A method and system for pumping concrete for ultra-high lattice columns

Abstract

This invention provides a method and system for pumping concrete for ultra-high lattice columns, relating to the field of building construction pumping control technology. The invention aims to solve the technical problems of high risk of pipe blockage and easy interruption in traditional single-point pumping. The method includes: acquiring a scheme containing primary and backup pumping paths; real-time acquisition of pumping pressure time-series data for the primary path, analyzing pressure gradients, fluctuations, and other characteristics, and combining them with a prediction model to calculate and make real-time judgments on an emergency switching index characterizing the risk of pipe blockage; when the index exceeds the limit, the system automatically triggers a switch to the optimal backup path, and ensures the absolute continuity of concrete pouring through intelligent routing and smooth flow control; furthermore, the system creates a digital construction log and adaptively optimizes the risk assessment model parameters using accumulated data. This invention effectively avoids the risk of single-point failure and improves the safety, reliability, and overall intelligence level of pumping construction for ultra-high lattice columns.

Description

Technical Field

[0001] This invention relates to the field of pumping control technology in building construction, specifically to a method and system for pumping concrete for ultra-high lattice columns. Background Technology

[0002] In modern high-rise buildings, three-limb steel tube lattice columns are widely used as a key composite structural load-bearing component due to their high load-bearing capacity, excellent seismic performance, and efficient space utilization. These components typically consist of three large-diameter steel tubes as main tubes, connected by web members to form a stable spatial lattice system. High-strength, high-flowability self-compacting concrete is poured inside to form a steel-concrete composite structure.

[0003] However, ultra-high lattice columns often reach heights of hundreds of meters. Combined with the high viscosity and stringent flow requirements of the high-grade concrete used (such as C70 and C80), the pumping pressure required far exceeds that of conventional buildings. While the theoretically calculated pumping pressure is already considerable, in actual production, various factors such as batch variations in concrete materials, aggregate gradation, admixture effects, and ambient temperature often result in actual flow resistance exceeding the theoretical value. Industry experience indicates that at least a 20% material effect margin should typically be considered. Furthermore, to ensure absolute safety during construction, a safety factor of at least 30% must be factored in. After all these factors are considered, the actual required pumping system outlet pressure may approach or even exceed 30 MPa. This places extremely stringent demands on the pressure-bearing capacity of concrete pumping equipment and piping systems, as well as the safety of the construction process.

[0004] Traditional construction methods typically involve pumping grout through a single injection port at the base of the lattice column. The biggest drawback of this method is its risk of a "single point of failure." If, during continuous pumping for several hours, this injection port or the connected pump pipe becomes blocked, the entire column pouring will be immediately interrupted. For the already poured concrete, if pumping cannot be resumed before initial setting, cold joints will form, severely impacting the structural integrity. Addressing blockages in extremely long vertical pipes is not only technically extremely difficult and time-consuming, but can also lead to the scrapping of the entire expensive steel structure, causing incalculable economic losses and significant project delays.

[0005] In ultra-tall columns, concrete needs to be lifted to an extremely high position in one go. During this process, air can easily be trapped inside the concrete, forming air pockets. As the concrete level rises, these air pockets may accumulate at the top of the column, resulting in loose concrete and voids in the top area, thus failing to reach the design strength and posing a potential safety hazard to the overall structure.

[0006] Therefore, there is an urgent need for a new construction method and system that can fundamentally solve the problems of ultra-high pumping pressure, single-point grouting failure risk, and casting quality control, so as to ensure the safety, reliability and high quality of such critical structure construction. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for pumping concrete for ultra-high lattice columns to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A concrete pumping construction method for ultra-high lattice columns includes the following steps: obtaining preset lattice column structural parameters and corresponding pumping construction schemes, wherein the pumping construction schemes include a main pumping path and at least one backup pumping path. During the pumping of concrete along the main pumping path, at least one process status data is collected simultaneously to form a time series data stream; based on a preset risk assessment model, the time series data stream is analyzed to extract multiple pumping risk characteristic parameters. Based on multiple pumping risk characteristic parameters and a preset comprehensive decision-making algorithm, the probability of pipe blockage risk, which characterizes the risk of pipe blockage, and the emergency switching index, which characterizes the stability of the current pumping process, are calculated. Based on the emergency switching index, the final pumping path control command is generated and executed.

[0009] Furthermore, process status data is collected, including: collecting pumping pressure data through pressure sensors deployed at the outlet of the main pumping path; extracting multiple pumping risk characteristic parameters, including: performing time-series analysis on the pumping pressure data to extract pressure gradient features and pressure fluctuation features; inputting the pressure gradient features and pressure fluctuation features into a pre-trained pipe blockage risk prediction model, and outputting a pipe blockage risk probability to characterize the likelihood of pipe blockage.

[0010] Furthermore, the emergency switching index is determined, including: calculating a first risk component based on pressure gradient characteristics, which is proportional to the rate of pressure rise, to quantify the risk of rapid pressure changes; calculating a second risk component based on pressure fluctuation characteristics, which is proportional to the variance of the pressure value within a preset time window, to quantify the instability of the pumping process; and obtaining the emergency switching index by weighting the first risk component, the second risk component, and the probability of pipe blockage with preset weights.

[0011] Furthermore, the final pumping path control command is generated and executed, including: when the emergency switching index is lower than a preset switching threshold, a first command is generated, specifically to maintain the main pumping path delivery; when the emergency switching index reaches or exceeds the switching threshold, a second command is generated to switch from the main pumping path to the backup pumping path delivery.

[0012] Furthermore, the step of generating and executing the second switching instruction further includes: starting the backup pumping unit connected to the backup pumping path, and controlling its pumping flow rate to smoothly increase from zero to the target flow rate according to a preset flow rate coordination curve; at the same time, controlling the pumping flow rate of the main pumping unit connected to the main pumping path to smoothly decrease from the current flow rate to zero according to the flow rate coordination curve, so as to ensure that the fluctuation of the total concrete flow rate injected into the lattice column is suppressed within a preset tolerance range during the path switching, thereby generating a flow rate smoothness coefficient to ensure the continuity of pouring.

[0013] Furthermore, multiple backup grouting ports are set along the height direction of the lattice column, corresponding to multiple backup pumping paths; the generation and execution of the final pumping path control command also includes: when the switching is triggered, calculating the estimated height of the blockage location based on the total amount of concrete pumped and the structural parameters of the lattice column; based on the estimated height, selecting the path with the smallest height difference and an inlet higher than the estimated height from the multiple backup pumping paths as the target switching path, and generating intelligent routing selection parameters for achieving optimal path restoration.

[0014] Furthermore, an independent digital construction log was created for this construction project. The time-series data streams collected at each pumping time point, the extracted pumping risk characteristic parameters, pumping stability index, emergency switching index, and the executed pumping path control instructions were stored in the digital construction log as a structured data record. The data record was then mapped spatially and temporally to the building information model (BIM) of the lattice columns to generate a traceability report containing quality parameters throughout the entire process.

[0015] Furthermore, when the number of data records accumulated in the digital construction log reaches the preset update threshold, an adaptive optimization process is triggered on the weight coefficients in the pipe blockage risk prediction model and the comprehensive decision-making algorithm to generate a risk assessment model for the current working conditions and concrete batch.

[0016] A concrete pumping construction system for ultra-high lattice columns includes: a scheme acquisition module, configured to acquire preset lattice column structural parameters and corresponding pumping construction schemes, wherein the pumping construction schemes include a main pumping path and at least one backup pumping path. The data acquisition module is configured to simultaneously acquire at least one process status data during the pumping of concrete along the main pumping path, forming a time-series data stream. The risk assessment module is configured to analyze time series data streams based on a preset risk assessment model and extract multiple pumping risk characteristic parameters. The decision generation module is configured to calculate the pipe blockage risk probability, which characterizes the pipe blockage risk, and the emergency switching index, which characterizes the stability of the current pumping process, based on multiple pumping risk characteristic parameters and a preset comprehensive decision algorithm. The instruction execution module is configured to generate and execute the final pumping path control instruction based on the emergency switching index.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention abandons the traditional single-point grouting construction mode and innovatively sets up a main pumping path and multiple backup pumping paths. When the emergency switching index calculated by the system through real-time data analysis exceeds a preset threshold, it can automatically trigger and execute a smooth switch from the main path to the optimal backup path. This process actively avoids impending pipe blockage events without interrupting the total concrete flow, fundamentally eliminating the risks of pouring interruption, cold joints, or even component scrapping caused by "single-point failure" in traditional methods, ensuring the absolute continuity and structural safety of long-term, high-volume pumping operations.

[0018] This invention departs from relying on a single pressure threshold or human experience. Instead, it acquires pressure data at high frequency and extracts pressure gradient and pressure fluctuation characteristics. These dynamic features, combined with a pre-trained pipe blockage risk prediction model, quantify the probability of a pipe blockage event. This method transforms risk identification from a passive, delayed response to an active, forward-looking prediction, enabling the system to issue warnings and trigger decisions when pipe blockage risks are still in their nascent stages. This provides a valuable time window for path switching and improves the intelligence and decision-making efficiency of the entire control system.

[0019] This invention stores the raw data, risk characteristic parameters, decision indices, and execution instructions at each time point as a complete structured data record. This not only provides a complete digital archive for the project but also enables precise retrospective review and auditing of the quality status at any stage of the pouring process. When quality concerns arise, it provides objective and detailed data evidence, changing the traditional construction approach that relies on sampling inspections and extensive management, and providing technical support for achieving high-quality construction and digital delivery.

[0020] When the accumulated data in the digital construction log reaches a preset update threshold, the system automatically triggers an optimization process. Using this new data, which includes labels of actual working conditions and results, the weight coefficients of the blockage risk prediction model and decision-making algorithm are incrementally trained and fine-tuned. This allows the model to "learn" from the project's historical data. Its prediction accuracy and decision-making logic become increasingly aligned with the characteristics of the current batch of concrete, equipment performance, and environmental conditions as construction progresses, thereby generating a dynamically evolving, personalized risk assessment model that continuously improves the accuracy of the system's decisions. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram illustrating the execution flow of generating the first or second instruction in this invention; Figure 3 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below. Example

[0024] Please see Figures 1 to 2 The present invention provides a technical solution: A method for pumping concrete for ultra-high lattice columns, comprising the following steps: The system acquires preset lattice column structural parameters and corresponding pumping construction plans, including a main pumping path and at least one backup pumping path. During concrete pumping along the main pumping path, at least one process status data is simultaneously collected to form a time-series data stream. Based on a preset risk assessment model, the time-series data stream is analyzed to extract multiple pumping risk characteristic parameters. Based on these parameters and a preset comprehensive decision-making algorithm, the system calculates the pipe blockage risk probability, which characterizes the pipe blockage risk, and an emergency switching index, which characterizes the stability of the current pumping process. Based on the emergency switching index, the system generates and executes the final pumping path control command.

[0025] The process involves collecting process status data, including: collecting pumping pressure data through pressure sensors deployed at the outlet of the main pumping path; extracting multiple pumping risk characteristic parameters, including: performing time-series analysis on the pumping pressure data to extract pressure gradient features and pressure fluctuation features; inputting the pressure gradient features and pressure fluctuation features into a pre-trained pipe blockage risk prediction model, and outputting a pipe blockage risk probability to characterize the likelihood of pipe blockage.

[0026] The emergency switching index is determined by: calculating a first risk component based on pressure gradient characteristics, which is proportional to the rate of pressure rise and is used to quantify the risk of rapid pressure changes; calculating a second risk component based on pressure fluctuation characteristics, which is proportional to the variance of the pressure value within a preset time window and is used to quantify the instability of the pumping process; and performing a weighted summation of the first risk component, the second risk component, and the probability of pipe blockage with preset weights to obtain the emergency switching index.

[0027] Concrete is a high-viscosity non-Newtonian fluid. When it is pumped stably in a pipeline, its pressure will exhibit a relatively stable and regular periodic fluctuation (caused by the piston movement of the pump).

[0028] However, when one or more air bladders (i.e., the entrained air masses) pass through the location of the pressure sensor, the following physical phenomena occur: a sudden pressure drop. Because air is much less dense than concrete and is highly compressible, when the air bladder reaches the sensor, the medium in the pipe changes from dense concrete to rarefied air, causing a sharp, instantaneous drop in the sensor reading. A subsequent pressure surge. When the high-density concrete fluid following the air bladder impacts the sensor again, a rebound-like pressure surge occurs. This "sudden drop followed by a surge" pattern creates a high-amplitude, short-period anomalous pressure fluctuation in the time-series data. This unique fluctuation pattern is like the fingerprint left by the air bladder on the pressure data.

[0029] The step in this solution, "calculating the second risk component based on pressure fluctuation characteristics, where the second risk component is proportional to the variance of the pressure value within a preset time window," is the key to identifying the fingerprint left by the airbag on the pressure data.

[0030] Variance is a measure of the dispersion of a set of data. During stable pumping, although pressure data fluctuates, it generally changes within a small range around a mean, resulting in small variance over a short time window. When an airbag passes through, the pressure data deviates from the mean instantaneously (first dropping sharply, then rising sharply), causing the data points to become extremely scattered, and the calculated variance increases significantly. This method transforms the complex physical problem of "whether an airbag exists" into a mathematical problem that can be precisely calculated by calculating the variance of the pressure data in real time. An abnormally increased "secondary risk component" is the alarm issued by the system: "Severe instability detected, airbag may be present."

[0031] The system generates and executes the final pumping path control command, including: generating a first command when the emergency switching index is lower than a preset switching threshold, specifically maintaining the main pumping path; and generating a second command to switch from the main pumping path to the backup pumping path when the emergency switching index reaches or exceeds the switching threshold. When the system calculates a high "second risk component," causing the total "emergency switching index" to exceed a preset threshold, it triggers the generation and execution of the final pumping path control command (e.g., switching to the backup pumping path).

[0032] This action has a dual significance in resolving the airbag problem: it interrupts unstable flow: the act of switching paths itself alters the hydrodynamic state of the entire pumping system. This change can break up forming or moving airbags, or alter their trajectory, preventing them from rising smoothly to the top of the column. The reason for this avoidance is that continuous pumping instability is often the main cause of airbag formation. By switching to a new, potentially better pumping path, pumping conditions can be improved at the source, reducing the formation of new airbags.

[0033] The step of generating and executing the second switching instruction further includes: starting the backup pumping unit connected to the backup pumping path, and controlling its pumping flow rate to smoothly increase from zero to the target flow rate according to the preset flow rate coordination curve; at the same time, controlling the pumping flow rate of the main pumping unit connected to the main pumping path to smoothly decrease from the current flow rate to zero according to the flow rate coordination curve, so as to ensure that the fluctuation of the total concrete flow rate injected into the lattice column is suppressed within the preset tolerance range during the path switching, thereby generating a flow smoothness coefficient to ensure the continuity of pouring.

[0034] Multiple backup grouting ports are set along the height of the lattice column, corresponding to multiple backup pumping paths; the generation and execution of the final pumping path control command also includes: when the switching is triggered, calculating the estimated height of the blockage location based on the total amount of concrete pumped and the structural parameters of the lattice column; based on the estimated height, selecting the path with the smallest height difference and an inlet higher than the estimated height from the multiple backup pumping paths as the target switching path, and generating intelligent routing selection parameters for achieving optimal path restoration.

[0035] Create an independent digital construction log for this construction project; store the time-series data streams collected at each pumping time point, the extracted pumping risk characteristic parameters, pumping stability index, emergency switching index, and executed pumping path control instructions as a structured data record in the digital construction log; map the data record to the building information model (BIM) of the lattice columns in space and time to generate a traceability report containing quality parameters throughout the entire process.

[0036] When the number of data records accumulated in the digital construction log reaches the preset update threshold, an adaptive optimization process is triggered on the weight coefficients in the pipe blockage risk prediction model and the comprehensive decision-making algorithm to generate a risk assessment model for the current working conditions and concrete batch.

[0037] Figure 1 The isometric view on the left depicts a portion of a steel pipe lattice column, including a main pumping path driven by the bottom main pumping unit and a backup pumping path driven by the middle backup pumping unit. This corresponds to the step of "acquiring a pumping construction plan, which includes the main pumping path and at least one backup pumping path." The central intelligent control system shown in the figure, through connection with pressure sensors on both paths, executes the step of "synchronously acquiring at least one process status data during concrete pumping along the main pumping path, forming a time-series data stream." Figure 1 The technical roadmap on the right further illustrates the internal decision-making logic of the intelligent control system. The flowchart "Real-time Data Acquisition and Analysis" corresponds to the step of "analyzing the time-series data stream based on a preset risk assessment model and extracting multiple pumping risk characteristic parameters." The flowchart "Risk Index Calculation and Judgment" corresponds to the step of "calculating the emergency switching index based on multiple pumping risk characteristic parameters and a preset comprehensive decision-making algorithm." Finally, the flowcharts "Intelligent Path Switching Decision" and "Flow Coordinated Smooth Execution" together illustrate the complete process of "generating and executing the final pumping path control command based on the emergency switching index," intuitively demonstrating how this invention automatically, intelligently, and smoothly completes path switching when risks exceed limits, thereby ensuring the continuity and safety of construction.

[0038] Please refer to the following: A concrete pumping construction system for ultra-high lattice columns. Figure 3 ,include: The scheme acquisition module is configured to acquire preset lattice column structural parameters and corresponding pumping construction schemes. The pumping construction schemes include a main pumping path and at least one backup pumping path. The data acquisition module is configured to simultaneously acquire at least one process status data during the pumping of concrete along the main pumping path, forming a time-series data stream. The risk assessment module is configured to analyze time series data streams based on a preset risk assessment model and extract multiple pumping risk characteristic parameters. The decision generation module is configured to calculate the pipe blockage risk probability, which characterizes the pipe blockage risk, and the emergency switching index, which characterizes the stability of the current pumping process, based on multiple pumping risk characteristic parameters and a preset comprehensive decision algorithm. The instruction execution module is configured to generate and execute the final pumping path control instruction based on the emergency switching index.

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be noted that this embodiment is a specific application scenario provided to illustrate the invention and does not constitute a limitation on the scope of protection of this invention.

[0040] The implementation scenario is set up for intelligent concrete pumping construction of a 450-meter ultra-high three-limb steel pipe lattice column; This embodiment takes the core tube three-limb steel pipe lattice column of a super high-rise building with a design height of 450 meters as the construction object, and elaborates in detail the complete process of the intelligent pumping construction method provided by the present invention.

[0041] Step 1: Obtain the preset lattice column structural parameters and the corresponding pumping construction plan; Before construction began, the project's technical team entered the detailed parameters and construction plan of the target lattice column into the intelligent pumping control system described in this invention. The structural parameters of the lattice column were collected, including: total height Htotal = 450.0m, internal equivalent cross-sectional area Asection = 0.85m², and total designed concrete volume Vtotal = Htotal × Asection = 382.5m³. The concrete design grade was C70 high-strength self-compacting concrete. Pumping path configuration includes: main pumping path, denoted as P. main Specifically, the main pumping unit A, deployed on the ground, connects to the main grouting port (I) located at the bottom of the lattice column via the first set of high-pressure pump pipes. main ).

[0042] The backup pumping path is denoted as (P) backup1 ,Pbackup2 ,...), P backup1 and P backup2 The first and second backup pumping paths are defined as follows: a backup grouting port is set every 50 meters along the height of the lattice column. In this embodiment, the backup grouting ports are located at H=50m (Ib1), H=100m (Ib2), ..., H=400m (Ib8). Each backup grouting port is pre-connected to an independent backup pump pipe, which is then connected to the standby backup pumping unit B on the ground.

[0043] The target pumping rate is Qtarget = 25 m³ / h.

[0044] Risk assessment model parameters: Switching threshold T of the emergency switching index hreshold =0.75.

[0045] Weighting coefficients (initial values): w1 (pressure gradient weight) = 0.4, w2 (pressure fluctuation weight) = 0.3, w3 (model prediction probability weight) = 0.3.

[0046] Step 2: During pumping along the main pumping path, process status data is collected synchronously; Construction begins, and main pumping unit A starts, proceeding through main pumping path P. main Concrete is pumped into the lattice column. The data acquisition module of the intelligent pumping control system is activated simultaneously.

[0047] Data acquisition: Deployed at the outlet of the main pumping path (near the main grouting port I) main The high-precision pressure sensor continuously collects pumping pressure data P(t) at a sampling frequency of 100Hz, forming a high-density time-series data stream.

[0048] Step 3: Based on the risk assessment model, analyze the time series data stream and extract pumping risk characteristic parameters; The risk assessment module of the control system performs real-time rolling analysis on the collected P(t) data stream. It is assumed that the system detects a potential risk around t=3600s (1 hour) during pumping. Table 1 shows the data samples collected and processed by the system from t=3598.0s to t=3603.0s. Timestamp (t) Initial pressure P(t)(MPa) Pressure gradient characteristics Gp(t) (MPa / s) Pressure fluctuation characteristics Fp(t)(MPa²) The probability of pipe blockage, Pblockage(t). 3598.0s 21.52 0.15 0.08 0.17 3599.0s 21.68 0.16 0.09 0.18 3600.0s 21.95 0.27 0.15 0.25 3601.0s 22.45 0.50 0.28 0.45 3602.0s 23.10 0.65 0.45 0.65 3603.0s 23.85 0.75 0.62 0.78 Pressure gradient characteristic Gp(t): The rate of change of pressure in the system within a short time window Δt (e.g., Δt = 1.0 s).

[0049] Formula: Gp(t) = (P(t) - P(t - Δt)) / Δt; Example (t=3602.0s): Gp(3602.0)=(P(3602.0)-P(3601.0)) / 1.0=(23.10-22.45) / 1.0=0.65MPa / s.

[0050] Pressure fluctuation characteristic Fp(t): The variance of the pressure value is calculated within a sliding time window W (e.g., W=5.0s).

[0051] Formula: Fp(t) = Var(P(ti)), where ti belongs to the interval [t-W,t]. Var represents the variance calculation.

[0052] Example (t=3602.0s): The system will take all pressure sample points from t from 3597.0s to 3602.0s, calculate its variance, and obtain Fp(3602.0)=0.45MPa².

[0053] The probability of pipe blockage risk, Pblockage(t), is calculated in real time using Gp(t) and Fp(t), along with other auxiliary features (such as mean pressure, peak-to-valley difference, etc.), as input vectors. These vectors are then fed into a Long Short-Term Memory (LSTM) network model pre-trained with a large amount of historical data. The model outputs a probability value between [0,1].

[0054] Example (t=3602.0s): Input [Gp=0.65,Fp=0.45,...], Model output Pblockage(3602.0)=0.65.

[0055] The specific calculation steps involve calculating the comprehensive risk score z(t). This step integrates multiple independent risk features into a single linear score z(t). The specific calculation formula is z(t) = β0 + β1 × Gp(t) + β2 × Fp(t); where β0 (bias coefficient) is -2.15, representing the basic risk level when there are no risk features (i.e., both Gp(t) and Fp(t) are zero). The bias coefficient is a calibration parameter, usually negative; β1 is the pressure gradient weight, specifically 2.50; β2 is the pressure fluctuation weight, specifically 2.50. After obtaining the comprehensive risk score z(t), the final blockage risk probability Pblockage(t) is calculated using the logistic function. The unbounded comprehensive risk score z(t) is mapped to the probability interval (0,1) through a nonlinear function. The specific calculation formula is Pblockage(t) = 1 / (1 + e^(-1 / 2)). (-z(t)) Where e is the base of the natural logarithm (approximately 2.71828). In Table 1, the specific inputs at t=3603.0s are Gp=0.75 and Fp=0.62. The specific calculation of z(t) is z = -2.15 + (2.50 × 0.75) + (2.50 × 0.62)z = -2.15 + 1.875 + 1.55 = 1.275; The specific calculation of Pblockage(t) is P = 1 / (1 + e^(-t)). (-1.275) =1 / (1+0.2794)=1 / 1.2794≈0.7815; The specific result is a calculated value of 0.78 (rounded to two decimal places), and the table data is 0.78.

[0056] In Table 1, the specific inputs at t=3602.0s are Gp=0.65 and Fp=0.45. The specific calculation of z(t) is z = -2.15 + (2.50 × 0.65) + (2.50 × 0.45)z = -2.15 + 1.625 + 1.125 = 0.60; The specific calculation of Pblockage(t) is P = 1 / (1 + e^(-0.60)) = 1 / (1 + 0.5488) = 1 / 1.5488 ≈ 0.6456; The calculated result is 0.65 (rounded to two decimal places), which is completely consistent with the data in the table, which is 0.65.

[0057] The specific inputs at t=3601.0s in Table 1 are Gp=0.50 and Fp=0.28; The specific calculation of z(t) is z = -2.15 + (2.50 × 0.50) + (2.50 × 0.28)z = -2.15 + 1.25 + 0.70 = -0.20; The specific calculation of Pblockage(t) is P = 1 / (1 + e^(0.20)) = 1 / (1 + 1.2214) = 1 / 2.2214 ≈ 0.4501; The specific result is 0.45 (rounded to two decimal places); The specific inputs at t=3600.0s in Table 1 are Gp=0.27 and Fp=0.15; The specific calculation of z(t) is z = -2.15 + (2.50 × 0.27) + (2.50 × 0.15)z = -2.15 + 0.675 + 0.375 = -1.10; The specific calculation of Pblockage(t) is P = 1 / (1 + e^(1.10)) = 1 / (1 + 3.0041) = 1 / 4.0041 ≈ 0.2497; The calculated result is 0.25 (rounded to two decimal places), which is completely consistent with the data in the table.

[0058] Step 4: Calculate the emergency handover index based on the comprehensive decision-making algorithm; calculate the emergency handover index Iswitch(t) based on the feature parameters extracted in the previous step. The specific steps are as follows: S101. Based on the pressure gradient characteristics, the first risk component R1(t) is calculated using the following formula: R1(t) = min(1, Gp(t) / Gpmax); where Gpmax is the preset upper limit threshold of the pressure gradient safety, which is set according to the concrete grade and pump pipe conditions. In this example, Gpmax = 1.0 MPa / s. Example (t = 3602.0s): R1(3602.0) = min(1, 0.65 / 1.0) = 0.65.

[0059] S102. Based on the pressure fluctuation characteristics, the second risk component R2(t) is calculated using the following formula: R2(t) = min(1, Fp(t) / Fpmax); The lower-level parameter is defined as follows: Fpmax is the preset upper limit threshold for the pressure fluctuation variance safety margin; in this example, Fpmax = 0.8 MPa². Example (t = 3602.0 s): R2(3602.0) = min(1, 0.45 / 0.8) = 0.5625.

[0060] S103. The emergency switching index Iswitch(t) is obtained by weighting the first risk component R1(t), the second risk component R2(t), and the blockage risk probability Pblockage(t) with preset weights using the following weighted summation formula: Iswitch(t) = w1 × R1(t) + w2 × R2(t) + w3 × Pblockage(t); where w1, w2, and w3 are preset weight coefficients, with values ​​of 0.4, 0.3, and 0.3 respectively. Example (t = 3602.0s): Iswitch(3602.0) = 0.4 × 0.65 + 0.3 × 0.5625 + 0.3 × 0.65 = 0.26 + 0.16875 + 0.195 = 0.62375. Example (t = 3603.0s): Calculated, Iswitch(3603.0) = 0.4 × (0.75 / 1.0) + 0.3 × (0.62 / 0.8) + 0.3 × 0.78 = 0.3 + 0.2325 + 0.234 = 0.7665.

[0061] Step 5: Generate and execute the final pumping path control command based on the emergency switching index; The control system's instruction execution module continuously compares the calculated Iswitch(t) with the switching threshold T. hreshold =0.75 for comparison.

[0062] Maintenance command: At t=3602.0s, Iswitch(3602.0)=0.62375<0.75, the system generates and executes the command "maintain main pumping path", and pumping continues.

[0063] The second instruction is triggered: at t=3603.0s, Iswitch(3603.0)=0.7665>0.75, the condition is triggered! The system immediately generates and executes the second instruction to switch from the main pumping path to the backup pumping path.

[0064] Step Six: The specific process of executing the second instruction includes: Intelligent routing selection (optimal path recovery): Calculating the total amount of pumped concrete Vpumped: The system integrates the flow data since the start of pumping to obtain Vpumped(3603.0)=Qtarget×t=(25m³ / h)×(3603.0 / 3600h)≈25.02m³. Calculating the estimated height of the blockage location Hblock: Formula: Hblock=Vpumped / Asection; Example: Hblock=25.02m³ / 0.85m²≈29.44m.

[0065] Selecting the target switching path: The system iterates through all available grouting ports at heights {50m, 100m, ...}, selecting the one higher than the Hblock with the smallest height difference. In this example, grouting port Ib1 at 50m is the optimal choice. The system generates intelligent routing parameters, determining the target switching path to be P connected to Ib1. backup1 .

[0066] Smooth flow switching: The system immediately supplies power to the main pumping unit A and the standby pumping unit B (connected to P). backup1 Send coordinated control commands. The handover process is executed according to the preset traffic coordination curve, with a handover time T. transition =20s.

[0067] Flow rate Q of main pumping unit A main (t'): Formula: Qmain(t') = Qtarget × (1 - t' / Ttransition), where t' is the time after the switch begins, 0 ≤ t' ≤ 20s. The flow rate smoothly and linearly decreases from 25 m³ / h to 0.

[0068] The flow rate Qbackup(t') of the backup pumping unit B is calculated using the formula: Qbackup(t') = Qtarget × (t' / Ttransition). The flow rate smoothly and linearly increases from 0 to 25 m³ / h. Total flow control: During the switching process, the total flow rate injected into the lattice column, Qtotal(t') = Qmain(t') + Qbackup(t') = Qtarget, remains constant, ensuring absolute continuity of the pouring. Flow smoothness coefficient: Since theoretically the total flow rate is stable, the flow smoothness coefficient generated in this embodiment is 1.0, indicating a perfectly smooth switching process.

[0069] Step 7: Digital Construction Log Creation and Model Adaptive Optimization; Digital Construction Log: Throughout the process, each row of data shown in Table 1 (and other dimensions of information), along with executed instructions (such as "Switch to P" at t=3603.0s), backup1 The instructions were all stored as structured data records in a separate digital construction log created for this construction project. Through time and space mapping with the BIM model, the system could pinpoint the exact time t=3603.0s, when the concrete had reached a height of 29.44 meters and the first path switch occurred. Once the lattice column pouring was completed, or after accumulating sufficient similar case data (e.g., the log recorded over 1 million data points), the system triggered an adaptive optimization process. Using this new data with realistic labels (e.g., "stable," "high risk," "successful switch"), the LSTM blockage risk prediction model was incrementally trained. Based on the actual effect of the switching decision (e.g., whether the backup path also experienced pressure anomalies after the switch), the weight coefficients w1, w2, and w3 in the decision-making algorithm were fine-tuned using reinforcement learning and other methods. This generated a risk assessment model more adapted to the current project's concrete batch and environmental conditions.

[0070] Rolling Iterative Optimization Decision Scenario 1: Scenario A: Pumping process reaches 85 meters (risk stable, maintain main path). Data Acquisition and Analysis (Corresponding Steps: Acquiring Process Status Data, Extracting Multiple Pumping Risk Characteristic Parameters) When the concrete pouring level approaches 85 meters, the system continuously collects pumping pressure data through pressure sensors, forming a time-series data stream. The risk assessment module performs real-time time-series analysis on this data stream and extracts: Pressure gradient characteristic Gp(t): approximately 0.40 MPa / s (smooth pressure rise); Pressure fluctuation characteristic Fp(t): approximately 0.30 MPa² (good fluidity, small fluctuations); The system inputs the above characteristic parameters into the preset risk assessment model and comprehensive decision-making algorithm: Pipe blockage risk probability Pblockage(t): The pre-trained pipe blockage risk prediction model (LSTM) outputs a pipe blockage risk probability of 0.40 based on the input characteristics. Emergency switching index Iswitch(t): The comprehensive decision-making algorithm performs weighted summation: First risk component R1(t) = min(1, 0.40 / 1.0) = 0.40; Second risk component R2(t) = min(1, 0.30 / 0.8) = 0.375; Iswitch(t) = (0.4 × 0.40) + (0.3 × 0.375) + (0.3 × 0.40) = 0.3925. Based on the emergency switching index, the final pumping path control command is generated and executed. The decision generation module compares the calculated emergency switching index (0.3925) with the preset switching threshold (0.75). Since 0.3925 < 0.75, the system determines that the current operating condition risk is controllable. The system generates the "first command," which is to maintain the main pumping path. The command execution module does not need to change the pumping unit status, and construction proceeds as originally planned.

[0071] The system creates a digital construction log. It stores the complete data packet (timestamp, pressure data, Gp, Fp, Pblockage, Iswitch=0.39, execution instruction="first instruction") related to the "85-meter" node as a structured data record in the digital construction log of this construction project, and performs spatiotemporal mapping with the 85-meter elevation in the BIM model.

[0072] Scenario B: Pumping process reaches a height of 95 meters (risk increases sharply, execution path switch). When the pump reached a depth of 95 meters, significant anomalies appeared in the pressure data collected by the system due to changes in the batch of concrete and ambient temperature. The risk assessment module analysis revealed the following: pressure gradient characteristic Gp(t): a sharp increase to 0.80 MPa / s; pressure fluctuation characteristic Fp(t): an increase to 0.70 MPa². Calculate the blockage risk probability Pblockage(t): Based on the deteriorating characteristics, the model outputs a blockage risk probability that climbs to 0.82. Emergency switching index Iswitch(t): First risk component R1(t) = min(1, 0.80 / 1.0) = 0.80; Second risk component R2(t) = min(1, 0.70 / 0.8) = 0.875; Therefore, Iswitch(t) = (0.4 × 0.80) + (0.3 × 0.875) + (0.3 × 0.82) = 0.8285; Since 0.8285 > 0.75, the emergency switching index has exceeded the switching threshold. Instruction generation: The system immediately generates a "second instruction," namely, switching from the main pumping path to the backup pumping path. Detailed execution process of the second instruction: The instruction execution module begins to perform the complex switching operation: Intelligent routing selection: Based on the total pumped volume, the system estimates the blockage location to be approximately 94.8 meters high. Based on this height, the system selects from multiple backup grouting ports (50m, 100m, 150m...) the one with the smallest elevation difference above 94.8 meters, namely the backup grouting port (Ib2) at a height of 100 meters, and determines its corresponding backup pumping path (P). backup2 ( ) is the target switching path. Smooth flow switching: The system supplies main pump unit A and connected to P backup2 Simultaneously, backup pumping unit B issues a coordinated control command. The flow rate of main pumping unit A smoothly decreases from 25 m³ / h to 0 within 20 seconds. The flow rate of backup pumping unit B smoothly increases from 0 to 25 m³ / h within the same 20 seconds. This process ensures a constant total flow rate, generating a flow smoothness coefficient close to 1.0, thus guaranteeing the continuity of the pouring process.

[0073] The system creates a digital construction log, including the complete data packet for the "95-meter" node (including Iswitch=0.83, execution instruction="second instruction", and switch target=P). backup2 The estimated blockage height (e.g., 94.8m) is stored in the digital construction log.

[0074] After construction is completed, how the system utilizes the accumulated data from the entire process to evolve itself is the core of "iterative optimization." The optimization process is triggered when the number of data records accumulated in the digital construction log reaches a preset update threshold. Once the entire 450-meter lattice column is poured, the digital construction log has accumulated a massive amount (e.g., over 100,000) of structured data records with real-world conditions and decision-making results. When the data volume reaches the preset update threshold, the system automatically triggers the adaptive optimization process. The system uses all the data records in the log ([concrete parameters, environmental conditions, pump pressure sequence] as input, and [whether pipe blockage / high-risk events occurred] as labels) as a new, large-scale, and highly correlated training set to incrementally train the existing LSTM model. This allows the model to more deeply understand the risk evolution patterns under the specific materials and conditions of this project. After optimization, the accuracy of the risk prediction model on the independent validation set increased from the initial 92.5% to 96.8%.

[0075] The optimized integrated decision-making algorithm involves analyzing the calculation records of all "emergency switching indices" in the system logs and their subsequent actual pumping effects. Through reinforcement learning or similar algorithms, the weight coefficients (w1, w2, w3) for calculating Iswitch are recalibrated. For example, if pressure fluctuations are found to have a greater impact on pipe blockage than initially estimated, the system may increase the weight of w2. This optimized weight combination allows the "emergency switching index" to more accurately reflect the actual risk, reducing the average error in predicting future pumping pressure trends from 12.5% ​​to 7.2%. After this optimization, the system generates an "evolved" risk assessment model. This model can be deployed as an initial model in similar future projects, achieving higher initial prediction accuracy and decision-making efficiency, demonstrating continuous technological iteration and progress.

[0076] It should be noted that the switching threshold T hreshold This constitutes the core of the risk management and decision-making execution mechanism of this invention, and its value directly determines the sensitivity and stability of the system. There is a mutual constraint between the two: a lower T... hreshold A higher T value increases the system's sensitivity to risks and triggers switching earlier to avoid pipe blockage, but may lead to unnecessary "false alarm" switching due to normal, harmless instantaneous pressure fluctuations in concrete, affecting construction efficiency; conversely, a higher T value... hreshold A higher value enhances the system's tolerance to disturbances and reduces false alarms, but may lead to a slow response to real and rapidly evolving pipe blockage risks, resulting in "missed detections" and missed optimal switching opportunities. Therefore, the optimal value of this parameter is not determined in isolation, but is obtained through joint calibration experiments of the following systems: Experimental objective: Determine a T value. hreshold The system achieves a false alarm rate of less than 1.5% when facing typical instantaneous pressure fluctuations, and a false alarm rate of less than 0.5% when facing the risk of progressively worsening pipe blockage. This embodiment prepares a high-fidelity "concrete pumping process simulation platform" that can accurately simulate fluid dynamics under different operating conditions. By programmatically injecting "high-viscosity concrete lumps" of different durations (increasing from 1 second to 30 seconds) and strengths into the platform, the system simulates instantaneous pressure fluctuations caused by batch-to-batch unevenness of concrete, which are typically self-resolving. A dynamic boundary condition of "progressive pipe wall shrinkage," with an adjustable shrinkage rate, is set in the middle section of the pump pipe within the platform to simulate the progressively worsening pipe blockage process caused by aggregate segregation or material settling.

[0077] The data acquisition and analysis process involves running multiple sets of simulation experiments for the two types of simulation scenarios mentioned above, and fully recording the time series curve of the "emergency switching index Iswitch(t)" calculated by the system during the simulation process.

[0078] In offline data analysis, setting a switching threshold Threshold The search range is [0.50, 0.95], with a step size of 0.01. For each T... hreshold The parameter points are used, with all collected Iswitch(t) curves as input to evaluate the system's switching behavior. The number of times a path switch occurred in all "high-viscosity agglomerate" simulation scenarios (counted as false alarms) and the number of times a switch failed to be triggered before the simulated pressure exceeded the safety limit in all "progressive pipe wall contraction" simulation scenarios (counted as false alarms) are statistically analyzed. False alarm rate curves and false alarm rate curves are plotted as a function of T. hreshold A graph showing the changes in T values. Analysis revealed that as T... hreshold As the value increases, the false positive rate monotonically decreases, while the false negative rate monotonically increases. Finally, the threshold point that minimizes the weighted sum of the false positive and false negative rates (where the false negative rate has a higher weight due to its more severe consequences) is selected. Analysis shows that when T... hreshold When the value is set to 0.75, the system achieves the best engineering balance between false alarm rate and false negative rate. It can effectively filter out most harmless instantaneous disturbances and respond to real and continuously developing pipe blockage risks in a timely and reliable manner, thus achieving the optimal combination of construction safety and operational efficiency.

[0079] The rolling optimization process is as follows: The system inputs the latest operating data (such as concrete viscosity, ambient temperature, and current pump pressure of 24 MPa) as new boundary conditions into the pipe blockage risk prediction model. Simultaneously, the system executes the optimal path recovery algorithm, calculating the estimated height of the current blockage risk point to be 94.8 meters. Based on this, the system selects the path with the smallest elevation difference (inlet higher than this height) from among several preset backup pumping paths, namely the backup grouting port (Ib2) located at a height of 100 meters, as the target switching path.

[0080] The system generates an instruction for "Type I Dynamic Adjustment of Pumping Scheme" and automatically issues coordinated control instructions to the primary and backup pumping units. The instruction content is: "Starting from the current moment, execute path switching, switching from initial pumping scheme A (primary path P)..." main Smoothly switch to emergency pumping plan A' (backup path P) backup2 This ensures a constant total pumping flow rate. The system will package a complete set of data related to the "95-meter" node, including the new pumping scheme, and store it in the database as a new structured data record.

[0081] Pumping parameter evolution database: Each time pumping reaches a preset node (e.g., every 5 meters of pouring height), the system generates and stores a structured data record. Table 2 shows examples of records stored in the database for the two scenarios described above. Table 2: Example of Pumping Parameter Evolution Database Records Record ID (pouring height) Timestamp Risk characteristic parameters (Gp, Fp) Pipe blockage risk probability Emergency Switching Index Control commands Switch execution details Final pumping solution 85m T1 (0.40MPa / s, 0.30MPa²) 0.40 0.39 First instruction (maintain) not applicable <![CDATA[Solution A (P main )]]> 95m T2 (0.80MPa / s, 0.70MPa²) 0.82 0.83 Second instruction (switch) <![CDATA[Estimated blockage height: 94.8 m; Target path: P backup2 ; Flow smoothness coefficient: ≈ 1.0]]> <![CDATA[Solution A'(P backup2 )]]> As the pumping construction continued, when the entire 450-meter lattice column was poured, the number of data records accumulated in the "Pumping Parameter Evolution Database" reached the preset update threshold (for example, a total of more than 100,000 high-frequency data points). The system automatically triggered an adaptive optimization process for the core model.

[0082] The specific optimization process involves the system using all accumulated data records in the database—namely, [concrete parameters, environmental conditions, pump pressure sequence] → [blockage risk event labels]—as a larger training set with broader coverage of conditions. This set is used for incremental training and fine-tuning of the existing "blockage risk prediction model." After optimization, the generalization ability and prediction accuracy of the risk prediction model are significantly improved. For example, its risk prediction accuracy on the independent validation set increased from 92.5% to 96.8%, and its sensitivity in identifying early, subtle blockage symptoms improved by 15%. Simultaneously, the system extracts the emergency switching index and its corresponding long-term pumping performance evolution time series from all records in the database, and recalibrates the weight coefficients (w1, w2, w3) in the comprehensive decision-making algorithm for calculating the emergency switching index. The new weight combination more accurately reflects the risk evolution law of the specific concrete material under ultra-high pumping conditions in this project. After optimization, the model's average prediction error for the pumping pressure trend over the next 10 minutes decreased from 12.5% ​​to 7.2%.

[0083] In this embodiment, traditional pumping construction methods often result in catastrophic interruptions when encountering pipe blockages. This solution changes this passive situation by introducing a rolling optimization decision-making mechanism based on a forward-looking emergency switching index. As shown in the embodiment, when the risk is low (emergency switching index = 0.65), the system maintains the efficient main path pumping scheme; while when the risk exceeds the threshold (emergency switching index = 0.78), the system automatically and smoothly switches to a safer backup pumping path. This capability allows the concrete pouring process to "adapt to changing needs," employing the most appropriate pumping strategy under different risk conditions, thus perfectly balancing construction efficiency and absolute safety throughout the entire process. This solution creates a pumping parameter evolution database, storing the working parameters, risk assessment results, decision type, and final pumping scheme corresponding to each pumping operation as a complete structured data record. This not only provides the project with an unprecedentedly detailed digital archive, but more importantly, it transforms the entire construction process into a continuously learning data source, laying a solid foundation for the self-optimization of subsequent models. When the data in the evolution database accumulates to a certain amount, the system will trigger adaptive optimization of the risk prediction model and decision-making algorithm. Data from the examples shows that, after incremental training, the model's risk prediction accuracy improved from 92.5% to 96.8%, while the pressure trend prediction error decreased from 12.5% ​​to 7.2%. This indicates that the core model of this invention can learn from the project's historical data, and its prediction and decision-making capabilities continuously improve as construction progresses. This adaptive optimization characteristic enhances the accuracy and reliability of the system's decisions over time, achieving truly intelligent pumping construction.

[0084] Through the detailed steps described above, this embodiment fully demonstrates how the present invention utilizes real-time data analysis, intelligent decision-making, and automated control to achieve precise, reliable, and efficient management of the concrete pumping process for ultra-high three-limb steel pipe lattice columns, thereby effectively avoiding major risks in traditional construction methods.

[0085] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected relevant parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max and normalization, and Z-score standardization. The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for pumping concrete for ultra-high lattice columns, characterized in that, The specific steps include: Obtain the preset structural parameters of the lattice column and the corresponding pumping construction plan. The pumping construction plan includes the main pumping path and at least one backup pumping path. During the pumping of concrete along the main pumping path, at least one process status data is collected simultaneously to form a time series data stream; based on a preset risk assessment model, the time series data stream is analyzed to extract multiple pumping risk characteristic parameters. Based on multiple pumping risk characteristic parameters and a preset comprehensive decision-making algorithm, the probability of pipe blockage risk, which characterizes the risk of pipe blockage, and the emergency switching index, which characterizes the stability of the current pumping process, are calculated. Based on the emergency switching index, the final pumping path control command is generated and executed.

2. The method for pumping concrete for ultra-high lattice columns according to claim 1, characterized in that: The process involves collecting process status data, including: collecting pumping pressure data through pressure sensors deployed at the outlet of the main pumping path; extracting multiple pumping risk characteristic parameters, including: performing time-series analysis on the pumping pressure data to extract pressure gradient features and pressure fluctuation features; inputting the pressure gradient features and pressure fluctuation features into a pre-trained pipe blockage risk prediction model, and outputting a pipe blockage risk probability to characterize the likelihood of pipe blockage.

3. The method for pumping concrete for ultra-high lattice columns according to claim 1, characterized in that: The emergency switching index is determined by: calculating a first risk component based on pressure gradient characteristics, which is proportional to the rate of pressure rise and is used to quantify the risk of rapid pressure changes; calculating a second risk component based on pressure fluctuation characteristics, which is proportional to the variance of the pressure value within a preset time window and is used to quantify the instability of the pumping process; and performing a weighted summation of the first risk component, the second risk component, and the probability of pipe blockage with preset weights to obtain the emergency switching index.

4. The method for pumping concrete for ultra-high lattice columns according to claim 1, characterized in that: Generate and execute the final pumping path control command, including: when the emergency switching index is lower than the preset switching threshold, generate a first command, specifically to maintain the main pumping path delivery; when the emergency switching index reaches or exceeds the switching threshold, generate a second command to switch from the main pumping path to the backup pumping path delivery.

5. The method for pumping concrete for ultra-high lattice columns according to claim 4, characterized in that: The step of generating and executing the second switching instruction further includes: starting the backup pumping unit connected to the backup pumping path, and controlling its pumping flow rate to smoothly increase from zero to the target flow rate according to the preset flow rate coordination curve; at the same time, controlling the pumping flow rate of the main pumping unit connected to the main pumping path to smoothly decrease from the current flow rate to zero according to the flow rate coordination curve, so as to ensure that the fluctuation of the total concrete flow rate injected into the lattice column is suppressed within the preset tolerance range during the path switching, thereby generating a flow smoothness coefficient to ensure the continuity of pouring.

6. The method for pumping concrete for ultra-high lattice columns according to claim 1, characterized in that: Multiple spare grouting ports are provided along the height of the lattice column, corresponding to multiple spare pumping paths; The process of generating and executing the final pumping path control command also includes: calculating the estimated height of the blockage location based on the total amount of concrete pumped and the structural parameters of the lattice column when a switch is triggered. Based on the estimated height, the path with the smallest elevation difference and an inlet higher than the estimated height is selected from multiple backup pumping paths as the target switching path, generating intelligent routing selection parameters for achieving optimal path recovery.

7. The method for pumping concrete for ultra-high lattice columns according to claim 1, characterized in that: Create an independent digital construction log for this construction project; store the time-series data streams collected at each pumping time point, the extracted pumping risk characteristic parameters, pumping stability index, emergency switching index, and executed pumping path control instructions as a structured data record in the digital construction log; map the data record to the building information model (BIM) of the lattice columns in space and time to generate a traceability report containing quality parameters throughout the entire process.

8. The method for pumping concrete for ultra-high lattice columns according to claim 7, characterized in that: When the number of data records accumulated in the digital construction log reaches the preset update threshold, an adaptive optimization process is triggered on the weight coefficients in the pipe blockage risk prediction model and the comprehensive decision-making algorithm to generate a risk assessment model for the current working conditions and concrete batch.

9. A concrete pumping construction system for ultra-high lattice columns, applied to the concrete pumping construction method for ultra-high lattice columns as described in any one of claims 1 to 8, characterized in that: include: The scheme acquisition module is configured to acquire preset lattice column structural parameters and corresponding pumping construction schemes. The pumping construction schemes include a main pumping path and at least one backup pumping path. The data acquisition module is configured to simultaneously acquire at least one process status data during the pumping of concrete along the main pumping path, forming a time-series data stream. The risk assessment module is configured to analyze time series data streams based on a preset risk assessment model and extract multiple pumping risk characteristic parameters. The decision generation module is configured to calculate the pipe blockage risk probability, which characterizes the pipe blockage risk, and the emergency switching index, which characterizes the stability of the current pumping process, based on multiple pumping risk characteristic parameters and a preset comprehensive decision algorithm. The instruction execution module is configured to generate and execute the final pumping path control instruction based on the emergency switching index.

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