A method and system for non-destructive evaluation of the strength of microbially mineralized river sand

Abstract

The present application relates to the technical field of material performance detection, and discloses a kind of microorganism mineralization river sand strength non-destructive evaluation method and system, comprising: the sweep frequency acoustic wave excitation signal is applied to microorganism mineralization river sand standard sample model;According to sweep frequency acoustic wave excitation signal, microorganism mineralization river sand standard sample model is carried out penetration simulation operation;Through the penetration response signal, the mineralization specificity characteristic parameter corresponding to sample model is extracted;According to mineralization specificity characteristic parameter, the biological cementation index of microorganism mineralization is constructed, and the compressive strength of microorganism mineralization river sand standard sample model is analyzed according to biological cementation index;The destructive calibration curve of microorganism mineralization river sand standard sample model is constructed;The target compressive strength of sample model to be tested is analyzed using destructive calibration curve, and the microorganism river sand strength non-destructive of sample model to be tested is analyzed based on target compressive strength.The present application can improve the accuracy when microorganism mineralization river sand strength non-destructive evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material performance detection, and in particular to a method and system for non-destructive evaluation of the strength of microbially mineralized river sand. BACKGROUND

[0002] In the field of civil and geological engineering, microbially mineralized technology has been widely used in river sand foundation improvement, dam seepage prevention and other engineering scenarios as a green and environmentally friendly foundation reinforcement method. The technology improves the overall structural strength by cementing river sand particles through the mineralization produced by microbial metabolism. The core of the technology is to accurately control the progress of the mineralization reaction to ensure engineering quality.

[0003] In the prior art, the strength evaluation of microbially mineralized river sand relies on destructive sampling detection, that is, the overall strength is inferred by performing compression tests on partial samples. This approach has significant technical defects. On the one hand, the sampling process destroys the overall structure of the river sand, making the test results unable to accurately reflect the actual mineralization state. On the other hand, due to the spatial and temporal heterogeneity of the mineralization reaction, the test results of the local samples are difficult to represent the overall mineralization strength distribution, which can easily cause evaluation bias and affect engineering safety. SUMMARY

[0004] The present application provides a method and system for non-destructive evaluation of the strength of microbially mineralized river sand, which aims to solve the problem of low accuracy in non-destructive evaluation of the strength of microbially mineralized river sand.

[0005] To achieve the above-mentioned purpose, the present application provides a method for non-destructive evaluation of the strength of microbially mineralized river sand, comprising:

[0006] Constructing a microbially mineralized river sand standard sample model and a sample to be tested based on pre-acquired mineralization attributes, and applying a predetermined swept frequency acoustic excitation signal to the microbially mineralized river sand standard sample model;

[0007] Performing penetration simulation operation on the microbially mineralized river sand standard sample model according to the swept frequency acoustic excitation signal to obtain penetration response signals of different reaction stages of the microbially mineralized river sand;

[0008] Extracting mineralization-specific characteristic parameters corresponding to the microbially mineralized river sand standard sample model through the penetration response signals of different reaction stages;

[0009] Constructing a biological cementation index of microbially mineralized river sand according to the mineralization-specific characteristic parameters, and analyzing the compressive strength of the microbially mineralized river sand standard sample model according to the biological cementation index;

[0010] Constructing a destructive calibration curve of the microbially mineralized river sand standard sample model according to the biological cementation index and the compressive strength.

[0011] analyzing the target compressive strength of the sample model to be tested by using the destructive calibration curve, and analyzing the non-destructive microbial sand strength of the sample model to be tested based on the target compressive strength.

[0012] Optionally, the construction of the microbial mineralization sand standard sample model based on the pre-acquired mineralization attribute comprises:

[0013] analyzing the spatiotemporal variation gradient of the mineralization reaction concentration based on the microbial metabolic characteristics in the mineralization attribute;

[0014] generating a layered perfusion strategy of the microbial mineralization sand standard sample model according to the spatiotemporal variation gradient;

[0015] performing spatially sequential perfusion and solidification simulation operations of the microbial liquid and the sand in the pre-set simulation space according to the layered perfusion strategy;

[0016] adjusting the microbial liquid and the sand after the solidification simulation operation based on the spatiotemporal variation gradient, and generating the microbial mineralization sand standard sample model according to the parameter attribute corresponding to the adjusted microbial liquid and the sand.

[0017] Optionally, the application of the pre-set swept-frequency acoustic excitation signal to the microbial mineralization sand standard sample model comprises:

[0018] dynamically dividing the focal region and the scanning path of the acoustic excitation according to the concentration gradient distribution of the microbial mineralization sand standard sample model in the solidification process;

[0019] adaptively adjusting the beamforming parameters of the pre-set multi-element acoustic transducer array according to the scanning path;

[0020] applying the swept-frequency acoustic excitation signal in the focal region based on the beamforming parameters.

[0021] Optionally, the penetration simulation operation of the microbial mineralization sand standard sample model according to the swept-frequency acoustic excitation signal to obtain the penetration response signal of the microbial mineralization sand in different reaction stages comprises:

[0022] synchronously collecting the vibration micro-variation signal corresponding to the surface target point of the microbial mineralization sand standard sample model when the swept-frequency acoustic excitation signal is applied;

[0023] dividing the acoustic signal after the penetration simulation operation into different types of wave components, and extracting the signal energy attenuation curve of different types of wave components at different spatial positions on the scanning path;

[0024] The signal energy attenuation curve is time-domain aligned and fused with the vibration micro-variation signal to generate a penetration enhanced response signal;

[0025] A signal segment corresponding to a time window of different reaction stages of the microbially mineralized river sand is intercepted from the penetration enhanced response signal;

[0026] The response signal corresponding to the signal segment is taken as a penetration response signal of different reaction stages.

[0027] Optionally, the mineralization-specific characteristic parameters corresponding to the microbially mineralized river sand standard sample model are extracted from the penetration response signals of different reaction stages, including:

[0028] The wave velocity ratio of the longitudinal wave and the transverse wave of the penetration response signals of different reaction stages is calculated, and the variance of the wave velocity ratio and the spatial position change of the penetration response signal is analyzed;

[0029] The biological cementation spatial distribution index of the microbially mineralized river sand standard sample model is determined according to the variance;

[0030] The dispersion curve feature of the surface wave component of the penetration response signals of different reaction stages is extracted, and the deviation degree between the dispersion curve feature and a preset standard elastic half-space model is calculated;

[0031] The biological cementation spatial distribution index, the wave velocity ratio and the deviation degree are taken as the mineralization-specific characteristic parameters.

[0032] Optionally, the biological cementation index of the microbially mineralized is constructed according to the mineralization-specific characteristic parameters, including:

[0033] The mineralization reaction stage of the microorganism is identified, and the dynamic weight coefficient corresponding to different mineralization reaction stages is selected in a preset coefficient mapping table according to the dominant cementation parameter corresponding to the mineralization reaction stage;

[0034] The wave velocity ratio and the deviation degree in the mineralization-specific characteristic parameters are weighted according to the dynamic weight coefficient;

[0035] The weighted result is operated with the biological cementation spatial distribution index in the mineralization-specific characteristic parameters to obtain the biological cementation index of the microbially mineralized.

[0036] Optionally, the destructive calibration curve of the microbially mineralized river sand standard sample model is constructed according to the biological cementation index and the compressive strength, including:

[0037] An acoustic wave simulation is performed on a preset target standard sample model, and a target biological cementation index after acoustic wave simulation is calculated;

[0038] The preset target standard sample model is simulated in a destructive compression strength mode to obtain a real compression strength;

[0039] A preset Gaussian process regression model is trained by using the target biocementation index and the real compression strength, and a probabilistic mapping relationship is determined based on the trained Gaussian process regression model;

[0040] A destructive calibration curve of the microorganism mineralization river sand standard sample model is constructed according to the probabilistic mapping relationship.

[0041] Optionally, the target compression strength of the sample model to be tested is analyzed by using the destructive calibration curve, and the method comprises the following steps of:

[0042] Multi-modal response data generated by the sample model to be tested under the excitation of an acoustic wave is acquired;

[0043] The multi-modal response data is processed in time and space synchronization and fusion, and a target mineralization characteristic parameter after fusion is extracted;

[0044] A dynamic biocementation index of the sample model to be tested is calculated according to the target mineralization characteristic parameter;

[0045] The target strength distribution corresponding to the dynamic biocementation index is analyzed by using the destructive calibration curve;

[0046] The variance of the target strength distribution is taken as a confidence index of the sample model to be tested, and the target compression strength of the sample model to be tested is determined according to the confidence index and an expected value of the target strength distribution.

[0047] Optionally, the microorganism river sand strength non-destructive property corresponding to the sample model to be tested is analyzed based on the target compression strength, and the method comprises the following steps of:

[0048] The target compression strength and the confidence index are positioned in a preset multi-dimensional strength decision space in a destructive manner to obtain a destructive positioning point;

[0049] The strength grade of the sample model to be tested is obtained by querying a pre-generated microorganism mineralization health state atlas based on the destructive positioning point;

[0050] The completion degree and the stability of the sample model to be tested with respect to a mineralization reaction are analyzed based on the convergence characteristics and the fluctuation characteristics of the dynamic biocementation index;

[0051] The strength non-destructive state of the microorganism river sand is analyzed according to the strength grade, the completion degree and the stability.

[0052] In order to solve the above problems, the present application further provides a microorganism mineralization river sand strength non-destructive evaluation system, and the system comprises:

[0053] a sample model construction module, configured to construct a standard sample model of microbially mineralized river sand and a sample to be tested based on pre-acquired mineralization attributes, and apply a preset sweep frequency acoustic excitation signal to the standard sample model of microbially mineralized river sand;

[0054] a penetration simulation operation module, configured to perform a penetration simulation operation on the standard sample model of microbially mineralized river sand according to the sweep frequency acoustic excitation signal, to obtain a penetration response signal of the standard sample model of microbially mineralized river sand at different reaction stages;

[0055] a mineralization specificity characteristic parameter extraction module, configured to extract a mineralization specificity characteristic parameter corresponding to the standard sample model of microbially mineralized river sand from the penetration response signal at different reaction stages;

[0056] a compressive strength analysis module, configured to construct a biological cementation index of microbially mineralized river sand according to the mineralization specificity characteristic parameter, and analyze the compressive strength of the standard sample model of microbially mineralized river sand according to the biological cementation index;

[0057] a destructive calibration curve construction module, configured to construct a destructive calibration curve of the standard sample model of microbially mineralized river sand according to the biological cementation index and the compressive strength;

[0058] a non-destructive analysis module, configured to analyze a target compressive strength of the sample to be tested by using the destructive calibration curve, and analyze the non-destructive strength of the microbially mineralized river sand corresponding to the sample to be tested based on the target compressive strength.

[0059] The embodiment of the present application realizes accurate simulation and excitation of the mineralization process by constructing a standard sample model and applying a sweep frequency acoustic excitation signal, enriches information reflecting mineralization characteristics by obtaining penetration response signals at different reaction stages through penetration simulation operation, quantifies mineralization characteristics from multiple dimensions by extracting mineralization specificity characteristic parameters, provides accurate indexes for subsequent evaluation, realizes intuitive evaluation of the degree of mineralization by constructing a biological cementation index which comprehensively considers multiple characteristic parameters, analyzes the compressive strength accordingly, solves the problem of single evaluation method, establishes the correlation between the biological cementation index and the compressive strength by constructing a destructive calibration curve, provides a standard for non-destructive evaluation, analyzes the target compressive strength of the sample to be tested by using the destructive calibration curve and evaluates the non-destructive strength, realizes non-destructive detection, solves the problem of relying on destructive sampling in the prior art, and improves the evaluation efficiency and accuracy. Therefore, the microbially mineralized river sand strength non-destructive evaluation method and system can solve the problem of low accuracy in microbially mineralized river sand strength non-destructive evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1A flowchart of a microorganism mineralized river sand strength non-destructive evaluation method provided by an embodiment of the present application is shown in the figure.

[0061] Figure 2 A functional module diagram of a microorganism mineralized river sand strength non-destructive evaluation system provided by an embodiment of the present application is shown in the figure.

[0062] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are merely intended to explain the present application and not to limit the present application.

[0064] An embodiment of the present application provides a microorganism mineralized river sand strength non-destructive evaluation method. The execution subject of the microorganism mineralized river sand strength non-destructive evaluation method includes but is not limited to at least one of electronic devices such as a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the present application. In other words, the microorganism mineralized river sand strength non-destructive evaluation method can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster and the like. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms and the like basic cloud computing services.

[0065] Referring to Figure 1 A flowchart of a microorganism mineralized river sand strength non-destructive evaluation method provided by an embodiment of the present application is shown in the figure. In the embodiment, the microorganism mineralized river sand strength non-destructive evaluation method includes:

[0066] S1, based on the pre-acquired mineralization properties, a microorganism mineralized river sand standard sample model and a sample to be tested model are constructed, and a predetermined sweep frequency acoustic wave excitation signal is applied to the microorganism mineralized river sand standard sample model.

[0067] In the embodiment of the present application, the mineralization properties refer to characteristic parameters exhibited by microorganisms in the mineralization reaction process, including microorganism species, metabolic rate, mineralization product generation amount and the like; the microorganism mineralized river sand standard sample model refers to a model constructed based on known mineralization properties and used as a reference standard.

[0068] In the embodiment of the present application, the microorganism mineralized river sand standard sample model is constructed based on the pre-acquired mineralization properties, including:

[0069] analyze the spatial and temporal variation gradient of the mineralization reaction concentration based on the metabolic characteristics of the microorganisms in the mineralization attribute;

[0070] generate a hierarchical perfusion strategy for the microorganism mineralization river sand standard sample model according to the spatial and temporal variation gradient;

[0071] perform spatially sequential perfusion and solidification simulation operations on the microorganism bacterial solution and river sand in a pre-set simulation space according to the hierarchical perfusion strategy;

[0072] based on the spatial and temporal variation gradient, adjust the microorganism bacterial solution and river sand after the solidification simulation operation, and generate a microorganism mineralization river sand standard sample model according to the parameter attributes of the adjusted microorganism bacterial solution and river sand.

[0073] In detail, metabolic data in the microorganism mineralization process is collected in real time by a monitoring device, for example, urease activity data of microorganisms is collected every hour; a correlation model between metabolic characteristics and mineral generation is established according to these data, for example, a corresponding relationship between urease activity and calcium carbonate generation rate; finally, the concentration variation of mineral at different times and different spatial positions is calculated based on the model, and the spatial and temporal variation gradient is obtained. For example, in the initial stage, the concentration of mineral on the surface of river sand increases by 0.2 g / cm3 per hour, while the concentration of mineral in the deep layer increases by 0.1 g / cm3 per hour, forming a specific spatial and temporal variation gradient, which can accurately reflect the dynamic distribution of the mineralization reaction. The metabolic characteristics of microorganisms refer to the characteristics of material metabolism in the growth and reproduction process of microorganisms, such as acid production rate and enzyme activity; the spatial and temporal variation gradient of the mineralization reaction concentration refers to the variation rate of the concentration of mineral at different times and spatial positions.

[0074] Specifically, the hierarchical perfusion strategy refers to a perfusion scheme of the microorganism bacterial solution and river sand according to different spatial levels, and according to the spatial and temporal variation gradient, the space of the standard sample model is divided into multiple levels, each level corresponds to a different variation rate of the mineralization reaction concentration. For example, the model is divided into three layers from top to bottom, the first layer corresponds to the region with fast growth of mineral concentration, the second layer corresponds to the region with medium growth, and the third layer corresponds to the region with slow growth. Then different perfusion parameters such as perfusion rate and bacterial solution concentration are set for each level, and the hierarchical perfusion strategy can make the mineralization reaction proceed according to the expected spatial and temporal gradient, avoiding the problem of uneven perfusion leading to poor mineralization effect.

[0075] Further, in the preset three-dimensional simulation space, according to the hierarchical order determined by the hierarchical perfusion strategy, perfusion is sequentially performed from the upper layer, river sand is first filled into the simulation space and compacted, then the microbial liquid is injected into the first layer at a set rate and concentration through the perfusion pipeline, and at the same time, the solidification simulation device is started, and the environmental parameters such as temperature and humidity are controlled, for example, the temperature is kept at 25 DEG C, and the humidity is kept at 80%, and the solidification process is simulated. When the perfusion and solidification of the first layer reach a certain stage, for example, reach 30% of the set mineral concentration, the perfusion and solidification of the second layer are performed, and so on, so that the standard sample model can truly reflect the actual situation of the mineralization reaction, and the model is associated with the actual scene, wherein the simulation space refers to a virtual space or a physical experimental space used for simulating the actual mineralization environment.

[0076] Further, the parameter attribute refers to the characteristic parameters of the microbial liquid and the river sand after solidification, such as porosity, cementation degree and the like. According to the space-time variation gradient, the difference between the actual mineral concentration and the expected concentration of each layer after the solidification simulation operation is compared. If the actual concentration of a certain layer is lower than the expected concentration, a certain amount of microbial liquid is supplemented to the layer, and the supplement amount is calculated according to the concentration difference; if the actual concentration is higher than the expected concentration, the filling amount of the river sand is increased for adjustment. After the adjustment is completed, the parameter attributes of each layer are measured, such as the porosity being between 20%-30%, the cementation degree being between 50%-70%, and the like, the parameter attributes are integrated, and the microbial mineralized river sand standard sample model is generated.

[0077] In addition, the test sample model refers to a model constructed according to the initial state of the microbial mineralized river sand in the actual project for detection and evaluation, wherein the steps of generating the test sample model are consistent with those of generating the microbial mineralized river sand standard sample model, and will not be repeated here.

[0078] In the embodiment of the application, the sweep frequency acoustic excitation signal refers to an acoustic signal with a frequency continuously changing within a certain range, which is used to excite the model to obtain a response.

[0079] In the embodiment of the application, the method for generating the microbial mineralized river sand standard sample model comprises the following steps:

[0080] According to the concentration gradient distribution of the microbial mineralized river sand standard sample model in the solidification process, the focal region and the scanning path of the acoustic excitation are dynamically divided;

[0081] The beam forming parameters of the preset multi-element acoustic transducer array are adaptively adjusted according to the scanning path;

[0082] The sweep frequency acoustic excitation signal is applied in the focal region based on the beam forming parameters.

[0083] In detail, the concentration gradient distribution data of the standard sample model at each time during the curing process is obtained by monitoring the equipment, and the concentration gradient distribution refers to the change of the mineralization concentration at different positions of the model; the region with relatively sharp concentration change is determined as the focal point region, and the focal point region refers to the key region of the acoustic wave excitation, for example, the region where the mineralization concentration changes more than 0.15 g / cm3 per hour. According to the distribution of the focal point region, the scanning path is planned, the scanning path refers to the propagation path of the acoustic wave on the model, and the acoustic wave is ensured to cover all the focal point regions, and the path is as short as possible to improve the detection efficiency, for example, a spiral scanning path is adopted, which gradually expands from the center of the model to the periphery, so that the acoustic wave excitation can be targeted to act on the key region.

[0084] Specifically, the multi-element acoustic transducer array refers to an array composed of multiple acoustic transducers for transmitting and receiving acoustic wave signals; the beam forming parameter refers to the parameter affecting the shape and direction of the acoustic wave beam, such as the element spacing, phase difference, etc. According to the direction of the scanning path and the position of the focal point region, the phase and amplitude of each element in the multi-element acoustic transducer array are calculated in real time. For example, when the scanning path turns to the focal point region on the right side of the model, the phase of the right side element is adjusted so that the acoustic wave beam emitted by it focuses to the right side, and at the same time the element spacing is adjusted so that the beam width adapts to the size of the focal point region. Through this adaptive adjustment, it is ensured that the acoustic wave beam can accurately reach each focal point region along the scanning path, improving the accuracy of the acoustic wave excitation.

[0085] Further, according to the adjusted beam forming parameter, the multi-element acoustic transducer array is controlled to emit a swept frequency acoustic excitation signal. If the frequency range of the signal is set to 1kHz-10kHz, it propagates in the focal point region according to the set scanning path. For example, in the first focal point region, the frequency of the acoustic wave gradually increases from 1kHz to 5kHz, and then in the second focal point region, the frequency increases from 5kHz to 10kHz. By applying the swept frequency acoustic excitation in the focal point region, rich response signals can be excited from the model to provide sufficient data for subsequent penetration simulation operations.

[0086] Further, the standard sample model is to provide a reference benchmark with known mineralization properties, and the swept frequency acoustic excitation signal is applied to obtain the response characteristics of the model under different acoustic waves, which will serve as the basis for subsequent analysis of mineralization intensity.

[0087] S2, performing a penetration simulation operation on the microbial mineralization river sand standard sample model according to the swept frequency acoustic excitation signal, to obtain the penetration response signals of the microbial mineralization river sand at different reaction stages.

[0088] In the embodiment of the present application, the penetration simulation operation refers to the process of simulating the sweep frequency acoustic wave excitation signal penetrating the microbial mineralized river sand standard sample model, and the transmission response signal refers to the response signal generated after the acoustic wave penetrates the model.

[0089] In the embodiment of the present application, the penetration simulation operation of the sweep frequency acoustic wave excitation signal on the microbial mineralized river sand standard sample model obtains the transmission response signal of the microbial mineralized river sand in different reaction stages, which comprises:

[0090] When the sweep frequency acoustic wave excitation signal is applied, the vibration micro-change signal corresponding to the surface target point of the microbial mineralized river sand standard sample model is synchronously collected.

[0091] The acoustic wave signal after the penetration simulation operation is divided into different types of wave components, and the signal energy attenuation curves of different types of wave components at different spatial positions on the scanning path are extracted.

[0092] The signal energy attenuation curves and the vibration micro-change signal are time-domain aligned and fused to generate a transmission enhanced response signal.

[0093] The signal segment of the corresponding time window of the microbial mineralized river sand in different reaction stages is intercepted from the transmission enhanced response signal.

[0094] The response signal corresponding to the signal segment is taken as the transmission response signal of different reaction stages.

[0095] In detail, the surface target point refers to a specific point selected on the surface of the sample model for signal collection, and a plurality of target points are uniformly selected on the surface of the sample model, for example, a collection point is set every 5 centimeters on the surface of the model. While the multi-element acoustic transducer array emits the sweep frequency acoustic wave excitation signal, the vibration sensor installed on the target point collects the vibration micro-change signal in real time, and the sampling frequency is set to 10 kHz to ensure that the micro vibration changes can be captured. The vibration micro-change signal refers to the micro vibration signal generated by the target point under the excitation of the acoustic wave, and the synchronous collection process can obtain the vibration information of the model surface caused by the acoustic wave excitation, which supplements the deficiency of the transmission acoustic wave signal. The wave component refers to different types of waves contained in the acoustic wave signal, such as longitudinal wave, transverse wave, surface wave, etc. The received acoustic wave signal after the penetration simulation operation is processed, and wavelet decomposition method is used to decompose it into different types of wave components such as longitudinal wave, transverse wave and surface wave. Then, according to the spatial position coordinates on the scanning path, the energy values of each type of wave component at different positions are calculated, for example, the energy value of the longitudinal wave is recorded every 2 centimeters on the scanning path. Taking the spatial position as the abscissa and the energy value as the ordinate, the signal energy attenuation curve of each type of wave component is drawn. The signal energy attenuation curve refers to the curve of the energy of the wave component changing with the spatial position during the propagation process.

[0096] Specifically, according to the time stamp of signal collection, the signal energy attenuation curve and the vibration micro-variation signal are aligned in time, ensuring that the signals at the same time can be corresponded, and then the data fusion algorithm such as weighted average method is used to fuse the two signals. For example, for the energy attenuation curve of the longitudinal wave and the corresponding vibration micro-variation signal, the weight is determined according to the signal-to-noise ratio of the two, the signal-to-noise ratio of the signal is set to 0.6, and the weight of the low signal is set to 0.4. The weighted combination of the two obtains the corresponding penetration enhanced response signal of the wave component, thereby integrating different types of signal information and improving the quality and information amount of the response signal.

[0097] Further, according to the space-time variation gradient, different stages of the mineralization reaction are determined. For example, when the initial mineralization concentration is low and the concentration growth rate is slow, it is the initial stage, and the corresponding time window is 0-24 hours; when the concentration growth rate is accelerated, it is the middle stage, and the time window is 24-72 hours; when the concentration growth tends to be flat, it is the mature stage, and the time window is 72-120 hours. Then, according to these time windows, the corresponding signal segments are intercepted from the penetration enhanced response signal, and each signal segment corresponds to a reaction stage, and the division and interception process makes the response signal correspond to the mineralization reaction stage, providing a basis for subsequent extraction of characteristic parameters at different stages. The signal segment in each time window intercepted in the foregoing is determined as the penetration response signal of the reaction stage. For example, the signal segment in the initial stage time window is the penetration response signal of the initial stage, and the middle and mature stages are the same. These penetration response signals contain characteristic information of the microbial mineralization river sand at different reaction stages.

[0098] Further, the penetration response signals at different reaction stages are obtained by performing penetration simulation operation on the sample model, and the penetration response signals contain information reflecting the mineralization degree.

[0099] S3, extracting the mineralization specificity characteristic parameters corresponding to the microbial mineralization river sand standard sample model through the penetration response signals at different reaction stages.

[0100] In the embodiment of the application, the mineralization specificity characteristic parameter refers to a parameter that can reflect the unique mineralization characteristics of the microbial mineralization river sand, and is used to distinguish different mineralization states.

[0101] In the embodiment of the application, the mineralization specificity characteristic parameters corresponding to the microbial mineralization river sand standard sample model are extracted through the penetration response signals at different reaction stages, including:

[0102] calculating the wave velocity ratio of the longitudinal wave and the transverse wave of the penetration response signal at different reaction stages, and analyzing the variance of the wave velocity ratio and the spatial position change of the penetration response signal;

[0103] determine a biological cementation spatial distribution index of the microbial mineralized river sand standard sample model according to the variance;

[0104] extract a dispersion curve feature of a surface wave component of the penetration response signal of different reaction stages, and calculate a deviation degree between the dispersion curve feature and a preset standard elastic half-space model;

[0105] use the biological cementation spatial distribution index, the wave velocity ratio and the deviation degree as the mineralization-specific characteristic parameters.

[0106] In detail, the longitudinal wave refers to a wave with a particle vibration direction consistent with the wave propagation direction; the transverse wave refers to a wave with a particle vibration direction perpendicular to the wave propagation direction; the wave velocity ratio refers to a ratio of the propagation speed of the longitudinal wave to the propagation speed of the transverse wave, that is, the propagation times of the longitudinal wave and the transverse wave are identified from the penetration response signal of different reaction stages, the longitudinal wave speed and the transverse wave speed are calculated in combination with the known propagation distance, and thus the wave velocity ratio is obtained, the wave velocity ratio can reflect the compactness and the cementation state of the river sand, and then the variance of the wave velocity ratio data at different spatial positions on the scanning path is calculated, the variance refers to a statistical quantity used to measure the dispersion degree of the wave velocity ratio with the change of the spatial position, for example, the variance of the initial stage is 0.05, and the variance of the middle stage is 0.03. The variance reflects the uniformity of mineralization. The calculated variance is substituted into a preset index calculation formula, and the formula is: calculate a biological cementation spatial distribution index, wherein the biological cementation spatial distribution index, the variance, the wave velocity ratio average, and thus the biological cementation spatial distribution index refers to an index used to represent the uniformity of the spatial distribution of the biological cementation in the microbial mineralization process, and the closer the index value is to 1, the more uniform the biological cementation distribution is.

[0107] Specifically, the surface wave component is extracted from the penetration response signal, the surface wave component refers to a wave component propagating along the surface of the model, and the dispersion curve thereof is obtained through signal processing technology, the dispersion curve feature refers to a curve feature of the propagation speed of the surface wave with the change of the frequency, for example, when the frequency of the surface wave is 2 kHz, the propagation speed is 500 m / s; when the frequency is 5 kHz, the propagation speed is 600 m / s, and the like. The dispersion curve is compared with the dispersion curve of the preset standard elastic half-space model, the speed difference at the same frequency point is calculated, and then the deviation degree is obtained through integration or averaging. The deviation degree can reflect the difference between the mineralized river sand and the ideal elastic body, and indirectly reflect the mineralization degree, so as to evaluate the elastic characteristics of the mineralized body. The preset standard elastic half-space model refers to a model of an ideal elastic half-space preset as a reference.

[0108] Further, the biological cementation space distribution index, wave velocity ratio and deviation degree are integrated as parameters that can comprehensively reflect the mineralization characteristics of the microbial mineralized river sand standard sample model, which describe the mineralization state from different angles, can more intuitively evaluate the mineralization degree, and further analyze the compressive strength.

[0109] S4, constructing a biological cementation index of the microbial mineralization according to the mineralization specificity characteristic parameters, and analyzing the compressive strength of the microbial mineralized river sand standard sample model according to the biological cementation index.

[0110] In the embodiment of the present application, the biological cementation index refers to an index that comprehensively reflects the degree of biological cementation in the microbial mineralized river sand.

[0111] In the embodiment of the present application, the biological cementation index of the microbial mineralization constructed according to the mineralization specificity characteristic parameters comprises:

[0112] identifying the mineralization reaction stage of the microorganism, and selecting the dynamic weight coefficient corresponding to different mineralization reaction stages in a preset coefficient mapping table according to the dominant cementation parameter corresponding to the mineralization reaction stage;

[0113] weighting the wave velocity ratio and the deviation degree in the mineralization specificity characteristic parameters according to the dynamic weight coefficient;

[0114] operating the weighted result with the biological cementation space distribution index in the mineralization specificity characteristic parameters to obtain the biological cementation index of the microbial mineralization.

[0115] In detail, the dominant cementation parameter refers to the parameter that mainly affects the mineralization cementation in different reaction stages. The preset coefficient mapping table refers to a table of the corresponding relationship between the reaction stage and the dynamic weight coefficient. By analyzing the metabolic data and the concentration change of the mineralized substance in the mineralization reaction process, the current reaction stage can be determined, such as the initial stage, the middle stage or the mature stage. The dominant cementation parameter is different in different reaction stages. In the initial stage, the microbial activity may be dominant, in the middle stage, the mineralized substance generation rate may be dominant, and in the mature stage, the cementation solidification degree may be dominant. According to these dominant cementation parameters, the corresponding dynamic weight coefficient is found in the preset coefficient mapping table, for example, the weight coefficient of the wave velocity ratio in the initial stage is 0.3, and the weight coefficient of the deviation degree is 0.2; the weight coefficient of the wave velocity ratio in the middle stage is 0.4, and the weight coefficient of the deviation degree is 0.3; the weight coefficient of the wave velocity ratio in the mature stage is 0.3, and the weight coefficient of the deviation degree is 0.4. The selection of the dynamic weight coefficient makes the characteristic parameters in different stages get reasonable attention, and solves the problem of inaccurate evaluation caused by fixed weight in the prior art.

[0116] Specifically, the wave velocity ratio and the deviation degree are multiplied by the corresponding dynamic weight coefficients respectively, and then added to obtain a weighted result, and the weighted result is operated with the biological cementation space distribution index in the mineralization specificity feature parameter to obtain a biological cementation index of the microbial mineralization, that is , wherein is the biological cementation index, is the dynamic weight coefficient corresponding to the wave velocity ratio, is the dynamic weight coefficient corresponding to the dispersion curve deviation degree, is the longitudinal wave value, is the transverse wave value, is the dispersion curve deviation degree, is the biological cementation space distribution index, the biological cementation index integrates the information of multiple feature parameters, and can more comprehensively reflect the biological cementation degree,

[0117] Further, a correlation model between the biological cementation index and the compressive strength is established through a large amount of experimental data, the compressive strength refers to the ability of the microbial mineralization river sand standard sample model to resist pressure damage. For example, the biological cementation index increases by 0.1, and the compressive strength increases by 5MPa on average. Based on the model, the compressive strength is calculated according to the calculated biological cementation index, for example, when the biological cementation index is 0.834, the compressive strength is about 41.7MPa, and the compressive strength is evaluated in a non-destructive manner. The biological cementation index and the compressive strength are key data for constructing a destructive calibration curve, which will be used for subsequent evaluation of the test sample model.

[0118] S5, constructing a destructive calibration curve of the microbial mineralization river sand standard sample model according to the biological cementation index and the compressive strength.

[0119] In the embodiment of the application, the destructive calibration curve refers to a curve for describing the relationship between the biological cementation index and the compressive strength, and the compressive strength can be inferred from the biological cementation index through the curve.

[0120] In the embodiment of the application, the destructive calibration curve of the microbial mineralization river sand standard sample model is constructed according to the biological cementation index and the compressive strength, and comprises:

[0121] The target biological cementation index is calculated after the sound wave simulation of the preset target standard sample model is simulated;

[0122] The real compressive strength is obtained by simulating the destructive compressive strength of the preset target standard sample model;

[0123] The preset Gaussian process regression model is trained by using the target biological cementation index and the real compressive strength, and the probabilistic mapping relationship is determined based on the trained Gaussian process regression model;

[0124] According to the probabilistic mapping relationship, a destructive calibration curve of the microbial mineralized river sand standard sample model is constructed.

[0125] In detail, the target standard sample model refers to a plurality of different mineralization degree standard sample models pre-set for constructing the calibration curve. A plurality of pre-set target standard sample models with different mineralization degrees are selected, for example, 5 models with different biological cementation indices. Sound wave simulation operations are performed on each model, including applying a sweep frequency sound wave excitation signal, obtaining a penetration response signal, extracting mineralization specific characteristic parameters, and the like, and then the target biological cementation index corresponding to each model is calculated, thereby providing abundant biological cementation index data for constructing the calibration curve. The destructive compressive strength simulation refers to a process of simulating a destructive test on the sample model to obtain its compressive strength. Then, a mechanical simulation software is used to simulate the destructive compressive test on each pre-set target standard sample model, and the pressure value borne by the model when it is destroyed is recorded as the real compressive strength. For example, the real compressive strengths corresponding to the previous 5 models are 20 MPa, 25 MPa, 30 MPa, 35 MPa, and 40 MPa, respectively. The real compressive strength data is an important reference for constructing the calibration curve.

[0126] Specifically, the pre-set Gaussian process regression model refers to a pre-set Gaussian process model used for regression analysis. The target biological cementation index is taken as input data, and the real compressive strength is taken as output data, which are input into the pre-set Gaussian process regression model for training. By adjusting the model parameters, the model can accurately fit the relationship between the input and the output. After the training is completed, the model can output the probability distribution of the corresponding compressive strength according to the input biological cementation index. The probabilistic mapping relationship refers to the corresponding relationship between the target biological cementation index and the real compressive strength with probability distribution characteristics. For example, when the biological cementation index is 0.75, the probability of the compressive strength being between 32-33 MPa is 90%, thereby determining the probabilistic mapping relationship.

[0127] Further, the destructive calibration curve is plotted with the target biological cementation index as the abscissa and the real compressive strength as the ordinate, combined with the probabilistic mapping relationship. The curve not only contains the change trend of the average compressive strength, but also reflects the probability distribution characteristics through the confidence interval around the curve. For example, the curve shows that the average compressive strength increases linearly as the biological cementation index increases, and the confidence interval narrows as the biological cementation index increases, indicating that the reliability of the evaluation is improved.

[0128] S6, analyzing the target compressive strength of the sample model to be tested using the destructive calibration curve, and analyzing the microbial river sand strength of the sample model to be tested based on the target compressive strength.

[0129] In the embodiment of the present application, the target compressive strength refers to the compressive strength of the test sample model obtained by analyzing the destructive calibration curve.

[0130] In the embodiment of the present application, the target compressive strength of the test sample model is analyzed by using the destructive calibration curve, comprising:

[0131] Obtaining the multi-modal response data of the test sample model under the excitation of sound waves;

[0132] Performing time-space synchronization and fusion processing on the multi-modal response data, and extracting the target mineralization characteristic parameters after fusion;

[0133] Calculating the dynamic biological cementation index of the test sample model according to the target mineralization characteristic parameters;

[0134] Analyzing the target strength distribution corresponding to the dynamic biological cementation index by using the destructive calibration curve;

[0135] Taking the variance of the target strength distribution as the confidence index of the test sample model, and determining the target compressive strength of the test sample model according to the confidence index and the expected value of the target strength distribution.

[0136] In detail, the multi-modal response data refers to multiple types of response data generated by the test sample model under the excitation of sound waves. The same sweep frequency sound excitation signal as the standard sample model is applied to the test sample model, and through multi-element acoustic transducer array and distributed optical fiber sensor and other devices, the penetrating sound signal and the distributed optical fiber sensing strain signal generated by the sound-induced vibration are synchronously collected, and these signals are integrated into multi-modal response data. For example, the collected penetrating sound signal contains longitudinal wave, transverse wave and surface wave components, and the distributed optical fiber sensing strain signal reflects the strain changes at different positions. The multi-modal response data provides more comprehensive information. According to the time and space markers of data collection, the penetrating sound signal and the distributed optical fiber sensing strain signal are aligned in time and space, ensuring that different types of data at the same time and the same position can correspond. Then, using a data fusion algorithm, such as principal component analysis, the multi-modal data after synchronization is fused to extract the main features. The target mineralization characteristic parameters are extracted from the fused data, including wave velocity ratio, biological cementation spatial distribution index and deviation degree, etc.

[0137] Specifically, the dynamic biological cementation index refers to an index reflecting the current mineralization cementation degree of the sample model to be tested according to the target mineralization characteristic parameters of the sample model to be tested. First, the reaction stage in which the sample model to be tested is located is determined, the corresponding dynamic weight coefficient is selected, then the wave velocity ratio and the deviation degree in the target mineralization characteristic parameters are weighted, and then the dynamic biological cementation index is calculated by operating the biological cementation space distribution index. The dynamic biological cementation index can reflect the mineralization state of the sample model to be tested in real time. The calculated dynamic biological cementation index is input into the destructive calibration curve, and the corresponding probability distribution of compressive strength is obtained according to the probabilistic mapping relationship. For example, when the dynamic biological cementation index is 0.633, the target strength distribution shows that the probability of the compressive strength being between 28-30 MPa is 85%, so that the possible range of the compressive strength of the sample model to be tested can be comprehensively reflected.

[0138] Further, the confidence index refers to an index for measuring the reliability of the target strength distribution, and the smaller the variance, the higher the confidence. The expected value refers to the average value of the target strength distribution. The variance of the target strength distribution is calculated as the confidence index, and the expected value of the target strength distribution is calculated, and the expected value is taken as the target compressive strength of the sample model to be tested, and the reliability of the strength value can be known in combination with the confidence index.

[0139] In the embodiment of the present application, the non-destructive strength of microbial river sand refers to the strength characteristics and state of the microbial mineralized river sand without being destroyed.

[0140] In the embodiment of the present application, the non-destructive strength of microbial river sand corresponding to the sample model to be tested is analyzed based on the target compressive strength, comprising:

[0141] The destructive positioning is performed in a pre-set multi-dimensional strength decision space according to the target compressive strength and the confidence index, and a destructive positioning point is obtained;

[0142] The strength grade of the sample model to be tested is obtained by querying the pre-generated microbial mineralization health state map based on the destructive positioning point;

[0143] The completion degree and stability of the sample model to be tested for the mineralization reaction are analyzed based on the convergence characteristics and fluctuation characteristics of the dynamic biological cementation index;

[0144] The non-destructive strength state of the microbial river sand is analyzed according to the strength grade, the completion degree and the stability.

[0145] In detail, the multidimensional strength decision space refers to a space constructed for decision analysis using dimensions such as target compressive strength and confidence index. Therefore, a two-dimensional strength decision space is constructed with target compressive strength as the vertical axis and confidence index as the horizontal axis, and different regions are divided, such as safe regions, warning regions, and dangerous regions. Based on the target compressive strength and confidence index of the model to be tested, the corresponding failure location points are determined in this space. The failure location points are the points in the multidimensional strength decision space corresponding to the target compressive strength and confidence index. The location process can intuitively reflect the strength state of the model to be tested.

[0146] Specifically, the microbial mineralization health status map refers to a pre-generated map reflecting the correspondence between damage locations and intensity levels. Different regions in this map correspond to different intensity levels, such as Level 1 (high intensity), Level 2 (medium intensity), and Level 3 (low intensity). Based on the location of the damage location in the multi-dimensional intensity decision space, this map is consulted to determine the intensity level of the model to be tested. For example, the damage location might correspond to a Level 2 intensity level.

[0147] Furthermore, the changes in the dynamic biocementing index at different time points were analyzed. If it gradually tends towards a stable value, it indicates good convergence characteristics and a high degree of completion; if its fluctuation amplitude is small, it indicates good fluctuation characteristics and a high degree of stability, which can assess the progress and stability of the mineralization reaction. Here, convergence characteristics refer to the tendency of the dynamic biocementing index to stabilize over time; fluctuation characteristics refer to the degree of fluctuation of the dynamic biocementing index over time; completion refers to the extent to which the mineralization reaction has proceeded; and stability refers to the degree of stability of the mineralization reaction.

[0148] Furthermore, considering the comprehensive evaluation results of strength grade, completion rate, and stability, if the strength grade is level two or above, the completion rate is high, and the stability is good, it indicates that the non-destructive strength state of the microbial river sand is good and can meet engineering requirements; otherwise, further processing is required. For example, the test sample model has a strength grade of level two, a high completion rate, and good stability, therefore its non-destructive strength state is good, achieving a comprehensive assessment of the non-destructive strength of microbial mineralized river sand and solving the problem of relying on destructive testing in existing technologies.

[0149] like Figure 2 The diagram shown is a functional block diagram of a non-destructive assessment system for the strength of microbially mineralized river sand provided in an embodiment of the present invention.

[0150] The microorganism mineralized river sand strength non-destructive evaluation system 100 can be installed in an electronic device. According to the functions implemented, the microorganism mineralized river sand strength non-destructive evaluation system 100 can include a sample model construction module 101, a penetration simulation operation module 102, a mineralization specificity characteristic parameter extraction module 103, a compressive strength analysis module 104, a destructive calibration curve construction module 105, and a microorganism river sand strength non-destructive analysis module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0151] In the present embodiment, the functions of each module / unit are as follows:

[0152] The sample model construction module 101 is configured to construct a microorganism mineralized river sand standard sample model and a sample to be tested based on pre-acquired mineralization attributes, and to apply a predetermined swept frequency acoustic wave excitation signal to the microorganism mineralized river sand standard sample model.

[0153] The penetration simulation operation module 102 is configured to perform penetration simulation operations on the microorganism mineralized river sand standard sample model according to the swept frequency acoustic wave excitation signal, to obtain penetration response signals of the microorganism mineralized river sand at different reaction stages.

[0154] The mineralization specificity characteristic parameter extraction module 103 is configured to extract mineralization specificity characteristic parameters corresponding to the microorganism mineralized river sand standard sample model through the penetration response signals at different reaction stages.

[0155] The compressive strength analysis module 104 is configured to construct a biological cementation index of microorganism mineralization according to the mineralization specificity characteristic parameters, and to analyze the compressive strength of the microorganism mineralized river sand standard sample model according to the biological cementation index.

[0156] The destructive calibration curve construction module 105 is configured to construct a destructive calibration curve of the microorganism mineralized river sand standard sample model according to the biological cementation index and the compressive strength.

[0157] The microorganism river sand strength non-destructive analysis module 106 is configured to analyze the target compressive strength of the sample to be tested using the destructive calibration curve, and to analyze the microorganism river sand strength non-destructiveness of the sample to be tested based on the target compressive strength.

[0158] In detail, each module in the microorganism mineralized river sand strength non-destructive evaluation system 100 in the present embodiment is used as described above Figure 1The microorganism mineralization river sand strength non-destructive evaluation method described in the present application uses the same technical means as the method described in the background art and can achieve the same technical effects, and thus will not be described here.

[0159] In several embodiments provided by the present application, it should be understood that the disclosed system and method can be implemented in other manners. For example, the system embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. There can be another division manner for the actual implementation.

[0160] The modules described as separated components can or can not be physically separated, and the components displayed as modules can or can not be physical units, i.e., can be located in one place or can be distributed on a plurality of network units. Some or all of the modules can be selected according to the actual needs to achieve the purposes of the embodiments.

[0161] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0162] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0163] Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is not limited only by the above description, and all changes within the meaning and scope of equivalent elements falling within the scope of protection are intended to be included in the present application.

[0164] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (Artificial Intelligence, AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0165] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The plurality of units or systems stated in the system claims can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names and do not represent any particular order.

[0166] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. A method for non-destructive evaluation of the strength of microbially mineralized river sand, characterized in that, The method comprises: Based on the pre-acquired mineralization attribute, a microbial mineralization river sand standard sample model and a to-be-tested sample model are constructed, and a preset sweep frequency acoustic excitation signal is applied to the microbial mineralization river sand standard sample model; According to the sweep frequency acoustic excitation signal, a penetration simulation operation is performed on the microbial mineralization river sand standard sample model, and a penetration response signal of the microbial mineralization river sand at different reaction stages is obtained; The mineralization specific characteristic parameters corresponding to the microbial mineralization river sand standard sample model are extracted through the penetration response signals at different reaction stages, wherein the wave velocity ratio of the longitudinal wave and the transverse wave of the penetration response signal at different reaction stages is calculated, and the variance of the wave velocity ratio and the spatial position change of the penetration response signal is analyzed; the biological cementation spatial distribution index of the microbial mineralization river sand standard sample model is determined according to the variance; the dispersion curve characteristics of the surface wave component of the penetration response signal at different reaction stages are extracted, and the deviation degree between the dispersion curve characteristics and the preset standard elastic half-space model is calculated; the biological cementation spatial distribution index, the wave velocity ratio and the deviation degree are taken as the mineralization specific characteristic parameters; According to the mineralization specificity characteristic parameter, a biocementation index of microbial mineralization is constructed, wherein a mineralization reaction stage of microorganisms is identified, dynamic weight coefficients corresponding to different mineralization reaction stages are selected in a preset coefficient mapping table according to a dominant cementation parameter corresponding to the mineralization reaction stage, the wave velocity ratio and the deviation in the mineralization specificity characteristic parameter are weighted according to the dynamic weight coefficients, and the weighted results are operated with a biocementation space distribution index in the mineralization specificity characteristic parameter to obtain the biocementation index of microbial mineralization, that is wherein is the biocementation index, is a dynamic weight coefficient corresponding to the wave velocity ratio, is a dynamic weight coefficient corresponding to the dispersion curve deviation, is a longitudinal wave value, is a transverse wave value, is the dispersion curve deviation, is the biocementation space distribution index, and the compressive strength of the microbial mineralization river sand standard sample model is analyzed according to the biocementation index. According to the biological cementation index and the compressive strength, a destructive calibration curve of the microbial mineralization river sand standard sample model is constructed; The target compressive strength of the to-be-tested sample model is analyzed by using the destructive calibration curve, and the microbial river sand strength non-destructive of the to-be-tested sample model is analyzed based on the target compressive strength.

2. The method for non-destructive evaluation of the strength of microbially mineralized river sand according to claim 1, characterized in that, The microbial mineralization river sand standard sample model is constructed based on the pre-acquired mineralization attribute, comprising: Based on the microbial metabolic characteristic in the mineralization attribute, the time-space variation gradient of the mineralization reaction concentration is analyzed; According to the time-space variation gradient, a hierarchical perfusion strategy of the microbial mineralization river sand standard sample model is generated; According to the hierarchical perfusion strategy, the microbial bacteria liquid and the river sand are sequentially perfused and solidified in the preset simulation space; Based on the time-space variation gradient, the microbial bacteria liquid and the river sand after the solidification simulation operation are adjusted in reverse, and the microbial mineralization river sand standard sample model is generated according to the parameter attributes of the adjusted microbial bacteria liquid and the river sand.

3. The method for non-destructive evaluation of the strength of microbially mineralized river sand according to claim 1, characterized in that, The preset sweep frequency acoustic excitation signal is applied to the microbial mineralization river sand standard sample model, comprising: According to the concentration gradient distribution of the microbial mineralization river sand standard sample model in the solidification process, the focal region and the scanning path of the acoustic excitation are dynamically divided; According to the scanning path, the beam forming parameters of the preset multi-element acoustic transducer array are adaptively adjusted; Based on the beam forming parameters, the sweep frequency acoustic excitation signal is applied in the focal region.

4. The method for non-destructive evaluation of the strength of microbially mineralized river sand according to claim 3, characterized in that, According to the sweep frequency acoustic excitation signal, the penetration simulation operation is performed on the microbial mineralization river sand standard sample model, and the penetration response signal of the microbial mineralization river sand at different reaction stages is obtained, comprising: When the sweep frequency acoustic excitation signal is applied, the vibration micro-variation signal corresponding to the surface target point of the microbial mineralization river sand standard sample model is synchronously collected; Divide the acoustic wave signal after the penetration simulation operation into different types of wave components, and extract the signal energy attenuation curves of different types of wave components at different spatial positions on the scanning path; Perform time domain alignment and fusion processing on the signal energy attenuation curves and the vibration micro-variation signal to generate a penetration enhanced response signal; Extract the signal segment corresponding to the time window of different reaction stages of the microorganism mineralized river sand from the penetration enhanced response signal; Use the response signal corresponding to the signal segment as the penetration response signal of different reaction stages.

5. The method for non-destructive evaluation of the strength of microbially mineralized river sand according to claim 1, characterized in that, The method comprises the following steps: performing acoustic simulation on a preset target standard sample model and calculating the target biological cementation index after acoustic simulation; performing destructive compressive strength simulation on the preset target standard sample model to obtain the true compressive strength; training a preset Gaussian process regression model using the target biological cementation index and the true compressive strength, and determining a probabilistic mapping relationship based on the trained Gaussian process regression model; constructing a destructive calibration curve of the microorganism mineralized river sand standard sample model according to the probabilistic mapping relationship.

6. The method for non-destructive evaluation of the strength of microbially mineralized river sand according to claim 1, characterized in that, The method comprises the following steps: obtaining multi-modal response data generated by the sample model under acoustic excitation; performing time-space synchronization and fusion processing on the multi-modal response data, and extracting the target mineralization characteristic parameters after fusion; calculating the dynamic biological cementation index of the sample model according to the target mineralization characteristic parameters; analyzing the target strength distribution corresponding to the dynamic biological cementation index using the destructive calibration curve; using the variance of the target strength distribution as the confidence index of the sample model, and determining the target compressive strength of the sample model according to the confidence index and the expected value of the target strength distribution.

7. The method for non-destructive evaluation of the strength of microbially mineralized river sand according to claim 6, characterized in that, The method comprises the following steps: performing destructive positioning in a preset multi-dimensional strength decision space according to the target compressive strength and the confidence index to obtain a destructive positioning point; querying a pre-generated microorganism mineralization health state map based on the destructive positioning point to obtain the strength grade of the sample model; analyzing the completion degree and stability of the sample model for the mineralization reaction based on the convergence characteristics and fluctuation characteristics of the dynamic biological cementation index; analyzing the strength non-destructive state of the microorganism river sand according to the strength grade, the completion degree and the stability.

8. A system for non-destructive evaluation of the strength of microbially mineralized river sand, characterized in that, The system for performing the microorganism mineralized river sand strength non-destructive evaluation method according to any one of claims 1-7 comprises: a sample model construction module for constructing a microorganism mineralized river sand standard sample model and a sample model to be tested based on pre-acquired mineralization attributes, and applying a preset frequency-sweep acoustic excitation signal to the microorganism mineralized river sand standard sample model; a penetration simulation module, configured to perform a penetration simulation operation on the microbially mineralized river sand standard sample model according to the swept-frequency acoustic excitation signal, to obtain a penetration response signal of the microbially mineralized river sand standard sample model at different reaction stages; a mineralization specificity characteristic parameter extraction module, configured to extract a mineralization specificity characteristic parameter corresponding to the microbially mineralized river sand standard sample model through the penetration response signal at different reaction stages, wherein a wave velocity ratio of a longitudinal wave and a transverse wave of the penetration response signal at different reaction stages is calculated, and a variance of a spatial position change of the penetration response signal is analyzed; a biological cementation spatial distribution index of the microbially mineralized river sand standard sample model is determined according to the variance; a dispersion curve feature of a surface wave component of the penetration response signal at different reaction stages is extracted, and a deviation degree between the dispersion curve feature and a preset standard elastic half-space model is calculated; and the biological cementation spatial distribution index, the wave velocity ratio, and the deviation degree are taken as the mineralization specificity characteristic parameter; The anti-pressure strength analysis module is used to construct a biological cementation index of microbial mineralization according to the mineralization specificity characteristic parameter, wherein a mineralization reaction stage of microorganisms is identified, dynamic weight coefficients corresponding to different mineralization reaction stages are selected in a preset coefficient mapping table according to a dominant cementation parameter corresponding to the mineralization reaction stage, the wave velocity ratio and the deviation in the mineralization specificity characteristic parameter are weighted according to the dynamic weight coefficients, and the weighted results are operated with a biological cementation space distribution index in the mineralization specificity characteristic parameter to obtain the biological cementation index of microbial mineralization, that is wherein is the biological cementation index, is a dynamic weight coefficient corresponding to the wave velocity ratio, is a dynamic weight coefficient corresponding to the dispersion curve deviation, is a P-wave value, is a S-wave value, is the dispersion curve deviation, is a biological cementation space distribution index; and the anti-pressure strength of the microbial mineralization river sand standard sample model is analyzed according to the biological cementation index. a destructive calibration curve construction module, configured to construct a destructive calibration curve of the microbially mineralized river sand standard sample model according to the biological cementation index and the compressive strength; a microbially mineralized river sand strength non-destructive analysis module, configured to analyze a target compressive strength of the sample model to be tested by using the destructive calibration curve, and analyze a microbially mineralized river sand strength non-destructiveness corresponding to the sample model to be tested based on the target compressive strength.

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