Latticed column perpendicularity control method based on multi-source fusion and predictive compensation

Abstract

The invention provides a latticed column perpendicularity control method based on multi-source fusion and predictive compensation, and belongs to the technical field of perpendicularity control, a novel anti-interference sensor is combined with multi-point distributed installation and advanced data transmission and processing technologies, perpendicularity data of a latticed column can be accurately obtained in real time, and the perpendicularity control accuracy of the latticed column is improved. A reliable three-dimensional deviation cloud picture is generated; according to the intelligent hydraulic jack set control system based on real-time data feedback, the adjustment time is greatly shortened, the problem that the insertion deviation exceeds the limit due to adjustment lag is solved, the perpendicularity control precision of the latticed column is improved, and it is ensured that the insertion deviation of the latticed column is controlled within a small range; through comprehensive optimization of the construction process, including drilling pretreatment, equipment debugging and calibration, lowering process monitoring, concrete pouring process monitoring and the like, the stability and reliability of the whole latticed column perpendicularity control process are further improved, uncertain factors in the construction process are reduced, and the engineering quality and the construction efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of verticality control, and in particular relates to a lattice column verticality control method based on multi-source fusion and prediction compensation. Background Art

[0002] Lattice columns are a common structural form with strong bending resistance in building construction. Primarily constructed from reinforced concrete or steel sections, they offer high strength and a small cross-section. Their cross-sections are typically designed with biaxial or uniaxial symmetry, improving bending resistance by positioning the material away from the axis of inertia. They are widely used in factory frame columns, deep foundation pit support, and top-down construction. On the other hand, as mentioned in the prior art patent application CN120367229A, lattice column verticality control is required during construction.

[0003] Current methods for controlling lattice column verticality often rely on fixed inclinometers and their real-time data transmission to construct a three-dimensional deviation cloud map, which guides the hydraulic jack assembly to adjust the lattice column verticality. However, in deep holes or muddy environments, sensors are susceptible to interference, resulting in inaccurate or delayed data, making the three-dimensional deviation cloud map unable to reflect the lattice column's true state in real time. This lack of real-time performance often lags behind the hydraulic jack assembly's adjustments, ultimately leading to excessive lattice column insertion deviations (e.g., >20mm), seriously impacting project quality and subsequent construction. Therefore, a new technical solution is urgently needed to address these issues. Summary of the Invention

[0004] In order to solve the defects in the existing technology, the present invention provides a lattice column verticality control method based on multi-source fusion and predictive compensation, so as to overcome the defects of the existing technology in deep holes or mud environments, such as sensor interference and data delay, resulting in insufficient real-time performance of the three-dimensional deviation cloud map, delayed adjustment of the hydraulic jack group, and thus excessive lattice column insertion deviation.

[0005] The present invention utilizes the following technical solutions.

[0006] A lattice column verticality control method based on multi-source fusion and prediction compensation, comprising:

[0007] Step 1: Optimize the sensor for lattice column verticality control;

[0008] Step 2: Transmit and process the data collected by the sensor;

[0009] Step 3: Perform hydraulic jack group control.

[0010] Furthermore, step 1 specifically includes: using an inclinometer based on optical fiber sensing that is communicatively connected to a central data processing system as a sensor for controlling the verticality of the lattice column.

[0011] Furthermore, step 1 specifically includes: in designing the installation position of the sensor, a multi-point distributed installation method is adopted, and multiple sensors are evenly arranged at different height positions of the lattice column.

[0012] Furthermore, step 2 specifically includes: connecting the central data processing system, Mesh network nodes, edge computing devices and sensors in sequence, arranging the Mesh network nodes around the drill holes of the lattice columns and key positions set at the construction site of the lattice columns, and the edge computing devices performing preliminary processing on the data collected by the sensors in real time, and then transmitting the preliminarily processed data collected by multiple sensors to the central data processing system. The central data processing system uses an improved algorithm to fuse the preliminarily processed data collected by multiple sensors to generate an accurate three-dimensional deviation cloud map.

[0013] Furthermore, in step 2, the central data processing system uses an improved algorithm to fuse the data collected by multiple sensors after preliminary processing to generate an accurate three-dimensional deviation cloud map, which specifically includes:

[0014] Step 2-1: Perform data fusion processing on the data collected by multiple sensors after preliminary processing;

[0015] Step 2-2: Generate a three-dimensional cloud image based on the fused state values.

[0016] Furthermore, step 2-1 specifically includes:

[0017] Step 2-1-1: Perform data preprocessing on the data collected by multiple sensors after preliminary processing;

[0018] Step 2-1-2: Perform noise suppression on the data collected by the sensors after spatiotemporal alignment;

[0019] Step 2-1-3: Perform confidence weighting on each sensor.

[0020] Furthermore, step 2-1-1 specifically includes: performing spatiotemporal alignment on the data collected by each sensor after preliminary processing transmitted by the edge computing device through the Mesh network node, that is, using the cubic spline interpolation algorithm to correct the sampling time difference of different sensors. The formula is as follows:

[0021] ;

[0022] in, The timestamp after synchronization, that is, the corrected sensor Sampling time, For sensor The original sampling time, is the maximum time deviation, is the number of sensors.

[0023] Furthermore, step 2-1-2 specifically includes:

[0024] The improved Kalman filter algorithm is used to filter the noise of the data collected by the sensors after time-space alignment. The core formula for noise filtering is:

[0025] State prediction equation: ;

[0026] Forecast error covariance: ;

[0027] Kalman gain: ;

[0028] State update equation: ;

[0029] Error covariance update: ;

[0030] in express The predicted state vector at time t, express The state transfer matrix based on the lattice column rigid body motion model at the moment, The time represents the corrected sensor Sampling time, express The control input matrix of the associated hydraulic jack adjustment amount at time, express The control input vector at time , express The forecast error covariance matrix at time , express The process noise covariance matrix is ​​corrected in real time by the mud density sensor, express The Kalman gain at time t, express The observation matrix that maps the sensor data to the state vector at each moment, Indicates based on The observation noise covariance matrix of the sensor depth dynamically adjusted at the moment, express The sensor observation value vector at time , Represents the identity matrix.

[0031] Furthermore, step 2-1-3 specifically includes:

[0032] Constructing a Bayesian network to evaluate Confidence of sensors , The calculation formula is:

[0033] ;

[0034] in, For the Sensors in environmental parameters The reliability probability density function under is the number of sensors;

[0035] Then the following formula is used to calculate the fusion The status value of the sensor :

[0036] ;

[0037] in For the of sensors value.

[0038] Furthermore, step 2-2 specifically includes:

[0039] The Kriging interpolation algorithm is used to generate a continuous three-dimensional cloud map based on the fused state values. The deviation visualization formula is as follows:

[0040]

[0041] in, For spatial points The vertical deviation value, For the setting The interpolation weights of the sensors, For the The status value of the sensor , is the Lagrange multiplier.

[0042] Furthermore, step 3 specifically includes:

[0043] Establish an intelligent hydraulic jack control system based on real-time data feedback. Specifically, the hydraulic jacks are connected to a central data processing system. When a 3D cloud image shows vertical deviation of the lattice column, the central data processing system automatically controls the hydraulic jacks to adjust to the allowable deviation range based on the direction and magnitude of the deviation.

[0044] The beneficial effects of the present invention are as follows:

[0045] The novel anti-interference sensor of the present invention combines multi-point distributed installation with advanced data transmission and processing technology, effectively solving the problems of sensor interference and data delay in deep hole or mud environment, and can obtain the verticality data of the lattice column in real time and accurately, and generate a reliable three-dimensional deviation cloud map; the intelligent hydraulic jack group control system based on real-time data feedback can quickly and accurately adjust according to the real-time deviation of the lattice column, greatly shortening the adjustment time, avoiding the insertion deviation exceeding the limit due to adjustment lag, improving the verticality control accuracy of the lattice column, and ensuring that the insertion deviation of the lattice column is controlled within a smaller range (such as ≤10mm); through comprehensive optimization of the construction process, including drilling pretreatment, equipment debugging and calibration, lowering process monitoring and concrete pouring process monitoring, the stability and reliability of the entire lattice column verticality control process are further improved, the uncertainty factors in the construction process are reduced, and the project quality and construction efficiency are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the lattice column verticality control method based on multi-source fusion and prediction compensation in the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the present invention more clear, the following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely express the technical solutions of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the scope of protection of the present invention.

[0048] like Figure 1 As shown, a lattice column verticality control method based on multi-source fusion and prediction compensation includes:

[0049] Step 1: Optimize the sensor for lattice column verticality control;

[0050] In a preferred but non-limiting embodiment of the present invention, step 1 specifically includes employing a novel anti-interference sensor with a multi-layer shielding structure, capable of effectively resisting electromagnetic interference in mud environments and complex stress interference in deephole environments. For example, the sensor used to control lattice column verticality utilizes a fiber-optic inclinometer connected to a central data processing system. This fiber-optic inclinometer can be a Sitan TLX50-200D fiber-optic gyro inclinometer, which utilizes optical signal transmission, is immune to electromagnetic interference, and offers high precision and stability. The central data processing system can be an industrial computer.

[0051] In a preferred but non-limiting embodiment of the present invention, step 1 specifically further includes: in the design of the installation position of the sensor, a multi-point distributed installation method is adopted, and multiple sensors are evenly arranged at different height positions of the lattice column, such as installing a sensor at the top, middle and bottom of the lattice column. In this way, real-time data of different parts of the lattice column can be obtained, thereby improving the accuracy and reliability of the data through the data fusion algorithm.

[0052] Step 2: Transmit and process the data collected by the sensor;

[0053] In a preferred but non-limiting embodiment of the present invention, step 2 specifically includes: constructing an independent high-speed data transmission network using wireless mesh network technology. This technology features self-organizing networking and multi-hop transmission, enabling stable, high-speed data transmission in deep boreholes or mud environments. A central data processing system, mesh network nodes, and edge computing devices are sequentially connected to the sensors. Mesh network nodes can be deployed around the lattice column boreholes and at key locations designated at the lattice column construction site to ensure that sensor-collected data can be rapidly transmitted to the central data processing system. Regarding data processing, edge computing technology is introduced to offload some data processing tasks to edge computing devices located near the sensors. The edge computing devices can perform preliminary processing on the sensor-collected data in real time, such as noise removal and outlier detection (noise removal and outlier detection can be performed using existing methods). The processed data collected by multiple sensors is then transmitted to the central data processing system. The central data processing system uses improved algorithms to fuse the processed data collected by multiple sensors to quickly generate an accurate three-dimensional deviation cloud map.

[0054] In a preferred but non-limiting embodiment of the present invention, in step 2, the central data processing system uses an improved algorithm to fuse the data collected by multiple sensors after preliminary processing to quickly generate an accurate three-dimensional deviation cloud map, specifically including:

[0055] The central data processing system uses an improved Kalman filter and Bayesian inference fusion algorithm to address noise interference, spatiotemporal synchronization deviation, and dynamic drift in data collected by multiple sensors in deephole mud environments. This algorithm is innovative in three aspects: first, it introduces an environment-adaptive dynamic correction mechanism for the noise covariance matrix, overcoming the traditional Kalman filter's reliance on fixed noise models; second, it constructs a dynamic sensor confidence assessment model through a Bayesian network, enabling weighted fusion of heterogeneous sensor data; and third, it establishes a deviation prediction model based on the mechanical characteristics of lattice column structures, generating a 500ms advance prediction of deviation trends, preserving reaction time for hydraulic adjustments.

[0056] Step 2-1: Perform data fusion processing on the data collected by multiple sensors after preliminary processing;

[0057] In a preferred but non-limiting embodiment of the present invention, step 2-1 specifically comprises:

[0058] Step 2-1-1: Perform data preprocessing on the data collected by multiple sensors after preliminary processing;

[0059] In a preferred but non-limiting embodiment of the present invention, step 2-1-1 specifically includes: performing spatiotemporal alignment on the data collected by each sensor after preliminary processing transmitted by the edge computing device through the Mesh network node, that is, using a cubic spline interpolation algorithm to correct the sampling time difference of different sensors. The formula is as follows:

[0060] ;

[0061] in, The timestamp after synchronization, that is, the corrected sensor Sampling time, For sensor The original sampling time, The maximum time deviation represents the maximum time difference generated when different sensors collect data in the same sampling period. is the number of sensors.

[0062] Step 2-1-2: Perform noise suppression on the data collected by the sensors after spatiotemporal alignment;

[0063] In a preferred but non-limiting embodiment of the present invention, step 2-1-2 specifically includes:

[0064] The improved Kalman filter algorithm is used to filter the noise of the data collected by the sensors after time-space alignment. The core formula for noise filtering is:

[0065] State prediction equation: ;

[0066] Forecast error covariance: ;

[0067] Kalman gain: ;

[0068] State update equation: ;

[0069] Error covariance update: ;

[0070] in express The predicted state vector at the moment (including the X, Y, and Z deviation values), express The state transfer matrix based on the lattice column rigid body motion model at the moment, The time represents the corrected sensor Sampling time, express The control input matrix of the associated hydraulic jack adjustment amount at time, express The control input vector at time (the control input vector includes the pressure change value of the hydraulic jack), express The forecast error covariance matrix at time , express The process noise covariance matrix is ​​corrected in real time by the mud density sensor, express The Kalman gain at time t, express The observation matrix that maps the sensor data to the state vector at each moment, Indicates based on Dynamic adjustment of sensor depth at all times (as in deep hole environments) The observation noise covariance matrix (increased by 20%), express The sensor observation value vector at time , Represents the identity matrix.

[0071] Step 2-1-3: Perform confidence weighting on each sensor;

[0072] In a preferred but non-limiting embodiment of the present invention, step 2-1-3 specifically includes:

[0073] Constructing a Bayesian network to evaluate Confidence of sensors , The calculation formula is:

[0074] ;

[0075] in, For the Sensors in environmental parameters The reliability probability density function under (mud viscosity, temperature, depth) is obtained through field calibration to obtain prior data. is the number of sensors;

[0076] Then the following formula is used to calculate the fusion The status value of the sensor :

[0077] ;

[0078] in For the of sensors value.

[0079] Step 2-2: Generate a three-dimensional cloud image based on the fused state values.

[0080] In a preferred but non-limiting embodiment of the present invention, step 2-2 specifically comprises:

[0081] The Kriging interpolation algorithm is used to generate a continuous three-dimensional cloud map based on the fused state values. The deviation visualization formula is as follows:

[0082]

[0083] in, For spatial points The vertical deviation value, For the setting The interpolation weights of the sensors, For the The status value of the sensor , To ensure an unbiased estimate of the Lagrange multiplier, the cloud map uses the HSV color space. Verticality deviation values ​​between 0 and 5 mm are green, verticality deviation values ​​between 5 and 10 mm are yellow, and verticality deviation values ​​greater than 10 mm are red.

[0084] The technical effect of step 2 is as follows:

[0085] Environmental adaptability: through and Dynamic correction (correlated with real-time data from the mud hydrometer) enables the algorithm to maintain an angle measurement accuracy of 0.1° in deep hole environments over 30m, an improvement of 40% over traditional algorithms.

[0086] Predictive fusion: Combined with the elastic deformation model of lattice columns, a pre-deformation compensation term is introduced into the state equation to achieve early prediction of deviation trends and solve the problem of hydraulic adjustment lag.

[0087] Robust design: When a sensor fails, the Bayesian confidence model automatically reduces its weight to below 0.05, ensuring that the fusion result is not affected by single-point failures and extending the system's trouble-free operation time to 1,200 hours.

[0088] The parameter calibration method of step 2 is as follows:

[0089] The initial value of the noise covariance matrix is ​​obtained through ground no-load tests. Set to diag[0.01,0.01,0.005], Set according to sensor model (fiber optic inclinometer =0.02).

[0090] Environmental parameters and The mapping relationship is established through orthogonal experiment, and the fitting formula is: , is the depth (m), is the mud viscosity of the lattice column (Pa·s).

[0091] Confidence Weight Recalculate every 5 minutes to ensure that it adapts to dynamic changes in the environment.

[0092] Step 3: Perform hydraulic jack group control.

[0093] In a preferred but non-limiting embodiment of the present invention, step 3 specifically comprises:

[0094] An intelligent hydraulic jack control system based on real-time data feedback was established. Specifically, the hydraulic jacks are connected to the central data processing system. When the 3D cloud image indicates vertical deviation of the lattice column, the central data processing system automatically controls the hydraulic jacks to adjust to the allowable deviation range based on the direction and magnitude of the deviation. For example, if the lattice column deviates forward in the X direction, the central data processing system automatically controls the hydraulic jacks in the corresponding position to increase pressure, pushing the lattice column backward. The system also monitors the deviation changes in real time during the adjustment process. When the deviation approaches the allowable range, the system automatically reduces the hydraulic jack adjustment force, achieving precise control. To ensure timely adjustments, the central data processing system has an early warning mechanism. When the lattice column deviation reaches a certain threshold (e.g., 10mm), an alarm is immediately issued and the hydraulic jacks are activated for pre-adjustment to prevent further deviation.

[0095] The lattice column construction process can also be optimized: Before lowering the lattice column, the borehole is pre-treated, and mud purification equipment is used to purify the mud in the borehole to remove impurities and large particles, thereby reducing mud interference with sensors. The sensors and hydraulic jack assembly are also fully debugged and calibrated to ensure optimal performance. During the lattice column lowering process, sensor data and the column lowering speed are monitored in real time. The speed is adjusted based on actual conditions to avoid deviation caused by excessive lowering and wobbling of the lattice column. When the column is lowered close to the designed depth, the hydraulic jack assembly is used for fine-tuning to ensure that the lattice column accurately reaches the designed position and verticality requirements. During the concrete pouring process, the verticality of the lattice column is continuously monitored, and any deviation caused by the impact of the concrete pouring is promptly readjusted using the hydraulic jack assembly.

[0096] The beneficial effects of the present invention are as follows:

[0097] The novel anti-interference sensor of the present invention combines multi-point distributed installation with advanced data transmission and processing technology, effectively solving the problems of sensor interference and data delay in deep hole or mud environment, and can obtain the verticality data of the lattice column in real time and accurately, and generate a reliable three-dimensional deviation cloud map; the intelligent hydraulic jack group control system based on real-time data feedback can quickly and accurately adjust according to the real-time deviation of the lattice column, greatly shortening the adjustment time, avoiding the insertion deviation exceeding the limit due to adjustment lag, improving the verticality control accuracy of the lattice column, and ensuring that the insertion deviation of the lattice column is controlled within a smaller range (such as ≤10mm); through comprehensive optimization of the construction process, including drilling pretreatment, equipment debugging and calibration, lowering process monitoring and concrete pouring process monitoring, the stability and reliability of the entire lattice column verticality control process are further improved, the uncertainty factors in the construction process are reduced, and the project quality and construction efficiency are improved.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be covered within the protection space of the claims of the present invention.

Claims

1. A lattice column verticality control method based on multi-source fusion and prediction compensation, characterized in that: include: Step 1: Optimize the sensor for lattice column verticality control; Step 2: Transmit and process the data collected by the sensor; Step 3: Control the hydraulic jack group; Step 1 specifically includes: using an inclinometer based on optical fiber sensing that is connected to a central data processing system as a sensor for controlling the verticality of the lattice column; Step 1 specifically includes: in designing the installation position of the sensor, adopting a multi-point distributed installation method, and evenly arranging multiple sensors at different heights of the lattice column; Step 2 specifically includes: connecting the central data processing system, Mesh network nodes, edge computing devices and sensors in sequence, and arranging the Mesh network nodes around the drill holes of the lattice columns and key locations set at the construction site of the lattice columns. The edge computing devices perform preliminary processing on the data collected by the sensors in real time, and then transmit the preliminarily processed data collected by multiple sensors to the central data processing system. The central data processing system uses an improved algorithm to fuse the preliminarily processed data collected by multiple sensors to generate an accurate three-dimensional deviation cloud map.

2. The lattice column verticality control method based on multi-source fusion and prediction compensation according to claim 1 is characterized in that: In step 2, the central data processing system uses an improved algorithm to fuse the data collected by multiple sensors after preliminary processing to generate an accurate three-dimensional deviation cloud map. The method specifically includes: Step 2-1: Perform data fusion processing on the data collected by multiple sensors after preliminary processing; Step 2-2: Generate a three-dimensional cloud image based on the fused state values.

3. The lattice column verticality control method based on multi-source fusion and prediction compensation according to claim 2 is characterized in that: Step 2-1 specifically includes: Step 2-1-1: Perform data preprocessing on the data collected by multiple sensors after preliminary processing; Step 2-1-2: Perform noise suppression on the data collected by the sensors after spatiotemporal alignment; Step 2-1-3: Perform confidence weighting on each sensor.

4. The lattice column verticality control method based on multi-source fusion and prediction compensation according to claim 3 is characterized in that: Step 2-1-1 specifically includes: performing spatiotemporal alignment on the data collected by each sensor after preliminary processing transmitted by the edge computing device through the Mesh network node, that is, using the cubic spline interpolation algorithm to correct the sampling time difference of different sensors. The formula is as follows: ； in, The timestamp after synchronization, that is, the corrected sensor Sampling time, For sensor The original sampling time, is the maximum time deviation, is the number of sensors.

5. The lattice column verticality control method based on multi-source fusion and prediction compensation according to claim 4 is characterized in that: Step 2-1-2 specifically includes: The improved Kalman filter algorithm is used to de-noise the data collected by the sensors after time-space alignment. Acoustic filtering, the core formula for noise filtering is: State prediction equation: ; Forecast error covariance: ; Kalman gain: ; State update equation: ; Error covariance update: ; in express The predicted state vector at time t, express The state transfer matrix based on the lattice column rigid body motion model at the moment, The time represents the corrected sensor Sampling time, express The control input matrix of the associated hydraulic jack adjustment amount at time, express The control input vector at time , express The forecast error covariance matrix at time , express The process noise covariance matrix is ​​corrected in real time by the mud density sensor, express The Kalman gain at time t, express The observation matrix that maps the sensor data to the state vector at each moment, Indicates based on The observation noise covariance matrix of the sensor depth dynamically adjusted at the moment, express The sensor observation value vector at time , Represents the identity matrix.

6. The lattice column verticality control method based on multi-source fusion and prediction compensation according to claim 5 is characterized in that: Step 2-1-3 specifically includes: Constructing a Bayesian network to evaluate Confidence of sensors , The calculation formula is: ； in, For the Sensors in environmental parameters The reliability probability density function under is the number of sensors; Then the following formula is used to calculate the fusion The status value of the sensor : ； in For the of sensors value.

7. The method for controlling the verticality of lattice columns based on multi-source fusion and prediction compensation according to claim 6 is characterized in that: Step 2-2 specifically includes: The Kriging interpolation algorithm is used to generate a continuous three-dimensional cloud map based on the fused state values. The deviation visualization formula is as follows: ； in, For spatial points The vertical deviation value, For the setting The interpolation weights of the sensors, For the The status value of the sensor , is the Lagrange multiplier.

8. The lattice column verticality control method based on multi-source fusion and prediction compensation according to claim 7 is characterized in that: Step 3 specifically includes: An intelligent hydraulic jack group control system based on real-time data feedback is established. That is, the hydraulic jacks are connected to the central data processing system. When the three-dimensional cloud map shows that the lattice column has a vertical deviation, the central data processing system automatically controls the hydraulic jacks to adjust to the allowable deviation range according to the direction and size of the deviation.

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