A method and system for mixing for TRD

Abstract

The application relates to the technical field of construction engineering construction management, in particular to a TRD stirring method and system. The method comprises the following steps: collecting construction parameter data in real time through sensors and measuring devices arranged on a TRD device; preliminarily evaluating the construction parameter data to obtain construction preliminary evaluation data; optimizing and adjusting the construction parameters according to the construction preliminary evaluation data to obtain construction parameter optimization data; 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. The application can timely identify high-risk areas or parameters (such as uneven stirring, insufficient grouting and the like) in construction, and reduce the occurrence probability of construction quality problems through early adjustment.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of construction engineering construction management, and in particular to a TRD stirring method and system. BACKGROUND

[0002] TRD (deep mixing method) is a commonly used construction technology in modern construction engineering, and is widely used in the fields of foundation reinforcement, water curtain construction and underground structure support, and has remarkable advantages especially in soft soil foundation or complex geological conditions. However, the traditional TRD construction technology has technical bottlenecks and deficiencies in practical application, which restricts the further improvement of its efficiency and quality. Limited construction monitoring means is a big problem of the traditional TRD construction. The collection of key parameters (such as stirring depth, grouting amount, stirring uniformity, etc.) in the construction process mainly depends on manual recording and interval sampling, and the real-time and precision of data collection are difficult to guarantee, which is easy to cause the missing or delay of key construction data. Especially in the case of complex geological conditions or high real-time adjustment demand, the traditional monitoring means is difficult to meet the high frequency and high precision construction management demand. SUMMARY

[0003] The application provides a TRD stirring method and system to solve at least one of the above technical problems.

[0004] The application provides a TRD stirring method, which comprises the following steps:

[0005] S1, collecting construction parameter data in real time through sensors and measuring devices arranged on the TRD equipment;

[0006] S2, preliminarily evaluating the construction parameter data to obtain construction preliminary evaluation data;

[0007] S3, optimizing and adjusting the construction parameters according to the construction preliminary evaluation data to obtain construction parameter optimization data;

[0008] S4, 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.

[0009] In the present application, high-precision sensors are used to collect data in real time, such as stirring depth, grouting quantity, stirring speed and mixing uniformity, to ensure comprehensive monitoring of key construction parameters. Preliminary evaluation of the collected construction parameter data can identify problems such as insufficient construction depth, uneven grouting or speed deviation in a timely manner. Based on the preliminary evaluation data, intelligent parameter correction can be performed for construction deviations, such as automatic adjustment of grouting flow or stirring speed, reducing human intervention. The construction quality evaluation results are fed back to the operator through visualization, helping them quickly understand the construction progress and quality, and timely adjust the construction strategy.

[0010] Optionally, S1 comprises:

[0011] Construction data is collected in real time by sensors and measuring devices arranged on the TRD equipment;

[0012] According to the construction data, sampling partition construction is performed to obtain construction partition sampling data;

[0013] According to the construction partition sampling data, construction parameter preprocessing is performed to obtain construction parameter data.

[0014] In the present application, 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, ensuring the timeliness of the construction data. After dividing the entire construction area into small unit partitions, the system only needs to perform in-depth analysis and optimization on the data of key partitions, reducing the computational load of full-area processing. Precise partitioning can quickly locate the areas with problems in the construction process, providing a clear direction for construction adjustment.

[0015] Optionally, the sampling partition construction comprises:

[0016] According to the construction data, geological property partitioning is performed to obtain geological property sampling data;

[0017] Construction parameter deviation partitioning is performed on the construction data to obtain deviation weight sampling data;

[0018] Construction progress dynamic partitioning is performed on the construction data to obtain progress dynamic sampling data;

[0019] According to the geological property sampling data, deviation weight sampling data and progress dynamic sampling data, grid partitioning is performed to obtain construction partition sampling data.

[0020] The present application can accurately reflect the geological differences in the construction area by dividing different regions according to their geological characteristics (such as soil type, permeability, etc.), avoiding the "one-size-fits-all" problem of construction schemes. The geological characteristic zoning can provide reference for the setting of construction parameters (such as mixing speed, grouting amount of different geological layers), improving the pertinence and efficiency of construction. By calculating the deviation of construction parameters (such as depth deviation, grouting amount deviation), the area where deviation occurs in the construction process can be quickly located. The deviation zoning can guide the adjustment of construction equipment parameters in real time, quickly correct the deviation and reduce unnecessary rework in construction. Dynamic zoning can provide real-time decision basis for construction management, such as resource scheduling, equipment path optimization, etc., reducing waiting time and waste in construction. Integrating geological characteristics, deviation weight and progress dynamic data into the zoning grid model can more comprehensively reflect the complex characteristics of the construction area, avoiding the limitations of single-dimensional zoning. The grid zoning refines the construction area, divides the complex construction area into several small units, each of which can be independently evaluated and optimized, improving the accuracy and operability of construction data.

[0021] Optionally, wherein the geological characteristic zoning comprises:

[0022] According to the construction data, the geological unit is identified to obtain construction geological unit data;

[0023] According to the construction geological unit data, the hierarchical data extraction is performed to obtain geological hierarchical characteristic data;

[0024] According to the geological hierarchical characteristic data, spatial characteristic analysis is performed to obtain geological spatial characteristic hierarchical data;

[0025] The geological spatial characteristic hierarchical data is regionally graded to obtain geological grading data;

[0026] According to the geological grading data, characteristic clustering is performed to obtain geological characteristic sampling data.

[0027] In the present application, different geological units in the region are automatically identified by 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 adjusting construction equipment, such as adjusting the mixing speed for clay layer and optimizing the grouting amount for sand layer. Independent extraction and analysis of the stratified characteristics of different geological units (such as soil compression modulus, water content, etc.) can more accurately describe the characteristics of geological layers. Spatial analysis can locate key areas with significant changes in characteristics in the construction area, providing a basis for key monitoring and optimization of construction. According to the characteristic data, the importance of the construction area is classified (such as high-risk area, medium-risk area, and ordinary area), which facilitates the construction team to prioritize key areas. Cluster data provides a basis for intelligent adjustment of construction equipment, such as setting uniform construction parameters for areas with similar characteristics to reduce equipment adjustment frequency.

[0028] Optionally, wherein the construction parameter deviation zoning includes:

[0029] Performing deviation calculation on the construction data to obtain construction deviation matrix data;

[0030] Performing deviation weight calculation according to the construction deviation matrix data to obtain deviation weight matrix data;

[0031] Performing deviation distribution analysis according to the deviation weight matrix data to obtain deviation distribution data;

[0032] Performing deviation region division according to the deviation distribution data to obtain deviation weight sampling data.

[0033] In the present application, the actual construction parameters (such as mixing depth, grouting amount, mixing speed, etc.) are compared with the design parameters to automatically calculate the deviation value and quickly find the non-conformance items in the construction process. Deviation weight calculation quantifies the impact of each deviation on construction quality, highlighting high-risk key deviations to facilitate the concentration of resources to solve major problems. Deviation distribution analysis uses spatial statistical methods to visually display hotspots of deviation concentration, facilitating the construction team to quickly locate problem areas. Deviation weight sampling data is concentrated in high-risk deviation areas, improving the utilization efficiency of sampling resources and avoiding resource waste.

[0034] Optionally, wherein the construction progress dynamic zoning includes:

[0035] Performing equipment construction area coverage rate calculation on the construction data to obtain area coverage rate data;

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

[0037] Performing preliminary division of construction progress zoning according to the construction allocation data to obtain preliminary progress zoning data;

[0038] According to the progress hot spot area data, distribution density feature extraction is performed to obtain progress density feature data;

[0039] According to the progress hot spot area data, distribution density feature extraction is performed to obtain progress density feature data;

[0040] According to the progress density feature data, boundary optimization fitting is performed on the preliminary progress partition data to obtain progress dynamic sampling data.

[0041] The coverage data in the application can quantify the actual construction effect of the equipment, and provide a basis for the adjustment and path optimization of the construction equipment. The construction distribution is based on the coverage and preset area data, which can realize accurate matching of resources and areas, and avoid resource waste or insufficient distribution. The preliminary partition data can clearly define the construction range and responsibility area, reduce the confusion of area management, and improve the construction management efficiency. The progress hot spot area data can highlight the area where the construction activities are most concentrated, and provide a key direction for resource allocation. Through density feature extraction, the distribution of construction activities in each area is accurately quantified. The density feature is used to optimize and fit the preliminary partition data, so that the partition boundary is more consistent with the actual construction situation.

[0042] Optionally, wherein the grid partitioning comprises:

[0043] Obtain construction geological area data;

[0044] According to the construction geological area data, grid is performed through the preset grid parameter to obtain initial grid data;

[0045] According to the initial grid data, construction area division is performed to obtain preliminary grid partition data;

[0046] According to the geological characteristic sampling data, bias weight sampling data and progress dynamic sampling data, grid weight distribution is performed on the preliminary grid partition data to obtain grid weight data;

[0047] According to the grid weight data, partition optimization is performed to obtain construction partition sampling data.

[0048] In the present application, the geological data of the construction area (such as soil type, stratum structure, permeability, etc.) is collected to provide basic information for zoning and ensure that the zoning conforms to the actual geological conditions. The grid zoning simplifies the complex regional data into structured units, reducing the complexity of data processing and calculation. The zoning data clearly defines the scope of each construction unit, which helps to divide the construction responsibilities and develop regional construction plans. By integrating geological characteristics, bias weight and progress dynamic data, the grid weight can fully reflect the importance and priority of the construction unit, and the weight allocation can accurately identify high-priority areas (such as complex geological areas, large bias areas, and hot areas), providing clear resource allocation guidance for the construction team. Based on the weight data, the grid zoning boundary is adjusted to ensure that the zoning result is more reasonable and meets the actual construction needs.

[0049] Optionally, S2 comprises:

[0050] Parameter anomaly detection is performed on the construction parameter data to obtain construction parameter anomaly data;

[0051] According to the construction parameter anomaly data, spatial distribution mapping is performed to obtain abnormal spatial distribution data;

[0052] According to the abnormal spatial distribution data, grouting amount feature extraction and mixing feature extraction are performed to obtain grouting amount feature data and mixing feature data, respectively;

[0053] According to the grouting amount feature data and the mixing feature data, mixing fluid dynamics simulation is performed to obtain mixing fluid dynamics simulation data;

[0054] According to the mixing fluid dynamics simulation data, grout diffusion simulation and grout mixing simulation are performed to obtain grout diffusion data and grout mixing data, respectively;

[0055] According to the grout diffusion data and the grout mixing data, construction condition evaluation is performed to obtain construction preliminary evaluation data.

[0056] In the present application, the deviation value (such as insufficient grouting amount and shallow mixing depth) in the construction parameter can be accurately found, and the problem can be located in time. Through abnormal area positioning, invalid investigation of normal areas is reduced, and the efficiency of solving construction problems is improved. The extracted feature data can reveal the problems existing in the grouting and mixing process, providing direction for construction process improvement. The mixing fluid dynamics simulation can accurately describe the flow and mixing behavior of grout in soil, improving the understanding of the construction process. The grout diffusion simulation can accurately predict the diffusion range of grout in soil, ensuring that the construction covers the target area. The evaluation data can verify whether the construction result meets the design requirements, providing data support for acceptance.

[0057] Optionally, S3 comprises:

[0058] According to the construction preliminary evaluation data, the deviation calculation is performed to obtain the construction deviation data;

[0059] According to the construction deviation data, the construction parameter optimization adjustment is performed to obtain the construction parameter optimization data.

[0060] In the present application, the deviation calculation can reveal the deviation source of specific parameters (such as stirring depth, grouting amount or mixing uniformity), helping the construction team to quickly identify problems. The 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. The optimization adjustment can accurately adjust the construction parameters (such as stirring speed, grouting amount, construction depth, etc.) according to the deviation data, significantly improving the accuracy of the construction process and the accuracy of the results. According to the deviation data and the construction area characteristics, the optimization adjustment can develop construction parameters for different areas or conditions to ensure construction quality and efficiency.

[0061] Optionally, the present application also 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] The construction parameter data acquisition module is used to acquire the construction parameter data in real time through the sensors and measuring devices arranged on the TRD equipment;

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

[0064] The construction parameter optimization adjustment module is used to perform construction parameter optimization adjustment according to the construction preliminary evaluation data to obtain the construction parameter optimization data;

[0065] The real-time construction quality evaluation module is used to perform real-time construction optimization operation according to the construction parameter optimization data, and to perform real-time construction quality evaluation to obtain real-time construction quality evaluation data, so as to perform real-time construction quality feedback operation.

[0066] The purpose of the present application is to achieve real-time monitoring and collection of construction parameters by arranging various high-precision sensors (such as depth sensors, grouting flow meters, rotary encoders, etc.) on the TRD device, which can provide millisecond-level feedback on the construction state. The real-time nature significantly improves the sensitivity of construction monitoring, ensuring that construction problems can be detected and fed back at the moment they occur. The present application comprehensively analyzes construction parameter data, further refines the evaluation dimension of construction data through fluid dynamics simulation and hybrid simulation, and accurately reflects potential problems in the construction process from multiple angles. Preliminary evaluation of construction is achieved through data modeling and deviation calculation, identifying and quantifying the deviation of key construction parameters, which can output precise deviation data for different regions and conditions, providing a scientific basis for construction parameter optimization. Through real-time monitoring and parameter optimization, the present application can effectively reduce construction deviation, improve mixing uniformity and grouting effect, thereby ensuring the consistency of construction results and design requirements. The dynamic adjustment function of the present application enables the construction equipment to quickly switch to the optimal parameters in different regions, avoiding the efficiency loss caused by relying on fixed parameters in traditional construction. BRIEF DESCRIPTION OF DRAWINGS

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

[0068] Fig. 1 A step flow chart of a mixing method for TRD is shown in an embodiment;

[0069] Fig. 2 A step flow chart of a construction parameter data collection method is shown in an embodiment;

[0070] Fig. 3 A step flow chart of a sampling partition construction method is shown in an embodiment;

[0071] Fig. 4 A step flow chart of a preliminary construction evaluation method is shown in an embodiment;

[0072] The implementation of the purpose, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0073] The technical method of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0074] In addition, the accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the application and, together with the description, serve to explain the principles of the application. In the drawings:

[0075] It should be understood that, although terms such as "first", "second", and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the associated associated items.

[0076] Referring to Figs. 1 to 4 The present application provides a stirring method for TRD, which comprises the following steps:

[0077] S1, collecting construction parameter data in real time through sensors and measuring devices arranged on the TRD equipment;

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

[0079] S2, preliminarily evaluating the construction parameter data to obtain construction preliminary evaluation data;

[0080] In an embodiment, after data collection is completed, the system performs preliminary evaluation on the collected data. Comparative analysis is performed on real-time data through rule logic or mathematical models. For example, normal range and threshold range of preset construction parameters (e.g., rotation speed of 50-70 rpm, down pressure of 20-30 kN). Real-time collected data is compared with these thresholds, and if the data deviates from the normal range, it is recorded as an abnormal data point, and the evaluation result is output, including the position of the abnormal point, the impact degree, and the possible cause. At the same time, the system calculates the parameter rationality score based on the matching degree of historical construction data and current data. For example, the rationality score can be based on the number of abnormal points, the deviation amplitude, and the distribution trend.

[0081] S3, optimizing and adjusting construction parameters according to preliminary evaluation data to obtain optimized construction parameter data;

[0082] In an embodiment, based on the preliminary evaluation data, the system automatically generates an optimization adjustment scheme. This includes identifying construction abnormal points and determining their impact on construction results. For example, if the rotation speed is too low, it will cause uneven mixing; if the down pressure is too high, it will cause equipment damage. Optimization suggestions are generated based on the construction model. For example, if the rotation speed is less than 50 rpm, the system suggests increasing the rotation speed to 55 rpm, and limits the adjustment amplitude according to the equipment operating state to ensure equipment stability. The optimized data is output to form an adjustment parameter file, for example: the rotation speed is adjusted to 55 rpm, and the down pressure is adjusted to 25 kN. The adjustment data is transmitted to the control system of the TRD equipment through the wireless network, and the equipment parameters are dynamically updated.

[0083] S4, real-time construction optimization operation is performed according to the optimized construction parameter data, and real-time construction quality evaluation is performed to obtain real-time construction quality evaluation data for real-time construction quality feedback operation.

[0084] In an embodiment, the system enters the real-time construction optimization phase after parameter adjustment. The optimized parameters are written into the equipment control program, and the equipment performs the construction operation 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 evaluation is completed by comparing the optimized construction parameters with the real-time collected data. If the deviation is within an acceptable range, it is recorded as normal; otherwise, an alarm is issued to require re-adjustment. Based on the real-time construction depth, rotation speed, and mud distribution data, the construction quality index is calculated, such as mud uniformity score and mixing depth consistency score. After the real-time construction quality evaluation data is generated, the system outputs the result to the construction monitoring platform, and if the quality score is lower than the threshold (e.g., less than 80 points), the feedback operation is triggered to re-optimize the parameters.

[0085] Optionally, S1 includes:

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

[0087] In an embodiment, the following sensors are installed on the TRD equipment: a rotational speed sensor for monitoring the rotational speed of the drill bit in real time; a pressure sensor for monitoring the pressure under stirring; a displacement sensor for monitoring the displacement state of the equipment; a mud concentration sensor for monitoring the uniformity and concentration changes of the mud; a depth sensor for measuring the current construction depth of the drill bit. The sensors convert the signals into digitized data through the acquisition module and transmit them to the data processing unit. To ensure data accuracy: high-frequency sampling technology (such as 100 times per second) is used to capture the dynamic changes in the construction process; noise removal algorithms are used to filter out environmental interference signals (such as invalid signals caused by wind and mechanical vibration). The raw construction data collected in real time are as follows: rotational speed: 52 rpm; down pressure: 21 kN; equipment vertical depth: 9.7 m; mud concentration: 36%.

[0088] S12, constructing sampling data for construction zones according to the construction data;

[0089] In an embodiment, the real-time collected construction data is divided into zones in space and time to construct the sampling data for construction zones. Specifically, it includes spatial zoning: taking construction depth as the basis for division, for example, every 10 m depth as a zone, and collecting the construction parameters (rotational speed, pressure, concentration, etc.) in the corresponding zone. Time zoning: taking time as the unit (such as every 10 seconds as a sampling period), and obtaining the change trend of the construction parameters. Calculate the statistical values (mean, variance, etc.) and distribution characteristics of the sampling data for each zone. According to the zoning rules, the region at a depth of 97 m is divided into zones for sampling: depth zone 1 (0-10 m): rotational speed 53 rpm, pressure 22 kN, mud concentration 38%; depth zone 2 (10-20 m): rotational speed 51 rpm, pressure 21 kN, mud concentration 36%; depth zone 3 (20-30 m): rotational speed 52 rpm, pressure 20 kN, mud concentration 37%.

[0090] S13, preprocessing the construction parameters according to the sampling data for construction zones to obtain the construction parameter data.

[0091] In an embodiment, the pre-processing of the sampling partition data includes: removing small fluctuations by a smoothing algorithm (such as a sliding mean method) for fluctuations of high-frequency sampling data. For example: average the data of 3 consecutive time points to obtain a stable value. Use a statistical model to identify abnormal points in the partition data, such as a sudden increase or decrease in pressure value, and eliminate these irregular data. The original partition data (depth 2-3m): rotational speed [52, 51, 53] rpm, pressure [20, 22, 19] kN, mud concentration [37%, 35%, 36%]. The data after smoothing processing: rotational speed 52 rpm, pressure 20.3 kN, mud concentration 36%. After removing abnormal values, the normalized results (or not normalized, directly store the results, here are the results after normalization) are: rotational speed: 0.74; down pressure: 0.68; mud concentration: 0.72. The construction parameter data obtained: rotational speed 52 rpm, down pressure 20.3 kN, mud concentration 36%.

[0092] Optionally, wherein the sampling partition construction comprises:

[0093] S121, according to the construction data, the geological property partition is obtained, and the geological property sampling data is obtained;

[0094] In an embodiment, the geological property partition is based on the geological data collected by the sensor, such as the drill bit resistance, mud concentration, underground flow rate and the like. The collected data is layered according to the depth, and each 10m depth is defined as a preliminary partition. Statistical analysis is performed on the data in each partition, and the geological property index (such as average resistance, concentration gradient) is extracted. Then, similar geological property regions are classified into a category by clustering method. (Depth 0-30m): 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%]. Result: depth 0-10m and 10-20m are classified into one category (geological loose area); depth 20-30m is another category (geological dense area).

[0095] S122, the construction parameter deviation partition of the construction data is obtained, and the deviation weight sampling data is obtained;

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

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

[0098] In an embodiment, according to the real-time collected equipment depth change data, the drill bit moving speed and the residence time are calculated, and the dynamic change of the construction progress is analyzed. The area with slow progress is separately zoned and marked. Depth change and time relationship: 0-10m depth: time consumption 100 seconds, speed = 1 / 10 = 0.1m / s; 10-20m depth: time consumption 150 seconds, speed = 1 / 15 ≈ 0.067m / s; 20-30m depth: time consumption 250 seconds, speed = 1 / 25 = 0.04m / s. Zoning result: depth 0-10m: normal progress, marked as green area; depth 10-20m: slightly slow progress, marked as yellow area; depth 20-30m: slow progress, marked as red area.

[0099] S124, grid zoning according to the geological characteristic sampling data, the deviation weight sampling data, and the progress dynamic sampling data to obtain construction zoning sampling data.

[0100] In an embodiment, the data of the geological property zoning, bias weight zoning and progress dynamic zoning are superimposed to generate a multi-dimensional 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 property category, bias weight level, and construction progress status). Data matrix (three-dimensional data after superposition): depth 0-10m: geological category = loose zone, bias weight = 1, progress = normal; depth 10-20m: geological category = loose zone, bias weight = 2, progress = slightly slow; depth 20-30m: geological category = dense zone, bias weight = 3, progress = slower. Gridding result: grid 1 (0-10m): label = "loose zone, low bias, normal progress"; grid 2 (10-20m): label = "loose zone, medium bias, slightly slow progress"; grid 3 (20-30m): label = "dense zone, high bias, slower progress".

[0101] Optionally, wherein the geological property zoning comprises:

[0102] According to the construction data, the geological unit is identified to obtain construction geological unit data;

[0103] In an embodiment, the sensors on the TRD equipment are used to collect data such as resistance, mud concentration, and groundwater flow rate in the depth range during construction. The collected data is layered according to depth, with each 10 meters of depth as a preliminary zoning. The data in each zoning is statistically calculated to extract characteristic indicators (such as average resistance, concentration gradient, and water flow rate change). According to these characteristic indicators, regions with similar geological conditions are classified into a category through clustering calculation to form geological property zoning, such as loose zone, clay zone, and rock zone. For example, depth 0-10m: average resistance 20kN, mud concentration 30%, groundwater flow rate 0.1m / s; initially determined as loose sand layer. Depth 10-20m: average resistance 35kN, mud concentration 40%, groundwater flow rate 0.05m / s; initially determined as clay layer. Depth 20-30m: average resistance 50kN, mud concentration 50%, groundwater flow rate 0.02m / s; initially determined as dense rock layer.

[0104] According to the construction geological unit data, the stratified data is extracted to obtain geological stratified property data;

[0105] In an embodiment, real-time rotation speed, pressure value and other parameters during construction 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%). According to the deviation of each partition, a deviation weight is assigned, and a high weight region indicates that more attention is needed for construction. For example, at a depth of 0-10m: the target rotation speed is 60 rpm, and the actual value is [59, 61, 60], the average deviation is =|60-59| = 1 rpm; low deviation, weight is 1. At a depth of 10-20m: the target rotation speed is 60 rpm, and the actual value is [64, 62, 63], the average deviation is =|60-63| = 3 rpm; medium deviation, weight is 2. At a depth of 20-30m: the target rotation speed is 60 rpm, and the actual value is [68, 65, 67], the average deviation is =|60-67| = 7 rpm; high deviation, weight is 3.

[0106] According to the geological stratification characteristic data, spatial characteristic analysis is performed to obtain geological spatial characteristic stratification data;

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

[0108] The geological spatial characteristic stratification data is regionally classified to obtain geological classification data;

[0109] In an embodiment, according to the spatial characteristic analysis result, the characteristics of each layer are regionally graded and divided into different levels: the level division is based on factors such as continuity, stability, and construction difficulty. Level 1 (easy construction): high continuity, low resistance, and uniform mud; Level 2 (moderate construction difficulty): good continuity, moderate 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 construction); clay layer (10-20m): good continuity, Level 2 (moderate construction difficulty); rock layer (20-30m): high continuity but high resistance, Level 3 (difficult construction).

[0110] According to the geological grading data, the characteristics are clustered to obtain geological characteristic sampling data.

[0111] In an embodiment, the geological characteristic data of each graded region is clustered and analyzed to divide it into several similar characteristic groups, forming geological characteristic sampling data: according to resistance average, concentration distribution, and continuity, the geological layers are divided into different characteristic groups; loose sand layer and clay layer are grouped into the same group due to similar resistance; rock layer is separately grouped into another group due to higher resistance. The sampling data of each group includes representative resistance value, concentration range, and spatial distribution characteristics. For example, Group 1 (loose sand layer + clay layer): resistance average 30kN, concentration range 30%-40%, good spatial continuity; Group 2 (rock layer): resistance average 50kN, concentration range 50%, stable spatial distribution.

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

[0113] The construction data is calculated for deviation to obtain construction deviation matrix data;

[0114] In an embodiment, real-time construction parameter data, including rotation speed, downforce and mud concentration, is collected from the TRD device. In combination with the design target parameters, the actual values and target values at each depth are calculated for deviation, resulting in 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 depth and the type of construction parameter, 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 19 kN, mud concentration 32%; depth 10-20m: rotation speed 62 rpm, downforce 21 kN, mud concentration 37%; depth 20-30m: rotation speed 67 rpm, downforce 24 kN, mud concentration 42%. Design target parameters: rotation speed 60 rpm, downforce 20 kN, mud concentration 35%. Deviation matrix: depth 0-10m: [rotation speed -2, downforce -1, mud concentration -3]; depth 10-20m: [rotation speed +2, downforce +1, mud concentration +2]; depth 20-30m: [rotation speed +7, downforce +4, mud concentration +7].

[0115] According to the construction deviation matrix data, deviation weight calculation is performed to obtain deviation weight matrix data;

[0116] In an embodiment, according to the deviation matrix, the weight values of each construction parameter are calculated to represent the influence degree of each parameter on the construction quality. The deviation weight is determined according to the absolute value of the parameter deviation, and 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 to indicate the deviation weight grade of each parameter. For example, the deviation weight matrix: depth 0-10m: [rotation speed 1, downforce 1, mud concentration 1]; depth 10-20m: [rotation speed 1, downforce 1, mud concentration 1]; depth 20-30m: [rotation speed 3, downforce 2, mud concentration 3].

[0117] According to the deviation weight matrix data, deviation distribution analysis is performed to obtain deviation distribution data;

[0118] In an embodiment, the distribution trend of each depth construction parameter deviation is analyzed, including deviation size and regional distribution range: the regional depth range corresponding to different deviation levels (small, medium, large) is counted; the deviation concentration area (continuous depth range with larger 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 (part of the parameters, such as the following pressure); large deviation (level 3): depth 20-30m (rotation speed and mud concentration). Distribution characteristics: depth 0-20m is a small deviation continuous area; depth 20-30m is a medium to large deviation concentration area.

[0119] According to the deviation distribution data, the deviation area is divided to obtain the deviation weight sampling data.

[0120] In an embodiment, according to the deviation distribution result, the construction area is divided into several deviation weight areas: low deviation area: all parameter deviation levels are 1; medium deviation area: the area where the parameter deviation level is 2; high deviation area: the area where the parameter deviation level is 3. Each area generates a deviation weight sampling data, including depth range, main deviation parameter and deviation weight information. Deviation area division result: 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, down pressure, mud concentration deviation weight are all 1; high deviation area: depth 20-30m, rotation speed deviation weight 3, down pressure deviation weight 2, mud concentration deviation weight 3.

[0121] Optionally, wherein the construction progress dynamic zoning includes:

[0122] The equipment construction area coverage rate of the construction data is calculated to obtain the area coverage rate data;

[0123] In an embodiment, according to the depth sensor and displacement sensor data, the coverage track of the equipment construction head in each area is recorded. The construction area coverage rate 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 track in the depth range layer by layer. Construction depth division (0-30m, every 10m as a layer): depth 0-10m: construction head coverage track is 80%, coverage rate is 0.8; depth 10-20m: construction head coverage track is 60%, coverage rate is 0.6; depth 20-30m: construction head coverage track is 90%, coverage rate is 0.9. Area coverage rate data: [depth 0-10m: coverage rate 0.8]; [depth 10-20m: coverage rate 0.6]; [depth 20-30m: coverage rate 0.9].

[0124] Construction allocation is performed according to the area coverage data and preset construction area data, and construction allocation data is obtained.

[0125] In an embodiment, the area 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. The area with low coverage needs to be allocated more equipment time for supplementary construction, and the area with high coverage needs to reduce the construction frequency. The preset area data is that the coverage of each depth needs to reach 0.85. Construction allocation: depth 0-10m: actual coverage 0.8, lower than the target value 0.85, allocate additional equipment time 20 minutes; depth 10-20m: actual coverage 0.6, lower than the target value 0.85, allocate additional equipment time 40 minutes; depth 2-3m: actual coverage 0.9, higher than the target value 0.85, no additional construction is needed. Construction allocation data: [depth 0-10m: additional time 20 minutes]; [depth 10-20m: additional time 40 minutes]; [depth 20-30m: no additional time].

[0126] Preliminary progress zoning is performed according to the construction allocation data, and preliminary progress zoning data is obtained.

[0127] In an embodiment, according to the construction allocation data, the construction area is divided into "fast area", "normal area" and "slow area": fast area: coverage is higher than the target value, construction progress is fast; normal area: coverage is close to the target value, construction progress is normal; slow area: coverage is lower than the target value, construction progress is slow. Preliminary zoning: depth 0-10m: coverage 0.8, classified as slow area; depth 10-20m: coverage 0.6, classified as slow area; depth 2-3m: coverage 0.9, classified as fast area. Preliminary progress zoning data: [depth 0-10m: slow area]; [depth 10-20m: slow area]; [depth 20-30m: fast area].

[0128] Progress distribution processing is performed according to the preliminary progress zoning data, and progress hotspot area data is obtained.

[0129] In an embodiment, the preliminary progress zoning data is distributed and counted to identify the construction progress hotspot area, such as the area with slow or fast progress, which needs to be focused on. The range of the hotspot area is marked by superimposing the area distribution characteristics. For example, slow hotspot area: depth 0-20m, low coverage; fast hotspot area: depth 20-30m, high coverage. Progress hotspot area data: slow hotspot: [depth 0-20m]; fast hotspot: [depth 20-30m].

[0130] Distribution density feature extraction is performed according to the progress hotspot area data, and progress density feature data is obtained.

[0131] In an embodiment, the construction density characteristics are analyzed in each hotspot region, including construction frequency, coverage distribution, and equipment movement trajectory. Feature extraction includes: construction density distribution (frequent / sparse); equipment coverage uniformity (uniform / non-uniform). Feature analysis: slow region (depth 0-20m): construction density is sparse, coverage is uneven; fast region (depth 20-30m): construction density is high, coverage is uniform. Progress density characteristic data: slow region: [density is sparse, coverage is uneven]; fast region: [density is high, coverage is uniform].

[0132] According to the progress density characteristic data, the preliminary progress zoning data is boundary optimized fitting, and the progress dynamic sampling data is obtained.

[0133] In an embodiment, the progress density characteristic data is used to optimize and adjust the boundary of the preliminary zoning. The feature classification data is obtained by clustering calculation and clustering center classification of the progress density characteristic data. The to-be-optimized boundary data is determined according to the feature classification data. The optimization curve parameter data is obtained by performing optimization curve parameter mapping according to the feature classification data. The progress dynamic sampling data is obtained by performing Bezier curve fitting on the to-be-optimized boundary data according to the optimization curve parameter data. The boundary in the hotspot region is refitted according to the construction density, so that the zoning range is more accurate; the adjusted boundary ensures that the distribution of the slow region and the fast region is more consistent with the actual construction state. Optimization and adjustment: the slow region (depth 0-20m) boundary is fine-tuned, and the depth 5-20m is divided into a slow region; the fast region (depth 20-30m) boundary remains stable. Progress dynamic sampling data: slow region: [depth 5-20m]; fast region: [depth 20-30m].

[0134] Optionally, the gridded zoning includes:

[0135] Obtain construction geological region data;

[0136] In an embodiment, the construction geological region data is collected by sensors on the construction equipment, including geological characteristics, depth range, construction progress, and coverage rate, etc. The data is recorded according to depth and region, and the geological characteristic distribution and construction state of each region are clear, such as the differentiation of rock layer, clay layer, and loose layer. For example, depth 0-10m: loose sand layer, coverage rate 80%, rotation speed deviation low; depth 10-20m: clay layer, coverage rate 60%, rotation speed deviation medium; depth 20-30m: rock layer, coverage rate 90%, rotation speed deviation high. Geological region data: loose sand layer: [depth 0-10m]; clay layer: [depth 10-20m]; rock layer: [depth 20-30m].

[0137] According to the construction geological regional data, grid is carried out through preset grid parameters to obtain initial grid data;

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

[0139] According to the initial grid data, the construction area is divided to obtain preliminary grid zoning data;

[0140] In an embodiment, according to the geological characteristics and coverage 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, complex geological characteristics; secondary construction area: high coverage, simple geological characteristics. Preliminary division: 0-10m depth: coverage 80%, classified as secondary construction area; 10-20m depth: coverage 60%, classified as priority construction area; 20-30m depth: coverage 90%, classified as secondary construction area. Preliminary grid zoning 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, deviation weight sampling data and progress dynamic sampling data, grid weight distribution is carried out on the preliminary grid zoning data to obtain grid weight data;

[0142] In an embodiment, the comprehensive geological property sampling data, the deviation weight sampling data and the progress dynamic sampling data are calculated by preset weights, so as to assign weights to each grid: the weight of the grid with high geological complexity is high; the weight of the grid with large deviation is high; the weight of the grid with low coverage is high. The grid weight is the result of superposition of the above three data, which represents the priority of the grid construction. The geological property sampling data: loose sand layer weight = 1, clay layer weight = 2, rock layer weight = 3. The deviation weight sampling data: low deviation weight = 1, medium deviation weight = 2, high deviation weight = 3. The progress dynamic sampling data: coverage < 70%: weight = 3, coverage 70%-85%: weight = 2, coverage > 85%: weight = 1. The grid weight calculation result: 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. The grid weight data: grid 1 weight = 4; grid 2 weight = 7; grid 3 weight = 7.

[0143] According to the grid weight data, the partition optimization is performed to obtain construction partition sampling data.

[0144] In an embodiment, the grid weight is optimized according to the partition boundary: the high weight grid is preferentially divided into the “priority construction area”; the low weight grid boundary is adjusted to make the partition more accurate. The optimization rule: weight ≥ 6: divided into the priority construction area; weight < 6: divided into the secondary construction area. The optimization result: priority construction area: depth 10-20m grid, depth 20-30m part of the grid; secondary construction area: depth 0-10m grid, depth 20-30m part of the grid. The construction partition sampling data: priority construction area: [depth 10-20m grid, depth 20-30m part of the grid]; secondary construction area: [depth 0-10m grid, depth 20-30m part of the grid].

[0145] Optionally, S2 comprises:

[0146] S21, parameter anomaly detection is performed on the construction parameter data to obtain construction parameter anomaly data;

[0147] In an embodiment, the construction parameter data (rotation speed, down pressure, mud concentration, etc.) collected by the sensor is compared with the preset normal parameter range. It is detected whether there is an abnormal value (out of the preset range), and the abnormal parameter and its depth and time point are marked. The collected data: depth 0-10m: rotation speed 58r / min, down pressure 18kN, mud concentration 30%; depth 10-20m: rotation speed 62r / min, down pressure 22kN, mud concentration 35%; depth 20-30m: rotation speed 70r / min, down pressure 25kN, mud concentration 50%. Normal range: rotation speed 50-65r / min, down pressure 20-25kN, mud concentration 30%-40%. The detection result: 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 an embodiment, more detailed parameter abnormality comparison is performed based on the data of the foregoing partition, so as to obtain multi-dimensional construction parameter abnormal data.

[0149] S22, spatial distribution mapping is performed according to the construction parameter abnormal data, so as to obtain abnormal spatial distribution data;

[0150] In an embodiment, the distribution of the abnormal parameter in the construction area is spatially mapped: the abnormal area is marked according to the depth and horizontal position of the abnormal parameter; the abnormal parameter distribution map is drawn to clearly define the abnormal concentration area. Spatial mapping: depth 0-10m: the abnormal parameter covers the entire horizontal area; depth 20-30m: the abnormal parameter covers 30% of the 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 = partial coverage].

[0151] S23, grouting amount feature extraction and mixing feature extraction are performed according to the abnormal spatial distribution data, so as to obtain grouting amount feature data and mixing feature data, respectively;

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

[0153] S24, performing mixing fluid dynamics simulation according to the grouting amount feature data and the mixing feature data to obtain mixing fluid dynamics simulation data;

[0154] In an embodiment, the movement trajectory and distribution of the slurry under different fluid dynamics conditions in the mixing process of the construction equipment in the abnormal area are simulated by a mixing fluid dynamics mathematical equation: low mixing intensity area: weak fluid dynamics, slow slurry diffusion; high mixing intensity area: strong fluid dynamics, but local turbulence occurs, affecting uniformity. Simulation results: depth 0-10m: insufficient mixing fluid dynamics, small slurry diffusion radius; depth 20-30m: strong fluid dynamics, but local turbulence affects diffusion uniformity. Mixing fluid dynamics simulation data: depth 0-10m: fluid dynamics = weak; depth 20-30m: fluid dynamics = strong, local turbulence.

[0155] In an embodiment, the speed field, pressure field and shear stress distribution of the slurry are calculated by mixing fluid dynamics simulation, so as to evaluate the mixing efficiency and mixing effect, wherein the grouting amount feature data includes grouting speed and slurry volume flow, and the mixing feature data includes mixing speed, blade geometric parameters and mixing power.

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

[0157] The equation describing the kinetic behavior of the fluid in the mixing process is: ρ is the density of the slurry, is a partial derivative symbol, t is a time parameter of fluid motion, v is the velocity field of the fluid, is a gradient operator, is a pressure field, The Laplacian of the velocity field describes the diffusive changes of the velocity field, reflecting the viscous dissipation in the fluid, μ is the dynamic viscosity coefficient, and f is the body force representing the volume force (such as gravity, centrifugal force, electromagnetic force, etc.) acting on the fluid.

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

[0159] The velocity field during stirring can be obtained by superimposing the shear force generated by the stirrer 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, and 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 grout represents the force of the grout under the action of stirring, τ is the shear stress of the grout, μ is the dynamic viscosity of the fluid, is the partial derivative symbol, v is the velocity field during stirring, and r is the radial distance from the center of the stirrer. The magnitude of the shear stress determines the uniformity of the grout mixing.

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

[0162] The energy dissipation rate ∈ during stirring is an important indicator of stirring efficiency, ∈ is the energy dissipation rate, which represents the rate of kinetic energy dissipation per unit volume due to the viscosity of the fluid, μ is the dynamic viscosity, which represents the strength of the internal friction of the fluid, and is an important parameter affecting the shear stress and energy dissipation of the fluid.​ is the gradient operator, which represents the partial derivative of the fluid velocity field v with respect to the spatial coordinates, v is the fluid velocity field, which represents the velocity of the fluid at each spatial point, is a fundamental variable in fluid dynamics, : is the tensor operator, which represents the dot product between two second-order tensors, and the result is a scalar, is the double dot product of the velocity gradient tensor, which describes the intensity of the change of the fluid velocity field gradient, v is the double dot product of the velocity gradient tensor, which is used to calculate the energy dissipation caused by the change of the velocity. The larger the energy dissipation rate, the higher the effectiveness of the stirring, 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] wherein is the double dot product of the velocity gradient tensor, which represents the component-wise square sum of the velocity gradient tensor, and is used to describe the intensity of the change of the fluid velocity 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, v 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, and v3 is the component of the velocity in the z direction. j is the spatial coordinate component, which represents 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, and x3 = z: z coordinate of the space.

[0165] The continuous Navier-Stokes equation is discretized using the finite difference method, the finite element method, or the finite volume method to obtain discrete algebraic equations. The initial conditions and boundary conditions are set, and the 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 dynamics simulation data.

[0166] S25, according to the stirring fluid dynamics simulation data, the slurry diffusion simulation and the slurry mixing simulation are carried out, and the slurry diffusion data and the slurry mixing data are obtained, respectively;

[0167] In an embodiment, the diffusion range is determined by analyzing the velocity and range of slurry diffusion. In a weak power area, the diffusion range is small. In a strong power area, the diffusion range is large but not uniform. Slurry mixing simulation: evaluate the mixing efficiency of the slurry, analyze uniformity: insufficient power area, mixing is not uniform; power is too strong area, turbulence leads to low efficiency. Simulation results: depth 0-10m: insufficient diffusion, mixing is not uniform; depth 20-30m: large diffusion range but low mixing efficiency. Diffusion and mixing data: slurry diffusion data: depth 0-10m = insufficient, depth 20-30m = not uniform; slurry mixing data: depth 0-10m = poor, depth 20-30m = low efficiency.

[0168] In an embodiment, the velocity and range of slurry diffusion are analyzed to determine whether the diffusion is uniform: in a weak power area, the diffusion range is small; in a strong power area, the diffusion range is large but not uniform. Slurry mixing simulation: evaluate the mixing efficiency of the slurry, analyze uniformity: insufficient power area, mixing is not uniform; power is too strong area, turbulence leads to low efficiency. Simulation results: depth 0-10m: insufficient diffusion, mixing is not uniform; depth 20-30m: large diffusion range but low mixing efficiency. Diffusion and mixing data: slurry diffusion data: depth 0-10m = insufficient, depth 20-30m = not uniform; slurry mixing data: depth 0-10m = poor, depth 20-30m = low efficiency.

[0169] S26, according to the slurry diffusion data and the slurry mixing data, the construction situation is evaluated, and the preliminary construction evaluation data is obtained.

[0170] In an embodiment, the quality of the construction area is evaluated based on the diffusion and mixing data: low coverage area requires increasing the amount of grouting and stirring intensity; high coverage area requires optimizing the stirring speed to improve mixing uniformity. According to the evaluation results, optimization suggestions are generated to provide basis for construction adjustment. Evaluation results such as depth 0-10m: slurry coverage is insufficient, stirring intensity is low, grouting and stirring intensity need to be increased; depth 20-30m: slurry diffusion range is large but not uniform, stirring speed needs to be reduced. Preliminary construction evaluation data: depth 0-10m: coverage = insufficient, mixing = poor; depth 20-30m: coverage = large but not uniform, mixing = low efficiency.

[0171] Optionally, S3 comprises:

[0172] According to the preliminary construction evaluation data, deviation calculation is performed to obtain construction deviation data.

[0173] In an embodiment, the preliminary construction evaluation data is compared with the target construction standard parameters, and the deviations are calculated item by item to identify the parameter values that do not meet the expectations in the construction process. The deviation data includes the deviation size, deviation direction (high or low) and the depth range of occurrence of each parameter. The target construction parameters are: rotation speed: 60 rpm; down pressure: 20 kN; mud concentration: 35%. The preliminary construction evaluation data is: depth 0-10 m: rotation speed 58 rpm, down pressure 18 kN, mud concentration 32%; depth 10-20 m: rotation speed 62 rpm, down pressure 22 kN, mud concentration 37%; depth 20-30 m: rotation speed 70 rpm, down pressure 25 kN, mud concentration 50%. The deviation calculation results are: depth 0-10 m: rotation speed -2 rpm (low), down pressure -2 kN (low), mud concentration -3% (low); depth 10-20 m: rotation speed +2 rpm (high), down pressure +2 kN (high), mud concentration +2% (high); depth 20-30 m: rotation speed +10 rpm (high), down pressure +5 kN (high), mud concentration +15% (high). The construction deviation data is: depth 0-10 m: rotation speed -2, down pressure -2, mud concentration -3; depth 10-20 m: rotation speed +2, down pressure +2, mud concentration +2; depth 20-30 m: rotation speed +10, down pressure +5, mud concentration +15.

[0174] In an embodiment, according to the preliminary construction evaluation data, mapping is performed through a preset expert knowledge engine or a preset parameter library to obtain the construction deviation data. Specifically, the expert system will look up the corresponding deviation type and deviation value in the rule base according to the preliminary construction evaluation data. For example: when the depth is 0-10 m, the coverage is "insufficient", and the mixing is "poor", the system will generate the following deviation data according to the rule base: coverage is insufficient, deviation value = high. Mixing is uneven, deviation value = high. When the depth is 20-30 m, the coverage is "large but uneven", and the mixing is "low efficiency", the system will generate the following deviation data: diffusion is uneven, deviation value = medium. Mixing efficiency is low, deviation value = medium.

[0175] According to the construction deviation data, the construction parameter optimization adjustment is performed to obtain the construction parameter optimization data.

[0176] In an embodiment, according to the deviation data, an optimization adjustment scheme of construction parameters is formulated: for parameters that are too low, the operation intensity or the equipment setting is increased; for parameters that are too high, the operation intensity or the equipment setting is reduced; according to the depth range, the parameters are adjusted in a targeted manner to avoid the influence of overall parameter changes on normal areas. The adjusted parameters need to be re-input into the equipment control system to update the construction operation in real time. The optimization adjustment steps are as follows: for a depth of 0-10 m, the rotation speed is increased from 58 rpm to 60 rpm, the downforce is increased from 18 kN to 20 kN, and the mud concentration is increased from 32% to 35%. For a depth of 10-20 m, the rotation speed is adjusted from 62 rpm to 60 rpm, the downforce is adjusted from 22 kN to 20 kN, and the mud concentration is adjusted from 37% to 35%. For a depth of 20-30 m, the rotation speed is reduced from 70 rpm to 65 rpm, the downforce is reduced from 25 kN to 22 kN, and the mud concentration is reduced from 50% to 40%. The optimization data of the construction parameters are as follows: for a depth of 0-10 m, the rotation speed = 60, the downforce = 20, and the mud concentration = 35; for a depth of 10-20 m, the rotation speed = 60, the downforce = 20, and the mud concentration = 35; and for a depth of 20-30 m, the rotation speed = 65, the downforce = 22, and the mud concentration = 40.

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

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

[0179] a construction preliminary evaluation module, which is used to preliminarily evaluate the construction parameter data to obtain construction preliminary evaluation data;

[0180] a construction parameter optimization adjustment module, which is used to perform optimization adjustment of the construction parameters according to the construction preliminary evaluation data to obtain construction parameter optimization data;

[0181] a real-time construction quality evaluation module, which is used to perform real-time construction optimization operation according to the construction parameter optimization data, and perform real-time construction quality evaluation to obtain real-time construction quality evaluation data, so as to perform real-time construction quality feedback operation.

[0182] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the attached application file rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0183] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A stirring method for a TRD, characterized by, The TRD uses a stirring method comprising: S1, collecting construction data in real time through sensors and measuring devices arranged on the TRD equipment; constructing sampling partitioning according to the construction data to obtain construction partition sampling data; and preprocessing construction parameters according to the construction partition sampling data to obtain construction parameter data; S2, preliminarily evaluating the construction parameter data to obtain construction preliminary evaluation data; S3, optimizing and adjusting the construction parameters according to the construction preliminary evaluation data to obtain construction parameter optimization data; S4, performing real-time construction optimization work according to the construction parameter optimization data, and performing real-time construction quality evaluation to obtain real-time construction quality evaluation data for real-time construction quality feedback work; S2 comprises: detecting parameter abnormalities in the construction parameter data to obtain construction parameter abnormal data; mapping the spatial distribution according to the construction parameter abnormal data to obtain abnormal spatial distribution data; extracting grouting amount features and stirring features according to the abnormal spatial distribution data to obtain grouting amount feature data and stirring feature data, respectively; performing stirring fluid dynamics simulation according to the grouting amount feature data and the stirring feature data to obtain stirring fluid dynamics simulation data; performing grout diffusion simulation and grout mixing simulation according to the stirring fluid dynamics simulation data to obtain grout diffusion data and grout mixing data, respectively; evaluating the construction situation according to the grout diffusion data and the grout mixing data to obtain the construction preliminary evaluation data; wherein the sampling partitioning construction comprises: performing geological property partitioning according to the construction data to obtain geological property sampling data; performing construction parameter deviation partitioning on the construction data to obtain deviation weight sampling data; performing construction progress dynamic partitioning on the construction data to obtain progress dynamic sampling data; performing grid partitioning according to the geological property sampling data, the deviation weight sampling data, and the progress dynamic sampling data to obtain the construction partition sampling data.

2. The stirring method for TRD according to claim 1, characterized by, Wherein the geological property partitioning comprises: identifying geological units according to the construction data to obtain construction geological unit data; extracting stratified data according to the construction geological unit data to obtain geological stratified property data; performing spatial property analysis according to the geological stratified property data to obtain geological spatial property stratified data; performing regional classification on the geological spatial property stratified data to obtain geological classification data; performing property clustering according to the geological classification data to obtain the geological property sampling data.

3. The method of claim 1, wherein the TRD is stirred by a magnetic stirrer. Wherein the construction parameter deviation partitioning comprises: performing deviation calculation on the construction data to obtain construction deviation matrix data; performing deviation weight calculation according to the construction deviation matrix data to obtain deviation weight matrix data; performing deviation distribution analysis according to the deviation weight matrix data to obtain deviation distribution data; performing deviation region division according to the deviation distribution data to obtain the deviation weight sampling data.

4. The TRD stirring method according to claim 1, wherein Wherein the construction progress dynamic partitioning comprises: performing equipment construction region coverage rate calculation on the construction data to obtain region coverage rate data; performing construction distribution according to the region coverage rate data and preset construction region data to obtain construction distribution data; performing preliminary division of construction progress partitioning according to the construction distribution data to obtain preliminary progress partitioning data; and According to the progress distribution processing of the preliminary progress partition data, progress hotspot area data is obtained; According to the progress density feature extraction of the progress hotspot area data, progress density feature data is obtained; According to the progress density feature data, the boundary optimization fitting of the preliminary progress partition data is performed, and progress dynamic sampling data is obtained.

5. The TRD stirring method according to claim 1, wherein The grid partitioning includes: Obtain construction geological area data; According to the construction geological area data, grid is performed through the preset grid parameter, and initial grid data is obtained; According to the initial grid data, construction area is divided, and preliminary grid partition data is obtained; According to the geological characteristic sampling data, the bias weight sampling data and the progress dynamic sampling data, the grid weight distribution of the preliminary grid partition data is performed, and grid weight data is obtained; According to the grid weight data, the partition optimization is performed, and construction partition sampling data is obtained.

6. The TRD stirring method according to claim 1, wherein S3 It includes: According to the construction preliminary evaluation data, bias calculation is performed, and construction bias data is obtained; According to the construction bias data, construction parameter optimization adjustment is performed, and construction parameter optimization data is obtained.

7. A stirring system for a TRD, characterized by, The TRD stirring method for performing the TRD stirring system as claimed in claim 1 includes: A construction parameter data acquisition module for acquiring construction parameter data in real time through sensors and measuring devices arranged on the TRD equipment; A construction preliminary evaluation module for performing preliminary evaluation on the construction parameter data to obtain construction preliminary evaluation data; A construction parameter optimization adjustment module for performing construction parameter optimization adjustment according to the construction preliminary evaluation data to obtain construction parameter optimization data; A real-time construction quality evaluation module for 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 for real-time construction quality feedback operation.

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