A method for instant volume detection of concrete in a feeding line

Abstract

The application discloses a kind of instant concrete quantity intelligent detection methods for feeding line, it is related to concrete processing field, comprising the following steps: non-contact measurement;Echo signal processing;By data registration and splicing, according to the actual environmental conditions of belt conveyor left and right two side point clouds are aligned and spliced into continuous one geometric model, intuitively present concrete shape;Data differential intercept, radar server will point cloud data according to differential method cut out section, calculate continuous section area;Calculate concrete volume;Algorithm parameter selection adapts to the running speed of feeding line;Three-dimensional visualization output;The application is through bilateral laser radar non-contact scanning, point cloud intelligent processing and the cooperative architecture of PLC real-time integration, solves the technical problems that traditional weighing method, mixing building discharge metering and other methods are poor in real-time, low accuracy, cannot detect local overload, realizes the high-precision instant detection of concrete feeding line conveying quantity and the active early warning of plugging risk.

Description

Technical Field

[0001] This invention relates to the field of concrete processing technology, and in particular to an intelligent method for real-time detection of concrete volume in a material supply line. Background Technology

[0002] Concrete supply lines are key equipment in the construction of hydropower dams, responsible for the continuous and stable delivery of concrete produced by the mixing plant to the pouring surface. Their technological development continuously integrates mechanical engineering, computer information technology, and modern automatic control technology, driving a comprehensive improvement in construction efficiency and quality. During concrete delivery, to achieve precise control and intelligent management, real-time monitoring of the conveyor's volume is necessary. However, traditional methods such as weighing and mixing plant output metering have significant shortcomings: First, these methods can only measure the average weight within a section of the conveyor belt and then calculate the average flow rate, lacking real-time accuracy and failing to reflect the instantaneous delivery status, making it difficult to meet the management requirements of high-strength, fast-paced concrete pouring for dams. Second, due to the irregular shape of concrete piles on the conveyor belt, traditional methods are not accurate enough in measuring localized flow overload, failing to promptly identify instantaneous overload conditions in local sections of the supply line, leading to delayed warnings of material blockage risks and affecting production safety and construction progress. Taking a hydropower station dam project as an example, its concrete supply line is designed to convey 300 m³ / h and the belt speed is 3.8 m / s. When using the traditional weighing method, the flow detection lag time is as long as several minutes, and there have been many belt blockage accidents caused by the failure to detect local overload in time, resulting in interruption of pouring and equipment damage. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide an intelligent method for real-time detection of concrete volume in a concrete supply line, thereby solving the problem of inaccurate measurement in existing concrete supply lines.

[0004] Technical Solution: A method for intelligent detection of real-time concrete volume in a material supply line, comprising the following steps:

[0005] 1) Non-contact measurement, including lidar and lidar server, wherein the lidar is connected to a belt conveyor;

[0006] 2) Echo signal processing: Conventional processing methods such as signal preprocessing, feature extraction, target detection and classification are used to remove interference signals and invalid points;

[0007] 3) Through data registration and splicing, the point clouds on the left and right sides are aligned and spliced ​​according to the actual environmental conditions of the belt conveyor and converted into a continuous geometric model to intuitively present the shape of the concrete.

[0008] 4) Data differential truncation: The radar server cuts the point cloud data into sections according to the differential method and calculates the area of ​​the continuous section.

[0009] 5) To obtain the concrete volume, the radar server calculates the cross-sectional area and instantaneous volume, transmits the signal to the PLC for integration, and calculates the flow rate for each time period with high measurement accuracy; the algorithm parameters are selected to adapt to the operating speed of the material supply line.

[0010] 6) 3D visualization output: Generates visual graphics and provides alarms for materials exceeding the boundary, alerting users to the risk of material blockage.

[0011] Preferably, the lidar and the belt conveyor are movably connected.

[0012] Preferably, the lidar is provided in pairs, with the two lidars symmetrically arranged on both sides of the belt conveyor, and the scanning area of ​​the two lidars covering the belt conveyor.

[0013] As a preferred method, the radar server performs differential extraction on the point cloud data and then performs integral calculations to obtain the concrete transport volume at each time interval. The maximum accuracy is the average flow rate over a time interval of 0.1 seconds. Through high-frequency differential extraction and integral calculations, the time resolution of flow rate detection is improved to the level of 0.1 seconds. Compared with traditional minute-level average flow rate detection, it can accurately capture instantaneous fluctuations and local overloads during the transportation process, providing data support for refined production management.

[0014] Preferably, in step 4, the differential cutting step size is precisely matched with the running speed of the belt conveyor, the length of the point cloud cut within a single data acquisition cycle is matched with the speed of the belt conveyor, and the point cloud segment is evenly divided into several equidistant differential sections.

[0015] Preferably, in step 5, the instantaneous volume is calculated using the formula V=∑(S×1.9cm), where S is the cross-sectional area of ​​a single differential section and 1.9cm is the step size of the differential section. This calculation model is suitable for a belt conveyor with a rated conveying capacity of 300m³ / h.

[0016] Preferably, in step 6, the data acquisition cycle of the lidar is 100ms. The PLC controller, through high-frequency integration calculation, achieves data refresh and flow update ten times per second through the coordinated operation of the 100ms data acquisition cycle and the PLC's high-frequency integration calculation, ensuring the real-time and continuous nature of the detection results and enabling the system to quickly respond to changes in the conveying conditions.

[0017] Preferably, the PLC controller and the radar server are communicatively connected.

[0018] Preferably, in step 7, the three-dimensional visualization graphic includes the cross-sectional outline of the concrete material, real-time volume value, and conveying speed curve. Alarm signals are output synchronously through audible and visual prompts and a pop-up window on the host computer. The integrated display of multi-dimensional information and multi-channel alarm output enable operators to grasp the conveying status from different dimensions and receive prominent prompts as soon as an anomaly occurs, shortening the emergency response time and improving the efficiency and safety of on-site management.

[0019] Beneficial Effects: By employing a collaborative architecture of dual-sided lidar non-contact scanning, point cloud intelligent processing, and PLC real-time integration, this system solves the technical challenges of poor real-time performance, low accuracy, and inability to detect localized overloads in traditional weighing methods and batching plant discharge metering. It achieves high-precision real-time detection of the concrete supply line's conveying volume and proactive early warning of material blockage risks. This system improves detection accuracy to an average flow rate of 0.1 seconds, adapting to engineering conditions with high conveying speeds and large rated conveying capacities. It fills the technological gap in high-precision metering of dynamic concrete conveying, promoting the technological upgrade of concrete supply lines from traditional manual metering to intelligent real-time metering. This is of significant value in ensuring the safe, efficient, and intelligent construction of large-volume concrete projects such as hydropower dams. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the device of the present invention;

[0021] Figure 2 This is a schematic diagram of the detection process of the present invention. Detailed Implementation

[0022] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example

[0024] like Figure 1-2As shown, the intelligent real-time volume detection system for concrete in a feed line of the present invention includes a lidar, a lidar server (monitoring host computer), a PLC controller, and a 3D visualization output module. The lidar is a pair, symmetrically positioned on the left and right sides of the middle of the conveyor belt. The lidars are movably connected to the conveyor belt and fixed by adjustable support rods. These support rods can move up and down to adjust the scanning angle, ensuring that the scanning area of ​​the two lidars covers the conveyor belt and that there is appropriate overlap between the scanning areas on both sides for subsequent point cloud data registration and stitching. The lidar server communicates with the two lidars via an industrial Ethernet connection, receiving and processing the raw point cloud data collected by the lidars. After completing signal preprocessing, the lidar server transmits the stitched geometric model and the calculated cross-sectional area and instantaneous volume data to the PLC controller via a communication interface. The PLC controller communicates with the lidar server, performs high-frequency integration calculations on the received data, and feeds back the calculation results and system status information to the lidar server for 3D visualization display and alarm judgment.

[0025] After system power-on initialization, two lidar sensors start synchronously to perform non-contact scanning of the dynamically conveyed concrete material on the conveyor belt. Due to the high surface humidity and uneven particle size of the concrete material, as well as the presence of a large amount of dust in the conveyor belt's operating environment, the original echo signal contains noise and invalid data points. After receiving the original point cloud data, the radar server processes the echo signal. First, signal preprocessing is performed, using statistical filtering to remove outliers and Gaussian filtering to smooth signal fluctuations while retaining the main features of the concrete material outline. Then, feature extraction is performed, extracting edge contour features, surface texture features, and height distribution features from the filtered point cloud data. Target detection and classification are then performed, distinguishing the concrete material point cloud from the conveyor belt background point cloud, dust interference point cloud, and environmental stray point cloud based on the extracted features. Interference signals and invalid points are removed, retaining the valid concrete material point cloud data. After completing the signal processing of one side of the radar, the radar server performs data registration and stitching. Since the two radars scan from the left and right perspectives respectively, their coordinate systems are different. The system adopts a registration algorithm based on feature matching to extract common feature points in the point clouds of both sides, calculates the transformation matrix of the coordinate systems of both sides, and transforms the point cloud data of one side of the radar to the coordinate system of the other side of the radar, so as to realize the alignment and stitching of the point clouds of the left and right sides. The stitched point cloud data is converted into a continuous geometric model, which intuitively presents the three-dimensional shape of the concrete material on the conveyor belt.

[0026] The radar server performs differential segmentation on the stitched continuous point cloud data using the differential method. The differential segmentation step size is precisely matched with the operating speed of the conveyor belt. The length of the point cloud segmented within a single data acquisition cycle is matched with the speed of the conveyor belt, and the point cloud segment is evenly divided into several equidistant differential sections. For each differential section, its area is calculated using the shoelace formula. Let the vertex coordinates of the section polygon be defined as: (x1, y1), (x2, y2), ..., (xn, yn), where the nth vertex is connected to the 1st vertex to form a closed polygon. Then the formula for calculating the area S of the section is: S = |∑ni=1(xiyi+1-xi+1yi)| / 2.

[0027] After calculating the area of ​​each continuous cross-section sequentially, the radar server calculates the cross-sectional area and instantaneous volume based on the obtained cross-sectional area. The formula for calculating the instantaneous volume is V=∑(S×1.9cm), where S is the cross-sectional area of ​​a single differential cross-section, and 1.9cm is the step size of the differential cross-section. This calculation model is adapted to the rated conveying capacity of the belt conveyor of 300m³ / h, and the algorithm parameters are selected to adapt to the operating speed of the feeding line. The radar server transmits the calculated instantaneous volume signal to the PLC controller in real time through the communication interface. The PLC controller performs high-frequency integration on the received instantaneous volume signal. By integrating the instantaneous volume per unit time, the concrete conveying flow rate for each time period and the total conveying volume from the start time to the current time are obtained. The radar server performs differential extraction on the point cloud data and then performs integration to obtain the concrete transport volume for each time period, with a maximum accuracy of 0.1s for the average flow rate. The data acquisition cycle of the lidar is 100ms. The PLC controller synchronizes with the lidar data acquisition cycle through high-frequency integration calculation. By adjusting the time window of the integration calculation, the average flow rate in different time intervals can be obtained to meet the different accuracy requirements of production monitoring.

[0028] The radar server inputs the stitched point cloud geometric model, real-time calculated cross-sectional area, and flow rate data into the 3D visualization output module. The 3D visualization includes the concrete material cross-sectional outline, real-time volume values, and conveying speed curve. Operators can visually observe the shape, distribution, and conveying status of the concrete material on the conveyor belt through the monitoring interface. The system also features an out-of-boundary material detection function. A safe boundary range for material conveying is preset in the visualization model. When concrete material is detected exceeding this boundary range, the system automatically triggers an alarm. The alarm signal is simultaneously output via audible and visual prompts and a pop-up window on the host computer, alerting operators to the risk of material blockage and prompting them to take timely measures to prevent production interruptions and equipment damage caused by conveyor belt blockage.

[0029] During system operation, the lidar continuously scans and collects data at a data acquisition cycle of 100ms. The radar server sequentially performs echo signal processing, data registration and stitching to generate a continuous geometric model of concrete materials. Based on the geometric model, it performs data differentiation and interception, cross-sectional area calculation and instantaneous volume calculation, and transmits the results to the PLC controller. After performing high-frequency integral calculation, the PLC controller feeds the calculation results back to the radar server, drives the 3D visualization output module to update the display, and determines in real time whether there are materials exceeding the boundary. The PLC controller can also transmit flow data, alarm signals and system status information to external devices to realize intelligent management and control of concrete conveying in the supply line.

[0030] This invention can be adaptively configured according to the actual operating conditions of different material supply lines. For belt conveyors with different conveying speeds, the data acquisition frequency and differential cutting parameters of the lidar are adjusted accordingly. For belt conveyors with different widths, the installation spacing and scanning angle of the lidars on both sides are adjusted. For material supply lines conveying concrete with different slumps, the filtering parameters and target detection threshold in signal preprocessing are adjusted to adapt to the point cloud characteristics of different materials, ensuring measurement accuracy. This invention combines lidar point cloud processing technology with PLC integral calculation and is applied for the first time in the field of real-time concrete volume detection for belt conveyors. It achieves high-precision real-time detection of concrete conveying flow rate and early warning of material blockage risks, improving the intelligence level and production safety of concrete supply lines.

[0031] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for intelligent detection of real-time concrete volume in a material supply line, characterized in that: 1) Non-contact measurement, including lidar and lidar server, wherein the lidar is connected to a belt conveyor; 2) Echo signal processing: Conventional processing methods such as signal preprocessing, feature extraction, target detection and classification are used to remove interference signals and invalid points; 3) Through data registration and splicing, the point clouds on the left and right sides are aligned and spliced ​​according to the actual environmental conditions of the belt conveyor and converted into a continuous geometric model to intuitively present the shape of the concrete. 4) Data differential truncation: The radar server cuts the point cloud data into sections according to the differential method and calculates the area of ​​the continuous section. 5) To obtain the concrete volume, the radar server calculates the cross-sectional area and instantaneous volume, transmits the signal to the PLC for integration, and calculates the flow rate for each time period with high measurement accuracy; the algorithm parameters are selected to adapt to the operating speed of the material supply line. 6) 3D visualization output: Generates visual graphics and provides alarms for materials exceeding the boundary, alerting users to the risk of material blockage.

2. The method according to claim 1, wherein, The lidar and the belt conveyor are movably connected.

3. The method according to claim 2, wherein, The lidar is provided in pairs, with the two lidars symmetrically arranged on both sides of the belt conveyor, and the scanning area of ​​the two lidars covering the belt conveyor.

4. The method according to claim 1, wherein, The radar server performs differential extraction on the point cloud data and then performs integral calculations to obtain the concrete transport volume at each time interval, with the maximum accuracy being the average flow rate over a time interval of 0.1 seconds.

5. The intelligent detection method for real-time concrete volume in a feeding line according to claim 1, characterized in that, In step 4, the differential cutting step size is precisely matched with the running speed of the belt conveyor. The length of the point cloud cut within a single data acquisition cycle is matched with the speed of the belt conveyor, and the point cloud segment is evenly divided into several equidistant differential sections.

6. The intelligent detection method for real-time concrete volume in a material supply line according to claim 5, characterized in that, In step 5, the instantaneous volume is calculated using the formula V=∑(S×1.9cm), where S is the cross-sectional area of ​​a single differential section and 1.9cm is the step size of the differential section. This calculation model is suitable for a belt conveyor with a rated conveying capacity of 300m³ / h.

7. The intelligent detection method for real-time concrete volume in a material supply line according to claim 1, characterized in that, In step 6, the data acquisition cycle of the lidar is 100ms, and the PLC controller performs high-frequency integration calculations.

8. The intelligent detection method for real-time concrete volume in a material supply line according to claim 1, characterized in that, The PLC controller and the radar server are connected in communication.

9. The intelligent detection method for real-time concrete volume in a material supply line according to claim 1, characterized in that, In step 7, the three-dimensional visualization graphics include the cross-sectional outline of the concrete material, real-time volume value, and conveying speed curve. The alarm signal is output synchronously through audible and visual prompts and a pop-up window on the host computer.

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