A synchronous control and intelligent monitoring system and method for ultra-long steel pipe concrete pouring

Abstract

This invention discloses a synchronous control and intelligent monitoring system and method for ultra-long steel pipe concrete pouring. The system includes a data acquisition module, an analysis and decision-making module, and an execution module. The data acquisition module uses laser displacement sensors to monitor the concrete liquid level elevation in each steel pipe in real time and uses a camera device to acquire images of the concrete surface inside the pipe and images of the safety behavior of the pouring platform on the tower. The analysis and decision-making module incorporates an AI image analysis unit and a weighted average elevation benchmark-deviation compensation PID control model to identify concrete homogeneity, safety behavior, and generate pouring flow rate adjustment commands. The execution module includes an electric flow regulating valve and a pouring pump frequency converter to receive and execute the adjustment commands. This invention achieves precise control of the liquid level elevation difference among multiple steel pipes during the pouring process and integrates safety linkage functions, improving construction quality and safety.

Description

Technical Field

[0001] This invention relates to the field of steel-concrete composite construction technology. More specifically, this invention relates to a synchronous control and intelligent monitoring system and method for the entire process of ultra-long steel-concrete composite pouring. Background Technology

[0002] Concrete-filled steel tube structures are widely used in bridge towers, high-rise buildings, and other engineering projects due to their high load-bearing capacity and convenient construction. For structures that use multiple steel tubes (such as concrete-filled steel tube bridge towers), the concrete pouring process usually requires simultaneous pouring of multiple steel tubes to ensure uniform stress distribution and overall stability of the structure.

[0003] Currently, the construction of concrete-filled steel pipes mainly relies on manual observation combined with simple instruments for monitoring. Operators visually inspect the flow state and liquid level of the concrete inside the steel pipes using either conventional cameras or visual observation, and adjust the pouring speed and flow rate of each steel pipe based on experience. This traditional method has the following shortcomings: First, the narrow interior space of the steel pipe, insufficient lighting, and dense reinforcement make it difficult for humans to accurately determine the real-time liquid level and flow state of the concrete, easily leading to judgment errors. Especially when the pouring height is large, the safety risks of prolonged high-altitude work by humans are high, and the continuity and stability of monitoring are also difficult to guarantee.

[0004] Secondly, in scenarios involving the simultaneous casting of multiple steel pipes, the difference in liquid level between the pipes relies heavily on the experience of on-site operators for coordination. Due to the lack of real-time and accurate elevation data, liquid level deviations are often difficult to detect and correct in a timely manner, which may lead to some steel pipes being cast too quickly or too slowly, thereby affecting the structural stress balance and even requiring later repairs.

[0005] Furthermore, existing monitoring methods mostly remain at the level of data collection or simple early warning, such as transmitting images back to the monitoring room via cameras, where manual judgment of anomalies is made and pouring parameters are adjusted accordingly. This method has a slow response time, lagging control, and is highly dependent on the experience of operators, making it difficult to achieve precise, real-time synchronous control.

[0006] Furthermore, safety management at construction sites is usually independent of the pouring operation. Safety violations (such as not wearing safety helmets, gathering of people in dangerous areas, etc.) are handled solely through manual inspections and post-incident processing, failing to be linked with the pouring equipment, thus posing safety hazards.

[0007] In summary, existing steel-concrete composite pouring construction suffers from prominent problems such as low precision in synchronous control of multiple steel pipes, disconnect between monitoring and regulation, and separation of safety and construction. There is an urgent need for a system that can achieve intelligent monitoring and synchronous control throughout the entire process to improve pouring quality and construction safety. Summary of the Invention

[0008] The purpose of this invention is to provide a synchronous control and intelligent monitoring system and method for ultra-long steel pipe concrete pouring, to solve the problems of low synchronous control accuracy and disconnect between monitoring and regulation in existing technologies for multi-steel pipe concrete pouring. By constructing a closed-loop system of data acquisition, intelligent analysis, model calculation, and synchronous control, precise control of the liquid level difference in multiple steel pipes during the pouring process is achieved, and safety linkage functions are integrated to improve construction quality and safety.

[0009] The technical solution adopted by this invention to solve this technical problem is: a synchronous control and full-process intelligent monitoring system for ultra-long steel pipe concrete pouring, comprising: The data acquisition module includes a laser displacement sensor for real-time monitoring of the concrete liquid level elevation inside each steel pipe, and a camera device for acquiring images of the concrete surface inside each steel pipe and images of the safety behavior of the pouring platform on the tower. The analysis and decision-making module, which is communicatively connected to the data acquisition module, includes: The AI ​​image analysis unit is used to receive images captured by the camera device and identify the state of the concrete surface and safety behaviors in the construction area. The synchronous control unit has a built-in weighted average elevation benchmark-deviation compensation PID control model, which is used to calculate the weighted average elevation benchmark based on the liquid level elevation data of each steel pipe and generate adjustment instructions for the pouring flow of each steel pipe. The execution module, which is communicatively connected to the analysis and decision module, includes an electric flow regulating valve and a frequency converter for the casting pump installed on each steel pipe casting pipeline, for receiving and executing the regulation command to regulate the casting flow of each steel pipe; The system forms a closed-loop control loop of data acquisition, intelligent analysis, model calculation and synchronous control through the data acquisition module, analysis and decision-making module and execution module, which is used to control the difference in concrete liquid level in each steel pipe within a preset threshold during the pouring process.

[0010] As a further aspect of the present invention, the synchronization control unit is configured as follows: Based on formula Calculate the weighted average elevation H avg H i Let w be the real-time liquid level elevation of the i-th steel pipe. i The preset weighting coefficients; Based on formula Calculate the elevation deviation e of the i-th steel pipe i , where e i Let be the deviation of the liquid level elevation in the i-th steel pipe. Based on PID control algorithm formula Calculate the opening adjustment amount of the electric flow control valve for the i-th steel pipe. K p K i K d These are the proportional, integral, and derivative coefficients, respectively, and t is the control time.

[0011] As a further aspect of the present invention, the AI ​​image analysis unit includes a concrete homogeneity recognition module, which is configured as follows: Acquire the concrete surface image captured by the camera device (3), and calculate its grayscale standard deviation σ and mean μ; Based on formula Calculate the homogeneity coefficient U, where, The standard deviation of the grayscale value; This represents the average grayscale value. When the homogeneity coefficient U is lower than a preset threshold, it is determined that there is a risk of segregation, and the pouring speed is reduced through the execution module.

[0012] As a further aspect of the present invention, the AI ​​image analysis unit includes a security behavior recognition module; The analysis and decision-making module also includes a safety pouring linkage interlocking unit that is communicatively connected to the safety behavior recognition module; The safety pouring linkage interlocking unit is configured to: quantify and calculate the safety risk level based on preset violation weights and durations, and generate access control instructions for the execution module according to the safety risk level. The access control instructions include at least instructions to limit the pouring speed and instructions to stop the pouring operation.

[0013] As a further aspect of the present invention, the safety pouring linkage interlocking unit is configured as follows: Based on formula Calculate the risk level R, where α j The preset weight for the j-th type of violation, , t j The duration of the j-th type of violation.

[0014] As a further aspect of the present invention, the data acquisition module further includes a sensor for acquiring the pouring speed; The analysis and decision-making module further includes a pouring speed optimization unit, which is configured as follows: Obtain the current actual pouring speed v and the homogeneity coefficient U of the concrete; ; When the homogeneity coefficient U is lower than a preset threshold, based on formula v opt =k⋅v calculates the target velocity v. optWhere k is an adaptation coefficient greater than 0 and less than 1; the execution module adjusts the pouring speed to the optimized target speed v. opt .

[0015] As a further aspect of the present invention, the camera device includes an in-tube camera unit and a tower-mounted camera unit; The in-tube camera unit includes a zoom camera mounted on a mobile device, a track assembly that provides a moving track for the mobile device, and a rope retractor connected to the mobile device. The track assembly is arranged longitudinally along the inner wall of the steel pipe, and the rope retractor is used to adjust the height of the mobile device on the track assembly.

[0016] This invention also provides a method for synchronous control and intelligent monitoring of the entire process of ultra-long steel pipe concrete pouring. The method, using the aforementioned system, includes the following steps: S1. Real-time acquisition of concrete liquid level elevation data in each steel pipe, concrete surface images inside the pipe, and safety behavior images of the pouring platform on the tower. S2. Based on the liquid level elevation data, an adjustment command for the pouring flow of each steel pipe is generated through a weighted average elevation benchmark-deviation compensation PID control model. S3. Based on the image of the concrete surface inside the pipe, the homogeneity of the concrete is analyzed and identified by the AI ​​image analysis unit, and a pouring speed optimization command is generated. S4. Based on the safety behavior image, the AI ​​image analysis unit analyzes and identifies the violation, and generates an access control command that is linked to the pouring operation. S5. According to the adjustment command, pouring speed optimization command and / or permission control command, control the electric flow regulating valve and / or pouring pump frequency converter to adjust the pouring flow and / or pouring speed of each steel pipe. Among them, steps S2 to S5 form a closed-loop control during the execution process, which is used to control the difference in concrete liquid level in each steel pipe within a preset threshold.

[0017] As a further aspect of the present invention, in step S2, the weighted average elevation benchmark-deviation compensation PID control model specifically includes: Calculate the weighted average elevation ; Calculate the elevation deviation of the i-th steel pipe ; Through PID control algorithm Calculate the opening adjustment amount of the electric flow regulating valve for the i-th steel pipe. .

[0018] As a further aspect of the present invention, step S3, identifying the homogeneity of concrete includes: Acquire images of the concrete surface and calculate the standard deviation of its grayscale values. and mean gray value ; Calculate the homogeneity coefficient ; When the homogeneity coefficient U is lower than a preset threshold, it is determined that there is a risk of segregation, and an optimization instruction to reduce the pouring speed is generated.

[0019] The present invention has at least the following beneficial effects: First, it improves the synchronous control accuracy of multi-steel-pipe concrete pouring. By collecting real-time liquid level elevation data of each steel pipe through laser displacement sensors, and combining the weighted average elevation benchmark with the deviation compensation PID control model, the pouring flow rate of each steel pipe is automatically adjusted. This can stably control the difference in concrete liquid level elevation in each steel pipe within a preset threshold, ensuring uniform stress on the structure and avoiding later repairs or structural safety hazards caused by excessive liquid level deviation.

[0020] Secondly, it achieves intelligent identification and adaptive control of concrete pouring quality. Through the AI ​​image analysis unit, the image of the concrete surface inside the steel pipe is processed in real time to calculate the homogeneity coefficient, automatically determine whether there is a risk of segregation, and actively reduce the pouring speed when the risk occurs, thereby effectively preventing quality problems such as concrete segregation and blockage, and improving the quality of pouring and forming.

[0021] Third, it integrates the linkage and interlocking functions of safety behavior recognition and pouring operations. By capturing images of safety behavior on the pouring platform on the tower through camera devices, using AI image analysis units to identify violations, and generating corresponding access control instructions based on risk quantification models, it can automatically limit the pouring speed or stop the pouring operation according to the risk level, directly binding safety management and construction control, and significantly reducing the safety risks of high-altitude pouring operations.

[0022] Fourth, a fully closed-loop intelligent monitoring and control system was constructed. The system integrates data acquisition, intelligent analysis, model calculation, and execution control, and can respond to changes in the state during the pouring process in real time without human intervention. It has fast response speed and high control accuracy, reduces reliance on operator experience, and improves the automation and intelligence level of ultra-long steel pipe concrete pouring construction.

[0023] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the casting platform on the tower of the present invention; Figure 2 This is a diagram showing the layout for monitoring the state of concrete inside the pipe according to the present invention; Figure 3 yes Figure 2 A magnified view of a portion of the image; Figure 4 It is a track layout diagram; Figure 5 It is a layout diagram of the mobile device.

[0025] Among them, 1-management camera, 2-pouring platform, 3-moving device, 4-track assembly, 5-spring, 6-roller, 7-steel pipe, 8-pouring pipe, 9-integrated cable, 10-zoom camera, 11-rope retractor. Detailed Implementation

[0026] The present invention will now be described in detail and completely with reference to the accompanying drawings. Those skilled in the art will be able to implement the present invention based on these descriptions. Before describing the present invention with reference to the accompanying drawings, it should be particularly noted that the technical solutions and features provided in various parts of the present invention, including the following description, can be combined with each other without conflict.

[0027] Furthermore, the embodiments of the present invention described below are generally only some, not all, of the embodiments of the present invention. Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific implementation process is as follows: This invention provides a synchronous control and intelligent monitoring system for the entire process of ultra-long steel pipe concrete pouring. The implementation of this invention will be described in detail below with reference to specific application scenarios. Taking the pouring construction of a steel pipe concrete bridge tower as an example, the tower is composed of eight steel pipes. During construction, it is required that the concrete levels in the eight steel pipes rise as synchronously as possible to ensure the uniform stress distribution and overall stability of the tower structure.

[0029] like Figures 1-5As shown, the system is first deployed. A laser displacement sensor is installed directly above the top opening of each steel pipe. The sensor's laser beam is vertically aligned with the concrete molten surface inside the pipe to measure the height of the molten concrete in real time. Simultaneously, a camera device is installed on the inner wall of each steel pipe. This device consists of a zoom camera, a track assembly, a moving device, and a rope retractor. The track assembly is fixed longitudinally along the inner wall of the steel pipe. The moving device is embedded in the track and can slide up and down. The camera is fixed to the moving device. The rope retractor is connected to the moving device via a cable. Operators on the ground can adjust the camera's height position inside the steel pipe by controlling the raising and lowering of the rope retractor, thus tracking the changes in the molten concrete level and the flow state of the concrete surface throughout the pouring process. In addition, behavior management cameras are installed diagonally at each pouring platform on the tower to collect images of the safety behavior of workers in the construction area, such as whether they are wearing safety helmets, whether they are gathering in dangerous areas, and whether there are open flames present.

[0030] After system deployment, the pouring construction phase begins. All laser displacement sensors and cameras are activated, and data is transmitted to the control host in real time. The AI ​​image analysis unit within the control host processes images of the concrete surface inside the steel pipes, identifying the homogeneity of the concrete, such as the presence of segregation or blockage. Simultaneously, it analyzes images of safety behavior on the platform above the tower to determine if any violations have occurred. The synchronous control unit dynamically calculates the weighted average elevation benchmark for all steel pipes based on the liquid level elevation data returned by the laser displacement sensors of each pipe. It then compares the deviation of the current liquid level of each pipe from this benchmark and generates pouring flow adjustment commands for each steel pipe using a built-in deviation compensation PID control model.

[0031] After receiving the adjustment command, the execution module controls the opening of the electric flow regulating valve on the corresponding steel pipe pouring pipeline and adjusts the frequency of the pouring pump inverter, thereby precisely adjusting the concrete pouring flow rate of each steel pipe. For example, when the liquid level in a certain steel pipe is too low, the system automatically increases the opening of the corresponding flow valve to speed up the pouring; conversely, if the liquid level is too high, the opening is decreased to slow down the pouring. The entire process runs continuously at a millisecond-level sampling frequency, forming a closed loop of data acquisition, intelligent analysis, model calculation, and synchronous control, ensuring that the difference in liquid level elevation between the eight steel pipes is always controlled within the preset three centimeters.

[0032] During the pouring process, if the AI ​​image analysis unit determines that the homogeneity coefficient of the concrete is lower than a set threshold, indicating a risk of segregation, the system automatically reduces the current pouring speed and gradually increases it again once the concrete condition returns to normal. If the safety behavior recognition module on the tower detects violations such as workers not wearing safety helmets, abnormal personnel gathering, or open flames, the system quantifies and calculates the risk level based on the type weight and duration of the violation. When the risk level reaches medium risk, an audible and visual alarm is issued and the pouring speed is limited. When the risk level reaches high risk, all pouring operations are immediately stopped, and construction can only resume after the violation is eliminated. The entire pouring process requires no manual intervention; the control host automatically records all monitoring data and control commands, which can be reviewed after construction is completed. Through the above implementation methods, this system can efficiently and accurately achieve synchronous control and intelligent monitoring of the entire process of ultra-long steel pipe concrete pouring.

[0033] In another embodiment, the synchronization control unit is configured as follows: Based on formula Calculate the weighted average elevation H avg H i Let w be the real-time liquid level elevation of the i-th steel pipe. i The preset weighting coefficients; Based on formula Calculate the elevation deviation e of the i-th steel pipe i , where e i Let e ​​be the deviation of the liquid level elevation in the i-th steel pipe. i >0 indicates that the liquid level is too high, e i <0 indicates that the liquid level is too low. Based on PID control algorithm formula Calculate the opening adjustment amount of the electric flow control valve for the i-th steel pipe. K p K i K d These are the proportional, integral, and derivative coefficients, respectively; t is the control time; and K is the coefficient for the derivative. p For 0.8-1.2, K i For 0.05-0.1, K d It is 0.1-0.3.

[0034] In the construction of ultra-long steel pipe concrete pouring, the stress importance of the eight steel pipes is not entirely the same. For example, the core load-bearing steel pipes located at the four corners of the bridge tower require stricter liquid level control, while the secondary steel pipes can be more relaxed. The synchronous control unit of this invention solves this problem through a weighted average elevation benchmark and a deviation-compensated PID control model. Before construction begins, technicians assign a weight coefficient to each steel pipe based on the stress analysis results of the bridge tower structure, with higher weight coefficients for the core load-bearing steel pipes and lower weight coefficients for the secondary load-bearing steel pipes. All laser displacement sensors collect the liquid level elevation data of each steel pipe in real time. The synchronous control unit calculates a weighted average of these elevation data with the corresponding weight coefficients to obtain a dynamically changing weighted average elevation benchmark. This benchmark is not a simple arithmetic mean, but rather leans more towards the actual liquid level height of the core load-bearing steel pipe, thereby ensuring the pouring quality of critical parts.

[0035] The synchronous control unit then calculates the difference between the current liquid level elevation of each steel pipe and the weighted average elevation reference, i.e., the elevation deviation. If the liquid level of a steel pipe is lower than the reference, the deviation is negative; if it is higher than the reference, the deviation is positive. This deviation value is processed in real time by the deviation compensation PID control model. The PID control model comprehensively considers the magnitude of the current deviation, the cumulative amount of deviation over a period of time, and the trend of deviation changes. The adjustment intensity and direction are determined according to the magnitude of the current deviation. When the deviation is positive, it indicates that the liquid level is too high, and the opening of the flow valve needs to be reduced to decrease the pouring speed; when the deviation is negative, it indicates that the liquid level is too low, and the opening of the flow valve needs to be increased to increase the pouring speed. The larger the absolute value of the deviation, the larger the corresponding opening adjustment amount. The integral term is used to eliminate long-term small deviations and prevent the liquid level from continuously deviating from the reference; the derivative term reacts in advance according to the rate of change of the deviation. For example, when the liquid level of a steel pipe rises rapidly, i.e., the deviation increases rapidly towards a positive value, the derivative term will increase the adjustment amount of closing the valve in advance to avoid excessive overshoot of the liquid level.

[0036] The control model continuously calculates the opening adjustment of the electric flow regulating valve corresponding to each steel pipe, and sends this adjustment amount to the execution module in the form of a percentage or angle value. The electric flow regulating valve precisely increases or decreases its opening according to the adjustment amount, thereby changing the concrete pouring flow rate. The entire calculation and adjustment process is performed in millisecond-level cycles, ensuring that the liquid level in each steel pipe is always dynamically pulled back to near the weighted average benchmark. Even if an abnormal liquid level occurs in a steel pipe due to local blockage or pump pressure fluctuations, the PID control model can respond quickly and adjust automatically without manual intervention. In this way, the liquid level elevation difference of the eight steel pipes is stably controlled within the allowable range, and the adjustment process is smooth and oscillating, avoiding the frequent start-stop and flow change problems caused by traditional on / off control, ensuring the continuity of pouring and the uniformity of structural stress.

[0037] During the pouring of concrete into steel pipes, if the concrete falls too far from the pump outlet into the steel pipe or the pouring speed is too fast, segregation can easily occur. This means that the coarse aggregate separates from the cement paste, with the coarse aggregate settling and the paste floating to the top. Segregated concrete not only has reduced strength but can also cause blockages or voids inside the steel pipe, severely affecting the structural load-bearing capacity. In traditional construction, operators can only visually inspect the concrete surface at the top of the steel pipe to determine if segregation has occurred. However, due to poor lighting inside the steel pipe, obstructed vision, and the fact that segregation often occurs below the liquid surface or in the lower part of the steel pipe, it is almost impossible to detect manually in a timely manner. In this embodiment, the AI ​​image analysis unit includes a concrete homogeneity recognition module, which is configured as follows: Acquire the concrete surface image captured by the camera device (3), and calculate its grayscale standard deviation σ and mean μ; Based on formula Calculate the homogeneity coefficient U, where, The standard deviation of the grayscale value; This represents the average grayscale value. When the homogeneity coefficient U is lower than the preset threshold, it is determined that there is a risk of segregation, and the pouring speed is reduced by the execution module. In this embodiment, U≥0.85 is normal, and U<0.85 is a precursor to segregation.

[0038] The AI ​​image analysis unit of this invention includes a concrete homogeneity recognition module to address this problem in real time. During the pouring process, a camera device installed on the inner wall of the steel pipe continuously captures images of the concrete surface and transmits the image data to the homogeneity recognition module. This module first performs grayscale processing on the images, converting the color images into grayscale images, and then calculates the standard deviation and mean of the grayscale values ​​of all pixels in the image. The mean grayscale value reflects the overall brightness of the concrete surface, while the standard deviation of the grayscale value characterizes the dispersion of the pixel grayscale values. When the concrete mixture is homogeneous, its surface exhibits a relatively uniform texture and color, with a concentrated distribution of grayscale values ​​and a relatively small standard deviation. When segregation occurs in the concrete, the grayscale values ​​in areas with concentrated coarse aggregate are darker, while the grayscale values ​​in areas with rich paste are brighter, resulting in a significant increase in the dispersion of grayscale values ​​across the entire image, and a corresponding increase in the standard deviation. The homogeneity recognition module calculates a homogeneity coefficient based on the ratio of the standard deviation to the mean grayscale value. A higher coefficient indicates more homogeneous concrete, while a lower coefficient indicates a higher risk of segregation. The homogeneity identification module is pre-set with a threshold value, which is obtained through experimental calibration. During the pouring process, the module calculates the real-time homogeneity coefficient at a fixed frequency and compares it with the threshold. When the homogeneity coefficient falls below the preset threshold, the system automatically determines that there is a risk of concrete segregation, without manual intervention. At this point, the system issues a command to reduce the pouring speed through the execution module. The electric flow regulating valve appropriately reduces its opening, or the frequency converter of the pouring pump reduces its pumping frequency, allowing the concrete to enter the steel pipe at a slower and more stable speed. After reducing the pouring speed, the impact force of the falling concrete is reduced, and the aggregate and paste can maintain a mixed state, thus suppressing the segregation trend. Once the homogeneity coefficient returns to a safe range, the system can gradually restore the original pouring speed. The entire process achieves automatic identification and active control of segregation risk, effectively ensuring the pouring quality of the concrete inside the steel pipe.

[0039] In traditional construction methods, safety inspections rely on the visual observation of on-site safety officers. Violations are addressed through walkie-talkies or by physically intervening. This manual management model is slow, and there is no linkage between safety supervision and the pouring equipment. Even in cases of serious violations, the concrete pumps and flow valves continue to operate normally, and safety hazards cannot be immediately eliminated. The safety pouring linkage interlocking unit in this implementation method specifically addresses this problem.

[0040] The AI ​​image analysis unit includes a security behavior recognition module; The analysis and decision-making module also includes a safety pouring linkage interlocking unit that is communicatively connected to the safety behavior recognition module; The safety pouring linkage interlocking unit is configured to: quantify and calculate the safety risk level based on preset violation weights and durations, and generate access control instructions for the execution module according to the safety risk level. The access control instructions include at least instructions to limit the pouring speed and instructions to stop the pouring operation.

[0041] The safety pouring linkage interlocking unit is configured as follows: Based on formula Calculate the risk level R, where α j The preset weight for the j-th type of violation, , t j Let α represent the duration of the type j violation; in this implementation, R < 3 indicates low risk, 3 ≤ R < 7 indicates medium risk, and R ≥ 7 indicates high risk, where α j They are divided into three categories: not wearing a safety helmet = 2, people gathering = 3, and open flame = 8; Specifically, behavior management cameras are installed at both ends of each operating level of the pouring platform on the tower. These cameras have built-in AI behavior acquisition software, enabling them to capture real-time footage of workers within the construction area. A safety behavior recognition module analyzes the footage frame by frame, identifying various pre-defined violations, such as detecting personnel not wearing safety helmets, exceeding a certain personnel density limit in an area, or detecting open flames or smoke. Each violation is pre-assigned a weight value, with not wearing a safety helmet having a relatively low weight, abnormal personnel gathering having a medium weight, and open flames having the highest weight. When the safety behavior recognition module detects a violation, it immediately sends the violation type and start time to the safety pouring linkage interlocking unit. This unit continuously records the duration of the violation and quantifies the current safety risk level based on the weight and duration. The risk level is a dynamically changing value; the longer the violation lasts, the higher the risk level. The linkage interlocking unit generates corresponding access control instructions based on the risk level and sends them to the execution module. Specifically, when the risk level is low, such as a worker not wearing a safety helmet for a short period, the system only issues a voice prompt via the tower's public address system to remind them to correct the violation immediately, without affecting the pouring operation. When the same violation persists for a certain period, and the risk level rises to medium risk, the system activates an audible and visual alarm and sends a speed-limiting command to the pouring pump's frequency converter, automatically reducing the concrete pouring speed to a safe level. Simultaneously, it limits the maximum opening of the electric flow control valve to prevent high-speed pouring under risky conditions. If the violation continues, or high-risk behaviors such as open flames are detected, and the risk level reaches the high-risk threshold, the system immediately sends a stop-pouring command to the execution module. The pouring pump stops running, all flow valves are completely closed, and an emergency warning is pushed to the ground management platform, displaying the specific type and location of the violation. Only after all violations are eliminated and the risk level returns to a safe range will the system allow the resumption of pouring operations, and the resumption process must be remotely confirmed by on-site management personnel. This safety and pouring linkage mechanism enables proactive intervention and immediate response in safety management, directly converting safety monitoring results into equipment control commands and effectively avoiding potential hazards from continuing construction under high-risk conditions.

[0042] During the pouring of concrete into steel pipes, when the AI ​​image analysis unit determines that there is a risk of segregation, the system needs to actively reduce the pouring speed to suppress further segregation. However, determining an appropriate reduction rate is a practical challenge. If the reduction is too small, the segregation trend cannot be effectively curbed, and the concrete may continue to deteriorate; if the reduction is too large, it will not only slow down the overall construction progress but may also cause the concrete to remain in the pump pipe or steel pipe for too long due to the sudden drop in flow rate, thus increasing the risk of blockage. Traditional methods usually rely on operators to manually adjust the speed based on experience, but different personnel have different judgment standards, making it difficult to guarantee the rationality of each adjustment.

[0043] The pouring speed optimization unit of this invention specifically addresses this dynamic speed regulation problem. The data acquisition module also includes a sensor for acquiring the pouring speed. The analysis and decision-making module further includes a pouring speed optimization unit, which is configured as follows: Obtain the current actual pouring speed v and the homogeneity coefficient U of the concrete; ; When the homogeneity coefficient U is lower than a preset threshold, based on formula v opt =k⋅v calculates the target velocity v. opt Where k is an adaptation coefficient greater than 0 and less than 1; the execution module adjusts the pouring speed to the optimized target speed v. opt .

[0044] Specifically, during the pouring process, the speed sensor in the data acquisition module monitors the current concrete pouring speed in real time, while the homogeneity identification module continuously calculates the homogeneity coefficient. The pouring speed optimization unit acquires both parameters and has a built-in adaptation coefficient, the specific value of which can be pre-calibrated and determined based on construction conditions such as concrete mix proportions and steel pipe diameter. When the homogeneity coefficient falls below a preset threshold, indicating a risk of segregation, the optimization unit automatically triggers the speed optimization process.

[0045] The pouring speed optimization unit multiplies the current actual pouring speed by an adaptation coefficient to obtain the optimized target speed. For example, if the current pouring speed is 2 m / min and the adaptation coefficient is 0.8, the optimized target speed is reduced to 1.6 m / min. An adaptation coefficient less than 1 ensures that the speed reduction is a decrease rather than an increase; simultaneously, a coefficient greater than 0 avoids a direct drop to zero, which could interrupt the pouring process. The specific value of this coefficient can be set according to the severity of segregation risk; the higher the segregation risk, the smaller the adaptation coefficient and the greater the reduction. The pouring speed optimization unit sends the calculated optimized target speed to the execution module. The execution module adjusts the frequency of the pouring pump inverter or the opening of the electric flow regulating valve to smoothly transition the actual pouring speed to the optimized target speed. At the new speed, the impact force of concrete entering the steel pipe decreases, allowing the aggregate and slurry to remain mixed, and the homogeneity coefficient gradually recovers. Once the homogeneity coefficient recovers to above the safe threshold, the pouring speed optimization unit can gradually restore the pouring speed to a normal level according to a preset strategy, or maintain the current speed and continue construction. The entire process requires no manual calculation or intervention. The system automatically generates a reasonable deceleration target based on the real-time homogeneity coefficient and the current speed, thus achieving refined speed control under segregation risk.

[0046] In another embodiment, the camera device includes an in-tube camera unit and a tower-mounted camera unit; The in-tube camera unit includes a zoom camera mounted on a mobile device, a track assembly that provides a moving track for the mobile device, and a rope retractor connected to the mobile device. The track assembly is arranged longitudinally along the inner wall of the steel pipe, and the rope retractor is used to adjust the height of the mobile device on the track assembly.

[0047] In the above technical solution, after the steel pipe is hoisted and fixed, construction workers weld two combined angle steels longitudinally along the inner wall of the steel pipe to serve as tracks, extending from the top of the steel pipe to near the bottom. The moving device is welded from checkered steel plates, with springs and rollers installed on three sides. The rollers are embedded in the track grooves, and the springs are kept compressed to keep the rollers in close contact with the tracks, ensuring that the moving device can slide smoothly up and down within the tracks without shaking. One end of the moving device extends through the track gap, and a zoom camera is fixed to this extended end, with its lens pointing towards the lower part of the steel pipe. An outdoor water-resistant Category 6 network cable is used to transmit video signals, while a nylon rope is used to support the weight of the camera and the moving device; the two are twisted together to form an integrated cable. The lower end of the integrated cable is connected to the moving device, and the upper end passes around the guide wheel at the top of the steel pipe and connects to a rope retractor on the ground or platform. Before pouring begins, the operator uses the rope retractor to lift the moving device to the starting position near the top of the steel pipe. During the pouring process, as the concrete level rises, operators use the elevation data from the laser displacement sensor to control the rope retractor's release, allowing the mobile device carrying the camera to descend synchronously, maintaining a suitable shooting distance between the camera and the concrete surface. The zoom function allows operators to adjust the focus as needed, zooming out to observe the overall flow when the liquid level is low, and zooming in to observe the concrete surface texture and aggregate distribution when the liquid level is close. The camera features white light supplementary lighting, providing ample illumination in the dark environment inside the steel pipe to ensure clear images. The rope retractor's design allows for camera raising and lowering operations to be performed by a single operator on the ground or a safe platform, without needing to enter the steel pipe. Simultaneously, the integrated cable's network cable transmits high-definition video footage to the control host in real time, enabling the AI ​​image analysis unit to perform homogeneity identification and pouring speed optimization. After pouring, the rope retractor retrieves the mobile device and camera from inside the steel pipe, and the track is permanently embedded in the concrete. Through the above structure, the camera unit inside the pipe can flexibly adjust the camera height according to the change of liquid level, which solves the problem of continuous monitoring of the entire process of concrete pouring in ultra-long steel pipes and provides reliable and clear raw data for AI image analysis.

[0048] This embodiment also provides a method for synchronous control and intelligent monitoring of the entire process of ultra-long steel pipe concrete pouring. The method utilizes the aforementioned system and includes the following steps: S1. Real-time acquisition of concrete liquid level elevation data in each steel pipe, concrete surface images inside the pipe, and safety behavior images of the pouring platform on the tower. S2. Based on the liquid level elevation data, an adjustment command for the pouring flow of each steel pipe is generated through a weighted average elevation benchmark-deviation compensation PID control model. S3. Based on the image of the concrete surface inside the pipe, the homogeneity of the concrete is analyzed and identified by the AI ​​image analysis unit, and a pouring speed optimization command is generated. S4. Based on the safety behavior image, the AI ​​image analysis unit analyzes and identifies the violation, and generates an access control command that is linked to the pouring operation. S5. According to the adjustment command, pouring speed optimization command and / or permission control command, control the electric flow regulating valve and / or pouring pump frequency converter to adjust the pouring flow and / or pouring speed of each steel pipe. Among them, steps S2 to S5 form a closed-loop control during the execution process, which is used to control the difference in concrete liquid level in each steel pipe within a preset threshold.

[0049] In another embodiment, in step S2, the weighted average elevation benchmark-deviation compensation PID control model specifically includes: Calculate the weighted average elevation ; Calculate the elevation deviation of the i-th steel pipe ; Through PID control algorithm Calculate the opening adjustment amount of the electric flow regulating valve for the i-th steel pipe. .

[0050] In another embodiment, step S3, identifying the homogeneity of concrete includes: Acquire images of the concrete surface and calculate the standard deviation of its grayscale values. and mean gray value ; Calculate the homogeneity coefficient ; When the homogeneity coefficient U is lower than a preset threshold, it is determined that there is a risk of segregation, and an optimization instruction to reduce the pouring speed is generated.

[0051] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A synchronous control and full-process intelligent monitoring system for ultra-long steel pipe concrete pouring, characterized in that, include: The data acquisition module includes a laser displacement sensor for real-time monitoring of the concrete liquid level elevation inside each steel pipe, and a camera device for acquiring images of the concrete surface inside each steel pipe and images of the safety behavior of the pouring platform on the tower. The analysis and decision-making module, which is communicatively connected to the data acquisition module, includes: The AI ​​image analysis unit is used to receive images captured by the camera device and identify the state of the concrete surface and safety behaviors in the construction area. The synchronous control unit has a built-in weighted average elevation benchmark-deviation compensation PID control model, which is used to calculate the weighted average elevation benchmark based on the liquid level elevation data of each steel pipe and generate adjustment instructions for the pouring flow of each steel pipe. The execution module, which is communicatively connected to the analysis and decision module, includes an electric flow regulating valve and a frequency converter for the casting pump installed on each steel pipe casting pipeline, for receiving and executing the regulation command to regulate the casting flow of each steel pipe; The system forms a closed-loop control loop of data acquisition, intelligent analysis, model calculation and synchronous control through the data acquisition module, analysis and decision-making module and execution module, which is used to control the difference in concrete liquid level in each steel pipe within a preset threshold during the pouring process.

2. The synchronous control and intelligent monitoring system for ultra-long steel pipe concrete pouring as described in claim 1, characterized in that, The synchronization control unit is configured as follows: Based on formula Calculate the weighted average elevation H avg H i Let w be the real-time liquid level elevation of the i-th steel pipe. i The preset weighting coefficients; Based on formula Calculate the elevation deviation e of the i-th steel pipe i , where e i Let be the deviation of the liquid level elevation in the i-th steel pipe; Based on PID control algorithm formula Calculate the opening adjustment amount of the electric flow control valve for the i-th steel pipe. K p K i K d These are the proportional, integral, and derivative coefficients, respectively, and t is the control time.

3. The synchronous control and intelligent monitoring system for ultra-long steel pipe concrete pouring as described in claim 1, characterized in that, The AI ​​image analysis unit includes a concrete homogeneity recognition module, which is configured as follows: Acquire the concrete surface image captured by the camera device (3), and calculate its grayscale standard deviation σ and mean μ; Based on formula Calculate the homogeneity coefficient U, where, The standard deviation of the grayscale value; This represents the average grayscale value. When the homogeneity coefficient U is lower than a preset threshold, it is determined that there is a risk of segregation, and the pouring speed is reduced through the execution module.

4. The synchronous control and intelligent monitoring system for ultra-long steel pipe concrete pouring as described in claim 1, characterized in that, The AI ​​image analysis unit includes a security behavior recognition module; The analysis and decision-making module also includes a safety pouring linkage interlocking unit that is communicatively connected to the safety behavior recognition module; The safety pouring linkage interlocking unit is configured to: quantify and calculate the safety risk level based on preset violation weights and durations, and generate access control instructions for the execution module according to the safety risk level. The access control instructions include at least instructions to limit the pouring speed and instructions to stop the pouring operation.

5. The synchronous control and intelligent monitoring system for ultra-long steel pipe concrete pouring as described in claim 4, characterized in that, The safety pouring linkage interlocking unit is configured as follows: Based on formula Calculate the risk level R, where α j The preset weight for the j-th type of violation, , t j The duration of the j-th type of violation.

6. The synchronous control and intelligent monitoring system for ultra-long steel pipe concrete pouring as described in claim 1, characterized in that, The data acquisition module also includes a sensor for obtaining the pouring speed; The analysis and decision-making module further includes a pouring speed optimization unit, which is configured as follows: Obtain the current actual pouring speed v and the homogeneity coefficient U of the concrete; ; When the homogeneity coefficient U is lower than a preset threshold, based on the formula Calculate the target velocity v opt Where k is an adaptation coefficient greater than 0 and less than 1; the execution module adjusts the pouring speed to the optimized target speed v. opt .

7. The synchronous control and intelligent monitoring system for ultra-long steel pipe concrete pouring as described in claim 1, characterized in that, The camera device includes an in-tube camera unit and a tower-mounted camera unit; The in-tube camera unit includes a zoom camera mounted on a mobile device, a track assembly that provides a moving track for the mobile device, and a rope retractor connected to the mobile device. The track assembly is arranged longitudinally along the inner wall of the steel pipe, and the rope retractor is used to adjust the height of the mobile device on the track assembly.

8. A method for synchronous control and intelligent monitoring of the entire process of ultra-long steel pipe concrete pouring, applied to the system described in any one of claims 1-7, characterized in that, Includes the following steps: S1. Real-time acquisition of concrete liquid level elevation data in each steel pipe, concrete surface images inside the pipe, and safety behavior images of the pouring platform on the tower. S2. Based on the liquid level elevation data, an adjustment command for the pouring flow of each steel pipe is generated through a weighted average elevation benchmark-deviation compensation PID control model. S3. Based on the image of the concrete surface inside the pipe, the homogeneity of the concrete is analyzed and identified by the AI ​​image analysis unit, and a pouring speed optimization command is generated. S4. Based on the safety behavior image, the AI ​​image analysis unit analyzes and identifies the violation, and generates an access control command that is linked to the pouring operation. S5. According to the adjustment command, pouring speed optimization command and / or permission control command, control the electric flow regulating valve and / or pouring pump frequency converter to adjust the pouring flow and / or pouring speed of each steel pipe. Among them, steps S2 to S5 form a closed-loop control during the execution process, which is used to control the difference in concrete liquid level in each steel pipe within a preset threshold.

9. The method for synchronous control and intelligent monitoring of the entire process of ultra-long steel pipe concrete pouring as described in claim 8, characterized in that, In step S2, the weighted average elevation benchmark-deviation compensation PID control model specifically includes: Calculate the weighted average elevation ; Calculate the elevation deviation of the i-th steel pipe ; Through PID control algorithm Calculate the opening adjustment amount of the electric flow regulating valve for the i-th steel pipe. .

10. The method for synchronous control and intelligent monitoring of the entire process of ultra-long steel pipe concrete pouring as described in claim 8, characterized in that, In step S3, identifying the homogeneity of concrete includes: Acquire images of the concrete surface and calculate the standard deviation of its grayscale values. and mean gray value ; Calculate the homogeneity coefficient ; When the homogeneity coefficient U is lower than a preset threshold, it is determined that there is a risk of segregation, and an optimization instruction to reduce the pouring speed is generated.

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