Energy consumption intelligent optimization and regulation method and system for post-grouting process of cast-in-place pile

Abstract

The application provides a bored pile post-grouting process energy consumption intelligent optimization and regulation method and system, relates to the field of automatic control of industrial processes, and comprises the following steps: collecting grouting construction data to establish a database; establishing an energy consumption prediction benchmark value based on historical data, dynamically comparing to generate working condition optimization instructions; adjusting parameters and collecting energy consumption data to determine the best efficiency parameter combination; and when the energy consumption exceeds the preset value, switching to a main and auxiliary double-pulse grouting mode and optimizing pulse sequence parameters. The application can realize intelligent optimization and regulation of grouting process energy consumption, improve grouting efficiency, and reduce energy consumption.

Description

Technical Field

[0001] This invention relates to automation control technology for industrial processes, and more particularly to a method and system for intelligent optimization and control of energy consumption in the post-grouting process of bored piles. Background Technology

[0002] After the completion of bored pile foundations, post-grouting is usually required to improve the bonding conditions between the pile foundation and the surrounding soil. However, in current construction practices, energy consumption control and grouting efficiency improvement in the post-grouting process face significant challenges. Traditional grouting process control methods mainly rely on construction personnel setting parameters such as grouting pressure, grouting flow rate, and duration based on experience. This often involves fixed operating conditions or extensive adjustments, lacking adaptability to complex and variable geological conditions. This control method cannot identify energy consumption characteristics under different operating conditions in real time, and it is also difficult to ensure that grouting quality is maintained while reducing energy consumption.

[0003] Currently, some grouting equipment possesses basic sensor monitoring capabilities, but this monitoring data is primarily used for construction records or post-construction analysis, and does not enable real-time prediction and feedback control during construction. Existing control systems generally rely on a single threshold for judgment; when energy consumption exceeds limits or grouting effectiveness is insufficient, manual intervention is required to adjust grouting parameters. This not only leads to response lag but also easily results in energy waste, insufficient grout diffusion, or uneven grouting, lacking an efficient closed-loop control mechanism.

[0004] With the development of sensing technology, big data processing, and intelligent control, how to introduce data-driven energy consumption prediction and optimization mechanisms in post-grouting construction has become an urgent problem to be solved. By combining real-time construction data with historical databases to form a dynamic working condition model, intelligent prediction and adaptive parameter optimization of grouting energy consumption can be achieved, thereby effectively reducing energy consumption, improving grouting efficiency, and providing a more reliable control method for pile foundation reinforcement under complex geological conditions. Summary of the Invention

[0005] This invention provides a method and system for intelligent optimization and control of energy consumption during the post-grouting process of bored cast-in-place piles, which can solve the problems in the prior art.

[0006] A first aspect of this invention provides a method for intelligent optimization and control of energy consumption during the post-grouting process of bored cast-in-place piles, comprising:

[0007] Collect grouting construction site data, obtain grouting condition parameters, and establish a grouting database for the construction site;

[0008] Historical data from the grouting database is used to establish a baseline value for grouting energy consumption prediction. Real-time grouting process data is dynamically compared with the baseline value for grouting energy consumption prediction to identify grouting condition characteristics and generate condition optimization instructions.

[0009] The system receives the adjustment index of the working condition optimization command, adjusts the grouting working condition parameters and collects energy consumption data in real time, determines the parameter combination corresponding to the best grouting efficiency based on the energy consumption data, records the parameter combination as the preferred grouting parameters, and generates parameter optimization control command.

[0010] The grouting conditions are adjusted according to the parameter optimization control command. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to the main and auxiliary dual-pulse grouting mode, and the initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters, and the main and auxiliary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system.

[0011] In one alternative embodiment,

[0012] Collecting on-site grouting data, obtaining grouting condition parameters, and establishing a grouting database for the construction site includes:

[0013] Obtain the real-time change rate of pressure, flow, and grouting volume parameters at the inlet, outlet, and intermediate control valve of the grouting pipeline;

[0014] The sampling timing is dynamically adjusted based on the real-time rate of change, generating a dynamic sampling instruction sequence that includes sampling time points and sampling frequencies;

[0015] According to the dynamic sampling instruction sequence, grouting condition parameters are collected collaboratively, and the collected analog signals are converted into digital parameters with timestamps.

[0016] The digitized parameters are written into the original parameter layer in timestamp order, the parameter characteristics are calculated according to the time window and written into the feature parameter layer, and the analysis results are calculated based on the feature parameters and written into the analysis parameter layer to establish a grouting database for the construction site.

[0017] In one alternative embodiment,

[0018] Historical data from the grouting database is used to establish a baseline value for grouting energy consumption prediction. Real-time grouting process data is dynamically compared with this baseline value to identify grouting condition characteristics and generate condition optimization instructions, including:

[0019] Obtain historical grouting data containing grouting pressure and grouting flow rate from the grouting database, calculate the rate of change of the historical grouting data and set a dynamic threshold based on the variance of the rate of change, and adaptively divide the time window according to the dynamic threshold.

[0020] Features are extracted from historical grouting data within a time window to construct feature vectors. Based on the similarity of the feature vectors, grouting condition categories are obtained, and a grouting condition feature library is established.

[0021] Historical grouting energy consumption data corresponding to each grouting condition category are extracted from the grouting database to establish a grouting energy consumption prediction benchmark value that includes the average energy consumption level and fluctuation range.

[0022] Real-time acquisition of grouting process data, determination of the current time window, and extraction of feature vectors;

[0023] The feature vector of the current time window is matched with the grouting condition feature library to identify the current grouting condition category and select the corresponding grouting energy consumption prediction benchmark value.

[0024] The real-time collected grouting process data is dynamically compared with the grouting energy consumption prediction benchmark value, and an operating condition optimization command is generated when the data deviates from the fluctuation range.

[0025] In one alternative embodiment,

[0026] The system receives the adjustment index of the working condition optimization command, adjusts the grouting working condition parameters and collects energy consumption data in real time, determines the parameter combination corresponding to the optimal grouting efficiency based on the energy consumption data, records the parameter combination as the preferred grouting parameters, and generates parameter optimization control commands, including:

[0027] The adjustment index receives the working condition optimization command, sets up a pressure sensor array in the grouting pipeline, collects the formation pressure distribution data, calculates the spatial distribution of the pressure gradient, and takes the area with the largest pressure gradient as the target area for adjusting the grouting working condition parameters.

[0028] Monitor the grout flow state in the target area of ​​adjustment. When the grout reaches the target area of ​​adjustment, record the current grouting condition parameters as the reference parameter values.

[0029] The first adjustment range of grouting flow rate is divided according to the benchmark parameter value. The grouting flow rate is adjusted according to a fixed increment. Energy consumption data corresponding to each flow rate value is collected in real time to form a correspondence between grouting flow rate and energy consumption data.

[0030] Based on the correspondence between grouting flow rate and energy consumption data, the changing trend of grouting energy consumption data is identified. When the energy consumption data begins to rise, a second adjustment range is determined with the current flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to the reduced fixed increment and energy consumption data is collected.

[0031] The combination of grouting parameters corresponding to the lowest grouting energy consumption is recorded as the optimal grouting parameters, and parameter optimization control instructions are generated.

[0032] In one alternative embodiment,

[0033] Based on the aforementioned correspondence, the changing trend of grouting energy consumption data is identified. When the energy consumption data begins to rise, a second adjustment range is determined with the current flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to a reduced fixed increment, and energy consumption data is collected, including:

[0034] Based on the correspondence between grouting flow rate and energy consumption data, within the first adjustment range, the grouting flow rate is gradually adjusted from the upper and lower limits of the range towards the middle. Energy consumption data is collected in a bidirectional convergence manner. When the difference in the rate of change of energy consumption data collected in the upper and lower directions is less than the preset deviation, the corresponding grouting flow rate segment is determined as the target flow rate range.

[0035] Multiple monitoring points are set within the target flow range. When the energy consumption data starts to rise, the current grouting flow rate is determined based on the spatial distribution characteristics of the energy consumption data at each monitoring point. The second adjustment range is then determined with the current grouting flow rate as the center.

[0036] Within the second adjustment range, the grouting flow rate is adjusted according to the reduced fixed increment, and the grout diffusion radius and grouting pressure data are collected simultaneously. The transport power consumption per unit volume of grout is calculated, and the transport power consumption is compared with the corresponding grouting flow rate to form an energy consumption characteristic curve.

[0037] The energy consumption gradient is calculated based on the energy consumption characteristic curve. When the energy consumption gradient is less than the preset gradient threshold and the energy consumption data is the lowest, the corresponding grouting flow rate is recorded as the preferred grouting parameter.

[0038] In one alternative embodiment,

[0039] The grouting conditions are adjusted according to parameter optimization control commands. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to a dual-pulse grouting mode with primary and secondary pulses. Initial parameters are set, and the grouting efficiency coefficient is collected by adjusting the initial parameters. Based on the grouting efficiency coefficient, the primary and secondary pulse sequence parameters are optimized and fed back to the control system, including:

[0040] Adjust the grouting conditions according to the parameter optimization control command, continuously collect grouting pressure and flow data, calculate the instantaneous power by multiplying the pressure and flow, and obtain the actual energy consumption value of the grouting process;

[0041] When the grouting energy consumption exceeds the preset control value, the conventional grouting method is switched to the main and auxiliary dual-pulse grouting mode. The pressure distribution ratio of the main and auxiliary pulses is determined based on the fluctuation law of instantaneous power, and the initial parameters of the main and auxiliary dual-pulse sequence are set. The main pulse provides the basic grouting pressure through continuous action, and the auxiliary pulse generates periodic pressure disturbances through intermittent action.

[0042] By combining the continuous action of the main pulse with the intermittent action of the auxiliary pulse, the initial parameters of the main and auxiliary double pulse sequence are adjusted, and the grouting pressure and grout diffusion range after pressure superposition are collected. The ratio of grout diffusion range per unit time to grouting energy consumption is used as the grouting efficiency coefficient.

[0043] Based on the grouting efficiency coefficient, the duration, intermittent period, and pressure distribution of the main and auxiliary pulses are optimized, and the optimized main and auxiliary pulse sequence parameters are fed back to the grouting control system.

[0044] A second aspect of this invention provides an intelligent optimization and control system for energy consumption during the post-grouting process of bored cast-in-place piles, comprising:

[0045] The first unit is used to collect data from the grouting construction site, obtain grouting condition parameters, and establish a grouting database for the construction site.

[0046] The second unit is used to acquire historical data from the grouting database to establish a grouting energy consumption prediction benchmark value, dynamically compare the real-time collected grouting process data with the grouting energy consumption prediction benchmark value, identify grouting working condition characteristics, and generate working condition optimization instructions.

[0047] The third unit is used to receive the adjustment index of the working condition optimization instruction, adjust the grouting working condition parameters and collect energy consumption data in real time, determine the parameter combination corresponding to the best grouting efficiency based on the energy consumption data, record the parameter combination as the preferred grouting parameters, and generate parameter optimization control instructions.

[0048] The fourth unit is used to adjust the grouting conditions according to the parameter optimization control command. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to the main and auxiliary dual-pulse grouting mode, and the initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters, and the main and auxiliary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system.

[0049] A third aspect of the present invention provides an electronic device, comprising:

[0050] processor;

[0051] Memory used to store processor-executable instructions;

[0052] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0053] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0054] In this embodiment, by establishing a grouting database and setting energy consumption prediction benchmarks, real-time identification and dynamic comparison of grouting process characteristics are achieved, providing data support for grouting parameter optimization and effectively improving the intelligence level of the grouting construction process. A parameter combination optimization strategy is adopted, adjusting grouting condition parameters and collecting energy consumption data in real time to determine the parameter combination corresponding to the optimal grouting efficiency, forming preferred grouting parameters, thereby achieving refined control of the grouting process and reducing construction energy consumption. A primary and secondary dual-pulse grouting mode is introduced as a response mechanism for abnormal energy consumption, and the primary and secondary pulse sequence parameters are optimized through the grouting efficiency coefficient, achieving adaptive control of the grouting process, improving grouting quality and efficiency, and reducing resource waste. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the intelligent optimization and control method for energy consumption during the post-grouting process of bored cast-in-place piles, as described in an embodiment of the present invention.

[0056] Figure 2 This is a flowchart illustrating the optimized control process for grouting conditions in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0059] Figure 1 This is a flowchart illustrating the intelligent optimization and control method for energy consumption during the post-grouting process of bored cast-in-place piles, as described in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] Collect grouting construction site data, obtain grouting condition parameters, and establish a grouting database for the construction site;

[0061] Historical data from the grouting database is used to establish a baseline value for grouting energy consumption prediction. Real-time grouting process data is dynamically compared with the baseline value for grouting energy consumption prediction to identify grouting condition characteristics and generate condition optimization instructions.

[0062] The system receives the adjustment index of the working condition optimization command, adjusts the grouting working condition parameters and collects energy consumption data in real time, determines the parameter combination corresponding to the best grouting efficiency based on the energy consumption data, records the parameter combination as the preferred grouting parameters, and generates parameter optimization control command.

[0063] The grouting conditions are adjusted according to the parameter optimization control command. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to the main and auxiliary dual-pulse grouting mode, and the initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters, and the main and auxiliary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system.

[0064] In one optional implementation, collecting on-site grouting data, obtaining grouting condition parameters, and establishing an on-site grouting database includes:

[0065] Obtain the real-time change rate of pressure, flow, and grouting volume parameters at the inlet, outlet, and intermediate control valve of the grouting pipeline;

[0066] The sampling timing is dynamically adjusted based on the real-time rate of change, generating a dynamic sampling instruction sequence that includes sampling time points and sampling frequencies;

[0067] According to the dynamic sampling instruction sequence, grouting condition parameters are collected collaboratively, and the collected analog signals are converted into digital parameters with timestamps.

[0068] The digitized parameters are written into the original parameter layer in timestamp order, the parameter characteristics are calculated according to the time window and written into the feature parameter layer, and the analysis results are calculated based on the feature parameters and written into the analysis parameter layer to establish a grouting database for the construction site.

[0069] In a specific implementation, sensors are installed at the inlet, outlet, and intermediate control valve of the grouting pipeline to collect pressure, flow, and grouting volume parameters in real time. The inlet sensor is located at the grouting pump outlet, the outlet sensor is located at the grouting orifice, and the intermediate control valve sensor is located at the flow regulating valve. Each measuring point is equipped with a pressure sensor, a flow sensor, and a grouting volume accumulation metering device. The system samples every 500 milliseconds as the initial sampling frequency, records the readings of each parameter, and calculates the rate of change between two adjacent sampling data points. For example, when the inlet pressure changes from 5.2 MPa to 5.6 MPa and the flow rate changes from 28 L / min to 26 L / min, the pressure change rate is 0.8 MPa / s, and the flow rate change rate is -4 L / min·s.

[0070] The sampling timing is automatically adjusted based on the real-time rate of change of parameters. When the rate of change of a parameter exceeds a preset threshold, the sampling frequency is increased; when the parameter tends to stabilize, the sampling frequency is decreased to save storage space. Optionally, the threshold for the rate of change of pressure is set to 0.5 MPa / s, the threshold for the rate of change of flow rate is 3 L / min·s, and the threshold for the rate of change of grouting volume is 5 L / min·s. When the rate of change of any parameter exceeds the threshold, the sampling frequency is increased to once every 100 milliseconds; when the rate of change of all parameters is less than 50% of the threshold for 10 consecutive seconds, the sampling frequency is reduced to once every 1000 milliseconds. For example, if a rapid increase in inlet pressure is detected, with the rate of change reaching 0.8 MPa / s, exceeding the threshold of 0.5 MPa / s, the sampling frequency is immediately adjusted from once every 500 milliseconds to once every 100 milliseconds to capture the details of pressure fluctuations.

[0071] A dynamic sampling instruction sequence is generated, containing precise sampling time points and corresponding sampling frequencies. The sampling instruction sequence is stored in JSON format, with each instruction containing three attributes: timestamp, sampling frequency, and sampling duration. For example, {"timestamp": "2023-05-10 14:30:22.500", "frequency": 10, "duration": 30} indicates continuous sampling at a frequency of 10 times per second for 30 seconds, starting from the specified time point. The system predicts parameter change trends and sends sampling instructions 100 milliseconds in advance to ensure that the sampling device can respond promptly to frequency changes.

[0072] Grouting condition parameters are collected collaboratively according to a dynamic sampling command sequence. Sensors at each measuring point are connected via an industrial fieldbus for synchronous sampling. The clocks of all sensors are synchronized with the central controller via the NTP protocol, achieving a time synchronization accuracy of ±1 millisecond. The collected analog signals are converted into digital signals by a 16-bit ADC and appended with microsecond-level timestamps. For example, an imported pressure sensor outputs a 4-20mA current signal, corresponding to a pressure range of 0-10MPa. After conversion, this yields a digital pressure value of 5.67MPa, with an appended timestamp "2023-05-10 14:30:22.768394".

[0073] Digital parameters are written to the raw parameter layer of the grouting database at the construction site in timestamp order. The database adopts a hierarchical structure. The raw parameter layer stores unprocessed basic data, with the record format as: {measuring point ID, parameter type, parameter value, timestamp, equipment status code}. For example: {"point_id": "P001", "param_type": "pressure", "value": 5.67, "timestamp": "2023-05-10 14:30: 22.768394", "status_code": 0}. When new data is collected, the timestamp order is checked to ensure that it is stored in chronological order. If a timestamp is found to be reversed, an anomaly is recorded and sorted.

[0074] The parameter characteristics are calculated based on time windows and written into the feature parameter layer. A sliding time window technique is used, with a window width of 10 seconds and a sliding step size of 5 seconds. Statistical characteristics are calculated for the raw data within each time window, including the mean, maximum, minimum, standard deviation, and mean rate of change. For example, for the inlet pressure data within a 10-second window from 14:30:20 to 14:30:30, the calculated values ​​are: mean pressure 5.45 MPa, maximum pressure 5.89 MPa, minimum pressure 5.12 MPa, standard deviation 0.21 MPa, and mean rate of change 0.38 MPa / s. These characteristic parameters are written into the feature parameter layer in the format: {window start time, window end time, measurement point ID, parameter type, feature type, feature value}.

[0075] The analysis results are calculated based on characteristic parameters and written into the analysis parameter layer. Advanced analyses are performed using these characteristic parameters, including grouting effect evaluation, abnormal condition detection, and grouting process segmentation. For example, correlation analysis between pressure and flow rate is used to determine if the grouting pipeline is blocked; the grouting saturation is assessed based on the rate of change in grouting volume; and the grouting stage is identified based on pressure fluctuation characteristics. When a continuous increase in inlet pressure and a decrease in flow rate are detected, it is determined that pipeline blockage may occur, the blockage probability is calculated to be 78%, and this is recorded in the analysis parameter layer. The analysis result format is: {Analysis Time, Analysis Type, Analysis Result, Correlation Parameter, Confidence Level}.

[0076] The database is equipped with automatic backup and data security mechanisms. Upon completion of each grouting process, the data is automatically packaged and archived, generating a unique identifier. Data storage employs a three-tiered backup strategy: local storage, edge server caching, and cloud backup. This approach establishes a complete on-site grouting database, providing a reliable data foundation for grouting process monitoring and subsequent analysis.

[0077] In one optional implementation, historical data from the grouting database is used to establish a grouting energy consumption prediction benchmark. Real-time collected grouting process data is dynamically compared with the grouting energy consumption prediction benchmark to identify grouting condition characteristics and generate condition optimization instructions, including:

[0078] Obtain historical grouting data containing grouting pressure and grouting flow rate from the grouting database, calculate the rate of change of the historical grouting data and set a dynamic threshold based on the variance of the rate of change, and adaptively divide the time window according to the dynamic threshold.

[0079] Features are extracted from historical grouting data within a time window to construct feature vectors. Based on the similarity of the feature vectors, grouting condition categories are obtained, and a grouting condition feature library is established.

[0080] Historical grouting energy consumption data corresponding to each grouting condition category are extracted from the grouting database to establish a grouting energy consumption prediction benchmark value that includes the average energy consumption level and fluctuation range.

[0081] Real-time acquisition of grouting process data, determination of the current time window, and extraction of feature vectors;

[0082] The feature vector of the current time window is matched with the grouting condition feature library to identify the current grouting condition category and select the corresponding grouting energy consumption prediction benchmark value.

[0083] The real-time collected grouting process data is dynamically compared with the grouting energy consumption prediction benchmark value, and an operating condition optimization command is generated when the data deviates from the fluctuation range.

[0084] This embodiment first obtains historical grouting data, including grouting pressure and flow rate, from a grouting database. Historical grouting data can include key parameters such as grouting pressure, flow rate, pumping rate, and grout viscosity from different engineering projects. For the obtained historical grouting data, its rate of change is calculated, and a dynamic threshold is set based on the variance of the rate of change. The rate of change is calculated by dividing the difference between data points at adjacent time points by the time interval. For example, for grouting pressure data in a certain time series, the rate of change of pressure between two adjacent sampling points (t1, p1) and (t2, p2) is (p2-p1) / (t2-t1). After calculating the rate of change over a period of time, the variance of these rates of change is calculated. When the variance is large, it indicates that the data fluctuates drastically, and a smaller dynamic threshold is set; when the variance is small, it indicates that the data is relatively stable, and a larger dynamic threshold is set. For example, when the variance of the grouting pressure rate of change is 0.02 MPa... 2 / s 2 When the dynamic threshold is set to 0.05 MPa / s, the variance of the rate of change can be increased to 0.08 MPa. 2 / s 2 At that time, the dynamic threshold can be adjusted to 0.03 MPa / s.

[0085] The time window is adaptively divided based on a set dynamic threshold. When the rate of change of multiple consecutive data points exceeds the dynamic threshold, it can be considered that the working condition has changed, and a new time window is defined. For example, during a grouting process, when the rate of change of grouting pressure at 5 consecutive data points exceeds 0.05 MPa / s, a new time window can be defined. In this way, the entire grouting process can be divided into multiple time windows, and the working condition within each window is relatively stable.

[0086] Feature vectors are constructed by extracting features from historical grouting data within each time window. Feature extraction includes statistical and morphological features. Statistical features include the average, standard deviation, maximum, minimum, and median of grouting pressure and flow rate; morphological features include the rate of rise, rate of fall, and frequency of fluctuation of the pressure curve. For example, for a time window lasting 10 minutes, features such as an average grouting pressure of 3.5 MPa, a standard deviation of 0.2 MPa, and a pressure rise rate of 0.01 MPa / s can be extracted to form the feature vector for that window.

[0087] Grouting condition categories are derived based on the similarity of feature vectors, and a grouting condition feature library is established. Similarity calculation can employ methods such as Euclidean distance or cosine similarity. By calculating the similarity between different feature vectors, feature vectors with similarity higher than a certain threshold are grouped into the same category. For example, setting the similarity threshold to 0.85, when the similarity between two feature vectors is greater than 0.85, they are grouped into the same condition category. After classification, different condition categories such as "stable grouting," "pressure fluctuation," "insufficient flow," and "pump blockage" can be obtained, and these categories and their typical feature vectors are stored in the grouting condition feature library.

[0088] Historical grouting energy consumption data for each grouting condition category was extracted from the grouting database to establish a baseline value for grouting energy consumption prediction, including average energy consumption level and fluctuation range. For each condition category, the average value and standard deviation of its historical energy consumption data were calculated. For example, for the "stable grouting" condition, historical data showed an average energy consumption of 25 kWh / m³. 3 The standard deviation is 3 kWh / m 3 Then the energy consumption prediction baseline value for this operating condition can be set to 25 kWh / m³. 3 The fluctuation range is [19kWh / m 3 31kWh / m 3 (mean ± 2 standard deviations).

[0089] During actual grouting, grouting process data is collected in real time, including parameters such as grouting pressure, flow rate, and motor power. The current time window is determined using the aforementioned method, and a feature vector is extracted. For example, in a grouting operation, the feature vector extracted within the current 10-minute window includes features such as an average grouting pressure of 3.6 MPa and a standard deviation of 0.25 MPa.

[0090] The feature vector of the current time window is matched with the grouting condition feature library to identify the current grouting condition category. The matching process calculates the similarity between the current feature vector and the typical feature vectors of each condition category in the feature library, and selects the category with the highest similarity as the current condition. For example, if the current feature vector has a similarity of 0.92 with the "stable grouting" condition, and its similarity with other conditions is less than 0.8, then the current condition is identified as "stable grouting," and the corresponding grouting energy consumption prediction benchmark value is selected.

[0091] The real-time grouting process data is dynamically compared with the selected grouting energy consumption prediction benchmark value. The deviation between the real-time energy consumption and the prediction benchmark value is calculated to determine whether it deviates from the fluctuation range. For example, when the real-time monitored energy consumption under the "stable grouting" condition is 33 kWh / m³, 3 It exceeded the preset upper limit of the fluctuation range of 31 kWh / m 3 The system detected abnormal energy consumption.

[0092] When real-time energy consumption deviates from the preset fluctuation range, operating condition optimization instructions are generated. Different optimization strategies are formulated according to the direction and degree of deviation. For example, when energy consumption is too high, specific optimization instructions such as "reduce grouting pressure by 5%", "adjust grout mix ratio", and "check for blockages in the pumping system" can be generated; when energy consumption is too low but the grouting effect is poor, optimization instructions such as "appropriately increase grouting pressure" and "increase grout concentration" can be generated.

[0093] The above methods enable intelligent optimization and control of energy consumption during the grouting process of bored piles, improving grouting efficiency, reducing energy consumption, and ensuring grouting quality.

[0094] In one optional implementation, the adjustment index of the working condition optimization command is received, the grouting working condition parameters are adjusted and energy consumption data is collected in real time, the parameter combination corresponding to the optimal grouting efficiency is determined based on the energy consumption data, the parameter combination is recorded as the preferred grouting parameters, and the parameter optimization control command is generated, including:

[0095] The adjustment index receives the working condition optimization command, sets up a pressure sensor array in the grouting pipeline, collects the formation pressure distribution data, calculates the spatial distribution of the pressure gradient, and takes the area with the largest pressure gradient as the target area for adjusting the grouting working condition parameters.

[0096] Monitor the grout flow state in the target area of ​​adjustment. When the grout reaches the target area of ​​adjustment, record the current grouting condition parameters as the reference parameter values.

[0097] The first adjustment range of grouting flow rate is divided according to the benchmark parameter value. The grouting flow rate is adjusted according to a fixed increment. Energy consumption data corresponding to each flow rate value is collected in real time to form a correspondence between grouting flow rate and energy consumption data.

[0098] Based on the correspondence between grouting flow rate and energy consumption data, the changing trend of grouting energy consumption data is identified. When the energy consumption data begins to rise, a second adjustment range is determined with the current flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to the reduced fixed increment and energy consumption data is collected.

[0099] The combination of grouting parameters corresponding to the lowest grouting energy consumption is recorded as the optimal grouting parameters, and parameter optimization control instructions are generated.

[0100] This implementation method adjusts the grouting parameters by receiving the adjustment index of the working condition optimization command and collects energy consumption data in real time. Based on the energy consumption data, it determines the parameter combination corresponding to the optimal grouting efficiency, records the parameter combination as the preferred grouting parameters, and generates parameter optimization control command.

[0101] Specifically, a pressure sensor array is installed on the grouting pipeline. This array consists of multiple pressure sensors, each capable of measuring the grout pressure at its installation location. These pressure sensors are distributed linearly or in a grid pattern along the grouting pipeline, covering the grouting influence area. When collecting formation pressure distribution data, the pressure values ​​at each measuring point are read in real time through these pressure sensors, forming a pressure data matrix. The spatial distribution of the pressure gradient is calculated by dividing the pressure difference between adjacent measuring points by the distance between the measuring points. For two-dimensional or three-dimensional space, the pressure gradient in each direction is calculated separately, and a composite gradient value is obtained. By comparing the pressure gradient values ​​of all areas, the area with the largest pressure gradient is identified as the target area for adjusting the grouting parameters. This area typically represents the location with the greatest grout diffusion resistance and requires focused optimization of the grouting parameters.

[0102] Monitoring the grout flow status in the target area is achieved by installing flow sensors or resistivity sensors in the target area. When these sensors detect that the grout flow or resistivity change exceeds a preset threshold, it is determined that the grout has reached the target area. At this time, the current grouting pressure, grouting flow rate, grout viscosity, and other operating parameters are recorded as baseline parameter values, which will serve as the starting point for subsequent optimization.

[0103] The first adjustment range for grouting flow rate, based on baseline parameter values, is achieved by setting a reasonable range of flow rate variation. Centered on the baseline flow rate, the first adjustment range is formed by expanding upwards and downwards by 30% to 50%. Within this range, the grouting flow rate is gradually adjusted in fixed increments (typically 5% of the baseline flow rate). At each flow rate adjustment point, keeping other parameters constant, the grouting process is run for at least 3 minutes to ensure the system reaches a stable state. Simultaneously, data such as motor power and pumping pressure are recorded, and the energy consumption per unit time is calculated. This establishes a correspondence between grouting flow rate and energy consumption data.

[0104] Based on the established correspondence, a curve fitting method is used to identify the changing trend of grouting energy consumption data. As the flow rate increases, energy consumption typically decreases first and then increases, exhibiting a "U"-shaped curve. The direction of energy consumption change is determined by calculating the energy consumption difference between adjacent measuring points. When the energy consumption values ​​at three consecutive measuring points show an upward trend, the system determines that the region near the inflection point of the energy consumption curve has been found. Using the current flow rate as the center, a range of 10% to 15% is expanded to both sides to determine the second adjustment interval. Within the second adjustment interval, the fixed increment is reduced to 1 / 5 of the increment in the first adjustment interval, allowing for finer step adjustments to the grouting flow rate, and the process of collecting energy consumption data is repeated.

[0105] After fine-tuning in the second adjustment range, the energy consumption data of all test points are compared, and the point with the lowest energy consumption is identified. The corresponding combination of all operating parameters, including grouting flow rate, grouting pressure, and grout viscosity, is recorded and stored as the optimal grouting parameters. Based on these optimal grouting parameters, a parameter optimization control command is generated. This command includes the target values ​​and allowable fluctuation ranges for each operating parameter. Upon receiving this command, the control system adjusts the grouting pump speed, the opening of the flow control valve, and the grout mix ratio to ensure that the actual grouting conditions match the optimal parameters.

[0106] In practical applications, the optimization strategy is adjusted in real time based on changes in geological conditions. When there is a significant change in the formation pressure distribution, a new round of parameter optimization is automatically triggered. A database linking grouting parameters and geological conditions has also been established, and the efficiency of the optimization process continuously improves with accumulated engineering experience. To cope with complex geological conditions, a regional optimization strategy is adopted, dividing the grouting area into multiple sub-regions based on geological characteristics, and optimizing parameters for each sub-region separately. During parameter adjustment, a safety threshold is also set; when the grouting pressure exceeds the safety threshold, the flow rate is automatically reduced to ensure construction safety.

[0107] The intelligent optimization and control method for energy consumption during the post-grouting process of bored piles provided by this invention achieves precise control of operating parameters during grouting, effectively reducing energy consumption and improving grouting efficiency. Simultaneously, real-time monitoring and pressure gradient analysis using a pressure sensor array make the grouting process more targeted, improving the uniformity of grout penetration and the effective grouting depth. The automated parameter optimization process reduces human intervention and the technical dependence of operators, making grouting quality more stable and controllable. Furthermore, the parameter-geological condition correlation database established by this method provides valuable reference for similar projects and has significant potential for widespread application.

[0108] In one optional implementation, the trend of grouting energy consumption data is identified based on the correspondence. When the energy consumption data begins to rise, a second adjustment range is determined with the current flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to a reduced fixed increment, and energy consumption data is collected, including:

[0109] Based on the correspondence between grouting flow rate and energy consumption data, within the first adjustment range, the grouting flow rate is gradually adjusted from the upper and lower limits of the range towards the middle. Energy consumption data is collected in a bidirectional convergence manner. When the difference in the rate of change of energy consumption data collected in the upper and lower directions is less than the preset deviation, the corresponding grouting flow rate segment is determined as the target flow rate range.

[0110] Multiple monitoring points are set within the target flow range. When the energy consumption data starts to rise, the current grouting flow rate is determined based on the spatial distribution characteristics of the energy consumption data at each monitoring point. The second adjustment range is then determined with the current grouting flow rate as the center.

[0111] Within the second adjustment range, the grouting flow rate is adjusted according to the reduced fixed increment, and the grout diffusion radius and grouting pressure data are collected simultaneously. The transport power consumption per unit volume of grout is calculated, and the transport power consumption is compared with the corresponding grouting flow rate to form an energy consumption characteristic curve.

[0112] The energy consumption gradient is calculated based on the energy consumption characteristic curve. When the energy consumption gradient is less than the preset gradient threshold and the energy consumption data is the lowest, the corresponding grouting flow rate is recorded as the preferred grouting parameter.

[0113] In this embodiment, a bidirectional convergent adjustment mode is adopted within the first adjustment interval, that is, the grouting flow rate is gradually adjusted from both the upper and lower limits of the interval towards the middle. Specifically, the upper limit flow rate of the first adjustment interval is set to Qupper limit, the lower limit flow rate is set to Qlower limit, and the adjustment step size is ΔQ. Starting from the upper limit, the flow rate is sequentially set to Qupper limit, Qupper limit - ΔQ, Qupper limit - 2ΔQ...; simultaneously, starting from the lower limit, the flow rate is sequentially set to Qlower limit, Qlower limit + ΔQ, Qlower limit + 2ΔQ... After each flow rate value is set, the flow rate is kept stable for a period of time (usually 3-5 minutes), and the corresponding energy consumption data E is recorded. The energy consumption data is calculated by measuring the motor power and running time of the grouting pump, and the unit is kilowatt-hours.

[0114] During the bidirectional convergence process, the energy consumption change rate between every two adjacent flow points is calculated. The energy consumption change rate is expressed as the ratio of the energy consumption difference to the flow rate difference between two adjacent points, i.e., the change rate calculated from the upper limit direction is (E_upper_limit - iE_upper_limit - i-1) / (Q_upper_limit - iQ_upper_limit - i-1), and the change rate calculated from the lower limit direction is (E_lower_limit + iE_lower_limit + i-1) / (Q_lower_limit + iQ_lower_limit + i-1). When the difference in the change rate of energy consumption data collected from the upper and lower directions is less than the preset deviation value δ (usually set to 5%), it is considered that a region with relatively gentle energy consumption change has been found, and the corresponding grouting flow segment is determined as the target flow range. The upper and lower boundaries of the target flow range are the current flow rate value adjusted from the upper limit direction (Q_upper_limit - n) and the flow rate value adjusted from the lower limit direction (Q_lower_limit + m), respectively.

[0115] Within a defined target flow range, multiple monitoring points are set up for detailed observation. Typically, 5-7 monitoring points are evenly distributed within the target flow range, each corresponding to a flow value. At each monitoring point, the grouting flow rate is kept constant, and the system is run continuously for a relatively long time (usually 5-10 minutes) to collect energy consumption data under stable conditions. The collected data is smoothed to eliminate the influence of random fluctuations. By comparing the energy consumption data of adjacent monitoring points, the trend of energy consumption change is determined. When the energy consumption data of three consecutive monitoring points show an upward trend, the energy consumption data is confirmed to have started to rise, and the corresponding flow value at this point is the current grouting flow rate Qcurrent.

[0116] Based on the spatial distribution characteristics of energy consumption data at each monitoring point, i.e., the shape of the curve relating energy consumption data to flow rate, the regional characteristics near the lowest energy consumption point are determined. If the curve presents a gentle "U" shape near the lowest point, it indicates a relatively wide optimal flow rate range; if it presents a sharp "V" shape, it indicates a narrower optimal flow rate range. Taking the current grouting flow rate Qcurrent as the center, the range of the second adjustment interval is determined based on the curve characteristics. Typically, the range of the second adjustment interval is Qcurrent ± (5% - 10%) × Qcurrent. The narrower the range, the higher the subsequent adjustment accuracy.

[0117] Within the second adjustment range, the grouting flow rate is adjusted according to a reduced fixed increment, which is typically 1 / 5 to 1 / 3 of the increment in the first adjustment range. For example, if the increment in the first adjustment range is 0.5 cubic meters per hour, then the increment in the second adjustment range is 0.1-0.15 cubic meters per hour. While adjusting the flow rate within the second adjustment range, grout diffusion radius and grouting pressure data are collected simultaneously. The grout diffusion radius is obtained through measurements taken at monitoring points embedded at different distances around the pile, and the grouting pressure is acquired through pressure sensors on the grouting pipeline.

[0118] The transport power consumption per unit volume of grout is calculated by dividing the total energy consumption of the grouting process by the diffusion volume. The diffusion volume is approximately calculated as the square of the diffusion radius multiplied by the grouting height and then by pi. Transport power consumption represents the energy consumed in transporting a unit volume of grout to the target location, expressed in kilowatt-hours per cubic meter. Correlating transport power consumption with the corresponding grouting flow rate creates an energy consumption characteristic curve, which visually reflects the variation in grouting efficiency under different flow rates.

[0119] The energy consumption gradient, i.e., the slope of the curve at each point, is calculated based on the energy consumption characteristic curve. The energy consumption gradient represents the rate at which transport power consumption changes with flow rate, and is numerically equal to the difference in transport power consumption between two adjacent points divided by the difference in flow rate. In practical applications, to improve calculation accuracy, the central difference method is used to calculate the energy consumption gradient. When the energy consumption gradient is less than a preset gradient threshold (usually set to 0.05 kWh / m³ per unit flow rate) and the energy consumption data reaches its minimum value, the optimal grouting flow rate is determined to have been found. The corresponding grouting flow rate is recorded as the preferred grouting parameter, along with the corresponding grouting pressure, grout viscosity, and other supporting parameters, forming a complete parameter combination.

[0120] The aforementioned intelligent optimization and control method for energy consumption during the post-grouting process of bored piles achieves precise control and optimization of energy consumption. Through a bidirectional convergent adjustment strategy, the time for parameter optimization is significantly reduced, and the refined flow rate adjustment strategy ensures that grouting parameters remain within the optimal operating range, avoiding over-grouting and energy waste. The energy consumption gradient-based determination method effectively solves the instability problem that may arise from relying solely on the lowest energy consumption point in traditional methods, making the optimization results more reliable. Furthermore, the parameter optimization model established by this method can be extended to post-grouting projects of bored piles under different geological conditions, demonstrating good adaptability and practical value.

[0121] like Figure 2 As shown, the optimized control process for grouting conditions in this embodiment is illustrated.

[0122] In one optional implementation, the grouting conditions are adjusted according to parameter optimization control commands. When the grouting energy consumption exceeds a preset control value, the conventional grouting mode is switched to a dual-pulse grouting mode with primary and secondary components, and initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters, and the primary and secondary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system, including:

[0123] Adjust the grouting conditions according to the parameter optimization control command, continuously collect grouting pressure and flow data, calculate the instantaneous power by multiplying the pressure and flow, and obtain the actual energy consumption value of the grouting process;

[0124] When the grouting energy consumption exceeds the preset control value, the conventional grouting method is switched to the main and auxiliary dual-pulse grouting mode. The pressure distribution ratio of the main and auxiliary pulses is determined based on the fluctuation law of instantaneous power, and the initial parameters of the main and auxiliary dual-pulse sequence are set. The main pulse provides the basic grouting pressure through continuous action, and the auxiliary pulse generates periodic pressure disturbances through intermittent action.

[0125] By combining the continuous action of the main pulse with the intermittent action of the auxiliary pulse, the initial parameters of the main and auxiliary double pulse sequence are adjusted, and the grouting pressure and grout diffusion range after pressure superposition are collected. The ratio of grout diffusion range per unit time to grouting energy consumption is used as the grouting efficiency coefficient.

[0126] Based on the grouting efficiency coefficient, the duration, intermittent period, and pressure distribution of the main and auxiliary pulses are optimized, and the optimized main and auxiliary pulse sequence parameters are fed back to the grouting control system.

[0127] In this embodiment, when adjusting the grouting conditions according to parameter optimization control commands, high-precision pressure sensors and flow meters need to be installed on the grouting pipeline to achieve continuous acquisition of grouting pressure and flow data. The sampling frequency of the pressure sensor is set to 50Hz, and the sampling frequency of the flow meter is set to 20Hz to ensure the real-time performance and accuracy of data acquisition. The pressure and flow data are transmitted to the processing unit through the data acquisition module. The processing unit calculates the instantaneous power based on the product of the pressure value and the flow value. The calculation formula is: instantaneous power equals pressure value multiplied by flow value multiplied by a constant coefficient. This constant coefficient is related to the characteristics of the grouting fluid and pipeline losses, and is usually obtained through experimental calibration, generally ranging from 0.8 to 0.95. By accumulating the instantaneous power data over a certain period of time and integrating it, the actual energy consumption value of the grouting process can be obtained.

[0128] During actual grouting, when the grouting energy consumption exceeds the preset control value, the operating condition adjustment mechanism must be triggered immediately. The preset control value is determined comprehensively based on factors such as pile diameter, pile length, and geological conditions, and is generally set to 1.2 to 1.5 times the conventional grouting energy consumption value. The method for determining whether the preset control value is exceeded is to continuously monitor the average energy consumption value over 30 seconds. When the average energy consumption value exceeds the preset control value three times consecutively, it is determined that the energy consumption exceeds the standard, and at this time, the conventional grouting method is switched to the main-auxiliary dual-pulse grouting mode. The switching process is completed through the mode conversion module in the control system. This module controls the grouting pump group to change its operating mode from continuous constant pressure or flow output to dual-pulse sequence output.

[0129] When determining the pressure distribution ratio of the primary and secondary pulses, it is necessary to analyze the fluctuation pattern of instantaneous power. Spectral analysis is performed on the instantaneous power data from the most recent 5 minutes to extract the dominant frequency and amplitude characteristics of the power fluctuations. The dominant frequency component in the power fluctuation spectrum corresponds to the characteristic frequency of the formation response, which reflects the formation's ability to absorb grouting pressure. The principle for determining the pressure distribution ratio is to match the pressure of the primary pulse with the mean value of the power fluctuation, and the pressure of the secondary pulse with the amplitude of the power fluctuation. Generally, the pressure distribution ratio of the primary and secondary pulses is between 7:3 and 8:2, with specific values ​​determined through experimental adjustments.

[0130] The initial parameters of the main and auxiliary dual-pulse sequence include the main pulse pressure value, auxiliary pulse pressure value, main pulse duration, auxiliary pulse duration, and auxiliary pulse interval period. The initial parameter settings follow these principles: the main pulse pressure value is set to 80% to 90% of the average pressure of conventional grouting, and the auxiliary pulse pressure value is set to 110% to 130% of the average pressure of conventional grouting; the main pulse duration is set to 15 to 30 seconds, and the auxiliary pulse duration is set to 3 to 8 seconds; the auxiliary pulse interval period is set to 1 / 3 to 1 / 2 of the main pulse duration. These initial parameters will be adjusted based on the grouting effect during subsequent optimization.

[0131] In the dual-pulse grouting mode, the main pulse provides the basic grouting pressure through continuous action, ensuring that the grout can stably penetrate into the formation; the auxiliary pulse generates periodic pressure disturbances through intermittent action, breaking down the blockage layer formed by the grout during penetration and improving grouting efficiency. The synergistic effect of the two pulses creates a pressure superposition effect, which can maintain the continuity of grouting pressure while achieving local high-pressure impact, effectively improving the diffusion performance of the grout in the formation.

[0132] During the adjustment of the initial parameters of the main and auxiliary dual-pulse sequences, an orthogonal experimental method was used to optimize the parameter combinations. An experimental scheme was designed to adjust the main pulse duration (15, 20, 25, 30 seconds), auxiliary pulse duration (3, 5, 8 seconds), auxiliary pulse interval (5, 8, 10, 15 seconds), and pressure distribution ratio (7:3, 7.5:2.5, 8:2) while keeping other parameters constant. Each parameter combination was run for at least 10 minutes to ensure the grouting effect reached a stable state.

[0133] When collecting the superimposed grouting pressure and grout diffusion range, the grouting pressure is directly obtained through a pressure sensor on the pipeline, while the grout diffusion range is measured by resistivity sensors or acoustic testing devices embedded at different distances around the pile. After processing the collected data, the ratio of the grout diffusion range per unit time to the grouting energy consumption is calculated to obtain the grouting efficiency coefficient. The grouting efficiency coefficient is calculated by dividing the increase in the grout diffusion range per unit time (usually 10 minutes) by the cumulative energy consumption within the same time period. This coefficient reflects the diffusion capacity of the grout per unit energy consumption; a larger coefficient value indicates higher grouting efficiency.

[0134] Based on the calculated grouting efficiency coefficient, the duration, intermittent period, and pressure distribution of the main and auxiliary pulses were optimized. The optimization process employed response surface methodology to establish a model relating the grouting efficiency coefficient to each parameter. By analyzing the influence and interaction of different parameters on the grouting efficiency coefficient, the optimal values ​​for each parameter were determined. Generally, the optimal value for the main pulse duration is between 20 and 25 seconds, the optimal value for the auxiliary pulse duration is between 4 and 6 seconds, the optimal value for the auxiliary pulse intermittent period is between 8 and 12 seconds, and the optimal value for the pressure distribution ratio is between 7.5:2.5 and 8:2.

[0135] The optimized primary and secondary pulse sequence parameters are fed back to the grouting control system. The execution module of the grouting control system adjusts the operating parameters of the grouting pump set, achieving precise control of the primary and secondary pulse sequences. The execution module includes a pressure regulating valve, a flow regulating valve, and a pump speed controller, which can adjust the grouting pressure and flow rate in real time according to the set pulse sequence parameters. Simultaneously, the control system also has a parameter adaptive function, capable of fine-tuning the pulse sequence parameters based on real-time collected grouting effect data to maintain optimal grouting efficiency.

[0136] A smart optimization and control method for energy consumption during the post-grouting process of bored piles, employing a dual-pulse grouting mode, significantly improves grouting efficiency and reduces energy consumption. It also increases the grout diffusion radius. The synergistic effect of the dual pulses effectively overcomes the "choke point effect" during grout penetration, allowing the grout to be distributed more evenly in the soil surrounding the pile, significantly improving the pile's bearing capacity. The introduction of a grouting efficiency coefficient provides a scientific basis for parameter optimization, making the optimization process more quantitative and precise. The optimized dual-pulse sequence parameters can automatically adjust according to different geological conditions, exhibiting strong adaptability and wide applicability to various bored pile projects. Furthermore, this method shortens grouting time, improves construction efficiency, and reduces project costs, demonstrating significant economic and social benefits.

[0137] A second aspect of this invention provides an intelligent optimization and control system for energy consumption during the post-grouting process of bored piles, the system comprising:

[0138] The first unit is used to collect data from the grouting construction site, obtain grouting condition parameters, and establish a grouting database for the construction site.

[0139] The second unit is used to acquire historical data from the grouting database to establish a grouting energy consumption prediction benchmark value, dynamically compare the real-time collected grouting process data with the grouting energy consumption prediction benchmark value, identify grouting working condition characteristics, and generate working condition optimization instructions.

[0140] The third unit is used to receive the adjustment index of the working condition optimization instruction, adjust the grouting working condition parameters and collect energy consumption data in real time, determine the parameter combination corresponding to the best grouting efficiency based on the energy consumption data, record the parameter combination as the preferred grouting parameters, and generate parameter optimization control instructions.

[0141] The fourth unit is used to adjust the grouting conditions according to the parameter optimization control command. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to the main and auxiliary dual-pulse grouting mode, and the initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters, and the main and auxiliary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system.

[0142] A third aspect of the present invention provides an electronic device, comprising:

[0143] processor;

[0144] Memory used to store processor-executable instructions;

[0145] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0146] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0147] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent optimization and control of energy consumption during the post-grouting process of bored cast-in-place piles, characterized in that, include: Collect grouting construction site data, obtain grouting condition parameters, and establish a grouting database for the construction site; Historical data from the grouting database is used to establish a baseline value for grouting energy consumption prediction. Real-time grouting process data is dynamically compared with the baseline value for grouting energy consumption prediction to identify grouting condition characteristics and generate condition optimization instructions. The system receives the adjustment index of the working condition optimization command, adjusts the grouting working condition parameters and collects energy consumption data in real time, determines the parameter combination corresponding to the optimal grouting efficiency based on the energy consumption data, records the parameter combination as the preferred grouting parameters, and generates parameter optimization control commands, including: The adjustment index receives the working condition optimization command, sets up a pressure sensor array in the grouting pipeline, collects the formation pressure distribution data, calculates the spatial distribution of the pressure gradient, and takes the area with the largest pressure gradient as the target area for adjusting the grouting working condition parameters. Monitor the grout flow state in the target area of ​​adjustment. When the grout reaches the target area of ​​adjustment, record the current grouting condition parameters as the reference parameter values. The first adjustment range of grouting flow rate is divided according to the benchmark parameter value. The grouting flow rate is adjusted according to a fixed increment. Energy consumption data corresponding to each flow rate value is collected in real time to form a correspondence between grouting flow rate and energy consumption data. Based on the correspondence between grouting flow rate and energy consumption data, the changing trend of grouting energy consumption data is identified. When the energy consumption data begins to rise, a second adjustment range is determined with the current flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to the reduced fixed increment and energy consumption data is collected. Record the combination of grouting parameters corresponding to the lowest grouting energy consumption as the optimal grouting parameters, and generate parameter optimization control instructions; The grouting conditions are adjusted according to the parameter optimization control command. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to the main and auxiliary dual-pulse grouting mode, and the initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters. The main and auxiliary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system. The grouting efficiency coefficient is the ratio of the grout diffusion range per unit time to the grouting energy consumption.

2. The method according to claim 1, characterized in that, Collecting on-site grouting data, obtaining grouting condition parameters, and establishing a grouting database for the construction site includes: Obtain the real-time change rate of pressure, flow, and grouting volume parameters at the inlet, outlet, and intermediate control valve of the grouting pipeline; The sampling timing is dynamically adjusted based on the real-time rate of change, generating a dynamic sampling instruction sequence that includes sampling time points and sampling frequencies; According to the dynamic sampling instruction sequence, grouting condition parameters are collected collaboratively, and the collected analog signals are converted into digital parameters with timestamps. The digitized parameters are written into the original parameter layer in timestamp order, the parameter characteristics are calculated according to the time window and written into the feature parameter layer, and the analysis results are calculated based on the feature parameters and written into the analysis parameter layer to establish a grouting database for the construction site.

3. The method according to claim 1, characterized in that, Historical data from the grouting database is used to establish a baseline value for grouting energy consumption prediction. Real-time grouting process data is dynamically compared with this baseline value to identify grouting condition characteristics and generate condition optimization instructions, including: Obtain historical grouting data containing grouting pressure and grouting flow rate from the grouting database, calculate the rate of change of the historical grouting data and set a dynamic threshold based on the variance of the rate of change, and adaptively divide the time window according to the dynamic threshold. Features are extracted from historical grouting data within a time window to construct feature vectors. Based on the similarity of the feature vectors, grouting condition categories are obtained, and a grouting condition feature library is established. Historical grouting energy consumption data corresponding to each grouting condition category are extracted from the grouting database to establish a grouting energy consumption prediction benchmark value that includes the average energy consumption level and fluctuation range. Real-time acquisition of grouting process data, determination of the current time window, and extraction of feature vectors; The feature vector of the current time window is matched with the grouting condition feature library to identify the current grouting condition category and select the corresponding grouting energy consumption prediction benchmark value. The real-time collected grouting process data is dynamically compared with the grouting energy consumption prediction benchmark value, and an operating condition optimization command is generated when the data deviates from the fluctuation range.

4. The method according to claim 1, characterized in that, Based on the aforementioned correspondence, the changing trend of grouting energy consumption data is identified. When the energy consumption data begins to rise, a second adjustment range is determined with the current flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to a reduced fixed increment, and energy consumption data is collected, including: Based on the correspondence between grouting flow rate and energy consumption data, within the first adjustment range, the grouting flow rate is gradually adjusted from the upper and lower limits of the range towards the middle. Energy consumption data is collected in a bidirectional convergence manner. When the difference in the rate of change of energy consumption data collected in the upper and lower directions is less than the preset deviation, the corresponding grouting flow rate segment is determined as the target flow rate range. Multiple monitoring points are set within the target flow range. When the energy consumption data starts to rise, the current grouting flow rate is determined based on the spatial distribution characteristics of the energy consumption data at each monitoring point. The second adjustment range is then determined with the current grouting flow rate as the center. Within the second adjustment range, the grouting flow rate is adjusted according to the reduced fixed increment, and the grout diffusion radius and grouting pressure data are collected simultaneously. The transport power consumption per unit volume of grout is calculated, and the transport power consumption is compared with the corresponding grouting flow rate to form an energy consumption characteristic curve. The energy consumption gradient is calculated based on the energy consumption characteristic curve. When the energy consumption gradient is less than the preset gradient threshold and the energy consumption data is the lowest, the corresponding grouting flow rate is recorded as the preferred grouting parameter.

5. The method according to claim 1, characterized in that, The grouting conditions are adjusted according to parameter optimization control commands. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to a dual-pulse grouting mode with primary and secondary pulses. Initial parameters are set, and the grouting efficiency coefficient is collected by adjusting the initial parameters. Based on the grouting efficiency coefficient, the primary and secondary pulse sequence parameters are optimized and fed back to the control system, including: Adjust the grouting conditions according to the parameter optimization control command, continuously collect grouting pressure and flow data, calculate the instantaneous power by multiplying the pressure and flow, and obtain the actual energy consumption value of the grouting process; When the grouting energy consumption exceeds the preset control value, the conventional grouting method is switched to the main and auxiliary dual-pulse grouting mode. The pressure distribution ratio of the main and auxiliary pulses is determined based on the fluctuation law of instantaneous power, and the initial parameters of the main and auxiliary dual-pulse sequence are set. The main pulse provides the basic grouting pressure through continuous action, and the auxiliary pulse generates periodic pressure disturbances through intermittent action. By combining the continuous action of the main pulse with the intermittent action of the auxiliary pulse, the initial parameters of the main and auxiliary double pulse sequence are adjusted, and the grouting pressure and grout diffusion range after pressure superposition are collected. The ratio of grout diffusion range per unit time to grouting energy consumption is used as the grouting efficiency coefficient. Based on the grouting efficiency coefficient, the duration, intermittent period, and pressure distribution of the main and auxiliary pulses are optimized, and the optimized main and auxiliary pulse sequence parameters are fed back to the grouting control system.

6. An intelligent optimization and control system for energy consumption during the post-grouting process of bored cast-in-place piles, used to implement the method described in any one of claims 1-5, characterized in that, include: The first unit is used to collect data from the grouting construction site, obtain grouting condition parameters, and establish a grouting database for the construction site. The second unit is used to acquire historical data from the grouting database to establish a grouting energy consumption prediction benchmark value, dynamically compare the real-time collected grouting process data with the grouting energy consumption prediction benchmark value, identify grouting working condition characteristics, and generate working condition optimization instructions. The third unit is used to receive the adjustment index of the working condition optimization instruction, adjust the grouting working condition parameters and collect energy consumption data in real time, determine the parameter combination corresponding to the best grouting efficiency based on the energy consumption data, record the parameter combination as the preferred grouting parameters, and generate parameter optimization control instructions. The fourth unit is used to adjust the grouting conditions according to the parameter optimization control command. When the grouting energy consumption exceeds the preset control value, the conventional grouting mode is switched to the main and auxiliary dual-pulse grouting mode, and the initial parameters are set. The grouting efficiency coefficient is collected by adjusting the initial parameters, and the main and auxiliary pulse sequence parameters are optimized based on the grouting efficiency coefficient and fed back to the control system.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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