Soft soil reinforcing material performance analysis method and system

Abstract

The invention discloses a soft soil reinforcement material performance analysis method and system, particularly relates to the technical field of material testing, and is used for solving the problem that an existing discretization testing method cannot accurately judge a material performance stabilization critical time point. Performance parameter time sequence data are obtained through continuous monitoring of a sensor, the mode stability quantity representing evolution characteristic consistency and the complexity equilibrium quantity representing multi-time scale behavior regularity are calculated, candidate critical points are identified when the mode stability quantity and the complexity equilibrium quantity are continuously higher than and lower than set threshold values respectively, and then the candidate critical points are identified through comparison verification with a standard performance development envelope band. Finally, an effective performance stabilization critical time point is output, continuous monitoring and accurate analysis of the whole material performance development process are achieved, and a reliable technical means is provided for performance evaluation of the soft soil reinforcement material.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material testing, and more particularly, to a soft soil reinforcement material performance analysis method and system. BACKGROUND

[0002] The performance evaluation of soft soil reinforcement materials usually relies on standardized laboratory test methods. The existing technology obtains performance data at discrete time points by destructive mechanical testing of specimens cured to a specific age, to characterize the strength development law of the material. This method is based on the assumption that the material performance monotonically increases with age and gradually stabilizes, and the long-term performance is extrapolated from limited data points.

[0003] However, since the performance development of soft soil reinforcement materials is a continuous time-varying process, the existing discrete testing method cannot capture the complete dynamic characteristics of the performance evolution, resulting in the inability to accurately determine the critical time point of material performance stabilization. The inherent time resolution of this testing method is insufficient, resulting in significant uncertainty in the performance development law obtained based on discrete data, making it difficult to make reliable predictions about the long-term behavior of the material. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a soft soil reinforcement material performance analysis method and system to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A soft soil reinforcement material performance analysis method, comprising the following steps: S1, continuously monitoring the performance parameters of a soft soil reinforcement material specimen during curing through a sensor to obtain a continuous data sequence of the performance parameters varying with time; S2, based on the continuous data sequence, calculating a pattern stability quantity for characterizing the consistency of its evolution characteristics and a complexity balance quantity for characterizing its behavior law under multiple time scales, respectively; S3, when the pattern stability quantity continuously exceeds a first threshold value and the complexity balance quantity continuously falls below a second threshold value, identifying the corresponding time point as a candidate critical point of performance stabilization; S4, comparing and analyzing the continuous data sequence with a pre-set standard performance development envelope band of the corresponding class of materials; S5, according to the comparison and analysis result, when the continuous data sequence is within the range of the pre-set standard performance development envelope band of the corresponding class of materials throughout, confirming the corresponding candidate critical point as an effective performance stabilization critical time point; S6, outputting the effective performance stabilization critical time point.

[0006] Further, the performance parameters of the soft soil reinforcement material test piece during the curing process are continuously monitored by sensors to obtain continuous data sequences of the performance parameters changing with time, including: The change of ultrasonic wave velocity of the soft soil reinforcement material test piece during the curing process is continuously monitored by the piezoelectric ceramic sensor embedded in the soft soil reinforcement material test piece; The change of resistivity of the soft soil reinforcement material test piece during the curing process is continuously monitored by the four-electrode resistance probe embedded in the soft soil reinforcement material test piece; To obtain continuous data sequences of ultrasonic wave velocity changing with time and continuous data sequences of resistivity changing with time.

[0007] Further, based on the continuous data sequences, the pattern stability quantity for characterizing the consistency of its evolution characteristics and the complexity balance quantity for characterizing the behavior regularity under multiple time scales are calculated respectively, including: Based on the continuous data sequences of ultrasonic wave velocity changing with time, the warping path distance between the current data sequence and the reference stable pattern sequence is calculated by the dynamic time warping algorithm as the pattern stability quantity; Based on the continuous data sequences of resistivity changing with time, the variance of entropy values under different time scales is calculated by the multi-scale entropy analysis method as the complexity balance quantity.

[0008] Further, based on the continuous data sequences of ultrasonic wave velocity changing with time, the warping path distance between the current data sequence and the reference stable pattern sequence is calculated by the dynamic time warping algorithm as the pattern stability quantity, including: Obtain the reference stable pattern sequence established in advance by a large number of stable period material samples; The minimum cumulative distance between the current ultrasonic wave velocity data sequence and the reference stable pattern sequence is calculated by the dynamic time warping algorithm, and the minimum cumulative distance is quantified as the warping path distance; The smaller the warping path distance value, the higher the consistency of the current data sequence with the stable pattern.

[0009] Further, based on the continuous data sequences of resistivity changing with time, the variance of entropy values under different time scales is calculated by the multi-scale entropy analysis method as the complexity balance quantity, including: The resistivity data sequence is coarsely granulated to obtain sub-sequences under multiple time scales; The sample entropy value of each scale sub-sequence is calculated; The dispersion degree of sample entropy values under all scales is calculated, and the variance of sample entropy values is taken as the complexity balance quantity; The smaller the variance of sample entropy values, the more consistent the behavior of the material under different time scales.

[0010] Further, when the mode stability quantity continuously exceeds the first threshold value and the complexity balance quantity continuously falls below the second threshold value, the corresponding time point is identified as a candidate critical point of performance stabilization, including: Based on the mode stability quantity, the first threshold value is set as the statistical significance level of the distance value obtained by comparing the reference stable mode sequence with itself using the dynamic time warping algorithm; Based on the complexity balance quantity, the second threshold value is set as the relative stable state reference value of the entropy value variance obtained by analyzing the reference material sequence in a completely stable state using the multi-scale entropy analysis method; When the time length that the mode stability quantity continuously exceeds the first threshold value reaches the preset monitoring period and the time length that the complexity balance quantity continuously falls below the second threshold value reaches the preset monitoring period, the first time point that meets the duration requirement is recorded as a candidate critical point of performance stabilization.

[0011] Further, the continuous data sequence is compared and analyzed with the preset corresponding class material standard performance development envelope band, including: Obtain the preset corresponding class material standard performance development envelope band, including the ultrasonic wave velocity standard development envelope band and the resistivity standard development envelope band; Point-by-point comparison and analysis of the continuous data sequence of the change of ultrasonic wave velocity with time and the ultrasonic wave velocity standard development envelope band; Synchronously, point-by-point comparison and analysis of the continuous data sequence of the change of resistivity with time and the resistivity standard development envelope band; Record the positional relationship of each data point in the continuous data sequence and the corresponding standard development envelope band.

[0012] Further, according to the comparison and analysis results, when the continuous data sequence is within the preset corresponding class material standard performance development envelope band range throughout, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point, including: Based on the recorded positional relationship of each data point in the continuous data sequence and the corresponding standard development envelope band, it is determined whether all data points of the continuous data sequence of the change of ultrasonic wave velocity with time are within the ultrasonic wave velocity standard development envelope band range; Synchronously, it is determined whether all data points of the continuous data sequence of the change of resistivity with time are within the resistivity standard development envelope band range; When both the ultrasonic wave velocity continuous data sequence and the resistivity continuous data sequence are within the corresponding standard development envelope band range throughout, the candidate critical point is confirmed as an effective performance stabilization critical time point.

[0013] Further, output the effective performance stabilization critical time point, including: The confirmed effective performance stabilization critical time point is stored in association with its corresponding ultrasonic wave velocity data sequence feature and resistivity data sequence feature; and a report file containing the effective performance stabilization critical time point and its corresponding monitoring data analysis result is generated; The effective performance stabilization critical time point is synchronously displayed in the position mark in the continuous data sequence of the ultrasonic wave velocity and the continuous data sequence of the resistivity with time, and the mode stability value and the complexity balance value corresponding to the time point and the comparison relationship with the respective threshold value are displayed, and the comparison analysis result of the continuous data sequence and the preset corresponding class material standard performance development envelope band is displayed.

[0014] In another aspect, the present application provides a soft soil reinforcement material performance analysis system, comprising the following modules: A continuous monitoring module is configured to continuously monitor the performance parameters of the soft soil reinforcement material test piece during the curing process through a sensor to obtain a continuous data sequence of the performance parameters changing with time; A feature calculation module is configured to calculate a mode stability value for characterizing the consistency of the evolution feature and a complexity balance value for characterizing the behavior regularity in multiple time scales based on the continuous data sequence; A critical identification module is configured to identify the corresponding time point as a candidate critical point of performance stabilization when the mode stability value continuously exceeds the first threshold value and the complexity balance value continuously falls below the second threshold value; A comparison analysis module is configured to compare and analyze the continuous data sequence with the preset corresponding class material standard performance development envelope band; A confirmation judgment module is configured to confirm the corresponding candidate critical point as an effective performance stabilization critical time point according to the comparison analysis result when the continuous data sequence is within the preset corresponding class material standard performance development envelope band throughout the whole process; A result output module is configured to output the effective performance stabilization critical time point.

[0015] Compared with the prior art, the present application has the following beneficial effects: 1. The complete time sequence data of the performance development of the soft soil reinforcement material is obtained through continuous monitoring means, which breaks through the time resolution limit of the traditional discrete testing method. The multi-dimensional feature quantization analysis is adopted, and the mode stability value and the complexity balance value are cooperatively calculated to realize the dynamic characterization of the material performance evolution process, which can accurately capture the critical features of the material from the non-steady state to the steady state. This analysis method based on the whole process continuous monitoring eliminates the uncertainty caused by the extrapolation of the traditional method, and provides a reliable data basis for the material performance stabilization determination.

[0016] 2. A multi-level verification mechanism is established, and the accuracy of the critical time point identification is ensured through the double verification of pattern stability judgment and standard development envelope band comparison, which not only reflects the macroscopic law of material performance development, but also reveals the microcosmic behavior characteristics through multi-time scale analysis, and realizes comprehensive performance evaluation from phenomenon to essence. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a soft soil reinforcement material performance analysis method according to the present application; Figure 2 A structural schematic diagram of a soft soil reinforcement material performance analysis system according to the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. 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 the present application.

[0019] Embodiment 1: Figure 1 A soft soil reinforcement material performance analysis method according to the present application is given, which includes the following steps: S1, continuously monitoring the performance parameters of the soft soil reinforcement material test piece in the curing process through the sensor to obtain a continuous data sequence of the performance parameters changing with time; S2, based on the continuous data sequence, respectively calculating a pattern stability quantity for characterizing the consistency of its evolution characteristics and a complexity balance quantity for characterizing its behavior regularity under multi-time scale; S3, when the pattern stability quantity continuously exceeds a first threshold value and the complexity balance quantity continuously falls below a second threshold value, identifying the corresponding time point as a candidate critical point of performance stabilization; S4, comparing and analyzing the continuous data sequence with the pre-set corresponding class material standard performance development envelope band; S5, according to the comparison and analysis result, when the continuous data sequence is within the pre-set corresponding class material standard performance development envelope band range all the time, confirming the corresponding candidate critical point as an effective performance stabilization critical time point; S6, outputting the effective performance stabilization critical time point.

[0020] S1, continuously monitoring the performance parameters of the soft soil reinforcement material test piece in the curing process through the sensor to obtain a continuous data sequence of the performance parameters changing with time, which is specifically implemented as: To realize continuous monitoring of the performance development of soft soil reinforcement material, the following is specifically implemented. First, a standard cylindrical soft soil reinforcement material specimen is prepared, with a diameter of 50 mm and a height of 100 mm. During preparation of the specimen, two piezoelectric ceramic sensors are embedded at the center of the two ends of the specimen, with a diameter of 10 mm, a thickness of 2 mm, and a working frequency of 1 MHz. Meanwhile, a four-electrode resistance probe is embedded in the middle of the specimen, with stainless steel electrodes, an electrode spacing of 10 mm, and an electrode diameter of 1 mm. When embedding the sensors, ensure that they are in full contact with the surrounding material without any gaps, and the sensor leads are drawn out from the side of the specimen and connected to the data acquisition system.

[0021] The data acquisition system includes an ultrasonic wave transmitting and receiving instrument and a resistance measuring instrument. The ultrasonic wave transmitting and receiving instrument can generate a pulse voltage signal to drive the transmitting sensor and receive signals from the receiving sensor; the resistance measuring instrument uses the four-terminal method to measure the resistance value, eliminating the influence of wire resistance. During monitoring, the specimen is kept in standard curing conditions, with a temperature controlled at 20±2 degrees Celsius and a relative humidity maintained above 95%.

[0022] During monitoring, the measurement of ultrasonic wave speed uses the pulse transmission method, for example, collecting data every 1 hour. The specific process is as follows: the ultrasonic wave transmitting and receiving instrument applies a pulse voltage signal to the transmitting sensor, which converts the electrical signal into mechanical vibration to generate ultrasonic waves propagating in the specimen; the receiving sensor converts the received ultrasonic wave signal into an electrical signal, which is amplified by an amplifier and recorded by a data acquisition card; by measuring the propagation time of the ultrasonic wave from transmission to reception, and combining the calibrated distance between the two sensors, the ultrasonic wave speed value is calculated. The formula for calculating the ultrasonic wave speed is: wave speed = propagation distance ÷ propagation time, where the propagation distance is the actual distance between the two sensors, which is determined to be 100 mm by precise measurement.

[0023] The measurement of resistivity is synchronized with the measurement of ultrasonic wave speed, for example, collecting data every 1 hour. The four-electrode resistance probe applies a constant alternating current through the outer two electrodes, with a current frequency of 1000 Hz and a current intensity of 1 mA; the voltage drop is measured through the inner two electrodes, and the resistance value is calculated according to Ohm's law, and then the resistivity is calculated according to the geometric size parameters of the probe. The formula for calculating the resistivity is: resistivity = resistance value × cross-sectional area ÷ electrode spacing, where the cross-sectional area is the cross-sectional area of the specimen, and the electrode spacing is the distance between the inner two electrodes.

[0024] All monitoring data are automatically stored in the computer to form time series data. The ultrasonic wave velocity data are stored as data pairs containing time stamp and wave velocity value, and the resistivity data are stored as data pairs containing time stamp and resistivity value. Data collection continues until the performance of the test piece is stable, usually more than 28 days. In this way, complete continuous data sequences of ultrasonic wave velocity and resistivity changing with time are obtained, providing a data basis for subsequent analysis.

[0025] To ensure data quality, a number of measures are taken during implementation. After the sensor is installed, an impedance analyzer is used to detect its working state to ensure that the sensor is intact and well coupled with the material; the data acquisition system is calibrated once a week to verify the measurement accuracy using standard test pieces, such as using a standard test piece of organic glass with known acoustic properties to calibrate the ultrasonic measurement system, and using a standard resistance box to calibrate the resistance measurement system; real-time inspection of data outliers is performed during monitoring, and when data outliers are found, the sensor connection and instrument state are immediately checked, such as when 3 consecutive data points deviate from the normal range by more than 10%, the system automatically issues an alarm. All monitoring data are automatically backed up to a cloud storage system to prevent data loss.

[0026] In the specific implementation process, the measurement of ultrasonic propagation time uses threshold detection method, and the time when the voltage amplitude reaches 10% of the maximum amplitude is set as the wave arrival time; when measuring resistance, an alternating current excitation signal is used to avoid electrode polarization effect, and the measurement frequency is selected in the range of 100 Hz to 10 kHz, for example, the frequency point of 1000 Hz is preferred for measurement. The data collection time interval can be adjusted as needed, for example, a shorter interval such as 30 minutes is used in the initial hardening stage, and a longer interval such as 2 hours is used later, but the consistency of the collection interval needs to be maintained.

[0027] The storage format of the monitoring data uses the standard CSV format, each row of data contains three data items of time stamp, ultrasonic wave velocity value and resistivity value, and the time stamp is accurate to the second level. To eliminate the influence of environmental temperature changes on the measurement results, the ambient temperature around the test piece is recorded synchronously, and the measurement results are compensated and corrected according to the temperature coefficient of the material, for example, the temperature compensation coefficient of ultrasonic wave velocity is taken as -0.5 m / s / ℃, and the temperature compensation coefficient of resistivity is taken as 2% / ℃.

[0028] S2, based on the continuous data sequence, respectively calculating the pattern stability quantity for characterizing the consistency of its evolution characteristics and the complexity balance quantity for characterizing the behavior regularity under multiple time scales, the specific implementation is: Based on the continuous data sequence of ultrasonic wave velocity changing with time and the continuous data sequence of resistivity changing with time, the step is implemented as follows. Firstly, the original data obtained by monitoring are preprocessed, including removing outliers and data smoothing. The 3σ criterion is adopted for removing outliers, that is, the mean and standard deviation of the data sequence are calculated, and the data points exceeding the mean ± 3 times the standard deviation range are regarded as outliers and removed. The moving average method is adopted for data smoothing, for example, the window size is 5 data points for smoothing processing, so as to eliminate the influence of random fluctuations and ensure that the data quality meets the requirements of subsequent analysis.

[0029] The establishment of the reference stable mode sequence is realized by collecting a large number of ultrasonic wave velocity data sequences of soft soil reinforcement material test pieces which have been determined to reach the stable state. After normalization processing and time alignment of these data sequences, the average value of each time point is taken to construct the reference stable mode sequence. Specifically, at least 30 groups of stable period test piece data of different formulations are selected, each group of data is normalized to the same length according to the time dimension, for example, unified to 100 time points by linear interpolation method, and then the average value of all data at each time point is calculated to form the reference stable mode sequence. The sequence represents the stable mode of material performance development in an ideal state, which serves as a reference benchmark for subsequent mode stability calculation.

[0030] When the dynamic time warping algorithm is used to calculate the warping path distance between the current ultrasonic wave velocity data sequence and the reference stable mode sequence, first, the two sequences are time-warped to find the warping path with the minimum cumulative distance between the two sequences. In specific implementation, a distance matrix is constructed, and each element in the matrix represents the Euclidean distance between the i-th point of the current sequence and the j-th point of the reference sequence. i is the sequence number of the data point in the current ultrasonic wave velocity data sequence, and j is the sequence number of the data point in the reference stable mode sequence.

[0031] A path is found from the top left corner to the bottom right corner of the matrix by the dynamic programming method, so that the sum of the distances of the points on the path is minimized. This minimum cumulative distance is the warping path distance. The calculation of the warping path distance considers the possible nonlinear time deformation between the sequences, which can effectively evaluate the similarity between the current sequence and the stable mode. The smaller the warping path distance value is, the higher the consistency between the current data sequence and the stable mode is.

[0032] For the resistivity data sequence, the multiscale entropy analysis method is used to calculate the complexity balance measure. Firstly, the continuous data sequence of resistivity changing with time is coarsely granulated, and by selecting different scale factors, for example, the scale factor τ takes integer values from 1 to 10, the original sequence is divided into multiple coarse-grained subsequences. Coarse-grained processing is to take the average value of τ consecutive data points in the original sequence to form a new coarse-grained sequence. Each scale factor corresponds to a coarse-grained sequence, so that a plurality of time scale subsequences are obtained.

[0033] Then the sample entropy values of the sub-sequences at each scale are calculated. When calculating the sample entropy, the pattern dimension m and the similarity tolerance r need to be set. Usually m = 2, and r is 0.1 to 0.25 times the standard deviation of the original sequence, for example, 0.2 times. For each coarse-grained sub-sequence, its sample entropy value is calculated, which reflects the complexity of the sequence. The larger the sample entropy value, the more complex the sequence, and the worse the regularity. The specific calculation process includes: first, define a set of m-dimensional vectors in the sequence, then count the proportion of vectors whose distance is less than r, then calculate the proportion of m+1-dimensional vectors, and finally take the natural logarithm of the ratio of the two as the sample entropy value.

[0034] Finally, the dispersion degree of the sample entropy values at all scales is counted, and the variance of these sample entropy values is calculated as a measure of complexity balance. The variance is calculated using the standard variance formula, that is, the average of the square sum of the difference between each sample entropy value and the mean of all scale sample entropy values. This variance value reflects the consistency of the behavior regularity of the material at different time scales, and the smaller the variance value, the more consistent the material performance development at different time scales, indicating that the material performance tends to be stable.

[0035] In the specific implementation process, the parameters of the dynamic time warping algorithm are set as follows: the warping window width is limited to 10% of the sequence length to avoid excessive warping; the local path constraint uses a symmetric Sakoe-Chiba band constraint. The scale factor selection range in multi-scale entropy analysis is usually 1 to 10, but it can be adjusted according to the actual data length, for example, when the data points are fewer, the maximum scale factor can be appropriately reduced. The value of the similarity tolerance r in sample entropy calculation needs to be adjusted according to the characteristics of the data, and generally determined by trial calculation to make the sample entropy value have good discrimination.

[0036] All calculations are implemented through programming, and the calculation program is written in Python language, and numerical calculation is performed using standard scientific calculation libraries such as NumPy. To ensure the reliability of the calculation results, cross-validation is performed on the key calculation steps, such as repeated calculation with different parameter settings to observe the stability of the results. The intermediate results and final results generated during the calculation process are saved as structured data files for subsequent analysis and verification. Through the above specific implementation, the pattern stability measure representing the consistency of evolution characteristics and the complexity balance measure representing the behavior regularity at multiple time scales can be accurately calculated, providing a quantitative basis for subsequent identification of performance stabilization critical points.

[0037] S3、When the pattern stability measure continuously exceeds the first threshold value and the complexity balance measure continuously falls below the second threshold value, the corresponding time point is identified as a candidate critical point of performance stabilization, and the specific implementation is: Based on the calculation results of the pattern stability measure and the complexity balance measure, the candidate critical point identification process is implemented in this step. The first threshold is determined by statistical analysis method, which establishes the reference standard by calculating the distribution characteristics of the regularized path distance of the benchmark stable pattern sequence compared with itself. Specifically, the dynamic time warping algorithm is used to calculate the self-similarity distance values of the benchmark stable pattern sequence, and then the statistical significance level is determined based on the distribution characteristics of these distance values. For example, the average of these self-similarity distance values plus 2 times the standard deviation is taken as the first threshold, which represents that when the similarity between the current sequence and the benchmark sequence reaches the statistical significance level comparable to the self-similarity of the benchmark sequence, the material performance is considered to be stable.

[0038] The second threshold is determined based on the entropy variance analysis of the reference material sequence in the completely stable state. A variety of soft soil reinforcement material specimens that have reached the completely stable state are selected, and their resistivity data sequences are collected. The entropy variance of each specimen is calculated by the multi-scale entropy analysis method, and then the distribution characteristics of these variance values are statistically analyzed. For example, the average of the entropy variance of these completely stable specimens is taken as the second threshold, which represents the level of regularity of the material's multi-scale behavior in the completely stable state. When the complexity balance measure of the current material is lower than this threshold, it indicates that the regularity of its behavior has reached the reference level of the stable state.

[0039] The determination of the preset monitoring period needs to consider the time-varying characteristics of material performance development, and is usually based on the statistical analysis results of a large amount of experimental data. For example, by analyzing the time distribution characteristics of the material from the beginning of curing to the stable state, a certain percentile of the time distribution is taken as the preset monitoring period. The preset monitoring period should ensure that the continuous stable trend can be captured, and avoid misjudgment due to too short time. In practical applications, the preset monitoring period can be adjusted according to the specific material type and environmental conditions, for example, a shorter monitoring period can be used for materials that harden quickly, and a longer monitoring period can be used for materials that harden slowly.

[0040] When the pattern stability measure continuously exceeds the first threshold and the complexity balance measure continuously falls below the second threshold, the system starts recording the duration. The duration calculation starts from the first time point that meets both conditions, and resets when either condition is not met. For example, when the pattern stability measure first meets both the first threshold and the complexity balance measure falls below the second threshold, the timer is started, and the subsequent data at the time points is continuously monitored. If all time points within the preset monitoring period meet both conditions, the starting point of this time period is recorded as the candidate critical point.

[0041] To ensure the reliability of the identification results, a verification mechanism is also set up in the implementation process. For example, for the identified candidate critical point, the data trend in the period before and after it needs to be checked to confirm the persistence of the stability trend. At the same time, a maximum allowed fluctuation range is set, for example, the mode stability quantity of an individual time point is allowed to be slightly lower than the first threshold value or the complexity balance quantity is allowed to be slightly higher than the second threshold value, but as long as the fluctuation is within the allowed range and the overall trend meets the requirements, it can still be determined as continuously meeting the conditions. The setting of the fluctuation range is based on statistical analysis of historical data, for example, taking the average of the normal fluctuation range plus 2 times the standard deviation as the maximum allowed fluctuation range.

[0042] All threshold parameters and judgment conditions are managed through configuration files, which are convenient for adjustment according to actual conditions. The system automatically saves the intermediate results and final results of the identification process regularly, including the mode stability quantity and complexity balance quantity values of each time point, comparison results with threshold values, duration count, etc., for subsequent tracing and verification.

[0043] S4, compare the continuous data sequence with the pre-set corresponding class material standard performance development envelope band, specifically implemented as: Based on the continuous data sequence of ultrasonic wave speed changing with time and the continuous data sequence of resistivity changing with time, and the identified candidate critical point, the specific implementation of the comparative analysis process is carried out. The establishment of the pre-set corresponding class material standard performance development envelope band is a reference standard range constructed by collecting a large number of long-term monitoring data of the same type of soft soil reinforcement material test pieces under standard curing conditions. Specifically, at least 50 groups of data of the same type of material test pieces of different formulations are selected, covering the whole process development data of the material from the initial state to the completely stable state, the data are normalized according to the time dimension, and then the statistical characteristic values of all data at each time point are calculated, for example, the average value, standard deviation and other statistical quantities at each time point are calculated to determine the boundary range of the envelope band.

[0044] The construction of the ultrasonic wave speed standard development envelope band is to determine the envelope band boundary by statistical analysis of the distribution characteristics of the ultrasonic wave speed values at each time point. For example, the average value of all data values at each time point plus or minus 2 times the standard deviation is taken as the upper and lower boundaries of the envelope band, so that the envelope band formed can cover more than 95% of the normal development data. The construction of the resistivity standard development envelope band uses the same method, and the distribution characteristics of the resistivity values at each time point are statistically analyzed to determine the envelope band boundary. Both envelope bands use the same time reference and normalization method to ensure consistency in the time dimension. The envelope band data is stored in matrix form, including time point sequence, upper boundary value sequence and lower boundary value sequence, which is convenient for fast query and call in subsequent comparative analysis.

[0045] When comparing the continuous data sequence of ultrasonic wave velocity change over time with the standard development envelope band point by point, first align the time axis of the current data sequence with the envelope band time axis. Linear interpolation is used to interpolate the envelope band boundary values to each time point of the current data sequence, ensuring that each monitoring time point has a corresponding envelope band boundary value. Then for each time point, compare the current ultrasonic wave velocity value with the upper and lower boundary values of the envelope band at the corresponding time point to determine whether the data point is within the envelope band range, and calculate the relative distance of the data point from the nearest boundary. This distance value is used to quantify the degree of deviation.

[0046] The same processing flow is used for synchronous point-by-point comparison and analysis of the continuous data sequence of resistivity change over time with the resistivity standard development envelope band. First, align the time axis and interpolate the resistivity standard development envelope band boundary values to each time point of the current data sequence, then compare the current resistivity value with the envelope band boundary value at each time point. The two comparison and analysis processes are time-synchronized to ensure consistent judgment of both parameters at the same time point. This synchronous comparison and analysis helps to comprehensively evaluate the coordination of material performance development.

[0047] When recording the position relationship of each data point in the continuous data sequence with the corresponding standard development envelope band, a classification recording method is used. For each time point, record the position state of the ultrasonic wave velocity data point relative to the ultrasonic wave velocity standard development envelope band, such as recording as within the envelope band, below the lower boundary or above the upper boundary. At the same time, record the position state of the resistivity data point relative to the resistivity standard development envelope band. In addition, the specific deviation degree is also recorded, such as calculating the relative distance between the data point and the nearest boundary, which is expressed in percentage form for subsequent analysis. All recorded information is stored as a structured data table, including fields such as timestamp, data value, boundary value, position state and deviation degree.

[0048] To ensure the accuracy of the comparison and analysis, a data quality control mechanism is implemented during the process. For example, when a significant difference is found between the time range of the current data sequence and the envelope band time range, data extension or truncation processing methods are used to ensure consistency in the comparison time range. At the same time, abnormal data processing rules are set, such as when encountering obvious abnormal data points, data verification is performed before comparison and analysis, and verification methods include checking sensor status, reviewing original data, etc. A tolerance range is also set during the comparison and analysis process, such as allowing data points to fluctuate within a certain range to be considered as meeting the requirements. This tolerance range is determined based on the statistical characteristics of historical data.

[0049] All comparative analysis results are saved as structured data records, including timestamp, current data value, upper envelope band value, lower envelope band value, position state marker and other information at each time point. These records facilitate subsequent queries and verification, and also provide data support for final performance stabilization critical time point confirmation. Through the above specific embodiments, comprehensive and accurate comparative analysis of data sequences and standard development envelope bands can be performed, ensuring the reliability and effectiveness of the analysis results.

[0050] S5、According to the comparative analysis results, when the continuous data sequence is within the preset corresponding material standard performance development envelope band range throughout, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point, which is specifically implemented as: Based on the position relationship between each data point in the recorded continuous data sequence and the corresponding standard development envelope band, the performance stabilization critical time point confirmation process is specifically implemented. When all data points of the continuous data sequence of ultrasonic wave velocity changing with time are within the ultrasonic wave velocity standard development envelope band range, first read the position state information of each data point recorded in the comparative analysis process. These position state information includes three states: within the envelope band, below the lower boundary or above the upper boundary. Traverse the position state information to confirm that all data points from the candidate critical point to the current time point are within the envelope band. Set data integrity verification during the checking process to ensure that all time points have corresponding position state records. For missing data points, data completion or reanalysis is required. Data completion methods include linear interpolation or trend extrapolation based on adjacent data points.

[0051] The same judgment logic is used to synchronously judge whether all data points of the continuous data sequence of resistivity changing with time are within the resistivity standard development envelope band range. Read the position state record of the resistivity data point, traverse the position state of all data points from the candidate critical point to the current time point, and confirm that they are all within the envelope band. The two judgment processes are time-synchronized to ensure consistent verification of the two parameters within the same time range. The density and uniformity of data points are also considered during the judgment process. For example, for a time period with a large monitoring interval, interpolation points need to be added to ensure the accuracy of the judgment. The interpolation method uses cubic spline interpolation to ensure data smoothness.

[0052] When both the ultrasonic wave velocity continuous data sequence and the resistivity continuous data sequence are within the corresponding standard development envelope band range throughout, the system performs a confirmation operation of the candidate critical point. The confirmation process includes multiple verification links, such as checking the continuity of the data sequence to ensure that there is no data missing or abnormal interruption; verifying the reasonableness of the envelope band boundary to confirm that the envelope band version used matches the current material type; checking the consistency of the time range to ensure that the time period judged covers the complete maintenance process. The setting of these verification links is based on the analysis results of historical data, such as setting corresponding check rules according to common error types in previous tests, including data jump detection, abnormal value identification, etc.

[0053] To ensure the reliability of the confirmation results, a review mechanism is also set up during implementation. For example, for the performance stabilization critical time point confirmed as valid, the data development trend in a period of time before and after it needs to be checked to confirm the continuity of the stability trend. At the same time, a maximum allowed deviation is set, for example, individual data points are allowed to deviate within a certain range from the envelope band center line, but as long as they remain within the envelope band range and the overall trend is stable, it can still be confirmed as valid. The setting of the deviation is based on the statistical characteristics of a large amount of stable state data, for example, taking the average distance of stable period data from the envelope band center line plus 2 times the standard deviation as the maximum allowed deviation range, which is usually controlled within ±5% of the envelope band width.

[0054] The confirmation process also takes into account the special nature of material performance development, for example, for some special formula materials, their performance development trend may have systematic differences with the standard envelope band. For this reason, an adaptive adjustment mechanism is set up, when systematic differences are found, the envelope band can be adjusted appropriately according to the historical data of this type of material, but the adjustment range needs to be controlled within the statistical allowed range, for example, the adjusted envelope band boundary cannot exceed the original boundary by ±10%. All adjustment operations need to record the adjustment reason, adjustment amplitude and adjustment basis for subsequent tracing. The adjustment basis includes the statistical characteristics of the historical data of this type of material, the formula difference analysis report, etc.

[0055] The final confirmed effective performance stabilization critical time point and all related analysis data are stored in the result database, including the original data sequence, the envelope band data, the position state record, the various check results in the confirmation process, etc. The storage format uses a standardized data structure, containing time stamp, data value, state marker, verification result, etc. fields, each field has a clear definition and value range. These data provide complete information support for subsequent result output and verification, and also facilitate other system calls and analysis. Through the above specific implementation, the performance stabilization critical time point can be accurately and reliably confirmed, ensuring the scientificity and effectiveness of the analysis results.

[0056] S6, output the effective performance stabilization critical time point, specifically implemented as: Based on the confirmed effective performance stabilization critical time point and its related analysis data, the specific implementation result output process is performed. When the confirmed effective performance stabilization critical time point is associated with its corresponding ultrasonic wave velocity data sequence characteristics and resistivity data sequence characteristics and stored, a structured database table is first established, which includes time point identification, critical time stamp, ultrasonic wave velocity data sequence characteristic abstract, resistivity data sequence characteristic abstract, etc. The ultrasonic wave velocity data sequence characteristic abstract includes average value, standard deviation, maximum value, minimum value, etc. The resistivity data sequence characteristic abstract uses the same statistical indicators. The associated storage is implemented using a relational database, and the primary and foreign key relationship is established through the time point identification to ensure the consistency and integrity of the data. A data checking mechanism is set in the storage process, such as checking the rationality of the data range to prevent abnormal data from being stored. The data checking includes range checking and logical consistency checking. The range checking confirms that the value is within a reasonable physical range, and the logical consistency checking ensures that the time sequence is correct and there is no contradictory data.

[0057] When generating a report file containing the effective performance stabilization critical time point and its corresponding monitoring data analysis results, a standardized report template is used. The report file contains multiple chapters, including test basic information, monitoring data overview, critical time point analysis results, data quality evaluation, etc. The test basic information records the test piece number, material ratio, curing condition, etc. The monitoring data overview contains statistical information such as data collection time range, data point number, data integrity index, etc. The critical time point analysis results record the identified critical time points and their related parameters in detail, including time point value, confidence index, etc. The data quality evaluation includes sensor state record, data abnormality processing record, etc. The report file is output in PDF format, and editable document format is also saved for subsequent modification. The report generation process is automatically implemented, and the data and analysis results are automatically filled according to the template. Format checking and content verification are performed during the filling process.

[0058] When the effective performance stabilization critical time point is synchronously displayed in the continuous data sequence of ultrasonic wave velocity and the continuous data sequence of resistivity through the visual interface, the multi-view coordinated display technology is used. The main view displays the curve graphs of the two data sequences, and the critical time point is marked with special markers on the curve, such as vertical dashed lines. The ultrasonic wave velocity and resistivity data are distinguished by different colors. The auxiliary view displays the enlarged detail data, which is convenient for observing the data characteristics near the critical time point. The display interface supports interactive operations, such as mouse hovering to display detailed data values, zooming and panning data views, etc. The visual interface is implemented using Web technology, supporting cross-platform access and real-time data updating.

[0059] When the mode stability value and the complexity balance value corresponding to the corresponding time point and their comparison with the respective threshold values are displayed simultaneously, a combination of a dashboard and a column chart is used for display. The dashboard displays the relative relationship between the current value and the threshold value, and distinguishes between the normal range, the warning range and the abnormal range by color, for example, green for normal, yellow for warning, and red for abnormal; the column chart displays the historical value change trend, and the threshold position is marked with a reference line. The display interface supports multi-time point comparison, and can simultaneously display the value comparison of multiple critical time points, facilitating the analysis of the trend and law of performance development. The threshold value display adopts a dynamic adjustment mechanism, which automatically adjusts the display ratio according to the data characteristics.

[0060] When the comparison analysis result of the continuous data sequence and the pre-set corresponding standard performance development envelope band of the material of the same type is displayed, a superimposed display method is used. The current data sequence curve and the envelope band region are displayed in the same coordinate system at the same time, and the envelope band is represented by a semi-transparent color band, and the current data sequence is represented by a solid line. The display interface supports envelope band display option setting, for example, the complete envelope band or only the boundary line can be displayed, and the transparency of the envelope band can be adjusted. At the same time, the difference analysis function is provided, for example, the average deviation of the data sequence and the envelope band center line is calculated, and the proportion of data points exceeding the envelope band range is counted. The difference analysis result is displayed in numerical and chart form at the same time.

[0061] All output results are set with version management and access control mechanisms. Each output result records the generation time, generation parameters and operator information, facilitating traceability and review. Access control sets different permission levels, for example, ordinary users can only view the results, senior users can modify the display parameters, administrators can adjust the output template and threshold parameters, etc. The output data is backed up regularly, and an automatic backup strategy is set, for example, incremental backup every day and full backup every week, to ensure data security. The backup data is stored in an encrypted manner, and access logs are set to record all data operations. Through the above specific embodiments, the performance stabilization critical time point and its related analysis results can be output comprehensively, accurately and reliably, providing complete and intuitive analysis reports and visual displays for users.

[0062] Embodiment 2: Figure 2 The structural diagram of a soft soil reinforcement material performance analysis system is given, and the soft soil reinforcement material performance analysis system comprises the following modules: A continuous monitoring module is used to continuously monitor the performance parameters of the soft soil reinforcement material test piece during the curing process through a sensor, and to obtain a continuous data sequence of the performance parameters changing with time; A feature calculation module is used to calculate a mode stability value for representing the consistency of the evolution characteristics and a complexity balance value for representing the behavior regularity in multiple time scales based on the continuous data sequence; a critical recognition module, configured to recognize the corresponding time point as a candidate critical point of performance stabilization when the mode stability quantity continuously is higher than the first threshold and the complexity balance quantity continuously is lower than the second threshold; a comparative analysis module, configured to compare the continuous data sequence with a preset corresponding class material standard performance development envelope band; a confirmation judgment module, configured to, according to the comparative analysis result, confirm the corresponding candidate critical point as an effective performance stabilization critical time point when the continuous data sequence is in the range of the preset corresponding class material standard performance development envelope band all the time; a result output module, configured to output the effective performance stabilization critical time point.

[0063] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and threshold values in the calculations are set by a person skilled in the art according to actual conditions.

[0064] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product, wholly or partially.

[0065] Those skilled in the art can appreciate that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application of the technical solutions and the constraints of the invention. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0066] In addition, the functional modules in each of the embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0067] In the several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the modules is only a logical function division. There can be another division manner in actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the modules shown or discussed can be indirect coupling or communication connection through some interface, device or module, and can be electrical, mechanical or other forms.

[0068] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0069] Finally, the above merely provides the preferred embodiments of the present application, but is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be covered by the protection scope of the present application.

Claims

1. A method for analyzing the performance of soft soil reinforcement materials, characterized in that, Includes the following steps: S1. Continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process using sensors to obtain a continuous data sequence of performance parameters changing over time; S2. Based on continuous data sequences, calculate the pattern stability quantity used to characterize the consistency of its evolutionary characteristics and the complexity balance quantity used to characterize the regularity of its behavior across multiple time scales. S3. When the mode stability value is consistently higher than the first threshold and the complexity balance value is consistently lower than the second threshold, the corresponding time point will be identified as a candidate critical point for performance stabilization. S4. Compare and analyze the continuous data sequence with the pre-set standard performance development envelope of the corresponding material class; S5. Based on the comparative analysis results, when the continuous data sequence is within the preset envelope of the standard performance development of the corresponding material, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point. S6 outputs the effective performance stabilization critical time point.

2. The method for analyzing the performance of soft soil reinforcement materials according to claim 1, characterized in that, The performance parameters of soft soil reinforcement material specimens were continuously monitored by sensors during the curing process, and a continuous data sequence of performance parameters changing over time was obtained, including: The ultrasonic wave velocity changes of the soft soil reinforcement material specimens during the curing process were continuously monitored by a piezoelectric ceramic sensor embedded in the specimens. The resistivity changes of the soft soil reinforcement material specimens during the curing process were continuously monitored by a four-electrode resistance probe embedded in the specimens. To obtain continuous data sequences of ultrasonic wave velocity and resistivity as a function of time.

3. The method for analyzing the performance of soft soil reinforcement materials according to claim 2, characterized in that, Based on continuous data sequences, we calculate the pattern stability quantity, which characterizes the consistency of their evolutionary features, and the complexity balance quantity, which characterizes the regularity of their behavior across multiple time scales, including: Based on a continuous data sequence of ultrasonic wave velocity changing with time, the normalized path distance between the current data sequence and the reference stable mode sequence is calculated using a dynamic time warping algorithm as a mode stability quantity. Based on a continuous data sequence of resistivity changing over time, the variance of entropy values ​​at different time scales is calculated using a multi-scale entropy analysis method as a complexity balancing measure.

4. The method for analyzing the performance of soft soil reinforcement materials according to claim 3, characterized in that, Based on a continuous data sequence of ultrasonic wave velocity varying over time, the normalized path distance between the current data sequence and the reference stable mode sequence is calculated using a dynamic time warping algorithm as a mode stability quantity, including: Obtain a baseline stability mode sequence established in advance using a large number of stable-period material samples; The minimum cumulative distance between the current ultrasonic wave velocity data sequence and the reference stable mode sequence is calculated using a dynamic time warping algorithm, and the minimum cumulative distance is quantized into a warped path distance. The smaller the regular path distance value, the higher the consistency between the current data sequence and the stable pattern.

5. The method for analyzing the performance of soft soil reinforcement materials according to claim 3, characterized in that, Based on a continuous data sequence of resistivity changing over time, the variance of entropy values ​​at different time scales is calculated using a multi-scale entropy analysis method as a complexity balancing measure, including: Coarsening the resistivity data sequence yields subsequences at multiple time scales. Calculate the sample entropy value of the subsequence at each scale; The dispersion of sample entropy values ​​across all scales is statistically analyzed, and the variance of sample entropy values ​​is used as a measure of complexity balance. The smaller the variance of the sample entropy, the more consistent the material's behavior is across different time scales.

6. The method for analyzing the performance of soft soil reinforcement materials according to claim 3, characterized in that, When the pattern stability value consistently exceeds the first threshold and the complexity balance value consistently falls below the second threshold, the corresponding time point is identified as a candidate critical point for performance stabilization, including: Based on the mode stability measure, its first threshold is set as the statistical significance level of the distance value obtained by comparing the benchmark stable mode sequence with itself using the dynamic time warping algorithm. Based on the complexity balance, its second threshold is set as the relative stable state reference value of the variance of the entropy value obtained by calculating the reference material sequence under the fully stable state using the multi-scale entropy analysis method. When the duration of the pattern stability value exceeding the first threshold reaches the preset monitoring period and the duration of the complexity balance value falling below the second threshold reaches the preset monitoring period, the first time point that simultaneously meets the duration requirement will be recorded as a candidate critical point for performance stabilization.

7. The method for analyzing the performance of soft soil reinforcement materials according to claim 6, characterized in that, The continuous data sequence is compared and analyzed with the pre-defined standard performance development envelope of the corresponding material class, including: Obtain the pre-defined standard performance development envelope of the corresponding material type, including the standard development envelope of ultrasonic wave velocity and the standard development envelope of resistivity; A point-by-point comparative analysis was conducted between the continuous data sequence of ultrasonic wave velocity changing over time and the standard development envelope of ultrasonic wave velocity. Simultaneously, a point-by-point comparative analysis is performed between the continuous data sequence of resistivity changes over time and the resistivity standard development envelope. Record the positional relationship between each data point in a continuous data sequence and the corresponding standard developmental envelope.

8. The method for analyzing the performance of soft soil reinforcement materials according to claim 7, characterized in that, Based on the comparative analysis results, when the entire continuous data sequence falls within the pre-defined envelope of the standard performance development of the corresponding material type, the corresponding candidate critical point is confirmed as an effective performance stabilization critical time point, including: Based on the positional relationship between each data point in the recorded continuous data sequence and the corresponding standard development envelope, it is determined whether all data points in the continuous data sequence of ultrasonic wave velocity changing with time are within the range of the standard development envelope of ultrasonic wave velocity. Simultaneously determine whether all data points in a continuous data sequence of resistivity changes over time are within the resistivity standard development envelope. When both the continuous data sequence of ultrasonic wave velocity and the continuous data sequence of resistivity are within the corresponding standard development envelope range throughout the entire process, the candidate critical point is confirmed as an effective performance stabilization critical time point.

9. The method for analyzing the performance of soft soil reinforcement materials according to claim 8, characterized in that, Output the effective performance stabilization critical time points, including: The confirmed effective performance stabilization critical time points are associated and stored with their corresponding ultrasonic wave velocity data sequence characteristics and resistivity data sequence characteristics; a report file containing the effective performance stabilization critical time points and their corresponding monitoring data analysis results is generated. The system displays the position markers of effective performance stabilization critical time points in continuous data sequences of ultrasonic wave velocity and resistivity over time through a visual interface. It also displays the mode stability value and complexity equilibrium value corresponding to the time point and their comparison with their respective thresholds. Furthermore, it displays the comparative analysis results of the continuous data sequence with the preset standard performance development envelope of the corresponding material type.

10. A soft soil reinforcement material performance analysis system, used to implement the soft soil reinforcement material performance analysis method according to any one of claims 1-9, characterized in that, Includes the following modules: The continuous monitoring module is used to continuously monitor the performance parameters of soft soil reinforcement material specimens during the curing process through sensors, and obtain a continuous data sequence of performance parameters changing over time. The feature calculation module is used to calculate, based on continuous data sequences, the pattern stability quantity used to characterize the consistency of their evolutionary features and the complexity balance quantity used to characterize the regularity of their behavior across multiple time scales. The critical identification module is used to identify the corresponding time point as a candidate critical point for performance stabilization when the pattern stability value is continuously higher than the first threshold and the complexity balance value is continuously lower than the second threshold. The comparative analysis module is used to compare and analyze continuous data sequences with preset standard performance development envelopes for corresponding material types. The confirmation and judgment module is used to confirm the corresponding candidate critical point as an effective performance stabilization critical time point when the continuous data sequence is within the preset standard performance development envelope of the corresponding material type throughout the entire process, based on the comparative analysis results. The results output module is used to output the effective performance stabilization critical time point.

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