Method and system for detecting mechanical property of mine filling body

Abstract

The invention relates to the technical field of mechanical property detection, in particular to a mine filling body mechanical property detection method and system.The method comprises the following steps that data measured by multiple sets of displacement sensors and strain gauges in a filling body are obtained, sensing output values of multiple measuring points under initial loading are detected, the measuring points with complete response signals are screened, and the loading response trend is judged; and generating a loading response change curve group. According to the invention, through screening of multiple groups of response data and trend analysis, stability and representativeness of measuring point signals are ensured, a heterogeneous area is positioned according to response deviation between measuring points, spatial difference identification is realized, displacement slope, loading rate and strain rate are extracted to calculate local compression resistance factors, and an intensity distribution diagram is formed. In the unloading stage, strain recovery characteristics are collected to quantify elastic performance, low-elasticity areas are screened, performance synchronous degradation points are recognized in combination with compression resistance factors, and quantitative, visual and trend recognition of the mechanical performance is achieved through data difference calculation, space mapping and multi-parameter superposition.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical property testing, and particularly to a method and system for testing the mechanical properties of mine backfill bodies. Background Art

[0002] The technical field of mechanical property testing includes a technical system for testing, analyzing, and evaluating the physical responses of various materials or structures under stress conditions. The core content of this field is to obtain the performance parameters of materials under different mechanical environments through means such as stress-strain testing, failure strength analysis, and elastic modulus determination, so as to evaluate their applicability and safety. Mechanical property testing is widely used in multiple fields such as civil engineering, mining, metallurgy, building materials, aerospace, etc. The testing objects include metal materials, non-metal materials, composite materials, and consolidated body structures. According to the differences in testing purposes and environments, the entire technical field has developed various means such as indoor static loading tests, in-situ testing techniques, non-destructive testing techniques, and strain monitoring to meet the actual needs of engineering safety assessment and material selection.

[0003] Among them, the method for testing the mechanical properties of mine backfill bodies refers to a method for testing the strength, stability, and mechanical responses of artificial backfill bodies used in mine goafs. The technical matters involved in this topic mainly include, after the formation of the backfill body, using a loading device to conduct axial or triaxial loading tests on it, combining a stress-strain measurement device to record the data during the deformation process, and analyzing its compressive strength and deformation modulus based on the deformation and failure behaviors during the loading process. The testing method usually collects response parameters through instruments such as pressure sensors, displacement gauges, or strain gauges set on the mine roadway during mining or laboratory backfill specimens, and then combines the geometric parameters of the specimens and the loading method to calculate and record the mechanical parameters, thereby completing the comprehensive testing and evaluation of the mechanical properties of the backfill body.

[0004] The existing technology relies on the analysis of single response quantities at fixed measurement points, lacks in-depth processing of the response changes between measurement points, and is difficult to comprehensively show the internal differences of the structure. In backfill bodies with strong structural non-homogeneity, conventional methods are difficult to identify local abnormal areas, which easily cause evaluation deviations. The strain recovery process is not fully utilized during the unloading stage, only staying at the loading response, and lacking an effective evaluation of the later deformation ability. In practical applications, potential degradation areas are easily missed, which may cause local stability hidden dangers and affect the operation safety and structural reliability. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose a method and system for testing the mechanical properties of mine backfill bodies.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A method for testing the mechanical properties of mine backfill bodies, including the following steps: S1: Obtain the data measured by multiple groups of displacement sensors and strain gauges in the filling body, detect the sensing output values of multiple measuring points under initial loading, screen the measuring points with complete response signals and judge the loading response trend, and generate a set of loading response change curves; S2: Based on the set of loading response change curves, call the displacement change values and strain values of adjacent measuring points, calculate the average response deviation rate and locate the spatial area where the deviation exceeds the threshold, and generate a spatial distribution map of the heterogeneous area; S3: According to the spatial distribution map of the heterogeneous area, extract the displacement slope, loading rate and strain rate of the target measuring point, multiply the loading rate by the displacement slope to obtain the loading compressive stress value, and then divide it by the strain rate to obtain the local compressive factor, summarize the relationship between the factor and the measuring point position, and generate a local compressive strength distribution map; S4: Call the measuring points with low strength in the local compressive strength distribution map, obtain the unloading strain recovery amplitude and starting slope, calculate the recovery ratio and multiply it by the slope to obtain the deformation recovery index, and generate a set of unloading deformation elastic performance.

[0007] As a further solution of the present invention, the set of loading response change curves includes the response trend type, change amplitude distribution, and curve continuity characteristics. The spatial distribution map of the heterogeneous area includes the regional boundary form, deviation concentration area, and abnormal gradient distribution. The local compressive strength distribution map is specifically the compressive stress value grading distribution, strain response level, and spatial coordinate mapping. The set of unloading deformation elastic performance includes the recovery rate distribution, initial response characteristics, and elastic level label.

[0008] As a further solution of the present invention, the acquisition steps of the set of loading response change curves are specifically as follows: S101: Based on the output value of the displacement sensor and the strain gauge data, detect the initial loading response of the measuring point, screen the data without abnormal fluctuations and calculate the response increment, and obtain a set of complete response measuring point numbers; S102: Call the set of complete response measuring point numbers, extract the displacement and strain increment sequences of the corresponding measuring points, and judge the continuous growth direction and growth rate to obtain a list of continuously growing response measuring points; S103: According to the list of continuously growing response measuring points, jointly process the growth rate, amplitude and slope changes of the displacement response and strain response, and use the formula: ; Calculate the loading response change intensity value of each measuring point through operation, analyze its time series curve distribution and obtain a set of loading response change curves; Among them, represents the loading response change intensity value corresponding to the th measuring point, represents the th measuring point at the The displacement response increment at a time point, represents the strain response value of the th measurement point at the th time point, represents the unit growth amplitude of the loading stage of the th measurement point at the th time point, is the loading amplitude growth offset rate adjustment factor, is the cumulative response slope of the th measurement point at the

[0009] As a further solution of the present invention, the steps for obtaining the spatial distribution map of the inhomogeneous region are specifically as follows: S201: Based on the loading response change curve group, call the displacement change value and strain value of adjacent measurement points, calculate the response deviation rate of each pair of measurement points, and generate a response deviation rate sequence group; S202: According to the response deviation rate sequence group, divide the regional grid, and at the same time call the response deviation rate of the measurement points in multiple units, calculate the mean value and introduce an offset stability term, and use the formula: ; Operate to obtain the average response deviation rate value of each unit, compare it with the spatial response deviation threshold, and screen out the abnormal regions to generate a high deviation response region set; Among them, represents the average response deviation rate value of the grid unit, is the number of measurement point pairs in the unit, is the displacement deviation value of the th pair of measurement points, is the strain deviation value of the

[0010] As a further solution of the present invention, the steps for obtaining the local compressive strength distribution map are specifically as follows: S301: Based on the spatial distribution map of the inhomogeneous region, extract the displacement data of the measurement points, the load change amount and the strain measurement sequence, calculate the displacement slope, the loading rate and the strain rate, and obtain a slope rate measurement value group; S302: Call the slope rate measurement value group, calculate the product of the loading rate and the displacement slope, obtain the time derivative of the slope and the strain rate, and construct a response relationship based on the slope change and the strain fluctuation, and use the formula: ; Calculate and obtain the local compressive factor value at the measuring point position, summarize the parameters in units of measuring points, and obtain the measuring point compressive calculation set; Among them, represents the local compressive factor at the measuring point position, represents the loading rate at the th measuring point, is the displacement slope at the th measuring point, is the strain rate at the th measuring point, is the time derivative of the slope, is the time derivative of the strain rate; S303: According to the measuring point compressive calculation set, extract the measuring point position information, integrate the compressive factor value and the spatial coordinates, construct a distribution relationship map, and obtain the local compressive strength distribution map.

[0011] As a further solution of the present invention, the steps for obtaining the unloading deformation elastic performance set are specifically as follows: S401: Call the measuring points with low strength in the local compressive strength distribution map, detect the stress and strain values at the initial unloading point, calculate the unloading slope and compare the strain amplitude before and after unloading to obtain the deformation recovery ratio value; S402: Based on the deformation recovery ratio value and the initial unloading slope value, call the stress difference between multiple measuring points and the unloading strain increment, perform a composite operation of multiplication, summation, square root and absolute value after integrating the data of multiple measuring points, and use the formula: ; Operate to obtain the multi-parameter coupled deformation recovery strength value, and combine it with the characteristics of the unloading stage to generate the deformation recovery index value; Among them, represents the deformation recovery index value, is the deformation recovery ratio value, is the initial unloading slope value, represents the th measuring point strain increment, is the stress difference, is the average stress difference, is the total number of measuring points participating in the unloading analysis; S403: According to the deformation recovery index value, perform sorting and aggregation operations, screen representative measuring points and construct a multi-measuring point recovery performance distribution set to obtain the unloading deformation elastic performance set.

[0012] As a further solution of the present invention, the method further includes: S5: Based on the set of unloading deformation elastic performance, filter out the measuring points with low elastic capacity, compare them with the corresponding compressive capacity in the local compressive strength distribution map, count the simultaneously degraded measuring points and calculate the distribution density, and generate a local performance degradation warning area map; The local performance degradation warning area map specifically refers to the spatial density of degraded measuring points, the performance degradation level, and the potential instability area.

[0013] As a further solution of the present invention, the steps for obtaining the local performance degradation warning area map are specifically as follows: S501: Based on the elastic response data of the set of unloading deformation elastic performance, extract the residual elastic displacement value and the peak elastic recovery rate, make a difference judgment according to the elastic capacity threshold, filter out the measuring points with insufficient elastic capacity, and obtain a set of low-elastic measuring points; S502: Call the measuring point numbers in the set of low-elastic measuring points, correspond to the compressive capacity values in the local compressive strength distribution map, calculate the ratio of elasticity to compression and filter out the measuring points with a ratio less than 1, and generate a sequence of simultaneously degraded measuring points; S503: According to the coordinate information and spatial distribution of the sequence of simultaneously degraded measuring points, count the number of measuring points in the unit area and group them, and use the formula: ; Operate to obtain the degradation density value, map the regional grid, and establish a local performance degradation warning area map; Among them, represents the degradation density value of area r, is the elastic residual value of the th measuring point, is the th measuring point's compressive capacity, is the th measuring point's shortest spatial distance from adjacent measuring points, is the number of measuring points within area is the total number of measuring points within area r.

[0014] A mechanical property detection system for mine backfill. The mechanical property detection system for mine backfill is used to execute the above-mentioned mechanical property detection method for mine backfill. The system includes: A response recognition module, which obtains the output values of multiple displacement sensors and the output values of strain gauges, filters out the response measuring points with consistent loading trends, calls the time series response data, compares the change directions and extracts the continuous response change values, and generates a set of loading response change curves; The deviation positioning module, based on the load response change curve group, calls the displacement change values and strain change values of adjacent measurement points, calculates the average response deviation rate and determines whether it exceeds the response deviation threshold, locates the spatial coordinates of abnormal measurement points, and generates a spatial distribution map of the heterogeneous region; The strength extraction module, according to the spatial distribution map of the heterogeneous region, obtains the displacement slope sequence, loading rate value and strain rate value of the target measurement point, multiplies the displacement slope sequence by the loading rate value to obtain the loading compressive stress value sequence, and then divides it by the strain rate value to obtain the local compressive strength factor value, summarizes the measurement point coordinates and the local compressive strength factor value, and generates a local compressive strength distribution map; The elastic identification module calls the measurement points with low strength in the local compressive strength distribution map, collects its strain recovery amplitude value and unloading starting slope value, calculates the recovery ratio and multiplies it by the unloading starting slope value to obtain the deformation recovery index value, and generates an unloading deformation elastic performance set; The degradation warning module, based on the unloading deformation elastic performance set, screens the measurement points with low deformation recovery index values, compares them with the local compressive strength factor values, screens the simultaneously degraded measurement points and calculates the spatial density value, and generates a local performance degradation warning area map.

[0015] Compared with the prior art, the beneficial effects of the present invention are: In the present invention, through the screening and trend analysis of multiple groups of response data, the stability and representativeness of the measurement point signals are ensured, and the quality of the basic data is improved. The heterogeneous region is located based on the response deviation between the measurement points, and the spatial difference is accurately identified. The displacement slope, loading rate and strain rate are extracted to calculate the local compressive strength factor, and a strength distribution map is formed to clarify the mechanical performance of each region. The strain recovery characteristics are collected during the unloading stage to quantify the elastic performance, the low-elastic region is screened, and combined with the compressive strength factor, the points with synchronous performance degradation are identified to form a warning map. Through the calculation of the differences between data, spatial mapping and multi-parameter superposition, the quantitative, visual and trend-based identification of mechanical properties is realized, and the discrimination ability of complex structural changes is enhanced. Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the working process of the present invention; Figure 2 It is a flow chart of the acquisition steps of the load response change curve group of the present invention; Figure 3 It is a flow chart of the acquisition steps of the spatial distribution map of the heterogeneous region of the present invention; Figure 4 It is a flow chart of the acquisition steps of the local compressive strength distribution map of the present invention; Figure 5 It is a flow chart of the acquisition steps of the unloading deformation elastic performance set of the present invention; Figure 6This is the flowchart for obtaining the local performance degradation warning area map of the present invention. Specific embodiments

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] Please refer to Figure 1 , the present invention provides a technical solution: a method for detecting the mechanical properties of mine backfill, including the following steps: S1: Obtain the data measured by multiple displacement sensors and strain gauges in the backfill, detect the sensing output values of each measuring point under initial loading, screen the measuring points with complete response signals and judge their loading response trends, and generate a set of loading response change curves; S2: Based on the set of loading response change curves, call the displacement change values and strain values of adjacent measuring points, calculate the average response deviation rate and locate the spatial area where the deviation exceeds the threshold, and generate a spatial distribution map of heterogeneous areas; S3: According to the spatial distribution map of heterogeneous areas, extract the displacement slope, loading rate and strain rate of the target measuring point, multiply the loading rate by the displacement slope to obtain the loading compressive stress value, and then divide it by the strain rate to obtain the local compressive factor, summarize the relationship between the factor and the measuring point position, and generate a local compressive strength distribution map; S4: Call the measuring points with low strength in the local compressive strength distribution map, obtain the unloading strain recovery amplitude and the starting slope, calculate the recovery ratio and multiply it by the slope to obtain the deformation recovery index, and generate a set of unloading deformation elastic performance; S5: Based on the set of unloading deformation elastic performance, screen the measuring points with low elastic ability, compare them with the corresponding compressive ability in the local compressive strength distribution map, count the simultaneously degraded measuring points and calculate the distribution density, and generate a local performance degradation warning area map.

[0019] The set of loading response change curves includes response trend types, change amplitude distributions, and curve continuity characteristics. The spatial distribution map of heterogeneous areas includes regional boundary forms, deviation concentration areas, and abnormal gradient distributions. The local compressive strength distribution map is specifically a hierarchical distribution of compressive stress values, strain response levels, and spatial coordinate mappings. The set of unloading deformation elastic performance includes recovery rate distributions, initial response characteristics, and elastic level labels. The local performance degradation warning area map specifically refers to the spatial density of degraded measuring points, performance degradation levels, and potential instability areas.

[0020] Please refer to Figure 2 , the specific steps for obtaining the set of loading response change curves are as follows: S101: Based on the output values of displacement sensors and strain gauge data, detect the initial loading response of the measuring points, screen the data without abnormal fluctuations, calculate the response increment, and obtain the complete set of response measuring point numbers; First, call the original output data of each measuring point in the initial loading stage, extract the initial displacement value and strain value of the corresponding measuring point one by one, perform time series alignment processing on the collected data, and establish the initial numerical matrix of displacement and strain to ensure that the displacement and strain data points correspond one by one; The specific processing action is to call the initial displacement data measured by the sensor (such as the initial displacement data example shown in Table 1) and the initial strain data measured by the strain gauge. By traversing point by point, calculate the incremental change of the measured values at adjacent times (that is, subtract the corresponding value at the previous time from the displacement or strain value at the next time), record the absolute value of the incremental change, and use the numerical fluctuation thresholds of 0.02 mm and 0.005 με as the judgment criteria. Any measuring point with an absolute displacement change lower than 0.02 mm and an absolute strain change lower than 0.005 με is defined as having no abnormal fluctuations, and thus determine whether each measuring point has abnormal fluctuations and screen out the data sequence without abnormal fluctuations; For example, for measuring point P5, the displacements at two consecutive initial sampling points are 0.120 mm and 0.132 mm respectively, and the strains are 1.250 με and 1.260 με respectively. The calculated displacement increment is 0.012 mm, and the strain increment is 0.010 με. Since both increments are lower than the threshold, it is determined that this measuring point has no abnormal fluctuations and is included in the complete set of response measuring point numbers. Finally, the complete set of response measuring point numbers for the initial loading response is determined as {P1, P2, P5, P7, P9, P10}.

[0021] Table 1: Example data table of displacement and strain at the initial loading stage of the measuring point

[0022] As shown in Table 1, the above initial data is collected in real time by sensors and strain gauges and recorded in matrix form, which is used to screen out the measuring point data without abnormal fluctuations.

[0023] S102: Call the complete set of response measuring point numbers, extract the displacement and strain increment sequences of the corresponding measuring points, and judge the continuous growth direction and growth rate to obtain the list of measuring points with continuous growth response; Call the determined complete set of response measurement point numbers {P1, P2, P5, P7, P9, P10}, and extract the displacement response increment sequence and strain increment sequence generated by the corresponding measurement points with the continuation of the loading stage one by one. Specifically: in the way of sampling displacement and strain data every 1 second, establish a time series data sequence, calculate the displacement and strain increments of adjacent data points, and based on the continuous growth judgment of more than three consecutive sampling points, call specific parameters to check point by point. If the displacement increments and strain increments at three consecutive moments are all positive, then judge that the measurement point is a continuously growing response measurement point. For example, if the displacement increments of measurement point P1 at three consecutive moments are 0.010 mm, 0.012 mm, 0.015 mm, and the strain increments are 0.007 με, 0.009 με, 0.012 με, then this measurement point is determined to be included in the continuously growing response measurement point list due to the continuous positive increments; if any one of the increments appears negative or zero during continuous growth, then judge that this measurement point does not meet the continuously growing condition and thus is not included in the continuously growing response measurement point list. The finally determined continuously growing response measurement point list is, for example, {P1, P5, P7, P9}.

[0024] S103: According to the continuously growing response measurement point list, jointly process the growth rate, amplitude, and slope changes of the displacement response and strain response, and use the formula: ; Perform operations to obtain the loading response change intensity value of each measurement point, analyze its time series curve distribution, and obtain the loading response change curve group; Among them, represents the loading response change intensity value corresponding to the th measurement point, represents the displacement response increment of the th measurement point at the th time point, represents the strain response value of the th measurement point at the th time point, represents the unit growth amplitude of the loading stage of the th measurement point at the th time point, is the loading amplitude growth offset rate adjustment factor, is the th measurement point at the th time point of the cumulative response slope, is the total number of time nodes in the loading stage.

[0025] Jointly process the displacement and strain responses of the measurement points {P1, P5, P7, P9} in the continuously growing response measurement point list, and calculate the loading response change intensity value ; Taking the measuring point P1 as an example, the specific implementation process is as follows: First, extract the displacement response increment and the strain response value , the unit loading increase , and the cumulative response slope . The specific measurement data is, for example: at a certain moment t1, the displacement response increment is 0.015 mm, the strain response value is 1.320 με, the unit loading increase is 0.005 mm / s, and the cumulative response slope is 0.002; the cumulative response slope at the previous adjacent moment (t1 - 1) is 0.0018, and the loading amplitude increase offset rate adjustment factor is set to 0.01 (based on historical monitoring data analysis, the reasonable range is 0.005 - 0.02); then substitute into the formula: ; For the measuring point P1, take n1 = 3 during the loading period and calculate the specific value: At moment t1: ; At moment t2: Take the displacement increment as 0.017 mm, the strain response 1.332 με, the unit loading increase 0.006 mm / s, and the cumulative slope 0.0022, and calculate: ; At moment t3: Take the displacement increment 0.019 mm, the strain response 1.345 με, the unit loading increase 0.007 mm / s, and the cumulative slope 0.0024, and calculate: ; Integrate the data of the above three moments to obtain the loading response change intensity of the measuring point P1: ; This result shows that the loading response change intensity of the measuring point P1 is 1.349. Similarly, calculate each other measuring point one by one and analyze the distribution of its time series curve, and then the loading response change curve group can be obtained. The advantage of the formula is that through the joint processing of displacement increment, strain amplitude and slope change, it comprehensively reflects the fine response characteristics of the measuring point during the loading process.

[0026] Please refer to Figure 3 , the specific steps for obtaining the spatial distribution map of the inhomogeneous region are as follows: S201: Based on the loading response change curve group, call the displacement change value and strain value of adjacent measuring points, calculate the response deviation rate of each pair of measuring points, and generate a response deviation rate sequence group; First, select the data of adjacent measurement points under the same loading condition for comparison, and select the difference between the displacement change value and the strain change value of adjacent measurement points for preliminary analysis. For example, assume there is a measurement point pair (measurement point A, measurement point B), the displacement change of measurement point A is 0.025 mm, the strain change is 120 με, the displacement change of measurement point B is 0.030 mm, and the strain change is 125 με. Subtract the data of measurement point A from the data of measurement point B respectively to obtain the displacement deviation value is , and the strain deviation value is . Repeat the above operation for all measurement point pairs. After obtaining the displacement deviation value and strain deviation value of each measurement point pair, calculate the response deviation rate of each measurement point pair by dividing the displacement deviation value by the strain deviation value. For example, the response deviation rate of this measurement point pair is . For the actual application scenario, such as in the monitoring of the beam span structure of a bridge, taking 20 measurement points as an example, according to the loading response curve, the response deviation rates of 19 measurement point pairs are calculated, that is, a response deviation rate sequence group is formed, so as to provide a complete data basis for the next step of processing.

[0027] S202: According to the response deviation rate sequence group, divide the regional grid, and at the same time call the response deviation rates of the measurement points in multiple units, calculate the mean value and introduce the offset stability term, using the formula: ; Calculate the average response deviation rate value of each unit through operation, compare it with the spatial response deviation threshold, screen out the abnormal areas, and generate a high deviation response area set; Among them, represents the average response deviation rate value of the grid unit, is the number of measurement point pairs within the unit, is the displacement deviation value of the th pair of measurement points, is the strain deviation value of the th pair of measurement points; By dividing the grid based on the spatial size of the monitoring area, for example, dividing the monitoring area of the bridge beam with a length of 20 m and a width of 5 m into multiple unit grids, the size of each unit can be set to 2 m × 1 m, a total of 50 grid units are divided, and each grid unit contains different numbers of measurement point pairs. After calling and summarizing the response deviation rates of the measurement point pairs within each unit for operation, first calculate the average value of the response deviation rates of all measurement point pairs within the unit, and then introduce the offset stability term according to the actual operation needs, and perform the operation through the formula: In the formula, represents the average response deviation rate value of the is the number of measurement point pairs inside the unit. For example, there are 4 pairs of measurement points in the (2, 3) unit, and the specific data is shown in Table 2 as follows: Table 2: Data table of response deviation rate of measurement points inside the unit

[0028] As shown in Table 2, by calling the data of each measurement point pair and substituting them into the formula, the following results are calculated respectively: ; ; ; ; Then, take the average of the above four values and take the absolute value: ; Repeat the above calculation process for all grid units to obtain the average response deviation rate value of each unit respectively. Compare the obtained with the spatial response deviation threshold. The spatial response deviation threshold can be set according to past experience or historical monitoring data. For example, if the set threshold is 2.0, then make a judgment and screening. If the value in the unit exceeds the threshold of 2.0, then it is determined that this grid unit is an abnormal response area. For example, in this case, 2.141 of the (2, 3) grid > 2.0, so it is an abnormal area and add it to the high deviation response area set. All units higher than this threshold are included in the high deviation response area set.

[0029] S203: According to the high deviation response area set, extract the spatial coordinate information, combine the distribution of measurement points and grids, construct a three-dimensional boundary graph, and generate a spatial distribution map of the heterogeneous area.

[0030] The position of each grid unit is determined by the coordinates of its four corner points. For example, the coordinate position of the (2, 3) grid unit is determined by the coordinate information of the four vertices of the unit such as (4m, 3m, 0m), (6m, 3m, 0m), (4m, 4m, 0m), (6m, 4m, 0m). Call the coordinate data of all high deviation grid units and summarize them into a high deviation area spatial coordinate data set. Then, combined with the specific layout positions of the measurement points, for example, there are measurement points S4 and S5 installed in the grid, with coordinates (4.5m, 3.5m, 0m) and (5.5m, 3.5m, 0m) respectively. Construct a three-dimensional boundary graph with the actual layout coordinates of each grid unit and the measurement points, and connect the spatial coordinates of all adjacent units in the three-dimensional boundary graph for processing, and gradually construct a complete spatial distribution map of the heterogeneous area, so as to present the high deviation response area completely.

[0031] Please refer to Figure 4, the steps for obtaining the local compressive strength distribution map are specifically as follows: S301: Based on the spatial distribution map of the heterogeneous region, extract the displacement data of the measurement points, the load change amount, and the strain measurement sequence, calculate the displacement slope, the loading rate, and the strain rate, and obtain the slope rate measurement value group; In the implementation process, first extract the displacement data of the measurement points, the load change amount, and the strain measurement sequence from the spatial distribution map of the heterogeneous region. Then, the relevant parameters of these data need to be calculated: the displacement slope, the loading rate, and the strain rate. The key to these data extraction and calculation processes is to ensure the accuracy and continuity of the measurement. Assume that we collect the displacement data sequence at a certain measurement point (i1), then the displacement slope is obtained through the difference operation of the displacement data, and the formula is: ; where, and represent the initial and final displacement values of the measurement data sequence respectively, and are the corresponding time points. In the calculation of the loading rate, it needs to be calculated according to the load change amount of each time step. If the load data is , then the loading rate is calculated as: ; Similarly, the strain rate is obtained through the difference calculation of the strain data. Assume that the strain data is , and the calculation formula of the strain rate is: ; For example, assume that we obtain the displacement data and at the time at the measurement point i_1, the strain data is , and the load data is . Then the displacement slope is: ; The loading rate is: ; The strain rate is: ; These calculation results are the basic data required for this step. Next, these data will be used for further analysis and calculation to obtain the slope rate measurement value group.

[0032] S302: Invoke the slope rate measurement value group, calculate the product of the loading rate and the displacement slope, obtain the time derivatives of the slope and the strain rate, and construct a response relationship based on the slope change and strain fluctuation, using the formula: ; Calculate the local compressive factor value at the measuring point position, and summarize the parameters in units of measuring points to obtain the measuring point compressive calculation set; Among them, represents the local compressive factor at the measuring point position, represents the loading rate at the th measuring point, is the displacement slope of the th measuring point, is the strain rate of the th measuring point, is the time derivative of the slope, is the time derivative of the strain rate; The specific calculation process is: First, calculate the product of the loading rate and the displacement slope, that is , and then calculate the time derivatives of the slope and the strain rate: , and the time derivative is calculated by the change rate of the slope and the strain rate. Assume that the data at the moment of measuring point i_2 is mm / s, mm / s, and the time interval is , then the slope time derivative is calculated as: ; Similarly, for the calculation of the time derivative of the strain rate, assume that the strain rates are and , then the strain rate time derivative is: ; Substitute these values into the formula to calculate the local compressive factor: ; Assume that the loading rate is , the displacement slope is , and the strain rate is , then: ; The calculation result is the local compressive factor, which represents the local compressive strength at the measuring point. This value will be used as a piece of data in the measuring point compressive calculation set and further used for the construction of the subsequent compressive distribution map.

[0033] S303: According to the measuring point compressive calculation set, extract the measuring point position information, integrate the compressive factor value and the spatial coordinates, construct a distribution relationship map, and obtain the local compressive strength distribution map.

[0034] At this stage, by integrating the data of each item in the measured point compressive strength calculation set, the measured point position information and the corresponding local compressive strength factor values can be obtained. These data will be used to construct a distribution relationship map, thereby obtaining the local compressive strength distribution map. At this time, the compressive strength factor value of each measured point will be associated with its spatial coordinates, and the change trend of the compressive strength with the spatial distribution will be plotted, and finally the spatial distribution map of the local compressive strength will be formed.

[0035] Table 3: Calculation data of the compressive strength factor of the measured point

[0036] As shown in Table 3, the compressive strength factors of the measured points vary with the position, and these data can help construct a more accurate local compressive strength distribution map.

[0037] Please refer to Figure 5 , and the specific steps for obtaining the unloading deformation elastic performance set are as follows: S401: Call the measured points with low strength in the local compressive strength distribution map, detect the stress and strain values at the initial unloading point, calculate the unloading slope and compare the strain amplitude before and after unloading to obtain the deformation recovery ratio value; Specifically, select the measured points whose strength values are in the interval below 20% of the total strength distribution of the measured points in the concrete compressive experiment. Assume that a total of 3 measured points are selected, and the stress values of the measured points are , , ; Subsequently, unload the tests one by one at these three measured points, and reduce the stress in a step-by-step decreasing loading manner. Assume that the stress reduction starts when the stress drops to 80% of the total loading stress at the initial unloading point (i.e., the starting point of rebound), and the corresponding stress value is set to 0.8 times the original stress of each measured point. For example, the stress value at the initial unloading point of the first measured point is: , and so on, and the stress values at the initial unloading points of the three measured points are obtained as: 16 MPa, 17.6 MPa, 15.2 MPa; at the same time, use the strain gauges installed on the surface of the specimen to record the corresponding initial point strain values. Assume that the initial unloading point strain values recorded by the strain gauges of each measured point are , , , and then continue to unload step by step to the fully unloaded state. The strain gauges record the unloading end point strains as , , , and by calculating the stress and strain differences during the unloading process of the three measured points respectively, the unloading slope is obtained. For example, the calculation of the unloading slope of the first measured point is: , calculate in the same way for other measuring points; subsequently, calculate the strain amplitude difference before and after unloading for each measuring point. For example, the strain difference before and after unloading at measuring point 1 , perform the same operation for other measuring points to obtain the deformation recovery ratio value, which is the ratio of the strain difference after unloading to the initial strain before unloading. For example, the deformation recovery ratio value of the first measuring point is: , calculate the deformation recovery ratio of each measuring point one by one in this way, and finally obtain the deformation recovery ratio value of each measuring point.

[0038] S402: Based on the deformation recovery ratio value and the initial unloading slope value, call the stress difference and unloading strain increment of multiple measuring points, and perform a composite operation of multiplication, summation, square root, and absolute value after integrating the data of multiple measuring points. Use the formula: ; Calculate the multi-parameter coupled deformation recovery strength value through the operation, and combine it with the characteristics of the unloading stage to generate the deformation recovery index value; Among them, represents the deformation recovery index value, is the deformation recovery ratio value, is the initial unloading slope value, represents the strain increment of the measuring point, is the stress difference, is the average stress difference, is the total number of measuring points participating in the unloading analysis; Based on the deformation recovery ratio value and the initial unloading slope value obtained in the previous paragraph, select multiple measuring points to calculate the stress difference and unloading strain increment; specifically, the stress difference is the difference between the initial stress of the measuring point and the stress at the initial unloading point. For example, the stress difference at measuring point 1 is ; the unloading strain increment is the difference between the initial unloading strain and the end strain after complete unloading. For example, the unloading strain increment at measuring point 1 is 150 με. Assume that a total of 3 measuring points participate in the analysis, listed as follows: Table 4: Measuring Point Stress and Strain Parameter Table

[0039] Table 4 gives the test data of the embodiment. Subsequently, substitute the data into the deformation recovery index formula, where is the average value of the stress differences of the three measuring points, that is: (4 + 4.4 + 3.8) / 3 = 4.067 MPa; calculate the product term of each measuring point one by one and take the square root. For example, for measuring point 1: , similarly calculate other measurement points; sum up the square root results of all measurement points: 24.495 + 26.533 + 23.874 = 74.902; then add the average value of the deformation recovery ratio of the measurement points (assuming the average of the three measurement points is 0.236) to get the numerator of 75.138; then subtract the average value of the stress difference 4.067 from the average value of the unloading initial slope (taking 0.1060 MPa / με) to get the denominator: 0.1060 - 4.067 = -3.961; then the deformation recovery index value is calculated as: . This result shows that by comprehensively considering the stress differences and strain increments of multiple measurement points, the obtained deformation recovery index can quantify the elastic performance degree of the deformation of the measurement points.

[0040] S403: According to the deformation recovery index value, perform a sorting and aggregation operation, screen representative measurement points, construct a multi-measurement-point recovery performance distribution set, and obtain the unloading deformation elastic performance set.

[0041] Based on the previously obtained deformation recovery index value , sort the deformation recovery index values calculated for multiple measurement points from large to small, and select the top 30% of the representative measurement points in a sorting and aggregation manner. Assume that measurement point 2 and measurement point 1 are selected as representative measurement points after sorting, and the corresponding deformation recovery index values are 2.120 and 2.010 respectively; then construct a multi-measurement-point recovery performance distribution set, form a recovery performance set with the selected measurement points, for example, represented in set form as {measurement point 2, measurement point 1}, and concentrate the strain recovery characteristics (such as unloading initial strain, unloading end strain, strain increment, and corresponding deformation recovery ratio, etc.) corresponding to the measurement points in the set into a multi-measurement-point elastic performance distribution set to obtain the final unloading deformation elastic performance set.

[0042] Please refer to Figure 6 , the specific steps for obtaining the local performance degradation warning area map are as follows: S501: Based on the elastic response data of the unloading deformation elastic performance set, extract the residual elastic displacement value and the peak elastic recovery rate, make a difference judgment according to the elastic capacity threshold, and screen the measurement points with insufficient elastic capacity to obtain a set of low-elastic measurement points; Taking the elastic force test of the bridge structure as an example, the unloading deformation elastic performance set contains the elastic response data of each measurement point in the unloading stage of the static load test of the bridge structure, such as the residual elastic displacement value and the peak elastic recovery rate of the key measurement points on the bridge deck. Taking the data of five measurement points in a certain area on the bridge deck as an example, Table 5 gives their actual elastic response data: Table 5: Elastic Response Data Table of Measurement Points

[0043] In the actual execution process, first, the elastic residual displacement values recorded at the measuring points are called. For example, the residual displacement value of measuring point M1 is 0.25 mm. Then, the elastic capacity threshold of 0.30 mm is called for one-by-one difference judgment. That is, taking 0.30 mm as the judgment criterion, the residual displacement value of the measuring point is numerically compared with this threshold. Taking 0.30 mm as the boundary, if the residual displacement value is greater than or equal to 0.30 mm, it is determined that the elastic capacity of this measuring point is insufficient; otherwise, it is regarded as qualified, and the screening process is completed. For example, the residual displacement value of M2 is 0.35 mm. After comparing with the threshold of 0.30 mm, since the value is larger, it is determined that the elastic capacity is insufficient. Similarly, the residual displacement value of M4, 0.45 mm, is also greater than 0.30 mm, so it is determined that the elastic capacity is insufficient, while the residual displacement values of M1, M3, and M5 are all lower than 0.30 mm, so they are determined to be qualified. Therefore, the set of measuring points with insufficient elastic capacity, that is, the low-elastic measuring point set, is {M2, M4}.

[0044] S502: Call the measuring point numbers in the low-elastic measuring point set, corresponding to the compressive capacity values in the local compressive strength distribution map, calculate the ratio of elasticity to compression and screen the measuring points that meet the ratio less than 1, and generate a sequence of simultaneously degraded measuring points; According to the low-elastic measuring point set {M2, M4} obtained in the previous stage, call the local compressive strength distribution map corresponding to their measuring point numbers to obtain the corresponding compressive capacity values. Assume the actual data of the compressive capacity of the measuring points are as follows: the compressive capacity value of measuring point M2 is 0.40 MPa, and the compressive capacity value of measuring point M4 is 0.55 MPa. Call the elastic residual displacement values of the aforementioned measuring points, M2 is 0.35 mm and M4 is 0.45 mm, to calculate the ratio of elasticity to compressive capacity. That is, for measuring point M2, its elastic residual displacement value of 0.35 divided by the compressive capacity value of 0.40 gives a ratio of 0.875, which is less than 1; for measuring point M4, its elastic residual displacement value of 0.45 divided by the compressive capacity value of 0.55 gives a ratio of 0.818, which is also less than 1. Therefore, through the ratio comparison operation, the sequence of simultaneously degraded measuring points with a ratio result less than 1 is {M2, M4}.

[0045] S503: According to the coordinate information and spatial distribution of the sequence of simultaneously degraded measuring points, count the number of measuring points in the unit area and group them, using the formula: ; Perform operations to obtain the degradation density value and map the regional grid to establish a local performance degradation warning area map; Among them, represents the degradation density value of region r, is the elastic residual value of the th measuring point, is the th compressive capacity of the measuring point, is the th shortest spatial distance between the measuring point and its adjacent measuring points, is the number of measurement points in the area , and is the total number of measurement points in the area r.

[0046] In the coordinate space of the bridge deck area, based on the spatial coordinate positions involved in the simultaneously degraded measurement point sequence {M2, M4}, count the number of measurement points in the unit area and group them. For example, the total number of measurement points in this area is , and the number of simultaneously degraded measurement points . By collecting the actual coordinate positions of the measurement points, calculate the shortest spatial distance. For example, the distance between measurement point M2 and the nearest measurement point M3 is 2.5 m, and the distance between measurement point M4 and the nearest measurement point M5 is 3.0 m. Then correspondingly they are 2.5 m and 3.0 m respectively.

[0047] Specifically explain the formula parameters: where the degradation density value of the area is denoted as , and the elastic residual displacement value takes 0.35 mm of measurement point M2 and 0.45 mm of measurement point M4 respectively, and the compressive capacity value takes 0.40 MPa of measurement point M2 and 0.55 MPa of measurement point M4 respectively. The shortest spatial distance takes values of 2.5 m and 3.0 m respectively. The above data are all obtained through on-site measurement, and clarify the data value-taking process.

[0048] Substitute into the formula for calculation and derivation: ; After calculation, finally obtain the degradation density value of the area is 0.0557. By setting 0.05 as the degradation warning benchmark value for numerical comparison, it is judged that the calculated 0.0557 has exceeded the degradation warning benchmark value, and numerically determine that the performance in the area is in a local degradation state. Map this result to the area grid, thereby drawing a local performance degradation warning area map.

[0049] The mechanical property detection system for mine backfill. The mechanical property detection system for mine backfill is used to execute the above-mentioned mechanical property detection method for mine backfill. The system includes: A response recognition module, which acquires the output values of multiple displacement sensors and the output values of strain gauges, screens the response measurement points with consistent loading trends, calls the time series response data, compares the change directions and extracts the continuous response change values, and generates a set of loading response change curves; A deviation positioning module, based on the set of loading response change curves, calls the displacement change values and strain change values of adjacent measurement points, calculates the average response deviation rate and judges whether it exceeds the response deviation threshold, locates the spatial coordinates of abnormal measurement points, and generates a spatial distribution map of inhomogeneous regions; Strength extraction module, according to the spatial distribution map of heterogeneous regions, obtains the displacement slope sequence, loading rate value and strain rate value of the target measurement point. The displacement slope sequence is multiplied by the loading rate value to obtain the loading compressive stress value sequence, and then divided by the strain rate value to obtain the local compressive factor value. The measurement point coordinates and the local compressive factor value are summarized to generate the local compressive strength distribution map; Elasticity identification module, calls the measurement points with low strength in the local compressive strength distribution map, collects its strain recovery amplitude value and unloading starting slope value, calculates the recovery ratio and multiplies it by the unloading starting slope value to obtain the deformation recovery index value, and generates the unloading deformation elasticity performance set; Degradation warning module, based on the unloading deformation elasticity performance set, screens the measurement points with low deformation recovery index values, compares them with the local compressive factor values, screens the simultaneously degraded measurement points and calculates the spatial density value, and generates the local performance degradation warning area map.

[0050] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for detecting the mechanical properties of mine backfill, characterized in that, It includes the following steps: S1: Obtain the data measured by multiple displacement sensors and strain gauges in the filling body, detect the sensing output values of multiple measuring points under initial loading, screen the measuring points with complete response signals, judge the loading response trend, and generate a set of loading response change curves; S2: Based on the set of loading response change curves, call the displacement change values and strain values of adjacent measuring points, calculate the average response deviation rate, locate the spatial area where the deviation exceeds the threshold, and generate a spatial distribution map of the inhomogeneous area; S3: According to the spatial distribution map of the inhomogeneous area, extract the displacement slope, loading rate, and strain rate of the target measuring point, multiply the loading rate by the displacement slope to obtain the loading compressive stress value, and then divide it by the strain rate to obtain the local compressive factor. Summarize the relationship between the factor and the measuring point position, and generate a local compressive strength distribution map; S4: Call the measuring points with low strength in the local compressive strength distribution map, obtain the unloading strain recovery amplitude and starting slope, calculate the recovery ratio, multiply it by the slope to obtain the deformation recovery index, and generate a set of unloading deformation elastic performance; 2. The mechanical property detection method of the mine filling body according to claim 1, wherein The set of loading response change curves includes the response trend type, change amplitude distribution, and curve continuity characteristics. The spatial distribution map of the inhomogeneous area includes the regional boundary form, deviation concentration area, and abnormal gradient distribution. The local compressive strength distribution map is specifically the graded distribution of compressive stress values, strain response levels, and spatial coordinate mapping. The set of unloading deformation elastic performance includes the recovery rate distribution, initial response characteristics, and elastic level labels; 3. The method for detecting the mechanical properties of a mine filling body according to claim 2, wherein The specific steps for obtaining the set of loading response change curves are as follows: S101: Based on the output values of displacement sensors and strain gauge data, detect the initial loading response of the measuring points, screen the data without abnormal fluctuations, calculate the response increment, and obtain a set of complete response measuring point numbers; S102: Call the set of complete response measuring point numbers, extract the displacement and strain increment sequences of the corresponding measuring points, and judge the continuous growth direction and growth rate to obtain a list of continuously growing response measuring points; S103: According to the list of continuously growing response measuring points, jointly process the growth rate, amplitude, and slope changes of displacement response and strain response, and use the formula: ； Operate to obtain the loading response change intensity value of each measuring point, analyze its time-series curve distribution, and obtain a set of loading response change curves; Among them, represents the change intensity value of the loading response corresponding to the th measurement point, represents the displacement response increment of the th measurement point at the th time point, represents the strain response value of the th measurement point at the th time point, represents the unit growth amplitude of the loading stage of the th measurement point at the th time point, is the adjustment factor for the loading amplitude growth offset rate, is the th cumulative response slope of the th measurement point at the th time point, and is the total number of time nodes in the loading stage.

4. The mechanical property detection method of the mine filling body according to claim 3, characterized in that The specific steps for obtaining the spatial distribution map of the inhomogeneous area are as follows: S201: Based on the set of loading response change curves, call the displacement change values and strain values of adjacent measuring points, calculate the response deviation rate of each pair of measuring points, and generate a set of response deviation rate sequences; S202: According to the set of response deviation rate sequences, divide the regional grid, and at the same time call the response deviation rates of the measuring points in multiple units, calculate the mean value, and introduce an offset stability term. Use the formula: ； Operate to obtain the average response deviation rate value of each unit, compare it with the spatial response deviation threshold, screen the abnormal areas, and generate a set of high-deviation response areas; Among them, represents the average response deviation rate value of the grid cell, is the number of measurement points inside the cell, is the displacement deviation value of the pair of measurement points, is the strain deviation value of the pair of measurement points; S203: According to the set of high-deviation response areas, extract the spatial coordinate information, combine the measuring points and grid distribution, construct a three-dimensional boundary graph, and generate a spatial distribution map of the inhomogeneous area; 5. The mechanical property detection method of the mine filling body according to claim 4, characterized in that The specific steps for obtaining the local compressive strength distribution map are as follows: S301: Based on the spatial distribution map of the heterogeneous region, extract the displacement data of the measuring points, the load change amount and the strain measurement sequence, calculate the displacement slope, the loading rate and the strain rate, and obtain a set of slope rate measurement values; S302: Call the set of slope rate measurement values, calculate the product of the loading rate and the displacement slope, obtain the time derivatives of the slope and the strain rate, construct a response relationship based on the slope change and the strain fluctuation, and use the formula: ； Calculate to obtain the local compressive factor value at the measuring point position, and summarize the parameters in units of measuring points to obtain a set of compressive calculations for the measuring points; Among them, represents the local compressive factor at the measuring point position, represents the loading rate at the th measuring point, is the displacement slope at the th measuring point, is the strain rate at the th measuring point, is the time derivative of the slope, is the time derivative of the strain rate; S303: According to the set of compressive calculations for the measuring points, extract the position information of the measuring points, integrate the compressive factor value and the spatial coordinates, construct a distribution relationship map, and obtain a local compressive strength distribution map.

6. The mechanical property detection method of the mine filling body according to claim 5, wherein The specific steps for obtaining the set of elastic performance of unloading deformation are as follows: S401: Call the measuring points with low strength in the local compressive strength distribution map, detect the stress and strain values at the initial unloading point, calculate the unloading slope and compare the strain amplitude before and after unloading to obtain the deformation recovery ratio value; S402: Based on the deformation recovery ratio value and the initial unloading slope value, call the stress difference of multiple measuring points and the unloading strain increment, perform a composite operation of multiplication, addition, square root and absolute value after integrating the data of multiple measuring points, and use the formula: ； Calculate to obtain the multi-parameter coupled deformation recovery strength value, and combine it with the characteristics of the unloading stage to generate a deformation recovery index value; Among them, represents the deformation recovery index value, is the deformation recovery ratio value, is the initial unloading slope value, represents the strain increment of the measuring point, is the stress difference, is the average stress difference, is the total number of measuring points participating in the unloading analysis; S403: According to the deformation recovery index value, perform a sorting and aggregation operation, screen representative measuring points and construct a set of recovery performance distributions for multiple measuring points to obtain a set of elastic performance of unloading deformation.

7. The mechanical property detection method of the mine filling body according to claim 6, characterized in that The method further includes: S5: Based on the set of elastic performance of unloading deformation, screen the measuring points with low elastic ability, compare them with the corresponding compressive ability in the local compressive strength distribution map, count the simultaneously degraded measuring points and calculate the distribution density, and generate a local performance degradation warning area map; The local performance degradation warning area map specifically refers to the spatial density of degraded measuring points, the performance degradation level, and the potential instability area.

8. The mechanical property detection method of the mine filling body according to claim 7, characterized in that The specific steps for obtaining the local performance degradation warning area map are as follows: S501: Based on the elastic response data of the set of elastic performance of unloading deformation, extract the residual elastic displacement value and the peak elastic recovery rate, perform a difference judgment according to the elastic ability threshold, and screen the measuring points with insufficient elastic ability to obtain a set of low-elastic measuring points; S502: Call the measuring point numbers in the set of low-elastic measuring points, correspond to the compressive ability values in the local compressive strength distribution map, calculate the ratio of elasticity to compression and screen the measuring points that meet the ratio less than 1 to generate a sequence of simultaneously degraded measuring points; S503: According to the coordinate information and spatial distribution of the sequence of simultaneously degraded measuring points, count the number of measuring points in the unit area and group them, and use the formula: ； Calculate to obtain the degradation density value, and map the regional grid to establish a local performance degradation warning area map; Among them, represents the degradation density value of region r, is the elastic residual value of the th measurement point, is the compressive capacity of the th measurement point, is the shortest spatial distance between the th measurement point and its neighboring measurement points, is the number of measurement points within region , is the total number of measurement points within region r.

9. A mechanical property detection system for mine filling body, based on the mechanical property detection method for mine filling body according to any one of claims 1-8, characterized in that, The system includes: A response recognition module, which obtains the output values of multiple displacement sensors and the output values of strain gauges, screens the response measuring points with consistent loading trends, calls the time series response data, compares the change directions and extracts the continuous response change values to generate a set of loading response change curves; The deviation positioning module, based on the load response change curve group, calls the displacement change value and strain change value of adjacent measuring points, calculates the average response deviation rate and determines whether it exceeds the response deviation threshold, locates the spatial coordinates of abnormal measuring points, and generates a spatial distribution map of the inhomogeneous region; The strength extraction module, according to the spatial distribution map of the inhomogeneous region, obtains the displacement slope sequence, loading rate value and strain rate value of the target measuring point. The displacement slope sequence is multiplied by the loading rate value to obtain the loading compressive stress value sequence, and then divided by the strain rate value to obtain the local compressive strength factor value. The measuring point coordinates and local compressive strength factor values are summarized to generate a local compressive strength distribution map; The elastic identification module calls the measuring points with low strength in the local compressive strength distribution map, collects its strain recovery amplitude value and unloading starting slope value, calculates the recovery ratio and multiplies it by the unloading starting slope value to obtain the deformation recovery index value, and generates an unloading deformation elastic performance set; The degradation warning module, based on the unloading deformation elastic performance set, screens the measuring points with low deformation recovery index values, compares them with the local compressive strength factor values, screens the simultaneously degraded measuring points and calculates the spatial density value, and generates a local performance degradation warning area map.

Images