Intelligent detection system for concrete high tower pumping pipeline

Abstract

The application relates to a concrete high-tower pumping pipe intelligent detection system, belonging to the technical field of concrete construction, which comprises an intelligent pipe and a control module, the inner wall of the intelligent pipe is provided with a partition layer, a plurality of groups of sensors are arranged in the partition layer, and the plurality of groups of sensors are distributed along the axial direction of the pipe; any group of the sensors comprises a pressure sensor and a distance sensor which are electrically connected with the control module; the pressure sensor is used for monitoring the pressure received by the partition layer and uploading the pressure to the control module; the distance sensor is used for monitoring the thickness of the partition layer and uploading the thickness data to the control module; after receiving the pressure data and the thickness data, the control module respectively judges whether the plurality of groups of data exceed threshold values, and an early warning signal is sent when the threshold values are exceeded.

Description

Technical Field

[0001] This invention belongs to the field of concrete construction technology, specifically relating to an intelligent detection system for concrete high-tower pumping pipelines. Background Technology

[0002] Concrete tower pumping is one of the key construction steps in the pouring of concrete for the main tower of a bridge. If the internal condition of the pipeline is not accurately monitored, improper construction can easily lead to problems such as pipe jamming, blockage, and bursting. Since manually checking for problems in the pipeline and repairing or replacing them would take a lot of time and seriously slow down the construction progress, sensors are usually required.

[0003] Traditional monitoring devices involve installing pressure detection devices near the beginning and end sections of the pipeline. Sensors in these devices obtain pressure data based on the concrete flow velocity within the pipeline. Pressure gauges on the two devices then display the pressure values ​​for the first and last sections, respectively. Workers rely solely on the magnitude and changes in these pressure values ​​to determine the overall internal condition of the pipeline, resulting in low accuracy. To address this, for example, Chinese Patent Publication CN111443130A discloses a concrete pumping pipeline blockage monitoring device. This device collects audio signals from the surrounding area of ​​the pipeline using audio acquisition equipment and uses changes in the surrounding sound to determine the blockage status, thereby enabling monitoring of the internal blockage status of the pipeline. However, actual construction sites are filled with various mechanical noises, which can affect the acquisition of audio signals, making the above solution inaccurate.

[0004] For example, Chinese patent CN113309987B discloses a device for detecting blockages in concrete pumping pipelines. Although its excitation component can emit vibration waves of different frequencies by impacting the pipeline and determine whether the pipeline is blocked based on the feedback of the waves, thereby locating the problematic pipeline, this device is used when checking after the pipeline is blocked and cannot perform real-time monitoring during the concrete pumping process.

[0005] Therefore, a smart detection system for concrete high-tower pumping pipelines that can be monitored in real time with high accuracy is needed. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides an intelligent detection system for concrete high-tower pumping pipelines, which features real-time monitoring and high accuracy.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A smart detection system for concrete high-tower pumping pipelines includes a smart pipeline and a control module. The inner wall of the smart pipeline is provided with a partition, and several sets of sensors are arranged in the partition. The several sets of sensors are distributed along the pipeline axis.

[0009] Any set of sensors includes a pressure sensor and a distance sensor electrically connected to the control module. The pressure sensor is used to monitor the pressure on the interlayer and upload the data to the control module. The distance sensor is used to monitor the thickness of the interlayer and upload the thickness data to the control module.

[0010] After receiving the pressure data and thickness data, the control module determines whether several data exceed the threshold and issues a warning signal when the threshold is exceeded.

[0011] As a preferred technical solution of the present invention, after receiving the pressure data, the control module calculates the variance of several pressure data and thickness data, determines whether the variance exceeds the variance threshold, and issues a warning signal when the variance threshold is exceeded. The control module is connected to a terminal for communication, and when the variance threshold is exceeded, it finds several data with the largest dispersion in the currently uploaded data.

[0012] As a preferred embodiment of the present invention, the control module is electrically connected to a flow sensor. The flow sensor is used to monitor the flow rate of concrete in the pipeline and transmit the flow data L to the control module. When the variance threshold is exceeded, the control module finds the n data points with the largest dispersion in the currently uploaded data, where n = L / L0 × d, d is a pre-input constant, and L0 is the standard flow value.

[0013] As a preferred embodiment of the present invention, any group of the sensors includes a plurality of pressure sensors and a plurality of distance sensors electrically connected to the control module. The plurality of pressure sensors belonging to the same group are arranged in a ring array within the partition, and the plurality of distance sensors belonging to the same group are arranged in a ring array within the partition.

[0014] As a preferred embodiment of the present invention, the control module calculates the variance of several pressure data uploaded by several pressure sensors belonging to the same group, and issues an alarm when it determines whether the variance is greater than a second variance threshold.

[0015] As a preferred embodiment of the present invention, the control module is used to increase the second variance threshold by a factor of A1, where A1 = L / L0 × e, and e is a pre-input correction coefficient.

[0016] As a preferred embodiment of the present invention, it further includes an input panel electrically connected to the control module. The input panel is used to input the values ​​of L0, e, and d, and to display several data points with the largest dispersion in the currently uploaded data.

[0017] The beneficial effects of this invention are as follows:

[0018] By setting up several sets of sensor groups containing pressure sensors and distance sensors along the pipeline axis, multiple sets of sensors distributed along the pipeline can collect pressure values ​​and pipe wall deformation data in real time. Then, the control module can identify sections with excessive pressure or abnormal pipe wall deformation based on whether each data point distributed along the pipeline exceeds the threshold, thus achieving real-time monitoring while improving monitoring accuracy.

[0019] By having the control module calculate the variance based on the data uploaded by each sensor group, when no single data point is judged as abnormal, but the overall pressure value of the pipeline is uneven, indicating a probability of blockage or abnormality, potential risks can be identified by the variance exceeding a set threshold.

[0020] When the variance threshold is exceeded, identify the data with the largest dispersion in the currently uploaded data so that operators can locate the abnormal location through the data source and take better and more timely preventive measures.

[0021] When the variance threshold is exceeded, the control module identifies the n data points with the largest dispersion in the currently uploaded data, where n = L / L0 × d. In cases of high flow and high risk, n is taken as a larger value to expand the scope of investigation, thereby realizing dynamic perception and early warning of pipeline status under complex working conditions.

[0022] By setting the second variance threshold to be increased by a factor of A1, where A1 = L / L0 × e, the system can dynamically correct the judgment threshold under conditions where the flow rate is high and the pipeline will be subjected to greater pressure and deformation during normal operation, thus avoiding false alarms. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 This is a schematic diagram of the cross-sectional structure of the present invention;

[0025] Figure 2 This is a cross-sectional structural diagram of a portion of the present invention.

[0026] Explanation of key component symbols:

[0027] In the diagram: 1. Sensor; 2. Data cable; 3. Divider. Detailed Implementation

[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0029] Please see Figure 1-2A smart detection system for concrete high tower pumping pipelines includes a smart pipeline and a control module. The inner wall of the smart pipeline is provided with a partition, and several sets of sensors are installed in the partition. The several sets of sensors are distributed along the pipeline axis.

[0030] Any set of sensors includes a pressure sensor and a distance sensor electrically connected to the control module. The pressure sensor is used to monitor the pressure on the partition and upload the data to the control module, while the distance sensor is used to monitor the thickness of the partition and upload the thickness data to the control module.

[0031] After receiving the pressure and thickness data, the control module determines whether several data points exceed the threshold and issues a warning signal when the threshold is exceeded.

[0032] Pipelines consist of straight sections and curved sections, and different sections are connected together by lap joints, flanges, and sockets to form a whole.

[0033] During use, the pressure sensor and distance sensor collect pressure and deformation data of the pipe partition once per second and send it to the control module in real time. The distance sensor determines the deformation of the partition by monitoring its distance from the partition.

[0034] By setting up several sets of sensors, including pressure sensors and distance sensors, along the pipeline axis, multiple sets of sensors distributed along the pipeline can collect pressure values ​​and pipe wall deformation data in real time. Then, the control module can identify sections with excessive pressure or abnormal pipe wall deformation based on whether each data point distributed along the pipeline exceeds a threshold, thus achieving real-time monitoring while improving monitoring accuracy.

[0035] In some cases, no single data point is judged as abnormal, but the overall pressure or deformation value of the pipeline is uneven. Therefore, after receiving the pressure data from each group of sensors, the control module calculates the variance of several pressure data points uploaded by each group of sensors, determines whether the variance exceeds the variance threshold, and issues an early warning signal when the variance threshold is exceeded. The control module is connected to a terminal, and when the variance threshold is exceeded, it finds the several data points with the largest dispersion in the currently uploaded data.

[0036] The control module pre-numbers each group of sensors. The numbers of several groups of sensors are arranged sequentially along the pipeline axis. When the variance of the data uploaded by a certain group exceeds the variance threshold, the control module marks the pipeline section corresponding to the data as an abnormal fluctuation area and uses the number of this group of sensors as the location basis for the abnormal fluctuation section. Then, further processing such as issuing abnormal location information can be carried out.

[0037] By having the control module calculate the variance based on the data uploaded by each sensor group, when no single data point is judged as abnormal, but the overall pressure value of the pipeline is uneven, indicating a probability of blockage or abnormality, potential risks can be identified by the variance exceeding a set threshold.

[0038] Specifically, the control module is electrically connected to a flow sensor, which is used to monitor the flow rate of concrete in the pipeline and transmit the flow data L to the control module. When the variance threshold is exceeded, the control module finds the n data with the largest dispersion in the currently uploaded data, where n = L / L0 × d, d is a pre-input constant, and L0 is the standard flow value.

[0039] The control module identifies the sensor locations corresponding to the n data points with the largest dispersion and marks them as high-risk areas.

[0040] When the variance threshold is exceeded, the control module identifies the n data points with the largest dispersion in the currently uploaded data, where n = L / L0 × d. In cases of high flow and high risk, n is taken as a larger value to expand the scope of investigation, thereby realizing dynamic perception and early warning of pipeline status under complex working conditions.

[0041] When the variance threshold is exceeded, identify the data points with the largest dispersion in the currently uploaded data. This allows operators to locate the anomaly by the data source and take better preventative measures in a timely manner.

[0042] For the same pipeline section, setting only a single sensor may not achieve accurate monitoring. For example, when the deformation point occurs on the opposite side of the sensor contact surface, single-point monitoring is difficult to reflect the true deformation. Therefore, any group of sensors includes several pressure sensors and several distance sensors that are electrically connected to the control module. Several pressure sensors belonging to the same group are arranged in a ring array in the partition, and several distance sensors belonging to the same group are arranged in a ring array in the partition.

[0043] Specifically, each pressure sensor and displacement sensor constitutes a sensor set, and each sensor group includes four sensor sets. The angle between the line connecting two adjacent sensor sets and the central axis of the pipe is 90 degrees. At this time, the four sensor sets of each sensor group are symmetrically distributed in a cross shape and in a ring array, covering the stress and deformation around the pipe wall.

[0044] Meanwhile, the control module can determine whether there is local uneven stress or abnormal deformation in the pipeline section where a certain group of sensors is located by judging the degree of data dispersion in the same group of sensors, thereby identifying potential risks of pipeline structural instability. To this end, the control module calculates the variance of several pressure data uploaded by several pressure sensors belonging to the same group and issues an alarm when it determines whether the variance is greater than the second variance threshold.

[0045] Whenever sensors from the same group upload pressure and distance data to the control module, the control module performs variance calculation on the data from the same group.

[0046] However, when the flow rate is large, the pressure fluctuation itself will increase, which will cause the pipeline to have greater pressure and deformation under normal operation, making the variance calculation susceptible to interference and misjudgment; therefore, the control module is used to increase the second variance threshold by A1 times, where A1=L / L0×e, and e is a pre-input correction coefficient.

[0047] By setting the second variance threshold to be increased by a factor of A1, where A1 = L / L0 × e, the system can dynamically correct the judgment threshold under conditions where the flow rate is high and the pipeline will be subjected to greater pressure and deformation during normal operation, thus avoiding false alarms.

[0048] Concrete differs from ordinary pipeline transport materials in terms of physical and chemical properties: Concrete has higher density and viscosity, and is prone to uneven settlement and local accumulation during pipeline transportation. Furthermore, when the pumping pressure is too high, bleeding may occur, which is the separation of solids in the water. Bleeding can lead to local aggregate accumulation, which in this scheme means that the local stress on the pipe wall is further uneven.

[0049] Therefore, the control module needs to further introduce a joint judgment of pipeline pressure gradient and variance persistence;

[0050] Specifically, after receiving pressure data uploaded by several sets of sensors, the control module calculates the pressure gradient between two adjacent sets of sensors.

[0051] The method for calculating the pressure gradient is as follows: Several groups of sensors are numbered in advance. After each data is received, the average pressure value Px of each group is calculated, where x is the sensor group number. Then, the pressure difference ΔPx = P(x+1) - Px between any two adjacent groups is calculated, where P(x+1) is the average pressure value of the adjacent downstream sensor of the sensor group numbered x.

[0052] When axial unevenness occurs during normal concrete pumping, even if there is a section of pipeline with high pressure, the pressure gradient will not rise abnormally because pumping continues and the pressure is transmitted smoothly along the pipeline, with the gradient change within the normal range. However, when local blockage or severe aggregate accumulation occurs, the upstream pressure rises significantly and the downstream pressure decreases relatively, resulting in an abnormally large pressure gradient between adjacent groups. Therefore, the magnitude of the pressure gradient is positively correlated with the probability of bleeding or blockage.

[0053] Subsequently, after the control module calculates the variance of each group of sensor-uploaded data, it stores the variance data from the same group in chronological order and derives the function Fx(t) that the variance changes with time.

[0054] Subsequently, the integral representation of the function Fx(t) calculated by the control module over the past period is denoted as Sx= At this point, Sx represents the recent variance accumulation of the pipe segment where the sensor group numbered x is located;

[0055] When radial unevenness occurs during ordinary concrete pumping, even if there is uneven pressure distribution in a certain section of the pipeline, resulting in a large variance, it will not be maintained for a long time, so the Sx value is small. However, when bleeding or aggregate accumulation occurs, the uneven pressure distribution will continue for a long time, resulting in a significant increase in the Sx value. Therefore, it can be seen that the value of Sx is positively correlated with the probability of bleeding.

[0056] Once the control module obtains Sx, it calculates the comprehensive congestion coefficient Dx = ΔPx × j × Sx × k for each sensor group, where j is the pre-input correction coefficient for ΔPx and k is the pre-input correction coefficient for Sx.

[0057] Subsequently, the control module determines whether Dx is greater than the pre-input threshold D0. If it is, it determines that there is a serious blockage or risk of water leakage in the pipe section and triggers an alarm.

[0058] When ΔPx or Sx is larger, the probability of blockage or oozing is greater. At this time, the larger the D value, the greater the probability of exceeding D0 and triggering an alarm, thus completing the early warning of blockage or oozing.

[0059] Furthermore, when a single value of Sx or ΔPx is high, the value of Dx can still be limited by the normal level of another parameter to avoid false alarms;

[0060] When ΔPx and Sx both rise abnormally, Dx will increase significantly since it is obtained by multiplying ΔPx and Sx. This greatly increases the probability of alarm triggering and improves the system's sensitivity to risks.

[0061] Therefore, this model improves the accuracy and sensitivity of early warnings by combining two parameters in its calculations.

[0062] Since the probability of bleeding is positively correlated with pumping pressure, when the pumping pressure is too high, the squeezing effect on the concrete is intensified, and the water in the concrete is more easily squeezed out, resulting in an increase in the slurry concentration and a decrease in fluidity near the pipe wall. Therefore, pumping pressure can be used as a reference indicator for whether bleeding occurs. For this reason, after the control module calculates Dx, it further corrects Dx using flow data.

[0063] The control module will calculate the correction coefficient m based on the flow data L, m≥1, m=L / L0×g, where g is the pre-input correction coefficient. When m<1, the control module will set m=1.

[0064] When the flow rate is large, it indicates that the pumping speed is fast. At this time, the pumping pressure is high, and the concrete is more prone to bleeding under high pressure. At this time, m>1, the control module will introduce a correction coefficient m according to the flow rate to amplify Dx. Dx is more likely to exceed D0, thereby improving the sensitivity of bleeding warning in scenarios where bleeding is more likely to occur.

[0065] When the flow rate is normal or low, it indicates that the pumping speed is slow. At this time, the pumping pressure is low and the probability of water leakage is small. At this time, m is close to or 1, and Dx is close to or equal to the original value, avoiding false alarms in low-risk operating conditions.

[0066] By introducing a flow correction coefficient m, Dx can be dynamically amplified under high flow conditions, enhancing the system's ability to identify the risk of high-pressure water leakage, while remaining stable under low flow conditions, thus suppressing the probability of false alarms.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An intelligent detection system for concrete high-tower pumping pipelines, characterized in that: It includes an intelligent pipeline and a control module. The inner wall of the intelligent pipeline is provided with a partition, and several sets of sensors are provided in the partition. The several sets of sensors are distributed along the pipeline axis. Any set of sensors includes a pressure sensor and a distance sensor electrically connected to the control module. The pressure sensor is used to monitor the pressure on the interlayer and upload the data to the control module. The distance sensor is used to monitor the thickness of the interlayer and upload the thickness data to the control module. After receiving the pressure data and thickness data, the control module determines whether several data exceed the threshold, and issues a warning signal when the threshold is exceeded. After receiving the pressure data, the control module calculates the variance of several pressure data from each group of sensors, determines whether the variance exceeds the variance threshold, and issues an early warning signal when the variance threshold is exceeded. The control module is connected to a terminal and finds the data with the largest dispersion in the currently uploaded data when the variance threshold is exceeded. The control module is electrically connected to a flow sensor, which is used to monitor the flow rate of concrete in the pipeline and transmit the flow data L to the control module. When the variance threshold is exceeded, the control module finds the n data with the largest dispersion in the currently uploaded data, where n = L / L0 × d, d is a pre-input constant, and L0 is the standard flow value. Any group of sensors includes several pressure sensors and several distance sensors electrically connected to the control module. Several pressure sensors belonging to the same group are arranged in a ring array within the partition, and several distance sensors belonging to the same group are arranged in a ring array within the partition. The control module calculates the variance of several pressure data uploaded by several pressure sensors belonging to the same group, and issues an alarm when the variance is greater than the second variance threshold. The control module is used to increase the second variance threshold by a factor of A1, where A1 = L / L0 × e, and e is a pre-input correction coefficient.

2. The intelligent detection system for concrete high-tower pumping pipelines according to claim 1, characterized in that: It also includes an input panel electrically connected to the control module, which is used to input the values ​​of L0, e, and d, and to display several data points with the largest dispersion in the currently uploaded data.

Images