CN115928810A - Foundation pit intelligent monitoring method based on multi-sensor data fusion - Google Patents
Foundation pit intelligent monitoring method based on multi-sensor data fusion Download PDFInfo
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Abstract
The invention discloses a foundation pit intelligent monitoring method based on multi-sensor data fusion, and belongs to the technical field of foundation pit intelligent monitoring. The method comprises the steps of constructing a multi-sensor data fusion time-varying model, sensors of all foundation pit monitoring subsystems and filters of a main sensor, carrying out independent time updating on each filter and carrying out measurement updating on a local filter, and fusing estimation information of the local filter LF and the main filter MF into new global state estimation information to obtain a latest foundation pit monitoring state value. The method comprehensively applies various sensor data used in foundation pit monitoring, integrates theoretical values, measured values and change information of the monitoring periods of all foundation pit monitoring sensors before and after, realizes the complementation of the foundation pit monitoring data, can find the abnormal condition of the foundation pit engineering in time, is suitable for different monitoring of different foundation pits, thereby ensuring the safety of the foundation pits and ensuring the smooth proceeding of the engineering.
Description
Technical Field
The invention belongs to the technical field of foundation pit monitoring, and particularly relates to an intelligent foundation pit monitoring method based on multi-sensor data fusion.
Background
Excavation of the foundation pit is a dynamic process, and the surrounding environment is also affected and is also in dynamic change. Therefore, in the construction process, the foundation pit support structure and the surrounding environment must be monitored in a three-dimensional all-dimensional and all-around manner. The construction monitoring mainly aims to compare the previous monitoring data with the previous monitoring data, calculate whether the variable quantity meets the expected requirement and discover the possible dangerous aura. Once the instability of the foundation pit occurs, the engineering progress is influenced, the construction cost is increased, the construction period is seriously delayed when casualties are caused by the accident of the foundation pit, and severe social influence is caused. Therefore, research and development and optimization of the intelligent foundation pit safety monitoring technology with high timeliness and high precision are imminent. And judging the safety of the engineering so as to take necessary engineering measures in advance, prevent the occurrence of engineering damage accidents or peripheral environment accidents, ensure the smooth proceeding of the engineering and ensure the construction safety.
At present in the foundation ditch monitoring process, often need a plurality of sensors to arrange the use, if: a measuring robot, a water level gauge, an inclinometer, a static level gauge and the like. The foundation pit monitoring data mainly acquired by the sensors mainly comprise foundation pit slope top displacement data, deep horizontal displacement data, anchor cable stress data, underground water bit data and the like. However, a single foundation pit monitoring sensor often cannot provide a relatively comprehensive foundation pit monitoring and early warning result, and therefore, the accuracy of foundation pit monitoring and early warning judgment is often improved by integrating a plurality of sensors. However, when a plurality of sensors are introduced, more system errors and abnormal errors are introduced, and how to scientifically and reasonably use the results of the plurality of sensors obtains a reasonable early warning result for the change condition of the foundation pit, so that the method has important guiding significance for the whole process of foundation pit construction.
Through search, the Chinese patent application numbers are: 201811617346.0, filed as follows: in 2018, 12 and 28 months, the invention and creation name is: a method and a system for foundation pit monitoring intelligent early warning and data storage are provided. The method in this application comprises the steps of: constructing a network; the method comprises the following steps of collecting foundation pit monitoring data and sending the foundation pit monitoring data to Internet of things equipment, and uploading the foundation pit monitoring data to a side chain by the Internet of things equipment for storage; the Internet of things equipment analyzes the foundation pit monitoring data through a real-time data analysis and early warning system based on machine learning to obtain a result; judging whether the foundation pit is safe or not according to the analysis result; when the analysis result is unsafe or has potential risks, sending an early warning signal to each main body of the foundation pit engineering and sending a certificate storing instruction to a side chain; after receiving the evidence storage instruction, the side chain stores the foundation pit monitoring data and the analysis result and uploads the foundation pit monitoring data and the analysis result to the main chain for evidence storage. The machine learning model is used for processing and early warning the foundation pit monitoring data, but the machine learning model is used as a statistical model, mass data support is often needed, and different foundation pit engineering characteristics are different, so that different requirements of different foundation pit engineering are difficult to meet.
Disclosure of Invention
1. Problems to be solved
Aiming at the defects of the prior art, the invention designs the foundation pit intelligent monitoring method based on multi-sensor data fusion, which can comprehensively apply various sensor data used in the foundation pit monitoring engineering, and fuses a theoretical value, an actual measurement value and change information of the front and rear monitoring periods of all foundation pit monitoring sensors, thereby realizing the complementation of the foundation pit monitoring data, finding the abnormal condition of the foundation pit engineering in time, ensuring the safety of the foundation pit and ensuring the smooth operation of the engineering.
2. Technical scheme
In order to solve the problems, the technical scheme adopted by the invention is as follows:
the invention discloses a foundation pit intelligent monitoring method based on multi-sensor data fusion, which comprises the following steps of:
firstly, constructing a time-varying model for data fusion of multiple sensors in foundation pit engineering;
secondly, constructing a filter of a sensor of each monitoring subsystem of the foundation pit and a filter of a main sensor constructed by a foundation pit deformation theoretical model;
step three, carrying out independent time updating on each filter;
measuring and updating a local filter of the foundation pit monitoring subsystem;
and step five, fusing the estimation information of the local filter of the foundation pit monitoring subsystem and the main filter of the foundation pit monitoring system into new global state estimation information according to the fused time-varying model in the step one, and further obtaining the latest foundation pit monitoring state value.
It should be noted that, compared with the prior art, the advantages of the present invention are: the method has the advantages that the theoretical value of the theoretical research of the deformation of the foundation pit at present and the measured value monitored by various sensors are fused together to construct the Federal Kalman filter, the result obtained by the filter corresponding to different sensors and the theoretical result are fused through the Federal Kalman filter, and meanwhile, the variable quantity of the front observation period and the rear observation period of different foundation pit monitoring sensors is transmitted, so that the early warning on the change condition of the foundation pit can be more accurately carried out, and the method has important guiding significance on the construction of the foundation pit.
Furthermore, when the foundation pit is monitored, the existing sensor equipment is adopted to collect various index data of the foundation pit in real time, different filters are respectively and correspondingly constructed according to measured values monitored by different sensors, and results obtained by the filters corresponding to the different sensors and theoretical values of foundation pit deformation are fused by adopting a Federal Kalman filter. The actual measurement value is related data obtained by monitoring the foundation pit by using a corresponding sensor, and mainly comprises foundation pit slope top displacement data, deep horizontal displacement data, anchor cable stress data, underground water bit data and the like of the foundation pit.
Furthermore, the principal filter of the federal kalman filter adopts a theoretical value of foundation pit deformation, and the federal kalman filter integrates the theoretical value, an actual measurement value and the variable quantity of the monitoring period before and after all the foundation pit monitoring sensors.
Furthermore, the Federal Kalman filter adopts a fusion reset mode, no information is distributed in the main filter, the main filter feeds the fusion result back to the filters of all foundation pit monitoring sensors, each filter obtains global information, the precision is obviously improved, and finally the global optimal fusion result can be obtained.
Drawings
FIG. 1 is a schematic diagram of a federated filtering algorithm for multi-sensor fusion in the present invention;
fig. 2 is a diagram showing the result of observation data of a certain foundation pit item in example 1 of the present invention.
Detailed Description
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention is further described with reference to specific examples.
The deformation process of the whole foundation pit is a long-term time-varying process, the deformation process is influenced by factors such as the form, the rigidity and the construction method of a supporting structure during construction, the supporting structure can be expressed in various deformation modes, and further the deformation characteristics of buildings at different positions outside the foundation pit and the deformation characteristics of deep soil outside the foundation pit are different. The theoretical value of the foundation pit deformation can be obtained by combining the theoretical study of the current foundation pit deformation and the theoretical equation of the displacement of the outer surface soil body and the displacement of the outer deep layer soil body in the current foundation pit deformation. In addition, in the current foundation pit monitoring, the observed values of the deformation of the foundation pit in different dimensions can be obtained by using different foundation pit monitoring sensor devices. However, a single foundation pit monitoring sensor cannot provide a relatively comprehensive foundation pit monitoring and early warning result, and results of a plurality of sensors need to be synthesized and manually researched to determine the foundation pit deformation condition. According to the invention, a unified filter is constructed in the monitoring process, different filters are constructed aiming at different sensor monitoring items, the results obtained by all sensors are set as a unified state matrix, and the unification of a deformation monitoring result equation is realized.
Specifically, as shown in fig. 1, the method for intelligently monitoring a foundation pit based on multi-sensor data fusion provided by the invention comprises the following steps:
firstly, constructing a foundation pit engineering multi-sensor data fusion time-varying model; the multi-sensor adopts various monitoring sensors, for example, in the figure 1, foundation pit monitoring sensors 1-i are sensors which are determined and selectively used according to the requirements of monitoring indexes during actual monitoring, for example, the existing measuring robot, a water level gauge, an inclinometer, a static level gauge and the like are adopted, the technology of the invention can integrate the detection item data of various sensors and combine the theoretical research value of foundation pit deformation, thereby representing the state of the foundation pit in an all-around and real-time manner and being beneficial to guiding the smooth construction of the foundation pit.
Secondly, constructing a filter of a sensor of each monitoring subsystem of the foundation pit and a filter of a main sensor constructed by a foundation pit deformation theoretical model;
step three, carrying out independent time updating on each filter;
measuring and updating a local filter of the foundation pit monitoring subsystem;
and step five, fusing the estimation information of the local filter of the foundation pit monitoring subsystem and the main filter of the foundation pit monitoring system into new global state estimation information according to the fused time-varying model in the step one, and further obtaining the latest foundation pit monitoring state value.
According to the invention, the theoretical value of the theoretical research of the deformation of the foundation pit at present and the measured value monitored by various sensors are fused together to construct the Federal Kalman filter, the result obtained by the filter corresponding to different sensors is fused with the theoretical result by the Federal Filter, and the variation of the observation periods before and after different foundation pit monitoring sensors is transmitted, so that the early warning on the change condition of the foundation pit can be more accurately carried out, and the method has important guiding significance on the construction of the foundation pit.
In the invention, the following multi-sensor data fusion time-varying model is constructed:
X k ＝Φ k，k-1 X k-1 +W k
wherein, X k ＝[x 1 (k) x 2 (k) L x n (k)]，x n (k) As eigenvalues x of a time-varying model n A state matrix at the kth observation period; phi k，k-1 Is a state transition matrix from k-1 observation periods to the k-th observation period; w k Is a gaussian white noise process error vector.
Because in the monitoring of foundation ditch at present, involve a plurality of sensors commonly and use in coordination, the foundation ditch monitoring data who obtains mainly includes: displacement data of the top of the foundation pit, deep horizontal displacement data, anchor cable stress data, underground water bit data and the like. According to the observation method of the corresponding monitoring subsystem established by the used sensor, the observation equation of the ith sensor monitoring subsystem in the invention is as follows:
L ik ＝A ik X k +e ik
L ik for the observation of the kth observation period of the ith sensor monitoring subsystem, A ik An array of observation coefficients of the subsystem is monitored for the sensor. e.g. of the type ik To observe the noise vector. E is desired.
At t k The time-varying model prediction value of the state vector of the foundation pit deformation at the moment is as follows:
in the above-mentioned description,
for a deformation prediction value>
Is the deformation fusion solution of the k-1 observation period. />
And recursion of the covariance matrix for the corresponding theory. />
Is the theoretical error value of the k-th observation period.
The corresponding error equation is:
The weight matrix is:
and then combining an observation equation of the monitoring subsystem of the ith sensor to deduce an error equation of the ith sensor as follows:
the sensor filtering estimation of the system i, the filtering estimation of the main sensor m and the global fusion solution are respectively set as
The corresponding weight matrix is ^ 4>
And &>
Covariance matrix ≥ respectively>
And &>
Due to the fact that
Statistically independent, the federate filtering fusion solution is:
the federal filtering algorithm is executed according to the following four steps:
(1) Assume an initial time global state estimate of
The weight matrix is ^ 4>
According to the principle of information conservation, information is distributed by a factor beta i The sensors and the main sensors are distributed to each foundation pit monitoring subsystem, it should be noted that the main sensors are theoretical equations constructed based on state values of multiple sensors and combined with the existing foundation pit deformation theory, and the distribution principle of information distribution factors is as follows:
wherein the information distribution factor beta i Satisfy the requirement of
(2) And (3) independently updating time of the local filter LF of each foundation pit monitoring subsystem and the main filter MF of the foundation pit monitoring system:
in the formula (I), the compound is shown in the specification,
and &>
Covariance matrices for local filter and main filter based on deformation model prediction, respectively>
And &>
An error covariance matrix is appended to the time-varying models of the local sensor and the main sensor, respectively.
(3) And (3) carrying out measurement updating on each local sensor LF:
(4) The estimation information of the local filter LF and the main filter MF is fused into new global state estimation information according to the above to obtain global state estimation
Covariance matrix>
Weight matrix>
Then returning to step (1) and passing the information through beta i And distributing to each LF and MF again for the next fusion.
It should be mentioned that the federal kalman filter uses the fused reset mode, that is, in each period, the information allocation factor of the filter uses the following formula:
in the mode, no information is distributed in the main filter, the main filter feeds the fusion solution back to the filters of all foundation pit monitoring sensors, each filter obtains global information, the precision is highest, and finally the result of global optimal fusion can be obtained.
Example 1
Taking a foundation pit monitoring project as an example, the main filter adopts a hard clay settlement distribution theoretical model proposed by Clough & O' Rourke: the sedimentation is maximum at the edge of the building envelope, the sedimentation is linearly reduced along with the increase of the distance, and the curve is distributed in an approximate triangle shape.
Taking a total station and an inclinometer as two sub-sensors to participate in data fusion calculation, wherein an observation value result of the inclinometer needs to be converted into a displacement, a measurement error of the two sensors refers to an instrument calibration value and an engineering empirical value, a configuration proportion in the project is 4, namely 0.36. In deformation monitoring, the states of the front observation period and the rear observation period do not change, so that the state transition matrix is defined as a unit matrix, and the process noise is 0..5.
Data of 20 observation periods are selected for calculation and plotted as figure 2. The theoretical values in fig. 2 are the theoretical model values selected by the main filter. In actual engineering practice, observation conditions are influenced by various factors, and the data deviation of a single sensor is usually large, which is shown as an observation value 1 and an observation value 2 in fig. 2. After the Federal Kalman filtering solution is carried out, the estimated value and the theoretical value keep better consistency, and powerful theoretical support can be provided for deformation monitoring and early warning.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A foundation pit intelligent monitoring method based on multi-sensor data fusion is characterized in that: the method comprises the following steps:
firstly, constructing a time-varying model for data fusion of multiple sensors in foundation pit engineering;
secondly, constructing a filter of a sensor of each monitoring subsystem of the foundation pit and a filter of a main sensor constructed by a foundation pit deformation theoretical model;
step three, carrying out independent time updating on each filter;
measuring and updating a local filter of the foundation pit monitoring subsystem;
and step five, fusing the estimation information of the local filter of the foundation pit monitoring subsystem and the main filter MF of the foundation pit monitoring system into new global state estimation information according to the fused time-varying model in the step one, and further obtaining the latest foundation pit monitoring state value.
2. The intelligent foundation pit monitoring method based on multi-sensor data fusion as claimed in claim 1, wherein: and respectively and correspondingly constructing different filters for measured values monitored by different sensors, and fusing results obtained by the filters corresponding to the different sensors and the theoretical value of the foundation pit deformation by adopting a Federal Kalman filter.
3. The foundation pit intelligent monitoring method based on multi-sensor data fusion as claimed in claim 2, characterized in that: the principal filter of the federal Kalman filter adopts the theoretical value of foundation pit deformation, and the federal Kalman filter fuses the theoretical value, the measured value and the variable quantity of the monitoring period of all foundation pit monitoring sensors.
4. The intelligent foundation pit monitoring method based on multi-sensor data fusion as claimed in claim 2, characterized in that: the Federal Kalman filter adopts a fusion reset mode, no information is distributed in the main filter, the main filter feeds a fusion result back to the filters of all foundation pit monitoring sensors, and each filter obtains global information.
5. The foundation pit intelligent monitoring method based on multi-sensor data fusion is characterized by comprising the following steps of: the foundation pit monitoring items comprise foundation pit slope top displacement data, deep horizontal displacement data, anchor cable stress data and underground water bit data.
6. A foundation pit intelligent monitoring method based on multi-sensor data fusion according to any one of claims 1-5, characterized in that: the multi-sensor data fusion time-varying model is X k ＝Φ k，k-1 X k-1 +W k Wherein X is k ＝[x 1 (k) x 2 (k) L x n (k)]，x n (k) As eigenvalues x of a time-varying model n A state matrix at the kth observation period; phi (phi) of k，k-1 Is a state transition matrix from k-1 observation periods to the k-th observation period; w k Is a gaussian white noise process error vector.
7. The intelligent foundation pit monitoring method based on multi-sensor data fusion as claimed in claim 6, wherein: according to a multi-sensor data fusion time-varying model, firstly, an observation equation of the ith sensor monitoring subsystem is constructed, and then t is determined k And (3) predicting a time-varying model of a state vector of foundation pit deformation at the moment to obtain a corresponding error equation and a weight matrix, obtaining an error equation of the ith sensor by an observation equation of the subsystem, and finally obtaining a federal filtering fusion solution.
8. The foundation pit intelligent monitoring method based on multi-sensor data fusion of claim 7, characterized in that: the observation equation of the ith sensor monitoring subsystem is as follows:
L ik ＝A ik X k +e ik
L ik for the observation of the kth observation period of the ith sensor monitoring subsystem, A ik Array of observation coefficients for the sensor monitoring subsystem, e ik To observe the noise vector. E is desired.
9. The foundation pit intelligent monitoring method based on multi-sensor data fusion of claim 7, characterized in that: t is t k The time-varying model prediction value of the state vector of the foundation pit deformation at the moment is as follows:
in the above-mentioned description,
is a deformation prediction value>
For the deformation fusion solution of the (k-1) th observation period>
Recur the covariance matrix for the corresponding theory, which is the kth->
And measuring a period theoretical error value. />
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