implementing the method, such as a computer-readable storage medium storing a corresponding computer program according to the technical solution of the present invention and a computer device including a corresponding computer program for operating the computer program, should also be within the scope of the present invention.
In some possible embodiments, a correlation-based subway construction long-term monitoring abnormal information mining system is provided, which comprises the following modules,
the system comprises a first module, a second module and a third module, wherein the first module is used for collecting monitoring data of a construction site and establishing a data warehouse;
the second module is used for selecting data aiming at the data warehouse and determining the accumulated deformation data of the underground continuous wall as a research object;
the third module is used for calculating correlation coefficients of accumulated deformation of the underground continuous walls at different depths of the same measuring point, determining an empirical reference threshold of the correlation coefficients, comparing the correlation coefficients of the underground continuous walls at the depths with the empirical reference threshold, and searching and judging abnormal data existing in monitoring data of the underground continuous walls at each depth;
the fourth module is used for dividing the time window of the underground continuous wall monitoring period at a certain depth according to the judgment result of the third module, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall under each time window and positioning abnormal data;
and the fifth module is used for obtaining a data mining result of the long-term monitoring abnormal information based on the correlation based on the abnormal data positioned at each depth, and early warning the actual engineering risk.
In some possible embodiments, a related relationship-based excavation system for long-term monitoring anomaly information in subway construction is provided, which includes a processor and a memory, where the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a related relationship-based excavation method for long-term monitoring anomaly information in subway construction as described above.
In some possible embodiments, a related relationship-based excavation system for monitoring abnormal information during subway construction long time is provided, and includes a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed, the related relationship-based excavation method for monitoring abnormal information during subway construction long time is implemented.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (8)
1. A method for mining long-time monitoring abnormal information of subway construction based on correlation is characterized by comprising the following steps:
1) collecting monitoring data of a construction site and establishing a data warehouse;
2) data selection is carried out on the data warehouse, and accumulated deformation data of the underground continuous wall are determined as research objects;
3) calculating correlation coefficients of accumulated deformation of the underground continuous walls at different depths of the same measuring point, determining an empirical reference threshold of the correlation coefficients, comparing the correlation coefficients of the underground continuous walls at the depths with the empirical reference threshold, and searching and judging abnormal data existing in monitoring data of the underground continuous walls at each depth;
4) according to the judgment result of the step 3), carrying out time window division on the underground continuous wall monitoring period at a certain depth, calculating the correlation coefficient of the accumulated deformation of the underground continuous wall under each time window, and positioning abnormal data;
5) and obtaining a data mining result of the long-term monitoring abnormal information based on the correlation based on the abnormal data positioned at each depth, and early warning the actual engineering risk.
2. The method for mining the long-term monitoring abnormal information of subway construction based on the correlation relationship as claimed in claim 1, wherein: the data warehouse is a set of monitoring data of a construction site.
3. The method for mining the long-term monitoring abnormal information of subway construction based on the correlation relationship as claimed in claim 2, wherein: the method for acquiring the deformation data of the underground continuous wall body comprises the steps of arranging a plurality of groups of underground continuous wall monitoring points along the periphery of a foundation pit, and arranging one deformation monitoring point at a fixed distance from the top of the wall body to the bottommost end of each monitoring point.
4. The method for mining the long-term monitoring abnormal information of subway construction based on the correlation relationship as claimed in claim 1, wherein: and 3) calculating correlation coefficients of the accumulated deformation of the underground continuous walls at different depths and a certain specific depth of the same measuring point, and if the correlation coefficients of the accumulated deformation of the underground continuous walls at other depths and the specific depth are far smaller than an empirical reference threshold, prejudging that abnormal data exists in the accumulated deformation data of the underground continuous walls at the depth.
5. The method for mining the long-term monitoring abnormal information of subway construction based on the correlation relationship as claimed in claim 4, wherein: and 4) according to the pre-judgment result, dividing time windows of the underground diaphragm wall monitoring period at the depth where the abnormal data exists and the specific depth, calculating, comparing and analyzing correlation coefficients under each time window, and positioning the abnormal data.
6. The method for mining long-term monitoring abnormal information of subway construction based on correlation relationship as claimed in claim 5, wherein: the duration of dividing the time window can be determined according to engineering characteristics.
7. The method for mining the long-term monitoring abnormal information of subway construction based on the correlation relationship as claimed in claim 5, wherein: the abnormal data positioning is realized by judging whether the correlation coefficient of a certain time window is far smaller than the correlation coefficients of other time windows, and if so, determining that the abnormal data is located in the monitoring data of the underground continuous wall in the time window.
8. The method for mining the abnormal information of long-term monitoring during the construction of the subway based on the correlation according to claim 1, 2, 3, 4, 5, 6 or 7, wherein: and the correlation coefficient calculation adopts the Pearson correlation coefficient.
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| CN114897421B (en) * | 2022-06-06 | 2023-02-10 | 深圳大学 | Method, system and storage medium for deep foundation pit excavation and construction safety early warning |
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