CN117494483B - Numerical optimization method for deformation data of double-hole tunnel section - Google Patents

Numerical optimization method for deformation data of double-hole tunnel section Download PDF

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CN117494483B
CN117494483B CN202410002607.7A CN202410002607A CN117494483B CN 117494483 B CN117494483 B CN 117494483B CN 202410002607 A CN202410002607 A CN 202410002607A CN 117494483 B CN117494483 B CN 117494483B
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data
item
change
moment
significance
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CN117494483A (en
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谭飞
丁大维
易明伟
殷现宾
张东林
孙鹏飞
迟文博
杨杰
刘家栋
段平威
王文聪
邵博鑫
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Shandong Weiyan High Speed Railway Co ltd
Shanghai Civil Engineering Co Ltd of CREC
Seventh Engineering Co Ltd of Shanghai Civil Engineering Co Ltd of CREC
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Shandong Weiyan High Speed Railway Co ltd
Shanghai Civil Engineering Co Ltd of CREC
Seventh Engineering Co Ltd of Shanghai Civil Engineering Co Ltd of CREC
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

The invention relates to the technical field of data processing, in particular to a numerical optimization method of deformation data of a section of a double-hole tunnel, which comprises the following steps: acquiring each item of data of a tunnel section, acquiring local change saliency and overall change saliency of the data at the current moment in each item of data, further acquiring the change saliency, acquiring the change synchronicity of the data at the current moment in each item of data and other items of data according to the difference between the local change saliency and the overall change saliency of the data at the same moment in each item of data, acquiring the smoothing coefficient of the data at the current moment in each item of data according to the change saliency and the change synchronicity, and acquiring the predicted value of each item of data at the next moment in the tunnel section according to the smoothing coefficient. The method eliminates the interference of measurement errors and noise, so that the predicted value can more accurately reflect the change condition of each item of data of the tunnel section at the next moment, and ensures the timeliness of the deformation monitoring and treatment of the tunnel section.

Description

Numerical optimization method for deformation data of double-hole tunnel section
Technical Field
The invention relates to the technical field of data processing, in particular to a numerical optimization method for deformation data of a section of a double-hole tunnel.
Background
Tunnels are important infrastructure and bear important functions in transportation, water conservancy, energy and the like. The safety of the tunnel structure is critical for maintaining normal operation of society. By monitoring the deformation of the tunnel section, the possible structural problems can be found and solved in time, and the safety of the tunnel is ensured.
At present, the deformation of the tunnel section is monitored, the settlement of the tunnel section is monitored through a displacement sensor, and when the settlement reaches a certain threshold value, an alarm is generated to remind related personnel to carry out treatment. However, in the process of monitoring the tunnel section settlement by the displacement sensor, measurement errors and inaccuracy of acquired data may exist, so that the monitoring accuracy of tunnel section deformation is affected.
Disclosure of Invention
In order to solve the above problems, the present invention provides a numerical optimization method for deformation data of a section of a double-hole tunnel, which comprises the following steps:
collecting various data of a tunnel section, including settlement displacement and inclination angles of various monitoring points of the tunnel section;
for each item of data, acquiring the local change significance of the data at the current moment in the item of data according to the difference between the data at the current moment in the item of data and the data at the previous moment; acquiring the overall change significance of the data at the current moment in the item of data according to the difference between the data at all adjacent two moments in the item of data; acquiring the change significance of the data at the current moment in the item of data according to the local change significance and the overall change significance of the data at the current moment in the item of data;
according to the difference between the local change significance and the overall change significance of the data at the same moment in each item of data, the change synchronism of the data at the current moment in each item of data and other items of data is obtained;
acquiring a smoothing coefficient of the data at the current moment in each item of data according to the change significance of the data at the current moment in each item of data and the change synchronicity of the data at the current moment in each item of data and other items of data; and obtaining a predicted value of each item of data of the tunnel section at the next moment by using an exponential smoothing method according to the smoothing coefficient.
Preferably, the obtaining the local variation significance of the current time data in the item of data according to the difference between the current time data and the previous time data in the item of data includes the following specific steps:
and acquiring the range of the item of data, acquiring the absolute value of the difference value between the data at the current moment and the data at the previous moment in the item of data, and taking the ratio of the absolute value of the difference value to the range as the local variation significance of the data at the current moment in the item of data.
Preferably, the method for obtaining the overall change significance of the data at the current time in the item of data according to the differences between the data at all adjacent two times in the item of data comprises the following specific steps:
wherein (1)>Representing the current time;represents the%>Item data item->The overall change significance of the data at each moment; />Indicate->Extremely bad item data; />Indicate->Item data item->Data and->Difference in data at a time immediately preceding each timeA value; />Representing absolute value symbols; />Indicate->The absolute value of the maximum difference between all the data of two adjacent moments in the item data; />Indicate->The time closest to the current time in the adjacent two times corresponding to the maximum difference absolute value between the data of all the adjacent two times in the item data; />An exponential function based on a natural constant is represented.
Preferably, the obtaining the change significance of the data at the current time in the item of data according to the local change significance and the overall change significance of the data at the current time in the item of data comprises the following specific steps:
and taking the maximum value of the local change significance and the overall change significance of the data at the current moment in the item of data as the change significance of the data at the current moment in the item of data.
Preferably, the step of obtaining the change synchronicity of the data at the current time in each item of data and other items of data according to the difference between the local change significance and the overall change significance of the data at the same time in each item of data comprises the following specific steps:
wherein (1)>Representing the current time; />Represents the%>Item data item->The change synchronicity of the data at each moment and other items of data;indicate->Item data item->Local variation significance of the data at each moment; />Indicate->Item data item->Local variation significance of the data at each moment; />Indicate->Item data item->The overall change significance of the data at each moment; />Indicate->Item data item->The overall change significance of the data at each moment; />Representing the number of data items collected; />Representing absolute value symbols; />An exponential function based on a natural constant is represented.
Preferably, the method includes the specific steps of:
wherein (1)>Representing the current time; />Represents the first of the tunnel sectionItem data item->Smoothing coefficients of the data at each instant; />Represents the%>Item data item->Data of individual momentsSignificance of change; />Represents the%>Item data item->The change synchronicity of the data at each moment and other items of data; />Is a super parameter.
The technical scheme of the invention has the beneficial effects that: according to the method, the local change saliency and the overall change saliency of the data at the current moment in each item of data of the tunnel section are obtained, the local change saliency and the overall change saliency are combined to obtain the change saliency, the smooth coefficient of the data is obtained according to the change saliency, and the data at the next moment is predicted, so that the predicted value is more concerned with the change of each item of data of the tunnel section, and the predicted value can reflect the deformation condition of the tunnel section as much as possible;
according to the method and the device, the change synchronicity of the data at the current moment and other data in each item of data is obtained according to the difference between the local change significance and the overall change significance of the data at the same moment in each item of data, the data change caused by measurement errors and noise and the data change caused by tunnel section deformation can be distinguished through the change synchronicity, the smooth coefficient of the data is obtained according to the change synchronicity, and the prediction of the data at the next moment is carried out, so that the predicted value eliminates the interference of the measurement errors and the noise, the change condition of each item of data of the tunnel section at the next moment can be reflected more accurately, and the timeliness of tunnel section deformation monitoring and treatment is ensured.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a method for optimizing the deformation data of the section of a double-hole tunnel according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of a numerical optimization method for the deformation data of the double-hole tunnel section according to the present invention, which is provided by the present invention, with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the numerical optimization method for the deformation data of the section of the double-hole tunnel provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for optimizing the numerical value of deformation data of a double-hole tunnel section according to an embodiment of the invention is shown, and the method includes the following steps:
s001, collecting various data of the tunnel section.
A plurality of monitoring points are uniformly distributed on the section of the tunnel, and displacement sensors and inclinometers are deployed at each monitoring point. And the displacement sensor and the inclinometer are used for periodically measuring the settlement displacement and the inclination angle of the monitoring point. In this embodiment, the acquisition frequency is once a week, and in other embodiments, the operator may set the acquisition frequency according to the actual implementation.
The settlement displacement of each monitoring point is respectively regarded as one item of data of the tunnel section, and each monitoring point is treatedThe dip angle of the measuring point is respectively regarded as one item of data of the tunnel section, so that a plurality of items of data of the tunnel section can be obtained, and the acquired data item number is recorded as。
Thus, the acquisition of each item of data of the tunnel section is realized.
S002, acquiring the local change saliency and the overall change saliency of the data at the current moment in each item of data of the tunnel section, and acquiring the change saliency of the data at the current moment in each item of data according to the local change saliency and the overall change saliency.
If the change of the data at the current time in each item of data of the tunnel section is more obvious, it is explained that the tunnel section is more likely to be deformed at the current time, so that the local change significance of the data at the current time in each item of data of the tunnel section is obtained according to the change condition of the data at the current time in each item of data of the tunnel section.
Specifically, the current time is recorded as the first timeAt each moment, obtaining the local change significance of the data at the current moment in each item of data:
wherein,representing the current time; />Represents the%>Item data item->Local significance of change of data at each instant, i.e.>Local change significance of data at the current moment in the item data; />Indicate->Item data item->Data at each moment, i.e.)>The data of the current moment in the item data; />Indicate->Item data item->Data at each time; />Indicate->Extreme difference of item data, i.e. +.>The difference between the maximum and minimum values in the item data; />Representing absolute value symbols; if the difference between the data at the current time and the data at the previous time is larger, the change of the data at the current time relative to the data at the previous time is more remarkable, at the moment +.>Local part of data at current time in item dataThe greater the significance of the change.
It should be noted that there may be a case where the current time is not significantly changed from the data at the previous time, but the tunnel section is significantly deformed from the initial stage of monitoring, and the local significance of the change of the current time data in the respective data cannot reflect the case. Therefore, the embodiment combines the change condition of the data between any adjacent time in the data before the current time to acquire the integral change significance of the data at the current time in the data.
Specifically, the overall change significance of the data at the current moment in each item of data is acquired:
wherein (1)>Representing the current time;represents the%>Item data item->The significance of the overall change of the data at the individual moments, i.e.>The overall change significance of the data at the current moment in the item data; />Indicate->Extreme difference of item data, i.e. +.>The difference between the maximum and minimum values in the item data; />Indicate->Item data item->Data and->The difference in data at a time preceding the time; />Representing absolute value symbols; />Indicate->The absolute value of the maximum difference between all the data of two adjacent moments in the item data; />Indicate->The time closest to the current time in the adjacent two times corresponding to the maximum difference absolute value between the data of all the adjacent two times in the item data;an exponential function that is based on a natural constant; when one item of data is smoother, the probability of deformation of the tunnel section is smaller, when one item of data is changed, the probability of deformation of the tunnel section is shown, and the previous data change is accumulated to the current moment due to accumulation of the data change, so the embodiment adopts the +.>Maximum absolute difference between data of all adjacent two moments in item data +.>Reflects the maximum accumulated change of the current moment to a certain extent, when the adjacent two moments corresponding to the absolute value of the maximum difference value are closer to the current moment, namelyThe smaller indicates that the cumulative change is more pronounced the closer to the current time, at which time the +.>The overall change of the data at the present moment in the item data is more significant than the early monitoring, corresponding +.>The greater the overall significance of the change in the data at the current time in the item data.
Obtaining the change significance of the current moment in each item of data according to the local change significance and the overall change significance of the current moment in each item of data:
wherein,representing the current time; />Represents the%>Item data item->Significance of change of data at each moment, i.e.>The significance of the change of the data at the current moment in the item data; />Indicate->Item data item->Local significance of change of data at each instant, i.e.>Local change significance of data at the current moment in the item data; />Indicate->Item data item->The significance of the overall change of the data at the individual moments, i.e.>The overall change significance of the data at the current moment in the item data; will be->Local variation significance of data at the present moment in item data and maximum value in global variation significance as +.>The significance of the change of the data at the current time in the item data.
So far, the significance of the change of the data at the current moment in each item of data of the tunnel section is obtained.
S003, according to the difference between the local change significance and the overall change significance of the data at the same moment in each item of data, the change synchronism of the data at the current moment in each item of data and other items of data is obtained.
When a major change occurs in one item of data of the tunnel section, the data may be changed due to deformation of the tunnel section, or may be changed due to errors or noise in the measurement process. If the tunnel section is deformed, the settlement displacement and the inclination angle of each monitoring point are synchronously changed, namely, the data of each item of data at the same moment are synchronously changed, and the data change caused by errors or noise in the measuring process is only reflected in a single data item, so that the method obtains the change synchronism of the data of the current moment in each item of data and other items of data according to the local change significance and the integral change significance of the data of the current moment in each item of data, and is used for reflecting the possibility that the change of the data of the current moment in each item of data is caused by the tunnel section deformation.
Specifically, for each item of data, the change synchronism of the data at the current moment in the item of data and other items of data is obtained:
wherein,representing the current time; />Represents the%>Item data item->The synchronicity of the change of the data at the moment with the other data, i.e. +.>The change synchronism of the data at the current moment in the item data and other item data; />Indicate->Item data item->Local variation significance of the data at each moment; />Indicate->Item data item->Local variation significance of the data at each moment; />Indicate->Item data item->The overall change significance of the data at each moment; />Indicate->Item data item->The overall change significance of the data at each moment; />Representing the number of data items collected; />Representing absolute value symbols; />An exponential function that is based on a natural constant; when the first isItem data item->Local change significance of data at each moment and +.>Item data item->The more similar the local variation significance of the data at the moment is, while at the same time when +.>Item data item->The significance of the overall change of the data at the individual moments and +.>Item data item->The more similar the overall change significance of the data at the individual moments, the +.>Item data item->Data and->Item data item->The more synchronous the change of the data at each moment; when->Item data item->Data at each time and each of the remaining dataThe more synchronous the change of data at the same instant in time +.>Item data item->The greater the synchronicity of the change of the data at a moment with the other item of data, the +.>Item data item->The more likely the data at each moment is the data change caused by tunnel section deformation; conversely, when->Item data item->The more unsynchronized the data at one time is with the change of the data at the same time in each of the other data +.>Item data item->The smaller the change synchronicity of the data at the moment with the other items of data, the +.>Item data item->The more likely the data at each instant is the change in data due to measurement errors or noise.
So far, the change synchronism of the data at the current moment in each item of data and other items of data is obtained.
S004, obtaining a smoothing coefficient of the data at the current moment in each item of data according to the change significance of the data at the current moment in each item of data and the change synchronism of the data at the current moment in each item of data and other items of data, and obtaining a predicted value of each item of data at the next moment of the tunnel section according to the smoothing coefficient.
In order to find out the deformation of the tunnel section in time and dispose it, the present embodiment predicts each item of data at the next moment according to each item of data at the history moment of the tunnel section by using an exponential smoothing method. The change of the current moment data in the data of the tunnel section may be caused by deformation of the tunnel section or may be caused by errors and noise in the measurement process, so that in order to avoid the interference of the errors and noise in the measurement process on the prediction result, the prediction result can more accurately reflect the deformation condition of the tunnel section.
Specifically, for each item of data, a smoothing coefficient of the data at the current time in the item of data is obtained according to the change significance of the data at the current time in the item of data and the change synchronicity of the data at the current time in the item of data and other items of data:
wherein,representing the current time; />Represents the%>Item data item->Smoothing coefficients of the data at the respective instants, i.e.>Smoothing coefficients of data at the current time in the item data; />Represents the%>Item data item->Significance of change of data at each moment, i.e.>The significance of the change of the data at the current moment in the item data;represents the%>Item data item->The synchronicity of changes in the data at each instant with other data, i.e. the firstThe change synchronism of the data at the current moment in the item data and other item data; />Is a superparameter, empirical value->The method is not particularly limited, and an operator can set super parameters according to actual implementation conditions;
when the first isIf the change synchronicity between the data at the current time and other item data is greaterThe greater the significance of the change in the data at the current time in the item data, the description +.>The change of the data at the current moment in the item data is more likely to be the data change caused by the deformation of the tunnel section, when the data is predicted, the more the actual value of the data at the current moment is required to be paid attention to, so that the predicted value can reflect the data change condition of the deformation of the tunnel section more, and if the predicted value is the first data change condition, the more the data change condition of the deformation of the tunnel section is>The smaller the significance of the change of the data at the current moment in the item data, the description +.>The more stable the data at the current moment in the item data is, the less the possibility that the tunnel section is deformed is, and the more attention is required to be paid to the data at the historical moment when the data is predicted at the moment, so that the predicted value can reflect the whole condition of the tunnel section. Thus when->The change synchronism of the data at the current moment in the item data and other item data is greater than or equal to the super parameter +.>When using +.>As->Smoothing coefficient of data at the present moment in item data, when +.>The greater the change significance of the data at the current moment in the item data, the greater the smoothing coefficient, and the more attention is paid to the actual value of the data at the current moment when the data is predicted, so that the predicted value can reflect the change of the tunnel sectionData change of shape, when +.>The smaller the change significance of the data at the current moment in the item data is, the smaller the smoothing coefficient is, and the more attention is paid to the data at the historical moment when the data is predicted;
when the first isIf the change synchronicity between the data at the current time and other item data is smaller in the item dataThe greater the significance of the change in the data at the current time in the item data, the description +.>The change of the data at the current moment in the item data is more likely to be the data change caused by measurement errors or noise, and the more attention is required to be paid to the data at the historical moment when the data is predicted, the larger interference of the data change caused by the measurement errors or noise at the current moment on the predicted value is avoided. Thus when->The change synchronicity of the data at the current moment in the item data and other item data is smaller than the super parameter +.>When usingAs->Smoothing coefficients of the data at the present time in the item data such that +.>The smaller the smoothing coefficient of the data at the current moment in the item data is, the more attention is paid to the data at the historical moment when the data is predicted, and the measurement error or noise at the current moment is avoidedThe data changes of (a) cause a large disturbance to the predicted value.
So far, the smoothing coefficient of the data at the current moment in each item of data is obtained.
And obtaining the predicted value of each item of data at the next moment by using an exponential smoothing method according to the smoothing coefficient of the data at the current moment in each item of data. The predicted value at the next moment eliminates the interference of measurement errors and noise, and can more accurately reflect the change condition of each item of data of the tunnel section at the next moment. Reminding related personnel to intervene and treat the deformation condition of the tunnel section in time according to the predicted value of each item of data at the next moment, eliminating potential safety hazards and avoiding safety accidents.
According to the embodiment of the invention, the local change saliency and the overall change saliency of the data at the current moment in each item of data of the tunnel section are obtained, the local change saliency and the overall change saliency are combined to obtain the change saliency, the smooth coefficient of the data is obtained according to the change saliency, and the data at the next moment is predicted, so that the predicted value is more concerned with the change of each item of data of the tunnel section, and the predicted value can reflect the deformation condition of the tunnel section as far as possible; according to the method and the device, the change synchronicity of the data at the current moment and other data in each item of data is obtained according to the difference between the local change significance and the overall change significance of the data at the same moment in each item of data, the data change caused by measurement errors and noise and the data change caused by tunnel section deformation can be distinguished through the change synchronicity, the smooth coefficient of the data is obtained according to the change synchronicity, and the prediction of the data at the next moment is carried out, so that the predicted value eliminates the interference of the measurement errors and the noise, the change condition of each item of data of the tunnel section at the next moment can be reflected more accurately, and the timeliness of tunnel section deformation monitoring and treatment is ensured.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.

Claims (4)

1. The numerical optimization method for the deformation data of the double-hole tunnel section is characterized by comprising the following steps of:
collecting various data of a tunnel section, including settlement displacement and inclination angles of various monitoring points of the tunnel section;
for each item of data, acquiring the local change significance of the data at the current moment in the item of data according to the difference between the data at the current moment in the item of data and the data at the previous moment; acquiring the overall change significance of the data at the current moment in the item of data according to the difference between the data at all adjacent two moments in the item of data; acquiring the change significance of the data at the current moment in the item of data according to the local change significance and the overall change significance of the data at the current moment in the item of data;
according to the difference between the local change significance and the overall change significance of the data at the same moment in each item of data, the change synchronism of the data at the current moment in each item of data and other items of data is obtained;
acquiring a smoothing coefficient of the data at the current moment in each item of data according to the change significance of the data at the current moment in each item of data and the change synchronicity of the data at the current moment in each item of data and other items of data; obtaining a predicted value of each item of data of the tunnel section at the next moment by using an exponential smoothing method according to the smoothing coefficient;
according to the difference between the local change significance and the overall change significance of the data at the same moment in each item of data, the change synchronicity of the data at the current moment in each item of data and other items of data is obtained, and the method comprises the following specific steps:
wherein,representing the current time; />Represents the%>Item data item->The change synchronicity of the data at each moment and other items of data; />Indicate->Item data item->Local variation significance of the data at each moment; />Indicate->Item data item->Local variation significance of the data at each moment; />Indicate->Item data item->The overall change significance of the data at each moment; />Indicate->Item data item->The overall change significance of the data at each moment; />Representing the number of data items collected; />Representing absolute value symbols; />An exponential function that is based on a natural constant;
the method comprises the following specific steps of:
wherein,representing the current time; />Represents the%>Item data item->Smoothing coefficients of the data at each instant;represents the%>Item data item->Significance of change in data at each time; />Represents the%>Item data item->The change synchronicity of the data at each moment and other items of data; />Is a super parameter.
2. The method for optimizing the numerical value of the deformation data of the section of the double-hole tunnel according to claim 1, wherein the obtaining the local variation significance of the data at the current moment in the data according to the difference between the data at the current moment and the data at the previous moment in the data comprises the following specific steps:
and acquiring the range of the item of data, acquiring the absolute value of the difference value between the data at the current moment and the data at the previous moment in the item of data, and taking the ratio of the absolute value of the difference value to the range as the local variation significance of the data at the current moment in the item of data.
3. The method for optimizing the numerical value of the deformation data of the section of the double-hole tunnel according to claim 1, wherein the method for acquiring the overall change significance of the data at the current moment in the data according to the difference between the data at all adjacent two moments in the data comprises the following specific steps:
wherein,representing the current time; />Represents the%>Item data item->The overall change significance of the data at each moment; />Indicate->Extremely bad item data; />Indicate->Item data item->Data and->The difference in data at a time preceding the time; />Representing absolute value symbols; />Indicate->The absolute value of the maximum difference between all the data of two adjacent moments in the item data; />Indicate->The time closest to the current time in the adjacent two times corresponding to the maximum difference absolute value between the data of all the adjacent two times in the item data;an exponential function based on a natural constant is represented.
4. The method for optimizing the numerical value of the deformation data of the section of the double-hole tunnel according to claim 1, wherein the obtaining the change saliency of the data at the current moment in the item of data according to the local change saliency and the overall change saliency of the data at the current moment in the item of data comprises the following specific steps:
and taking the maximum value of the local change significance and the overall change significance of the data at the current moment in the item of data as the change significance of the data at the current moment in the item of data.
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