A trench cover concrete pouring device

Abstract

This invention relates to the field of trench cover concrete pouring technology, and proposes a trench cover concrete pouring device and its control method, including a hopper, a peristaltic pump, a generator, a control system, traveling wheels, a motor, auxiliary wheels, an image acquisition system, a frame, and a support. Driven by the traveling wheels, it can automatically move to the top of the trench cover mold for pouring. The control system performs path planning based on image information acquired by the image acquisition system, and controls the start, stop, and speed of the motor and peristaltic pump to achieve automatic movement and quantitative concrete pouring. The image acquisition system acquires images through a camera and transmits the image information to the control system. The control system processes the images, plans the walking route, and controls the movement of the equipment. Matlab and control configuration software are used for route planning and motor control, combined with a fuzzy PID control algorithm to adjust the flow rate of the peristaltic pump, ensuring the accuracy and efficiency of trench cover concrete pouring.

Description

Technical Field

[0001] This invention relates to the field of trench cover concrete pouring technology, and in particular to a trench cover concrete pouring device and its control method. Background Technology

[0002] Most cable trench covers for urban (suburban) railway bridges are prefabricated, using fiberglass formwork. Currently, the pouring of these prefabricated cable trench covers primarily involves manually pushing concrete using wheelbarrows and controlling the amount poured. This often results in over- or under-pouring of concrete, leading to inconsistent cover sizes and affecting construction quality. Furthermore, manually pushing concrete and pouring the prefabricated cable trench covers is inefficient and labor-intensive.

[0003] Therefore, there is an urgent need to develop a concrete pouring device for trench covers and its control method to overcome the shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a concrete pouring device and control method for trench covers, so as to improve the pouring efficiency and reduce the labor intensity of workers.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A concrete pouring device for trench covers includes: a hopper, a peristaltic pump, a discharge pipe, a generator, a control system, traveling wheels, a motor, auxiliary wheels, an image acquisition system, a frame, and a support.

[0007] The traveling wheels are installed on both sides of the front end of the frame, and the auxiliary wheels are installed on both sides of the rear end of the frame. The two traveling wheels are driven by two motors installed on the frame.

[0008] The hopper is mounted on the vehicle frame via a bracket, and the peristaltic pump is mounted on the vehicle frame. The inlet of the peristaltic pump is connected to the outlet of the hopper, and the outlet of the peristaltic pump is connected to a downward-pointing discharge pipe.

[0009] The control system and image acquisition system are installed at the front end of the vehicle frame. The image acquisition system is electrically connected to the control system. The image acquisition system acquires image information and inputs the image information into the control system for processing. The control system plans the walking route based on the image information and controls the start, stop and speed of the motor and peristaltic pump to realize automatic walking and concrete pouring.

[0010] The generator is mounted on the frame behind the control system and is electrically connected to the peristaltic pump, the control system, the motor, and the image acquisition system.

[0011] Preferably, the control system includes:

[0012] PC, controller, DC relay group, motor drive module, AC relay group, AC / DC conversion module and frequency converter;

[0013] The PC is equipped with Matlab and control configuration software. Matlab provides the Simulink algorithm modeling environment, and the control configuration software performs route planning and motor operation control calculations.

[0014] The controller is connected to a PC via a communication interface. The controller is connected to the coil terminals of the DC relay group and the motor drive module. The controller sends commands to the DC relay group and the motor drive module based on the calculation results of the PC to control the start, stop and speed of the motor.

[0015] The common terminal of the DC relay group is connected to the AC / DC conversion module, which converts the generator's output voltage into DC power suitable for the motor's operation.

[0016] The coil end of the AC relay group is connected to the controller, the common end is connected to the generator and the frequency converter, the frequency converter is connected to the peristaltic pump, and the frequency converter adjusts the speed of the peristaltic pump.

[0017] Preferably, the image acquisition system includes:

[0018] Camera and image acquisition card;

[0019] The camera is connected to an image acquisition card for communication. The camera captures video information, which is then sent to the image acquisition card for video image acquisition.

[0020] Preferably, the control system is equipped with a photoelectric sensor and a speed sensor at its front end; the photoelectric sensor and the speed sensor are connected to the input terminal of the controller, and the photoelectric sensor is installed at the front end of the vehicle frame;

[0021] The speed sensor is used to detect the speed deviation of the trench cover concrete pouring device and to correct it.

[0022] The photoelectric sensor is used to detect obstacles in front and feed the obstacle information back to the controller, which then performs an emergency stop operation on the motor based on the feedback information.

[0023] Preferably, a high-concentration flow meter is installed in the discharge port of the peristaltic pump; the high-concentration flow meter is used to detect the concrete flow rate in real time, and adjust the output flow rate of the peristaltic pump based on the measurement results to achieve quantitative concrete pouring;

[0024] The high-concentration flow meter is also used to detect the number of trench covers that have been poured when the concrete in the hopper has been poured.

[0025] Another objective of this invention is to provide a control method for a trench cover concrete pouring device, applicable to the aforementioned trench cover concrete pouring device, comprising the following steps:

[0026] S101. Pour a certain amount of concrete into the hopper, collect video information through the image acquisition system, process the collected image information, and plan the running route of the trench cover concrete pouring device.

[0027] S102. Calculate the rotational speed of the left and right motors in each time period, control the motor drive module to modulate the PWM signal, and control the trench cover concrete pouring device to move above the trench cover mold.

[0028] S103. Based on the required concrete for a single trench cover mold, control the concrete output flow rate and running time of the peristaltic pump to complete the concrete pouring of the trench cover mold.

[0029] Preferably, the processing of the acquired image information in step S101 specifically includes the following steps:

[0030] Step a: Image grayscale processing. The image is processed in the YCbCr color space to separate the brightness and chromaticity information. The Y, Cb and Cr components in the YCrCb color space are extracted respectively. The color component image that best represents the difference between the mold and the ground is extracted as the image to be processed.

[0031] Step b: Use the threshold segmentation method to segment the image to be processed, determine the gray level thresholds of the segmentation, and classify the entire image.

[0032] Step c: Apply median filtering to the segmented image. Create a window template and move it sequentially through the row sequence at a step size of 2. At each position, sort the elements to obtain the median pixel value and replace the original center pixel's grayscale value with the median value to eliminate isolated noise points in the image. From the obtained median, take the first column pixels of the first window and repeat the above steps to obtain the median of the second window. Process the entire image from row to column until the end.

[0033] Step d: Pixel statistics. Count the number of road pixels and target ditch cover mold pixels in the image after threshold segmentation. That is, count the sum of all vertical coordinate values ​​corresponding to each horizontal coordinate. Build matrices to represent them and draw the pixel statistics curves of the walking road image and the target ditch cover mold.

[0034] Step e: Perform Gaussian fitting on the pixel statistics curves of the walking road image and the target ditch cover mold respectively to determine the driving path and the center position of the target.

[0035] Preferably, controlling the concrete output flow rate and running time of the peristaltic pump in step S103 specifically includes the following steps:

[0036] Step 1: Start the peristaltic pump and make the peristaltic pump motor speed reach the speed value of the expected delivery flow rate;

[0037] Step 2: Monitor the actual concrete pouring flow rate using a high-viscosity flow meter, compare the actual measured value with the theoretical value, and obtain the flow deviation value and the rate of change of the difference as the input signal for fuzzy PID control;

[0038] Step 3: Perform fuzzy inference according to the fuzzy control rules to obtain the membership table of the input and output control quantities, and obtain the PID adjustment value of the fuzzy output control quantity after defuzzification.

[0039] Step 4: The PID adjustment values ​​ΔKp, ΔKi, and ΔKd of the fuzzy output control quantity are transmitted to the PID controller to obtain the fuzzy output control quantity, which is used as the input of the frequency converter to control the speed of the peristaltic pump motor and realize the flow rate regulation.

[0040] Preferably, step S102 involves calculating the rotational speeds of the left and right motors at various time intervals, specifically including the following steps:

[0041] Step 1: Divide the planned route into straight sections and turning sections. Calculate the speeds of the left and right motors for the straight and turning sections respectively. The turning section is achieved by adjusting the speed difference between the motors.

[0042] Step 2: Calculate the motor control parameters using the PID algorithm, send them to the drive module for PWM modulation, and realize the movement of the trench cover concrete pouring device.

[0043] The present invention discloses a concrete pouring device for trench covers, which has the following beneficial effects.

[0044] This invention includes a hopper, a peristaltic pump, a discharge pipe, a generator, a control system, traveling wheels, a motor, auxiliary wheels, an image acquisition system, a frame, and a support. Driven by the traveling wheels, the device can automatically move to the top of the trench cover mold for concrete pouring. The control system plans the path based on image information acquired by the image acquisition system, controlling the start / stop and speed of the motor and peristaltic pump to achieve automatic movement and quantitative concrete pouring. The image acquisition system captures images through a camera and transmits the image information to the control system, which processes the images, plans the travel route, and controls the equipment movement. The flow rate of the peristaltic pump can be monitored and adjusted in real time using a high-concentration flow meter to ensure accurate concrete output. This patent also uses Matlab and control configuration software for route planning and motor control, combined with a fuzzy PID control algorithm to adjust the flow rate of the peristaltic pump, ensuring the accuracy and efficiency of trench cover concrete pouring. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of the trench cover concrete pouring device shown in the embodiments of this application.

[0046] Figure 2 This is a schematic diagram of the control frame of the trench cover concrete pouring device shown in the embodiments of this application.

[0047] Figure 3 This is a schematic flowchart illustrating the control method of the trench cover concrete pouring device in an embodiment of this application.

[0048] In the attached diagram: 1-Hopper; 2-Peristaltic pump; 3-Discharge pipe; 4-Generator; 5-Control system; 501-PC; 502-Controller; 503-DC relay group; 504-Motor drive module; 505-AC relay group; 506-AC / DC conversion module; 507-Frequency converter; 6-Walking wheel; 7-Motor; 8-Auxiliary wheel; 9-Speed ​​sensor; 10-Image acquisition system; 101-Camera; 102-Image acquisition card; 11-Frame; 12-Bracket; 13-Photoelectric sensor. Detailed Implementation

[0049] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0050] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0051] Most cable trench covers for urban (suburban) railway bridges are prefabricated, using fiberglass formwork. Currently, the pouring of these prefabricated cable trench covers primarily involves manually pushing concrete using wheelbarrows and controlling the amount poured. This often results in over- or under-pouring of concrete, leading to inconsistent cover sizes and affecting construction quality. Furthermore, manually pushing concrete and pouring the prefabricated cable trench covers is inefficient and labor-intensive.

[0052] To address the aforementioned issues, this application provides a trench cover concrete pouring device and control method. This device utilizes a combination of a walking wheel, a motor, a control system, and an image acquisition system. Image processing technology is used to plan the operating route, enabling the trench cover concrete pouring device to move independently. A peristaltic pump and control system, combined with a fuzzy PID control system, achieve precise control of the concrete pouring volume. This improves the efficiency and accuracy of trench cover concrete pouring, ensures construction quality, and reduces the labor intensity of operators.

[0053] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0054] See Figures 1 to 2 A concrete pouring device for trench covers includes: a hopper 1, a peristaltic pump 2, a discharge pipe 3, a generator 4, a control system 5, a PC 501, a controller 502, a DC relay group 503, a motor drive module 504, an AC relay group 505, an AC / DC conversion module 506, a frequency converter 507, a walking wheel 6, a motor 7, an auxiliary wheel 8, a speed sensor 9, an image acquisition system 10, a camera 101, an image acquisition card 102, a frame 11, a bracket 12, and a photoelectric sensor 13.

[0055] The traveling wheels 6 are installed on both sides of the front end of the frame 11, and the auxiliary wheels 8 are installed on both sides of the front and rear ends of the frame 11. The two traveling wheels 6 are respectively connected to the motors 7 installed on the frame 11. The motors 7 drive the traveling wheels 6 to rotate and move forward. The speed difference between the two motors 7 controls the traveling wheels 6 to turn.

[0056] Hopper 1 is mounted on top of frame 11 via bracket 12. Hopper 1 is funnel-shaped. Peristaltic pump 2 is mounted on frame 11. The inlet of peristaltic pump 2 is connected to the outlet of hopper 1, and the outlet of peristaltic pump 2 is connected to a downward-pointing discharge pipe 3. Concrete is pumped by the pressure roller of peristaltic pump 2 alternately squeezing and releasing the elastic conveying hose. The concrete flow rate depends on the pump head rotation speed and the size of the elastic conveying hose. When the size of the elastic conveying hose is fixed, the concrete flow rate is controlled by adjusting the pump head rotation speed. A high-concentration flow meter is also installed in the outlet of peristaltic pump 2. The high-concentration flow meter is connected to control system 5 to detect the concrete flow rate in real time and adjust the flow rate of peristaltic pump 2 to achieve quantitative concrete pouring. The high-concentration flow meter is also used to detect the amount of trench cover that has been poured when the concrete in hopper 1 is finished, thereby obtaining the current trench cover pouring progress.

[0057] The control system 5 and image acquisition system 10 are installed at the front end of the frame 11. The image acquisition system 10 is electrically connected to the control system 5. The image acquisition system 10 acquires image information and inputs it to the control system 5 for processing. The control system 5 plans the walking route based on the image information and controls the start, stop, and speed of the motor 7 and peristaltic pump 2 to achieve automatic walking and concrete pouring. The control system 5 includes a PC 501, a controller 502, a DC relay group 503, a motor drive module 504, an AC relay group 505, an AC / DC conversion module 506, and a frequency converter 507. The PC 501 is equipped with Matlab and control configuration software. Matlab provides the Simulink algorithm modeling environment, and the control configuration software performs route planning and motor 7 operation control calculations. The controller 502 is communicatively connected to the PC 501 and connects to the coil terminals of the DC relay group 503 and the motor drive module 507. 4. Controller 502 sends commands to DC relay group 503 and motor drive module 504 based on the calculation results of PC 501 to control the start, stop, and speed of motor 7. The common terminal of DC relay group 503 is connected to AC / DC conversion module 506, which is connected to generator 4 and motor 7. AC / DC conversion module 506 converts the AC power output from generator 4 into DC power, which is then transmitted to motor 7 through DC relay group 503 to realize the start and stop control of motor 7. Motor drive module 504 receives commands from controller 502 to regulate speed. The coil terminal of AC relay group 505 is connected to controller 502, and the common terminal is connected to generator 4 and frequency converter 507. AC relay group 505 controls the power supply to and from frequency converter 507. Frequency converter 507 is connected to peristaltic pump 2 and controller 502, and controls the speed of peristaltic pump 2 through frequency converter 507.

[0058] The image acquisition system 10 includes a camera 101 and an image acquisition card 102. The camera 101 is communicatively connected to the image acquisition card 102, and video information is acquired through the camera 101 and transmitted to the image acquisition card 102 for video image acquisition. The image acquisition card 102 transmits the image data to the control system 5 for planning the movement path and determining the movement target of the trench cover concrete pouring device.

[0059] Photoelectric sensor 13 and speed sensor 9 are connected to the input terminal of controller 502 and are located at the front end of control system 5. Photoelectric sensor 13 is installed at the front end of frame 11. Speed ​​sensor 9 is used to detect the speed deviation of trench cover concrete pouring device and correct it. Photoelectric sensor 13 is used to detect obstacles in front and feed back the obstacle information to controller 502. Controller 502 performs emergency stop operation of motor 7 according to the feedback information.

[0060] The generator 4 is mounted on the frame 11 behind the control system 5, and is connected to the peristaltic pump 2, the control system 5, the motor 7 and the image acquisition system 10 to provide power.

[0061] Please refer to Figure 3 Corresponding to the aforementioned application function implementation device embodiments, this application also provides a control method for a trench cover concrete pouring device and corresponding embodiments, including the following steps:

[0062] S101. Pour a certain amount of concrete into the hopper 1, collect video information through the image acquisition system 10, process the collected image information, and plan the running route of the trench cover concrete pouring device.

[0063] Specifically, the acquired image information is processed, including:

[0064] Step a: Each time movement is required, capture two video frames of the video information acquired by the image acquisition system 10, standardize the image, scale it to 640x640, and convert it into an RGB image.

[0065] In this application, the road is a typical gray-white cement surface, and the trench cover mold is black. The RGB image is processed using the YCbCr color space to separate luminance and chrominance information, extracting the Y, Cb, and Cr components from the YCrCb color space to ensure good differentiation between the mold and the road surface. In the conventional Y component image, the difference between the road and the mold is small, making them difficult to distinguish. In the Cr component image, the road appears white, while the mold appears dark black, showing a clear difference. Both the road and mold have stable and uniform color distributions, which is highly beneficial for subsequent image segmentation. While the Cb component shows a stable and uniform distribution of the road surface and is prominent in the overall image, the mold's color distribution is highly unstable, significantly impacting the accuracy of road segmentation in later images. Therefore, the Cr component image from the YCrCb color space is directly used as the image to be processed.

[0066] Step b: Segment the image to be processed using a thresholding method, determine the gray-level thresholds for the division, and classify the entire image. Treat the image to be processed as a whole composed of background and target, use variance to describe the difference between the background and target, and take the gray level with the largest inter-class variance as the optimal threshold. Let the image size be M*N, the gray level be L, and the number of pixels with gray level i in the image be Ni, then its probability of occurrence is:

[0067]

[0068] The optimal threshold formula is expressed as:

[0069]

[0070] To enhance the noise resistance of the image segmentation method, a two-dimensional maximum inter-class variance method is adopted. In the same image of size M×N and gray level L, if f(x,y) is the gray value of the image at (x,y), and g(x,y) is the average gray value in the neighborhood centered at (x,y) with k×k as the range, its expression is:

[0071]

[0072] In the formula, k is the size of the neighborhood, 1≤x+m≤M, 1≤y+n≤M, M and N are the width and height of the image, respectively, f(x,y) and g(x,y) form a pair (f(x,y), g(x,y)), let N ij Let f(x, y) = i and g(x, y) = j be the number of pixels in the image (0 ≤ i, j ≤ L-1), then their joint probability density is:

[0073]

[0074] The probabilities of the image target and background appearing are as follows:

[0075]

[0076] The overall mean vector of the image is:

[0077]

[0078] The mean vectors of the image target and background are:

[0079]

[0080] The between-class variance is defined as:

[0081] S B =P0[(μ0-μ T )(μ0-μ T )]+P1[(μ1-μ T )(μ1-μ T (10)

[0082] Taking the trace of the variance between classes as a measure, we have

[0083]

[0084] The optimal threshold formula is:

[0085]

[0086] Iterate through the gray levels of the image and find the gray level that maximizes the trace of the inter-class variance between the target and the background to obtain the optimal segmentation threshold (s*, t*).

[0087] Step c: Apply median filtering to the segmented image. Create a window template and move it sequentially through the row sequence at a step size of 2. At each position, sort the elements to obtain the median pixel value, and replace the original center pixel's grayscale value with the median value. The expression is as follows:

[0088]

[0089] Where, {x1, x2, ..., x n The order of size decreases gradually.

[0090] From the obtained median, take the first column pixels of the first window, repeat the above steps to obtain the median of the second window, and process the entire image in order from row to column until the end.

[0091] The basic process includes: First, create a template with a window size of (2N+1). Move the template sequentially through the row sequence at a step size of 2. At each position, sort the elements to obtain the median pixel value as (2N+1) / 2+1. Then, take the first column pixels of the first window from the median obtained in the previous step, and repeat the above two steps to obtain the median of the second window. Finally, process the entire image from row to column until finished. In Matlab, the medfilt2 function can be used directly to perform filtering operations on the grayscale image.

[0092] Step d: Pixel statistics. Count the number of road pixels and target ditch cover mold pixels in the image after threshold segmentation, respectively. This involves summing the values ​​of all ordinates corresponding to each abscissa and representing them in matrices.

[0093]

[0094] After pixel statistics, the horizontal coordinates Y and X are represented as follows:

[0095]

[0096] X = [1, 2, ..., j] (16)

[0097] Plot the pixel statistics curves of the walking road image and the target trench cover mold;

[0098] Step e: Perform Gaussian fitting on the pixel statistics curves of the walking road image and the target ditch cover mold, respectively.

[0099]

[0100] In the above formula, a, b, and c are real constants and a > 0. The standard form of the Gaussian function is:

[0101]

[0102] The expected value μ shown in equation (18) is equal to the fitted expected value b shown in equation (17).

[0103] b = μ (19)

[0104] The b-value determines the position of the curve on the X-axis, the standard deviation σ determines the amplitude of the curve distribution, and the expected value b is the center position x of the walking path determined by Gaussian fitting. a .

[0105] S102. Calculate the rotational speed of the left and right motors 7 in each time period, control the motor drive module 504 to modulate the PWM signal, and control the trench cover concrete pouring device to move above the trench cover mold.

[0106] Specifically, the rotational speeds of the left and right motors 7 are calculated separately for each time period, including:

[0107] Step 1: Divide the planned route into straight sections and turning sections. Calculate the rotational speeds of the left and right motors 7 for both straight and turning sections. The speed difference between the left and right motors 7 is used to achieve the turning section.

[0108] Step 2: Calculate the control parameters of motor 7 using the PID algorithm, send them to the drive module for PWM modulation, and realize the movement of the trench cover concrete pouring device.

[0109] The linear function is used as the output of the PID control device. The control deviation is the main factor affecting the actual speed of motor 7 and is also a key control point in the system design. This parameter can be calculated as follows:

[0110] e(t) = x r (t)-y(t) (20)

[0111] Where, x r y(t) represents the target speed parameter, and y(t) represents the actual speed parameter of motor 7, which is a DC motor. Following the method shown in equation (20), the control objective is e(t) = e(t)min. Based on this, the difference between the parameter and the target speed is calculated to avoid the influence of the oscillation inherent in motor 7 on the numerical calculation.

[0112] Using control deviation as the basis for PID control parameter calculation and correction, and relying on PID control theory, proportional, integral, and derivative control error parameters are set through direct digital control mode to clarify the control inputs for motor 7 speed. Fuzzy rules are used to obtain the incremental values ​​of the PID control parameters, and the motor 7 speed parameters are adjusted in real time to ensure optimal dynamic control performance. The specific PID control calculation method can be expressed as follows:

[0113]

[0114] In formula (21), P represents the proportionality factor, I represents the integral factor, D represents the differential factor, and K represents the integral factor. P K represents the proportional control parameter. I K represents the integral control parameter. D K represents the differential control parameter. P0 K I0 K D0 This represents the initial values ​​of the three control parameters, ΔK. P ΔK I ΔK D This represents the increment of fuzzy control.

[0115] The PID control parameters are set using a quadratic performance function to complete the signal output of the PID control device, expressed as follows:

[0116]

[0117] Where, x j This represents the collected rotational speed data, and δ represents the weighting coefficient. The specific calculation method for the weighting coefficient can be expressed as follows:

[0118]

[0119] Here, η represents the learning rate, and to ensure control accuracy, this parameter is set to a range of (0, 1). It is important to note that... These are theoretical values ​​and cannot be directly applied to specific parameter settings. They are approximated using a difference quotient method, and the calculation can be expressed as:

[0120]

[0121] Substituting equation (24) into equation (23) yields the output parameters of the motor speed PID control device, ensuring that the final control result meets the requirements and realizing PID-based motor speed control.

[0122] S103. Based on the concrete required for a single trench cover mold, control the concrete output flow rate and running time of the peristaltic pump 2 to complete the concrete pouring of the trench cover mold.

[0123] Specifically, control the concrete output flow rate and running time of peristaltic pump 2:

[0124] Step 1: Start the peristaltic pump 2 and make the motor speed of the peristaltic pump 2 reach the speed value of the expected delivery flow rate;

[0125] Step 2: Monitor the actual concrete pouring flow rate using a high-viscosity flow meter, compare the actual measured value with the theoretical value, and obtain the flow deviation value and the rate of change of the difference as the input signal for fuzzy PID control;

[0126] Based on the working principle of peristaltic pump 2, the flow rate of concrete transported per revolution is fixed, so the instantaneous flow rate can be controlled by adjusting the pump speed. The calculation of the fixed flow rate of peristaltic pump 2 involves the pressure roller rotor moving from position A to position B, and the volume of concrete transported is the volume of the arc-shaped pipe AB, i.e.

[0127]

[0128] In the formula, D is the pitch circle diameter of the pump casing, V is the volume of the arc-shaped pipe section AB, θ is the angle of rotation from A to B, and d is the diameter of the pump pipe.

[0129] The theoretical flow rate delivered by a peristaltic pump in one revolution is:

[0130]

[0131] The theoretical flow rate of a peristaltic pump is,

[0132] Q = q × n (27)

[0133] In the formula, Q is the theoretical flow rate of concrete transported by peristaltic pump 2, and n is the number of revolutions of peristaltic pump 2.

[0134] The input to the fuzzy controller 502 is obtained by the difference e(k) between the theoretical flow rate Q calculated from the motor speed of the peristaltic pump 2 and the actual fabric flow rate y(k) monitored by the high viscosity flow meter, and the rate of change of the difference Δe(k).

[0135] e(k)=Qy(k) (28)

[0136]

[0137] After e(k) and Δe(k) are input into the PID controller, they are fuzzified and then flow control is completed through the PID controller 502.

[0138] Step 3: Perform fuzzy inference according to the fuzzy control rules to obtain the membership table of the input and output control quantities, and obtain the fuzzy output control quantity after defuzzification.

[0139] Designing the universe of discourse based on fuzzy control variables is a prerequisite for constructing fuzzy control rules. The fuzzy subsets of the input and output variables are set as negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively, and represented by letters as {NB, NM, NS, ZO, PS, PM, PB}. Then its fuzzy universe of discourse can be represented as {-3, -2, -1, 0, 1, 2, 3}, making it a control rule base.

[0140] When the absolute values ​​of the input quantities e(k) and Δe(k) are large, e(k) is either large or medium, corresponding to a universe of discourse of ±large or ±medium, respectively. To reduce the settling time, the value of Kp should be large (Kp corresponds to universes of discourse of PB and NB); to prevent large overshoot in the system during control and to ensure that it remains within the controllable range, the integral element should be limited, and the values ​​of Ki and Kd should be small (corresponding to universes of discourse of PS and NS).

[0141] When the absolute values ​​of the input quantities e(k) and Δe(k) are moderate, e(k) is medium or small, corresponding to ±positive or ±medium universes of discourse, respectively. To reduce the settling time, the value of Kp should be slightly reduced (Kp corresponds to universes of discourse PM and ZO); to enhance the control effect and ensure the system response speed, the values ​​of Ki and Kd should be moderate (corresponding to universes of discourse PM and NM, respectively).

[0142] When the absolute values ​​of the input quantities e(k) and Δe(k) are small, e(k) is small or 0, corresponding to ±small or 0 in the universe of discourse, respectively. To obtain better steady-state performance, the values ​​of Kp and Ki should be large (corresponding to the universes of discourse PM and NM, respectively). At this time, the system may oscillate. When the error tends to decrease, Kd takes a large value (corresponding to the universe of discourse PB), and when the error tends to increase, Kd is 0 (corresponding to the universe of discourse ZO).

[0143] Defuzzification is the process of converting fuzzy quantities into precise quantities. After obtaining a fuzzy set through reasoning, the quantity with the highest membership degree is selected as the sharp quantity. Let set A be the fuzzy output quantity. If the membership function has a maximum value μA, then this value can be directly determined as the sharp quantity, and its corresponding mathematical expression is:

[0144] μ A (z0)=μ A (z) z0∈z (6)

[0145] In the formula, z0 is the sharpness factor; z is the blurriness factor;

[0146] Assuming that the universe of discourse does not have one or more maximum elements, its succinct value can be determined as the average value of the elements. By calculating the average value of each element, the precise output quantities ΔKp, ΔKi, and ΔKd can be obtained.

[0147] Step 4: The PID adjustment values ​​△Kp, △Ki, and △Kd of the fuzzy output control quantity are transmitted to the PID controller 502 to obtain the fuzzy output control quantity, which is used as the input of the frequency converter 507 to control the speed of the peristaltic pump 2 motor and realize the flow rate regulation.

[0148] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Substitutions may include replacements for some structures, devices, or method steps, or may be complete technical solutions. Equivalent substitutions or modifications made to the technical solutions and inventive concepts of the present invention should all be covered within the scope of protection of the present invention.

Claims

1. A trench cover concrete pouring device, characterized by, Include: hopper (1), peristaltic pump (2), discharge pipe (3), generator (4), control system (5), walking wheel (6), motor (7), auxiliary wheel (8), image acquisition system (10), frame (11) and support (12); The walking wheel (6) is installed on both sides of the front end of the frame (11), and the auxiliary wheel (8) is installed on both sides of the rear end of the frame (11). Two walking wheels (6) are respectively driven by two motors (7) installed on the frame (11); The hopper (1) is installed above the frame (11) by the support (12), and the peristaltic pump (2) is installed on the frame (11). The inlet of the peristaltic pump (2) is connected with the outlet of the hopper (1), and the outlet of the peristaltic pump (2) is connected with the downward pointing discharge pipe (3); The control system (5) and the image acquisition system (10) are installed on the front end of the frame (11), and the image acquisition system (10) is electrically connected with the control system (5). The image acquisition system (10) collects image information and inputs the image information to the control system (5) for processing. The control system (5) plans the walking route according to the image information, controls the start and stop and the rotating speed of the motor (7) and the peristaltic pump (2), and realizes automatic walking and concrete pouring; The generator (4) is installed on the frame (11) behind the control system (5), and the generator (4) is electrically connected with the peristaltic pump (2), the control system (5), the motor (7) and the image acquisition system (10) respectively; The control method of the above device for trench cover plate concrete pouring includes the following steps: S101, a certain amount of concrete is poured into the hopper (1), video information is collected by the image acquisition system (10), the collected image information is processed, and the running route of the trench cover plate concrete pouring device is planned; The step S101 of processing the collected image information specifically includes the following steps: Step a, image gray processing, YCbCr color space processing is performed on the image, the luminance information and the chrominance information are separated, the Y, Cb and Cr components in the YCrCb color space are extracted respectively, and the color component image which best represents the difference between the mold and the ground is extracted as the image to be processed; Step b, the threshold segmentation method is used to segment the image to be processed, and the gray threshold of the boundary is determined to classify the whole image; Step c, the median filter method is used to filter the segmented image, a window template is created, and the moving speed is 2. When moving to a position, the median pixel value is obtained by sorting the elements at this time, and the gray value of the original center pixel is replaced by the median value, so that the isolated noise points in the image are eliminated. From the obtained median value, the front column pixels of the first window are taken, and the above steps are repeated to obtain the median value of the second window. The whole image is processed in the order of row to column until the end. Step d, pixel statistics, respectively, the number of road pixel points and target trench cover plate mold pixel points in the image after threshold segmentation, that is, the sum of all vertical coordinate values corresponding to each horizontal coordinate, respectively, to establish a matrix to represent, draw the pixel statistical curve of the walking road image and the target trench cover plate mold; Step e, respectively, the pixel statistical curve of the walking road image and the target trench cover plate mold is fitted by Gaussian, and the center position of the driving path and the target is determined; S102, the speed of the left and right motors (7) in each time period is calculated, the motor drive module (504) is controlled to modulate the PWM signal, and the trench cover plate concrete pouring device is controlled to move above the trench cover plate mold; S103, according to the concrete required by a single trench cover plate mold, the output flow and running time of the peristaltic pump (2) are controlled, and the trench cover plate mold concrete pouring is completed; The control of the peristaltic pump (2) in the step S103 includes the following steps: Step 1, start the peristaltic pump (2), so that the motor speed of the peristaltic pump (2) reaches the speed value of the expected delivery flow; Step 2, the actual concrete pouring flow value is monitored by the high-viscosity flowmeter, the actual measured value is compared with the theoretical value, and the flow deviation value and the difference change rate are obtained as the input signals of the fuzzy PID control; Step 3, according to the fuzzy control rule, the membership table of the input and output control quantity is obtained by fuzzy reasoning, and the PID adjustment value of the fuzzy output control quantity is obtained by de-fuzzification processing; Step 4, the PID adjustment value of the fuzzy output control quantity △Kp, △Ki and △Kd is transmitted to the PID controller (502), the fuzzy output control quantity is obtained, which is used as the input of the frequency converter (507), the speed of the peristaltic pump (2) motor is controlled, and the flow is adjusted.

2. A trench cover concrete pouring device according to claim 1, characterized in that, The control system (5) comprises: PC (501), controller (502), DC relay group (503), motor drive module (504), AC relay group (505), AC / DC conversion module (506) and frequency converter (507); Matlab and control configuration software are carried on the PC (501), wherein Matlab provides Simulink algorithm modeling environment, and control configuration software is used for route planning and motor (7) operation control calculation; The controller (502) is connected with the PC (501) through a communication interface, the controller (502) is connected with the coil end of the DC relay group (503) and the motor drive module (504), the controller (502) sends instructions to the DC relay group (503) and the motor drive module (504) according to the calculation result of the PC (501), and controls the start and stop and speed of the motor (7); The common end of the DC relay group (503) is connected with the AC / DC conversion module (506), and the AC / DC conversion module (506) converts the output voltage of the generator (4) into direct current suitable for the operation of the motor (7); The alternating current relay group (505) coil end connects the controller (502), the common end connects the generator (4) and the frequency converter (507), the frequency converter (507) connects the peristaltic pump (2), and the frequency converter (507) adjusts the rotating speed of the peristaltic pump (2).

3. A trench cover concrete pouring device according to claim 1, characterized in that, The image acquisition system (10) comprises: a camera (101) and an image acquisition card (102); The camera (101) is in communication connection with the image acquisition card (102), video information is collected by the camera (101), and the video information collected by the camera (101) is transmitted to the image acquisition card (102) for video image acquisition.

4. A trench cover concrete pouring device according to claim 2, wherein The control system (5) is provided with a photoelectric sensor (13) and a speed sensor (9) at the front end; the photoelectric sensor (13) and the speed sensor (9) are connected with the input end of the controller (502), and the photoelectric sensor (13) is installed at the front end of the vehicle frame (11); The speed sensor (9) is used for detecting the speed deviation of the trench cover plate concrete pouring device and correcting deviation; The photoelectric sensor (13) is used for detecting the front obstacles and feeding back the obstacle information to the controller (502), and the controller (502) performs emergency stop operation of the motor (7) according to the feedback information.

5. A trench cover concrete pouring device according to claim 1, characterized in that, A high-concentration flowmeter is arranged in the discharge port of the peristaltic pump (2); the high-concentration flowmeter is used for detecting the concrete flow in real time, adjusting the output flow of the peristaltic pump (2) based on the measurement result, and realizing quantitative pouring of the concrete; The high-concentration flowmeter is also used for detecting the current trench cover pouring quantity when the concrete pouring in the hopper (1) is completed.

6. A trench cover concrete pouring apparatus according to claim 1, wherein In the step S102, the rotating speeds of the left and right motors (7) in each time period are calculated, specifically including the following steps: Step one, the planned route is segmented, the planned route is divided into straight line sections and turning sections, and the rotating speeds of the left and right motors (7) in the straight line sections and the turning sections are calculated, wherein the turning sections are realized by adjusting the speed difference of the motor (7); Step two, the motor (7) control parameters are calculated by the PID algorithm and sent to the driving module for PWM modulation, so as to realize the walking of the trench cover concrete pouring device.

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