A concrete pressure monitoring system and method for tunnel lining trolley pouring process

Abstract

The present invention relates to the field of tunnel engineering technology, specifically to a concrete pressure monitoring system and method for a tunnel lining trolley pouring process, comprising: a pressure sensor installation working window provided on the trolley template, which is a detection through hole provided on the template, and a mounting plate rotatably connected in the detection through hole; a pressure sensor installation position is provided on the side of the mounting plate in contact with the concrete to install the pressure sensor; a step-adjustable signal acquisition and adaptive denoising module performs pressure signal acquisition and denoising through TDPA, a communication and distributed adaptive pressure prediction module receives the denoised pressure signal, and predicts the pressure signal; a real-time distributed pressure monitoring and early warning module performs pressure parameter analysis and early warning according to the received pressure signal. This solution can monitor the pressure of the template in real time and comprehensively, and the use cost of the pressure sensor is low, and it is easy to install and replace, and the accuracy of pressure monitoring is improved to ensure the pouring effect and improve the safety of the pouring process.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel engineering, and in particular to a concrete pressure monitoring system and method during a tunnel lining trolley casting process. Background Art

[0002] At present, in the pressure monitoring of lining trolleys and other concrete pouring processes, the main method used for pressure detection is to drill holes at several points on the trolley template to install pressure sensors for liquid and gas pressure monitoring.

[0003] There are some problems with this pressure detection method, as follows:

[0004] (1) Existing pressure sensors are not suitable for use in concrete scenarios such as lining trolleys. Pressure sensors are usually installed on the template by drilling threaded holes in the template. The pressure sensors are easily damaged during demolding, and the cost is high and it is inconvenient to replace.

[0005] (2) Due to the cost of pressure sensors and the complicated arrangement and replacement process, certain technical requirements are imposed on operators, and the number of pressure sensors usually arranged is small. As a result, the existing lining trolley cannot grasp the overall stress of the formwork in real time during the pouring process. The pouring process is mainly controlled based on the experience of the pouring staff, and the actual pressure of the formwork and the trolley cannot be understood. Especially during the top mold grouting process, if the pouring pressure is insufficient, it may lead to unreal pouring and quality defects such as hollowing. If the pouring pressure is too high, it may lead to safety accidents such as grouting explosion or even collapse of the trolley.

[0006] (3) During the pressure monitoring process, data collection and transmission will be disturbed, the accuracy of the pressure signal will be affected, and thus the accuracy of pressure monitoring will be affected.

[0007] Therefore, there is an urgent need for a concrete pressure monitoring system and method for the tunnel lining trolley pouring process, which can monitor the pressure of the formwork in real time and comprehensively. The pressure sensor has low cost and is easy to install and replace, which can improve the accuracy of pressure monitoring, thereby ensuring the pouring effect and improving the safety of the pouring process. Summary of the invention

[0008] One of the purposes of the present invention is to provide a concrete pressure monitoring system and method for the pouring process of a tunnel lining trolley, which can monitor the pressure of the formwork in real time and comprehensively, and the pressure sensor has low cost and is easy to install and replace, thereby improving the accuracy of pressure monitoring to ensure the pouring effect and improve the safety of the pouring process.

[0009] The first basic solution provided by the present invention is a concrete pressure monitoring system for the pouring process of a tunnel lining trolley, comprising: a step-adjustable signal acquisition and adaptive denoising module, a communication and distributed adaptive pressure prediction module, and a real-time distributed pressure monitoring and early warning module;

[0010] The step-adjustable signal acquisition and adaptive denoising module includes: a pressure sensor installation working window and a pressure sensor set on the template of the trolley; the pressure sensor adopts a flexible film pressure sensor;

[0011] The pressure sensor installation working window includes: a detection through hole opened on the template, and a mounting plate rotatably connected in the detection through hole; a pressure sensor installation position is arranged on the side of the mounting plate in contact with the concrete, for installing the pressure sensor;

[0012] The step-adjustable signal acquisition and adaptive denoising module is used to collect the pressure signals collected by all pressure sensors, and adopt the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and send the denoised pressure signals to the communication and distributed adaptive pressure prediction module;

[0013] The communication and distributed adaptive pressure prediction module is used to receive the denoised pressure signal, predict the pressure signal based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module;

[0014] The real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal to determine whether the pressure value meets the preset requirements. If not, an early warning is issued. It is also used to determine whether the difference between the received pressure signal and the predicted pressure signal meets the corresponding preset difference range. If not, an early warning is issued.

[0015] Furthermore, the template and the mounting plate are provided with locking devices for locking the mounting plate when the mounting plate is closed;

[0016] A pressure sensor wire connection socket is provided on one side of the pressure sensor installation position, and the pressure sensor wire connection socket passes through the installation plate and is connected to the step-adjustable signal acquisition module; the pressure sensor is connected to the external device by connecting the pressure sensor wire connection socket.

[0017] Furthermore, a plurality of pressure sensor installation working windows and pressure sensors are arranged on the template of the trolley, including: equidistantly arranged pressure sensor installation working windows and pressure sensors, arranged directly below the pouring port of the template, and arranged one pressure sensor on the left and right sides of the bottom of the template, or arranged directly above the pouring port, and arranged pressure sensor installation working windows and pressure sensors on the left and right sides of the bottom of the pouring port;

[0018] A plurality of pressure sensor installation working windows and pressure sensors are arranged on the top of the template along the axial direction;

[0019] A handle is arranged on the side of the template opposite to the pressure sensor installation position.

[0020] Furthermore, the pressure signals collected by all pressure sensors are collected, and a real-time distributed pressure denoising model TDPA is used to denoise the pressure signals, including:

[0021] According to the collection requirements, adjust the sampling frequency or amplification factor to collect the pressure signals collected by all pressure sensors;

[0022] The real-time distributed pressure denoising model TDPA is used to denoise the pressure signal;

[0023] The denoising process of TDPA is:

[0024] Window the pressure signal, divide it into time series, and obtain the current acquisition time series signal and the historical N acquisition time series signals include:

[0025] The signal received by the first pressure sensor at time t is expressed as:

[0026]

[0027] Where y represents the pressure signal, t represents the time scale, m represents the serial number of the pressure sensor, m takes an integer from 1 to M, μ represents the prediction coefficient, k represents the sequence number of the prediction coefficient, k takes an integer from 1 to K; arrive Respectively represent the pressure signal received by the first pressure sensor at time t to the pressure signal received by the m-th pressure sensor at time t;

[0028]

[0029] As the source signal s t The pressure signal is regarded as a prediction error, so the prediction coefficient group μ is regarded as an inverse filter for denoising, and the prediction error becomes the expected signal of the denoised signal required to be obtained; as shown in the above formula, represents the expected signal of the mth pressure sensor. When the prediction coefficient is known, the denoised pressure signal is obtained;

[0030] Estimating linear prediction coefficients The linear coefficients are estimated using the time-varying normal distribution function, the likelihood maximization function and the iterative solution formula. include:

[0031] The idea of ​​maximum likelihood estimation is adopted, because the pure pressure signal can be regarded as a time-varying normal distribution model;

[0032]

[0033] in Represents a time series vector of length N. The above formula represents a zero mean autocorrelation function is a time-varying function of a normally distributed process;

[0034] The TDPA model is transformed into a set of parameters: Get the likelihood maximization function:

[0035]

[0036] in A time series signal representing a pressure signal;

[0037] The linear prediction coefficient can be obtained by maximizing the likelihood function. The iterative solution formula is as follows:

[0038]

[0039] The residual of the above formula is iterated into Indicates convergence;

[0040] At the end of each iteration, the expected residual signal Update to r t The value of is calculated again by iteration until the expected signal reaches the preset expected value;

[0041] By means of a linear predictor, the noise component is estimated;

[0042] According to the noise component, the current acquisition time series signal is de-noised to obtain the de-noised pressure signal. and as output.

[0043] Furthermore, the step-adjustable signal acquisition and adaptive denoising module further includes: an analog channel, a signal conditioning circuit, a first processor, and an encoder connected in sequence;

[0044] The analog channel is connected to the pressure sensor. The pressure signal collected by the pressure sensor is an analog signal, which is transmitted to the signal conditioning circuit through the analog channel.

[0045] Signal conditioning circuit, used to convert analog signals into digital signals;

[0046] The first processor is used to control the collection work of the real-time distributed pressure collection module, coordinate and control the pressure sensor to collect and send the pressure signal, and is also used to use the real-time distributed pressure denoising model TDPA to denoise the pressure signal and send the denoised pressure signal to the encoder;

[0047] The encoder is used to encode the digital signal and send the encoded digital signal to the communication and distributed adaptive pressure prediction module through the communication module.

[0048] Further, the prediction of the pressure signal based on the Fourier power spectrum FPS of the expected signal includes:

[0049] The exponential decay model and the calculated denoising time are used to estimate the FPS of the distributed pressure components at each point of the current pressure, and then the FPS of the expected signal is obtained by geometric spectrum subtraction. The specific contents are as follows:

[0050] Initialize parameters of the received pressure signal; cyclically extract timing of the pressure signal; perform windowing and time division; perform Fourier transform; estimate the FPS of the expected signal; update the gain vector A of the expected signal d calculation; update the prediction coefficient μ; update the covariance matrix Φ; perform Fourier transform timing synthesis; determine whether all timing calculations are completed. If so, output a multi-channel denoised signal, that is, the denoised pressure signal. If not, continue to cyclically extract timing of the pressure signal.

[0051] Furthermore, the communication and distributed adaptive pressure prediction module is also used to use a prediction error compensation algorithm to supplement the prediction error of the pressure signal, and send the supplemented pressure signal to the real-time distributed pressure monitoring and early warning module;

[0052] The prediction error compensation algorithm used to compensate the prediction error of the pressure signal includes: initializing parameters of the received pressure signal; cyclically extracting the timing of the pressure signal; windowing and time-series division; Fourier transform; noise timing estimation; noise timing component FPS estimation; power spectrum estimation of expected signal FPS; initial expected signal FPS estimation; gain vector A update; prediction coefficient μ update; covariance matrix φ update; compensation expected signal d calculation; expected signal FPS estimation; Fourier transform timing synthesis; determine whether all timing calculations are completed, if so, output a multi-channel denoised signal, that is, the denoised pressure signal, if not, continue to cyclically extract the timing of the pressure signal.

[0053] Furthermore, the communication and distributed adaptive pressure prediction module includes: a communication submodule, a decoder, and a second processor;

[0054] A communication submodule, connected to the encoder, for receiving the encoded digital signal;

[0055] A decoder, used for decoding the received digital signal to obtain a pressure signal;

[0056] The second processor is used to predict the pressure signal based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module;

[0057] It is also used to supplement the prediction error of the pressure signal by using a prediction error compensation algorithm, and send the supplemented pressure signal to the real-time distributed pressure monitoring and early warning module.

[0058] Further, the real-time distributed pressure monitoring and early warning module includes: a real-time pressure monitoring submodule and an early warning submodule;

[0059] The real-time distributed pressure monitoring submodule is pre-set with lower and upper thresholds of each pressure sensor;

[0060] During the pouring process, the real-time distributed pressure monitoring submodule is used to determine whether the pressure value monitored by each pressure sensor is less than the upper limit threshold, or whether the pressure change value of each sensor conforms to the preset concrete side pressure change law, or whether the difference between the total pressure value monitored by the left pressure sensor and the total pressure value monitored by the right pressure sensor conforms to the preset difference range according to the received pressure signal. If not, the first warning information of abnormal pouring volume at the corresponding pouring port is generated and sent to the warning submodule;

[0061] During the top mold grouting process, the real-time distributed pressure monitoring submodule is used to determine whether the pressure value monitored by the pressure sensor set along the axial direction at the top of the template is the same as the pressure value monitored by other pressure sensors on the same axis, or whether it is greater than the lower limit threshold, according to the received pressure signal. If not, the second warning information of the corresponding top pouring port having the risk of being emptied is generated and sent to the warning submodule;

[0062] The real-time distributed pressure monitoring submodule is also used to determine whether the difference between the received pressure signal and the predicted pressure signal or the supplemented pressure value meets the corresponding preset difference range. If not, a corresponding warning message is generated and sent to the warning submodule;

[0063] The early warning submodule is used to display early warning and sound and light alarm according to the first early warning information;

[0064] It is also used to give an audible and visual alarm according to the second warning information;

[0065] The early warning submodule includes: a display unit and an audible and visual alarm;

[0066] A display unit, used for displaying a warning according to the first warning information;

[0067] The sound and light alarm is used to make sound and light alarm according to the first warning information or the second warning information.

[0068] The beneficial effects of this solution are as follows: in tunnel engineering, a trolley is used to pour concrete, and this system can be used to monitor concrete pressure. Specifically, first, a trolley is set in the tunnel, and then the mounting plate on the template is opened, and the flexible film pressure sensor is installed on the pressure sensor mounting position, and the socket is connected to the analog channel of the step-adjustable signal acquisition and adaptive denoising module through the pressure sensor wire connection socket, and the mounting plate is closed; then during the pouring process, the step-adjustable signal acquisition module collects the pressure signals collected by all pressure sensors, and uses the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and the denoised pressure signals are converted into The communication and distributed adaptive pressure prediction module receives the denoised pressure signal, predicts the pressure signal based on the Fourier power spectrum FPS of the expected signal, generates a predicted pressure signal, and sends the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module; the real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal, determine whether the pressure value meets the preset requirements, and if not, issue an early warning; it is also used to determine whether the difference between the received pressure signal and the predicted pressure signal meets the corresponding preset difference range, and if not, issue an early warning;

[0069] The pressure sensor is easy to install and disassemble. It can be directly installed and disassembled by opening the mounting plate without turning the fixing bolts, etc., which is convenient for replacement. In addition, the flexible film sensor is low-cost and does not require special installation. It can be pasted as needed, which can effectively reduce the cost of the pressure sensor.

[0070] The overall arrangement of the pressure sensor does not need to rely on the experience of the staff. Several pressure sensor installation working windows are pre-set on the template. The installation of the pressure sensor can be completed accordingly. The installation distribution of the pressure sensor in this scheme is comprehensively considered, including: equidistantly arranging the pressure sensor installation working windows and pressure sensors, arranging the pressure sensor installation working windows and pressure sensors just below the pouring port of the template, and arranging a pressure sensor on the left and right sides of the bottom of the template, or arranging the pressure sensor installation working windows and pressure sensors just above the pouring port, and arranging the pressure sensor installation working windows and pressure sensors on the left and right sides of the bottom of the pouring port; and several pressure sensor installation working windows and pressure sensors are arranged on the top of the template along the axial direction; thereby, the concrete pressure of each pouring process can be comprehensively and accurately monitored;

[0071] During the entire pouring process, the real-time distributed pressure monitoring and early warning module will also analyze the pressure parameters according to the pressure signal to determine whether the pressure value meets the preset requirements. If not, an early warning will be issued to promptly remind the staff to handle it and ensure the pouring effect and pouring safety; and the pressure signal analyzed by the real-time distributed pressure monitoring and early warning module is a pressure signal after noise processing. Among them, TDPA has a simple structure and small calculation amount, without any matrix inversion operation. The streaming processing method of pressure time-series calculation can ensure timely tracking of changes when the source position changes, adaptively track the pulse response of the sensor, and the calculation speed can adapt to the pressure sensor.

[0072] In summary, this solution can monitor the pressure of the mold plate in real time and comprehensively, and the pressure sensor has low cost and is easy to install and replace, which improves the accuracy of pressure monitoring to ensure the pouring effect and improve the safety of the pouring process.

[0073] The second purpose of the present invention is to provide a method for monitoring concrete pressure during the pouring process of a tunnel lining trolley, which can monitor the pressure of the formwork in real time and comprehensively. The pressure sensor has low cost and is easy to install and replace, thereby improving the accuracy of pressure monitoring to ensure the pouring effect and improve the safety of the pouring process.

[0074] The present invention provides a second basic solution: a method for monitoring concrete pressure during a tunnel lining trolley casting process, using the above-mentioned concrete pressure monitoring system during a tunnel lining trolley casting process, comprising:

[0075] Setting up a trolley in the tunnel;

[0076] Open the mounting plate on the template, install the flexible film pressure sensor on the pressure sensor mounting position, and connect it to the analog channel through the pressure sensor wire connection socket;

[0077] Close the mounting plate;

[0078] Carry out concrete pouring;

[0079] During the pouring process, the step-adjustable signal acquisition and adaptive denoising module collects the pressure signals collected by all pressure sensors 2, and uses the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and sends the denoised pressure signals to the communication and distributed adaptive pressure prediction module; the communication and distributed adaptive pressure prediction module is used to receive the denoised pressure signals, and predict the pressure signals based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signals and the predicted pressure signals to the real-time distributed pressure monitoring and early warning module; the communication and distributed adaptive pressure prediction module is also used to use the prediction error compensation algorithm to supplement the prediction error of the pressure signals, and send the supplemented pressure signals to the real-time distributed pressure monitoring and early warning module;

[0080] The real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal, determine whether the pressure value meets the preset requirements, and issue an early warning if not; it is also used to determine whether the difference between the received pressure signal and the predicted pressure signal or the supplemented pressure value meets the corresponding preset difference range, and issue an early warning if not;

[0081] After the casting is completed, demoulding is performed and the flexible film pressure sensor is peeled off.

[0082] Beneficial effects of this solution: This solution can monitor the pressure of the mold in real time and comprehensively, and the pressure sensor has low cost and is easy to install and replace, which improves the accuracy of pressure monitoring to ensure the casting effect and improve the safety of the casting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a logic block diagram of an embodiment of a concrete pressure monitoring system for a tunnel lining trolley pouring process of the present invention;

[0084] Figure 2 It is a structural schematic diagram of a pressure acquisition module in an embodiment of a concrete pressure monitoring system for a tunnel lining trolley pouring process of the present invention;

[0085] Figure 3 Schematic diagram of the first setting mode of the pressure sensor installation working window on the template in the embodiment of the concrete pressure monitoring system for the pouring process of the tunnel lining trolley of the present invention Figure 1 ;

[0086] Figure 4 Schematic diagram of the first setting mode of the pressure sensor installation working window on the template in the embodiment of the concrete pressure monitoring system for the pouring process of the tunnel lining trolley of the present invention Figure 2 ;

[0087] Figure 5 It is a schematic diagram of a second setting mode of the working window for installing the pressure sensor on the template in an embodiment of a concrete pressure monitoring system for a tunnel lining trolley casting process of the present invention;

[0088] Figure 6 It is a schematic diagram of a third setting mode of the working window for installing the pressure sensor on the template in an embodiment of a concrete pressure monitoring system for a tunnel lining trolley casting process of the present invention;

[0089] Figure 7 It is a structural schematic diagram of a mounting plate in an embodiment of a concrete pressure monitoring system for a tunnel lining trolley pouring process of the present invention;

[0090] Figure 8 It is a TDPA denoising flow chart in an embodiment of a concrete pressure monitoring system for a tunnel lining trolley pouring process of the present invention;

[0091] Fig. 9 It is a TDPA flow chart of real-time pressure signal sequence-by-sequence processing in a concrete pressure monitoring system for a tunnel lining trolley pouring process according to the present invention;

[0092] Fig.10 This is a flow chart of prediction error compensation in an embodiment of a concrete pressure monitoring system for a tunnel lining trolley casting process of the present invention. DETAILED DESCRIPTION

[0093] The following is further described in detail through specific implementation methods:

[0094] The figure marks in the drawings of the specification include: pressure sensor installation working window 1, pressure sensor 2, template 3, pouring port 4, detection through hole 5, mounting plate 6, pressure sensor installation position 7, handle 8, pressure sensor wire connection socket 9.

[0095] In the description of this application, unless otherwise clearly specified and limited, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance; unless otherwise specified or explained, the term "plurality" refers to two or more; the terms "connected" and "fixed" should be understood in a broad sense, for example, "connected" can be a fixed connection, a detachable connection, an integral connection, or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0096] In the description of this specification, it should be understood that the directional words such as "upper", "lower", "left", and "right" described in the embodiments of the present application are described at the angles shown in the accompanying drawings and should not be understood as limitations on the embodiments of the present application. In addition, in the context, it should also be understood that when it is mentioned that an element is connected to the "upper", "lower", "left", or "right" of another element, it can not only be directly connected to the "upper", "lower", "left", or "right" of another element, but can also be indirectly connected to the "upper", "lower", "left", or "right" of another element through an intermediate element.

[0097] The embodiment is basically as shown in the attached Figure 1 As shown: A concrete pressure monitoring system for the pouring process of a tunnel lining trolley, comprising: a step-adjustable signal acquisition and adaptive denoising module, a communication and distributed adaptive pressure prediction module, and a real-time distributed pressure monitoring and early warning module;

[0098] The step-adjustable signal acquisition and adaptive denoising module comprises: a pressure sensor installation working window 1 and a pressure sensor 2 arranged on a template 3 of a trolley; in this embodiment, the pressure sensor 2 adopts a flexible film pressure sensor 2, such as Figure 2 As shown;

[0099] The formwork 3 of the trolley is provided with a plurality of pressure sensor installation windows 1 and pressure sensors 2. When the pressure sensor installation windows 1 and pressure sensors 2 are provided, the pressure sensor installation windows 1 and pressure sensors 2 can be provided at equal distances according to the surface of the formwork 3 to monitor the pressure distribution at each pouring moment, such as Figure 3 and Figure 4 as shown; or Figure 5 As shown, a pressure sensor installation window 1 and a pressure sensor 2 are set just below the pouring port 4 of the template 3, and a pressure sensor 2 is set on the left and right sides of the bottom of the template 3. On the basis of monitoring the pressure change at the edge of the bottom, the pressure distribution is calculated by monitoring the change of the pressure sensor 2 at the building port; it can also be as shown in FIG. Figure 6 As shown, by arranging a pressure sensor installation working window 1 and a pressure sensor 2 just above the pouring port 4, and arranging a pressure sensor installation working window 1 and a pressure sensor 2 on the left and right sides of the bottom of the pouring port 4, the pressure sensors 2 around a single pouring port 4 are in a triangular shape, the two pressure sensors 2 at the bottom serve as a basis for monitoring pressure, and the pressure sensor 2 on the upper part of the window serves as a calibration sensor.

[0100] The number and positions of the pressure sensor installation working windows 1 and the pressure sensors 2 are set according to demand. In the present embodiment, as shown in FIG4 , taking the center line of the template 3 as the boundary and taking the right side as an example, 5 rows of pressure sensor installation working windows 1 and pressure sensors 2 are set from bottom to top in the height direction on the entire template 3 of the trolley. The numbers increase from bottom to top, with the bottom being the first row and the top being the fifth row. Eight pressure sensor installation working windows 1 and pressure sensors 2 are set in each row in the length direction of the trolley.

[0101] In addition, several pressure sensor installation working windows 1 and pressure sensors 2 are arranged along the axial direction on the top of the formwork 3 to monitor the force changes of the trolley during the top pouring process. Multiple adhesive film pressure sensors 2 are arranged and pasted to the top of the tunnel in advance before the trolley works. They are pasted along the axis at a certain distance, usually one is arranged at an interval of 1.5 meters, and 9 are arranged for a 12-meter trolley. During the lining construction process, since all the weight of the top concrete is applied to the formwork 3, and in order to ensure the density of the top lining, pressurized pouring is usually required. Therefore, the top formwork 3 of the trolley is a key monitoring part, and it is necessary to prevent safety accidents such as top formwork explosion or collapse of the trolley structure due to excessive pressure. Therefore, multiple pressure sensors 2 are arranged along the axial direction on the top of the trolley formwork 3 to monitor the force changes of the trolley during the top pouring process.

[0102] The pressure sensor installation working window 1 comprises: a detection through hole 5 provided on a template 3, and a mounting plate 6 rotatably connected to the detection through hole 5; and a locking device is provided on the template and the mounting plate, which is used to lock the mounting plate when the mounting plate is closed to prevent it from being opened during the concrete pouring process; in this embodiment, the locking device adopts a latch;

[0103] A pressure sensor mounting position 7 is provided on the side of the mounting plate 6 that contacts the concrete, for mounting the pressure sensor 2. Figure 7 As shown; and a handle 8 is provided on the side opposite to the pressure sensor mounting position 7, so as to facilitate the rotation of the mounting plate 6;

[0104] A pressure sensor wire connection socket 9 is provided on one side of the pressure sensor installation position 7, and the pressure sensor wire connection socket 9 passes through the installation plate 6 and is connected to the step-adjustable signal acquisition module; the pressure sensor 2 is connected to the analog channel by connecting the pressure sensor wire connection socket 9, and the pressure sensor wire connection socket 9 is waterproof and sealed around to prevent water leakage;

[0105] The step-adjustable signal acquisition and adaptive denoising module is used to collect the pressure signals collected by all pressure sensors 2, and use the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and send the denoised pressure signals to the communication and distributed adaptive pressure prediction module;

[0106] Specifically, Figure 8 As shown, according to the acquisition requirements, the sampling frequency or the amplification factor is adjusted to collect the pressure signals collected by all the pressure sensors 2 to reduce the interference in the pressure signals;

[0107] The sampling frequency f 采 =f / N, f is the pressure signal, N is an integer greater than zero (8, 12, 16, 20, 24, ....);

[0108] Amplification factor A = kx + b, k is a constant, x is the electrical signal of f, and b is a non-zero constant; in actual engineering, the sampling frequency or amplification factor is adjusted according to the acquisition requirements to reduce the interference in the signal to a minimum;

[0109] A real-time distributed pressure denoising model TDPA is used to denoise the pressure signal; TDPA inputs pressure signals of multiple pressure sensors, i.e., multi-channel pressure sensor signals, and outputs pressure signals of multiple pressure sensors after noise removal, i.e., denoised multi-channel pressure sensor signals; TDPA does not require prior knowledge of the noise environment and has no requirements on the type of pressure sensor array, so it is a highly versatile blind denoising algorithm; in this embodiment, the real-time distributed pressure denoising model TDPA is designed using the principle of the MCLP algorithm;

[0110] TDPA, the specific denoising process is as follows:

[0111] Window the pressure signal, divide it into time series, and obtain the current acquisition time series signal and the historical N acquisition time series signals

[0112] Specifically, the signal received by the first pressure sensor at time t is expressed as:

[0113]

[0114] Where y represents the pressure signal, t represents the time scale, m represents the serial number of the pressure sensor (m is an integer from 1 to M), μ represents the prediction coefficient, and k represents the sequence number of the prediction coefficient (k is an integer from 1 to K); y t (1) to y t (m) Respectively represent the pressure signal received by the first pressure sensor at time t to the pressure signal received by the m-th pressure sensor at time t;

[0115]

[0116] As the source signal s tThe pressure signal is regarded as a prediction error, so the prediction coefficient group μ is regarded as an inverse filter for denoising, and the prediction error becomes the expected signal of the denoised signal required to be obtained; as shown in the above formula, represents the expected signal of the mth pressure sensor. When the prediction coefficient is known, the denoised multi-channel output, i.e. the denoised pressure signal, can be obtained;

[0117] Estimating linear prediction coefficients The linear coefficients are estimated using the time-varying normal distribution function, the likelihood maximization function and the iterative solution formula.

[0118] Specifically, a certain optimization criterion is used to determine the prediction coefficient. In this embodiment, the TDPA model adopts the idea of ​​maximum likelihood estimation, because the pure pressure signal can be regarded as a time-varying normal distribution model;

[0119]

[0120] in Represents a time series vector of length N. The above formula represents a zero mean autocorrelation function is a time-varying function of a normally distributed process;

[0121] A set of parameters for TDPA algorithmization is set as Get the likelihood maximization function:

[0122]

[0123] in A time series signal representing a pressure signal;

[0124] The linear prediction coefficient can be obtained by maximizing the likelihood function. The iterative solution formula is as follows:

[0125]

[0126] The residual of the above formula is iterated into It means convergence;

[0127] At the end of each iteration, the expected residual signal Update to r t The value of is calculated again by iteration until the expected signal reaches the preset expected value.

[0128] By means of a linear predictor, the noise component is estimated;

[0129] According to the noise component, the current acquisition time series signal is de-noised to obtain the de-noised pressure signal. That is, multi-channel denoising signal, and as output, output noise-free pressure signal

[0130] The step-adjustable signal acquisition and adaptive denoising module also includes: an analog channel, a signal conditioning circuit, a first processor, and an encoder connected in sequence;

[0131] The analog channel is connected to the pressure sensor 2. The pressure signal collected by the pressure sensor 2 is an analog signal, which is transmitted to the signal conditioning circuit through the analog channel.

[0132] Signal conditioning circuit, used to convert analog signals into digital signals;

[0133] The first processor is used to control the collection work of the real-time distributed pressure collection module, coordinate and control the pressure sensor to collect and send the pressure signal, and is also used to use the real-time distributed pressure denoising model TDPA to denoise the pressure signal and send the denoised pressure signal to the encoder;

[0134] An encoder, used for encoding the digital signal, and sending the encoded digital signal to the communication and distributed adaptive pressure prediction module through the communication module;

[0135] The communication and distributed adaptive pressure prediction module is used to receive the denoised pressure signal, predict the pressure signal based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module;

[0136] The communication and distributed adaptive pressure prediction module is also used to use a prediction error compensation algorithm to supplement the prediction error of the pressure signal, and send the supplemented pressure signal to the real-time distributed pressure monitoring and early warning module;

[0137] A communication and distributed adaptive pressure prediction module, comprising: a communication submodule, a decoder, and a second processor;

[0138] A communication submodule, connected to the encoder, for receiving the encoded digital signal;

[0139] A decoder, used for decoding the received digital signal to obtain a pressure signal;

[0140] The second processor is used to predict the pressure signal based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module;

[0141] It is also used to use a prediction error compensation algorithm to supplement the prediction error of the pressure signal, and send the supplemented pressure signal to the real-time distributed pressure monitoring and early warning module;

[0142] The pressure signal is predicted based on the Fourier power spectrum FPS of the expected signal. Specifically, in order to improve the accuracy of the estimation and avoid the problem of over-spectrum subtraction caused by conventional spectral subtraction, Fig. 9 As shown, including:

[0143] The exponential decay model and the calculated denoising time are used to estimate the FPS of the distributed pressure components at each point of the current pressure, and then the FPS of the desired signal is obtained through geometric spectrum subtraction, so as to stabilize the denoising effect more quickly.

[0144] Specifically, it includes: initializing parameters of the received pressure signal; cyclically extracting the timing of the pressure signal; performing windowing and time division; performing Fourier transform; estimating the FPS of the expected signal; calculating the expected signal d; updating the gain vector A; updating the prediction coefficient μ; updating the covariance matrix Φ; performing Fourier transform timing synthesis; judging whether all timing calculations are completed, if so, outputting a multi-channel denoised signal, that is, the denoised pressure signal, if not, continuing to cyclically extract the timing of the pressure signal;

[0145] The real-time pressure signal is processed sequentially in the TDPA process, such as Fig. 9 As shown, it is derived based on the weighted recursive least squares algorithm. The advantages are simple structure and small calculation amount. There is no matrix inversion operation. The streaming processing method of pressure time-series calculation can ensure timely tracking of changes when the source position changes, adaptively track the pulse response of the sensor, and the calculation speed can adaptively track the sensor.

[0146] The specific real-time processing process is as follows:

[0147] First, in the algorithm for real-time processing of multi-channel pressure signals, the signal observed by the pressure sensor at the t-th frame is expressed as:

[0148]

[0149] And the set of all historical observation signals of the sensor at this moment is expressed as:

[0150]

[0151] Assume the current prediction coefficient (filter) is:

[0152]

[0153] Then the posterior probability density function of the currently measured pressure signal is:

[0154]

[0155] in Indicates the expected signal FPS;

[0156] Assume that the posterior probability density of the current prediction coefficient under the pressure signal of the previous frame is μ k (t-1); the covariance matrix is ​​Φ k The complex Gaussian distribution of (t-1) is transformed into the variance matrix:

[0157]

[0158] Where α is the number of common forgetting factors;

[0159] The weighted recursive least squares algorithm is introduced to solve the above solution, and the online frame-by-frame pressure algorithm calculation formula and process are obtained:

[0160] Initialize the initial prediction coefficient μ for each pressure channel k (0) = 0 and the covariance matrix Φ k (0) is set to the unit diagonal matrix;

[0161] Frame division, FT, estimates the expected signal and prediction coefficient for each channel pressure signal frame and each frequency point in turn;

[0162] For the expected signal d t,k Perform inverse Fourier transform to synthesize frames and output multi-channel denoised and predicted pressure signals d t.k .

[0163] The frame division, FT, estimates the expected signal and prediction coefficient for each channel pressure signal frame and each frequency point in turn, including:

[0164] The denoised pressure signal components of each channel are calculated as:

[0165] d t,k =x t,k -μ k (t-1) H x t-τ,k ;

[0166] The observed signal of each channel estimates the expected signal FPS:

[0167]

[0168] New gain A for each channel downfilter k (t), filtering system (i.e. prediction coefficient) μ k (t) and the covariance matrix Φ k (t);

[0169]

[0170]

[0171]

[0172] where τfilter is the delay time.

[0173] The prediction error compensation algorithm is used to compensate the prediction error of the pressure signal. Specifically, Fig.10 As shown, it includes: initializing parameters for the received pressure signal; cyclically extracting the timing of the pressure signal; performing windowing and time division; performing Fourier transform; performing noise timing estimation; performing noise timing component FPS estimation; power spectrum estimation of expected signal FPS; initial expected signal FPS estimation; gain vector A update; prediction coefficient μ update; covariance matrix Φ update; compensation expected signal d calculation; expected signal FPS estimation; Fourier transform timing synthesis; judging whether all timing calculations are completed, if so, outputting a multi-channel denoised signal, that is, a denoised pressure signal, if not, continuing to cyclically extract timing for the pressure signal; designing the effects of different pressure channel numbers and linear prediction coefficient orders on the performance and computational overhead of the TDPA algorithm; compared with increasing the number of channels, increasing the prediction order can more effectively improve the denoising effect while increasing the same amount of computational complexity; in other embodiments, after the initial expected signal FPS estimation, the error gain w calculation is also performed;

[0174] The real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal, determine whether the pressure value meets the preset requirements, and issue an early warning if not;

[0175] It is also used to determine whether the difference between the received pressure signal and the predicted pressure signal or the supplemented pressure value meets the corresponding preset difference range. If not, an early warning is issued and it is recommended to remove the corresponding pressure signal;

[0176] Specifically, the real-time distributed pressure monitoring and early warning module includes: a real-time pressure monitoring submodule and an early warning submodule;

[0177] The real-time distributed pressure monitoring submodule is used to analyze the pressure parameters according to the received pressure signal, and determine whether the pressure value meets the preset requirements. If not, it generates corresponding warning information and sends it to the warning submodule;

[0178] Specifically, the real-time distributed pressure monitoring submodule is pre-set with the lower threshold and upper threshold of each pressure sensor 2;

[0179] During the pouring process, the real-time distributed pressure monitoring submodule is used to determine whether the pressure value monitored by each pressure sensor 2 is less than the upper limit threshold value, or whether the pressure change value of each sensor conforms to the preset concrete side pressure change law, or whether the difference between the total pressure value monitored by the left pressure sensor 2 and the total pressure value monitored by the right pressure sensor 2 conforms to the preset difference range according to the received pressure signal. If not, the first warning information of abnormal pouring amount of the corresponding pouring port 4 is generated and sent to the warning submodule;

[0180] According to the requirements of the construction process specifications, each pouring port 4 should be poured evenly during pouring. In order to prevent the operator from pouring at only one pouring port 4 during the construction process, the pressure value of the pressure sensor 2 sent by the monitoring pouring port 4 is monitored; the real-time distributed pressure monitoring submodule records the pouring start time of this window according to the pressure value (pressure signal), calculates the pouring volume according to the pouring speed, and sets the maximum value of the single pouring volume of the single pouring port 4 (upper threshold); when pouring, according to the received pressure signal, it is judged whether the pressure value is less than the upper threshold. If not, that is, the pressure value exceeds the upper threshold, a first warning message is generated to remind the management personnel to handle it, so as to avoid excessive pouring volume of a single pouring port 4, resulting in uneven force on the trolley and causing safety accidents, and also ensure the balanced strength of the poured concrete structure.

[0181] According to the pouring work experience, the change of the lateral pressure value of concrete on the same point of the formwork 3 of the trolley during the pouring process can be regarded as a linear increase process, which increases with the increase of the concrete pouring height. When the pouring height reaches a certain critical value, the lateral pressure of the concrete on the formwork 3 will no longer increase. The lateral pressure at this time is the maximum lateral pressure (the pouring height at which the lateral pressure reaches the maximum value is called the effective pressure head of concrete); through theoretical and engineering practice experience, the lateral pressure can be calculated according to the following two formulas, and the smaller result is taken as the maximum lateral pressure F:

[0182]

[0183] F=γ c H;

[0184] Where F is the maximum lateral pressure, γ c is the gravity density of concrete, t0 is the initial setting time of concrete, generally 5h, V is the concrete pouring speed, H is the total height from the concrete side pressure calculation position to the top surface of concrete, β1 is the influence coefficient of admixture, 1 is taken without admixture, β2 is the slump influence coefficient, when the slump is less than 30mm, take 0.85; when the slump is 50-90mm, take 1; when the slump is 110-150mm, take 1.5;

[0185] In the lining trolley pouring construction scenario, the pressure of formwork 3 is calculated by taking F as the sub-item of the superimposed concrete horizontal load in addition to the lateral pressure in accordance with the construction specifications. 水平=5KN / M 2 ; During the calculation process, based on the on-site construction experience of the lining trolley, the coefficients of the lateral pressure and horizontal load were taken as 1.3 and 1.45 respectively when calculating the total pressure;

[0186] The total concrete lateral pressure F received by a point on the formwork 3 during the pouring construction of the lining trolley 总 For: F 总 =1.3F+1.45F 水平 ;

[0187] The real-time distributed pressure monitoring submodule determines whether the pressure change value of each sensor conforms to the preset concrete side pressure change law according to the real-time received pressure signal, where the preset concrete side pressure change law is the pressure change between F and F during the pouring process. 总 If not, the first warning information of abnormal pouring volume of the corresponding pouring port 4 is generated and sent to the warning submodule;

[0188] The real-time distributed pressure monitoring submodule determines whether the difference between the total pressure value monitored by the left pressure sensor 2 and the total pressure value monitored by the right pressure sensor 2 meets the preset difference range according to the real-time received pressure signal, that is, determines whether the pressure on the left side is within the preset difference range. 总 and F on the right 总 Whether the difference of meets the preset difference range, if not, a first warning message of abnormal pouring volume of the corresponding pouring port 4 is generated and sent to the warning submodule;

[0189] Taking into account the changes in the on-site environment during the implementation of the project, in other embodiments, it is allowed to exceed the theoretical pressure value by 10% during the monitoring process, that is, 10% lower than the lower limit threshold, or 10% higher than the upper limit threshold. During the top mold grouting process, the real-time distributed pressure monitoring submodule is used to determine whether the pressure value monitored by the pressure sensor 2 set along the axial direction at the top of the template 3 is the same as the pressure value monitored by other pressure sensors 2 on the same axis, or whether it is greater than the lower limit threshold, if not, then generate the corresponding top pouring port 4 has a second warning information of the risk of emptying, and send it to the warning submodule; during the top mold grouting process, when the pressure value monitored by the pressure sensor 2 set along the axial direction at the top of the template 3 is less than the pressure value monitored by other pressure sensors 2 on the same axis, or less than the lower limit threshold, it is determined that there is a risk of emptying, and the grouting amount corresponding to a certain grouting pipe can be increased, so the second warning information is generated to remind the management personnel, thereby preventing the emptying defect during the grouting process, rather than opening the hole for grouting after the grouting solidifies, which greatly reduces the construction cost, shortens the construction period, and improves the construction reliability.

[0190] The real-time distributed pressure monitoring submodule is also used to determine whether the received pressure signal is different from the predicted pressure signal or the supplemented pressure value within the corresponding preset difference range. If not, a corresponding warning message is generated and sent to the warning submodule; the corresponding warning message in this embodiment is the first warning message; if the pressure signal received by the pressure sensor exceeds a certain range of the predicted pressure signal, it indicates excessive pouring and an alarm should be given. In addition, if the pressure signal in the pouring area remains unstable during the pouring process, a warning can also be given, because the sensor may have a problem, or when the pouring area is completed, the pressure signal of the pressure sensor is less than the predicted pressure signal, and a warning can also be given, indicating that the pouring is not dense and there may be holes. The warning submodule is used to issue a warning based on the warning information.

[0191] Specifically, the early warning submodule is used to display early warning and sound and light alarm according to the first early warning information; wherein the displayed early warning is to display the number of the pressure sensor 2, so as to assist the management personnel to quickly identify the pouring port 4 with abnormal pouring volume;

[0192] It is also used to issue an audible and visual alarm according to the second warning information.

[0193] The early warning submodule includes: a display unit and an audible and visual alarm;

[0194] A display unit, used for displaying a warning according to the first warning information;

[0195] The sound and light alarm is used to make sound and light alarm according to the first warning information or the second warning information.

[0196] This embodiment also provides a method for monitoring concrete pressure during a tunnel lining trolley casting process, using the above-mentioned concrete pressure monitoring system during a tunnel lining trolley casting process, comprising:

[0197] Setting up a trolley in the tunnel;

[0198] Open the mounting plate on the template, install the flexible film pressure sensor on the pressure sensor mounting position, and connect it to the analog channel through the pressure sensor wire connection socket;

[0199] Close the mounting plate;

[0200] Carry out concrete pouring;

[0201] During the pouring process, the step-adjustable signal acquisition and adaptive denoising module collects the pressure signals collected by all pressure sensors 2, and uses the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and sends the denoised pressure signals to the communication and distributed adaptive pressure prediction module; the communication and distributed adaptive pressure prediction module is used to receive the denoised pressure signals, and predict the pressure signals based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signals and the predicted pressure signals to the real-time distributed pressure monitoring and early warning module; the communication and distributed adaptive pressure prediction module is also used to use the prediction error compensation algorithm to supplement the prediction error of the pressure signals, and send the supplemented pressure signals to the real-time distributed pressure monitoring and early warning module;

[0202] The real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal, determine whether the pressure value meets the preset requirements, and issue an early warning if not; it is also used to determine whether the difference between the received pressure signal and the predicted pressure signal or the supplemented pressure value meets the corresponding preset difference range, and issue an early warning if not;

[0203] After the casting is completed, demoulding is performed and the flexible film pressure sensor is peeled off.

[0204] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A concrete pressure monitoring system for a tunnel lining trolley pouring process, characterized in that: include: Step-adjustable signal acquisition and adaptive denoising module, communication and distributed adaptive pressure prediction module, real-time distributed pressure monitoring and early warning module; The step-adjustable signal acquisition and adaptive denoising module includes: a pressure sensor installation working window and a pressure sensor set on the template of the trolley; the pressure sensor adopts a flexible film pressure sensor; The pressure sensor installation working window includes: a detection through hole opened on the template, and a mounting plate rotatably connected in the detection through hole; a pressure sensor installation position is arranged on the side of the mounting plate in contact with the concrete, for installing the pressure sensor; The step-adjustable signal acquisition and adaptive denoising module is used to collect the pressure signals collected by all pressure sensors, and adopt the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and send the denoised pressure signals to the communication and distributed adaptive pressure prediction module; The communication and distributed adaptive pressure prediction module is used to receive the denoised pressure signal, predict the pressure signal based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module; The real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal, determine whether the pressure value meets the preset requirements, and issue an early warning if not; it is also used to determine whether the difference between the received pressure signal and the predicted pressure signal meets the corresponding preset difference range, and issue an early warning if not; The pressure signals collected by all pressure sensors are collected, and the real-time distributed pressure denoising model TDPA is used to denoise the pressure signals, including: According to the collection requirements, adjust the sampling frequency or amplification factor to collect the pressure signals collected by all pressure sensors; The real-time distributed pressure denoising model TDPA is used to denoise the pressure signal; The denoising process of TDPA is: The pressure signal is windowed and divided into time series to obtain the current acquisition time series signal and the historical N acquisition time series signals, including: The signal received by the first pressure sensor at time t is expressed as: ； Where y represents the pressure signal, t represents the time scale, m represents the serial number of the pressure sensor, and m is an integer from 1 to M. represents the prediction coefficient, k represents the sequence number of the prediction coefficient, and k is an integer from 1 to K; arrive Respectively represent the pressure signal received by the first pressure sensor at time t to the pressure signal received by the mth pressure sensor at time t , and it is a noisy pressure signal; It is a noise-free pressure signal source; ； As a source signal The pressure signal is regarded as the prediction error, so the prediction coefficient group As an inverse filter for denoising, the prediction error becomes the expected signal of the denoised signal required to be obtained; as shown in the above formula, represents the expected signal of the mth pressure sensor. When the prediction coefficient is known, the denoised pressure signal is obtained; Indicates the number of the pressure sensor, A pressure sensor; Estimating linear prediction coefficients :Use time-varying normal distribution function, likelihood maximization function and iterative solution formula to estimate linear coefficients ,include: The idea of ​​maximum likelihood estimation is adopted, since the pure pressure signal can be regarded as a time-varying normal distribution model; ； in represents a time series vector of length N, and , the above formula represents a zero mean, autocorrelation function , is a time-varying function of the normal distribution process; function is the probability density function; function is the matrix expression of the probability distribution function; The TDPA model is transformed into a set of parameters: , and obtain the likelihood maximization function: ； ； in A time series signal representing a pressure signal; For a given parameter The probability of the latter variable t is L; and ; The linear prediction coefficient can be obtained by maximizing the likelihood function. The iterative solution formula is as follows: ； ； The residual of the above formula is iterated into Indicates convergence; is the error prediction parameter value obtained for each iteration; Characterize the values ​​of the error prediction parameters of each sensor after matrix calculation; At the end of each iteration, the expected residual signal Updated to The value of is calculated again by iteration until the expected signal reaches the preset expected value; By means of a linear predictor, the noise component is estimated; According to the noise component, the current acquisition time series signal is subjected to denoising calculation to obtain the denoised pressure signal, that is, the multi-channel denoised signal, and output it.

2. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 1 is characterized in that: The template and the mounting plate are provided with a locking device for locking the mounting plate when the mounting plate is closed; A pressure sensor wire connection socket is provided on one side of the pressure sensor installation position, and the pressure sensor wire connection socket passes through the installation plate and is connected to the step-adjustable signal acquisition module; the pressure sensor is connected to the external device by connecting the pressure sensor wire connection socket.

3. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 1 is characterized in that: The template of the trolley is provided with a plurality of pressure sensor installation working windows and pressure sensors, including: equidistantly arranged pressure sensor installation working windows and pressure sensors, arranged directly below the pouring port of the template, and arranged one pressure sensor on the left and right sides of the bottom of the template, or arranged directly above the pouring port, and arranged pressure sensor installation working windows and pressure sensors on the left and right sides of the bottom of the pouring port; A plurality of pressure sensor installation working windows and pressure sensors are arranged on the top of the template along the axial direction; a handle is arranged on the side of the template opposite to the pressure sensor installation position.

4. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 1 is characterized in that: The step-adjustable signal acquisition and adaptive denoising module further includes: an analog channel, a signal conditioning circuit, a first processor, and an encoder connected in sequence; The analog channel is connected to the pressure sensor. The pressure signal collected by the pressure sensor is an analog signal, which is transmitted to the signal conditioning circuit through the analog channel. Signal conditioning circuit, used to convert analog signals into digital signals; The first processor is used to control the collection work of the real-time distributed pressure collection module, coordinate and control the pressure sensor to collect and send the pressure signal, and is also used to use the real-time distributed pressure denoising model TDPA to denoise the pressure signal and send the denoised pressure signal to the encoder; The encoder is used to encode the digital signal and send the encoded digital signal to the communication and distributed adaptive pressure prediction module through the communication module.

5. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 4 is characterized in that: The prediction of the pressure signal based on the Fourier power spectrum FPS of the expected signal includes: The exponential decay model and the calculated denoising time are used to estimate the FPS of the distributed pressure components at each point of the current pressure, and then the FPS of the expected signal is obtained by geometric spectrum subtraction. The specific contents are as follows: Initialize parameters of the received pressure signal; extract timing of the pressure signal in a loop; perform windowing and time division; perform Fourier transform; estimate the FPS of the expected signal; calculate the gain vector A update of the expected signal d; predict the coefficients Update; covariance matrix Update; perform Fourier transform timing synthesis; determine whether all timing calculations are completed, if so, output a multi-channel denoised signal, that is, a denoised pressure signal, if not, continue to cyclically extract the timing of the pressure signal.

6. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 5 is characterized in that: The communication and distributed adaptive pressure prediction module is also used to use a prediction error compensation algorithm to supplement the prediction error of the pressure signal, and send the supplemented pressure signal to the real-time distributed pressure monitoring and early warning module; The prediction error compensation algorithm is used to compensate the prediction error of the pressure signal, including: initializing parameters of the received pressure signal; cyclically extracting the time series of the pressure signal; windowing and time series division; Fourier transform; noise time series estimation; noise time series component FPS estimation; power spectrum estimation of expected signal FPS; initial expected signal FPS estimation; gain vector A update; prediction coefficient Update; covariance matrix Update; calculate the compensation expected signal d; estimate the expected signal FPS; perform Fourier transform timing synthesis; determine whether all timing calculations are completed. If so, output the multi-channel denoised signal, that is, the denoised pressure signal. If not, continue to extract the timing of the pressure signal in a cyclic manner.

7. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 6 is characterized in that: The communication and distributed adaptive pressure prediction module includes: a communication submodule, a decoder, and a second processor; A communication submodule, connected to the encoder, for receiving the encoded digital signal; A decoder, used for decoding the received digital signal to obtain a pressure signal; The second processor is used to predict the pressure signal based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signal and the predicted pressure signal to the real-time distributed pressure monitoring and early warning module; It is also used to supplement the prediction error of the pressure signal by using a prediction error compensation algorithm, and send the supplemented pressure signal to the real-time distributed pressure monitoring and early warning module.

8. The concrete pressure monitoring system for the tunnel lining trolley pouring process according to claim 7 is characterized in that: The real-time distributed pressure monitoring and early warning module includes: a real-time pressure monitoring submodule and an early warning submodule; The real-time distributed pressure monitoring submodule is pre-set with lower and upper thresholds of each pressure sensor; During the pouring process, the real-time distributed pressure monitoring submodule is used to determine whether the pressure value monitored by each pressure sensor is less than the upper limit threshold, or whether the pressure change value of each sensor conforms to the preset concrete side pressure change law, or whether the difference between the total pressure value monitored by the left pressure sensor and the total pressure value monitored by the right pressure sensor conforms to the preset difference range according to the received pressure signal. If not, the first warning information of abnormal pouring volume at the corresponding pouring port is generated and sent to the warning submodule; During the top mold grouting process, the real-time distributed pressure monitoring submodule is used to determine whether the pressure value monitored by the pressure sensor set along the axial direction at the top of the template is the same as the pressure value monitored by other pressure sensors on the same axis, or whether it is greater than the lower limit threshold, according to the received pressure signal. If not, the second warning information of the corresponding top pouring port having the risk of being emptied is generated and sent to the warning submodule; The real-time distributed pressure monitoring submodule is also used to determine whether the difference between the received pressure signal and the predicted pressure signal or the supplemented pressure value meets the corresponding preset difference range. If not, a corresponding warning message is generated and sent to the warning submodule; The early warning submodule is used to display early warning and sound and light alarm according to the first early warning information; It is also used to give an audible and visual alarm according to the second warning information; The early warning submodule includes: a display unit and an audible and visual alarm; A display unit, used for displaying a warning according to the first warning information; The sound and light alarm is used to make sound and light alarm according to the first warning information or the second warning information.

9. A method for monitoring concrete pressure during the pouring process of a tunnel lining trolley, characterized in that: The concrete pressure monitoring system for the tunnel lining trolley pouring process according to any one of claims 1 to 8 comprises: Setting up a trolley in the tunnel; Open the mounting plate on the template, install the flexible film pressure sensor on the pressure sensor mounting position, and connect it to the analog channel through the pressure sensor wire connection socket; Close the mounting plate; Carry out concrete pouring; During the pouring process, the step-adjustable signal acquisition and adaptive denoising module collects the pressure signals collected by all pressure sensors 2, and uses the real-time distributed pressure denoising model TDPA to denoise the pressure signals, and sends the denoised pressure signals to the communication and distributed adaptive pressure prediction module; the communication and distributed adaptive pressure prediction module is used to receive the denoised pressure signals, and predict the pressure signals based on the Fourier power spectrum FPS of the expected signal, generate a predicted pressure signal, and send the pressure signals and the predicted pressure signals to the real-time distributed pressure monitoring and early warning module; the communication and distributed adaptive pressure prediction module is also used to use the prediction error compensation algorithm to supplement the prediction error of the pressure signals, and send the supplemented pressure signals to the real-time distributed pressure monitoring and early warning module; The real-time distributed pressure monitoring and early warning module is used to analyze the pressure parameters according to the received pressure signal, determine whether the pressure value meets the preset requirements, and issue an early warning if not; it is also used to determine whether the difference between the received pressure signal and the predicted pressure signal or the supplemented pressure value meets the corresponding preset difference range, and issue an early warning if not; After the casting is completed, demoulding is performed and the flexible film pressure sensor is peeled off.

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