Grouting quality evaluation result generation method and device, electronic equipment and storage medium

Abstract

This application discloses a method, device, electronic equipment, and storage medium for generating grouting quality evaluation results. It determines the risk tolerance of secondary indicators at corresponding construction stages; analyzes the interaction relationships between different secondary indicators at different construction stages through attribute coupling; determines the quality of secondary indicators and the degree of performance control based on risk tolerance and interaction relationships; and generates grouting quality evaluation results for each grouting hole within a grouting unit based on indicator quality and performance control degree. This introduces a stage-differentiated risk tolerance mechanism and indicator coupling quantification logic, achieving a precise two-dimensional evaluation of grouting hole quality from "macro-level comprehensive assessment" to "core performance control status." It can effectively identify seepage hazards caused by local failure of key indicators and provide accurate parameter support for subsequent graded and quantified supplementary grouting decisions.

Description

Technical Field

[0001] This invention relates to the field of grouting quality evaluation result generation technology, and in particular to a method for generating grouting quality evaluation results, a device for generating grouting quality evaluation results, an electronic device, and a readable storage medium. Background Technology

[0002] In curtain grouting construction of water conservancy and hydropower projects, quality evaluation is a core aspect of ensuring the seepage prevention safety of dams. Existing methods for evaluating curtain grouting quality typically treat the grouting process as a static system. However, in actual engineering projects, grouting quality is influenced by a complex interplay of factors, presenting the following pressing technical challenges: First, the relevant technologies suffer from a "static" nature in weight allocation, failing to reflect the dynamic evolutionary logic between indicators. Traditional weighting methods typically treat each evaluation factor as an independent entity, lacking quantitative means to quantify the complex coupling relationships such as mutual constraints and mutual drives between different technical indicators. This makes it difficult for the evaluation system to accurately reflect the logical connections between various process stages on the construction site.

[0003] Secondly, the relevant technologies suffer from "homogenization" in terms of quantitative benchmarks, ignoring the differences in risk sensitivity at different stages and lacking stage-specific data processing methods. This can easily lead to quality fluctuations in key stages being masked by mediocre data in secondary stages, weakening the early warning accuracy of the evaluation results and causing evaluation distortion. Summary of the Invention

[0004] The present invention provides a method, apparatus, electronic device, and readable storage medium for generating grouting quality evaluation results, in order to overcome or at least partially solve the above-mentioned problems.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for generating grouting quality evaluation results, including: The indicators for dredging feasibility and design rationality before dredging, construction standardization during dredging, and quality evaluation after dredging are defined as primary indicators; multiple primary indicators correspond one-to-one with multiple construction stages. Several secondary indicators are established under different categories of primary indicators, and coupling attributes are assigned to the secondary indicators; Determine the risk tolerance of the secondary indicators at the corresponding construction stage; The interaction relationships between different secondary indicators under different construction stages are analyzed using the aforementioned coupling properties. Based on the risk tolerance and the interaction relationship, the quality of the secondary indicators and the degree of performance control of the indicator quality are determined. The grouting quality evaluation results for each grouting hole within the grouting unit are generated based on the quality of the aforementioned indicators and the degree of performance control.

[0006] Optionally, the step of determining the risk tolerance of the secondary indicator at the corresponding construction stage includes: Obtain the construction stage attribute data to which the secondary indicator belongs; Based on the construction stage attribute data, a differential relative membership calculation formula is used to match the secondary indicators, which includes at least linear formulas, nonlinear formulas, and exponential formulas. Obtain the measured values ​​of the secondary indicators and the quality levels of the measured values ​​of the secondary indicators. Substitute the measured values ​​of the secondary indicators and the quality levels into the differential relative membership degree calculation formula to generate a dynamic relative membership matrix that expresses the risk tolerance of the secondary indicators in the corresponding construction stage.

[0007] Optionally, the coupling attributes include strong coupling attributes and weak coupling attributes, and the step of analyzing the interaction relationship between different secondary indicators under different construction stages through the coupling attributes includes: The secondary index corresponding to the strongly coupled attribute is determined as the strongly coupled index; The secondary index corresponding to the weakly coupled attribute is determined as the weakly coupled index; Assign a first fixed coupling weight coefficient to the strong coupling index; Assign a second fixed coupling weight coefficient to the weak coupling index; The off-diagonal terms in the interaction matrix of the fuzzy rock engineering system are corrected using the first fixed coupling weight coefficient and / or the second fixed coupling weight coefficient to obtain the target interaction matrix; The interaction relationships between different secondary indicators are determined based on the target interaction matrix.

[0008] Optionally, the step of determining the quality of the secondary indicator and the degree of performance control of the indicator quality based on the risk tolerance and the interaction relationship includes: The modified target interaction matrix is ​​subjected to criterion optimization calculation and normalization processing to obtain the final index weights that characterize the importance of the secondary indexes. The final indicator weights are weighted and integrated with the dynamic relative membership matrix to determine the weighted average level feature value used to characterize the quality of the secondary indicators. The measured values ​​of the strong coupling indicators are statistically analyzed, and the number of indicators whose measured values ​​of the strong coupling indicators meet the preset quality level requirements is determined. The ratio of the number of indicators to the total number of strongly coupled indicators is used to determine the single-hole strongly coupled indicator compliance rate, which is used to characterize the degree of performance control over the quality of the indicators.

[0009] Optionally, the step of generating the grouting quality evaluation result for each grouting hole within the grouting unit based on the index quality and the degree of performance control includes: The weighted average level feature value and the single-hole strong coupling index compliance rate are used to determine the grouting hole as the judgment condition, and single-hole qualified status evaluation information is generated to characterize the grouting quality evaluation result.

[0010] Optionally, it also includes: Based on the single-hole qualification status evaluation information, unqualified grouting holes are identified, and the unqualified grouting holes are identified as grouting holes to be repaired. Based on the weighted average level feature value or the single-hole strong coupling index compliance rate, the graded quantitative grouting parameters for the grouting hole to be repaired are calculated; the graded quantitative grouting parameters are used to control the grouting equipment to perform local grouting operation on the grouting hole to be repaired.

[0011] Optionally, it also includes: Summarize the grouting data of individual grouting holes that have undergone localized grouting operations; Based on the single-hole grouting data, a unit-level characteristic is determined to express the overall seepage prevention and reinforcement effect of the grouting unit. Determine the average compliance rate of the strong coupling index of the grouting unit in terms of the strong coupling index; The grouting quality evaluation result of the grouting unit is generated by using the overall level characteristics, the average compliance rate of the unit strong coupling index, and the preset unit overall quality qualification standard.

[0012] Secondly, embodiments of this application provide a grouting quality evaluation result generation device, comprising: The primary indicator determination module is used to determine the pre-irrigation irrigability and design rationality indicators, the construction standardization indicators during irrigation, and the post-irrigation quality evaluation indicators as primary indicators; multiple primary indicators correspond one-to-one with multiple construction stages. The secondary indicator setting module is used to set up several secondary indicators under different categories of primary indicators and assign coupling attributes to the secondary indicators. The risk tolerance determination module is used to determine the risk tolerance of the secondary indicators at the corresponding construction stage; The interaction relationship analysis module is used to analyze the interaction relationship between different secondary indicators under different construction stages through the coupling attributes; The indicator quality and performance controllability determination module is used to determine the indicator quality of the secondary indicator and the performance controllability of the indicator quality based on the risk tolerance and the interaction relationship. The grouting quality evaluation result generation module is used to generate grouting quality evaluation results for each grouting hole within the grouting unit based on the quality indicators and the degree of performance control. Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0013] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0014] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0015] The embodiments of the present invention have the following advantages: This invention, through the introduction of a phase-differentiated risk tolerance mechanism and index-coupled quantitative logic, achieves a precise two-dimensional evaluation of grouting hole quality, ranging from "macroscopic comprehensive level" to "core performance controllable state." This effectively identifies potential seepage hazards caused by local failure of key indicators and provides accurate parameter support for subsequent graded and quantitative supplementary grouting decisions. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps in a method for generating grouting quality evaluation results provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a grouting quality evaluation result generation device provided in an embodiment of the present invention; Figure 3 This is a hardware structure block diagram of an electronic device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Figure 1 The diagram illustrates a flowchart of a method for generating grouting quality evaluation results according to an embodiment of the present invention, which may specifically include the following steps: Step 101: The pre-irrigation irrigability and design rationality indicators, the construction standardization indicators during irrigation, and the post-irrigation quality evaluation indicators are determined as primary indicators; multiple primary indicators correspond one-to-one with multiple construction stages. Step 102: Establish several secondary indicators under the primary indicators of different categories, and assign coupling attributes to the secondary indicators; Step 103: Determine the risk tolerance of the secondary indicators at the corresponding construction stage; Step 104: Analyze the interaction relationship between different secondary indicators under different construction stages using the coupling properties; Step 105: Based on the risk tolerance and the interaction relationship, determine the quality of the secondary indicator and the degree of performance control of the indicator quality; Step 106: Based on the quality of the indicators and the degree of performance control, generate the grouting quality evaluation result for each grouting hole in the grouting unit.

[0021] In specific implementation, embodiments of the present invention can define the pre-grouting groutability and design rationality indicators, the construction standardization indicators during grouting, and the post-grouting quality evaluation indicators as primary indicators, so as to establish an evaluation dimension synchronized with the construction progress, deconstruct the complex grouting project into a logical hierarchy with time attributes, and ensure that the evaluation work can cover the entire life cycle of the project.

[0022] In practical applications, multiple primary indicators can correspond one-to-one with multiple construction stages. For example, three primary indicators correspond to the three construction stages of pre-grouting, during-grouting, and post-grouting. The pre-grouting groutability and design rationality index (D1) refers to the set of indicators used to pre-evaluate the formation's ability to absorb grout (groutability) and the preset construction parameters (design rationality) before the formal commencement of grouting operations.

[0023] The grouting construction standard indicators (D2) refer to the set of indicators used to monitor in real time whether the construction operation meets the design specifications and technical requirements during the grouting process.

[0024] Post-grouting quality evaluation index (D3) refers to the set of indicators used to conduct a final evaluation of the performance of the formed seepage barrier curtain through testing methods after the grouting operation is completed.

[0025] Primary indicators are the highest-level classification standards in the evaluation system, representing the main direction of the evaluation.

[0026] The construction phase refers to the operational process in curtain grouting engineering, divided according to the sequence of time.

[0027] In specific implementation, embodiments of the present invention can establish several secondary indicators under different categories of primary indicators, and assign coupling attributes to the secondary indicators to refine the evaluation granularity. By giving the indicators "coupling attributes", the limitation of the isolation between indicators in traditional evaluation is broken, laying the foundation for quantifying the complex interaction between indicators.

[0028] Secondary indicators are the specific measured parameters under primary indicators (such as permeability, pressure, skewness, etc.), and are the direct source of evaluation data.

[0029] For example, the indicators are divided into three primary indicators according to the grouting process, and their coupling attributes (strong coupling or weak coupling) are clearly marked, as follows: 1. The secondary indicators of the pre-grouting groutability and design rationality index (D1) may include the number of rock mass joint groups (strong coupling), joint opening degree (strong coupling), pre-grouting permeability (strong coupling), grout water-cement ratio (strong coupling), design parameter rationality (weak coupling), and grouting pressure (strong coupling).

[0030] For example, before construction, the original permeability characteristics of the rock strata can be quantitatively assessed by measuring the number of rock joint groups and the pre-grouting permeability (Lu) through engineering geological surveys, which can serve as the basis for subsequent grouting design.

[0031] 2. The secondary indicators of the construction standard indicators (D2) during the grouting process may include borehole position deviation (weak coupling), borehole deviation rate (weak coupling), and the results of the supervision spot checks (strong coupling).

[0032] For example, by calculating the hole position deviation (cm) and the borehole deviation rate (%), it is possible to monitor in real time whether the construction process meets the design requirements and ensure that the geometric spatial layout of the grouting holes is reasonable.

[0033] 3. The secondary indicators of the post-irrigation quality evaluation index (D3) include post-irrigation permeability (strong coupling), post-irrigation integrity (strong coupling), and post-irrigation durability (strong coupling).

[0034] For example, by detecting the post-grouting permeability (measured Lu value) and acoustic velocity (representing integrity) of the inspection holes, the seepage prevention and reinforcement effect after curtain grouting can be directly quantified.

[0035] Coupling attributes are physical labels used to characterize the degree of influence and correlation between a certain indicator and the system as a whole or other indicators. In practical applications, they can be divided into "strong coupling" and "weak coupling".

[0036] In specific implementation, embodiments of the present invention can determine the risk tolerance of the secondary indicators at the corresponding construction stage, so as to establish differentiated evaluation criteria for different stages with different sensitivities to quality deviations, and prevent the quality risks of key stages (such as post-grouting acceptance) from being excessively diluted.

[0037] Risk tolerance refers to the degree to which an evaluation system tolerates deviations from an ideal state.

[0038] For example, during the pre-grouting stage with high tolerance, if the borehole position deviates slightly by 2 cm, the system has a high tolerance for this "deviation" because subsequent grouting pressure and grout diffusion compensate for it. Using a linear formula, the score decreases very gradually. In the post-grouting stage with extremely low tolerance, in the final permeability test, if the standard is 1 Lu and the measured value is 1.1 Lu, although the difference is only 0.1, because this is the final stage and the impermeable barrier cannot have any cracks, the system quickly downgrades the quality grade from "excellent" to "unacceptable" using an exponential formula.

[0039] In specific implementation, embodiments of the present invention can analyze the interaction relationship between different secondary indicators under different construction stages through the coupling attribute analysis, so as to quantify the dynamic dominance and subordination relationship between indicators. By modifying the weight allocation logic, the evaluation results are tilted towards the core driving indicators, thereby improving the objectivity of the evaluation.

[0040] Interaction relationship refers to the logical connection between indicators that drive and constrain each other.

[0041] Take the grouting pressure and the water-cement ratio (concentration) of the grout as examples, which are two indicators of strong coupling effect.

[0042] If high pressure is applied but the grout is very thin, the grout may travel too far, resulting in insufficient curtain thickness; if the grout is very thick but the pressure is insufficient, the grout will not be able to penetrate the micro-cracks.

[0043] In the calculation, the weights of these two indicators are dynamically linked. If one fails to meet the standard, it will drag down the influence of the other indicator through an "interaction relationship".

[0044] Take the number of spot checks in construction supervision with weak coupling effects as an example.

[0045] Although multiple spot checks by the supervisor can increase the confidence level, there is no direct physical causal relationship between it and "formation permeability". They are relatively independent in the calculation and have very weak interaction.

[0046] In specific implementation, embodiments of the present invention can determine the quality of the secondary indicators and the degree of performance control of the indicator quality based on the risk tolerance and the interaction relationship, so as to create a three-dimensional profile of the quality of a single hole from the two dimensions of "macro level" and "micro safety", thereby solving the blind spot of identification in traditional evaluation where "the average score is qualified but the core item fails".

[0047] The quality index expresses the "average score" or "energy level positioning" of the overall quality of the grouting hole. It is a comprehensive index obtained by weighting and summing all secondary indicators (such as hole position deviation, grout concentration, pressure, and permeability) before, during, and after grouting according to their respective weights. It reflects the overall grouting effect of this grouting hole.

[0048] The degree of control over the performance of the indicator quality reflects the stability of the "core bottom line" that determines the safety of the project. It no longer focuses on secondary auxiliary indicators, but specifically on strongly coupled indicators (i.e., key items that play a decisive role in seepage prevention, such as post-grouting permeability). It calculates the percentage of these key items that meet the preset qualification standards. Indicator quality reflects whether core risks have been completely eliminated. For example, even if the score for the indicator quality is high, if one or two key indicators fail to meet the standards, the hole still has potential risks. Suppose a grouting hole has H = 4.0 (which seems good overall), but its P = 60% (compliance rate of strongly coupled indicators). This means that out of the five most critical strongly coupled indicators, only three meet the standards, while the remaining two (such as the most crucial "post-grouting permeability") fail. This implies that although most of the construction process of this hole was perfect, its core seepage prevention performance is unreliable.

[0049] In a specific implementation, embodiments of the present invention can generate grouting quality evaluation results for each grouting hole in the grouting unit by using the quality of the indicators and the degree of performance control, so as to produce a judgment conclusion with dual-dimensional verification, providing a direct decision basis for whether subsequent supplementary grouting is needed and how to match supplementary grouting parameters.

[0050] A grouting unit is the smallest engineering acceptance and evaluation organization consisting of several grouting holes.

[0051] This invention, through the introduction of a phase-differentiated risk tolerance mechanism and index-coupled quantitative logic, achieves a precise two-dimensional evaluation of grouting hole quality, ranging from "macroscopic comprehensive level" to "core performance controllable state." This effectively identifies potential seepage hazards caused by local failure of key indicators and provides accurate parameter support for subsequent graded and quantitative supplementary grouting decisions.

[0052] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0053] In an optional embodiment of the present invention, the step of determining the risk tolerance of the secondary index at the corresponding construction stage includes: Obtain the construction stage attribute data to which the secondary indicator belongs; Based on the construction stage attribute data, a differential relative membership calculation formula is used to match the secondary indicators, which includes at least linear formulas, nonlinear formulas, and exponential formulas. Obtain the measured values ​​of the secondary indicators and the quality levels of the measured values ​​of the secondary indicators. Substitute the measured values ​​of the secondary indicators and the quality levels into the differential relative membership degree calculation formula to generate a dynamic relative membership matrix that expresses the risk tolerance of the secondary indicators in the corresponding construction stage.

[0054] The embodiments of the present invention can obtain construction stage attribute data. In specific implementation, the construction stage attribute data to which the secondary indicators belong can be obtained to clarify the position of the evaluation indicators on the project time chain, and provide a classification basis for subsequent selection of differentiated quantitative evaluation standards.

[0055] Secondary indicators refer to the most basic and directly measurable technical parameters that constitute the evaluation system, such as permeability, borehole deviation rate, and slurry water-cement ratio.

[0056] The construction phase refers to a specific period in the curtain grouting project, divided according to the order of operations. In this plan, it specifically refers to the three key periods: before grouting, during grouting, and after grouting.

[0057] Attribute data refers to label information or logical identifiers used to describe the category, source, and stage characteristics of secondary indicators.

[0058] The embodiments of the present invention can match differentiated relative membership calculation formulas. In specific implementation, differentiated relative membership calculation formulas, including at least linear formulas, nonlinear formulas and exponential formulas, can be matched for the secondary indicators based on the construction stage attribute data. Different mathematical transformation logics can be set for the sensitivity of different stages to quality risks, so as to achieve quantitative control of "risk tolerance" from the algorithm bottom layer.

[0059] A linear formula is a mathematical model in which the output changes proportionally to the input. It represents a stable and balanced evaluation scale and usually corresponds to a stage with moderate risk tolerance.

[0060] Nonlinear formulas are mathematical models in which the output and input are not proportional, and are used to handle evaluation processes with a certain degree of fluctuation elasticity or fuzziness.

[0061] An exponential formula is a mathematical model in which the output changes explosively or decayingly with the input. It has extremely high sensitivity and is used to express a "zero tolerance" attitude towards quality risks.

[0062] The formula for calculating the differential relative membership degree refers to a set of different mathematical functions selected based on the different characteristics of the indicators, which can convert physical measured values ​​into a degree of membership.

[0063] This invention can generate a dynamic relative membership matrix. In a specific implementation, the measured values ​​of the secondary indicators and the quality levels of the measured values ​​of the secondary indicators can be obtained. The measured values ​​of the secondary indicators and the quality levels are then substituted into the differential relative membership calculation formula to generate a dynamic relative membership matrix that expresses the risk tolerance of the secondary indicators in the corresponding construction stage. This transforms discrete engineering measurement data into a continuous probability distribution and utilizes the "strict or lenient" characteristics of the formula to form a quantitative data set that reflects the true risk status.

[0064] The measured values ​​of the secondary indicators are the specific values ​​of each secondary indicator obtained through on-site sensors, physical tests, or supervisory observations.

[0065] Quality grades are determined based on the pre-defined levels of excellence or inferiority in engineering specifications, such as Grade I (excellent) and Grade II (good).

[0066] The dynamic relative membership matrix is ​​a matrix composed of a set of values ​​describing the degree of belonging of each secondary indicator to different quality levels. Its values ​​are updated in real time as the formula type and measured values ​​change.

[0067] Risk tolerance refers to the degree to which the evaluation system accepts or allows quality defects. In this step, it is quantified by using a formula to "amplify" or "reduce" the data.

[0068] This invention eliminates the drawbacks of the traditional "one-size-fits-all" approach by matching differentiated mathematical calculation models to different construction stages. It significantly improves the system's sensitivity in identifying quality risks at key nodes such as post-grouting acceptance, ensuring that the evaluation criteria are accurately aligned with the project's safety requirements.

[0069] In an optional embodiment of the present invention, the coupling attribute includes strong coupling attribute and weak coupling attribute, and the step of analyzing the interaction relationship between different secondary indicators under different construction stages through the coupling attribute includes: The secondary index corresponding to the strongly coupled attribute is determined as the strongly coupled index; The secondary index corresponding to the weakly coupled attribute is determined as the weakly coupled index; Assign a first fixed coupling weight coefficient to the strong coupling index; Assign a second fixed coupling weight coefficient to the weak coupling index; The off-diagonal terms in the interaction matrix of the fuzzy rock engineering system are corrected using the first fixed coupling weight coefficient and / or the second fixed coupling weight coefficient to obtain the target interaction matrix; The interaction relationships between different secondary indicators are determined based on the target interaction matrix.

[0070] The embodiments of the present invention can classify strong and weak coupling indicators. In specific implementation, the secondary indicators corresponding to the strong coupling attributes can be determined as strong coupling indicators, and the secondary indicators corresponding to the weak coupling attributes can be determined as weak coupling indicators. By labeling the indicators with category attributes, the core driving factors and auxiliary factors in the evaluation system can be established, providing a logical starting point for subsequent differentiated weight correction.

[0071] Strong coupling property refers to the property that a certain indicator has a very high correlation with the quality and safety of engineering projects, and that its fluctuation will cause drastic changes in the system state.

[0072] Weak coupling properties refer to the properties of a certain indicator that have a relatively small impact on the overall system, or that are secondary in logical relationships.

[0073] Secondary indicators refer to specific, quantifiable construction or testing parameters.

[0074] Strongly coupled indicators are specific parameters (such as post-irrigation permeability) that are labeled as strongly coupled attributes and play a key role in the evaluation results.

[0075] Weakly coupled indicators are supporting parameters that are labeled as weakly coupled attributes and have a relatively small impact on the evaluation results (such as pore position deviation).

[0076] In this embodiment of the invention, a fixed coupling weight coefficient can be assigned. In a specific implementation, a first fixed coupling weight coefficient can be assigned to the strongly coupled index, and a second fixed coupling weight coefficient can be assigned to the weakly coupled index, so as to introduce artificially preset gain and attenuation factors, thereby strengthening the weight influence of the core index from a mathematical perspective, while suppressing the interference of secondary indexes on the evaluation results.

[0077] The first fixed coupling weight coefficient is an amplification factor (e.g., k=1.2) assigned to the strongly coupled index, used to enhance the index's "voice" in the system.

[0078] The second fixed coupling weight coefficient is a reduction coefficient (e.g., k=0.8) assigned to the weakly coupled index, used to weaken the index's contribution to the overall evaluation.

[0079] The embodiments of the present invention can modify the interaction matrix and determine the interaction relationship. In a specific implementation, the off-diagonal terms in the interaction matrix of the fuzzy rock engineering system can be modified using the first fixed coupling weight coefficient and / or the second fixed coupling weight coefficient to obtain the target interaction matrix. Based on the target interaction matrix, the interaction relationship between different secondary indicators can be determined. By modifying the internal logical values ​​of the matrix, the dynamic dominance strength between indicators can be quantified, so that the evaluation model can truly reflect the complex physical relationship between engineering parameters.

[0080] The Fuzzy Rock Engineering System (FRES) is a logical framework system used to handle uncertain information and analyze the interactions between indicators in rock engineering.

[0081] An interaction matrix can be used to describe the degree of pairwise interaction between indicators in a system, with its rows and columns representing different indicators.

[0082] Off-diagonal terms are the elements in the interaction matrix other than the main diagonal, representing the strength of one indicator's influence on another.

[0083] The modified interaction matrix refers to the process of performing a weighted product operation on the original data of the interaction matrix using fixed coupling weight coefficients.

[0084] The target interaction matrix is ​​the final matrix that, after being corrected for weight coefficients, can accurately express the distribution of the true influence between indicators.

[0085] Interaction relationship refers to the logical connection between indicators involving dominance, control, and energy transfer.

[0086] This embodiment introduces a strong-weak coupling correction factor to dynamically adjust the interaction matrix, successfully solving the problems of weight amortization and dilution of core indicators in traditional evaluation. This enables the evaluation model to accurately capture and amplify the impact of key parameters on system stability, greatly improving the scientific nature and risk identification of the evaluation conclusions.

[0087] In an optional embodiment of the present invention, the step of determining the quality of the secondary indicator and the degree of performance control of the indicator quality based on the risk tolerance and the interaction relationship includes: The modified target interaction matrix is ​​subjected to criterion optimization calculation and normalization processing to obtain the final index weights that characterize the importance of the secondary indexes. The final indicator weights are weighted and integrated with the dynamic relative membership matrix to determine the weighted average level feature value used to characterize the quality of the secondary indicators. The measured values ​​of the strong coupling indicators are statistically analyzed, and the number of indicators whose measured values ​​of the strong coupling indicators meet the preset quality level requirements is determined. The ratio of the number of indicators to the total number of strongly coupled indicators is used to determine the single-hole strongly coupled indicator compliance rate, which is used to characterize the degree of performance control over the quality of the indicators.

[0088] The embodiments of the present invention can solve for the final index weights. In specific implementation, the modified target interaction matrix can be subjected to criterion optimization calculation and normalization processing to solve for the final index weights that characterize the importance of the secondary indexes. This transforms the abstract index interaction relationship into a specific mathematical ratio, establishes the true contribution of each parameter in the evaluation system, and ensures that the core indexes occupy a dominant position in the calculation.

[0089] The target interaction matrix is ​​a numerical square matrix that records the pairwise influence strength between indicators after the coupling coefficient is corrected.

[0090] Criterion optimization calculation refers to the process of logically adjusting matrix data based on specific evaluation criteria (such as causal and effect analysis) to eliminate redundancy or contradictions.

[0091] Normalization is a mathematical transformation that converts data of different dimensions into values ​​within the interval [0, 1], so that the sum of the weights of each indicator equals 1.

[0092] The final indicator weight is a numerical value that quantifies the impact of each secondary indicator on the overall quality evaluation of the system.

[0093] The embodiments of the present invention can determine the weighted average level feature value. In specific implementation, the final indicator weight and the dynamic relative membership matrix can be weighted and integrated to determine the weighted average level feature value used to characterize the quality of the secondary indicator, so as to summarize the performance of each scattered indicator into a comprehensive score and realize the macro-positioning of the overall quality level of the evaluation object.

[0094] The dynamic relative membership matrix is ​​a probability distribution matrix describing the degree of belonging of each indicator to different quality levels. Its value is affected by the risk tolerance formula for the construction stage.

[0095] Weighted integration operation refers to the mathematical process of multiplying and summing the weights of each indicator with their corresponding membership data.

[0096] The weighted average level eigenvalue (H) is a continuous numerical value used to express the overall quality level of the evaluated object, reflecting its specific position in the quality spectrum.

[0097] This invention can determine the compliance rate of a single-hole strong coupling index. In specific implementation, the measured values ​​of the strong coupling index can be counted, and the number of indexes whose measured values ​​meet the preset quality level requirements can be determined. The ratio of the number of indexes to the total number of strong coupling indexes is calculated to determine the compliance rate of the single-hole strong coupling index, which characterizes the degree of performance control of the index quality. This allows for a special verification of the qualification status of core safety indicators, assessment of the reliability of the engineering quality baseline, and prevention of the risk of high overall scores masking the failure of key items.

[0098] Strong coupling indicators are core parameters that play a decisive role in engineering safety.

[0099] The measured values ​​of strong coupling indicators are the original measurement data of strong coupling indicators obtained at the construction site or during the testing process.

[0100] The preset quality level requirement is the threshold for acceptance specified in the engineering specifications or design requirements.

[0101] The ratio calculation is used to calculate the percentage of the number of qualified indicators out of the total number of core indicators.

[0102] Performance control is a reliability indicator used to reflect whether key quality objectives are consistently within a safe range.

[0103] The single-hole strong coupling index compliance rate (P) is a percentage value used to measure the compliance of key seepage prevention performance items of a single grouting hole.

[0104] In this embodiment of the invention, through dual-path calculation of "weight integration" and "key item statistics", it is possible not only to determine the overall quality level of the grouting hole from a macroscopic perspective, but also to accurately pinpoint the failure risk of the core seepage prevention performance, thereby achieving a dual improvement in evaluation depth and safety assurance.

[0105] In an optional embodiment of the present invention, the step of generating a grouting quality evaluation result for each grouting hole within the grouting unit based on the index quality and the degree of performance control includes: The weighted average level feature value and the single-hole strong coupling index compliance rate are used to determine the grouting hole as the judgment condition, and single-hole qualified status evaluation information is generated to characterize the grouting quality evaluation result.

[0106] This invention can perform dual-dimensional logical judgment. In specific implementation, the weighted average level feature value and the single-hole strong coupling index compliance rate can be used to determine the grouting hole as the judgment condition, and generate single-hole qualified status evaluation information to characterize the grouting quality evaluation result. This changes the traditional single score judgment mode. By introducing parallel verification of two dimensions, "comprehensive energy level" and "core bottom line", the quality status of the grouting hole can be accurately characterized, ensuring that the evaluation conclusion can truly guide the project acceptance.

[0107] The weighted average level characteristic value (H) reflects the comprehensive quantitative score of the overall construction quality level of the grouting hole.

[0108] The single-hole strong coupling index compliance rate (P) reflects the proportion of core key indicators (strong coupling index) that determine seepage prevention performance that meet the qualified standards.

[0109] The judgment condition refers to a pre-defined set of logical criteria (such as H≥H). 阈值 And P≥P 阈值 This is used to perform logical filtering of input data based on whether it is true or false, or whether it is good or bad. Grouting hole: The smallest physical work unit drilled and filled with grout in the rock and soil mass during curtain grouting construction.

[0110] The single-hole qualification status evaluation information is a conclusive label (such as qualified, unqualified, and to be repaired) generated based on two-dimensional judgment to describe whether the final quality of a specific grouting hole meets the design requirements.

[0111] A grouting unit is an engineering statistical area consisting of several grouting holes arranged at specific intervals, which has an overall seepage prevention function.

[0112] The grouting quality evaluation result is the final qualitative or quantitative description of the quality of the construction of the evaluated object.

[0113] This embodiment combines the overall quality level with the compliance rate of core performance indicators to construct a complementary dual judgment mechanism, effectively eliminating the risk of local key defects being masked by the overall passing score, and significantly improving the reliability and accuracy of grouting hole qualification judgment.

[0114] In an optional embodiment of the present invention, it further includes: Based on the single-hole qualification status evaluation information, unqualified grouting holes are identified, and the unqualified grouting holes are identified as grouting holes to be repaired. Based on the weighted average level feature value or the single-hole strong coupling index compliance rate, the graded quantitative grouting parameters for the grouting hole to be repaired are calculated; the graded quantitative grouting parameters are used to control the grouting equipment to perform local grouting operation on the grouting hole to be repaired.

[0115] The embodiments of the present invention can pinpoint the target to be repaired. In specific implementation, unqualified grouting holes can be identified based on the single hole qualification status evaluation information. The unqualified grouting holes are identified as grouting holes to be repaired, so as to establish a logical interface from "evaluation conclusion" to "construction instruction". Individuals with potential quality hazards are accurately screened from the group of grouting holes, providing target objects for subsequent precise repair.

[0116] The single-hole qualification status evaluation information refers to the qualitative conclusions generated in the aforementioned steps, used to identify whether the quality of the grouting hole meets the engineering design standards.

[0117] Unqualified grouting holes refer to physical holes where the comprehensive level characteristic value (H) or the compliance rate of strong coupling index (P) does not reach the preset threshold, indicating quality defects.

[0118] The grouting holes to be repaired are target hole sections that are labeled "requires secondary reinforcement work" in the evaluation system.

[0119] The embodiments of the present invention can calculate graded and quantitative grouting parameters. In specific implementation, graded and quantitative grouting parameters for the grouting hole to be repaired can be calculated based on the weighted average level feature value or the single hole strong coupling index compliance rate, so as to realize the evaluation results directly feed back to the construction parameters. According to the severity of the defect (the specific value of H or P), a personalized repair plan is automatically generated to ensure that the grouting operation has a scientific and quantitative basis.

[0120] The weighted average level eigenvalue (H) is a numerical value that quantifies the degree of overall quality deficiency in a single borehole. The single borehole strong coupling index compliance rate (P) is a numerical value that quantifies the proportion of missing core safety items in a single borehole.

[0121] In practical applications, graded and quantified re-irrigation parameters are specific technical parameters generated by evaluation scores to guide re-irrigation construction, including re-irrigation pressure coefficient, pore size increment, slurry ratio adjustment value, etc.

[0122] Grouting equipment is a mechanical system used to inject grout into the formation, such as grouting pumps and automatic recorders. Localized supplementary grouting is a targeted grouting reinforcement operation performed only on identified boreholes with quality defects.

[0123] This embodiment establishes a digital mapping relationship between evaluation parameters and re-grouting process, enabling precise location and quantitative repair of defective holes. This avoids the waste of resources or insufficient repair caused by blind re-grouting in traditional construction, and significantly improves the pertinence and efficiency of engineering defect handling.

[0124] In an optional embodiment of the present invention, it further includes: Summarize the grouting data of individual grouting holes that have undergone localized grouting operations; Based on the single-hole grouting data, a unit-level characteristic is determined to express the overall seepage prevention and reinforcement effect of the grouting unit. Determine the average compliance rate of the strong coupling index of the grouting unit in terms of the strong coupling index; The grouting quality evaluation result of the grouting unit is generated by using the overall level characteristics, the average compliance rate of the unit strong coupling index, and the preset unit overall quality qualification standard.

[0125] This invention can summarize single-hole grouting data after supplementary grouting. In specific implementations, it can summarize the single-hole grouting data of grouting holes that have undergone local supplementary grouting operations to comprehensively collect construction feedback information during the repair phase, providing a data foundation for evaluating the quality contribution of "point-based repair" to "overall surface repair." Local supplementary grouting operation: directional grouting repair work performed on specific holes that failed previous evaluations. Grouting hole to be repaired: a defective hole labeled "requires secondary reinforcement work" in the evaluation system. Single-hole grouting data: including but not limited to technical parameters such as the final grouting volume, termination pressure, unit grout consumption, and grout transformation after single-hole supplementary grouting. Summarization: the process of collecting, screening, and classifying statistically analyzing all measured data related to supplementary grouting holes within a unit.

[0126] The embodiments of the present invention can determine the overall characteristics of the unit. In specific implementation, the overall characteristics of the unit used to express the seepage prevention and reinforcement effect of the grouting unit can be determined based on the single-hole grouting data, so as to realize the spatial dimensional upgrade of the evaluation scale. By converting the physical data of the single hole into the macroscopic energy level of the unit, the overall performance of the entire grouting unit as a seepage barrier is scientifically presented.

[0127] Unit-level features (H) 总 The energy level index, which characterizes the average mass level of the entire unit, is obtained by performing a weighted integration operation on the mass data of each pore within the unit.

[0128] The seepage prevention and reinforcement effect refers to the objective state in which the soil and rock mass's ability to resist seepage erosion and the overall strength of the structure are improved after grouting construction.

[0129] The embodiments of the present invention can determine the average compliance rate of the unit's strong coupling index. In specific implementation, the average compliance rate of the unit's strong coupling index on the strong coupling index can be determined to evaluate the "homogeneity" and "reliability" of the key quality barriers inside the unit, and ensure that there are no systematic and large-scale key performance failure risks within the unit.

[0130] Strongly coupled indicators are core parameters that play a decisive role in engineering safety and have strong physical correlation attributes (such as post-irrigation permeability).

[0131] Average compliance rate of unit strong coupling index (P) 总 The average level of the strong coupling index compliance rate of all single holes in the unit represents the controlled coverage of core safety items in the spatial dimension.

[0132] The embodiments of the present invention can generate unit grouting quality evaluation results. In specific implementation, the overall level characteristics, the average compliance rate of the unit strong coupling index and the preset unit overall quality qualification standard can be used to generate the grouting quality evaluation results of the grouting unit, so as to perform a final logical judgment. Through the dual verification of "macro level" and "safety bottom line", the final evaluation conclusion that can be directly used to guide the project acceptance or the overall treatment decision can be output.

[0133] The preset overall quality qualification standard of the unit is a combination of judgment logic determined by the design or specification, including level characteristic value threshold and compliance rate threshold.

[0134] The grouting quality evaluation result is a conclusive assessment of the entire grouting unit, including the quality level determination and whether further processing (such as full-area supplementary grouting) is required.

[0135] As illustrated, embodiments of the present invention can determine "unit-level characteristics" based on "grouting data" in the following manner: Assuming a unit has three openings, its behavior after refilling is as follows: Extract the level feature values ​​(4.5, 4.0, and 3.2, respectively) and the corresponding grouting data (such as grouting volumes of 100L, 200L, and 700L, respectively) of the three holes A, B, and C.

[0136] Weights are assigned based on the size of the grouting data. The larger the grouting data (indicating more fractured geology), the higher its weight.

[0137] Assume hole C accounts for 70% of the weight. Weighted calculation: H 总= 4.5 * 10% + 4.0 * 20% + 3.2 * 70% = 3.49. This calculation result reflects that the lower score of C-holes with large slurry intake leads to a lower overall characteristic value of the unit, truly reflecting the restrictive effect of geological defects on the overall seepage prevention effect.

[0138] This invention, through the introduction of a weighted aggregation mechanism based on single-hole grouting data, achieves a scientific transformation from "single-hole point control" to "unit surface control," which can more realistically reflect the impact of fractured strata areas on the overall seepage prevention quality. It provides a dual-dimensional judgment basis with macroscopic representativeness and core safety assurance for the final acceptance of the project.

[0139] To enable anyone skilled in the art to better understand the embodiments of the present invention, a complete example is provided below to illustrate the embodiments of the present invention.

[0140] Step 1: Construct a quality evaluation index system for curtain grouting with coupled properties: 1.1 Indicator hierarchy and composition: A two-tiered indicator system was constructed, consisting of 3 primary indicators and 12 secondary indicators. The primary indicators are pre-grouting groutability and design rationality (D1), construction standardization during grouting (D2), and post-grouting quality evaluation (D3). Each secondary indicator was clearly labeled with its coupling attribute (strong coupling / weak coupling). Strongly coupled indicators are those that play a decisive role in grouting quality, while weakly coupled indicators are those that have auxiliary influence. The measured values ​​and acquisition methods of all indicators are quantified as shown in Table 1. Table 1:

[0141] 1.2 Quantification of Indicator Levels and Coupling Weight Coefficients: (1) The evaluation level is still divided into 5 levels: Level I (poor), Level II (qualified), Level III (medium), Level IV (good), and Level V (excellent). The numerical range of each indicator level is strictly quantified according to the grouting engineering specifications. (2) Assign fixed coupling weight coefficients to strong / weak coupling indices: strong coupling index k=1.2, weak coupling index k=0.8, which are used for weighted correction of subsequent FRES interaction matrix encoding to realize the quantitative representation of coupling degree.

[0142] Step 2: Solve for index weights based on the improved FRES theory of coupling degree hierarchy: This step breaks through the limitations of the traditional "indiscriminate coding" of the FRES interaction matrix, proposes a fuzzy ESQ coding method with hierarchical coupling degree, and designs a weight calculation model for coupling coefficient weighting. Specific steps are as follows: 2.1 Determination of Coupling Influence: The degree of mutual influence between indicators within the same level is classified into coupling degree, and the results are divided into "no coupling, weak coupling (influence degree 1-2), medium coupling (influence degree 3-4), strong coupling (influence degree 5-6), and extremely strong coupling (influence degree 7-8)", and the direction of influence (positive / negative) is also marked.

[0143] 2.2 Constructing the FRES interaction matrix with hierarchical coupling: For the primary indicator set {D1, D2, D3}, the 6 secondary indicators under D1, the 3 secondary indicators under D2, and the 3 secondary indicators under D3, construct an n×n coupling degree hierarchical FRES interaction matrix (where n is the number of indicators at that level): (1) Diagonal terms: Arrange the indicators whose weights are to be calculated and label their coupling attributes (strong / weak); (2) Non-diagonal items: Enter the coupling influence value determined by experts, and mark it as positive or negative according to the direction of influence (positive influence is +, negative influence is -), and 0 if there is no coupling; (3) Coupling correction: Multiply the coupling influence value of the off-diagonal term by the coupling weight coefficient k of the affected index (strong coupling k=1.2, weak coupling k=0.8) to obtain the corrected coupling influence value Iij′, which is used as the final encoding value of the matrix to realize the quantitative weighting of coupling degree.

[0144] 2.3 Coupling Optimization of Fuzzy ESQ Coding: (1) Initial coding mapping: The modified coupling influence value Iij′ is mapped to the traditional ESQ coding value 0-4 according to “0=no coupling, 1-2=weak coupling, 3-4=medium coupling, 5-6=strong coupling, 7-8=extremely strong coupling”; (2) Optimization of Fuzzy Inference System (FIS): ① Input layer: The percentage of coupling levels determined by statistical experts for each off-diagonal item (n0 no coupling, n1 weak coupling, n2 moderate coupling, n3 strong coupling, n4 extremely strong coupling) are used as 5 inputs, with the input value range [0,1]. ②Membership function: Establish a trapezoidal membership function that adapts to the coupling degree (to replace the traditional triangular membership function), and set the hierarchical threshold of the input value to 0.2, 0.4, 0.6, and 0.8 to achieve precise fuzzification of the coupling degree ratio; ③ Output layer: Establish 8 engineering-specific fuzzy sets, design 20 coupling-related fuzzy criteria (remove criteria that do not conform to the coupling logic of grouting engineering indicators), and finally output the coupling-weighted FRES encoding value Iij to complete matrix encoding.

[0145] 2.4 Calculation of Causality Values ​​and Weights for Coupling Coefficient Weighting: (1) Calculate the cause value Ci′ and effect value Ei′ of the coupling weighting:

[0146]

[0147] In the formula: I ij The FRES encoding value is a coupled weighted value, where n is the number of indicators at this level, and k is the number of indicators at this level. i The coupling weight coefficient for the i-th indicator (1.2 for strong coupling, 0.8 for weak coupling) is used for the first time to incorporate the coupling attribute of the indicator itself into the causal value calculation, breaking through the limitations of the traditional equal weight calculation of FRES. (2) Calculate the coupling interaction strength and single-level weight ωi′: The coupling interaction strength is Ci′+Ei′, and the single-level weight is calculated using normalization:

[0148] (3) Calculate the total weight wj of the secondary indicators: Let the weighted average weight of the primary indicator be w*′, and the weighted average weight of the secondary indicator be ws*′, then the total weight is:

[0149] The total weight of the 12 secondary indicators is 1, and the weight of strongly coupled indicators accounts for ≥70%, which is consistent with the actual quality impact law of grouting projects.

[0150] 2.5 Weighted Two-Dimensional Robustness Verification: Based on the traditional OAT method, a coupling degree perturbation verification is added to form a two-dimensional verification system, with the verification standard being a quantization threshold: (1) Weight sensitivity verification: RPC=±30%, IPC=±2%, generate 360 ​​sets of weight values, and the absolute change rate of the evaluation result is <8% to be qualified; (2) Coupling perturbation verification: Perturb the strong / weak coupling coefficient k by ±10% respectively, calculate the weight change rate, and the weight change rate <5% is qualified to ensure that the influence of coupling quantification on the weight is controllable.

[0151] Step 3: Calculate the evaluation level based on the improved VFS theory using a dynamic adaptive model: This step breaks through the limitations of the traditional VFS unified membership formula. Based on the different quality characteristics of the "pre-grouting / in-grouting / post-grouting" indicators in the grouting project (pre-grouting is a basic characteristic, in-grouting is a process characteristic, and post-grouting is a result characteristic), it designs three differentiated relative membership calculation formulas and optimizes the weighted calculation model of level feature values. Specific steps are as follows: 3.1 Constructing the indicator interval matrix: (1) Standard interval matrix Iab: 12×5 matrix, the elements [aih,bih] are the quantitative intervals of each level of each indicator. The pre-grouting indicators are divided into "from small to large" and the mid-grouting / post-grouting indicators are divided into "from high to low", which is adapted to the characteristics of grouting engineering indicators. (2) Variable interval matrix Icd: Based on Iab transformation, the traditional boundary error is corrected. The transformation rule is as follows:

[0152] 3.2 Calculate the hierarchical point value matrix Mih: Based on the characteristics of pre-irrigation, during-irrigation, and post-irrigation indicators, a tiered point value calculation rule was designed, and a 12×5 point value matrix was constructed:

[0153] The pre-irrigation indicators are conservative, while the post-irrigation indicators are strict, which meets the actual needs of project quality evaluation.

[0154] 3.3 Calculation of Relative Membership Degree in Dynamic Adaptation: Based on the stage to which the indicator belongs (pre-irrigation / during-irrigation / post-irrigation), three different relative membership formulas are adopted, each with a clearly defined scope of application and calculation logic, which can be directly implemented by those in the field: 3.3.1 Relative membership degree μA1(xi)h of pre-irrigation indicators (6 secondary indicators under D1): Based on pre-irrigation fundamental characteristics, a conservative linear membership formula is adopted to amplify the membership differences in lower-level intervals: When xi∈[aih,Mih]:

[0155] When xi∈[Mih,bih]:

[0156] 3.3.2 Relative membership degree μA2(xi)h of irrigation index (3 secondary indexes under D2): The irrigation process is characterized by a balanced nonlinear membership formula, which takes into account the tolerance of process deviations. When xi∈[aih,Mih]:

[0157] When xi∈[Mih,bih]:

[0158] 3.3.3 Relative membership degree μA3(xi)h of post-irrigation indicators (3 secondary indicators under D3) Post-irrigation results are characterized by a stricter exponential membership formula to rigorously control deviations in the results indicators. When \(x_i\in[a_{ih},M_{ih}]\):

[0159] When \(x_i\in[M_{ih},b_{ih}]\):

[0160] All the membership degree results of the formulas are normalized to \([0,1]\), and finally a \(12\times5\) dynamic relative membership matrix is obtained, realizing the accurate matching of index characteristics and membership degree calculation.

[0161] 3.4 Calculation of stage-weighted comprehensive membership degree and level characteristic value: (1) Stage-weighted comprehensive membership degree \(U_A(u)_h\): Introduce stage weight coefficients (\(\beta_1 = 0.3\) before grouting, \(\beta_2 = 0.2\) during grouting, \(\beta_3 = 0.5\) after grouting, which conforms to the quality evaluation principle of "result orientation" in grouting projects). The formula:

[0162] In the formula: \(\omega_j\) is the total weight of the secondary index, \(p\) is the variable distance parameter (\(1 =\) Hamming, \(2 =\) Euclidean), \(\alpha\) is the optimization criterion parameter (\(1 =\) least absolute deviation, \(2 =\) least squares), and the 4 parameter combinations remain unchanged; (2) Calculation of weighted level characteristic value: Level characteristic value of a single group of parameters (\(C = [1,2,3,4,5]\)), introduce parameter credibility weights (Hamming distance \(w_p = 0.4\), Euclidean distance \(w_p = 0.6\); least absolute deviation \(w_{\alpha}=0.3\), least squares \(w_{\alpha}=0.7\)), and calculate the weighted average level characteristic value \(H\):

[0163] 3.5 Quantification level judgment rule: [[ID=Z5]]According to the weighted average level characteristic value \(H\), formulate the quantification level judgment rule to clarify the level and bias: 1.0 ≤ \(H\) ≤ 1.5, Level I (poor); 1.5 < \(H\) ≤ 2.0, Level II (qualified), biased towards Level I by 0; 2.0 < \(H\) ≤ 2.5, Level II (qualified), biased towards Level III; 2.5 < \(H\) ≤ 3.0, Level III (medium), biased towards Level II; 3.0 < \(H\) ≤ 3.5, Level III (medium), biased towards Level IV; 3.5 < \(H\) ≤ 4.0, Level IV (good), biased towards Level III; 4.0 < \(H\) ≤ 4.5, Level IV (good), biased towards Level V; 4.5 < \(H\) ≤ 5.0, Level V (excellent); Step 4: Quality assessment and handling of grouting projects based on dual-dimensional quality thresholds: This step breaks through the limitations of traditional single-level judgment, proposing a two-dimensional quality threshold of "level feature value + strong coupling indicator compliance rate", and designing a graded and measure-based engineering treatment plan. All treatment measures are quantified parameters and can be directly implemented. 4.1 Two-dimensional assessment of single-hole quality: For each grouting hole within the grouting unit, the weighted average level characteristic value H and the compliance rate P of the strong coupling index were calculated (compliance rate = number of strong coupling indices reaching level III or above / total number of strong coupling indices × 100%). The results of the two-dimensional judgment are shown in Table 2. Table 2:

[0164] Single-hole qualification requirements: Both dimensions must meet the standards; neither can be missing.

[0165] 4.2 Local quality assessment and quantitative processing measures: (1) Judgment of local quality problems: If the proportion of unqualified single holes in a unit is ≥10%, it is judged as a local quality problem; (2) Classification and quantification measures: ① If H < 2.0 and P ≥ 80%: Use low-pressure supplementary grouting, with the grouting pressure being 0.8-0.9 times the original pressure, and the water-cement ratio being consistent with the original grout. ②If H≥2.0 and P<80%: Targeted supplementary grouting is adopted, and supplementary grouting is only carried out on the grouting section corresponding to the strong coupling index that did not meet the standard. The supplementary grouting hole diameter is increased by 15% compared with the original hole diameter. ③ If H < 2.0 and P < 80%: adopt densified enlarged hole filling, the spacing between filling holes is 50% of the original hole spacing, the enlarged hole diameter is increased by 20%, and the grouting pressure is 1.0-1.1 times the original pressure; (3) Local treatment acceptance criteria: After re-irrigation, both dimensions of a single hole meet the standards, and the proportion of unqualified holes is <10%.

[0166] 4.3 Overall Quality Assessment and Engineering Handling Decisions: Based on the premise that the local quality meets the requirements, calculate the overall unit-level characteristic value H_total (the weighted average of H_total for each individual hole, with the weight being the proportion of the grouting volume of each individual hole to the total grouting volume of the unit) and the average compliance rate P_total for the unit's strong coupling index (the arithmetic mean of P_total for each individual hole). Combined with the excellent rate requirement R_designed in the engineering design, make an overall judgment: (1) Overall qualification standard: H total ≥ 3.5 (≥ Grade IV) and P total ≥ 90% and the percentage of excellent holes ≥ R set; (2) Overall handling measures: ① If the standard is not met but H total ≥ 3.0: Use full-area light supplementary irrigation, add 1 supplementary irrigation hole between every 2 original holes in the unit, and perform low-pressure supplementary irrigation; ②If H<3.0: Use full-area re-grouting, redistribute grouting holes with a hole spacing of 60% of the original spacing, and grout according to 1.1 times the original design parameters; (3) Overall acceptance criteria: Unit project acceptance can only be carried out if both dimensions meet the standards and the excellent rate is ≥R.

[0167] Step 5: Engineering compliance verification of the evaluation results: This step overcomes the problem of "disconnect between theoretical evaluation and project acceptance" in traditional evaluation methods by adding a project compliance verification module. It links the evaluation results with the key control items / general item requirements of the "Technical Specification for Cement Grouting Construction of Hydraulic Structures" (SL62-2014), forming a closed loop of "theoretical evaluation - project acceptance." Specific steps are as follows: 5.1 Quantitative acceptance criteria for extraction specifications: From the current national standards for grouting engineering, quantitative acceptance thresholds are extracted for key control items (post-grouting permeability, grouting pressure, grout water-cement ratio) and general items (hole position deviation, borehole deviation rate, and supervisor's spot check results). For example, post-grouting permeability ≤ 5Lu (key control) and hole position deviation ≤ 10cm (general).

[0168] 5.2 Calculate the standard compliance rate of the evaluation results: (1) Compliance rate of key control items Pkey: Number of key control items that meet the standard acceptance threshold / Total number of key control items × 100%; (2) General project compliance rate P: Number of general projects that meet the standard acceptance threshold / Total number of general projects × 100%.

[0169] 5.3 Quantitative Judgment of Project Compliance Establish criteria for determining the conformity of evaluation results with engineering specifications: ① Fully compliant: Pmain = 100% and Pone ≥ 90%, the evaluation results can be directly used as the basis for project acceptance; ② Basically meets the following criteria: P_main = 100% and 80% ≤ P_one < 90%. After rectifying the deviations in general items, the evaluation results can be used as the basis for acceptance. ③ If not met: Pmain < 100% or Pone < 80%, grouting should be carried out again and a quality evaluation should be conducted again until the standard is met.

[0170] The key parameters are defined as shown in Table 3: Table 3:

[0171] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0172] Reference Figure 2 The diagram shows a structural block diagram of a grouting quality evaluation result generation device provided in an embodiment of the present invention, which may specifically include the following modules: The primary indicator determination module 201 is used to determine the pre-irrigation irrigability and design rationality indicators, the construction standardization indicators during irrigation, and the post-irrigation quality evaluation indicators as primary indicators; multiple primary indicators correspond one-to-one with multiple construction stages. The secondary indicator setting module 202 is used to set up several secondary indicators under different categories of primary indicators and to assign coupling attributes to the secondary indicators. Risk tolerance determination module 203 is used to determine the risk tolerance of the secondary indicators at the corresponding construction stage; The interaction relationship analysis module 204 is used to analyze the interaction relationship between different secondary indicators under different construction stages through the coupling attributes; The indicator quality and performance controllability determination module 205 is used to determine the indicator quality of the secondary indicator and the performance controllability of the indicator quality based on the risk tolerance and the interaction relationship. The grouting quality evaluation result generation module 206 is used to generate grouting quality evaluation results for each grouting hole in the grouting unit based on the index quality and the degree of performance control.

[0173] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0174] In addition, embodiments of the present invention also provide an electronic device, such as... Figure 3 As shown, it includes a processor 301, a communication interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304. Memory 303 is used to store computer programs; When the processor 301 executes the program stored in the memory 303, it implements the grouting quality evaluation result generation method described in any of the above embodiments: The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0175] The communication interface is used for communication between the aforementioned terminal and other devices.

[0176] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0177] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0178] like Figure 4 As shown, in another embodiment of the present invention, a computer-readable storage medium 401 is also provided, which stores instructions that, when run on a computer, cause the computer to execute the grouting quality evaluation result generation method described in the above embodiment.

[0179] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described method embodiment for generating grouting quality evaluation results, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0180] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0182] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0183] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for generating grouting quality evaluation results, characterized in that, include: The indicators for dredging feasibility and design rationality before dredging, construction standardization during dredging, and quality evaluation after dredging are defined as primary indicators; multiple primary indicators correspond one-to-one with multiple construction stages. Several secondary indicators are established under different categories of primary indicators, and coupling attributes are assigned to the secondary indicators; Determine the risk tolerance of the secondary indicators at the corresponding construction stage; The interaction relationships between different secondary indicators under different construction stages are analyzed using the aforementioned coupling properties. Based on the risk tolerance and the interaction relationship, the quality of the secondary indicators and the degree of performance control of the indicator quality are determined. The grouting quality evaluation results for each grouting hole within the grouting unit are generated based on the quality of the aforementioned indicators and the degree of performance control.

2. The method according to claim 1, characterized in that, The step of determining the risk tolerance of the secondary indicator at the corresponding construction stage includes: Obtain the construction stage attribute data to which the secondary indicator belongs; Based on the construction stage attribute data, a differential relative membership calculation formula is used to match the secondary indicators, which includes at least linear formulas, nonlinear formulas, and exponential formulas. Obtain the measured values ​​of the secondary indicators and the quality levels of the measured values ​​of the secondary indicators. Substitute the measured values ​​of the secondary indicators and the quality levels into the differential relative membership degree calculation formula to generate a dynamic relative membership matrix that expresses the risk tolerance of the secondary indicators in the corresponding construction stage.

3. The method according to claim 2, characterized in that, The coupling attributes include strong coupling attributes and weak coupling attributes. The step of analyzing the interaction relationship between different secondary indicators under different construction stages through the coupling attributes includes: The secondary index corresponding to the strongly coupled attribute is determined as the strongly coupled index; The secondary index corresponding to the weakly coupled attribute is determined as the weakly coupled index; Assign a first fixed coupling weight coefficient to the strong coupling index; Assign a second fixed coupling weight coefficient to the weak coupling index; The off-diagonal terms in the interaction matrix of the fuzzy rock engineering system are corrected using the first fixed coupling weight coefficient and / or the second fixed coupling weight coefficient to obtain the target interaction matrix; The interaction relationships between different secondary indicators are determined based on the target interaction matrix.

4. The method according to claim 3, characterized in that, The step of determining the quality of the secondary indicator and the degree of performance control of the indicator quality based on the risk tolerance and the interaction relationship includes: The modified target interaction matrix is ​​subjected to criterion optimization calculation and normalization processing to obtain the final index weights that characterize the importance of the secondary indexes. The final indicator weights are weighted and integrated with the dynamic relative membership matrix to determine the weighted average level feature value used to characterize the quality of the secondary indicators. The measured values ​​of the strong coupling indicators are statistically analyzed, and the number of indicators whose measured values ​​of the strong coupling indicators meet the preset quality level requirements is determined. The ratio of the number of indicators to the total number of strongly coupled indicators is used to determine the single-hole strongly coupled indicator compliance rate, which is used to characterize the degree of performance control over the quality of the indicators.

5. The method according to claim 4, characterized in that, The step of generating the grouting quality evaluation result for each grouting hole within the grouting unit based on the quality index and the degree of performance control includes: The weighted average level feature value and the single-hole strong coupling index compliance rate are used to determine the grouting hole as the judgment condition, and single-hole qualified status evaluation information is generated to characterize the grouting quality evaluation result.

6. The method according to claim 5, characterized in that, Also includes: Based on the single-hole qualification status evaluation information, unqualified grouting holes are identified, and the unqualified grouting holes are identified as grouting holes to be repaired. Based on the weighted average level feature value or the single-hole strong coupling index compliance rate, the graded quantitative grouting parameters for the grouting hole to be repaired are calculated; the graded quantitative grouting parameters are used to control the grouting equipment to perform local grouting operation on the grouting hole to be repaired.

7. The method according to claim 6, characterized in that, Also includes: Summarize the grouting data of individual grouting holes that have undergone localized grouting operations; Based on the single-hole grouting data, a unit-level characteristic is determined to express the overall seepage prevention and reinforcement effect of the grouting unit. Determine the average compliance rate of the strong coupling index of the grouting unit in terms of the strong coupling index; The grouting quality evaluation result of the grouting unit is generated by using the overall level characteristics, the average compliance rate of the unit strong coupling index, and the preset unit overall quality qualification standard.

8. A device for generating grouting quality evaluation results, characterized in that, include: The primary indicator determination module is used to determine the pre-irrigation irrigability and design rationality indicators, the construction standardization indicators during irrigation, and the post-irrigation quality evaluation indicators as primary indicators; multiple primary indicators correspond one-to-one with multiple construction stages. The secondary indicator setting module is used to set up several secondary indicators under different categories of primary indicators and assign coupling attributes to the secondary indicators. The risk tolerance determination module is used to determine the risk tolerance of the secondary indicators at the corresponding construction stage; The interaction relationship analysis module is used to analyze the interaction relationship between different secondary indicators under different construction stages through the coupling attributes; The indicator quality and performance controllability determination module is used to determine the indicator quality of the secondary indicator and the performance controllability of the indicator quality based on the risk tolerance and the interaction relationship. The grouting quality evaluation result generation module is used to generate grouting quality evaluation results for each grouting hole in the grouting unit based on the quality index and the degree of performance control.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the method as described in claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the method as described in claims 1-7.

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