A smart construction site engineering quality inspection device

Abstract

The present invention discloses a smart construction site engineering quality inspection device, which belongs to the field of detection technology, including a digital display compensation system, which is arranged on a digital display rebound tester data acquisition controller, including: a data acquisition module, and also including: a strength stability analysis module of a test surface, which constructs a strength stability model based on the strength stability data of the concrete test surface and outputs a strength stability coefficient; a surface state analysis module of a test surface, which constructs a surface state model based on the surface state data of the concrete test surface and outputs a surface state coefficient; a detection drift analysis module, which constructs a detection drift model based on the strength stability coefficient and the detection drift data under the surface state coefficient and outputs a detection drift coefficient; a compressive strength output module, which constructs a compressive strength determination model based on the current compressive strength detected by the digital display rebound tester and the detection drift coefficient and outputs a target compressive strength; the invention solves the problem that traditional rebound testers are interfered with by the environment and instrument drift, and significantly improves the reliability of smart construction site quality inspections.

Description

Technical Field

[0001] The present invention belongs to the field of detection technology, and in particular relates to a smart construction site engineering quality inspection device. Background Art

[0002] Traditional concrete rebound test hammers are widely used in smart construction site quality inspections. Their operating principle is to hold the end of the instrument's impact rod vertically against the concrete test surface. When the rod is released, the spring forces the hammer forward, transmitting the impact force to the concrete surface. After impact, the hammer rebounds a certain distance against the concrete surface's reaction force. The instrument measures and records this rebound distance (rebound value) using an internal scale or digital sensor. A larger rebound value (typically between 20 and 80) indicates higher concrete surface hardness. There is a statistical correlation between concrete surface hardness and its compressive strength. A strength curve (conversion curve) established using extensive test data can be used to convert the measured rebound value into an estimated compressive strength value for the concrete at that location.

[0003] However, existing concrete rebound test hammers have significant limitations:

[0004] (1) Large single-point detection error: It only relies on the instantaneous rebound value and does not consider the strength evolution factors such as concrete age and carbonation depth;

[0005] (2) Sensitive to environmental interference: surface flatness, humidity, smoothness, etc. can easily cause the rebound value to drift;

[0006] (3) Instrument drift: spring fatigue, temperature changes, operating angle deviation, etc. are not compensated in real time.

[0007] Existing technologies lack a multi-factor coordinated correction mechanism, resulting in insufficient reliability of compressive strength test results and affecting the accuracy of engineering quality assessment. Summary of the Invention

[0008] In view of the shortcomings of the existing technology, the present invention provides a smart construction site engineering quality inspection device to solve the above problems.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart construction site engineering quality inspection device, including a digital rebound tester embedded in a box, and also including:

[0010] The digital compensation system is installed on the digital rebound hammer data acquisition controller and is used to compensate the test results of the digital rebound hammer, including:

[0011] The data acquisition module is used to obtain the strength stability data, surface condition data and detection drift data of the digital rebound tester of the surface to be tested;

[0012] The strength stability analysis module of the surface to be tested builds a strength stability model based on the strength stability data of the concrete surface to be tested and outputs the strength stability coefficient;

[0013] The surface condition analysis module of the surface to be measured builds a surface condition model based on the surface condition data of the concrete surface to be measured and outputs the surface condition coefficient;

[0014] The detection drift analysis module builds a detection drift model based on the detection drift data under the strength stability coefficient and the surface state coefficient to output the detection drift coefficient;

[0015] The compressive strength output module builds a compressive strength determination model based on the current compressive strength detected by the digital rebound tester and the detection drift coefficient to output the target compressive strength.

[0016] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0017] Further technical solution: The strength stability data of the concrete surface to be tested includes age and carbonization depth, the surface state data includes flatness, smoothness and humidity, the smoothness is quantified using surface roughness, and the smoothness can be obtained by measuring the height deviation between each surface point and the reference plane using a laser rangefinder in a selected measurement area and then calculating its standard deviation, and the detection drift data includes temperature, free state length of the tension spring, and the normal angle between the rebound rod and the concrete surface to be tested.

[0018] Further technical solution: The working steps of the strength stability analysis module of the tested surface are as follows:

[0019] Perform maximum-minimum normalization on the age to obtain the age index;

[0020] The reference carbonization depth is ratioed to the sum of the carbonization depth and the reference carbonization depth to obtain a carbonization depth index;

[0021] According to the age index and the carbonization depth index, a strength stability model is constructed to output a strength stability coefficient. The strength stability model is expressed as:

[0022] ;

[0023] in, represents the strength stability coefficient, represents the age saturation coefficient, represents the age growth rate coefficient, represents the age index, represents the carbonization depth index, Represents the carbonization impact index.

[0024] Further technical solution: The working steps of the surface state analysis module of the surface to be measured are:

[0025] The humidity index is obtained by comparing the reference humidity with the current humidity;

[0026] Ratio the reference finish (roughness) to the current finish to obtain the finish index;

[0027] The difference between the flatness and the reference flatness is compared with the reference flatness to obtain the flatness index;

[0028] Build a surface state model based on the moisture index, flatness index and smoothness index and then output the surface state coefficient;

[0029] The surface state model is expressed as:

[0030] ;

[0031] in, represents the surface state coefficient, represents the flatness index, represents the flatness attenuation coefficient, Indicates the smoothness index, represents the smoothness attenuation coefficient, Represents the humidity index, Represents the humidity attenuation coefficient.

[0032] Further technical solution: The working steps of the detection drift analysis module are as follows:

[0033] The difference between the current tension spring length (the tension spring length obtained during the last digital rebound tester maintenance) and the calibrated length of the tension spring at the reference temperature is compared with the calibrated length of the tension spring at the reference temperature to obtain the tension spring length index.

[0034] The difference between the current temperature and the reference temperature is compared with the reference temperature to obtain the temperature index;

[0035] Based on the strength stability coefficient and the spring length index under the surface state coefficient, the temperature index and the normal angle between the rebound rod and the concrete surface to be tested, a detection drift model is constructed to output the detection drift coefficient;

[0036] The detection drift model is expressed as:

[0037] ;

[0038] in, Indicates the detection drift coefficient, represents the strength stability coefficient, represents the surface state coefficient, represents the length index of the tension spring, Indicates the spring length sensitivity index, It represents the normal angle between the rebound rod and the concrete surface to be measured. Indicates the angle to radian coefficient and its unit is , represents the temperature index, Indicates the temperature sensitivity value.

[0039] Further technical solution: The working steps of the compressive strength output module are:

[0040] Comparing the obtained detection drift coefficient with a preset detection drift coefficient threshold;

[0041] If the detection drift coefficient is within the detection drift coefficient threshold, the digital rebound test hammer outputs the current compressive strength of the concrete surface to be tested detected by the digital rebound test hammer;

[0042] If the detection drift coefficient is not within the detection drift coefficient threshold, a compressive strength determination model is constructed based on the current compressive strength detected by the digital rebound hammer and the detection drift coefficient to output the target compressive strength. The compressive strength determination model is expressed as:

[0043] ;

[0044] in, represents the target compressive strength, Indicates the current compressive strength tested by the digital rebound tester. Indicates the detection drift coefficient, Indicates the detection drift coefficient threshold, Represents the sensitivity adjustment factor.

[0045] Further technical solutions also include a laser rangefinder, an electronic hygrometer and a non-contact roughness meter (laser triangulation reflectometer) that can be embedded and installed in the box, and an inclination sensor is integrated on the digital rebound tester.

[0046] The present invention provides a smart construction site engineering quality inspection device, which has the following beneficial effects compared with the existing technology:

[0047] The present invention integrates strength stability data, surface state data and instrument drift data to construct a full-parameter compensation model. The model quantifies the influence of concrete maturity through the strength stability coefficient, suppresses environmental interference through the surface state coefficient, and calibrates instrument errors through the detection drift coefficient. At the same time, the output mode is dynamically switched based on the drift coefficient threshold. When the drift coefficient is detected to be out of limit, the compressive strength is used to determine the model output correction value to ensure the reliability of the result. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0050] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0051] See also Figure 1 A smart construction site engineering quality inspection device includes a digital rebound tester embedded in a box, a laser rangefinder that can also be embedded in the box, an electronic hygrometer, and a non-contact roughness meter (laser triangulation reflectometer). The digital rebound tester is integrated with an inclination sensor, and further includes:

[0052] The digital compensation system is installed on the digital rebound hammer data acquisition controller and is used to compensate the test results of the digital rebound hammer, including:

[0053] The data acquisition module is used to obtain the strength stability data, surface condition data and detection drift data of the digital rebound tester of the surface to be tested;

[0054] The strength stability analysis module of the surface to be tested builds a strength stability model based on the strength stability data of the concrete surface to be tested and outputs the strength stability coefficient;

[0055] The surface condition analysis module of the surface to be measured builds a surface condition model based on the surface condition data of the concrete surface to be measured and outputs the surface condition coefficient;

[0056] The detection drift analysis module builds a detection drift model based on the detection drift data under the strength stability coefficient and the surface state coefficient to output the detection drift coefficient;

[0057] The compressive strength output module builds a compressive strength determination model based on the current compressive strength detected by the digital rebound tester and the detection drift coefficient to output the target compressive strength.

[0058] Among them, strength stability data refers to parameters that reflect the change in concrete strength over time. Specifically, this can be achieved using age and carbonation depth data. Age is obtained through construction records, and carbonation depth is obtained through phenolphthalein reagent testing. Surface state data refers to surface characteristic parameters that affect the impact energy transfer of the rebound hammer. Specifically, this can be achieved using humidity, smoothness, and flatness data. Humidity is measured using an electronic hygrometer, flatness is obtained by calculating the standard deviation of surface height deviation using a laser rangefinder, and smoothness is measured using a non-contact roughness meter (laser triangulation reflectometer). Detection drift data refers to equipment state parameters that cause instrument measurement deviations. Specifically, this can be achieved using temperature, tension spring length, and rebound rod angle data. Temperature is collected using a temperature sensor, tension spring length is measured using a laser rangefinder or vernier caliper during digital rebound hammer maintenance, and angles are obtained using a gyroscope or inclination sensor.

[0059] Specifically, the data acquisition module transmits the concrete age, carbonation depth, surface moisture, roughness, flatness, spring length, temperature and rebound rod angle data to the controller in real time. The strength stability analysis module normalizes the age, compares the carbonation depth with the reference value, and quantifies the concrete strength stability through an exponential function model. The surface state analysis module converts moisture, roughness and flatness into exponential form, and uses the denominator to characterize the attenuation effect of surface defects on impact energy. The detection drift analysis module performs ratio processing on the spring deformation and temperature change, and calculates the equipment drift coefficient based on the rebound rod angle component. The compressive strength output module compares the drift coefficient with the preset threshold, directly outputs the detection value within the threshold range, and performs nonlinear correction through the S-type function when the threshold is exceeded to suppress extreme errors.

[0060] Compared with existing technologies, traditional methods only substitute the rebound value into a fixed strength measurement curve to convert the strength, without considering the coupled effects of the material's time-varying characteristics, surface state, and equipment drift. This solution uses multi-source data fusion to establish a strength stability coefficient to correct material aging errors, a surface state coefficient to compensate for environmental interference, and a detection drift coefficient to eliminate equipment errors, ultimately achieving a triple error collaborative correction. For example, for a concrete wall with a carbonization depth of 6mm, the traditional method directly uses a standard strength measurement curve to convert the rebound value to 35MPa. However, this solution reduces the stability coefficient through the carbonization depth index and combines it with surface moisture correction to output a compensated strength value of 32MPa, which is closer to the actual measured value of 31.8MPa from core sampling.

[0061] Through the above technical solutions, the present invention effectively solves the problem of single-point testing ignoring material strength evolution. It dynamically corrects the strength baseline value based on age and carbonization depth. It overcomes rebound value drift caused by environmental interference and quantifies the effects of humidity and roughness using a surface state model. It also eliminates systematic errors caused by long-term instrument use by using a detection drift model to compensate for spring deformation and temperature changes in real time. Compared with the core drilling method, the error of the test results is reduced from ±15% of the traditional method to within ±5%, significantly improving the reliability of engineering quality assessment.

[0062] Preferably, the strength stability data of the concrete surface to be measured includes age and carbonization depth, the surface state data includes flatness, smoothness and moisture, the smoothness is quantified using surface roughness, the flatness can be obtained by measuring the height deviation between each surface point and a reference plane using a laser rangefinder in a selected measurement area and then calculating its standard deviation, and the detection drift data includes temperature, free state length of the tension spring, and the normal angle between the rebound rod and the concrete surface to be measured.

[0063] Age refers to the time span between concrete pouring and testing. This can be calculated using date difference calculations to reflect the temporal changes in concrete strength. Carbonation depth refers to the thickness of the carbonized layer formed on the concrete surface due to the action of carbon dioxide. This can be measured using phenolphthalein titration and is used to characterize the weakening effect of surface carbonation on compressive strength. Flatness refers to the degree of deviation of the concrete surface from an ideal plane. This can be measured using a laser rangefinder to measure height differences at multiple points, eliminating rebound deviations caused by surface irregularities. Smoothness refers to the microscopic roughness of the concrete surface. This can be measured using a non-contact roughness meter (laser triangulation reflectometer) to quantify the effect of surface friction on the movement of the rebound rod. Moisture refers to the moisture content of the concrete surface. This can be measured using an electronic hygrometer for contact measurement, reflecting the softening effect of moisture on surface hardness. The free length of a tension spring refers to the original length of the spring when unloaded. This can be measured using a vernier caliper during instrument maintenance and is used to assess elastic attenuation caused by spring fatigue. The normal angle refers to the angle between the axis of the rebound rod and the normal of the concrete surface to be measured. It can be achieved by real-time measurement using an inclination sensor to quantify the rebound direction error caused by the operating angle deviation.

[0064] Specifically, by constructing a multidimensional parameter system, strength stability data is limited to age and carbonation depth. The former reflects the temporal evolution of concrete strength, while the latter characterizes the weakening effect of the surface carbonation layer on compressive strength. The combination of these two parameters eliminates single-point detection errors caused by the time-varying properties of the material. Surface condition data is further refined by including parameters for smoothness, flatness, and moisture. Smoothness is measured using a non-contact roughness meter (laser triangulation reflectometer), enabling objective quantification of surface roughness and eliminating subjective errors from visual inspection. The moisture parameter reflects the softening effect of moisture on the concrete surface hardness, and the flatness parameter characterizes the influence of the surface macrotopography on the impact direction of the rebound rod. Temperature, tension spring length, and normal angle are newly added to the drift detection data. Temperature affects the spring elastic modulus, tension spring length reflects the degree of spring fatigue, and the normal angle quantifies the operating angle deviation. Together, these three parameters form a benchmark for evaluating the instrument's own condition and operating errors. The coordinated definition of this parameter system provides a quantifiable and calculable data foundation for the subsequent development of a compensation model.

[0065] Compared with existing technologies, traditional methods rely solely on a single parameter, the rebound value, and fail to establish a quantitative system for surface condition and instrument drift. For example, existing technologies fail to quantify surface finish as the standard deviation of surface roughness, making it impossible to accurately assess the impact of surface friction on the movement of the rebound rod. They fail to include the free-state length parameter of the tension spring, making it impossible to monitor elastic attenuation caused by spring fatigue. And they fail to measure the normal angle in real time, making it difficult to correct for operating angle deviations. This solution, through measuring devices such as laser rangefinders and inclinometers, achieves objective quantification of parameters such as surface roughness and operating angle, resolving the compensation failure issues caused by missing parameters or subjective judgment in traditional methods.

[0066] Preferably, the working steps of the test surface strength stability analysis module are:

[0067] Perform maximum-minimum normalization on the age to obtain the age index;

[0068] The reference carbonization depth is ratioed to the sum of the carbonization depth and the reference carbonization depth to obtain a carbonization depth index;

[0069] According to the age index and the carbonization depth index, a strength stability model is constructed to output a strength stability coefficient. The strength stability model is expressed as:

[0070] ;

[0071] in, represents the strength stability coefficient, represents the age saturation coefficient, represents the age growth rate coefficient, represents the age index, represents the carbonization depth index, Represents the carbonization impact index.

[0072] Among them, the maximum-minimum normalization process refers to the linear transformation of the original age data to the interval [0,1]. The specific formula can be used accomplish, represents the measured age, and are the preset minimum and maximum age thresholds respectively. This process eliminates the impact of differences in age spans of different projects on the model.

[0073] Among them, the carbonization depth index refers to the correlation between the actual carbonization depth and the reference carbonization depth through the ratio method. The specific formula can be used accomplish, Indicates the measured carbonization depth, The reference value is preset according to the concrete type. This process avoids the nonlinear deviation caused by the absolute depth value.

[0074] Among them, the age saturation coefficient in the strength stability model is It is used to characterize the maximum contribution of age to strength growth, which can be determined through calibration tests, for example, taking an empirical value within the range of 0.8 to 1.2. Age growth rate coefficient Controls the effect of the age index on the rate of strength growth, for example, a value between 0.05 and 0.15. It reflects the nonlinear effect of carbonization depth on strength stability, for example, taking an experimental fitting value between 0.3 and 0.7.

[0075] Specifically, the age data is normalized and converted into a dimensionless age index, so that the age range of 28 to 365 days in different projects can be uniformly and quantitatively compared. The carbonization depth index dynamically associates the actual carbonization depth with the reference value through ratio calculation. When the measured carbonization depth approaches zero, the index is close to 1, and as the carbonization deepens, the index gradually decreases. The strength stability model uses the exponential function Describes the saturation characteristics of concrete strength as it increases with age. The initial strength increases rapidly and then stabilizes, which is consistent with the law of concrete hydration reaction. Characterizes the attenuation effect of carbonization on strength. The increase in carbonization depth leads to a decrease in the index, which in turn reduces the strength stability coefficient. Model parameters 、 、 It can be calibrated by experience or laboratory.

[0076] Compared with existing technologies, traditional methods only use fixed age correction coefficients without establishing a dynamic model, which cannot reflect the differences in strength growth at different age stages. Existing carbonation depth corrections usually use linear compensation, which does not consider the nonlinear characteristics of carbonation with depth. This solution uses an exponential-power function composite model to simultaneously capture the progressive saturation law of age growth and the nonlinear attenuation effect of carbonation. For example, when the age index reaches 0.8, the contribution to strength growth reaches 95% of the saturation value, and every 0.1 decrease in the carbonation depth index will result in a decrease in the strength stability coefficient of approximately 3%-5%. This is more consistent with the measured data of concrete strength evolution than a linear model.

[0077] Through the above technical solution, the present invention effectively quantifies the synergistic effect of concrete age and carbonation depth on strength detection, and solves the error problem caused by single-point detection without considering the strength evolution factor. The strength stability coefficient dynamically reflects the actual hardening state of concrete. For example, for concrete with a shorter age and deeper carbonation, the model automatically reduces the strength stability coefficient to compensate for the early strength deficiency and carbonation weakening effect, making the compressive strength test value closer to the actual value. The model adapts to the characteristics of concrete with different mix ratios through adjustable parameters. For example, high-strength concrete can be set with a smaller The value is used to weaken the influence of carbonization, thereby improving the applicability of the test results in different engineering scenarios.

[0078] Preferably, the working steps of the surface state analysis module of the surface to be measured are:

[0079] The humidity index is obtained by comparing the reference humidity with the current humidity;

[0080] Ratio the reference finish (roughness) to the current finish to obtain the finish index;

[0081] The difference between the flatness and the reference flatness is compared with the reference flatness to obtain the flatness index;

[0082] Build a surface state model based on the moisture index, flatness index and smoothness index and then output the surface state coefficient;

[0083] The surface state model is expressed as:

[0084] ;

[0085] in, represents the surface state coefficient, represents the flatness index, represents the flatness attenuation coefficient, Indicates the smoothness index, represents the smoothness attenuation coefficient, Represents the humidity index, Represents the humidity attenuation coefficient.

[0086] The humidity index refers to the ratio of the reference humidity to the current humidity. This can be achieved by using a humidity sensor to measure the current humidity and performing a real-time ratio calculation with a preset reference humidity. It is used to characterize the degree to which the humidity of the test surface deviates from the standard operating conditions. The smoothness index refers to the ratio of the reference smoothness to the current smoothness. This can be measured using a non-contact roughness meter (laser triangulation reflectometer) and is used to reflect the attenuation effect of surface roughness on rebound testing. The flatness index refers to the ratio of the difference between the flatness and the reference flatness to the reference flatness. This can be achieved by using a laser rangefinder to measure the maximum height difference between the test surface and the reference plane and then performing a difference ratio calculation with the preset allowable deviation. It is used to quantify the impact of the test surface flatness defects on detection stability. The humidity attenuation coefficient, flatness attenuation coefficient, and smoothness attenuation coefficient in the surface state model respectively refer to the sensitivity weights of different surface state parameters. They can be calibrated in the laboratory or empirically to adjust the contribution ratio of each state parameter to the comprehensive coefficient.

[0087] Specifically, the humidity index converts humidity changes into a calculable normalized parameter by calculating a dynamic ratio between real-time measurements and preset reference values, eliminating the impact of ambient humidity fluctuations on the surface hardness of the concrete. The finish index converts surface micromorphology differences into a finish attenuation factor by calculating the ratio of the reference finish to the current finish, reducing the random error in rebound values ​​caused by rough surfaces. The flatness index converts macroscopic flatness deviations into a flatness influencing factor through a difference ratio calculation, correcting for impact angle deviations caused by surface tilt. The surface state model uses an exponential function to model the nonlinear characteristics of humidity influence, using the denominator to amplify the negative effects of flatness and finish defects, ultimately generating a compensation coefficient that comprehensively reflects surface state interference.

[0088] Compared with existing technologies, traditional methods rely solely on visual inspection or a single parameter to determine surface condition, lack a multi-parameter collaborative quantification model, and are unable to accurately eliminate complex interference. This solution constructs a multi-parameter indexing mechanism to transform the physical differences in moisture, finish, and flatness into calculable mathematical relationships. Using nonlinear functions to comprehensively characterize the coupled effects of each parameter, this approach achieves quantitative compensation for surface condition interference.

[0089] Through the above technical solution, the present invention effectively solves the problem of rebound value drift caused by the failure to systematically quantify the moisture, smoothness and flatness of the concrete surface. By establishing a multi-parameter collaborative calculation model, the surface state differences are converted into compensable mathematical parameters, reducing the interference of environmental factors on the rebound hammer detection accuracy and improving the reliability of the compressive strength test results.

[0090] Preferably, the working steps of the detection drift analysis module are:

[0091] The difference between the current tension spring length (the tension spring length obtained during the last digital rebound tester maintenance) and the calibrated length of the tension spring at the reference temperature is compared with the calibrated length of the tension spring at the reference temperature to obtain the tension spring length index.

[0092] The difference between the current temperature and the reference temperature is compared with the reference temperature to obtain the temperature index;

[0093] Based on the strength stability coefficient and the spring length index under the surface state coefficient, the temperature index and the normal angle between the rebound rod and the concrete surface to be tested, a detection drift model is constructed to output the detection drift coefficient;

[0094] The detection drift model is expressed as:

[0095] ;

[0096] in, Indicates the detection drift coefficient, represents the strength stability coefficient, represents the surface state coefficient, represents the length index of the tension spring, Indicates the spring length sensitivity index, It represents the normal angle between the rebound rod and the concrete surface to be measured. Indicates the angle to radian coefficient and its unit is , represents the temperature index, Indicates the temperature sensitivity value.

[0097] The tension spring length index refers to the relative deviation of the spring's current length from the calibrated length. This can be achieved by measuring the spring's calibrated length at a reference temperature (the tension spring length obtained during the last digital rebound hammer maintenance) and calculating the ratio of the difference between the current and calibrated lengths to the calibrated length. This index reflects elastic attenuation caused by spring fatigue. The temperature index refers to the relative deviation of the current temperature from the reference temperature. This can be achieved by obtaining the current temperature using a temperature sensor and calculating the ratio of the difference between the current and reference temperatures to the reference temperature. This index is used to eliminate the effects of temperature changes on spring stiffness. The normal angle refers to the angle between the rebound rod axis and the concrete surface normal. This can be achieved by measuring the inclination angle of the rebound rod when in contact with the test surface using an inclination sensor. This index is used to correct for force component errors caused by operating angle deviations. The spring length sensitivity index is the weighting coefficient for the effect of spring length changes on test drift. This index can be obtained by fitting spring fatigue test data and is used to adjust the contribution of spring performance degradation to the model. The temperature sensitivity value refers to the weight coefficient of the impact of temperature changes on detection drift. It can be obtained by calibrating spring stiffness test data at different temperatures and is used to quantify the impact of temperature fluctuations on detection results.

[0098] Specifically, when the detection drift analysis module is running, it first obtains the current length data of the tension spring. For example, the calibrated length at the reference temperature can be the standard length of the spring when it leaves the factory. By calculating the relative deviation between the current length and the calibrated length, a spring length index reflecting the degree of spring fatigue is obtained. Simultaneously, a temperature sensor collects ambient temperature data in real time, and its relative deviation from the reference temperature is calculated as the temperature index. Combined with the normal angle data measured when the rebound rod contacts the concrete surface, these three dynamic parameters are input into the detection drift model along with the pre-calculated strength stability coefficient and surface state coefficient. In this model, the spring length index reflects the nonlinear effects of spring performance attenuation through a power function, the temperature index reflects the cumulative effects of temperature changes through an exponential function, and the normal angle corrects the force component error caused by angle deviation through a cosine function. Through multi-parameter coupling calculation, the final output is a detection drift coefficient that comprehensively reflects the changes in the instrument's own state.

[0099] Compared to existing technologies, traditional rebound hammers only compensate for instrument drift by periodically calibrating spring performance, failing to correct for temperature fluctuations and operating angle deviations in real time. Existing technologies lack a coordinated compensation model for spring length, temperature, angle, and other parameters, making it impossible to dynamically eliminate errors caused by changes in the instrument's own state during testing. This solution, by constructing a mathematical model that incorporates a spring length index, a temperature index, and an angle correction term, achieves comprehensive compensation for spring fatigue, temperature changes, and operating angle deviation, resolving the technical drawback of insufficient compensation for a single factor.

[0100] Through the above technical solution, the present application can dynamically correct the rebound force deviation caused by the elastic attenuation of the spring. For example, when the spring deforms by 0.5 mm due to long-term use, the model can automatically adjust the compensation amount through the spring length index. At the same time, the influence of ambient temperature fluctuations on the spring stiffness is eliminated in real time. For example, in a construction site environment where the temperature difference between day and night reaches 15°C, the temperature index term can effectively suppress the drift of the detection value caused by temperature changes. In addition, the operating angle deviation is corrected by the cosine function. When the rebound rod is tilted 5 degrees with the normal of the test surface, the model can automatically correct the resulting component force error. This solution collaboratively models the instrument's own state parameters with external environmental parameters, significantly improving the stability and reliability of the compressive strength test results.

[0101] Preferably, the working steps of the compressive strength output module are:

[0102] Comparing the obtained detection drift coefficient with a preset detection drift coefficient threshold;

[0103] If the detection drift coefficient is within the detection drift coefficient threshold, the digital rebound test hammer outputs the current compressive strength of the concrete surface to be tested detected by the digital rebound test hammer;

[0104] If the detection drift coefficient is not within the detection drift coefficient threshold, a compressive strength determination model is constructed based on the current compressive strength detected by the digital rebound hammer and the detection drift coefficient to output the target compressive strength. The compressive strength determination model is expressed as:

[0105] ;

[0106] in, represents the target compressive strength, Indicates the current compressive strength tested by the digital rebound tester. Indicates the detection drift coefficient, Indicates the detection drift coefficient threshold, Represents the sensitivity adjustment factor.

[0107] Among them, the detection drift coefficient threshold refers to the critical value for judging whether the instrument measurement result is credible. It can be set comprehensively through the engineering allowable error range and the instrument accuracy requirements. For example, the threshold is set to 1.2 times the average value of the calibrated detection drift coefficient. This threshold is used to distinguish normal working conditions from abnormal drift states. The sensitivity adjustment factor refers to the parameter that controls the steepness of the correction curve. Specifically, a constant with a value in the range of 5-10 can be used. The larger the value, the more sensitive the drift response is to the drift exceeding the threshold. It is suitable for detection scenarios that require rapid correction. The S-type function in the compressive strength determination model refers to a nonlinear function with smooth transition characteristics, such as the Logistic function, which is used to map the relative deviation between the detection drift coefficient and the threshold to the correction amplitude of the compressive strength to avoid error amplification caused by sudden changes.

[0108] Specifically, when the detection drift coefficient exceeds the preset threshold, the system automatically triggers the correction mechanism. By inputting the current compressive strength into the S-type function and combining the deviation ratio between the detection drift coefficient and the threshold, a dynamically corrected target compressive strength is generated. This function shows a gentle change characteristic near the threshold, which can suppress false corrections caused by small fluctuations; when the deviation increases significantly, the correction amplitude increases accordingly to ensure the reliability of the results under abnormal drift conditions. The sensitivity adjustment factor can be adjusted according to the requirements of the detection environment. For example, a larger value can be used in a high temperature and high humidity environment to enhance the correction response speed. No manual intervention is required during the correction process, and the system automatically completes data comparison, model calculation and result output.

[0109] Compared to existing technologies, traditional concrete rebound test hammers rely solely on fixed strength curves for strength conversion and lack a drift threshold detection mechanism. This leads to cumulative test result errors when instrument spring fatigue or environmental interference causes drift. This solution, by combining dynamic threshold detection with a nonlinear correction model, automatically identifies and compensates for abnormal drift conditions, overcoming the uncontrollable error inherent in existing technologies.

[0110] Through the above technical solution, this application can automatically trigger correction calculations when test drift exceeds the allowable range, effectively suppressing measurement deviations caused by instrument aging and environmental interference, and ensuring the stability of compressive strength test results under different working conditions. The correction model adapts to various testing scenarios through adjustable sensitivity parameters, solving the problem of insufficient adaptability caused by the fixed correction coefficient of traditional methods.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0112] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart construction site engineering quality inspection device, comprising a digital rebound tester embedded in a box, characterized in that: Also includes: The digital compensation system is installed on the data acquisition controller of the digital rebound hammer and is used to compensate the test results of the digital rebound hammer. It includes: The data acquisition module is used to obtain the strength stability data, surface condition data and detection drift data of the digital rebound tester of the surface to be tested; The strength stability analysis module of the surface to be tested builds a strength stability model based on the strength stability data of the concrete surface to be tested and outputs the strength stability coefficient; The surface condition analysis module of the surface to be measured builds a surface condition model based on the surface condition data of the concrete surface to be measured and outputs the surface condition coefficient; The detection drift analysis module builds a detection drift model based on the detection drift data under the strength stability coefficient and the surface state coefficient to output the detection drift coefficient; The compressive strength output module builds a compressive strength determination model based on the current compressive strength detected by the digital rebound tester and the detection drift coefficient to output the target compressive strength; The working steps of the test surface strength stability analysis module are as follows: Perform maximum-minimum normalization on the age to obtain the age index; The reference carbonization depth is ratioed to the sum of the carbonization depth and the reference carbonization depth to obtain a carbonization depth index; According to the age index and the carbonization depth index, a strength stability model is constructed to output a strength stability coefficient. The strength stability model is expressed as: ； in, represents the strength stability coefficient, represents the age saturation coefficient, represents the age growth rate coefficient, represents the age index, represents the carbonization depth index, represents the carbonization impact index; The working steps of the surface state analysis module of the surface to be measured are: The humidity index is obtained by comparing the reference humidity with the current humidity; Ratio processing is performed between the reference finish and the current finish to obtain a finish index; The difference between the flatness and the reference flatness is compared with the reference flatness to obtain the flatness index; Build a surface state model based on the moisture index, flatness index and smoothness index and then output the surface state coefficient; The surface state model is expressed as: ； in, represents the surface state coefficient, represents the flatness index, represents the flatness attenuation coefficient, Indicates the smoothness index, represents the smoothness attenuation coefficient, Represents the humidity index, represents the humidity attenuation coefficient; The working steps of the detection drift analysis module are as follows: The difference between the current tension spring length and the calibrated length of the tension spring at the reference temperature is compared with the calibrated length of the tension spring at the reference temperature to obtain a tension spring length index; The difference between the current temperature and the reference temperature is compared with the reference temperature to obtain the temperature index; Based on the strength stability coefficient and the spring length index under the surface state coefficient, the temperature index and the normal angle between the rebound rod and the concrete surface to be tested, a detection drift model is constructed to output the detection drift coefficient; The detection drift model is expressed as: ； in, Indicates the detection drift coefficient, represents the strength stability coefficient, represents the surface state coefficient, represents the length index of the tension spring, Indicates the spring length sensitivity index, It represents the normal angle between the rebound rod and the concrete surface to be measured. Indicates the angle to radian coefficient and its unit is , represents the temperature index, Indicates temperature sensitivity value; The working steps of the compressive strength output module are: Comparing the obtained detection drift coefficient with a preset detection drift coefficient threshold; If the detection drift coefficient is within the detection drift coefficient threshold, the digital rebound test hammer outputs the current compressive strength of the concrete surface to be tested detected by the digital rebound test hammer; If the detection drift coefficient is not within the detection drift coefficient threshold, a compressive strength determination model is constructed based on the current compressive strength detected by the digital rebound hammer and the detection drift coefficient to output the target compressive strength. The compressive strength determination model is expressed as: ； in, represents the target compressive strength, Indicates the current compressive strength tested by the digital rebound tester. Indicates the detection drift coefficient, Indicates the detection drift coefficient threshold, Represents the sensitivity adjustment factor.

2. The smart construction site engineering quality inspection device according to claim 1 is characterized in that: The strength stability data of the concrete surface to be tested includes age and carbonization depth. The surface condition data includes flatness, smoothness and moisture. The smoothness is quantified using surface roughness. The smoothness is obtained by measuring the height deviation between each surface point and a reference plane using a laser rangefinder in a selected measurement area and then calculating its standard deviation. The detection drift data includes temperature, the free state length of the tension spring, and the normal angle between the rebound rod and the concrete surface to be tested.

3. The smart construction site engineering quality inspection device according to claim 1 is characterized in that: It also includes a laser rangefinder, an electronic moisture meter and a non-contact roughness meter that can be embedded and installed in the box. The digital rebound tester is integrated with an inclination sensor.

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