Stirring method and system for TRD

Abstract

The invention relates to the technical field of building engineering construction management, in particular to a stirring method and system for TRD. The method comprises the following steps: collecting construction parameter data in real time through a sensor and a measuring device which are arranged on TRD equipment; performing preliminary evaluation on the construction parameter data to obtain construction preliminary evaluation data; performing construction parameter optimization adjustment according to the construction preliminary evaluation data to obtain construction parameter optimization data; and performing real-time construction optimization operation according to the construction parameter optimization data, and performing real-time construction quality evaluation to obtain real-time construction quality evaluation data so as to perform real-time construction quality feedback operation. According to the method, high-risk areas or parameters (such as non-uniform stirring and insufficient grouting) in construction can be recognized in time, and the probability of occurrence of construction quality problems is reduced by adjusting in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering management, and in particular to a stirring method and system for TRD. Background Art

[0002] TRD (deep mixing method) is a construction technology commonly used in modern construction projects. It is widely used in foundation reinforcement, water-stop curtain construction, underground structure support and other fields, especially in soft soil foundations or complex geological conditions. However, traditional TRD construction technology has technical bottlenecks and shortcomings in practical applications, which restricts the further improvement of its efficiency and quality. Limited construction monitoring methods are a major problem in traditional TRD construction. The collection of key parameters (such as mixing depth, grouting volume, mixing uniformity, etc.) during the construction process mainly relies on manual records and intermittent sampling. The real-time and accuracy of data collection are difficult to guarantee, which can easily lead to the loss or delay of key construction data. Especially when the geological conditions are complex or the demand for real-time adjustment is high, traditional monitoring methods are difficult to meet the high-frequency and high-precision construction management needs. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a stirring method and system for TRD to solve at least one of the above technical problems.

[0004] The present application provides a stirring method for TRD, the method comprising:

[0005] S1. Real-time collection of construction parameter data through sensors and measuring devices arranged on TRD equipment;

[0006] S2. Conduct a preliminary evaluation of the construction parameter data to obtain preliminary construction evaluation data;

[0007] S3. Optimize and adjust the construction parameters according to the preliminary construction evaluation data to obtain construction parameter optimization data;

[0008] S4. Perform real-time construction optimization operations based on the construction parameter optimization data, and perform real-time construction quality evaluation to obtain real-time construction quality evaluation data for real-time construction quality feedback operations.

[0009] In the present invention, high-precision sensors are used to collect data in real time, such as mixing depth, grouting volume, mixing speed and mixing uniformity, to ensure comprehensive monitoring of key construction parameters. A preliminary evaluation of the collected construction parameter data can promptly identify problems such as insufficient construction depth, uneven grouting or speed deviation. Optimization and adjustment based on preliminary evaluation data can perform intelligent parameter correction for construction deviations, such as automatically adjusting the grouting flow rate or mixing speed to reduce human intervention. The construction quality assessment results are visually fed back to the operator to help them quickly understand the construction progress and quality and adjust the construction strategy in a timely manner.

[0010] Optionally, S1 includes:

[0011] Real-time collection of construction data through sensors and measuring devices arranged on TRD equipment;

[0012] Sampling partitions are constructed according to the construction data to obtain construction partition sampling data;

[0013] The construction parameter data is obtained by preprocessing the construction parameters according to the sampling data of the construction zones.

[0014] In the present invention, high-precision sensors (such as depth sensors, flow meters, rotary encoders, etc.) are used to achieve millisecond-level real-time monitoring of the construction process to ensure the timeliness of construction data. After the entire construction area is divided into small unit partitions, the system only needs to perform in-depth analysis and optimization on the data of key partitions, reducing the amount of calculation for processing the entire area. Accurate partitioning can quickly locate areas with problems during construction and provide a clear direction for construction adjustments.

[0015] Optionally, the sampling partition construction includes:

[0016] Carry out geological characteristic zoning according to construction data to obtain geological characteristic sampling data;

[0017] Perform construction parameter deviation partitioning on the construction data to obtain deviation weight sampling data;

[0018] Dynamically partition the construction progress of the construction data to obtain dynamic sampling data of the progress;

[0019] Grid partitioning is performed based on geological characteristic sampling data, deviation weight sampling data and progress dynamic sampling data to obtain construction partition sampling data.

[0020] The present invention is divided into zones according to the geological characteristics (such as soil type, permeability, etc.) of different regions, which can accurately reflect the geological differences in the construction area and avoid the "one size fits all" problem of the construction plan. The geological characteristics zoning can provide a reference for the setting of construction parameters (such as the stirring speed and grouting volume of different geological layers), and improve the pertinence and efficiency of the construction. By calculating the deviation of the construction parameters (such as depth deviation and grouting volume deviation), the area where the deviation occurs during the construction process can be quickly located. The deviation zoning can guide the construction equipment to adjust the parameters in real time, quickly correct the deviation, and reduce unnecessary rework during construction. Dynamic zoning can provide real-time decision-making basis for construction management, such as resource scheduling, equipment path optimization, etc., to reduce waiting time and waste in construction. The geological characteristics, deviation weights and progress dynamic data are integrated into the zoning grid model, which can more comprehensively reflect the complex characteristics of the construction area and avoid the limitations of single-dimensional zoning. Grid zoning divides the complex construction area into several small units by refining the construction area, and each unit can be independently evaluated and optimized to improve the accuracy and operability of the construction data.

[0021] Optionally, the geological characteristics zoning includes:

[0022] Identify geological units based on construction data to obtain construction geological unit data;

[0023] Extract stratified data based on construction geological unit data to obtain geological stratification characteristic data;

[0024] Perform spatial characteristic analysis based on geological stratification characteristic data to obtain geological spatial characteristic stratification data;

[0025] Carry out regional classification on the geological spatial characteristic stratification data to obtain geological classification data;

[0026] Based on the geological classification data, characteristic clustering is performed to obtain geological characteristic sampling data.

[0027] In the present invention, different geological units in the region are automatically identified through construction data (such as soil density, permeability coefficient, etc.), avoiding the subjectivity and errors of manual identification in traditional methods. Identifying geological units can provide basic data for construction equipment adjustment, such as adjusting the stirring speed for the clay layer and optimizing the grouting amount for the sand layer. Independent extraction and analysis of the stratified characteristics of different geological units (such as the compression modulus and water content of the soil) can more accurately describe the characteristics of the geological layer. Spatial analysis can locate key areas where the characteristics change significantly within the construction area, providing a basis for key construction monitoring and optimization. The construction area is graded in importance (such as high-risk areas, medium-risk areas, and ordinary areas) according to the characteristic data, so that the construction team can give priority to key areas. Clustering data provides a basis for the intelligent adjustment of construction equipment, such as setting unified construction parameters for areas with similar characteristics to reduce the frequency of equipment adjustment.

[0028] Optionally, the construction parameter deviation partition includes:

[0029] Calculate the deviation of the construction data to obtain the construction deviation matrix data;

[0030] Deviation weights are calculated based on the construction deviation matrix data to obtain deviation weight matrix data;

[0031] Perform deviation distribution analysis based on deviation weight matrix data to obtain deviation distribution data;

[0032] Deviation regions are divided according to the deviation distribution data to obtain deviation weight sampling data.

[0033] The present invention compares actual construction parameters (such as mixing depth, grouting volume, mixing speed, etc.) with design parameters to automatically calculate deviation values ​​and quickly discover non-conformities in the construction process. Deviation weight calculation quantifies the impact of each deviation on construction quality, highlights high-risk key deviations, and facilitates the concentration of resources to solve major problems. Deviation distribution analysis uses spatial statistical methods to intuitively display hot spots where deviations are concentrated, making it easier for construction teams to quickly locate problem areas. Deviation weight sampling data is concentrated in high-risk areas of deviations, which improves the utilization efficiency of sampling resources and avoids waste of resources.

[0034] Optionally, the dynamic partitioning of the construction progress includes:

[0035] Calculate the equipment construction area coverage rate for the construction data to obtain the area coverage rate data;

[0036] Perform construction allocation according to the area coverage data and the preset construction area data to obtain construction allocation data;

[0037] Perform preliminary division of construction progress zones according to the construction allocation data to obtain preliminary progress zone data;

[0038] Perform progress distribution processing based on preliminary progress partition data to obtain progress hotspot area data;

[0039] Extract distribution density features based on progress hotspot area data to obtain progress density feature data;

[0040] The preliminary progress partition data is subjected to boundary optimization fitting based on the progress density feature data to obtain the progress dynamic sampling data.

[0041] The coverage data in the present invention can quantify the actual construction effect of the equipment and provide a basis for the adjustment of construction equipment and path optimization. Construction allocation is based on coverage and preset area data, which can achieve accurate matching of resources and areas, avoiding waste of resources or insufficient allocation. Preliminary zoning data can clarify the construction scope and responsibility area, reduce regional management chaos, and improve construction management efficiency. Progress hot spot area data can highlight the areas with the most concentrated construction activities and provide key directions for resource allocation. Through density feature extraction, the distribution of construction activities in various areas can be accurately quantified. The preliminary zoning data is optimized and fitted using density features to ensure that the zoning boundaries are more in line with the actual construction situation.

[0042] Optionally, the gridded partitioning includes:

[0043] Obtain construction geological area data;

[0044] Gridding is performed according to the construction geological area data using preset gridding parameters to obtain initial grid data;

[0045] Divide the construction area according to the initial grid data to obtain preliminary grid partition data;

[0046] According to the geological characteristic sampling data, the deviation weight sampling data and the progress dynamic sampling data, the grid weight data is allocated to the preliminary grid partition data to obtain the grid weight data;

[0047] The partition optimization is carried out according to the grid weight data to obtain the construction partition sampling data.

[0048] In the present invention, by collecting geological data (such as soil type, stratigraphic structure, permeability, etc.) of the construction area, basic information is provided for the zoning to ensure that the zoning meets the actual geological conditions. Grid division simplifies complex regional data into structured units, reducing the complexity of data processing and calculation. The zoning data clarifies the scope of each construction unit, which is helpful for the division of construction responsibilities and the formulation of regional construction plans. Comprehensive geological characteristics, deviation weights and progress dynamic data are used to ensure that the grid weights can fully reflect the importance and priority of the construction units, and the weight allocation can accurately identify high-priority areas (such as geologically complex areas, large deviation areas, and hot spots), providing clear resource allocation guidelines for the construction team. The grid partition boundaries are adjusted based on the weight data to ensure that the partition results are more reasonable and meet the actual construction needs.

[0049] Optionally, S2 includes:

[0050] Perform parameter anomaly detection on the construction parameter data to obtain abnormal construction parameter data;

[0051] Perform spatial distribution mapping based on abnormal construction parameter data to obtain abnormal spatial distribution data;

[0052] Grouting volume feature extraction and stirring feature extraction are performed according to the abnormal spatial distribution data, and grouting volume feature data and stirring feature data are obtained respectively;

[0053] Perform stirring fluid dynamics simulation according to the grouting volume characteristic data and the stirring characteristic data to obtain stirring fluid dynamics simulation data;

[0054] Perform slurry diffusion simulation and slurry mixing simulation according to the stirring fluid dynamics simulation data to obtain slurry diffusion data and slurry mixing data respectively;

[0055] The construction situation is evaluated based on the slurry diffusion data and slurry mixing data to obtain preliminary construction evaluation data.

[0056] The present invention can accurately detect deviations in construction parameters (such as insufficient grouting volume, too shallow mixing depth), and locate problems in a timely manner. By locating abnormal areas, ineffective inspections of normal areas can be reduced, and the efficiency of solving construction problems can be improved. The extracted feature data can reveal problems in the grouting and mixing process, and provide direction for improving construction technology. The stirring fluid dynamics simulation can accurately describe the flow and mixing behavior of the slurry in the soil, and enhance the understanding of the construction process. The slurry diffusion simulation can accurately predict the diffusion range of the slurry in the soil to ensure that the construction covers the target area. The evaluation data can verify whether the construction results meet the design requirements and provide data support for acceptance.

[0057] Optionally, S3 includes:

[0058] Deviation calculation is performed based on the preliminary construction assessment data to obtain construction deviation data;

[0059] The construction parameters are optimized and adjusted according to the construction deviation data to obtain the construction parameter optimization data.

[0060] The deviation calculation in the present invention can reveal the source of deviation of specific parameters (such as mixing depth, grouting amount or mixing uniformity), helping the construction team to quickly identify problems. Deviation calculation covers multi-dimensional parameters such as depth, speed, grouting amount, etc., providing comprehensive construction quality diagnosis to ensure the integrity and accuracy of the analysis results. Optimization and adjustment can accurately adjust construction parameters (such as mixing speed, grouting amount, construction depth, etc.) according to deviation data, significantly improving the accuracy of the construction process and the accuracy of the results. According to the deviation data and the characteristics of the construction area, optimization and adjustment can formulate construction parameters for different areas or conditions to ensure construction quality and efficiency.

[0061] Optionally, the present application further provides a stirring system for TRD, which is used to perform the stirring method for TRD as described above, and the stirring system for TRD comprises:

[0062] A construction parameter data acquisition module is used to collect construction parameter data in real time through sensors and measuring devices arranged on the TRD equipment;

[0063] A preliminary construction evaluation module is used to perform a preliminary evaluation on the construction parameter data to obtain preliminary construction evaluation data;

[0064] A construction parameter optimization and adjustment module is used to optimize and adjust the construction parameters according to the preliminary construction evaluation data to obtain construction parameter optimization data;

[0065] The real-time construction quality assessment module is used to perform real-time construction optimization operations according to the construction parameter optimization data, and to perform real-time construction quality assessment to obtain real-time construction quality assessment data for real-time construction quality feedback operations.

[0066] The purpose of the present invention is to achieve real-time monitoring and collection of construction parameters by arranging a variety of high-precision sensors (such as depth sensors, grouting flow meters, rotary encoders, etc.) on TRD equipment, and to be able to feedback the construction status in milliseconds. Real-time performance significantly improves the sensitivity of construction monitoring, ensuring that construction problems can be detected and fed back at the moment of occurrence. The present invention conducts a comprehensive analysis of construction parameter data, further refines the evaluation dimensions of construction data through fluid dynamics simulation and hybrid simulation, and can accurately reflect potential problems in the construction process from multiple angles. The preliminary construction assessment identifies and quantifies the deviations of key construction parameters through data modeling and deviation calculation, and can output accurate deviation data for different regions and conditions, providing a scientific basis for construction parameter optimization. Through real-time monitoring and parameter optimization, the present invention can effectively reduce construction deviations, improve mixing uniformity and grouting effects, thereby ensuring the consistency of construction results with design requirements. The dynamic adjustment function of the present invention enables construction equipment to quickly switch optimal parameters in different areas, avoiding the efficiency loss caused by relying on fixed parameters in traditional construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0068] Figure 1 A flowchart showing a stirring method for TRD according to an embodiment of the present invention is shown;

[0069] Figure 2 A flowchart showing the steps of a construction parameter data collection method according to an embodiment is shown;

[0070] Figure 3 A flowchart showing a method for constructing a sampling partition according to an embodiment is shown;

[0071] Figure 4 A flowchart showing the steps of a preliminary construction assessment method according to an embodiment is shown;

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] See also Figures 1 to 4 , the present application provides a stirring method for TRD, the method comprising:

[0077] S1. Real-time collection of construction parameter data through sensors and measuring devices arranged on TRD equipment;

[0078] In one embodiment, a plurality of sensors and measuring devices are installed on the TRD equipment, including displacement sensors, pressure sensors, rotation speed sensors, and depth measuring devices. Through these sensors, the operation data of the TRD equipment, such as the rotation speed of the drill bit, downforce, lifting speed, equipment displacement, and operating depth, are collected in real time. Each sensor converts the signal into digital data through the data acquisition module and transmits it to the central data processing system. To ensure the real-time nature of the data, high-frequency sampling is adopted, and the sampling rate is set to 100 times per second. The original signal is processed by a denoising algorithm to eliminate environmental noise and equipment interference to ensure data accuracy.

[0079] S2. Conduct a preliminary evaluation of the construction parameter data to obtain preliminary construction evaluation data;

[0080] In one embodiment, after the data collection is completed, the system performs a preliminary evaluation of the collected data. The real-time data is compared and analyzed through rule logic or mathematical models. For example, the normal range and threshold range of the construction parameters are preset (such as a rotation speed of 50-70 rpm and a downward force of 20-30 kN). The real-time collected data is compared with these thresholds. If the data deviates from the normal range, it is recorded as an abnormal data point, and the evaluation results are output, including the location of the abnormal point, the degree of impact, and the possible causes. At the same time, the system calculates the parameter rationality score based on the degree of match between the historical construction data and the current data. For example, the rationality score can be derived based on the number of abnormal points, the deviation amplitude, and the distribution trend.

[0081] S3. Optimize and adjust the construction parameters according to the preliminary construction evaluation data to obtain construction parameter optimization data;

[0082] In one embodiment, based on the preliminary evaluation data, the system automatically generates an optimization adjustment plan. Including: identifying construction anomalies and determining their impact on construction results. For example, if the rotation speed is low, uneven mixing will result; if the downforce is too high, equipment damage will result. Generate optimization suggestions based on the construction model. For example, if the rotation speed is lower than 50 rpm, the system recommends increasing the rotation speed to 55 rpm, and limiting the adjustment range according to the operating status of the equipment to ensure equipment stability. Output optimization data to form an adjustment parameter file, for example: the rotation speed is adjusted to 55 rpm, and the downforce is adjusted to 25kN. The adjustment data is transmitted to the control system of the TRD equipment via a wireless network, and the equipment parameters are dynamically updated.

[0083] S4. Perform real-time construction optimization operations based on the construction parameter optimization data, and perform real-time construction quality evaluation to obtain real-time construction quality evaluation data for real-time construction quality feedback operations.

[0084] In one embodiment, the system enters the real-time construction optimization stage after the parameters are adjusted. The optimized parameters are written into the equipment control program, and the equipment performs construction operations according to the new parameters. During the operation, the system continues to monitor the construction parameters in real time through sensors to ensure the execution of the optimized parameters. Real-time construction quality assessment is completed in the following way: the optimized construction parameters are compared with the real-time collected data. If the deviation is within an acceptable range, it is recorded as normal; otherwise, an alarm is issued and readjustment is required. Based on the real-time construction depth, rotation speed and mud distribution data, the construction quality index is calculated, such as the mud uniformity score and the mixing depth consistency score. After the real-time construction quality assessment data is generated, the system outputs the results to the construction monitoring platform. If the quality score is lower than the threshold (such as less than 80 points), the feedback operation is triggered to re-optimize the parameters.

[0085] Optionally, S1 includes:

[0086] S11, collect construction data in real time through sensors and measuring devices arranged on TRD equipment;

[0087] In one embodiment, the following sensors are installed on the TRD equipment: rotation speed sensor: real-time monitoring of the rotation speed of the drill bit; pressure sensor: monitoring the stirring down pressure; displacement sensor: monitoring the displacement state of the equipment; mud concentration sensor: monitoring the mud uniformity and concentration changes; depth sensor: measuring the current construction depth of the drill bit. The sensor converts the signal into digital data through the acquisition module and transmits it to the data processing unit. To ensure data accuracy: high-frequency sampling technology (such as 100 times / second) is used to capture the dynamic changes of the construction process; and a denoising algorithm is used to filter out environmental interference signals (such as invalid signals caused by wind and mechanical vibration). The original construction data collected in real time are as follows: rotation speed: 52 rpm; down pressure: 21kN; equipment vertical depth: 9.7m; mud concentration: 36%.

[0088] S12, constructing sampling partitions according to the construction data to obtain construction partition sampling data;

[0089] In one embodiment, the construction data collected in real time is partitioned in space and time to construct construction partition sampling data. Specifically, it includes spatial partitioning: taking the construction depth as the basis for division, for example, every 10m depth is a partition, and collecting the construction parameters (rotation speed, pressure, concentration, etc.) in the corresponding partition. Time partitioning: taking time as the unit (such as a sampling cycle every 10 seconds) to obtain the change trend of the construction parameters. Calculate the statistical values ​​(mean, variance, etc.) and distribution characteristics of the sampling data of each partition. According to the partitioning rules, the area with a depth of 97m is partitioned and sampled: Depth partition 1 (0-10m): rotation speed 53 rpm, pressure 22kN, mud concentration 38%; Depth partition 2 (10-20m): rotation speed 51 rpm, pressure 21kN, mud concentration 36%; Depth partition 3 (20-30m): rotation speed 52 rpm, pressure 20kN, mud concentration 37%.

[0090] S13. Perform construction parameter preprocessing according to the construction zone sampling data to obtain construction parameter data.

[0091] In one embodiment, the sampled partition data is preprocessed, including: for the fluctuation of high-frequency sampled data, a smoothing algorithm (such as a sliding mean method) is used to remove small fluctuations. For example, the data of three consecutive time points are averaged to obtain a stable value. The statistical model is used to identify abnormal points in the partition data, such as a sudden increase or decrease in the pressure value, and these irregular data are eliminated. Original partition data (depth 2-3m): rotation speed [52,51,53] rpm, pressure [20,22,19] kN, mud concentration [37%,35%,36%]. Data after smoothing: rotation speed 52 rpm, pressure 20.3 kN, mud concentration 36%. After eliminating the abnormal values, the standardized results (normalization can also be omitted, and the results are directly stored directly, here is the result after normalization) are: rotation speed: 0.74; down pressure: 0.68; mud concentration: 0.72. The construction parameter data were obtained: rotation speed 52 rpm, downward pressure 20.3 kN, and mud concentration 36%.

[0092] Optionally, the sampling partition construction includes:

[0093] S121, performing geological characteristic zoning according to the construction data to obtain geological characteristic sampling data;

[0094] In one embodiment, the geological characteristic partition is based on geological data collected by sensors, such as drill resistance, mud concentration, underground flow rate and other characteristics. The collected data is layered according to depth, and each 10-meter depth is defined as a preliminary partition. The data in each partition is statistically analyzed to extract geological characteristic indicators (such as average resistance, concentration gradient). Then, similar geological characteristic areas are classified into one category through clustering method. (Depth 0-30 meters): Depth 0-10m: resistance value [20,21,22] kN, mud concentration [30%, 32%, 31%]; Depth 10-20m: resistance value [25,26,24] kN, mud concentration [35%, 36%, 34%]; Depth 20-30m: resistance value [40,42,41] kN, mud concentration [50%, 52%, 51%]. Results: Depths of 0-10m and 10-20m are classified into one category (geologically loose area); depth of 20-30m is classified into another category (geologically dense area).

[0095] S122, performing construction parameter deviation partitioning on the construction data to obtain deviation weight sampling data;

[0096] In one embodiment, the construction parameters (rotation speed, downforce, mud concentration, etc.) collected in real time are compared with the design parameters to calculate the deviation value. According to the size of the deviation value, the construction area is divided into zones, and the area with large deviation is given a higher weight. Actual data and design parameters: Depth 0-10m: Design rotation speed 60 rpm, actual [58,57,59] rpm; deviation mean = |60-58| = 2 rpm. Depth 10-20m: Design rotation speed 60 rpm, actual [62,65,64] rpm; deviation mean = |60-64| = 4 rpm. Depth 20-30m: Design rotation speed 60 rpm, actual [55,53,54] rpm; deviation mean = |60-54| = 6 rpm. Bias weight: Depth 0-10m: bias weight = 1 (low); Depth 1-2m: bias weight = 2 (medium); Depth 2-3m: bias weight = 3 (high).

[0097] S123, dynamically partitioning the construction progress of the construction data to obtain dynamic progress sampling data;

[0098] In one embodiment, according to the real-time collected equipment depth change data, the drill bit movement speed and dwell time are calculated to analyze the dynamic changes in the construction progress. The areas with slow progress are partitioned separately and marked. Relationship between depth change and time: 0-10m depth: takes 100 seconds, speed = 1 / 10 = 0.1m / s; 10-20m depth: takes 150 seconds, speed = 1 / 15≈0.067m / s; 20-30m depth: takes 250 seconds, speed = 1 / 25 = 0.04m / s. Partition results: Depth 0-10m: normal progress, marked as green area; Depth 10-20m: slightly slower progress, marked as yellow area; Depth 20-30m: slower progress, marked as red area.

[0099] S124. Grid partitioning is performed according to the geological characteristic sampling data, the deviation weight sampling data and the progress dynamic sampling data to obtain the construction partition sampling data.

[0100] In one embodiment, the data of geological characteristic partition, deviation weight partition and progress dynamic partition are superimposed to generate a multidimensional data matrix. The construction area is divided into small unit grids using a gridding method, and the characteristics of each grid are determined by the superimposed data. Each grid is assigned a label (such as geological characteristic category, deviation weight level, construction progress status). Data matrix (superimposed three-dimensional data): Depth 0-10m: geological category = loose area, deviation weight = 1, progress = normal; Depth 10-20m: geological category = loose area, deviation weight = 2, progress = slightly slow; Depth 20-30m: geological category = dense area, deviation weight = 3, progress = slow. Gridding results: Grid 1 (0-10m): label = "loose area, low deviation, normal progress"; Grid 2 (10-20m): label = "loose area, medium deviation, slightly slow progress"; Grid 3 (20-30m): label = "dense area, high deviation, slow progress".

[0101] Optionally, the geological characteristics zoning includes:

[0102] Identify geological units based on construction data to obtain construction geological unit data;

[0103] In one embodiment, the sensors on the TRD equipment are used to collect data such as resistance, mud concentration, and groundwater flow rate within the depth range during the construction process. The collected data is layered by depth, and each 10-meter depth is a preliminary partition. The data in each partition is statistically calculated to extract characteristic indicators (such as resistance mean, concentration gradient, and water flow velocity change). According to these characteristic indicators, areas with similar geological conditions are classified into one category through cluster calculation to form geological characteristic partitions, such as loose areas, clay areas, and rock areas. For example, at a depth of 0-10m: the average resistance is 20kN, the mud concentration is 30%, and the groundwater flow rate is 0.1m / s; it is initially determined to be a loose sand layer. At a depth of 10-20m: the average resistance is 35kN, the mud concentration is 40%, and the groundwater flow rate is 0.05m / s; it is initially determined to be a clay layer. At a depth of 20-30m: the average resistance is 50kN, the mud concentration is 50%, and the groundwater flow rate is 0.02m / s; it is initially determined to be a dense rock layer.

[0104] Extract stratified data based on construction geological unit data to obtain geological stratification characteristic data;

[0105] In one embodiment, the real-time rotation speed, pressure value and other parameters during the construction process are collected, and compared with the design target parameters item by item to calculate the deviation. The deviation is defined as the absolute difference between the actual value and the target value. The deviation range is divided into three categories: low deviation (deviation ≤ 3%), medium deviation (deviation 3%-5%), and high deviation (deviation> 5%). Deviation weights are assigned according to the deviation of each partition, and areas with high weights indicate that construction requires more attention. For example, at a depth of 0-10m: the rotation speed target is 60 rpm, the actual value is [59, 61, 60], and the deviation mean = |60-59| = 1 rpm; the deviation is low, and the weight is 1. At a depth of 10-20m: the rotation speed target is 60 rpm, the actual value is [64, 62, 63], and the deviation mean = |60-63| = 3 rpm; the deviation is medium, and the weight is 2. Depth 20-30m: The rotation speed target is 60 rpm, the actual value is [68,65,67], the deviation mean = |60-67| = 7 rpm; the deviation is high, and the weight is 3.

[0106] Perform spatial characteristic analysis based on geological stratification characteristic data to obtain geological spatial characteristic stratification data;

[0107] In one embodiment, a depth sensor is used to record the depth change and time data of the drill bit, calculate the construction depth per unit time, and analyze the construction speed. The construction speed threshold is defined as normal speed (0.1m / s≤speed≤0.15m / s), slow speed (speed<0.1m / s), and too fast speed (speed>0.15m / s). The construction progress is dynamically partitioned according to the speed status and marked as "normal area", "slow area" or "fast area". For example, at a depth of 0-10m: the drill bit takes 80 seconds to move, and the construction speed = 1 / 8 = 0.125m / s; it is judged to be normal speed and marked as "normal area". At a depth of 10-20m: the drill bit takes 120 seconds to move, and the construction speed = 1 / 12≈0.083m / s; it is judged to be slow speed and marked as "slow area". At a depth of 20-30m: the drill bit takes 60 seconds to move, and the construction speed = 1 / 6≈0.167m / s; it is judged to be too fast speed and marked as "fast area".

[0108] Carry out regional classification on the geological spatial characteristic stratification data to obtain geological classification data;

[0109] In one embodiment, according to the results of the spatial characteristic analysis, the characteristics of each layer are regionally graded and divided into different levels: the basis for the grade division: factors such as continuity, stability and construction difficulty. Level 1 (easy to construct): high continuity, low resistance, and uniform mud; Level 2 (medium construction difficulty): good continuity, medium resistance, and local fluctuations; Level 3 (difficult construction): poor continuity, high resistance, and uneven mud. For example, loose sand layer (0-10m): high continuity, level 1 (easy to construct); clay layer (10-20m): good continuity, level 2 (medium construction difficulty); rock layer (20-30m): high continuity but high resistance, level 3 (difficult construction).

[0110] Based on the geological classification data, characteristic clustering is performed to obtain geological characteristic sampling data.

[0111] In one embodiment, the geological characteristic data of each graded area are clustered and divided into several similar characteristic groups to form geological characteristic sampling data: geological layers are classified into different characteristic groups based on characteristics such as resistance mean, concentration distribution and continuity; loose sand layers and clay layers are classified into the same group because of their similar resistance; rock layers are classified into another group because of their higher resistance. The sampling data of each group includes representative resistance values, concentration ranges and spatial distribution characteristics. For example, Group 1 (loose sand layer + clay layer): resistance mean 30kN, concentration range 30%-40%, good spatial continuity; Group 2 (rock layer): resistance mean 50kN, concentration range 50%, stable spatial distribution.

[0112] Optionally, the construction parameter deviation partition includes:

[0113] Calculate the deviation of the construction data to obtain the construction deviation matrix data;

[0114] In one embodiment, real-time construction parameter data including rotation speed, downforce and mud concentration are collected from the TRD equipment. Combined with the design target parameters, the deviation between the actual value and the target value of each depth is calculated to obtain a deviation value matrix: the deviation value is defined as the difference between the actual value and the target value. The deviation matrix is ​​organized according to the categories of depth and construction parameters, and each unit represents the parameter deviation corresponding to a certain depth. Actual construction data (depth 0-3m): Depth 0-10m: rotation speed 58 rpm, downforce 19kN, mud concentration 32%; Depth 10-20m: rotation speed 62 rpm, downforce 21kN, mud concentration 37%; Depth 20-30m: rotation speed 67 rpm, downforce 24kN, mud concentration 42%. Design target parameters: rotation speed 60 rpm, downforce 20kN, mud concentration 35%. Deviation matrix: Depth 0-10m: [rotation speed -2, down pressure -1, mud concentration -3]; Depth 10-20m: [rotation speed +2, down pressure +1, mud concentration +2]; Depth 20-30m: [rotation speed +7, down pressure +4, mud concentration +7].

[0115] Deviation weights are calculated based on the construction deviation matrix data to obtain deviation weight matrix data;

[0116] In one embodiment, the weight value of each construction parameter is calculated based on the deviation matrix to indicate the degree of influence of each parameter on the construction quality. The deviation weight is based on the absolute value of the parameter deviation. The larger the deviation, the higher the weight: small deviation: absolute deviation ≤ 2, weight = 1; medium deviation: absolute deviation 3-5, weight = 2; large deviation: absolute deviation ≥ 6, weight = 3. A weight matrix is ​​generated for each depth, indicating the deviation weight level of each parameter. For example, the deviation weight matrix: depth 0-10m: [rotation speed 1, down pressure 1, mud concentration 1]; depth 10-20m: [rotation speed 1, down pressure 1, mud concentration 1]; depth 20-30m: [rotation speed 3, down pressure 2, mud concentration 3].

[0117] Perform deviation distribution analysis based on deviation weight matrix data to obtain deviation distribution data;

[0118] In one embodiment, the distribution trend of the deviation of construction parameters at each depth is analyzed, including the deviation size and regional distribution range: the regional depth range corresponding to different deviation levels (small, medium, and large) is statistically analyzed; and the deviation concentration area (continuous depth range with large deviation) is identified. The deviation distribution data includes the deviation level of each parameter at different depths and its distribution characteristics. Deviation level statistics: small deviation (level 1): depth 0-20m; medium deviation (level 2): ​​depth 20-30m (partial parameters, such as lower pressure); large deviation (level 3): depth 20-30m (rotation speed and mud concentration). Distribution characteristics: depth 0-20m is a continuous area with small deviations; depth 20-30m is a concentrated area of ​​medium to large deviations.

[0119] Deviation regions are divided according to the deviation distribution data to obtain deviation weight sampling data.

[0120] In one embodiment, according to the deviation distribution results, the construction area is divided into several deviation weight areas: low deviation area: all parameter deviation levels are 1; medium deviation area: areas with parameter deviation level 2; high deviation area: areas with parameter deviation level 3. Each area generates a deviation weight sampling data, including depth range, main deviation parameters and deviation weight information. Deviation area division results: Depth 0-20m: low deviation area; Depth 20-30m: high deviation area (rotation speed and mud concentration deviation level 3). Deviation weight sampling data: low deviation area: depth 0-20m, rotation speed, downforce, mud concentration deviation weights are all 1; high deviation area: depth 20-30m, rotation speed deviation weight 3, downforce deviation weight 2, mud concentration deviation weight 3.

[0121] Optionally, the dynamic partitioning of the construction progress includes:

[0122] Calculate the equipment construction area coverage rate for the construction data to obtain the area coverage rate data;

[0123] In one embodiment, based on the data of the depth sensor and the displacement sensor, the coverage trajectory of the equipment construction head in each area is recorded. The coverage rate of the construction area is defined as the proportion of the area covered by the equipment in a certain area to the total area of ​​the area. The coverage rate data is generated by calculating the coverage trajectory within the depth range layer by layer. Construction depth division (0-30m, every 10m is a layer): Depth 0-10m: the construction head coverage trajectory is 80%, and the coverage rate is 0.8; Depth 10-20m: The construction head coverage trajectory is 60%, and the coverage rate is 0.6; Depth 20-30m: The construction head coverage trajectory is 90%, and the coverage rate is 0.9. Area coverage data: [Depth 0-10m: coverage rate 0.8]; [Depth 10-20m: coverage rate 0.6]; [Depth 20-30m: coverage rate 0.9].

[0124] Perform construction allocation according to the area coverage data and the preset construction area data to obtain construction allocation data;

[0125] In one embodiment, the regional coverage is matched with the preset construction area data (such as area, priority), and the working time and frequency of the construction equipment are allocated. Areas with low coverage need to allocate more equipment time for supplementary construction, and areas with high coverage reduce the construction frequency. The preset regional data is that each layer depth must reach a coverage rate of 0.85. Construction allocation: Depth 0-10m: actual coverage rate 0.8, lower than the target value 0.85, allocate additional equipment time of 20 minutes; Depth 10-20m: actual coverage rate 0.6, lower than the target value 0.85, allocate additional equipment time of 40 minutes; Depth 2-3m: actual coverage rate 0.9, higher than the target value 0.85, no additional construction is required. Construction allocation data: [Depth 0-10m: additional time 20 minutes]; [Depth 10-20m: additional time 40 minutes]; [Depth 20-30m: no additional time].

[0126] Perform preliminary division of construction progress zones according to the construction allocation data to obtain preliminary progress zone data;

[0127] In one embodiment, the construction area is divided into "fast area", "normal area" and "slow area" according to the construction allocation data: Fast area: the coverage rate is higher than the target value and the construction progress is fast; Normal area: the coverage rate is close to the target value and the construction progress is normal; Slow area: the coverage rate is lower than the target value and the construction progress is slow. Preliminary division: Depth 0-10m: coverage rate 0.8, classified as a slow area; Depth 10-20m: coverage rate 0.6, classified as a slow area; Depth 2-3m: coverage rate 0.9, classified as a fast area. Preliminary progress partition data: [Depth 0-10m: Slow area]; [Depth 10-20m: Slow area]; [Depth 20-30m: Fast area].

[0128] Perform progress distribution processing based on preliminary progress partition data to obtain progress hotspot area data;

[0129] In one embodiment, the preliminary progress partition data is distributed and counted to identify the hot spots of construction progress. For example, the hot spots are areas with slow or fast progress and need to be paid special attention to. By superimposing the regional distribution characteristics, the scope of the hot spots is marked. For example, the slow hot spots: depth 0-20m, low coverage; the fast hot spots: depth 20-30m, high coverage. Progress hot spots data: slow hot spots: [depth 0-20m]; fast hot spots: [depth 20-30m].

[0130] Extract distribution density features based on progress hotspot area data to obtain progress density feature data;

[0131] In one embodiment, construction density features are analyzed in each hotspot area, including construction frequency, coverage distribution, and equipment movement trajectory. Feature extraction includes: construction density distribution (frequent / sparse); equipment coverage uniformity (uniform / uneven). Feature analysis: Slow area (depth 0-20m): sparse construction density, uneven coverage; fast area (depth 20-30m): high construction density, uniform coverage. Progress density feature data: Slow area: [sparse density, uneven coverage]; fast area: [high density, uniform coverage].

[0132] The preliminary progress partition data is subjected to boundary optimization fitting based on the progress density feature data to obtain the progress dynamic sampling data.

[0133] In one embodiment, the progress density feature data is used to optimize and adjust the boundaries of the preliminary partitions, such as performing cluster calculation on the progress density feature data and classifying the cluster centers to obtain feature classification data, determining the boundaries to be optimized based on the feature classification data to obtain the boundary data to be optimized, performing optimization curve parameter mapping based on the feature classification data to obtain optimization curve parameter data, and performing Bezier curve fitting on the boundary data to be optimized based on the optimization curve parameter data to obtain progress dynamic sampling data. The boundaries within the hot spot area are refitted according to the construction density to make the partition range more accurate; the adjusted boundaries ensure that the distribution of slow areas and fast areas is more in line with the actual construction status. Optimization and adjustment: The boundaries of the slow area (depth 0-20m) are fine-tuned to classify the depth 5-20m as a slow area; the boundaries of the fast area (depth 20-30m) remain stable. Progress dynamic sampling data: slow area: [depth 5-20m]; fast area: [depth 20-30m].

[0134] Optionally, the gridded partitioning includes:

[0135] Obtain construction geological area data;

[0136] In one embodiment, the construction geological area data is collected by sensors on the construction equipment, including information such as geological characteristics, depth range, construction progress and coverage. The data is recorded by depth and area to clarify the distribution of geological characteristics and construction status of each area, such as the distinction between rock layer, clay layer and loose layer. For example, depth 0-10m: loose sand layer, coverage rate 80%, low rotation speed deviation; depth 10-20m: clay layer, coverage rate 60%, medium rotation speed deviation; depth 20-30m: rock layer, coverage rate 90%, high rotation speed deviation. Geological area data: loose sand layer: [depth 0-10m]; clay layer: [depth 10-20m]; rock layer: [depth 20-30m].

[0137] Gridding is performed according to the construction geological area data using preset gridding parameters to obtain initial grid data;

[0138] In one embodiment, the construction area is divided into several grids of equal size according to the depth and horizontal position, and the grid size is preset (such as 1m×1m×1m per grid). Each grid inherits the geological characteristics and construction data of the area where it is located to form the initial grid data. Grid division: Depth 0-10m: divided into 100×100 grids, each grid records the loose sand layer data; Depth 10-20m: divided into 100×100 grids, each grid records the clay layer data; Depth 20-30m: divided into 10×10 grids, each grid records the rock layer data. Initial grid data: Grid 1 (depth 0-10m): geology = loose sand layer, coverage = 80%, low deviation; Grid 2 (depth 10-20m): geology = clay layer, coverage = 60%, medium deviation; Grid 3 (depth 20-30m): geology = rock layer, coverage = 90%, high deviation.

[0139] Divide the construction area according to the initial grid data to obtain preliminary grid partition data;

[0140] In one embodiment, according to the geological characteristics and coverage rate of the area where the grid is located, the grid is preliminarily divided into "priority construction area" and "secondary construction area" through a preset parameter table: priority construction area: low coverage rate, complex geological characteristics; secondary construction area: high coverage rate, simple geological characteristics. Preliminary division: depth 0-10m: coverage rate 80%, classified as secondary construction area; depth 10-20m: coverage rate 60%, classified as priority construction area; depth 20-30m: coverage rate 90%, classified as secondary construction area. Preliminary grid partition data: priority construction area: [depth 10-20m grid]; secondary construction area: [depth 0-10m grid], [depth 20-30m grid].

[0141] According to the geological characteristic sampling data, the deviation weight sampling data and the progress dynamic sampling data, the grid weight data is allocated to the preliminary grid partition data to obtain the grid weight data;

[0142] In one embodiment, the comprehensive geological characteristic sampling data, deviation weight sampling data and progress dynamic sampling data are weighted by preset weights to assign weights to each grid: the grid with high geological complexity has a high weight; the grid with large deviation has a high weight; the grid with low coverage has a high weight. The grid weight is the result of the superposition of the above three types of data, indicating the priority of grid construction. Geological characteristic sampling data: loose sand layer weight = 1, clay layer weight = 2, rock layer weight = 3. Deviation weight sampling data: low deviation weight = 1, medium deviation weight = 2, high deviation weight = 3. Progress dynamic sampling data: coverage < 70%: weight = 3, coverage 70%-85%: weight = 2, coverage > 85%: weight = 1. Grid weight calculation results: Grid 1 (depth 0-10m): geology = 1 + deviation = 1 + coverage = 2, total weight = 4; Grid 2 (depth 10-20m): geology = 2 + deviation = 2 + coverage = 3, total weight = 7; Grid 3 (depth 20-30m): geology = 3 + deviation = 3 + coverage = 1, total weight = 7. Grid weight data: Grid 1 weight = 4; Grid 2 weight = 7; Grid 3 weight = 7.

[0143] The partition optimization is carried out according to the grid weight data to obtain the construction partition sampling data.

[0144] In one embodiment, the partition boundaries are optimized according to the grid weights: high-weight grids are preferentially included in the "priority construction area"; the boundaries of low-weight grids are adjusted to make the partition more accurate. Optimization rules: Weight ≥ 6: included in the priority construction area; Weight < 6: included in the secondary construction area. Optimization results: Priority construction area: 10-20m depth grid, 20-30m depth partial grid; secondary construction area: 0-10m depth grid, 20-30m depth partial grid. Construction partition sampling data: Priority construction area: [10-20m depth grid, 20-30m depth partial grid]; Secondary construction area: [0-10m depth grid, 20-30m depth partial grid].

[0145] Optionally, S2 includes:

[0146] S21, performing parameter anomaly detection on the construction parameter data to obtain construction parameter abnormality data;

[0147] In one embodiment, the construction parameter data (rotation speed, downforce, mud concentration, etc.) collected by the sensor are compared with the preset normal parameter range. Detect whether there are abnormal values ​​(outside the preset range), and mark the abnormal parameters and their depth and time points. Collected data: Depth 0-10m: rotation speed 58 rpm, downforce 18kN, mud concentration 30%; Depth 10-20m: rotation speed 62 rpm, downforce 22kN, mud concentration 35%; Depth 20-30m: rotation speed 70 rpm, downforce 25kN, mud concentration 50%. Normal range: rotation speed 50-65 rpm, downforce 20-25kN, mud concentration 30%-40%. Detection results: Depth 0-10m: abnormal rotation speed; Depth 20-30m: abnormal rotation speed and mud concentration. Abnormal construction parameter data: Depth 0-10m: [Abnormal rotation speed]; Depth 20-30m: [Abnormal rotation speed, abnormal mud concentration].

[0148] In one embodiment, a more detailed parameter anomaly comparison is performed based on the aforementioned partitioned data, thereby obtaining multi-dimensional construction parameter anomaly data.

[0149] S22, performing spatial distribution mapping according to the abnormal data of the construction parameters to obtain abnormal spatial distribution data;

[0150] In one embodiment, the distribution of abnormal parameters is spatially mapped in the construction area: the abnormal area is marked according to the depth and horizontal position of the abnormal parameters; the abnormal parameter distribution map is drawn to clarify the abnormal concentration area. Spatial mapping: Depth 0-10m: the abnormal parameter coverage range is the entire horizontal area; Depth 20-30m: the abnormal parameter coverage range is the 30% grid in the middle of the area. Abnormal spatial distribution data: Depth 0-10m: [abnormal parameter = rotation speed, distribution = full coverage]; Depth 20-30m: [abnormal parameter = rotation speed and mud concentration, distribution = local coverage].

[0151] S23, extracting grouting volume characteristics and stirring characteristics according to the abnormal spatial distribution data, and obtaining grouting volume characteristic data and stirring characteristic data respectively;

[0152] In one embodiment, the grouting volume feature extraction is to analyze the mud injection volume and diffusion characteristics of the abnormal area. Low grouting area: the mud concentration is low and the diffusion is insufficient; high grouting area: the mud concentration is too high and the diffusion is too fast. The stirring feature extraction is to extract the stirring uniformity and strength characteristics of the abnormal area by combining the rotation speed and the downward pressure. Depth 0-10m: Grouting volume feature: low mud concentration, insufficient grouting volume; stirring feature: low rotation speed, insufficient stirring intensity. Depth 20-30m: Grouting volume feature: high mud concentration, too fast diffusion; stirring feature: too high rotation speed, poor stirring uniformity. In summary, grouting volume feature: depth 0-10m = low, depth 20-30m = high; stirring feature: depth 0-10m = weak, depth 20-30m = uneven.

[0153] S24, performing stirring fluid dynamics simulation according to the grouting volume characteristic data and the stirring characteristic data to obtain stirring fluid dynamics simulation data;

[0154] In one embodiment, the mathematical equation of stirring fluid dynamics is used to simulate the movement trajectory and distribution of the slurry under different fluid dynamics conditions during the stirring process of the construction equipment in the abnormal area: low stirring intensity area: the fluid dynamics are weak and the slurry diffuses slowly; high stirring intensity area: the fluid dynamics are strong, but local turbulence is prone to occur, affecting uniformity. Simulation results: Depth 0-10m: insufficient stirring fluid dynamics, small slurry diffusion radius; Depth 20-30m: strong fluid dynamics, but local turbulence affects diffusion uniformity. Stirring fluid dynamics simulation data: Depth 0-10m: fluid dynamics = weak; Depth 20-30m: fluid dynamics = strong, local turbulence.

[0155] In one embodiment, the velocity field, pressure field and shear stress distribution of the slurry are calculated through stirring fluid dynamics simulation to evaluate the stirring efficiency and mixing effect, wherein the grouting volume characteristic data include the grouting velocity and the slurry volume flow rate, and the stirring characteristic data include the stirring speed, blade geometric parameters, stirring power, etc.

[0156] For an incompressible fluid, the mass conservation equation of the fluid is: in is a vector differential operator (gradient operator), which represents the vector combination of partial derivatives with respect to the spatial coordinates x, y, and z, and V is the velocity field of the fluid (v = [u, v, w], representing the velocity components in the x, y, and z directions, respectively).

[0157] The dynamic behavior of the fluid during stirring is described by the equation: ρ is the density of the slurry, is the symbol of partial derivative, t is the time parameter of fluid motion, v is the velocity field of the fluid, is the gradient operator, is the pressure field, is the Laplace operator of the velocity field, which describes the diffusion change of the velocity field and reflects the viscous dissipation in the fluid. μ is the dynamic viscosity coefficient and f is the volume force, which indicates the volume force (such as gravity, centrifugal force, electromagnetic force, etc.) acting on the fluid.

[0158] Grouting volume characteristic data include grouting speed (Q): the volume of slurry injected per unit time, in m 3 / s. Grouting position (x, y, z): the spatial position of the grouting point, which affects the initial velocity distribution of the fluid. Stirring characteristic data include stirring speed (ω): the angular velocity of the stirrer, in rad / s. Stirring radius (R): the radius of the stirrer blade, which affects the shear force distribution. Power The output power of the agitator affects the stirring intensity. The agitator power is related to the viscosity and density of the slurry and can be calculated by the formula: in is the agitator power, K is the power coefficient, ρ is the density of the slurry, N is the density of the slurry, and D is the diameter of the agitator blade.

[0159] The velocity field during the mixing process can be obtained by superimposing the shear force generated by the agitator and the grouting flow rate: V = V inj +V shear , where V inj is the initial velocity field generated by grouting, and the calculation formula is: V inj =Q / A, where Q is the grouting speed, A is the cross-sectional area of ​​the grouting pipe, V shear is the shear velocity field generated by the stirrer, which is determined by the stirring speed and radius, V shear =ωR.

[0160] The shear stress τ of the slurry represents the force of stirring on the slurry. τ is the shear stress of the slurry, μ is the dynamic viscosity of the fluid, is the sign of the partial derivative, v is the velocity field during the stirring process, and r is the radial distance from the center of the stirrer. The magnitude of the shear stress determines the uniformity of the slurry mixing.

[0161] The vortex intensity Ω is used to evaluate the strength of the vortex during stirring. Where Ω is the vortex strength, is a gradient operator used to calculate the change in velocity field, v is the fluid velocity field, which is the motion distribution of the fluid in space. A higher vortex strength usually means better mixing effect.

[0162] The energy dissipation rate ∈ during the stirring process is an important indicator of stirring efficiency. ∈ is the energy dissipation rate, which indicates the rate of kinetic energy dissipated per unit volume due to the viscosity of the fluid. μ is the dynamic viscosity, which indicates the intensity of friction in the fluid and is an important parameter that affects the shear stress and energy dissipation of the fluid. is a gradient operator, which indicates the change of partial derivative of fluid velocity field in space coordinates. v is the fluid velocity field, which indicates the velocity of fluid at each spatial point and is a basic variable in fluid dynamics. : is a tensor operator, which indicates the dot product between two second-order tensors. The result is a scalar. is the double dot product of the velocity gradient tensor, which describes the intensity of the change in the fluid velocity field gradient and represents the velocity gradient tensor The double dot product with itself is used to calculate the energy dissipation of the fluid due to velocity changes. The greater the energy dissipation rate, the higher the stirring effectiveness, but too large a dissipation rate leads to energy waste or over-mixing of the slurry. The specific calculation process of the double dot product of the velocity gradient tensor is:

[0163]

[0164] in is the double dot product of the velocity gradient tensor, which represents the component-by-component sum of the velocity gradient tensor and is used to describe the severity of the fluid velocity change and the local shear behavior of the fluid. i is the index of the velocity direction, j is the index of the spatial coordinate direction, and v is the index of the velocity direction. i is the velocity component, which represents the components of the velocity field v in the x, y, and z directions, respectively: v1 is the component of the velocity in the x direction, v2 is the component of the velocity in the y direction, v3 is the component of the velocity in the z direction, and x j Are spatial coordinate components, representing the coordinate components of the space where the fluid is located, respectively: x1=x: x coordinate of the space, x2=y: y coordinate of the space, x3=z: z coordinate of the space.

[0165] The continuous Navier-Stokes equations are discretized using the finite difference method, finite element method or finite volume method to obtain discrete algebraic equations. The initial conditions and boundary conditions are set, and computational fluid dynamics (CFD) software (such as ANSYS Fluent, OpenFOAM) is used to solve the velocity field, pressure field, shear stress distribution, vortex and energy distribution during the stirring process to obtain the stirring fluid dynamic simulation data.

[0166] S25, performing slurry diffusion simulation and slurry mixing simulation according to the stirring fluid dynamics simulation data, and obtaining slurry diffusion data and slurry mixing data respectively;

[0167] In one embodiment, the index of the diffusion range is determined, and the diffusion range is defined by taking the slurry concentration or fluid velocity attenuation as an index. For example, the diffusion radius is set to the position when the fluid velocity decreases to 10% of the initial velocity. The slurry concentration distribution is to analyze the concentration gradient change and determine the diffusion boundary of the slurry. According to the velocity field and concentration field, the isoline or isosurface of the fluid diffusion is drawn to intuitively display the diffusion range. The diffusion range of different regions is compared to determine whether the diffusion is uniform. The dynamic region is divided according to the stirring fluid dynamic simulation data to perform dynamic strength analysis, such as a strong dynamic region: the velocity field and vortex intensity are large, the diffusion range is large, but the diffusion uniformity is poor. Weak dynamic region: the velocity field and vortex intensity are small, the diffusion range is small and uneven. Regional classification: Combined with the dynamic data, the analysis area is divided into strong, medium and weak dynamic regions, and the diffusion characteristics of each region are evaluated. Diffusion uniformity is judged according to the stirring fluid dynamic simulation data to evaluate the diffusion uniformity and compare the concentration distribution of the slurry within the diffusion range. Calculate the concentration variance or other statistical indicators. The smaller the variance, the more uniform the diffusion. Judgment criteria: Uniform diffusion: The concentration distribution difference is small and the concentration field is stable. Inhomogeneous diffusion: The concentration field changes dramatically, and the local concentration is too high or too low.

[0168] In one embodiment, the speed and range of slurry diffusion are analyzed to determine whether the diffusion is uniform: in areas with weak power, the diffusion range is small; in areas with strong power, the diffusion range is large but uneven. Slurry mixing simulation: Evaluate the mixing efficiency of the slurry and analyze the uniformity: in areas with insufficient power, the mixing is uneven; in areas with excessive power, turbulence leads to low efficiency. Simulation results: Depth 0-10m: insufficient diffusion and uneven mixing; Depth 20-30m: large diffusion range but low mixing efficiency. Diffusion and mixing data: Slurry diffusion data: Depth 0-10m = insufficient, depth 20-30m = uneven; Slurry mixing data: Depth 0-10m = poor, depth 20-30m = low efficiency.

[0169] S26. Evaluate the construction situation based on the slurry diffusion data and the slurry mixing data to obtain preliminary construction evaluation data.

[0170] In one embodiment, the quality of the construction area is evaluated by combining diffusion and mixing data: low coverage areas need to increase the grouting volume and mixing intensity; high coverage areas need to optimize the mixing speed and improve mixing uniformity. Generate optimization suggestions based on the evaluation results to provide a basis for construction adjustments. Evaluation results such as depth 0-10m: insufficient slurry coverage, low mixing intensity, need to increase grouting and mixing intensity; depth 20-30m: slurry diffusion range is large but uneven, need to reduce the mixing speed. Preliminary construction evaluation data: depth 0-10m: coverage = insufficient, mixing = poor; depth 20-30m: coverage = large but uneven, mixing = low efficiency.

[0171] Optionally, S3 includes:

[0172] Deviation calculation is performed based on the preliminary construction assessment data to obtain construction deviation data;

[0173] In one embodiment, the preliminary construction assessment data is compared with the target construction standard parameters, the deviation is calculated item by item, and the parameter values ​​that do not meet expectations during the construction process are identified. The deviation data includes the deviation size, deviation direction (high or low) and depth range of each parameter. Target construction parameters: rotation speed: 60 rpm; down force: 20kN; mud concentration: 35%. Preliminary construction assessment data: Depth 0-10m: rotation speed 58 rpm, down force 18kN, mud concentration 32%; Depth 10-20m: rotation speed 62 rpm, down force 22kN, mud concentration 37%; Depth 20-30m: rotation speed 70 rpm, down force 25kN, mud concentration 50%. Deviation calculation results: Depth 0-10m: rotation speed -2 ​​rpm (too low), downforce -2kN (too low), mud concentration -3% (too low); Depth 10-20m: rotation speed +2 rpm (too high), downforce +2kN (too high), mud concentration +2% (too high); Depth 20-30m: rotation speed +10 rpm (too high), downforce +5kN (too high), mud concentration +15% (too high). Construction deviation data: Depth 0-10m: rotation speed -2, downforce -2, mud concentration -3; Depth 10-20m: rotation speed +2, downforce +2, mud concentration +2; Depth 20-30m: rotation speed +10, downforce +5, mud concentration +15.

[0174] In one embodiment, the construction deviation data is obtained by mapping the preliminary construction assessment data through a preset expert knowledge engine or a preset parameter library. Specifically, the expert system will search for the corresponding deviation type and deviation value in the rule library based on the preliminary construction assessment data. For example: when the depth is 0-10m, the coverage is "insufficient" and the mixing is "poor", then the system will generate the following deviation data according to the rule library: Insufficient coverage, deviation value = high. Uneven mixing, deviation value = high. When the depth is 20-30m, the coverage is "large but uneven" and the mixing is "inefficient", then the system will generate the following deviation data: Uneven diffusion, deviation value = medium. Low mixing efficiency, deviation value = medium.

[0175] The construction parameters are optimized and adjusted according to the construction deviation data to obtain the construction parameter optimization data.

[0176] In one embodiment, according to the deviation data, a construction parameter optimization adjustment plan is formulated: for low parameters, the operation force is increased or the equipment settings are increased; for high parameters, the operation force is reduced or the equipment settings are lowered; the parameters are adjusted according to the depth range to avoid the overall parameter changes affecting the normal area. The adjusted parameters need to be re-entered into the equipment control system to update the construction operation in real time. Optimization adjustment steps: Depth 0-10m: the rotation speed is increased from 58 rpm to 60 rpm; the downforce is increased from 18kN to 20kN; the mud concentration is increased from 32% to 35%. Depth 10-20m: the rotation speed is adjusted from 62 rpm to 60 rpm; the downforce is adjusted from 22kN to 20kN; the mud concentration is adjusted from 37% to 35%. Depth 20-30m: the rotation speed is reduced from 70 rpm to 65 rpm; the downforce is reduced from 25kN to 22kN; the mud concentration is reduced from 50% to 40%. Construction parameter optimization data: Depth 0-10m: rotation speed = 60, down pressure = 20, mud concentration = 35; Depth 10-20m: rotation speed = 60, down pressure = 20, mud concentration = 35; Depth 20-30m: rotation speed = 65, down pressure = 22, mud concentration = 40.

[0177] Optionally, the present application further provides a stirring system for TRD, which is used to perform the stirring method for TRD as described above, and the stirring system for TRD comprises:

[0178] A construction parameter data acquisition module is used to collect construction parameter data in real time through sensors and measuring devices arranged on the TRD equipment;

[0179] A preliminary construction evaluation module is used to perform a preliminary evaluation on the construction parameter data to obtain preliminary construction evaluation data;

[0180] A construction parameter optimization and adjustment module is used to optimize and adjust the construction parameters according to the preliminary construction evaluation data to obtain construction parameter optimization data;

[0181] The real-time construction quality assessment module is used to perform real-time construction optimization operations according to the construction parameter optimization data, and to perform real-time construction quality assessment to obtain real-time construction quality assessment data for real-time construction quality feedback operations.

[0182] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0183] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A stirring method for TRD, characterized in that: The stirring method for TRD includes: S1. Real-time collection of construction parameter data through sensors and measuring devices arranged on TRD equipment; S2. Conduct a preliminary evaluation of the construction parameter data to obtain preliminary construction evaluation data; S3. Optimize and adjust construction parameters according to preliminary construction evaluation data to obtain construction parameter optimization data; S4. Perform real-time construction optimization operations based on the construction parameter optimization data, and perform real-time construction quality evaluation to obtain real-time construction quality evaluation data for real-time construction quality feedback operations.

2. The stirring method for TRD according to claim 1, characterized in that: S1 includes: Real-time collection of construction data through sensors and measuring devices arranged on TRD equipment; Sampling partitions are constructed according to the construction data to obtain construction partition sampling data; The construction parameter data is obtained by preprocessing the construction parameters according to the sampling data of the construction zones.

3. The stirring method for TRD according to claim 2, characterized in that: The sampling partition construction includes: Carry out geological characteristic zoning according to construction data to obtain geological characteristic sampling data; Perform construction parameter deviation partitioning on the construction data to obtain deviation weight sampling data; Dynamically partition the construction progress of the construction data to obtain dynamic sampling data of the progress; Grid partitioning is performed based on geological characteristic sampling data, deviation weight sampling data and progress dynamic sampling data to obtain construction partition sampling data.

4. The stirring method for TRD according to claim 3, characterized in that: The geological characteristics of the zoning include: Identify geological units based on construction data to obtain construction geological unit data; Extract stratified data based on construction geological unit data to obtain geological stratification characteristic data; Perform spatial characteristic analysis based on geological stratification characteristic data to obtain geological spatial characteristic stratification data; Carry out regional classification on the geological spatial characteristic stratification data to obtain geological classification data; Based on the geological classification data, characteristic clustering is performed to obtain geological characteristic sampling data.

5. A stirring method for TRD according to claim 3, characterized in that: The construction parameter deviation zones include: Calculate the deviation of the construction data to obtain the construction deviation matrix data; Deviation weights are calculated based on the construction deviation matrix data to obtain deviation weight matrix data; Perform deviation distribution analysis based on deviation weight matrix data to obtain deviation distribution data; Deviation regions are divided according to the deviation distribution data to obtain deviation weight sampling data.

6. The stirring method for TRD according to claim 3, characterized in that: The dynamic partitioning of construction progress includes: Calculate the equipment construction area coverage rate for the construction data to obtain the area coverage rate data; Perform construction allocation according to the area coverage data and the preset construction area data to obtain construction allocation data; Perform preliminary division of construction progress zones according to the construction allocation data to obtain preliminary progress zone data; Perform progress distribution processing based on preliminary progress partition data to obtain progress hotspot area data; Extract distribution density features based on progress hotspot area data to obtain progress density feature data; The preliminary progress partition data is subjected to boundary optimization fitting based on the progress density feature data to obtain the progress dynamic sampling data.

7. The stirring method for TRD according to claim 3, characterized in that: The grid partitions include: Obtain construction geological area data; Gridding is performed according to the construction geological area data using preset gridding parameters to obtain initial grid data; Divide the construction area according to the initial grid data to obtain preliminary grid partition data; According to the geological characteristic sampling data, the deviation weight sampling data and the progress dynamic sampling data, the grid weight data is allocated to the preliminary grid partition data to obtain the grid weight data; The partition optimization is carried out according to the grid weight data to obtain the construction partition sampling data.

8. The stirring method for TRD according to claim 1, characterized in that S2 include: Perform parameter anomaly detection on the construction parameter data to obtain abnormal construction parameter data; Perform spatial distribution mapping based on abnormal construction parameter data to obtain abnormal spatial distribution data; Grouting volume feature extraction and stirring feature extraction are performed according to the abnormal spatial distribution data to obtain grouting volume feature data and stirring feature data respectively; Perform stirring fluid dynamics simulation according to the grouting volume characteristic data and the stirring characteristic data to obtain stirring fluid dynamics simulation data; Perform slurry diffusion simulation and slurry mixing simulation according to the stirring fluid dynamics simulation data to obtain slurry diffusion data and slurry mixing data respectively; The construction situation is evaluated based on the slurry diffusion data and slurry mixing data to obtain preliminary construction evaluation data.

9. The stirring method for TRD according to claim 1, characterized in that S3 include: Deviation calculation is performed based on the preliminary construction assessment data to obtain construction deviation data; The construction parameters are optimized and adjusted according to the construction deviation data to obtain the construction parameter optimization data.

10. A stirring system for TRD, characterized in that: For performing the stirring method for TRD according to claim 1, the stirring system for TRD comprises: A construction parameter data acquisition module is used to collect construction parameter data in real time through sensors and measuring devices arranged on the TRD equipment; A preliminary construction evaluation module is used to perform a preliminary evaluation on the construction parameter data to obtain preliminary construction evaluation data; A construction parameter optimization and adjustment module is used to optimize and adjust the construction parameters according to the preliminary construction evaluation data to obtain construction parameter optimization data; The real-time construction quality assessment module is used to perform real-time construction optimization operations according to the construction parameter optimization data, and to perform real-time construction quality assessment to obtain real-time construction quality assessment data for real-time construction quality feedback operations.

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