A vibration control method and system for concrete pouring process

Abstract

The present invention provides a vibration control method and system for a concrete pouring process, the method comprising the following steps: determining the vibration priority of each component to be vibrated, and generating a device vibration path of an automatic vibrating device; whenever the concrete automatic vibrating device reaches a component position, marking the current component to be vibrated as a target component to be vibrated, and configuring a vibration strategy for the concrete automatic vibrating device; automatically vibrating the target component to be vibrated according to the vibration strategy and using a horizontal vibration module or a mechanical arm vibration module; collecting the component surface image of the target component to be vibrated in real time through an image recognition module, and identifying whether the automatic vibration reaches a preset vibration standard according to the component surface image; if the automatic vibration does not reach the preset vibration standard, maintaining the automatic vibration until the vibration standard is reached. The present invention has the effect of completing automatic vibration with high efficiency and quality.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete construction, and in particular relates to a vibration control method and system in a concrete pouring process. Background Art

[0002] Concrete is one of the most commonly used materials in construction projects, and its quality is directly related to the safety and durability of building structures. In concrete construction, vibration is a key link. Vibration can remove bubbles in concrete, making it more compact, thereby improving the strength and durability of concrete. Traditional vibration methods mainly rely on manual operation. Workers use vibrators or vibrators to vibrate concrete to achieve a compact effect. However, with the continuous expansion of the scale and increase in complexity of construction projects, traditional manual vibration methods have gradually exposed problems such as low efficiency, unstable quality and poor safety. Especially in the construction of high-rise buildings, large bridges and complex structures, the workload of concrete pouring and vibration is huge, and manual operation is difficult to meet the requirements of efficient and high-quality construction. In addition, the working environment of construction sites is complex and changeable, and manual operation has certain safety risks. Summary of the invention

[0003] The present invention provides a vibration control method and system for a concrete pouring process, so as to solve the problem that manual vibration is difficult to meet the construction requirements of high efficiency and high quality and has certain safety risks.

[0004] In a first aspect, the present invention provides a method for controlling vibration during concrete pouring, which is applied to an automatic concrete vibrating device, wherein the automatic concrete vibrating device comprises an image recognition module, a vibration data acquisition module, a horizontal vibration module and a mechanical arm vibration module, and the method comprises the following steps:

[0005] Acquire component placement data and component position of a component to be vibrated that has completed concrete placement in the building component, and acquire the initial position of the automatic concrete vibrating device;

[0006] Determine the vibration priority of each component to be vibrated according to the initial position of the device, the component position and the component distribution data, and generate a device vibration path of the automatic concrete vibrating device in combination with the component position and the component vibration priority;

[0007] Controlling the automatic concrete vibrating device to move sequentially to the component position corresponding to each component to be vibrated according to the device vibration path;

[0008] Whenever the automatic concrete vibrating device reaches a component position, the component to be vibrated corresponding to the current component position is marked as a target component to be vibrated, and a vibration strategy of the automatic concrete vibrating device is configured based on the component type of the target component to be vibrated and the component material distribution data;

[0009] According to the vibration strategy and using the horizontal vibration module or the robotic arm vibration module, the target component to be vibrated is automatically vibrated;

[0010] During the automatic vibration process, the component surface image of the target component to be vibrated is collected in real time by the image recognition module, the real-time vibration data of the automatic vibration process is collected by the vibration data collection module, and the component surface image and the real-time vibration data are combined to identify whether the automatic vibration meets the preset vibration standard;

[0011] If the automatic vibration does not reach the preset vibration standard, maintaining the automatic vibration until the vibration standard is reached;

[0012] If the automatic vibration has reached the preset vibration standard, the automatic vibration of the target component to be vibrated is terminated, and the automatic concrete vibrating device is controlled to move to the next component position according to the device vibration path.

[0013] Optionally, the component distribution data includes concrete distribution ratio data, concrete distribution volume data and component size data, and the vibrating strategy of the automatic concrete vibrating device configured based on the component type of the target component to be vibrated and the component distribution data includes the following steps:

[0014] Determining whether the target component to be vibrated is a panel component, a wall component or a frame component;

[0015] If the component type is the panel component, configuring a first vibration strategy of the automatic concrete vibrating device in combination with the concrete distribution volume data and the component size data, wherein the first vibration strategy includes vibration time and vibration power, and the first vibration strategy is executed by the horizontal vibration module;

[0016] If the component type is the wall component or the frame component, the wall component or the frame component is preliminarily divided into a plurality of initial vibration grid areas in combination with the concrete distribution volume data and the component size data;

[0017] Adjusting each of the initial vibration grid areas to a target vibration grid area according to the concrete distribution ratio data;

[0018] Using the center point of the target vibration grid area as the vibration point of the vibration rod in the mechanical arm vibration module;

[0019] Determining the vibration power and vibration time of the vibrating rod according to the area size of the target vibration grid area;

[0020] The second vibration strategy of the automatic concrete vibrating device is configured in combination with the vibration power, the vibration time and all the target vibration grid areas, and the second vibration strategy is executed by the mechanical arm vibration module.

[0021] Optionally, the step of collecting the component surface image of the target component to be vibrated in real time by the image recognition module, collecting the real-time vibration data of the automatic vibration process by the vibration data acquisition module, and identifying whether the automatic vibration reaches a preset vibration standard by combining the component surface image and the real-time vibration data comprises the following steps:

[0022] The image recognition module is used to collect the component surface image of the target component to be vibrated in real time;

[0023] Using image recognition technology to identify the component surface image, and judging whether there are bubbles on the component surface concrete of the target component to be vibrated according to the recognition result;

[0024] If bubbles exist in the concrete on the surface of the target component to be vibrated, it is determined that the automatic vibration does not meet the preset vibration standard;

[0025] If there are no bubbles on the surface concrete of the target component to be vibrated, judging whether there is floating slurry on the surface concrete of the target component to be vibrated according to the recognition result;

[0026] If there is no laitance on the surface of the target component to be vibrated, it is determined that the automatic vibration does not meet the preset vibration standard;

[0027] If there is laitance on the surface concrete of the target component to be vibrated, the vibration data acquisition module is used to collect real-time vibration data of the automatic vibration process, and based on the real-time vibration data, it is identified whether the automatic vibration meets the preset vibration standard.

[0028] Optionally, the step of collecting real-time vibration data of the automatic vibration process by using the vibration data collection module, and identifying whether the automatic vibration reaches a preset vibration standard based on the real-time vibration data comprises the following steps:

[0029] Using the ultrasonic sensor, temperature sensor and pressure sensor in the vibration data acquisition module, respectively collect the ultrasonic data, temperature data and pressure data of the concrete in the target vibration grid area where the vibrating rod is currently located;

[0030] extracting data features of the ultrasonic data, the temperature data and the pressure data respectively, and fusing all the data features into sensor data features;

[0031] The sensor data features are input into a pre-trained concrete vibration prediction model, and a model output result of the concrete vibration prediction model is used to determine whether the automatic vibration reaches a preset vibration standard.

[0032] Optionally, the component distribution data includes concrete distribution ratio data and concrete distribution volume data, and the step of determining the vibration priority of each component to be vibrated according to the initial position of the device, the component position and the component distribution data, and generating the device vibration path of the automatic concrete vibrating device in combination with the component position and the component vibration priority includes the following steps:

[0033] Based on the concrete distribution volume data, respectively calculate the vibration completion time for the automatic concrete vibrating device to complete the automatic vibration of each component to be vibrated;

[0034] Obtaining environmental forecast data of the surrounding environment of all the components to be vibrated;

[0035] Determine the coagulation-related ingredients associated with the initial setting time of concrete in the concrete material distribution ratio data and the coagulation-related environmental data associated with the initial setting time of concrete in the environmental forecast data by using Pearson correlation analysis, and extract the associated ingredient ratio of the coagulation-related ingredients according to the concrete material distribution ratio data;

[0036] Combining the setting-related environmental data and the associated ingredient ratio of the setting-related ingredients, and using a genetic algorithm and a BP neural network to respectively predict the predicted initial setting time of concrete in each of the components to be vibrated;

[0037] The initial displacement time of the automatic concrete vibrating device moving from the initial position of the device to each of the component positions is calculated according to the device moving speed, and the inter-component displacement time of the automatic concrete vibrating device moving between any two component positions is calculated according to the device moving speed;

[0038] Constructing a time constraint condition by combining the vibration completion time, the predicted initial setting time, the initial displacement time and the displacement time between components;

[0039] Taking the completion of the automatic vibration once for each component to be vibrated within the predicted initial setting time as the optimization goal, the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint condition and in combination with the immune algorithm and the whale group algorithm;

[0040] Allocating a component vibration priority to each component to be vibrated according to the optimal vibration sequence;

[0041] A device vibration path of the automatic concrete vibrating device is generated based on the component position and in the order of the component vibration priorities from high to low.

[0042] Optionally, combining the setting-related environmental data and the associated ingredient ratio of the setting-related ingredients, and using a genetic algorithm and a BP neural network to respectively predict the predicted initial setting time of concrete in each of the components to be vibrated comprises the following steps:

[0043] Performing joint data enhancement on the condensation-related environment data and the associated ingredient ratios of the condensation-related ingredients to obtain an expanded set of condensation-related data;

[0044] Constructing a BP neural network and initializing network parameters of the BP neural network;

[0045] Inputting the condensed association data expansion set into the BP neural network to perform network model training, and calculating the loss function in the network model training process;

[0046] Encoding the network parameters into a parameter encoding vector, using the loss function as the fitness of the genetic algorithm, and optimizing the parameter encoding vector using the genetic algorithm to obtain an optimal parameter encoding vector;

[0047] Updating the network parameters based on the optimal parameter encoding vector;

[0048] The setting-related environmental data and the associated ingredient ratio of the setting-related ingredients corresponding to each component to be vibrated are input into the BP neural network after the network parameters are updated, and the predicted initial setting time of the concrete in each component to be vibrated is predicted by the BP neural network.

[0049] Optionally, taking the completion of the automatic vibration once for each component to be vibrated within the predicted initial setting time as the optimization goal, and calculating the optimal vibration sequence of all components to be vibrated based on the time constraint condition and in combination with the immune algorithm and the whale group algorithm comprises the following steps:

[0050] Randomly generate multiple initial vibration sequences of all the components to be vibrated based on the component positions, and each component to be vibrated appears only once in any of the initial vibration sequences;

[0051] encoding all of the initial vibration sequences into an antibody population of an immune algorithm;

[0052] Taking the optimization target as the target antigen of the immune algorithm, and iteratively updating the antibody population based on the time constraint and using the immune algorithm;

[0053] In the iterative updating process of the antibody population, a whale swarm algorithm is used to optimize and update immune algorithm parameters in the immune algorithm;

[0054] The immune algorithm is repeatedly used to iteratively update the antibody population until the antibody population meets the optimization target, and the antibody population that meets the optimization target is decoded into the optimal vibration sequence of all the components to be vibrated.

[0055] Optionally, the method further comprises the following steps:

[0056] Acquire engineering model data of the construction project where the building component is located and device model data of the automatic concrete vibrating device;

[0057] Combining the engineering model data and the device model data and using BIM software to construct an engineering three-dimensional model of the construction project;

[0058] Simulating the movement of the automatic concrete vibrating device between any two components to be vibrated based on the engineering three-dimensional model to obtain a simulated collision detection result;

[0059] Constructing device movement constraint conditions according to the simulation collision detection results;

[0060] Taking the completion of the automatic vibration once for each component to be vibrated within the predicted initial setting time as the optimization goal, the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint condition and the device movement constraint condition and in combination with the immune algorithm and the whale group algorithm;

[0061] Allocating a component vibration priority to each component to be vibrated according to the optimal vibration sequence;

[0062] A device vibration path of the automatic concrete vibrating device is generated based on the component position and in the order of the component vibration priorities from high to low.

[0063] Optionally, taking the completion of the automatic vibration once for each component to be vibrated within the predicted initial setting time as the optimization goal, and calculating the optimal vibration sequence of all components to be vibrated based on the time constraint condition and the device movement constraint condition and in combination with the immune algorithm and the whale group algorithm comprises the following steps:

[0064] Randomly generate multiple initial vibration sequences of all the components to be vibrated based on the component positions, and each component to be vibrated appears only once in any of the initial vibration sequences;

[0065] encoding all of the initial vibration sequences into an antibody population of an immune algorithm;

[0066] Taking the optimization target as the target antigen of the immune algorithm, and iteratively updating the antibody population based on the time constraint and the device movement constraint and using the immune algorithm;

[0067] In the iterative updating process of the antibody population, a whale swarm algorithm is used to optimize and update immune algorithm parameters in the immune algorithm;

[0068] The immune algorithm is repeatedly used to iteratively update the antibody population until the antibody population meets the optimization target, and the antibody population that meets the optimization target is decoded into the optimal vibration sequence of all the components to be vibrated.

[0069] In a second aspect, the present invention provides a vibration control system for a concrete pouring process, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vibration control method for a concrete pouring process as described in the first aspect is implemented.

[0070] The beneficial effects of the present invention are:

[0071] Through automation and intelligent means, the component laying data and component position of the components to be vibrated that have completed concrete laying in the building components can be accurately obtained, and combined with the initial position of the concrete automatic vibrating device, the vibration priority of each component to be vibrated is determined, and the device vibration path is generated, thereby realizing the efficient movement and precise vibration of the concrete automatic vibrating device. Whenever the device reaches the specified position, the vibration strategy is configured based on the component type and laying data, and automatic vibration is performed through the horizontal vibration module or the mechanical arm vibration module. At the same time, the image recognition module and the vibration data acquisition module are used to monitor the vibration effect in real time to ensure that the vibration meets the preset standards. This not only improves construction efficiency, reduces labor costs, and ensures the consistency and reliability of vibration quality, but also improves the safety of the construction site, reduces the risk of workers being exposed to dangerous environments, and ultimately achieves a more environmentally friendly and sustainable construction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of a method for controlling vibration during concrete pouring in one embodiment of the present application.

[0073] Figure 2A schematic diagram of a flow chart for configuring a vibration strategy in one embodiment of the present application.

[0074] Figure 3 This is a schematic diagram of a flow chart for determining whether automatic vibration reaches a preset vibration standard in one embodiment of the present application. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0076] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0077] Figure 1 FIG. 1 is a flow chart of a method for controlling the vibration during concrete pouring in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0078] Figure 1 The method is applied to an automatic concrete vibrating device, which specifically includes:

[0079] 1. Mobile chassis: used to support the entire device and provide mobility, so that the device can move freely on the construction site. The mobile chassis is the basis of all other components, and all components are installed on or connected to the mobile chassis.

[0080] 2. Perception module: responsible for perceiving the environmental information around the device and its own status, ensuring that the device can accurately perceive location and environmental changes.

[0081] The perception module specifically includes:

[0082] Posture sensor: detects the posture and tilt angle of the device to help adjust the balance and stability of the device.

[0083] LiDAR sensor: used for environment scanning and ranging, providing high-precision environment maps and obstacle detection.

[0084] Millimeter wave radar sensor: Provides high-precision distance measurement and object recognition, especially suitable for detection in harsh environments.

[0085] 3. Image recognition module: used to achieve real-time monitoring of the construction site and vibration effect through image acquisition and processing.

[0086] The image recognition module specifically includes:

[0087] Camera: Collects image data of the construction site and the vibration process.

[0088] Storage equipment: stores the collected image data and real-time data of the vibration process.

[0089] Image recognition processing module MCU: processes the collected images, identifies the vibration effect and construction environment information, and feeds back the processing results to the central control device.

[0090] 4. Positioning module: used to provide high-precision positioning information to ensure accurate movement of the device at the construction site.

[0091] The positioning module can be an RTK high-precision positioning device. The RTK high-precision positioning device uses real-time dynamic differential technology to provide high-precision position information to ensure accurate navigation of the device at the construction site.

[0092] 5. Robotic arm vibration module: responsible for performing specific vibration operations to ensure the density of concrete.

[0093] The mechanical arm vibration module specifically includes:

[0094] Multi-axis robot arm: provides flexible movement capabilities and can reach different vibration positions.

[0095] Vibrating rod: installed at the end of the robotic arm, actually performs the vibration operation.

[0096] 6. Horizontal vibration module: used to provide horizontal vibration to enhance the density and uniformity of concrete. The horizontal vibration module works in conjunction with the mechanical arm vibration module to perform vibration operations as needed.

[0097] 7. Dynamically adjustable leveling device: used to level the concrete surface according to the designed height of the top surface of the pouring, keep it consistent with the designed height, and ensure the accuracy and stability of the vibration process. The dynamically adjustable leveling device is connected to the attitude sensor and the central control device to adjust the horizontal state of the device in real time.

[0098] 8. Vibration data collection module: used to collect the vibration status data of concrete during automatic vibration.

[0099] The vibration data acquisition module specifically includes:

[0100] Ultrasonic sensor: used to detect the density and porosity inside concrete. The propagation speed and attenuation of ultrasonic waves in concrete can reflect the density of concrete.

[0101] Pressure sensor: used to monitor the pressure applied to concrete during vibration in real time. The pressure change can be used to determine whether the vibration is sufficient.

[0102] Temperature sensor: used to monitor the heat changes caused by friction and chemical reactions during the vibration process. The temperature change can indirectly reflect the vibration effect.

[0103] 9. Central control device: As the core of the entire device, it is responsible for coordinating and controlling the work of each component and executing the preset vibration strategy. The central control device is connected to all other components and realizes information transmission and execution of control instructions through data bus and communication interface.

[0104] like Figure 1 As shown, a method for controlling vibration during concrete pouring disclosed in the present invention specifically comprises the following steps:

[0105] S101. Obtain component placement data and component positions of components to be vibrated that have completed concrete placement in building components, and obtain an initial position of an automatic concrete vibrating device.

[0106] Among them, it is necessary to obtain the laying data and component position of the components to be vibrated in the building components that have completed the concrete laying through sensors and data acquisition modules. These data include the specific position, shape, size of each component and the specific conditions of the concrete laying, such as the thickness and uniformity of the concrete. Equipment such as laser radar, millimeter wave radar and camera can be used to conduct a comprehensive scan of the construction site, and the information can be converted into usable digital data through the data processing system. At the same time, it is necessary to determine the initial position of the automatic concrete vibrating device, which can be achieved through the RTK high-precision positioning device. The RTK high-precision positioning device uses real-time dynamic differential technology to provide centimeter-level positioning accuracy to ensure accurate acquisition of the initial position of the device. The acquisition of these initial data is the basis for subsequent steps, ensuring that the vibrating device can accurately identify the position and state of the components to be vibrated, so as to formulate reasonable vibration strategies and paths.

[0107] S102. Determine the vibration priority of each component to be vibrated according to the initial position of the device, the component position and the component distribution data, and generate a device vibration path of the automatic concrete vibrating device in combination with the component position and the component vibration priority.

[0108] Among them, first of all, it is necessary to calculate the path and distance for the device to reach each component based on the initial position of the device and the position of each component. This can be achieved through path planning algorithms, such as the Dijkstra algorithm or the A* algorithm, which can calculate the optimal path when the map and obstacles are known. Then, according to the component distribution data, the vibration priority of each component to be vibrated is determined. The vibration priority can be determined based on multiple factors, such as the time of concrete distribution, the initial setting time of concrete, the importance of the component, etc. For components with a long concrete distribution time and about to be initially set, vibration should be given priority. Combining the component position and the vibration priority, the vibration path of the automatic concrete vibrating device can be generated. This path should ensure that the device can reach the position of each component to be vibrated in turn with the shortest time and the least moving distance. The implementation effect of this step is to ensure that the vibration device can efficiently complete the vibration task and avoid affecting the vibration effect due to the initial setting of concrete.

[0109] S103. Control the automatic concrete vibrating device to move to the component position corresponding to each component to be vibrated in sequence according to the device's vibration path.

[0110] Among them, according to the vibration path generated in the previous step, the automatic concrete vibration device is controlled by the central control device to move along the path to the position of each component to be vibrated in turn. During the movement, it is necessary to monitor the position and status of the device in real time to ensure that the device can move accurately along the path. This can be achieved through the RTK high-precision positioning device and the attitude sensor. The RTK high-precision positioning device provides accurate position data, and the attitude sensor provides the attitude and tilt angle data of the device. The central control device adjusts the movement direction and speed of the device in real time based on these data to ensure that the device can reach each component position smoothly and accurately. During the movement, it is also necessary to avoid obstacles at the construction site. This can be done through laser radar and millimeter-wave radar for environmental scanning and obstacle detection. The central control device adjusts the movement path in real time based on the obstacle information to ensure safe arrival at the target position.

[0111] S104. Whenever the automatic concrete vibrating device reaches a component position, the component to be vibrated corresponding to the current component position is marked as a target component to be vibrated, and a vibration strategy of the automatic concrete vibrating device is configured based on the component type and component layout data of the target component to be vibrated.

[0112] Among them, when the automatic concrete vibrating device reaches the position of a component to be vibrated, it is first necessary to mark the component to be vibrated corresponding to the position as the target component to be vibrated. Then, based on the specific type and distribution data of the target component to be vibrated, the vibration strategy of the automatic concrete vibrating device is configured. Component types may include walls, columns, beams, etc. Different types of components have different requirements for vibration. For example, walls may require more uniform vibration, while columns may require deeper vibration. Distribution data include the thickness and uniformity of concrete. Based on these data, specific parameters of vibration can be determined, such as the frequency, amplitude, and vibration time of vibration. The central control device adjusts the working mode of the vibrating device according to these parameters to ensure that the vibration effect reaches the expected effect. The implementation effect of this step is to ensure that each component to be vibrated can adopt the most appropriate vibration strategy according to its specific situation, ensure the density and uniformity of concrete, and avoid problems such as uneven vibration or over-vibration.

[0113] S105. Automatically vibrate the target component to be vibrated according to the vibration strategy and using a horizontal vibration module or a robotic arm vibration module.

[0114] Among them, the horizontal vibration module is mainly used for large-area, flat vibration, such as walls or floors. Through horizontal vibration, the concrete is ensured to be uniformly compacted. The robotic arm vibration module is used for complex shapes or deep vibrations, such as columns or beams. Through the flexible movement of the robotic arm, it can penetrate deep into the concrete for vibration. The central control device controls the working parameters of the vibration module, such as the frequency, amplitude, and time of vibration, according to the vibration strategy to ensure that the vibration effect reaches the expected result. The implementation effect of this step is to ensure the density and uniformity of the concrete through automated vibration operations, improve construction efficiency, and reduce errors and labor intensity of manual operations.

[0115] S106. During the automatic vibration process, the component surface image of the target component to be vibrated is collected in real time through the image recognition module, and the real-time vibration data of the automatic vibration process is collected using the vibration data collection module. The component surface image and the real-time vibration data are combined to identify whether the automatic vibration meets the preset vibration standard.

[0116] Among them, during the automatic vibration process, the surface image of the target component to be vibrated is collected in real time through the image recognition module installed on the vibration device. These image data can reflect the state of the concrete surface, such as whether there are bubbles, cracks and other problems. At the same time, the vibration data acquisition module is used to collect real-time data during the vibration process. These real-time data are combined with the surface image of the component, and image processing and data analysis technology are used to identify whether the automatic vibration meets the preset vibration standard. The preset vibration standard may include indicators such as the density and uniformity of concrete. By comparing the actual data with the preset standard, it is judged whether the vibration effect meets the standard. If it is found that the vibration effect does not meet the standard, the vibration parameters can be adjusted in real time to increase the frequency or time of vibration until the expected effect is achieved.

[0117] S107. If the automatic vibration does not reach the preset vibration standard, maintain the automatic vibration until the vibration standard is reached.

[0118] S108. If the automatic vibration has reached the preset vibration standard, the automatic vibration of the target component to be vibrated is terminated, and the automatic concrete vibration device is controlled to move to the next component position according to the device vibration path.

[0119] Among them, when it is detected that the automatic vibration has reached the preset vibration standard, the vibration operation of the current target component to be vibrated can be ended. Through the image recognition module and the vibration data acquisition module, it is confirmed that the vibration effect has met the preset standard, for example, the density and uniformity of the concrete have met the requirements. Then, the central control device issues an instruction to stop the vibration module and end the vibration operation of the current component. Next, according to the vibration path generated in the previous step, the automatic concrete vibration device is controlled to move to the position of the next component to be vibrated. During the movement, the RTK high-precision positioning device and attitude sensor continue to be used to monitor the position and status of the device in real time to ensure that the device can accurately move along the path, avoid obstacles at the construction site, and safely reach the next target position.

[0120] In one embodiment, the component distribution data includes concrete distribution ratio data, concrete distribution volume data and component size data. The step of configuring the vibration strategy of the automatic concrete vibration device based on the component type and component distribution data of the target component to be vibrated in step S104 specifically includes the following steps:

[0121] Determine whether the target component to be vibrated is a panel component, a wall component or a frame component;

[0122] If the component type is a slab component, the first vibration strategy of the automatic concrete vibrating device is configured in combination with the concrete distribution volume data and the component size data. The first vibration strategy includes vibration time and vibration power. The first vibration strategy is executed by the horizontal vibration module.

[0123] If the component type is a wall component or a frame component, the wall component or the frame component is preliminarily divided into a plurality of initial vibration grid areas in combination with the concrete distribution volume data and the component size data;

[0124] Adjust each initial vibration grid area to a target vibration grid area according to the concrete distribution ratio data;

[0125] The center point of the target vibration grid area is used as the vibration point of the vibration rod in the robot arm vibration module;

[0126] Determine the vibration power and vibration time of the vibrating rod according to the size of the target vibration grid area;

[0127] The second vibration strategy of the automatic concrete vibrating device is configured in combination with the vibration power, the vibration time and all target vibration grid areas. The second vibration strategy is executed by the mechanical arm vibration module.

[0128] In this embodiment, it is first necessary to determine whether the component type of the target component to be vibrated is a plate component, a wall component or a frame component. This judgment process can be achieved through the geometric size and shape characteristics of the component. Plate components usually have a larger plane area and a smaller thickness, such as a floor or floor; wall components have a larger vertical height and a smaller thickness, such as a wall; frame components are composed of multiple columns and beams and have a complex three-dimensional structure. In actual operation, the geometric size and shape characteristics of the component can be obtained through the design drawings of the component, and then the type of component can be determined by a preset rule or algorithm. For example, a threshold can be set. If the thickness of the component is less than a certain value and the area is greater than a certain value, it is judged as a plate component; if the height of the component is greater than a certain value and the thickness is less than a certain value, it is judged as a wall component; if the component is composed of multiple columns and beams, it is judged as a frame component.

[0129] If the component type is a plate component, the first vibration strategy of the automatic concrete vibrator needs to be configured in combination with the concrete distribution volume data and the component size data. The first vibration strategy includes vibration time and vibration power. The specific configuration method can be determined by calculating the distribution volume of concrete and the area of ​​the component. The determination of vibration time and vibration power can refer to the density requirements of concrete and the performance parameters of the vibration equipment. The first vibration strategy is executed by the horizontal vibration module, which is usually composed of multiple vibrators, which can evenly distribute vibration energy on the plate surface, thereby ensuring uniform density of concrete.

[0130] If the component type is a wall component or a frame component, it is necessary to preliminarily divide the wall component or the frame component into a plurality of initial vibration grid areas in combination with the concrete distribution volume data and the component size data. This process can be achieved by dividing the overall size of the component into a number of small areas, each of which is called an initial vibration grid area. The division of the initial vibration grid area can be determined according to the geometric size of the component and the uniformity of the concrete distribution. Each initial vibration grid area is adjusted to a target vibration grid area according to the concrete distribution ratio data. The concrete distribution ratio data includes the proportion of each component in the concrete, such as the proportion of cement, sand, gravel and water. In some embodiments, the data used to divide the initial vibration grid area also need to consider the steel bar density, concrete number, etc. By analyzing these data, the uniformity and density of the concrete can be judged, so as to adjust the initial vibration grid area. For example, if the concrete distribution ratio in a certain initial vibration grid area is uneven, the vibration effect of the area can be adjusted by increasing or decreasing the vibration time and vibration power. In this way, it can be ensured that the concrete in each target vibration grid area is uniform and dense, and the vibration quality of the overall component is improved.

[0131] The center point of the target vibration grid area is used as the vibration point of the vibrating rod in the mechanical arm vibration module. The vibration point of the vibration grid area is planned based on the component size and shape data, the volume and coordinate data corresponding to the current pouring amount (volumetric and coordinate corresponding to each pouring amount in multiple pourings), and the distribution data of the steel bar truss in the component. The vibration point, motion trajectory, final target posture and coordinates of the vibrating rod are ensured to ensure that the vibrating rod can be placed in the specified position in the component without interference. The mechanical arm vibration module usually consists of a mechanical arm and a vibrating rod. The mechanical arm can move according to the preset trajectory, and the vibrating rod is responsible for vibrating at the target position. By using the center point of the target vibration grid area as the vibration point, it can be ensured that the vibrating rod is vibrated uniformly in each grid area, thereby improving the vibration effect. The vibration power and vibration time of the vibrating rod are determined according to the area size of the target vibration grid area. The determination of the vibration power and vibration time can refer to the density requirements of the concrete and the performance parameters of the vibration equipment. For example, for a target vibration grid area of ​​1 meter by 1 meter, the vibration time can be determined to be 2 minutes and the vibration power to be 300 watts according to the concrete distribution volume and density requirements. In another embodiment, the vibration power will change in real time with a preset vibration power curve, and the vibration power curve corresponding to the vibration trajectory of the vibration rod is determined according to the size of the target vibration grid area, and the horizontal coordinate of the vibration power curve is the vibration time, and the vertical coordinate is the vibration power.

[0132] The second vibration strategy of the automatic concrete vibrating device is configured in combination with the vibration power, vibration time, and all target vibration grid areas. The second vibration strategy is executed by the robotic arm vibration module. The robotic arm moves to the center point of each target vibration grid area according to the preset trajectory, and the vibrating rod performs a vibration operation at this position. In this way, it can be ensured that the concrete in each target vibration grid area is uniformly dense, and the vibration quality of the overall component is improved. The robotic arm moves to the center point of each grid area in turn according to the preset trajectory, and the vibrating rod performs a vibration operation at this position to ensure that the concrete of the entire wall component is uniformly dense.

[0133] In one embodiment, in step S106, the component surface image of the target component to be vibrated is collected in real time by the image recognition module, the real-time vibration data of the automatic vibration process is collected by the vibration data collection module, and the step of combining the component surface image and the real-time vibration data to identify whether the automatic vibration reaches the preset vibration standard specifically includes the following steps:

[0134] The surface image of the target component to be vibrated is collected in real time through the image recognition module;

[0135] The image recognition technology is used to identify the component surface image, and based on the recognition result, it is determined whether there are bubbles on the surface concrete of the target component to be vibrated;

[0136] If bubbles exist in the concrete on the surface of the target component to be vibrated, it is determined that the automatic vibration does not reach the preset vibration standard;

[0137] If there are no bubbles on the surface of the target component to be vibrated, determine whether there is floating slurry on the surface of the target component to be vibrated according to the recognition result;

[0138] If there is no laitance on the surface of the target component to be vibrated, it is determined that the automatic vibration does not reach the preset vibration standard;

[0139] If there is floating slurry on the surface concrete of the target component to be vibrated, the vibration data acquisition module is used to collect real-time vibration data of the automatic vibration process, and based on the real-time vibration data, it is identified whether the automatic vibration reaches the preset vibration standard.

[0140] In this embodiment, the position of the camera should ensure that the entire surface area of ​​the target component can be covered, and industrial protection measures such as dustproof and waterproof should be provided to adapt to the complex environment of the construction site. The camera transmits the real-time image to the image processing unit via a data cable or wireless transmission. After receiving the image, the image processing unit first performs preprocessing, including grayscale, binarization and noise filtering. Grayscale is to convert a color image into a grayscale image to reduce the amount of data and computational complexity; binarization is to convert a grayscale image into a black and white image, and divide the pixels into foreground and background by setting a threshold; noise filtering is to remove random noise in the image through a filter to improve the clarity and recognition accuracy of the image. The preprocessed image will be used as input data for subsequent image recognition algorithms to analyze and process.

[0141] Image recognition technology is used to identify the component surface image, and based on the recognition result, it is determined whether there are bubbles on the component surface concrete of the target component to be vibrated. Image recognition technology includes steps such as image segmentation, feature extraction and pattern recognition. Image segmentation is to divide the image into several small areas, each of which contains similar pixel values. This step can be achieved by edge detection, region growth and other methods. Feature extraction is to extract features that are helpful for identification from the image, such as shape, color and texture, and methods that can be used include Hough transform, Fourier transform and the like. Pattern recognition is to match the extracted features with predefined patterns to identify the target object, and machine learning algorithms such as support vector machines and neural networks can be used. In this technical solution, bubbles on the concrete surface can be identified by segmenting and feature extracting the component surface image. Bubbles usually appear as round or elliptical bright spots, and these bubbles can be accurately identified by pattern recognition algorithms. If the recognition result shows that there are bubbles on the component surface, it means that the automatic vibration does not meet the preset vibration standard, because the presence of bubbles will affect the density and strength of the concrete.

[0142] If there are no bubbles in the concrete on the surface of the target component to be vibrated, it is determined whether there is laitance on the surface of the target component to be vibrated based on the recognition result. Laitance is a layer of cement slurry on the surface of concrete, which usually appears as a smooth and bright surface. The presence of laitance can be identified through image recognition technology. The specific implementation method is to grayscale the component surface image, extract the brightness information in the image, and then separate the laitance area from the concrete main body area through the threshold segmentation method. The laitance area usually has a higher brightness value, and the laitance area can be accurately identified by setting an appropriate threshold. If the recognition result shows that there is no laitance on the surface of the component, it indicates that the automatic vibration has not reached the preset vibration standard, because the presence of laitance is one of the signs that the concrete is fully vibrated. If there is laitance on the surface of the target component to be vibrated, the vibration data acquisition module is used to collect real-time vibration data of the automatic vibration process.

[0143] In this embodiment, using the vibration data collection module to collect real-time vibration data of the automatic vibration process, and identifying whether the automatic vibration reaches the preset vibration standard based on the real-time vibration data includes the following steps:

[0144] Using the ultrasonic sensor, temperature sensor and pressure sensor in the vibration data acquisition module, the ultrasonic data, temperature data and pressure data of the concrete in the target vibration grid area where the vibrating rod is currently located are respectively collected;

[0145] The data features of ultrasonic data, temperature data and pressure data are extracted respectively, and all data features are fused into sensor data features;

[0146] The sensor data features are input into a pre-trained concrete vibration prediction model, and the model output results of the concrete vibration prediction model are used to determine whether the automatic vibration meets the preset vibration standard.

[0147] In this embodiment, in the vibration data acquisition module, ultrasonic sensors, temperature sensors and pressure sensors are used to respectively collect ultrasonic data, temperature data and pressure data of concrete in the target vibration grid area where the vibrating rod is currently located. The ultrasonic sensor measures the density and uniformity of the concrete by emitting and receiving ultrasonic signals. Specifically, the ultrasonic sensor emits a beam of ultrasonic waves. When the ultrasonic waves encounter particles or voids in the concrete, reflection and refraction will occur. After the sensor receives the echo signal, it calculates the density of the concrete by measuring the time and intensity of ultrasonic wave propagation. The temperature sensor monitors the hardening process of the concrete by measuring the temperature changes inside the concrete. Concrete releases heat during the hardening process, and the temperature sensor can monitor these temperature changes in real time to determine the degree of hardening of the concrete. The pressure sensor evaluates the stress of the concrete by measuring the pressure changes inside the concrete. During the vibration process, the concrete will be subject to the impact force of the vibrating rod and the interaction force of the internal particles. The pressure sensor can monitor the changes in these forces in real time to determine the vibration effect of the concrete.

[0148] Next, the data features of ultrasonic data, temperature data and pressure data are extracted respectively, and all data features are fused into sensor data features. The feature extraction of ultrasonic data can include parameters such as signal amplitude, frequency, phase and propagation time. By analyzing these parameters, the density and uniformity information inside the concrete can be obtained. The feature extraction of temperature data can include parameters such as the rate of change of temperature, the peak and valley values ​​of temperature. By analyzing these parameters, the hardening degree information of concrete can be obtained. The feature extraction of pressure data can include parameters such as the rate of change of pressure, the peak and valley values ​​of pressure. By analyzing these parameters, the stress condition information of concrete can be obtained. There are many methods to fuse these feature data, such as weighted average method, principal component analysis (PCA) or feature fusion method in deep learning. By fusing the features of different sensor data, a comprehensive sensor data feature can be obtained, which fully reflects the density, hardening degree and stress condition of concrete.

[0149] Finally, the sensor data features are input into the pre-trained concrete vibration prediction model, and the model output results of the concrete vibration prediction model are used to determine whether the automatic vibration meets the preset vibration standard. The concrete vibration prediction model can use a variety of machine learning or deep learning algorithms, such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN) or long short-term memory network (LSTM). In the model training stage, it is necessary to collect a large amount of vibration data and corresponding vibration effect data, perform data preprocessing and feature extraction, and then input these data into the model for training to obtain a model that can accurately predict the vibration effect. In practical applications, the sensor data features collected in real time are input into the trained model, and the model will output a prediction result of the vibration effect. According to the prediction results, it can be judged whether the current vibration meets the preset standard. If it does not meet the standard, the vibration parameters can be adjusted or the vibration can be continued until the preset vibration standard is met.

[0150] In one embodiment, the component distribution data includes concrete distribution ratio data and concrete distribution volume data, and step S102 specifically includes the following steps:

[0151] Based on the concrete distribution volume data, respectively calculate the time for the automatic concrete vibrator to complete the automatic vibration of each component to be vibrated;

[0152] Obtain environmental forecast data of the surrounding environment of all components to be vibrated;

[0153] Using Pearson correlation analysis, determine the coagulation-related ingredients associated with the initial setting time of concrete in the concrete distribution ratio data, and the coagulation-related environmental data associated with the initial setting time of concrete in the environmental forecast data, and extract the associated ingredient ratio of the coagulation-related ingredients according to the concrete distribution ratio data;

[0154] Combined with the setting-related environmental data and the setting-related ingredients ratio, the genetic algorithm and BP neural network are used to predict the predicted initial setting time of concrete in each component to be vibrated.

[0155] According to the moving speed of the automatic concrete vibrating device, the initial displacement time of the automatic concrete vibrating device moving from the initial position of the device to each component position is calculated respectively, and according to the moving speed of the device, the inter-component displacement time of the automatic concrete vibrating device moving between any two component positions is calculated respectively;

[0156] The time constraint condition is constructed by combining the vibration completion time, the predicted initial setting time, the initial displacement time and the displacement time between components;

[0157] Taking the optimization goal of completing an automatic vibration for each component to be vibrated within the predicted initial setting time, the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint condition and combined with the immune algorithm and the whale swarm algorithm.

[0158] Assign component vibration priority to each component to be vibrated according to the optimal vibration sequence;

[0159] A device vibration path of the automatic concrete vibrating device is generated based on the component position and in the order of component vibration priority from high to low.

[0160] In this embodiment, in order to obtain the environmental forecast data of the surrounding environment of all components to be vibrated, it is first necessary to deploy a series of environmental monitoring equipment at the construction site, which include temperature sensors, humidity sensors, wind speed and direction sensors, and rainfall sensors. The environmental monitoring equipment transmits real-time environmental data to the central data processing system by wireless or wired means. In order to improve the accuracy and coverage of the data, multiple sensors can be deployed at different locations on the construction site, and the data of multiple sensors can be comprehensively processed using data fusion technology. The data processing system preprocesses the received environmental data, including steps such as data cleaning, missing value filling, and data smoothing to eliminate noise and outliers. The preprocessed environmental data will be used as input data for subsequent analysis and prediction. In this way, the environmental forecast data of the construction site can be obtained in real time, providing important environmental parameters for the prediction of the initial setting time of concrete.

[0161] Pearson correlation analysis is used to determine the coagulation-related ingredients associated with the initial setting time of concrete in the concrete distribution ratio data, and the coagulation-related environmental data associated with the initial setting time of concrete in the environmental forecast data, and the associated ingredient ratio of the coagulation-related ingredients is extracted according to the concrete distribution ratio data. Pearson correlation analysis is a statistical method used to measure the degree of linear correlation between two variables, and the value range of its correlation coefficient is -1 to 1, which respectively represents complete negative correlation, no correlation and complete positive correlation. The specific implementation method is to use the concrete distribution ratio data and the environmental forecast data as input variables, and the initial setting time of concrete as the output variable, and calculate the Pearson correlation coefficient between each input variable and the output variable. By setting the threshold of the correlation coefficient, the ingredients and environmental data significantly related to the initial setting time of concrete can be screened out. For the screened coagulation-related ingredients, their ratio values ​​in the concrete distribution ratio data are further extracted as the input features of the subsequent prediction model. In this way, the key factors affecting the initial setting time of concrete can be identified, providing a reliable data basis for the accurate prediction of the initial setting time.

[0162] Combine the coagulation-related environmental data and the associated ingredient ratio of the coagulation-related ingredients, and use genetic algorithms and BP neural networks to predict the predicted initial setting time of concrete in each component to be vibrated. Genetic algorithm is an optimization algorithm based on natural selection and genetic mechanisms, which gradually optimizes the objective function through operations such as selection, crossover and mutation. The specific implementation method is to use the coagulation-related environmental data and the associated ingredient ratio as input features, the initial setting time of concrete as the objective function, and use genetic algorithms to optimize parameters to obtain a prediction model for the initial setting time. The BP neural network is a multi-layer feedforward neural network that is trained through a back propagation algorithm and can fit complex nonlinear relationships. The specific implementation method is to use the coagulation-related environmental data and the associated ingredient ratio as the input layer, the initial setting time as the output layer, and train to obtain a prediction model for the initial setting time by adjusting the weights and biases of the network. Through the combination of genetic algorithms and BP neural networks, the accuracy and robustness of the initial setting time prediction can be improved, and accurate time constraints can be provided for subsequent vibration optimization.

[0163] The initial displacement time of the automatic concrete vibrating device from the initial position of the device to the position of each component is calculated according to the device moving speed of the automatic concrete vibrating device, and the inter-component displacement time of the automatic concrete vibrating device between any two component positions is calculated according to the device moving speed. The specific implementation method is to first determine the moving speed of the vibrating device, which can be obtained through experimental measurement or equipment manual. Then, according to the initial position of the vibrating device and the position of each component, the initial displacement distance is calculated using the geometric distance formula, and the initial displacement time is calculated according to the moving speed. Similarly, according to the positions of any two components, the geometric distance between them is calculated, and the inter-component displacement time is calculated according to the moving speed.

[0164] Time constraints are constructed by combining the vibration completion time, predicted initial setting time, initial displacement time, and inter-component displacement time. The vibration completion time refers to the time required for each component to complete a vibration operation, and this parameter can be obtained through experimental measurement or equipment manual. The predicted initial setting time is the initial setting time of concrete predicted by the aforementioned genetic algorithm and BP neural network. The initial displacement time and inter-component displacement time are the moving time of the vibrating device obtained by the aforementioned calculation. By combining these time parameters, time constraints for each component can be constructed to ensure that each component completes the vibration operation within the predicted initial setting time. The specific implementation method is to add the vibration completion time, initial displacement time, and inter-component displacement time of each component to obtain the total time of the vibration operation, and compare it with the predicted initial setting time to ensure that the total time does not exceed the predicted initial setting time. In this way, reasonable time constraints can be provided for vibration sequence optimization and path planning.

[0165] The optimization goal is that each component to be vibrated completes an automatic vibration within the predicted initial setting time. The optimal vibration order of all components to be vibrated is calculated based on the time constraint and in combination with the immune algorithm and the whale swarm algorithm. The immune algorithm is an optimization algorithm that simulates the biological immune system. It gradually optimizes the objective function through operations such as antibody generation, selection and mutation. The whale swarm algorithm is an optimization algorithm that simulates the predation behavior of whales. It gradually optimizes the algorithm parameters of the immune algorithm through operations such as whale encirclement, expulsion and spiral update. Assign component vibration priority to each component to be vibrated according to the optimal vibration order. The specific implementation method is to assign priorities to each component in the optimal vibration order in order, and the components with higher priorities will be vibrated earlier.

[0166] The vibration path of the automatic concrete vibrating device is generated based on the component position and in the order of component vibration priority from high to low. The specific implementation method is to first generate the vibration path using the shortest path algorithm according to the position and vibration priority of each component. The shortest path algorithm can use the Dijkstra algorithm or the A* algorithm, etc., to generate the path planning of the vibration operation by calculating the shortest moving path of the vibration device at the construction site. The generated vibration path should take into account the actual conditions of the construction site, such as obstacles, traffic routes, etc., to ensure that the vibration device can successfully complete the vibration operation.

[0167] In one embodiment, combining the setting-related environmental data and the associated ingredient ratio of the setting-related ingredients, and using a genetic algorithm and a BP neural network to predict the predicted initial setting time of concrete in each to-be-vibrated component, comprises the following steps:

[0168] Performing joint data enhancement on the condensation-related environmental data and the associated ingredient ratio of the condensation-related ingredients to obtain an expanded set of condensation-related data;

[0169] Construct BP neural network and initialize the network parameters of BP neural network;

[0170] The expanded set of condensed correlation data is input into the BP neural network for network model training, and the loss function in the network model training process is calculated;

[0171] The network parameters are encoded into parameter encoding vectors, the loss function is used as the fitness of the genetic algorithm, and the genetic algorithm is used to optimize the parameter encoding vector to obtain the optimal parameter encoding vector;

[0172] Update network parameters based on the optimal parameter encoding vector;

[0173] The setting-related environmental data and the associated ingredient ratio of the setting-related ingredients corresponding to each component to be vibrated are input into the BP neural network after the network parameters are updated, and the predicted initial setting time of the concrete in each component to be vibrated is obtained through the BP neural network.

[0174] In this embodiment, the condensation-associated environmental data and the associated ingredient ratio of the condensation-associated ingredients are jointly data enhanced to obtain an expanded set of condensation-associated data. Data enhancement is a commonly used machine learning technique that generates more training samples by performing various transformations and extensions on the original data, thereby improving the generalization ability and robustness of the model. The specific implementation method is to first standardize the condensation-associated environmental data and the associated ingredient ratio so that they have the same scale and distribution. Then, the standardized data is expanded using data enhancement technology, including random noise addition, data translation, scaling, rotation and other operations. Through these operations, a large number of enhanced data samples can be generated to form an expanded set of condensation-associated data. In the process of data enhancement, it is necessary to pay attention to maintaining the physical meaning and rationality of the data to avoid generating invalid or erroneous data samples. In this way, the training data set can be enriched to improve the training effect and prediction accuracy of the model.

[0175] BP neural network is a multi-layer feedforward neural network that is trained through a back-propagation algorithm and can fit complex nonlinear relationships. The specific implementation method is to first determine the structure of the neural network, including the number of nodes in the input layer, hidden layer, and output layer. The number of nodes in the input layer is equal to the number of features in the expanded set of coagulation-related data, and the number of nodes in the output layer is 1, corresponding to the initial setting time of the concrete. The number of nodes in the hidden layer can be determined based on experience or through cross-validation. Usually 1 to 2 hidden layers are selected, and each layer contains 10 to 100 nodes. The network parameters include weights and biases. When initializing, random initialization or Xavier initialization and other methods can be used to give the parameters appropriate initial values. In this way, the BP neural network can be constructed and initialized, providing a basis for subsequent model training and optimization.

[0176] The expanded set of condensation-related data is input into the BP neural network for network model training, and the loss function in the network model training process is calculated. The specific implementation method is to input each sample in the expanded set of condensation-related data into the BP neural network in turn, perform forward propagation calculation, and obtain the predicted initial setting time. Then, the error between the predicted value and the true value is calculated. Commonly used loss functions include mean square error (MSE) and cross entropy loss. After the loss function is calculated, the network parameters are updated using the back propagation algorithm to gradually reduce the value of the loss function and improve the prediction accuracy of the model. In this way, the BP neural network can be trained to accurately predict the initial setting time of concrete.

[0177] The network parameters are encoded as parameter encoding vectors, and the loss function is used as the fitness of the genetic algorithm. The genetic algorithm is used to optimize the parameter encoding vector to obtain the optimal parameter encoding vector. The specific implementation method is to first arrange the weights and bias parameters of the BP neural network in a certain order to form a parameter encoding vector. Then, the value of the loss function is used as the fitness of the genetic algorithm to construct the initial population of the genetic algorithm, and each individual corresponds to a parameter encoding vector. The genetic algorithm gradually optimizes the parameter encoding vector through operations such as selection, crossover and mutation, so that the fitness (i.e., the value of the loss function) is continuously reduced. The specific operation includes selecting individuals with higher fitness for mating, generating new individuals, and mutating some parameters of the new individuals to increase the diversity of the population. After multiple generations of iterations, the genetic algorithm finally converges and obtains the optimal parameter encoding vector. In this way, the parameters of the BP neural network can be globally optimized to improve the prediction performance of the model.

[0178] Update the network parameters based on the optimal parameter encoding vector. The specific implementation method is to decode the optimal parameter encoding vector obtained by the genetic algorithm into the weight and bias parameters of the BP neural network, and update these parameters to the BP neural network. The updated network parameters can better fit the training data and reduce the prediction error. In order to verify the effect of the parameter update, a complete forward propagation and back propagation can be performed on the updated BP neural network, and the new loss function value is calculated to ensure that it is lower than before optimization. In this way, the prediction accuracy and robustness of the BP neural network can be further improved, providing a reliable model for the subsequent initial setting time prediction.

[0179] The coagulation-associated environmental data and the associated ingredient ratios of the coagulation-associated ingredients corresponding to each component to be vibrated are input into the BP neural network after the network parameters are updated, and the predicted initial setting time of the concrete in each component to be vibrated is predicted by the BP neural network. A specific implementation method is to process the coagulation-associated environmental data and the associated ingredient ratios of each component to be vibrated in accordance with the previous standardized method, and then input them into the updated BP neural network, perform forward propagation calculations, and obtain the predicted initial setting time. The prediction results can be compared with the actual initial setting time to verify the accuracy and reliability of the model. In this way, an accurate initial setting time prediction can be provided for each component to be vibrated, guiding subsequent vibration operations and construction plans, and improving construction efficiency and concrete quality.

[0180] In one embodiment, the optimization goal is to complete an automatic vibration for each component to be vibrated within the predicted initial setting time, and the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint condition and combined with the immune algorithm and the whale group algorithm, including the following steps:

[0181] A plurality of initial vibration sequences of all components to be vibrated are randomly generated based on the component positions, and each component to be vibrated appears only once in any initial vibration sequence;

[0182] Encode all initial vibration sequences into antibody populations of immune algorithms;

[0183] The optimization goal is used as the target antigen of the immune algorithm, and the antibody population is iteratively updated using the immune algorithm based on time constraints;

[0184] In the iterative update process of the antibody population, the whale swarm algorithm is used to optimize and update the immune algorithm parameters in the immune algorithm;

[0185] The immune algorithm is repeatedly used to iteratively update the antibody population until the antibody population meets the optimization target, and the antibody population that meets the optimization target is decoded into the optimal vibration sequence of all components to be vibrated.

[0186] In this embodiment, it is first necessary to obtain the position information of all components to be vibrated, which can be obtained through the layout diagram of the construction site or sensor data. Then a random algorithm is used to generate multiple initial vibration sequences. The specific implementation method is to randomly disrupt the order of the components to be vibrated to generate multiple different permutations and combinations, each of which is an initial vibration sequence. In order to ensure that each component appears only once in each sequence, the Fisher-Yates shuffle algorithm or other random permutation algorithms can be used. The steps of the Fisher-Yates shuffle algorithm include: starting from the last element of the list, randomly selecting an element to exchange position with it, then moving forward one position, and repeating the above steps until the first element of the list. In this way, multiple different initial vibration sequences can be generated to provide diverse initial solutions for the subsequent optimization process.

[0187] All initial vibration sequences are encoded into antibody populations of immune algorithms. Immune algorithm is an optimization algorithm that simulates the biological immune system, and finds the optimal solution through the interaction between antibodies and antigens. The specific implementation method is to encode each initial vibration sequence into an antibody to form an antibody population. The encoding method can be direct encoding, that is, arranging the vibration sequences in order to form a sequence. The size of the antibody population can be determined according to the complexity of the problem and the computing resources, and usually dozens to hundreds of antibodies are selected. Then the optimization target is used as the target antigen of the immune algorithm, and the antibody population is iteratively updated based on the time constraint and using the immune algorithm. The optimization target can be to minimize the vibration time, minimize the vibration path length, or other indicators related to construction efficiency and quality.

[0188] The specific implementation method is to use the optimization target as the target antigen of the immune algorithm and calculate the fitness of each antibody (vibration order). The fitness function can be designed according to the optimization target and time constraints. For example, the fitness function can be defined as the total time or total path length of the vibration order. Then, the immune algorithm is used to iteratively update the antibody population, including operations such as clone selection, mutation, and suppression. Clone selection is to select antibodies with higher fitness for cloning according to fitness to generate more similar antibodies; mutation is to randomly mutate the cloned antibodies to increase the diversity of the population; suppression is to delete antibodies with lower fitness or higher similarity to maintain the excellence and diversity of the population. In this way, the antibody population can be gradually optimized, the fitness can be improved, and the optimal vibration order can be found.

[0189] During the iterative update process of the antibody population, the whale swarm algorithm is used to optimize and update the immune algorithm parameters in the immune algorithm. The whale swarm algorithm is an optimization algorithm based on the foraging behavior of whales. It simulates the predation strategy of whales to find the optimal solution. The specific implementation method is to use the whale swarm algorithm to optimize the parameters of the immune algorithm, such as cloning rate, mutation rate and suppression threshold, during the iterative update process of the immune algorithm. The steps of the whale swarm algorithm include: initializing the whale swarm population and calculating the fitness of each individual; updating the position of the individual according to the fitness to simulate the predation behavior of whales; iterative update until the termination condition is met. In this way, the parameters of the immune algorithm can be dynamically adjusted to improve the optimization performance of the immune algorithm, accelerate the convergence process, and find a better vibration sequence.

[0190] Repeatedly use the immune algorithm to iteratively update the antibody population until the antibody population meets the optimization target, and decode the antibody population that meets the optimization target into the optimal vibration order of all components to be vibrated. The specific implementation method is to continuously iteratively update the antibody population, calculate the fitness of the optimal antibody after each iteration, and judge whether the optimization target is met. The optimization target can be set as the fitness reaching a certain threshold or the number of iterations reaching a certain upper limit. If the optimization target is met, stop the iteration, and decode the optimal antibody into a vibration order, and the decoding method is to convert the antibody sequence into the original vibration order. In one embodiment, the vibration control method of the concrete pouring process also includes the following steps:

[0191] Acquire engineering model data of the construction project where the building component is located and device model data of the automatic concrete vibrating device;

[0192] Combine engineering model data and device model data and use BIM software to build an engineering 3D model of the construction project;

[0193] Based on the engineering 3D model, the movement of the automatic concrete vibrator between any two components to be vibrated is simulated to obtain the simulated collision detection results;

[0194] Constructing device movement constraints based on simulation collision detection results;

[0195] Taking the optimization goal of completing an automatic vibration for each component to be vibrated within the predicted initial setting time, the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint and device movement constraint and combined with the immune algorithm and the whale swarm algorithm.

[0196] Assign component vibration priority to each component to be vibrated according to the optimal vibration sequence;

[0197] A device vibration path of the automatic concrete vibrating device is generated based on the component position and in the order of component vibration priority from high to low.

[0198] In this embodiment, it is first necessary to collect all relevant data of the construction site, including geometric information, location, size and material properties of building components. This information can usually be obtained through the building information model (BIM), which is a tool for digitally representing the physical and functional characteristics of a building and can provide comprehensive engineering model data. In addition, it is also necessary to obtain the model data of the automatic concrete vibrator, which includes the size, range of motion, working radius, weight, power system, etc. of the device. The device model data can be obtained through the technical manual or three-dimensional CAD model provided by the manufacturer. By obtaining these detailed engineering model data and device model data, accurate basic data can be provided for subsequent three-dimensional modeling and simulation, ensuring the reliability and accuracy of the simulation results.

[0199] Combine the engineering model data and the device model data and use BIM software to build an engineering 3D model of the construction project. Use BIM software (such as Revit, Navisworks, etc.) to import the acquired engineering model data and device model data into the software for 3D modeling. The specific implementation method is to first create a new engineering project in the BIM software, and then import the geometric information and position data of the building components to build a 3D model of the building. Next, import the model data of the automatic concrete vibrator and place the device model in the appropriate position of the building model. By combining the engineering model data and the device model data, a complete 3D model of the construction project can be created in the BIM software. This 3D model can not only intuitively display the spatial relationship between the building and the vibrator, but can also be used for subsequent collision detection and path planning.

[0200] The simulation function of BIM software can be used to simulate the movement process of the automatic concrete vibrator at the construction site. The specific implementation method is to set the initial position of the vibrator, define its movement path, and run the simulation program in the BIM software. During the simulation process, the BIM software will calculate whether the device will collide with the building components during the movement based on the motion parameters of the vibrator and the geometric information of the building. The simulation collision detection results include information such as the movement trajectory of the device, the collision position and the number of collisions. Based on the simulation collision detection results, the obstacles and restrictions that the automatic concrete vibrator needs to avoid during the movement can be determined. The specific implementation method is to analyze the simulation results, identify all the locations and causes of collisions, and then define the corresponding movement constraints. For example, if the device collides with a building component at a certain position, it is necessary to avoid this position in the path planning; if the movement range of the device is limited, these restrictions need to be considered in the path planning. The movement constraints can be expressed as mathematical constraint formulas or logical rules to guide subsequent path optimization.

[0201] Taking each component to be vibrated as the optimization goal to complete an automatic vibration within the predicted initial setting time, the optimal vibration order of all components to be vibrated is calculated based on the time constraint conditions and device movement constraint conditions and the immune algorithm and the whale swarm algorithm. First, the optimization goal is defined, that is, to ensure that each component to be vibrated completes the vibration within its initial setting time. The initial setting time is the time from pouring to initial solidification of concrete, which is usually predicted based on the concrete mix ratio and environmental conditions. Then, combined with the time constraint conditions and device movement constraint conditions, the immune algorithm and the whale swarm algorithm are used for optimization calculation. According to the calculated optimal vibration order, the vibration order of each component to be vibrated is determined. Then, according to the vibration order, the vibration priority is assigned to each component. The priority can be expressed as an integer value, and the smaller the value, the higher the priority.

[0202] According to the vibration priority of each component, all components to be vibrated are arranged in order from high to low. Then, according to the arranged component order and its position at the construction site, the moving path of the vibrating device is generated. The specific implementation method is to start from the component with the highest vibration priority, calculate the shortest path for the device to move from the current position to the position of the component, and then calculate the path for the device to move from one component to the next component in sequence until all components are vibrated. The path generation can use the shortest path algorithm, such as the Dijkstra algorithm or the A* algorithm, to ensure that the path is the shortest and avoids all movement constraints.

[0203] In this embodiment, the optimization goal is to complete an automatic vibration for each component to be vibrated within the predicted initial setting time. The optimal vibration sequence of all components to be vibrated is calculated based on the time constraint and the device movement constraint and combined with the immune algorithm and the whale group algorithm, including the following steps:

[0204] A plurality of initial vibration sequences of all components to be vibrated are randomly generated based on the component positions, and each component to be vibrated appears only once in any initial vibration sequence;

[0205] Encode all initial vibration sequences into antibody populations of immune algorithms;

[0206] Taking the optimization target as the target antigen of the immune algorithm, the antibody population is iteratively updated using the immune algorithm based on the time constraint and the device movement constraint;

[0207] In the iterative update process of the antibody population, the whale swarm algorithm is used to optimize and update the immune algorithm parameters in the immune algorithm;

[0208] The immune algorithm is repeatedly used to iteratively update the antibody population until the antibody population meets the optimization target, and the antibody population that meets the optimization target is decoded into the optimal vibration sequence of all components to be vibrated.

[0209] In this embodiment, by considering the movement constraints of the device, the optimal solution can be found under more complex constraints, so that the movement path of the vibrating device can be optimized to ensure that each component is vibrated within the optimal time during the construction process, while reducing unnecessary collisions of the device and improving work efficiency.

[0210] The present invention also discloses a concrete pouring process vibration control system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the concrete pouring process vibration control method described in any one of the above-mentioned embodiments is implemented.

[0211] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0212] Among them, the memory can be an internal storage unit of a computer device, such as a hard disk or memory of a computer device, or an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the computer device, and the memory can also be a combination of an internal storage unit and an external storage device of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0213] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0214] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A method for controlling vibration during concrete pouring, characterized in that: Applied to an automatic concrete vibrating device, the automatic concrete vibrating device includes an image recognition module, a vibration data acquisition module, a horizontal vibration module and a mechanical arm vibration module, and the method includes the following steps: Acquire component placement data and component position of a component to be vibrated that has completed concrete placement in the building component, and acquire the initial position of the automatic concrete vibrating device, wherein the component placement data includes concrete placement ratio data and concrete placement volume data; Based on the concrete distribution volume data, respectively calculate the vibration completion time for the automatic concrete vibrating device to complete the automatic vibration of each component to be vibrated; Obtaining environmental forecast data of the surrounding environment of all the components to be vibrated; Determine the coagulation-related ingredients associated with the initial setting time of concrete in the concrete material distribution ratio data and the coagulation-related environmental data associated with the initial setting time of concrete in the environmental forecast data by using Pearson correlation analysis, and extract the associated ingredient ratio of the coagulation-related ingredients according to the concrete material distribution ratio data; Combining the setting-related environmental data and the associated ingredient ratio of the setting-related ingredients, and using a genetic algorithm and a BP neural network to respectively predict the predicted initial setting time of concrete in each of the components to be vibrated; The initial displacement time of the automatic concrete vibrating device moving from the initial position of the device to each of the component positions is calculated according to the device moving speed, and the inter-component displacement time of the automatic concrete vibrating device moving between any two component positions is calculated according to the device moving speed; Constructing a time constraint condition by combining the vibration completion time, the predicted initial setting time, the initial displacement time and the displacement time between components; Taking the completion of the automatic vibration once for each component to be vibrated within the predicted initial setting time as the optimization goal, the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint condition and in combination with the immune algorithm and the whale group algorithm; Allocating a component vibration priority to each component to be vibrated according to the optimal vibration sequence; Generate a device vibration path of the automatic concrete vibrating device based on the component position and in descending order of the component vibration priority; Controlling the automatic concrete vibrating device to move sequentially to the component position corresponding to each component to be vibrated according to the device vibration path; Whenever the automatic concrete vibrating device reaches a component position, the component to be vibrated corresponding to the current component position is marked as a target component to be vibrated, and a vibration strategy of the automatic concrete vibrating device is configured based on the component type of the target component to be vibrated and the component material distribution data; According to the vibration strategy and using the horizontal vibration module or the robotic arm vibration module, the target component to be vibrated is automatically vibrated; During the automatic vibration process, the component surface image of the target component to be vibrated is collected in real time by the image recognition module, the real-time vibration data of the automatic vibration process is collected by the vibration data collection module, and the component surface image and the real-time vibration data are combined to identify whether the automatic vibration meets the preset vibration standard; If the automatic vibration does not reach the preset vibration standard, maintaining the automatic vibration until the vibration standard is reached; If the automatic vibration has reached the preset vibration standard, the automatic vibration of the target component to be vibrated is terminated, and the automatic concrete vibrating device is controlled to move to the next component position according to the device vibration path.

2. The method for controlling vibration during concrete pouring according to claim 1, characterized in that: The component distribution data includes concrete distribution ratio data, concrete distribution volume data and component size data. The vibrating strategy of the automatic concrete vibrating device configured based on the component type of the target component to be vibrated and the component distribution data includes the following steps: Determining whether the target component to be vibrated is a panel component, a wall component or a frame component; If the component type is the panel component, configuring a first vibration strategy of the automatic concrete vibrating device in combination with the concrete distribution volume data and the component size data, wherein the first vibration strategy includes vibration time and vibration power, and the first vibration strategy is executed by the horizontal vibration module; If the component type is the wall component or the frame component, the wall component or the frame component is preliminarily divided into a plurality of initial vibration grid areas in combination with the concrete distribution volume data and the component size data; Adjusting each of the initial vibration grid areas to a target vibration grid area according to the concrete distribution ratio data; Using the center point of the target vibration grid area as the vibration point of the vibration rod in the mechanical arm vibration module; Determining the vibration power and vibration time of the vibrating rod according to the area size of the target vibration grid area; The second vibration strategy of the automatic concrete vibrating device is configured in combination with the vibration power, the vibration time and all the target vibration grid areas, and the second vibration strategy is executed by the mechanical arm vibration module.

3. The method for controlling vibration during concrete pouring according to claim 2, characterized in that: The step of collecting the component surface image of the target component to be vibrated in real time by the image recognition module, collecting the real-time vibration data of the automatic vibration process by the vibration data acquisition module, and identifying whether the automatic vibration reaches the preset vibration standard by combining the component surface image and the real-time vibration data comprises the following steps: The image recognition module is used to collect the component surface image of the target component to be vibrated in real time; Using image recognition technology to identify the component surface image, and judging whether there are bubbles on the component surface concrete of the target component to be vibrated according to the recognition result; If bubbles exist in the concrete on the surface of the target component to be vibrated, it is determined that the automatic vibration does not meet the preset vibration standard; If there are no bubbles on the surface concrete of the target component to be vibrated, judging whether there is floating slurry on the surface concrete of the target component to be vibrated according to the recognition result; If there is no laitance on the surface of the target component to be vibrated, it is determined that the automatic vibration does not meet the preset vibration standard; If there is laitance on the surface concrete of the target component to be vibrated, the vibration data acquisition module is used to collect real-time vibration data of the automatic vibration process, and based on the real-time vibration data, it is identified whether the automatic vibration meets the preset vibration standard.

4. The method for controlling vibration during concrete pouring according to claim 3, characterized in that: The method of collecting the real-time vibration data of the automatic vibration process by using the vibration data collection module and identifying whether the automatic vibration reaches a preset vibration standard based on the real-time vibration data comprises the following steps: Using the ultrasonic sensor, temperature sensor and pressure sensor in the vibration data acquisition module, respectively collect the ultrasonic data, temperature data and pressure data of the concrete in the target vibration grid area where the vibrating rod is currently located; extracting data features of the ultrasonic data, the temperature data and the pressure data respectively, and fusing all the data features into sensor data features; The sensor data features are input into a pre-trained concrete vibration prediction model, and a model output result of the concrete vibration prediction model is used to determine whether the automatic vibration reaches a preset vibration standard.

5. The method for controlling vibration during concrete pouring according to claim 1, characterized in that: The method of combining the setting-related environmental data and the associated ingredient ratio of the setting-related ingredients and using a genetic algorithm and a BP neural network to respectively predict the predicted initial setting time of concrete in each of the components to be vibrated comprises the following steps: Performing joint data enhancement on the condensation-related environment data and the associated ingredient ratios of the condensation-related ingredients to obtain an expanded set of condensation-related data; Constructing a BP neural network and initializing network parameters of the BP neural network; Inputting the condensed association data expansion set into the BP neural network to perform network model training, and calculating the loss function in the network model training process; Encoding the network parameters into a parameter encoding vector, using the loss function as the fitness of the genetic algorithm, and optimizing the parameter encoding vector using the genetic algorithm to obtain an optimal parameter encoding vector; Updating the network parameters based on the optimal parameter encoding vector; The setting-related environmental data and the associated ingredient ratio of the setting-related ingredients corresponding to each component to be vibrated are input into the BP neural network after the network parameters are updated, and the predicted initial setting time of the concrete in each component to be vibrated is predicted by the BP neural network.

6. The method for controlling vibration during concrete pouring according to claim 1, characterized in that: The optimization goal is that each component to be vibrated completes the automatic vibration once within the predicted initial setting time, and based on the time constraint condition and in combination with the immune algorithm and the whale group algorithm, the optimal vibration sequence of all components to be vibrated is calculated, which includes the following steps: Randomly generate multiple initial vibration sequences of all the components to be vibrated based on the component positions, and each component to be vibrated appears only once in any of the initial vibration sequences; encoding all of the initial vibration sequences into an antibody population of an immune algorithm; Taking the optimization target as the target antigen of the immune algorithm, and iteratively updating the antibody population based on the time constraint and using the immune algorithm; In the iterative updating process of the antibody population, a whale swarm algorithm is used to optimize and update immune algorithm parameters in the immune algorithm; The immune algorithm is repeatedly used to iteratively update the antibody population until the antibody population meets the optimization target, and the antibody population that meets the optimization target is decoded into the optimal vibration sequence of all the components to be vibrated.

7. The method for controlling vibration during concrete pouring according to claim 1, characterized in that: The method further comprises the steps of: Acquire engineering model data of the construction project where the building component is located and device model data of the automatic concrete vibrating device; Combining the engineering model data and the device model data and using BIM software to construct an engineering three-dimensional model of the construction project; Simulating the movement of the automatic concrete vibrating device between any two components to be vibrated based on the engineering three-dimensional model to obtain a simulated collision detection result; Constructing device movement constraint conditions according to the simulation collision detection results; Taking the completion of the automatic vibration once for each component to be vibrated within the predicted initial setting time as the optimization goal, the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint condition and the device movement constraint condition and in combination with the immune algorithm and the whale group algorithm; Allocating a component vibration priority to each component to be vibrated according to the optimal vibration sequence; A device vibration path of the automatic concrete vibrating device is generated based on the component position and in the order of the component vibration priorities from high to low.

8. The method for controlling vibration during concrete pouring according to claim 7, characterized in that: The optimization goal is to complete the automatic vibration once for each component to be vibrated within the predicted initial setting time, and the optimal vibration sequence of all components to be vibrated is calculated based on the time constraint and the device movement constraint and combined with the immune algorithm and the whale group algorithm, including the following steps: Randomly generate multiple initial vibration sequences of all the components to be vibrated based on the component positions, and each component to be vibrated appears only once in any of the initial vibration sequences; encoding all of the initial vibration sequences into an antibody population of an immune algorithm; Taking the optimization target as the target antigen of the immune algorithm, and iteratively updating the antibody population based on the time constraint and the device movement constraint and using the immune algorithm; In the iterative updating process of the antibody population, a whale swarm algorithm is used to optimize and update immune algorithm parameters in the immune algorithm; The immune algorithm is repeatedly used to iteratively update the antibody population until the antibody population meets the optimization target, and the antibody population that meets the optimization target is decoded into the optimal vibration sequence of all the components to be vibrated.

9. A concrete pouring process vibration control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the concrete pouring process vibration control method as described in any one of claims 1 to 8 is implemented.

Images