An intelligent monitoring method, system, product and medium for the quality of concrete pouring

Abstract

An intelligent monitoring method, system, product and medium for concrete pouring quality, which are applied to the field of monitoring and regulation of control systems. In this method, the pouring area is divided into multiple grid units and an adjacency matrix is established. Based on this matrix, a diffusion prediction model is established. The coverage range of each pump truck's boom and the real-time height difference between grid units are obtained. The pumping parameters are adjusted according to the height difference, and the model is updated in real time and the pumping flow is optimized until the height difference meets the standard. Implementing the technical solution provided by this application effectively solves the technical problems of difficult real-time grasp of the concrete flow trend and poor coordination of multi-pump truck pouring during the construction of mountain viaducts, and realizes the uniform distribution of concrete in large-area pouring areas.

Description

Technical Field

[0001] The present application relates to the field of monitoring and regulation of G05B23 control systems, and particularly to an intelligent monitoring method, system, product, and medium for the quality of concrete pouring. Background Art

[0002] With the rapid development of China's transportation infrastructure construction, the number of mountain viaduct engineering projects is increasing day by day. During the construction of mountain viaducts, the quality of concrete pouring is directly related to the safety and durability of the bridge structure. Especially for extra-large bridges with a span of more than 200 meters and a pier height of more than 90 meters, the concrete pouring construction not only requires one-time forming but also needs to ensure the uniform distribution of concrete in a large pouring area.

[0003] Currently, the concrete pouring construction of mountain viaducts adopts a cooperative operation method of multiple concrete pump trucks and tank trucks. Construction workers lay a leveling point every 10 meters within the pouring area and use a DS3 level to measure the elevation data of each leveling point at fixed time intervals. The surveyors record the elevation data in the construction log, and the on-site construction supervisor determines the working parameters of the pump truck based on the height difference between adjacent leveling points. The specific adjustment method is as follows: when it is found that the elevation of a certain area is low, increase the pumping pressure and the placing speed of the pump truck corresponding to this area; when multiple pump trucks are operating in the overlapping area, the construction supervisor commands each pump truck to carry out pouring operations in turn through a walkie-talkie.

[0004] During the actual construction process, due to the limited number of leveling points and the long measurement interval time, it is impossible to grasp the overall height change trend within the pouring area in real time. Especially in foggy weather in the mountains, the measurement accuracy decreases, and it is difficult for construction workers to see the specific situation in the far-end pouring area. At the same time, the large temperature difference between day and night in the mountains will cause changes in the performance of concrete, and the manual observation method is difficult to detect in time the impact of the change in concrete performance on fluidity, resulting in the gradual accumulation of height differences between regions and the formation of uneven concrete accumulation. Summary of the Invention

[0005] The present application provides an intelligent monitoring method, system, product, and medium for the quality of concrete pouring, which is used to achieve intelligent collaborative control of the concrete pouring area in a complex mountain environment to improve the uniformity of concrete pouring.

[0006] In a first aspect, the present application provides an intelligent monitoring method for the quality of concrete pouring, including: dividing the pouring area into multiple grid units, and establishing a grid adjacency matrix based on the spatial position relationship of each grid unit; based on this grid adjacency matrix, establishing a diffusion prediction model for predicting the flow characteristics of concrete between grid units, the diffusion prediction model taking the current performance parameters of the concrete, the current pouring environment parameters, and the real-time height difference between adjacent grid units as inputs, calculating the flow coefficient of each grid unit in the grid adjacency matrix, and determining the predicted height difference between adjacent grid units after a preset time period as an output based on this flow coefficient; obtaining the coverage range of each pump truck boom and the real-time height difference between each grid unit, and determining the main pouring area of each pump truck and its corresponding initial pumping flow according to the distance relationship between the area where the real-time height difference is greater than the first preset threshold and each pump truck; for the overlapping pouring area of adjacent pump trucks, when the real-time height difference is greater than the second preset threshold, switching the pump truck on the lower side in height in the overlapping pouring area to the main pump truck, and increasing the pumping flow of this main pump truck in accordance with a preset flow adjustment step until the real-time height difference is less than the second preset threshold; based on the real-time height difference between each grid unit, updating the flow coefficient in the diffusion prediction model in real time to obtain the predicted height difference between adjacent grid units after a preset time period; calculating the height uniformity index corresponding to different pumping parameter combinations based on the predicted height difference output by this diffusion prediction model, and selecting the pumping parameter combination that can make the height difference between adjacent grid units less than the third preset threshold after this preset time period to adjust the pumping flow of each pump truck in real time until the actual height difference and the predicted height difference between each grid unit are both less than the third preset threshold.

[0007] By adopting the above technical solution, the embodiment of the present application divides the pouring area into grid units and establishes an adjacency matrix, constructs a diffusion prediction model for the flow characteristics of concrete, which can dynamically calculate the flow coefficient based on the concrete performance parameters, environmental parameters, and real-time height difference, so as to accurately predict the flow trend of concrete. Combining the real-time coverage range of the pump truck boom and the height difference data, the system can intelligently determine the main pouring area and pumping parameters of each pump truck, and adaptively adjust the pumping flow of the main pump truck in the overlapping area. Through the real-time update of the diffusion prediction model and the coordinated control of multiple pump trucks, the uniform distribution of concrete in a large-area pouring area is realized, effectively solving the problem of uneven concrete accumulation in the construction of mountain viaducts.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the establishment of a diffusion prediction model for predicting the flow characteristics of concrete specifically includes: obtaining the slump, initial setting time, external temperature, and concrete temperature of the concrete as the concrete performance parameters, and obtaining the temperature, humidity, and wind speed of the construction environment as the pouring environment parameters; constructing a directed graph model based on the grid adjacency matrix, where the nodes of the graph represent grid cells, and the weight of the edge is determined by the height difference, distance, and the concrete performance parameters of adjacent grid cells; calculating the flow coefficient D(i, j, t) between adjacent grid cells i and j at time t according to the following formula: ; where is the basic flow coefficient, i and j respectively represent the numbers of two adjacent grid cells, S is the slump, is the initial setting time, is the concrete temperature, is the ambient temperature, H is the humidity, W is the wind speed, t is the time since pouring started, , , , , , and λ are corresponding weight coefficients; substituting the flow coefficient into the diffusion equation to establish the diffusion prediction model: ; where K is the global correction coefficient, A(i, j) is the grid adjacency matrix element, and ΔZ(i, j, t) is the height gradient at the current moment.

[0009] By adopting the above technical solution, the embodiment of the present application constructs a diffusion prediction model considering the coupling effect of multiple factors. This model characterizes the spatial relationship between grid cells through a directed graph structure, and introduces performance parameters such as concrete slump, initial setting time, and temperature, as well as environmental parameters such as environmental temperature and humidity and wind speed, to establish a calculation formula for the flow coefficient. Based on this flow coefficient, the diffusion equation can accurately describe the flow characteristics of concrete between grid cells.

[0010] In some embodiments in combination with some embodiments of the first aspect, in some embodiments, UWB base stations are arranged at multiple high points in the pouring area, and an inertial navigation unit, an inclination sensor, and a laser rangefinder are installed on the concrete pump truck. The method for obtaining the coverage range of each concrete pump truck's distributing boom and the real-time height difference between each grid unit specifically includes: obtaining the real-time positioning data of the concrete pump truck by the UWB base station and monitoring the UWB signal quality. When it is detected that the signal quality decreases due to mountain blockage, the positioning scheme is switched from UWB positioning to inertial navigation positioning, and at the same time, the last set of valid UWB positioning data before the switch is recorded as the initial reference position; obtaining the acceleration and angular velocity data of the inertial navigation unit, calculating the relative displacement relative to the initial reference position, and obtaining the inclination data of the distributing boom through the inclination sensor, and calculating the real-time attitude angle of the distributing boom; based on the initial reference position, the relative displacement, the inclination data, and the vertical distance data measured by the laser rangefinder from the discharging port of the distributing boom to the concrete surface, determining the precise spatial position of the discharging port of the distributing boom; calculating the coverage range of each concrete pump truck's distributing boom according to the spatial position, attitude angle, maximum extension length, and rotation angle range of the distributing boom; calculating the real-time height difference between each grid unit based on the concrete surface elevation data collected by the laser rangefinder.

[0011] By adopting the above technical solution, the embodiments of the present application achieve high-precision positioning of the concrete pump truck position and real-time monitoring of the distributing boom attitude. The system automatically switches to inertial navigation positioning when the UWB signal quality decreases due to mountain blockage through the fusion positioning scheme of the UWB base station and inertial navigation, ensuring the continuity and reliability of positioning. Combining the data of the inclination sensor and the laser rangefinder, the system can accurately calculate the spatial position and coverage range of the discharging port of the distributing boom, providing a guarantee for the precise pouring of concrete.

[0012] In some embodiments in combination with some embodiments of the first aspect, in some embodiments, based on the real-time height difference between each grid unit, the flow coefficient in the diffusion prediction model is updated in real time to obtain the predicted height difference between adjacent grid units after a preset time period. Specifically, it includes: obtaining the ex-factory performance parameters of each batch of concrete; establishing the correspondence between the grid unit and the concrete batch based on the GPS positioning and discharging record of the concrete mixer truck, and mapping the performance parameters of each batch of concrete to the corresponding grid unit; extracting the steel bar arrangement information from the BIM model, and calculating the steel bar volume ratio and the main steel bar direction of each grid unit; determining the local concrete flow direction based on the real-time height difference between each grid unit; when it is detected that the performance parameter difference between adjacent grid units exceeds the preset performance difference threshold and the difference between the real-time height difference and the predicted height difference of the diffusion prediction model before the preset time period exceeds the prediction deviation threshold, the flow coefficient of the diffusion prediction model is corrected to: ; where: Φ(ρ, θ) is the steel bar correction function, ρ is the steel bar volume ratio, and θ is the angle between the local concrete flow direction and the main bar direction; substituting the corrected flow coefficient into the diffusion prediction model, and inputting the concrete performance parameters, pouring environment parameters, and the real-time height difference between adjacent grid cells at the current moment, the predicted height difference between adjacent grid cells after a preset time duration is obtained.

[0013] By adopting the above technical solution, the embodiment of the present application realizes the accurate correction of the diffusion prediction model at the boundary of sudden change in concrete performance. The system establishes a dual-threshold judgment mechanism by simultaneously monitoring two key indicators, namely the difference in performance parameters between adjacent grid cells and the prediction deviation of the height difference, and can accurately identify the interfaces of different batches of concrete. At these interfaces, due to the sudden change in concrete performance, the applicability of the original diffusion coefficient calculation method is reduced. However, in this solution, by introducing a correction function considering the steel bar arrangement information at the boundary of sudden change in performance, the model can more accurately describe the non-linear flow characteristics of concrete here. This correction function not only considers the influence of the steel bar volume ratio on the effective flow space, but also describes the anisotropic hindrance effect of the steel bar mesh on concrete flow through the relationship between the main bar direction and the flow direction angle, thereby realizing a more accurate flow prediction at the boundary of sudden change in performance and providing a more reliable basis for the optimization and adjustment of pumping parameters.

[0014] Combined with some embodiments of the first aspect, in some embodiments, the steel bar correction function Φ(ρ, θ) is specifically: ; where: ρ is the steel bar volume ratio, representing the volume proportion of steel bars in unit volume of concrete; θ is the angle between the local concrete flow direction and the main bar direction; γ is the directional influence coefficient, 0 ≤ γ ≤ 1; (1 - ρ) represents the reduction effect of the steel bar volume on the effective flow space; represents the influence of the angle between the flow direction and the main bar direction. The influence is the smallest when the flow direction is parallel to the main bar, at this time θ = 0°, and the influence is the largest when they are perpendicular, at this time θ = 90°.

[0015] By adopting the above technical solution, the embodiment of the present application proposes a correction function considering the influence of steel bar arrangement. This function reflects the reduction effect of steel bars on the effective flow space of concrete through the steel bar volume ratio, and describes the directional influence through the trigonometric function relationship between the flow direction and the main bar direction, enabling the diffusion prediction model to accurately reflect the hindrance effect of steel bar arrangement on concrete flow.

[0016] In some embodiments in combination with some embodiments of the first aspect, the method further includes: collecting concrete vibration response characteristics based on a vibration sensor array arranged at each grid unit; dynamically calculating the compactness of each grid unit based on spectral analysis of the concrete vibration response characteristics; when it is detected that the compactness of a local area is lower than a preset compactness threshold, adaptively adjusting the pumping flow rate of relevant concrete pumps until the compactness reaches the standard based on the area size of the low-compactness area, the compactness deviation value, and the coverage of surrounding concrete pumps.

[0017] By adopting the above technical solution, the embodiments of the present application achieve real-time monitoring and intelligent regulation of concrete compactness. The system collects concrete vibration response characteristics through the arranged vibration sensor array, dynamically evaluates the compactness based on spectral analysis, and when it is detected that the compactness of a local area is insufficient, adaptively adjusts the pumping flow rate of relevant concrete pumps to ensure the quality of concrete pouring.

[0018] In some embodiments in combination with some embodiments of the first aspect, the step of adaptively adjusting the pumping flow rate of relevant concrete pumps until the compactness reaches the standard based on the area size of the low-compactness area, the compactness deviation value, and the coverage of surrounding concrete pumps specifically includes: calculating the overlap degree between the coverage range of each concrete pump's boom and the low-compactness area, and selecting the concrete pump with the largest overlap degree as the target concrete pump; determining the required additional concrete volume based on the area size of the low-compactness area and the compactness deviation value, in combination with concrete performance parameters; calculating the target pumping flow rate based on the additional concrete volume and a preset maximum pumping time; gradually increasing the pumping flow rate of the target concrete pump from the current value to the target flow rate in a step-by-step manner, with the increment step not exceeding a preset maximum flow rate change rate until the compactness reaches the preset compactness threshold; when it is detected that the compactness growth rate exceeds a preset growth rate threshold, reducing the pumping flow rate to 80% of the current flow rate to reduce local stress concentration.

[0019] By adopting the above technical solution, the embodiments of the present application establish an adaptive regulation mechanism for pumping flow rate based on compactness. The system selects the optimal target concrete pump by calculating the overlap degree between the concrete pump's coverage range and the low-compactness area, and intelligently calculates the target pumping flow rate based on the additional concrete volume. The pumping flow rate is increased in a step-by-step manner, while monitoring the compactness growth rate and reducing the pumping flow rate when necessary to avoid stress concentration, achieving precise regulation of concrete compactness.

[0020] In a second aspect, the present application provides an intelligent monitoring system for the quality of concrete pouring, including: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, the present application provides a computer program product containing instructions, which, when the computer program product runs on an intelligent monitoring system for the quality of concrete pouring, enables the intelligent monitoring system for the quality of concrete pouring to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium including instructions, which, when the instructions run on an intelligent monitoring system for the quality of concrete pouring, enables the intelligent monitoring system for the quality of concrete pouring to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the intelligent monitoring system for the quality of concrete pouring provided in the above second aspect, the computer program product provided in the third aspect, and the computer-readable storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. Since a diffusion prediction model based on a grid adjacency matrix and a multi-pump truck collaborative control mechanism are adopted, the technical problems of difficult real-time mastery of the concrete flow trend and poor coordination of multi-pump truck pouring during the construction of mountain viaducts are effectively solved, and thus the uniform distribution of concrete in a large-area pouring area is realized.

[0026] 2. Since a fusion positioning scheme of UWB base stations and inertial navigation and a collaborative measurement mechanism of multiple sensors on the placing boom are adopted, the technical problems that the positioning signal of the pump truck is easily blocked in a mountainous environment and the spatial position of the placing boom is difficult to accurately determine are effectively solved, and thus the continuous and reliable positioning of the pump truck and the accurate spatial positioning of the discharging port of the placing boom are realized.

[0027] 3. Since a step-by-step adjustment mechanism for pumping flow based on density and a stress concentration prevention strategy are adopted, the technical problems of insufficient density in local areas and easy generation of stress concentration during the concrete pouring process are effectively solved, and thus the accurate regulation of the concrete density is realized. Description of the Drawings

[0028] Figure 1 It is a schematic structural diagram of a system architecture to which the intelligent monitoring method for the quality of concrete pouring in the embodiments of the present application can be applied;

[0029] Figure 2 It is a schematic flow diagram of an intelligent monitoring method for the quality of concrete pouring in the embodiments of the present application;

[0030] Figure 3 It is another schematic flow diagram of an intelligent monitoring method for the quality of concrete pouring in the embodiments of the present application;

[0031] Figure 4 It is a schematic diagram of an exemplary hardware structure of a control server in the intelligent monitoring system for the quality of concrete pouring in the embodiments of the present application. Detailed implementation manners

[0032] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term " / and / " used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0034] Figure 1 It is a schematic structural diagram of a system architecture to which the intelligent monitoring method for the quality of concrete pouring in the embodiments of the present application can be applied.

[0035] Please refer to Figure 1 , the intelligent monitoring system for the quality of concrete pouring includes:

[0036] A control server 100, configured to receive various data collected by other devices in the system and perform unified scheduling and control on the entire system;

[0037] A plurality of UWB base stations 110, deployed at the high points of the pouring area, for providing positioning signals;

[0038] Multiple concrete pump trucks 120, each pump truck is equipped with: an inertial navigation unit for obtaining the motion state data of the pump truck; an inclination sensor for measuring the attitude angle of the placing boom; a laser rangefinder for measuring the distance between the end of the placing boom and the pouring surface; a pumping control unit for adjusting the pumping parameters;

[0039] Multiple concrete mixer trucks 130, each mixer truck is equipped with: an inertial navigation unit for obtaining the motion state data of the mixer truck; an inclination sensor for monitoring the tilting state of the mixer truck;

[0040] An unmanned aerial vehicle 140, carrying a lidar system for obtaining the three-dimensional point cloud data of the construction site;

[0041] An environment monitoring unit 150 for collecting environmental parameters such as temperature, humidity, and wind speed at the construction site;

[0042] Among them, the control server 100 conducts data interaction with each device unit through a wireless network. The data of the UWB base station 110, the inertial navigation unit, and the laser rangefinder are used to achieve high-precision positioning. The data collected in real time by the environment monitoring unit 150 is used to assist construction decision-making. The three-dimensional point cloud data obtained by the regular cruise of the unmanned aerial vehicle 140 is used to update the terrain information of the construction site.

[0043] Next, in combination with the above exemplary system architecture diagram, the intelligent monitoring method for the quality of concrete pouring in the embodiments of the present application will be described:

[0044] Please refer to Figure 2 , which is a flowchart of an intelligent monitoring method for the quality of concrete pouring in the embodiments of the present application. The method includes the following steps:

[0045] S201. Divide the pouring area into multiple grid units, and establish a grid adjacency matrix based on the spatial position relationship of each grid unit;

[0046] Among them, the grid unit represents a regular small area obtained by dividing the entire pouring area according to a preset size, and is used to achieve refined management of the pouring area; the grid adjacency matrix refers to a two-dimensional matrix used to describe the spatial adjacent relationship between grid units, and is used to represent the connectivity and relative position relationship between each grid unit.

[0047] Before starting the concrete pouring operation, it is necessary to perform grid processing on the pouring area and establish the topological relationship between the grids. Specifically, first determine the boundary range of the pouring area based on the three-dimensional point cloud data obtained by the unmanned aerial vehicle, and then divide the area into several square grid units of equal size. The side length of each grid unit is set according to the construction requirements, usually 1-2 meters. For each grid unit, determine the adjacent grid units around it and establish the corresponding connection relationship in the grid adjacency matrix. If two grid units are adjacent, the value of the corresponding matrix element is 1, otherwise it is 0.

[0048] In some embodiments, the grid division of the pouring area and the construction of the adjacency matrix can be achieved in various ways: Optionally, the boundary coordinate points of the pouring area can be determined by RTK measurement first, and then the Delaunay triangulation algorithm can be used to divide the area into regular triangular grids. Finally, the grid adjacency matrix is constructed based on the node connection relationship of the triangular grids; Optionally, based on the orthophoto image collected by the unmanned aerial vehicle, the image segmentation algorithm can be used to divide the pouring area into regular rectangular grids, and the grid adjacency matrix can be automatically generated by scanning the four-neighborhood relationship of each grid. It can be understood that other grid division methods and adjacency matrix construction methods can also be used, which are not limited here.

[0049] S202. Based on this grid adjacency matrix, establish a diffusion prediction model for predicting the flow characteristics of concrete between grid units. This diffusion prediction model takes the current performance parameters of the concrete, the current pouring environment parameters, and the real-time height difference between adjacent grid units as inputs, calculates the flow coefficient of each grid unit in this grid adjacency matrix, and determines the predicted height difference between adjacent grid units after a preset time period as the output based on this flow coefficient.

[0050] Among them, the diffusion prediction model refers to a mathematical model used to describe the flow characteristics of concrete between grid units under the action of gravity, and is used to predict the flow trend of concrete; the flow coefficient is a parameter that characterizes the flow ability of concrete under specific conditions and is used to quantify the flow characteristics of concrete; the performance parameters include characteristic indexes such as the slump, initial setting time, and temperature of the concrete; the pouring environment parameters include external conditions such as environmental temperature, humidity, and wind speed.

[0051] After completing the grid division in step S201, a mathematical model that can accurately predict the flow characteristics of concrete can be established. Specifically, first construct a diffusion equation considering the action of gravity based on the theory of fluid mechanics, and simplify the flow process of concrete between grid units into a diffusion process. Then introduce the concrete performance parameters and environmental parameters to correct the flow coefficient and establish a calculation formula for the flow coefficient. Finally, substitute the corrected flow coefficient into the diffusion equation and obtain the height difference between each grid unit after a preset time period through numerical solution.

[0052] In some embodiments, the establishment of the diffusion prediction model can be achieved in various ways: Optionally, a basic diffusion equation can be established based on Fick's diffusion law, and then by introducing correction terms considering concrete properties and environmental impacts, a complete diffusion prediction model can be constructed and solved using the finite difference method; Optionally, machine learning methods can also be used to collect a large amount of historical pouring data to establish a training set, and a deep neural network can be used to fit the flow characteristics of concrete to accurately predict the height difference. It can be understood that other mathematical models or prediction methods can also be used to predict the flow characteristics of concrete, which are not limited here.

[0053] Preferably, in some embodiments, establishing a diffusion prediction model for predicting the flow characteristics of concrete may specifically include:

[0054] Obtain the concrete slump, initial setting time, external temperature, and concrete temperature as the concrete property parameters, and obtain the temperature, humidity, and wind speed of the construction environment as the pouring environment parameters;

[0055] Construct a directed graph model based on the grid adjacency matrix, where the nodes of the graph represent grid cells, and the weights of the edges are determined by the height difference, distance, and the concrete property parameters between adjacent grid cells;

[0056] Specifically, the steps of constructing the directed graph model may include:

[0057] 1) Take each grid cell as a node of the graph, and the node attributes include the spatial coordinates and the current concrete height of the grid cell;

[0058] 2) Determine the connection relationship between nodes according to the grid adjacency matrix A(i, j). If A(i, j) = 1, then establish a two-way edge between nodes i and j;

[0059] 3) Calculate the weight w(i, j) of the edge: ; where f(Δh, d) is a function of the height difference and distance: f(Δh, d) = |Δh| / d; Δh is the height difference between nodes i and j, and d is the Euclidean distance between the two nodes; is the influence function of the concrete property parameters: ; where 、 、 are the corresponding weight coefficients.

[0060] Then, the flow coefficient D(i, j, t) between adjacent grid cells i and j at time t can be calculated according to the following formula:

[0061] ;

[0062] Among them, is the base flow coefficient, i and j respectively represent the numbers of two adjacent grid cells, S is the slump, is the initial setting time, is the concrete temperature, is the ambient temperature, H is the humidity, W is the wind speed, t is the time since pouring started, 、 、 、 、 、 and λ are corresponding weight coefficients;

[0063] Substitute the flow coefficient into the diffusion equation to establish the diffusion prediction model:

[0064] ;

[0065] Among them, K is the global correction coefficient, A(i, j) is the grid adjacency matrix element, and ΔZ(i, j, t) is the height gradient at the current moment.

[0066] S203. Obtain the coverage range of each pump truck's boom and the real-time height difference between each grid cell. According to the distance relationship between the area where the real-time height difference is greater than the first preset threshold and each pump truck, determine the main pouring area of each pump truck and its corresponding initial pumping flow rate;

[0067] Among them, the coverage range of the boom represents the maximum working area that the pump truck's boom can reach and is used to determine the effective operation range of the pump truck; the real-time height difference refers to the height difference of the concrete surface between adjacent grid cells and is used to characterize the pouring uniformity; the first preset threshold represents the height difference critical value for triggering pump truck scheduling and is used to identify the area that needs to be poured first; the main pouring area refers to the priority pouring area assigned to each pump truck and is used to achieve the coordinated operation of multiple pump trucks.

[0068] After completing the grid division in step S201, the pump trucks can be reasonably scheduled according to the height difference between the grid cells to achieve uniform pouring. Specifically, first, obtain the spatial position and attitude data of the boom through the sensor system on each pump truck, and calculate its coverage range in combination with the maximum extension length and rotation angle range of the boom. Then, calculate the real-time height difference between each grid cell based on the laser ranging data and identify the area where the height difference exceeds the first preset threshold. Finally, according to the distance relationship between these areas and each pump truck, divide the main pouring area for each pump truck according to the principle of proximity, and set the initial pumping flow rate according to the area size and height difference.

[0069] In some embodiments, the scheduling and allocation of concrete pump trucks can be achieved in various ways: Optionally, the minimum spanning tree algorithm can be adopted. Taking the positions of the concrete pump trucks as the root nodes and the grid cells with a height difference exceeding a threshold as the leaf nodes, a minimum distance tree is constructed. The grid cells on the same subtree are assigned to the corresponding concrete pump trucks, and the pumping flow rate is determined based on the scale of the subtree. Optionally, the clustering algorithm can also be used to cluster the grid cells with a height difference exceeding the threshold, and then the distance matrix between each clustering center and the concrete pump trucks is calculated. The Hungarian algorithm is used to solve the optimal matching to achieve the optimal allocation of the concrete pump trucks and the pouring areas. It can be understood that other scheduling algorithms can also be adopted to achieve the matching of the concrete pump trucks and the pouring areas, which is not limited herein.

[0070] S204. For the overlapping pouring areas of adjacent concrete pump trucks, when the real-time height difference is greater than the second preset threshold, switch the concrete pump truck on the lower height side within the overlapping pouring area to the leading concrete pump truck, and increase the pumping flow rate of the leading concrete pump truck by the preset flow adjustment step until the real-time height difference is less than the second preset threshold.

[0071] Among them, the overlapping pouring area refers to the intersection area of the coverage ranges of the distributing arms of two or more concrete pump trucks, which is used to achieve the coordinated pouring of multiple concrete pump trucks; the second preset threshold represents the critical height difference value for triggering the switching of the concrete pump truck, which is used to control the pouring uniformity of the overlapping area; the leading concrete pump truck refers to the concrete pump truck that undertakes the main pouring task in the overlapping area, which is used to coordinate the pouring rhythm of multiple concrete pump trucks; the flow adjustment step represents the increment size of the pumping flow rate adjusted each time, which is used to achieve the smooth change of the pumping flow rate.

[0072] After determining the main pouring areas of each concrete pump truck in step S203, the overlapping pouring areas of adjacent concrete pump trucks need to be focused on. Specifically, first calculate the intersection of the coverage ranges of the distributing arms of each concrete pump truck to determine the range of the overlapping pouring area. Then, the height difference within the overlapping area is monitored in real time. When it is detected that the height difference exceeds the second preset threshold, the concrete pump truck covering the lower height area is set as the leading concrete pump truck. Finally, gradually increase the pumping flow rate of the leading concrete pump truck according to the preset flow adjustment step, and at the same time reduce the pumping flow rate of other concrete pump trucks in this area until the height difference drops below the threshold.

[0073] In some embodiments, the coordinated control of the overlapping area can be achieved in various ways: Optionally, the proportional-integral control algorithm is adopted, using the height difference as the feedback signal, eliminating the steady-state error through the integral term, and calculating the required flow rate increment of the leading concrete pump truck in real time to achieve the smooth transition of the overlapping area. Optionally, based on the fuzzy control method, taking the height difference and the change trend as the input variables, a fuzzy rule base is designed to guide the flow rate adjustment to achieve the adaptive control of the leading concrete pump truck. It can be understood that other control algorithms can also be adopted to achieve the coordinated pouring of the overlapping area, which is not limited herein.

[0074] S205. Based on the real-time height differences between grid cells, update the flow coefficient in the diffusion prediction model in real time to obtain the predicted height differences between adjacent grid cells after a preset time period.

[0075] Among them, real-time update means dynamically adjusting the model parameters according to the latest monitoring data to improve the prediction accuracy; the preset time period represents the time span of the prediction, used to determine the prediction time range; the predicted height difference refers to the height difference between adjacent grid cells at a future moment predicted by the model, used to guide the optimization and adjustment of pumping parameters.

[0076] After obtaining the diffusion prediction model in step S202, while performing pump truck scheduling and flow control in step S204, the diffusion prediction model can be continuously optimized. Specifically, first collect the real-time height difference data between grid cells, compare it with the model prediction value, and calculate the prediction error. Then, based on the error information, use a parameter optimization algorithm to correct the flow coefficient in real time to improve the prediction accuracy of the model. Finally, use the updated flow coefficient to recalculate the height difference prediction result after the preset time period, providing a basis for the subsequent optimization of pumping parameters.

[0077] In some embodiments, the real-time update of model parameters can be achieved in multiple ways: Optionally, use the Kalman filter algorithm, take the measured height difference as the observed quantity, the predicted height difference as the state quantity, and estimate the optimal flow coefficient in real time through the filter to improve the prediction accuracy of the model; Optionally, use an online learning method to construct a recurrent neural network model, take the historical data sequence as the input, and continuously optimize the network parameters through the backpropagation algorithm to achieve the adaptive update of the model. It can be understood that other parameter optimization methods can also be used to achieve the real-time update of the diffusion prediction model, which is not limited here.

[0078] Since there may be performance differences in different batches of concrete, resulting in abnormal boundary flow performance, therefore, in some embodiments, the factory performance parameters of each batch of concrete can also be obtained first; based on the GPS positioning and unloading records of the tank trucks, establish the correspondence between grid cells and concrete batches, and map the performance parameters of each batch of concrete to the corresponding grid cells; extract the steel bar layout information from the BIM model, calculate the steel bar volume ratio and the main reinforcement direction of each grid cell; determine the local concrete flow direction based on the real-time height differences between the grid cells; when it is detected that the performance parameter difference between adjacent grid cells exceeds the preset performance difference threshold and the difference between the real-time height difference and the predicted height difference of the diffusion prediction model before the preset time period exceeds the prediction deviation threshold, the flow coefficient of the diffusion prediction model is corrected to: ; where: Φ(ρ, θ) is the steel bar correction function, ρ is the steel bar volume ratio, and θ is the angle between the local concrete flow direction and the main bar direction; substituting the corrected flow coefficient into the diffusion prediction model, inputting the concrete performance parameters, pouring environment parameters, and real-time height difference between adjacent grid cells at the current moment, the predicted height difference between adjacent grid cells after a preset time duration is obtained. And in the case where the performance parameter difference between adjacent grid cells does not exceed the preset performance difference threshold, the flow coefficient of the original diffusion prediction model is continuously updated and the subsequent steps are executed.

[0079] Among them, the steel bar correction function Φ(ρ, θ) can specifically be: ; where: ρ is the steel bar volume ratio, representing the volume proportion of steel bars in unit volume of concrete; θ is the angle between the local concrete flow direction and the main bar direction; γ is the directional influence coefficient, 0 ≤ γ ≤ 1; (1 - ρ) represents the reduction effect of the steel bar volume on the effective flow space; represents the influence of the angle between the flow direction and the main bar direction. The influence is the smallest when the flow direction is parallel to the main bar, at this time θ = 0°, and the influence is the largest when perpendicular, at this time θ = 90°.

[0080] S206. Based on the predicted height difference output by the diffusion prediction model, calculate the height uniformity index corresponding to different pumping parameter combinations, and select the pumping parameter combination that can make the height difference between adjacent grid cells less than the third preset threshold after the preset time duration, and adjust the pumping flow rates of each pump truck in real time until the actual height difference and the predicted height difference between each grid cell are both less than the third preset threshold.

[0081] Among them, the pumping parameter combination represents the set of working parameters such as the pumping flow rate and pumping pressure of multiple pump trucks, and is used to realize the coordinated control of multiple pump trucks; the height uniformity index is a quantitative index used to evaluate the flatness of the concrete surface in the pouring area and is used to measure the pouring quality; the third preset threshold represents the target value of the pouring quality control and is used to judge whether the expected pouring effect is achieved.

[0082] After obtaining the updated prediction result in step S205, the pumping parameters can be optimized to achieve uniform pouring. Specifically, first construct a candidate combination set containing parameters such as the pumping flow rate and pumping pressure of each pump truck, and use the diffusion prediction model to calculate the predicted height difference corresponding to each combination respectively. Then calculate the height uniformity index based on the predicted height difference, and select the optimal parameter combination that can make the height difference between adjacent grid cells less than the third preset threshold after the preset time duration. Finally, send the optimal parameter combination to each pump truck for execution, and continuously monitor the actual effect until the expected goal is achieved.

[0083] In some embodiments, for a concrete pump truck located in the overlapping pouring area, when receiving instructions to adjust the pumping flow rate for both step S204 and step S206 simultaneously, the flow rate can be adjusted only according to the instruction for adjusting the pumping flow rate in step S206.

[0084] In some embodiments, the prediction calculation based on the candidate parameter combination can be achieved in the following manner:

[0085] First, for each concrete pump truck, discretely sample its pumping flow rate and pumping pressure within their respective reasonable ranges to obtain a set of parameter combinations for a single pump truck. For example, the pumping flow rate can take a value every 10 m³ / h within the range of 30 - 120 m³ / h, and the pumping pressure can take a value every 1 MPa within the range of 4 - 12 MPa, thereby constructing a candidate set containing multiple groups of parameter combinations.

[0086] Then, through the Cartesian product operation, combine the sets of parameter combinations of each pump truck into a complete set of multi - pump - truck parameter combinations. For each group of parameter combinations, substitute it into the diffusion prediction model and calculate the predicted height difference through the following steps:

[0087] 1) Based on the real - time height of each grid cell currently and the pumping flow rate in the parameter combination, calculate the concrete increment of each grid cell within a preset time period.

[0088] 2) Use the flow coefficient of the diffusion prediction model to calculate the height change caused by concrete flow within a preset time period.

[0089] Specifically, the steps of calculating the height change using the flow coefficient of the diffusion prediction model include: determine the adjacent cells of each grid cell according to the grid adjacency matrix; for each pair of adjacent grid cells (i, j), based on the current flow coefficient k_ij and height difference Δh_ij, calculate the flow rate q_ij per unit time using the following diffusion equation: q_ij = k_ij * Δh_ij; multiply the flow rate per unit time by the preset time period Δt to obtain the total flow rate Q_ij within the preset time period: Q_ij = q_ij * Δt; for each grid cell i, accumulate the flow rates between it and all its adjacent grid cells j to obtain the net height change amount ΔH_i of this grid cell caused by concrete flow: ΔH_i = Σ(Q_ij) / A_i; where A_i is the area of grid cell i. It can be understood that this calculation method is based on a simplified diffusion model, and in practical applications, non - linear characteristics such as the viscosity and yield stress of concrete can also be considered to correct the calculation formula, which is not limited here.

[0090] 3) Superimpose the concrete increment and the height change caused by flow to obtain the predicted height of each grid cell after a preset time period.

[0091] 4) Calculate the predicted height difference between adjacent grid cells.

[0092] Finally, evaluate the prediction results for all parameter combinations and select the optimal combination that can meet the requirements for the height difference. It can be understood that other methods can also be used to implement the prediction calculation of parameter combinations, which is not limited here.

[0093] In some embodiments, the height uniformity index can be calculated based on the predicted height difference in the following manner: First, for the prediction results of each set of pumping parameter combinations, calculate the height uniformity index using the following steps: 1) Calculate the root mean square error (RMSE) of the predicted height differences for all pairs of adjacent grid cells in the entire pouring area: RMSE = sqrt(Σ(Δh_ij²) / N); where Δh_ij is the predicted height difference between grid cell i and its adjacent cell j, and N is the total number of all adjacent cell pairs. 2) Calculate the local height coefficient (LHC): For each grid cell i, calculate the maximum height difference max(|Δh_ij|) with all its adjacent cells j; LHC = Σmax(|Δh_ij|) / M; where M is the total number of grid cells. This index reflects the degree of height mutation in the local area. 3) Calculate the global height standard deviation (GHS): First calculate the average value h_avg of the predicted heights of all grid cells; then calculate the standard deviation: GHS = sqrt(Σ(h_i - h_avg)² / M); where h_i is the predicted height of grid cell i. This index reflects the overall flatness level. 4) Comprehensively calculate the height uniformity index (HUI): HUI = w1*(1 - RMSE / RMSE_max) + w2*(1 - LHC / LHC_max) + w3*(1 - GHS / GHS_max); where w1, w2, and w3 are weight coefficients, and w1 + w2 + w3 = 1; RMSE_max, LHC_max, and GHS_max are the maximum allowable values of each index respectively. The value range of this height uniformity index HUI is 0 - 1, and the larger the value, the more uniform the predicted pouring effect. It can be understood that other statistical indices or calculation methods can also be used to evaluate the height uniformity, which is not limited here.

[0094] In some embodiments, the selection of the optimal parameter combination can be achieved in the following manner: First, screen the prediction results of all candidate parameter combinations and eliminate the combinations that do not meet the constraint conditions: 1) Eliminate the parameter combinations where the predicted height difference between any two adjacent grid cells exceeds the third preset threshold; 2) Eliminate the parameter combinations where the pumping flow rate or pressure exceeds the safe operating range of the equipment; 3) Eliminate the parameter combinations that may cause local stress concentration, such as the combinations with a large difference in the flow rates of adjacent concrete pump trucks. Then, conduct a multi-objective optimization evaluation on the remaining feasible parameter combinations: 1) Calculate the height uniformity objective function J1: J1 = α1*RMSE + α2*LHC + α3*GHS; where α1, α2, and α3 are weight coefficients; 2) Calculate the energy consumption objective function J2: J2 = Σ(β1*Q_i + β2*P_i); where Q_i and P_i are the pumping flow rate and pressure of concrete pump truck i respectively, and β1 and β2 are weight coefficients; 3) Calculate the adjustment amplitude objective function J3: J3 = Σ|Q_i - Q_i_current| / Q_i_max; where Q_i_current is the current flow rate and Q_i_max is the maximum allowable flow rate; 4) Construct the comprehensive evaluation function: J = w1*J1 + w2*J2 + w3*J3; where w1, w2, and w3 are the weights of each objective. Finally, select the parameter combination with the minimum value of the comprehensive evaluation function J as the optimal solution. It can be understood that other methods can also be used to select the optimal parameter combination, which is not limited herein.

[0095] In the embodiments of the present application, due to the adoption of the diffusion prediction model based on the grid adjacency matrix and the multi-concrete pump truck collaborative control mechanism, the concrete flow coefficient can be calculated in real time and the pumping parameters can be dynamically adjusted, effectively solving the technical problems of difficult real-time grasp of the concrete flow trend and poor coordination of multi-concrete pump truck pouring during the construction of mountain viaducts, and thus realizing the uniform distribution of concrete in a large-area pouring area.

[0096] In the above embodiments, the intelligent monitoring system for concrete pouring quality can achieve uniform pouring through the diffusion prediction model and multi-concrete pump truck collaborative control. In practical applications, when implementing the above monitoring method, due to the complex mountain terrain, the positioning signals of the concrete pump trucks are easily blocked and the spatial positions of the boom are difficult to accurately determine, which may lead to inaccurate measurement of the positions of the concrete pump trucks and the attitudes of the booms, and thus affect the concrete pouring quality. In the following embodiments, a fusion positioning scheme of UWB base stations and inertial navigation and a multi-sensor collaborative measurement mechanism for the boom can be adopted to solve this technical problem and improve the positioning accuracy of the concrete pump truck positions and the measurement accuracy of the boom attitudes.

[0097] Please refer to Figure 3 , which is another flow schematic diagram of the intelligent monitoring method for concrete pouring quality in the embodiments of the present application.

[0098] S301. Obtain the real-time positioning data of the pump truck by the UWB base station, and monitor the UWB signal quality. When it is detected that the signal quality decreases due to mountain blockage, switch the positioning scheme from UWB positioning to inertial navigation positioning, and record the last set of valid UWB positioning data before the switch as the initial reference position;

[0099] Among them, the UWB base station refers to an ultra-wideband positioning base station deployed at the highest point of the pouring area, which is used to provide high-precision positioning signals; the signal quality refers to the intensity and stability indicators of the UWB positioning signal, which are used to evaluate the positioning accuracy; the initial reference position refers to the last reliable position data before switching the positioning scheme, which is used as the reference point for subsequent inertial navigation positioning.

[0100] In the mountain construction environment, due to the complex terrain, the UWB signal is easily blocked, and a reliable positioning scheme switching mechanism needs to be established. Specifically, first, the pump truck is positioned in real time by multiple UWB base stations, and at the same time, signal quality indicators such as signal strength and multipath effect are monitored. When it is detected that the signal quality decreases to below the preset threshold due to mountain blockage, the system automatically switches the positioning scheme to the inertial navigation mode, and uses the last set of high-quality UWB positioning data before the switch as the initial reference position for inertial navigation.

[0101] In some embodiments, the intelligent switching of the positioning scheme can be realized in various ways: Optionally, a signal quality evaluation algorithm can be adopted. By calculating the weighted scores of indicators such as signal strength, carrier-to-noise ratio, and geometric dilution of precision, when the score is lower than the threshold, the switch is triggered, and at the same time, the Kalman filtering algorithm is used to smooth the last several sets of UWB data to obtain a reliable initial reference position; Optionally, based on the deep learning method, a neural network model can be trained to identify the signal quality degradation mode, predict possible signal blockages in advance, realize seamless switching to the inertial navigation mode, and use the Bayesian estimation method to fuse the UWB data at multiple moments to determine the optimal initial reference position. It can be understood that other ways can also be adopted to realize the switching of the positioning scheme and the determination of the initial reference position, which are not limited here.

[0102] S302. Obtain the acceleration and angular velocity data of the inertial navigation unit, calculate the relative displacement relative to the initial reference position, and obtain the inclination data of the boom through the inclination sensor, and calculate the real-time attitude angle of the boom;

[0103] Among them, the inertial navigation unit refers to an inertial measurement device installed on the pump truck, which is used to obtain motion state data; the acceleration and angular velocity data refer to physical quantities that describe the motion characteristics of the pump truck and are used to calculate displacement changes; the relative displacement refers to the spatial position change relative to the initial reference position and is used to update the position of the pump truck; the attitude angle refers to the direction angle of the boom in space and is used to determine the pouring direction.

[0104] After switching to the inertial navigation mode, continuous tracking of the boom pump truck's position needs to be achieved through multi-sensor data fusion. Specifically, first, triaxial acceleration and angular velocity data are obtained from the inertial navigation unit, and the displacement change of the boom pump truck relative to the initial reference position is calculated through integral operations. At the same time, an inclination sensor is used to measure the pitch angle and swing angle of the boom, and combined with the kinematic model of the robotic arm, the real-time attitude angle of the boom in space is calculated.

[0105] In some embodiments, precise calculation of the position and attitude can be achieved in various ways: Optionally, a zero-velocity correction algorithm can be adopted to eliminate the acceleration integration error by detecting the stationary state of the boom pump truck, and the quaternion method is used to represent the attitude to avoid gimbal lock. Finally, a high-precision position solution is obtained by fusing multi-source data through an extended Kalman filter; Optionally, based on deep learning methods, an end-to-end neural network model can be constructed to directly estimate the displacement and attitude from the original sensor data, and the weights of different sensors are adaptively adjusted through an attention mechanism to achieve robust state estimation. It can be understood that other methods can also be used to calculate the position and attitude, which are not limited here.

[0106] S303. Based on the initial reference position, the relative displacement, the inclination data, and the vertical distance data measured by the laser rangefinder from the discharge port of the boom to the concrete surface, determine the precise spatial position of the discharge port of the boom.

[0107] Among them, the laser rangefinder refers to a distance measurement device installed at the end of the boom for measuring the height of the discharge port; the vertical distance data refers to the height value from the discharge port to the concrete surface for determining the pouring point position; the spatial position refers to the specific position of the discharge port of the boom in the three-dimensional coordinate system for guiding precise pouring.

[0108] After obtaining various sensor data, it is necessary to determine the precise position of the discharge port of the boom through multi-source data fusion. Specifically, first, the current position of the boom pump truck is determined based on the initial reference position and the relative displacement calculated by inertial navigation, then the spatial configuration of the robotic arm is calculated in combination with the inclination data of the boom, and finally the height coordinate of the discharge port is corrected through the vertical distance data measured by the laser rangefinder to achieve a positioning accuracy of sub-meter level.

[0109] In some embodiments, the accurate calculation of the position of the discharge port can be achieved in various ways: Optionally, a forward kinematics model of the robotic arm can be adopted, and the coordinate transformation relationship of the cloth arm can be established by the DH parameter method. The end position can be calculated by combining the real-time angles of each joint and the arm length, and the spatial coordinates of the discharge port can be obtained by using the laser ranging data for height compensation; Optionally, based on the visual SLAM technology, a local three-dimensional map can be constructed by the binocular camera installed on the cloth arm, and the real-time tracking and positioning of the discharge port can be realized by combining the feature point matching and the pose graph optimization algorithm. It can be understood that other methods can also be used to determine the position of the discharge port, which is not limited here.

[0110] S304. Calculate the coverage range of each pump truck's cloth arm according to the spatial position, attitude angle, maximum extension length and rotation angle range of the cloth arm;

[0111] Among them, the maximum extension length represents the farthest distance that the cloth arm can extend and is used to determine the working radius; the rotation angle range refers to the angle limit by which the cloth arm can rotate and is used to determine the scanning range; the coverage range represents the set of all spatial points that the cloth arm can reach and is used to plan the pouring area.

[0112] After determining the position of the discharge port, it is necessary to calculate the effective working range of each pump truck. Specifically, first determine the working origin according to the spatial position of the cloth arm, then determine the current working direction based on the attitude angle, calculate the radial coverage range in combination with the maximum extension length of the cloth arm, and finally determine the circumferential coverage range according to the rotation angle limit, so as to obtain a complete three-dimensional working space.

[0113] In some embodiments, the calculation of the coverage range can be achieved in various ways: Optionally, a geometric modeling method can be adopted. The cloth arm is simplified into a multi-segment link structure, and a large number of configurations are generated in the joint angle space by Monte Carlo sampling, mapped to the Cartesian space to obtain a discrete set of reachable points, and finally a continuous coverage area is constructed by the convex hull algorithm; Optionally, based on the analytic geometry method, the motion of the cloth arm is described as a parametric equation, and the analytic expression of the working space is obtained by solving the boundary equation under the constraint conditions, so as to achieve the accurate calculation of the coverage range. It can be understood that other methods can also be used to calculate the coverage range, which is not limited here.

[0114] S305. Divide the pouring area into multiple grid cells, and establish a grid adjacency matrix based on the spatial position relationship of each grid cell;

[0115] Before the execution of step S304 ends, this step S305 can be executed simultaneously. Step S305 is similar to step S201 and will not be elaborated here.

[0116] S306. Based on the grid adjacency matrix, establish a diffusion prediction model for predicting the flow characteristics of concrete between grid cells. The diffusion prediction model takes the current performance parameters of the concrete, the current pouring environment parameters, and the real-time height difference between adjacent grid cells as inputs, calculates the flow coefficient of each grid cell in the grid adjacency matrix, and determines the predicted height difference between adjacent grid cells after a preset time duration as the output based on the flow coefficient;

[0117] Similar to step S202, it will not be elaborated here.

[0118] S307. Calculate the real-time height difference between each grid cell based on the concrete surface elevation data collected by the laser rangefinder;

[0119] Among them, the elevation data represents the three-dimensional terrain information of the concrete surface and is used to characterize the undulating state of the pouring surface; the real-time height difference refers to the height drop between adjacent grid cells and is used to evaluate the pouring uniformity; the grid cell represents the basic calculation unit for dividing the pouring area and is used to achieve refined control.

[0120] After performing step S305 to divide the grid cells, the height difference between each grid cell can be calculated. Specifically, first, the laser rangefinder on the placing boom is used to scan and measure within the working range to obtain the discrete elevation point cloud data of the concrete surface. Then, the point cloud data is mapped into the pre-divided grid cells through a spatial interpolation algorithm, and the average elevation of each grid is calculated. Finally, the height difference between adjacent grid cells is calculated to guide the subsequent pumping control.

[0121] In some embodiments, the height difference can be calculated in various ways: Optionally, the Kriging interpolation algorithm can be used to construct a variogram model based on the spatial correlation of the measurement points, obtain the elevation values of the grid nodes through the optimal linear unbiased estimation, and finally calculate the height gradient between adjacent grids through finite differences; Optionally, based on the deep learning method, a point cloud processing network can be used to directly reconstruct a continuous surface model from the original ranging data, and the height difference can be calculated by extracting local features through a graph convolutional network. It can be understood that other ways can also be used to calculate the height difference, which is not limited here.

[0122] S308. Determine the main pouring area of each pump truck and its corresponding initial pumping flow rate according to the distance relationship between the area where the real-time height difference is greater than the first preset threshold and each pump truck;

[0123] S309. For the overlapping pouring area of adjacent concrete pump trucks, when the real-time height difference is greater than the second preset threshold, switch the pump truck on the lower side in the overlapping pouring area to the leading pump truck, and increase the pumping flow rate of the leading pump truck according to the preset flow adjustment step until the real-time height difference is less than the second preset threshold;

[0124] S310. Based on the real-time height differences between grid cells, update the flow coefficient in the diffusion prediction model in real time to obtain the predicted height differences between adjacent grid cells after a preset time period;

[0125] S311. Based on the predicted height differences output by the diffusion prediction model, calculate the height uniformity indexes corresponding to different combinations of pumping parameters, select the combination of pumping parameters that can make the height difference between adjacent grid cells less than the third preset threshold after the preset time period, and adjust the pumping flow rates of each pump truck in real time until both the actual height difference and the predicted height difference between grid cells are less than the third preset threshold;

[0126] Steps S308 - S311 are similar to steps S203 - S206 and will not be elaborated here.

[0127] S312. Based on the vibration sensor array arranged at each grid cell, collect the concrete vibration response characteristics;

[0128] Among them, the vibration sensor array refers to a plurality of vibration sensors regularly arranged in the pouring area, used to collect vibration signals; the vibration response characteristics refer to the vibration waveform characteristics generated by the concrete under the action of external forces, used to characterize the flow state of the concrete; the response characteristics include parameters such as amplitude, frequency, and phase, used to evaluate the compactness of the concrete.

[0129] During the execution of step S311 to complete the concrete pouring or during the execution process, the pouring quality can be evaluated. Specifically, first, arrange the vibration sensor array at the characteristic positions of the grid cells, and synchronously collect the vibration response signals of the concrete at a high sampling rate. Then, preprocess the collected original signals, including operations such as filtering and noise reduction, and baseline correction, and extract the time-domain and frequency-domain characteristic parameters. Finally, use these characteristic parameters as the input data for the evaluation of the concrete compactness.

[0130] In some embodiments, the acquisition and processing of vibration response characteristics can be achieved in various ways: Optionally, a distributed data acquisition system can be adopted to realize multi-point synchronous sampling through a wireless sensor network, use wavelet transform for signal denoising and feature extraction, and finally reduce the feature dimension through principal component analysis to improve the calculation efficiency; Optionally, based on an edge computing architecture, signal preprocessing and feature extraction can be completed at the sensor node end, automatic learning of features can be realized through a lightweight neural network, and finally the extracted features can be transmitted to the central processing unit through the network. It can be understood that other ways can also be adopted to obtain vibration response characteristics, which are not limited herein.

[0131] S313. Dynamically calculate the density of each grid unit based on the spectral analysis of the concrete vibration response characteristics;

[0132] Among them, spectral analysis refers to a mathematical method for decomposing a vibration signal in the frequency domain, used to extract frequency characteristics; density refers to the filling degree of voids inside the concrete, used to evaluate the pouring quality; dynamic calculation refers to a calculation process of real-time update, used to achieve online monitoring.

[0133] After obtaining the vibration response characteristics, it is necessary to evaluate the density of the concrete through signal processing methods. Specifically, first perform a fast Fourier transform on the vibration signal to obtain the spectral distribution of the signal. Then analyze the energy distribution characteristics of the spectrum, including parameters such as the main frequency, energy ratio, and harmonic ratio. Finally, based on these spectral characteristic parameters, calculate the density value of the concrete through a pre-established mapping relationship. For example, first perform a fast Fourier transform on the vibration signal to obtain the spectrum, and then extract the following characteristic parameters: Main frequency characteristic: Determine the main frequency f0 by finding the frequency point corresponding to the maximum energy in the spectrum, and calculate the main frequency energy ratio η0 = E0 / Et, where E0 is the main frequency energy and Et is the total energy; Energy ratio characteristic: Divide the spectrum into a low-frequency band (0 - f1), a middle-frequency band (f1 - f2), and a high-frequency band (above f2), and calculate the energy ratio of each band ηL = EL / Et, ηM = EM / Et, ηH = EH / Et; Harmonic ratio characteristic: Extract the energy En at the nth harmonic frequency point fn = n * f0 of the main frequency, and calculate the harmonic ratio γn = En / E0; Frequency band energy characteristic: Calculate the normalized energy spectral density within each characteristic frequency band [fa, fb]: , f ∈ [fa, fb], where X(f) is the Fourier transform of the signal.

[0134] Establish a density calculation model based on the above characteristic parameters:

[0135] ρ = w0 * η0 + wL * ηL + wM * ηM + wH * ηH + Σ(wn * γn) + wp * P(f),

[0136] Among them, w0, wL, wM, wH, wn, and wp are the weight coefficients of each characteristic parameter, which are obtained through experimental calibration. This model comprehensively considers the frequency-domain characteristics of the vibration signal and can more accurately reflect the compactness of the concrete.

[0137] In some embodiments, the dynamic calculation of the compactness can be achieved in various ways: Optionally, a time-frequency analysis method can be adopted. The time-varying spectrum of the signal is obtained through short-time Fourier transform, the energy change of the characteristic frequency band is extracted by combining empirical mode decomposition, and finally the mapping relationship from the spectrum characteristics to the compactness is established through support vector regression; Optionally, based on the deep learning method, a one-dimensional convolutional neural network can be used to directly learn the compactness characteristics from the original vibration signal, and the time series dependence relationship is captured through a recurrent neural network to achieve the end-to-end prediction of the compactness. It can be understood that other ways can also be adopted to calculate the compactness, which is not limited here.

[0138] S314. When it is detected that the compactness of the local area is lower than the preset compactness threshold, the pumping flow rate of the relevant pump truck is adaptively adjusted based on the area size of the low-compactness area, the compactness deviation value, and the coverage of the surrounding pump trucks until the compactness reaches the standard.

[0139] Among them, the preset compactness threshold represents the standard value for judging whether the compactness is qualified and is used to trigger the regulation operation; the compactness deviation value refers to the difference between the actual compactness and the target value and is used to determine the supplementary amount; the adaptive adjustment represents the process of automatically adjusting according to the real-time state and is used to achieve intelligent control.

[0140] When it is found that the compactness of the local area is insufficient, remedial measures need to be taken to improve the pouring quality. Specifically, first determine the scope and degree of the low-compactness area and calculate the volume of concrete that needs to be supplemented. Then analyze the coverage of the pump trucks around this area and select the most suitable pump truck for supplementary pouring. Finally, increase the pumping flow rate in a step-by-step manner while monitoring the change trend of the compactness to ensure that no new problems are caused by excessive supplementation.

[0141] In some embodiments, the adaptive adjustment of the pumping flow rate can be achieved in various ways: Optionally, a fuzzy control algorithm can be adopted. The compactness deviation and the change rate are used as input variables, and the flow rate adjustment amount is calculated through fuzzy rule inference to achieve smooth flow control; Optionally, based on the reinforcement learning method, the adjustment of the pumping flow rate is modeled as a Markov decision process, and the adjustment strategy is optimized through a deep learning algorithm to achieve intelligent flow control. It can be understood that other ways can also be adopted to adjust the pumping flow rate, which is not limited here.

[0142] Preferably, in some embodiments, the overlap between the coverage range of the placing boom of each concrete pump truck and the low-density area can be calculated, and the concrete pump truck with the largest overlap is selected as the target pump truck; according to the area size and density deviation value of the low-density area, combined with the concrete performance parameters, the required supplementary concrete volume is determined; based on the supplementary concrete volume and the preset maximum pumping time, the target pumping flow rate is calculated; the pumping flow rate of the target pump truck is gradually increased from the current value to the target flow rate in a way of gradually increasing the pumping flow rate, and the increment step size does not exceed the preset maximum flow rate change rate until the density reaches the preset density threshold; when it is detected that the density growth rate exceeds the preset growth rate threshold, the pumping flow rate is reduced to 80% of the current flow rate value to reduce local stress concentration.

[0143] In the embodiments of the present application, due to the adoption of the fusion positioning scheme of UWB base stations and inertial navigation and the collaborative measurement mechanism of multiple sensors on the placing boom, it is possible to automatically switch to inertial navigation positioning when the UWB signal quality deteriorates due to mountain occlusion, effectively solving the technical problems that the positioning signal of the concrete pump truck is vulnerable to occlusion in mountainous environments and the spatial position of the placing boom is difficult to accurately determine, and thus realizing the continuous and reliable positioning of the pump truck position and the accurate spatial positioning of the placing boom discharge port. And due to the adoption of the gradually adjusted pumping flow rate mechanism based on density and the stress concentration prevention strategy, it is possible to dynamically evaluate the density according to the vibration response characteristics and intelligently control the pumping flow rate, effectively solving the technical problems of insufficient density in local areas and easy generation of stress concentration during the concrete pouring process, and thus realizing the accurate control of the concrete density.

[0144] The control server in the intelligent monitoring system for concrete pouring quality provided by the embodiments of the present application is introduced below. Figure 4 It is an exemplary hardware structure diagram of the control server provided by the embodiments of the present application.

[0145] In some embodiments, the control server 100 includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. The computer program, when executed by the processor, implements the method in the embodiments of the present application.

[0146] Those skilled in the art can understand, Figure 4The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. Specifically, the computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0147] As described above, the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0148] In the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0149] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

Claims

1. A method for intelligent monitoring of concrete pouring quality, characterized in that: include: The pouring area is divided into a plurality of grid units, and a grid adjacency matrix is ​​established based on the spatial position relationship of each grid unit; The pouring area is divided into a plurality of grid units, and a grid adjacency matrix is ​​established based on the spatial position relationship of each grid unit; Based on the grid adjacency matrix, a diffusion prediction model for predicting the flow characteristics of concrete between grid units is established. The diffusion prediction model takes the current performance parameters of concrete, the current pouring environment parameters and the real-time height difference between adjacent grid units as input, calculates the flow coefficient of each grid unit in the grid adjacency matrix, and determines the predicted height difference between adjacent grid units after a preset time based on the flow coefficient as output; the diffusion prediction model is: ； Wherein, i and j represent the numbers of two adjacent grid units, t is the time from the start of pouring, Δh(i, j, t+Δt) is the predicted height difference between adjacent grid units after the preset time Δt, K is the global correction coefficient, D(i, j, t) is the flow coefficient between adjacent grid units i and j at time t, which is determined according to the current performance parameters of concrete and the current pouring environment parameters, A(i, j) is the grid adjacency matrix element, and ΔZ(i, j, t) is the height gradient at the current moment; Obtain the coverage of the distributing arm of each pump truck and the real-time height difference between each grid unit, and determine the main pouring area of ​​each pump truck and its corresponding initial pumping flow rate according to the distance relationship between the area where the real-time height difference is greater than the first preset threshold and each pump truck; For the overlapping pouring area of ​​adjacent pump trucks, when the real-time height difference is greater than the second preset threshold, the pump truck located at the lower side of the overlapping pouring area is switched to the leading pump truck, and the pumping flow of the leading pump truck is increased according to the preset flow adjustment step length until the real-time height difference is less than the second preset threshold; Based on the real-time height difference between each grid unit, the flow coefficient in the diffusion prediction model is updated in real time to obtain the predicted height difference between adjacent grid units after a preset time period; Based on the predicted height difference output by the diffusion prediction model, the height uniformity index corresponding to different pumping parameter combinations is calculated, and the pumping parameter combination that can make the height difference between adjacent grid units less than the third preset threshold after a preset time is selected to adjust the pumping flow of each pump truck in real time until the actual height difference and the predicted height difference between each grid unit are both less than the third preset threshold.

2. The method according to claim 1, characterized in that The establishment of a diffusion prediction model for predicting concrete flow characteristics specifically includes: Acquire the concrete slump, initial setting time, external temperature and concrete temperature as concrete performance parameters, and acquire the temperature, humidity and wind speed of the construction environment as the casting environment parameters; Constructing a directed graph model based on the grid adjacency matrix, wherein the nodes of the graph represent grid units, and the weights of the edges are determined by the height difference, distance and the concrete performance parameters of adjacent grid units; The flow coefficient D(i, j, t) between adjacent grid cells i and j at time t is calculated according to the following formula: ； in, is the basic flow coefficient, i and j represent the numbers of two adjacent grid cells, S is the slump, is the initial setting time, is the concrete temperature, is the ambient temperature, H is the humidity, W is the wind speed, , , , , , and λ are the corresponding weight coefficients; Substituting the flow coefficient into the diffusion equation, the diffusion prediction model is established.

3. The method according to claim 1, characterized in that UWB base stations are deployed at multiple commanding heights in the pouring area, and the pump truck is equipped with an inertial navigation unit, a tilt sensor, and a laser rangefinder; the acquisition of the coverage range of each pump truck's placing arm and the real-time height difference between each grid unit specifically includes: Obtain the real-time positioning data of the pump truck from the UWB base station and monitor the UWB signal quality. When it is detected that the signal quality has decreased due to mountain obstruction, switch the positioning scheme from UWB positioning to inertial navigation positioning, and record the last set of valid UWB positioning data before the switch as the initial reference position; Acquire the acceleration and angular velocity data of the inertial navigation unit, calculate the relative displacement relative to the initial reference position, and obtain the inclination data of the fabric arm through the inclination sensor to calculate the real-time attitude angle of the fabric arm; Determine the precise spatial position of the material discharging port of the material discharging arm based on the initial reference position, the relative displacement, the inclination data, and the vertical distance data from the material discharging port of the material discharging arm to the concrete surface measured by the laser rangefinder; Calculate the coverage of each pump truck’s dispensing arm based on its spatial position, attitude angle, maximum extension length and rotation angle range; Based on the concrete surface elevation data collected by the laser rangefinder, the real-time height difference between each grid unit is calculated.

4. The method according to claim 1, characterized in that: The method of updating the flow coefficient in the diffusion prediction model in real time based on the real-time height difference between each grid unit to obtain the predicted height difference between adjacent grid units after a preset time period specifically includes: Obtain the factory performance parameters of each batch of concrete; Based on the tank truck GPS positioning and unloading records, the correspondence between grid units and concrete batches is established, and the performance parameters of each batch of concrete are mapped to the corresponding grid units; Extract reinforcement layout information from the BIM model and calculate the reinforcement volume ratio and main reinforcement direction of each grid unit; Determining the local concrete flow direction based on the real-time height difference between the grid cells; When it is detected that the performance parameter difference of adjacent grid cells exceeds the preset performance difference threshold and the difference between the real-time height difference and the predicted height difference of the diffusion prediction model before the preset time exceeds the prediction deviation threshold, the flow coefficient of the diffusion prediction model is corrected to: ； Where: Φ(ρ, θ) is the reinforcement correction function, ρ is the reinforcement volume ratio, θ is the angle between the local concrete flow direction and the main reinforcement direction; The corrected flow coefficient is substituted into the diffusion prediction model, and the concrete performance parameters, pouring environment parameters and real-time height difference between adjacent grid units at the current moment are input to obtain the predicted height difference between adjacent grid units after a preset time.

5. The method according to claim 4, characterized in that The steel bar correction function Φ(ρ,θ) is specifically: ； Where: ρ is the steel volume ratio, which indicates the volume ratio of steel bars in a unit volume of concrete; θ is the angle between the local concrete flow direction and the main reinforcement direction; γ is the directional influence coefficient, 0≤γ≤1; (1-ρ) indicates the reduction effect of the steel volume on the effective flow space; It represents the influence of the angle between the flow direction and the main reinforcement direction. When the flow direction is parallel to the main reinforcement, the influence is the smallest, at this time θ=0°, and when it is perpendicular, the influence is the largest, at this time θ=90°.

6. The method according to claim 1, characterized in that The method further comprises: Based on the vibration sensor array arranged at each grid unit, the vibration response characteristics of concrete are collected; Dynamically calculating the density of each grid unit based on the frequency spectrum analysis of the concrete vibration response characteristics; When it is detected that the density of a local area is lower than the preset density threshold, the pumping flow of the relevant pump trucks is adaptively adjusted until the density meets the standard based on the area size of the low-density area, the density deviation value and the coverage of surrounding pump trucks.

7. The method according to claim 6, characterized in that Based on the area size of the low-density area, the density deviation value and the coverage of surrounding pump trucks, the pumping flow rate of the relevant pump trucks is adaptively adjusted until the density reaches the standard, specifically including: Calculate the overlap between the coverage of each pump truck's distributing arm and the low-density area, and select the pump truck with the largest overlap as the target pump truck; Determine the required volume of additional concrete based on the area size and density deviation value of the low-density area and the concrete performance parameters; Calculate the target pumping flow rate based on the added concrete volume and the preset maximum pumping time; The pumping flow rate of the target pump truck is gradually increased from the current value to the target flow rate by gradually increasing the pumping flow rate, and the incremental step length does not exceed the preset maximum flow rate change rate, until the density reaches the preset density threshold value; When it is detected that the density growth rate exceeds the preset growth rate threshold, the pumping flow rate is reduced to 80% of the current flow rate value to reduce local stress concentration.

8. An intelligent monitoring system for concrete pouring quality, characterized in that: The intelligent monitoring system for concrete pouring quality includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product runs on a concrete pouring quality intelligent monitoring system, the concrete pouring quality intelligent monitoring system is enabled to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the intelligent monitoring system for concrete pouring quality, the intelligent monitoring system for concrete pouring quality executes the method according to any one of claims 1 to 7.

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