Device for rapidly detecting water content of tailings and testing method

Abstract

The invention discloses a device for rapidly detecting the moisture content of tailings and a test method, and belongs to the technical field of detection, and the method comprises the following steps: vibrating a tailings sample, loading the tailings sample into a test mold, connecting a test circuit to obtain a resistance value, and calculating the actually measured moisture content through a moisture content-resistance fitting formula; synchronously constructing a tailing state model, an environment state model, an electrode field model and an electric field model, and outputting a tailing state coefficient, an environment state coefficient, an electrode field coefficient and an electric field coefficient; obtaining a test stability coefficient based on the electric field coefficient and the electrode field coefficient, and generating an adaptation degree through a density-particle adaptation model based on the tailing state coefficient, the environment state coefficient, the test stability coefficient, the tailing compactness and the coarse particle mass ratio; and in combination with the test duration, the result reliability is judged through a confidence model, and recording, early warning or re-testing are executed in a graded manner. The method is suitable for on-site rapid monitoring of the tailing pond.

Description

Technical Field

[0001] This invention belongs to the field of detection technology, and in particular relates to a device and testing method for rapidly detecting the moisture content of tailings. Background Technology

[0002] The moisture content of tailings is a key indicator for safe mining operations, directly affecting the stability of tailings dams and disaster prevention. Traditional detection methods suffer from poor timeliness and complex operation, necessitating the development of rapid and accurate on-site detection technologies.

[0003] Existing technologies mainly rely on the drying and weighing method, which requires cumbersome steps such as sampling, drying, and weighing, taking up to several hours. Some technologies use the resistance method or nuclear moisture analyzer, which can shorten the detection time, but are easily affected by factors such as tailings particle distribution, density, ambient temperature and humidity, and electrode contact status, resulting in significant measurement errors. They also lack a confidence assessment system, cannot identify abnormal data, and are prone to misjudgment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a device and testing method for rapidly detecting the moisture content of tailings, thus solving the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides a method for rapidly detecting the moisture content of tailings, comprising the following steps: After the tailings sample is loaded into the mold and vibrated, a test circuit with a voltmeter and an ammeter is connected to obtain the resistance value of the tailings sample and import it into the moisture content-resistance fitting formula to obtain the measured moisture content. A tailings state model is constructed based on the length and cross-sectional area of ​​the tailings sample, and the tailings state coefficient is output. An environmental state model is constructed based on the test environment temperature and humidity, and the environmental state coefficients are output. Based on the electrode parallelism, electrode axial distance (the axial distance between two electrodes), and contact pressure between the electrode and the tailings sample of the test circuit, an electrode field model is constructed and the electrode field coefficients are output. An electric field model is constructed based on the voltage fluctuation amplitude and the current fluctuation amplitude, and the electric field coefficients are output. A test stability model is constructed based on the electrode field coefficient and the electric field coefficient, and the test stability coefficient is output. Based on the environmental state coefficient, test stability coefficient, tailings state coefficient, tailings sample density, and the proportion of coarse particles in the tailings (coarse particles refer to particles with a diameter between 0.5 mm and 1 mm), a density-particle fit model is constructed to output density-particle fit. A detection confidence model is constructed based on density-particle fit and test duration to determine the confidence of the detection results.

[0006] Based on the above technical solutions, the present invention also provides the following optional technical solutions: Further technical solution: The steps for judging the confidence of the detection results by constructing a detection confidence model based on density-particle fit and test duration are as follows: The test duration index is obtained by squaring the ratio of the difference between the test duration and the optimal test duration to the optimal test duration. The current test duration index and the current density-particle fit are imported into the detection confidence model to obtain the current detection confidence level. The detection confidence model is expressed as follows: ; in, Indicates the current detection confidence level. This indicates the current test duration index. This indicates the current density-particle fit. Represents the time sensitivity coefficient, the Furthermore, the higher the value, the more reliable the test results; Compare the current detection confidence level with the corresponding threshold: like If the result is high confidence, then routine recording should be performed. like If the result is within the range of 0, the test result is of moderate confidence, and a warning should be noted. like If the test result is unreliable, a retest will be mandatory.

[0007] Further technical solution: Based on the environmental state coefficient, test stability coefficient, tailings sample density, and the proportion of coarse particles in the tailings (coarse particles refer to particles with a diameter between 0.5mm and 1mm), the steps to construct a density-particle fit model and output density-particle fit are as follows: The density index is obtained by processing the ratio of the absolute difference between the density of the tailings sample and the optimum density to the optimum density. The coarse particle mass ratio index is obtained by taking the ratio of the absolute difference between the coarse particle mass ratio and the optimal coarse particle mass ratio to the optimal particle mass ratio. The current environmental state coefficient, current test stability coefficient, current density index, and coarse particle mass ratio index are imported into the density-particle size fit model to obtain the current density-particle size fit. The density-particle size fit model is expressed as follows: ; in, This indicates the current density-particle size fit. This represents the current environmental state coefficient. This represents the current stability coefficient during testing. This represents the current tailings state coefficient. This indicates the current density index. This represents the current coarse particle mass percentage index, the... Furthermore, the higher the value, the more ideal the tailings condition.

[0008] Further technical solution: The test stability model is expressed as: ; in, This represents the current stability coefficient during testing. Indicates the current electrode field coefficient. Indicates the current electric field coefficient, the Furthermore, the larger the value, the more stable the overall testing system.

[0009] Further technical solution: The steps for constructing an electric field model and outputting the electric field coefficients based on the voltage fluctuation amplitude and current fluctuation amplitude are as follows: Collect voltage values ​​during the test period and construct a voltage time series dataset. ; Will Import formula The voltage fluctuation amplitude is obtained from the data. Indicates the number of samples. This represents the average voltage value within the sampling period; Collect current values ​​during the test period and construct a current time series dataset. ; Will Import formula The amplitude of current fluctuation is obtained from the data. Indicates the number of samples. This represents the average current during the sampling period; The voltage fluctuation coefficient is obtained by comparing the voltage fluctuation amplitude with the nominal output voltage setting value of the constant voltage DC power supply, and the current fluctuation coefficient is obtained by comparing the current fluctuation amplitude with the average current value. The current voltage fluctuation coefficient and the current current fluctuation coefficient are imported into the constructed electric field model to output the current electric field coefficient. The electric field model is expressed as follows: ; in, Indicates the current electric field coefficient. This represents the current voltage fluctuation coefficient. This represents the current current fluctuation coefficient. Indicates the voltage fluctuation sensitivity coefficient. The current fluctuation sensitivity coefficient is represented by the following. Furthermore, the larger the value, the more stable the electric field.

[0010] Further technical solution: The steps for constructing an electrode field model and outputting electrode field coefficients based on the electrode parallelism, electrode axial distance (the axial distance between two electrodes), and contact pressure between the electrodes and the tailings sample of the test circuit are as follows: The electrode axial distance and electrode parallelism are processed using the maximum-minimum normalization method to obtain the electrode axial distance index and electrode parallelism index. The relative deviation index of contact pressure is obtained by comparing the absolute difference between the contact pressure and the reference contact pressure with the reference contact pressure. The current electrode axial distance index, current electrode parallelism index, and contact pressure relative deviation index are imported into the electrode field model to output the current electrode field coefficients. The electrode field model is expressed as follows: ; in, Indicates the current electrode field coefficient. Indicates the current electrode axial distance index. Indicates the current electrode parallelism index. This indicates the relative deviation index of the current contact pressure. Represents the wheelbase sensitivity coefficient. Represents the parallelism sensitivity coefficient. The pressure deviation sensitivity coefficient is represented by the following. Furthermore, the larger the value, the more ideal the electrode system.

[0011] Further technical solution: The steps for constructing an environmental state model and outputting environmental state coefficients based on the test environment temperature and humidity are as follows: The temperature index is obtained by squaring the ratio of the absolute difference between the ambient temperature and the reference temperature to the reference temperature. The humidity index is obtained by squaring the ratio of the absolute difference between the ambient humidity and the reference humidity to the reference humidity. The current temperature and humidity indices are imported into the environmental state model to output the current environmental state coefficients. The environmental state model is represented as follows: ; in, This represents the current environmental state coefficient. This indicates the current temperature index. This indicates the current humidity index. Indicates the temperature sensitivity coefficient. Indicates the humidity sensitivity coefficient, the Furthermore, the higher the value, the more ideal the detection environment.

[0012] Further technical solution: The steps for constructing a tailings state model and outputting tailings state coefficients based on the tailings sample length and cross-sectional area are as follows: The relative length deviation index is obtained by comparing the absolute difference between the length of the tailings sample and the standard length with the standard length. The relative deviation index of the cross-sectional area is obtained by comparing the absolute difference between the cross-sectional area of ​​the tailings sample and the standard cross-sectional area with the standard cross-sectional area. The current length relative deviation index and the current cross-sectional area relative deviation index are imported into the tailings state model to output the current tailings state coefficient. The tailings state model is expressed as follows: ; in, This represents the current tailings state coefficient. This represents the current length relative deviation index. This represents the relative deviation index of the current cross-sectional area. This represents the sensitivity coefficient to length deviation. The area deviation sensitivity coefficient is represented by the following. Furthermore, the larger the value, the more ideal the size of the tailings sample.

[0013] A further technical solution: The moisture content-resistivity fitting formula is expressed as: ; in, This represents the dimensionless resistance value. Represents the scaling factor. Represents the exponential coefficient. Indicates moisture content.

[0014] A device and method for rapidly detecting the moisture content of tailings are disclosed. The method includes a test circuit comprising a constant voltage DC power supply, an ammeter, and a voltmeter. The constant voltage DC power supply is connected in series with electrodes in a test mold via the ammeter, and the voltmeter is connected in parallel with the test mold. The test mold includes a housing, a side plate, and electrodes. The side plate is slidably fitted with the housing and fixedly connected to the output shaft of a linear motion component A detachably connected to the constant voltage DC power supply. The electrodes are connected to a pushing mechanism mounted on the side plate. The pushing mechanism includes a bracket, a pressure sensor, and a linear motion component B. The bracket is fixedly mounted on the side plate, one end of the pressure sensor is fixedly connected to the electrode, and the other end of the pressure sensor is fixedly connected to the output shaft of the linear motion component B detachably mounted on the bracket.

[0015] This invention provides a device and method for rapidly detecting the moisture content of tailings, which has the following advantages compared with the prior art: This invention calculates the measured moisture content using a moisture content-resistance fitting formula, and simultaneously constructs a tailings state model, an environmental state model, an electrode field model, and an electric field model. It outputs tailings state coefficients, environmental state coefficients, electrode field coefficients, and electric field coefficients. Then, based on the electric field coefficients and electrode field coefficients, it obtains the test stability coefficients. Based on the tailings state coefficients, environmental state coefficients, test stability coefficients, tailings density, and the proportion of coarse particles, it generates a density-particle fit degree using a density-particle fit model. The density-particle fit degree, combined with the test duration, is used to determine the reliability of the results through a confidence model. It then performs tiered recording, early warning, or retesting, systematically eliminating interference to ensure data reliability. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the process of the present invention.

[0017] Figure 2 This is a schematic diagram of the test circuit in this invention.

[0018] Figure 3 This is a schematic diagram of the structure of the prototype mold in this invention.

[0019] Figure 4 For the present invention Figure 3 Enlarged schematic diagram of part A in the diagram.

[0020] Figure reference numerals: 1. Constant voltage DC power supply; 2. Ammeter; 3. Voltmeter; 4. Test mold; 401. Box body; 402. Side plate; 403. Linear motion component A; 404. Electrode; 405. Pushing mechanism; 4051. Support; 4052. Pressure sensor; 4053. Linear motion component B; 5. Tailings sample. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] Please see Figure 1 as well as Figure 2 The present invention provides a method for rapidly detecting the moisture content of tailings, comprising the following steps: After the tailings sample is loaded into the mold and vibrated, a test circuit with a voltmeter and an ammeter is connected to obtain the resistance value of the tailings sample and import it into the moisture content-resistance fitting formula to obtain the measured moisture content. A tailings state model is constructed based on the length and cross-sectional area of ​​the tailings sample, and the tailings state coefficient is output. An environmental state model is constructed based on the test environment temperature and humidity, and the environmental state coefficients are output. Based on the electrode parallelism, electrode axial distance (the axial distance between two electrodes), and contact pressure between the electrode and the tailings sample of the test circuit, an electrode field model is constructed and the electrode field coefficients are output. An electric field model is constructed based on the voltage fluctuation amplitude and the current fluctuation amplitude, and the electric field coefficients are output. A test stability model is constructed based on the electrode field coefficient and the electric field coefficient, and the test stability coefficient is output. Based on the environmental state coefficient, test stability coefficient, tailings state coefficient, tailings sample density, and the proportion of coarse particles in the tailings (coarse particles refer to particles with a diameter between 0.5 mm and 1 mm), a density-particle fit model is constructed to output density-particle fit. A detection confidence model is constructed based on density-particle fit and test duration to determine the confidence of the detection results.

[0024] Specifically, this method first standardizes the sample preparation process, using vibration to eliminate internal structural differences in the samples and ensure consistent reference values ​​for resistance measurements. After obtaining the resistance values, a pre-established moisture content-resistance mathematical model enables rapid conversion, avoiding the time-consuming operation of traditional drying methods. To address multi-source interference, a layered compensation mechanism is established: a tailings state model quantifies the impact of sample size deviations on resistance measurement; an environmental state model compensates for resistance drift caused by temperature and humidity changes; and electrode field and electric field models evaluate the reliability of the testing system from the perspectives of hardware layout and electrical signal stability, respectively. A test stability model integrates electrode system and electric field stability indicators to form a comprehensive hardware-level assessment. A density-particle fit model further integrates sample physical property parameters, including density and coarse particle ratio, combined with environmental and system stability parameters to generate a fit index. Finally, a detection confidence model dynamically evaluates the reliability of the measurement results, automatically triggering warnings or retesting when the fit decreases or the test time deviates from the ideal value, forming a closed-loop quality control system.

[0025] Preferably, the moisture content-resistivity fitting formula is expressed as: ; in, This represents the dimensionless resistance value. Represents the scaling factor. Represents the exponential coefficient. Indicates moisture content.

[0026] Among them, the scaling factor These are parameters used to adjust the proportional relationships within the model. Specifically, they can be adjusted through calibration experiments to adapt to the electrical conductivity characteristics of different tailings materials. For example, a smaller parameter can be set for highly conductive minerals. The value is chosen to match its low resistance characteristic.

[0027] Among them, the exponential coefficient This refers to a parameter describing the nonlinear relationship between water content and electrical resistance. It can be specifically determined by fitting experimental data, for example, by using the least squares method to optimize the matching degree between the exponential curve and the measured data. This parameter reflects the difference in sensitivity of pore water distribution to the conductive path.

[0028] Among them, moisture content This refers to the mass percentage of water in the tailings sample to be tested. Specifically, calibration data can be obtained through the drying and weighing method, and then a correlation with the resistance value can be established.

[0029] Specifically, by establishing a nonlinear exponential function model, the resistance value is directly correlated with the moisture content, solving the problem that traditional linear models cannot adapt to the characteristics of different tailings materials. In the implementation process, the measured resistance values ​​are first dimensionless to eliminate the influence of electrode contact resistance and geometric dimensions; then, the model baseline is adjusted using a scaling factor to match the intrinsic conductivity characteristics of different mineral compositions; further, the exponential coefficient is used to capture the nonlinear response of the conductivity path caused by changes in moisture content, such as the abrupt change in resistance caused by capillary action in fine-particle tailings. Calibration experiments are used to determine... and The value of allows the model to adapt to the physical properties of specific tailings types.

[0030] In some specific implementations, the calibration experiment may include the following steps: selecting a tailings sample with a known moisture content, measuring its resistance value, and calculating the dimensionless resistance value. Nonlinear regression fitting was used. and Find the optimal solution; verify the error range between the moisture content predicted by the model and the measured value by the drying method.

[0031] Preferably, the steps for constructing a tailings state model and outputting tailings state coefficients based on the tailings sample length and cross-sectional area are as follows: The relative length deviation index is obtained by comparing the absolute difference between the length of the tailings sample and the standard length with the standard length. The relative deviation index of the cross-sectional area is obtained by comparing the absolute difference between the cross-sectional area of ​​the tailings sample and the standard cross-sectional area with the standard cross-sectional area. The current length relative deviation index and the current cross-sectional area relative deviation index are imported into the tailings state model to output the current tailings state coefficient. The tailings state model is expressed as follows: ; in, This represents the current tailings state coefficient. This represents the current length relative deviation index. This represents the relative deviation index of the current cross-sectional area. This represents the sensitivity coefficient to length deviation. The area deviation sensitivity coefficient is represented by the following. Furthermore, the larger the value, the more ideal the size of the tailings sample.

[0032] The length relative deviation index is used to quantify the impact of longitudinal dimension deviation on the electric field distribution. The cross-sectional area relative deviation index is used to characterize the current density distribution deviation caused by abnormal cross-sectional dimensions of the sample. Sensitivity coefficient. and These refer to the pre-calibrated weight parameters in the model, which can be determined through empirical calibration or regression analysis of experimental data. They are used to adjust the nonlinear influence of length deviation and area deviation on the state coefficient.

[0033] Specifically, when tailings samples are loaded into the mold, the actual length and cross-sectional area data of the sample are obtained through a measuring device. When calculating the relative length deviation index, the absolute value of the difference between the measured length and the standard length is divided by the standard length. For example, if the standard length is a pre-set 20cm, and the measured length is 21cm, the deviation index is 0.05. When calculating the relative cross-sectional area deviation index, if the standard cross-sectional area is 50cm²... 2 The actual measurement was 48cm. 2 The deviation index is 0.04. This model can enhance the influence of cross-sectional area changes on the state coefficient through the square term of the area deviation. For example, when the cross-sectional area deviation increases, the growth rate of its square term is higher than that of the linear term, resulting in an accelerated decrease in the state coefficient, thus more sensitively reflecting the influence of cross-sectional area anomalies on resistance measurement.

[0034] Through the above technical solution, this application can automatically identify the dimensional deviations of tailings samples in both length and cross-sectional area. By dynamically adjusting the state coefficient through an exponential model, it effectively suppresses abnormal electric field distribution caused by deviations of sample geometric parameters from standard conditions. When the sample dimensional deviation exceeds the allowable range, the state coefficient decreases significantly and triggers the early warning mechanism of the subsequent detection confidence model, avoiding misjudging dimensional errors as changes in moisture content and improving the anti-interference capability of the resistance method detection results.

[0035] Preferably, the step of constructing an environmental state model and outputting environmental state coefficients based on the test environment temperature and humidity is as follows: The temperature index is obtained by squaring the ratio of the absolute difference between the ambient temperature and the reference temperature to the reference temperature. The humidity index is obtained by squaring the ratio of the absolute difference between the ambient humidity and the reference humidity to the reference humidity. The current temperature and humidity indices are imported into the environmental state model to output the current environmental state coefficients. The environmental state model is represented as follows: ; in, This represents the current environmental state coefficient. This indicates the current temperature index. This indicates the current humidity index. Indicates the temperature sensitivity coefficient. Indicates the humidity sensitivity coefficient, the Furthermore, the higher the value, the more ideal the detection environment.

[0036] The reference temperature refers to a pre-set ideal ambient temperature for the test, typically a laboratory standard temperature of 25°C, used to eliminate the influence of temperature deviations on resistance measurements. The reference humidity refers to a pre-set ideal ambient humidity, typically a relative humidity of 50%, used to eliminate the interference of humidity changes on the sample's conductivity. Temperature sensitivity coefficient. This refers to the weighting parameter of the impact of temperature deviation on the detection system. It can be determined through empirical calibration or calibration experiments, for example, by taking a value in the range of 0.05 to 0.2 to amplify the model's sensitivity to temperature anomalies. Humidity sensitivity coefficient. This refers to the weighting parameter of the impact of humidity deviation on the detection system. It can be determined through empirical calibration or by using humidity gradient experiments. For example, a value in the range of 0.03 to 0.15 can be used to quantify the attenuation effect of humidity fluctuation on the detection results.

[0037] Specifically, when the ambient temperature deviates from the reference temperature, the nonlinear effect of temperature anomalies on the detection system can be amplified by calculating the absolute difference and performing normalized square processing. For example, when the actual temperature is 30℃ and the reference temperature is 25℃, the temperature index is calculated as [(30-25) / 25]^2 = 0.04. Similarly, when the ambient humidity deviates from the reference humidity, the humidity index reflects the square relationship of the humidity deviation using the same algorithm. After substituting these two indices into the environmental state model, the denominator structure... and The term will produce a product amplification effect, increasing the environmental state coefficient. The coefficient exhibits a decay characteristic as temperature and humidity deviations increase. As an input parameter for subsequent density-particle adaptation models, this coefficient can dynamically adjust the weighting of environmental factors on detection confidence.

[0038] Preferably, the steps for constructing an electrode field model and outputting electrode field coefficients based on the electrode parallelism, electrode axial distance (the axial distance between two electrodes), and contact pressure between the electrodes and the tailings sample of the test circuit are as follows: The electrode axial distance and electrode parallelism are processed using the maximum-minimum normalization method to obtain the electrode axial distance index and electrode parallelism index. The relative deviation index of contact pressure is obtained by comparing the absolute difference between the contact pressure and the reference contact pressure with the reference contact pressure. The current electrode axial distance index, current electrode parallelism index, and contact pressure relative deviation index are imported into the electrode field model to output the current electrode field coefficients. The electrode field model is expressed as follows: ; in, Indicates the current electrode field coefficient. Indicates the current electrode axial distance index. Indicates the current electrode parallelism index. This indicates the relative deviation index of the current contact pressure. Represents the wheelbase sensitivity coefficient. Represents the parallelism sensitivity coefficient. The pressure deviation sensitivity coefficient is represented by the following. Furthermore, the larger the value, the more ideal the electrode system.

[0039] Specifically, the electrode axial distance index can be achieved by linearly mapping the measured axial distance to a standard axial distance range. The electrode parallelism index refers to the normalization of the tilt angle between electrodes obtained through an angle measuring device into a standardized deviation parameter. This can be achieved using a laser rangefinder or optical sensor, and is used to quantify the degree of interference of electrode parallelism deviation on the electric field distribution. The contact pressure relative deviation index refers to the deviation calculated by measuring the actual pressure value at the contact surface between the electrode and the tailings sample using a pressure sensor and comparing it to a preset reference pressure. This can be achieved using a piezoelectric sensor or strain gauge, and is used to reflect the risk of resistance measurement error caused by contact pressure fluctuations.

[0040] Specifically, electrode wheelbase and parallelism are normalized into comparable exponential parameters, eliminating the interference of dimensional differences on model calculations. Contact pressure deviation is calculated as a relative ratio, directly reflecting the degree of deviation between actual pressure and ideal conditions. When the three types of indices are input into the exponential function model, wheelbase deviation affects the coefficient as a linear term, parallelism deviation enhances the sensitivity to angular deviation as a squared term, and contact pressure deviation reflects the impact of continuous pressure fluctuations as a linear term. By adjusting the weights of different parameters through sensitivity coefficients, the electrode field coefficient can dynamically characterize the overall state of the electrode system. When the wheelbase exceeds the standard range, parallelism decreases, or contact pressure is unstable, the negative exponential term in the exponential function increases, causing the electrode field coefficient to decrease exponentially, thus directly reflecting the negative impact of the electrode system on the test results.

[0041] Preferably, the steps for constructing an electric field model and outputting the electric field coefficients based on the voltage fluctuation amplitude and the current fluctuation amplitude are as follows: Collect voltage values ​​during the test period and construct a voltage time series dataset. ; Will Import formula The voltage fluctuation amplitude is obtained from the data. Indicates the number of samples. This represents the average voltage value within the sampling period; Collect current values ​​during the test period and construct a current time series dataset. ; Will Import formula The amplitude of current fluctuation is obtained from the data. Indicates the number of samples. This represents the average current during the sampling period; The voltage fluctuation coefficient is obtained by comparing the voltage fluctuation amplitude with the nominal output voltage setting value of the constant voltage DC power supply, and the current fluctuation coefficient is obtained by comparing the current fluctuation amplitude with the average current value. The current voltage fluctuation coefficient and the current current fluctuation coefficient are imported into the constructed electric field model to output the current electric field coefficient. The electric field model is expressed as follows: ; in, Indicates the current electric field coefficient. This represents the current voltage fluctuation coefficient. This represents the current current fluctuation coefficient. Indicates the voltage fluctuation sensitivity coefficient. The current fluctuation sensitivity coefficient is represented by the following. Furthermore, the larger the value, the more stable the electric field.

[0042] Specifically, voltage and current data are continuously collected during the test period to form a time-series dataset. The fluctuation amplitudes of voltage and current are quantified by calculating the standard deviation. The ratio of the voltage fluctuation amplitude to the nominal output voltage setting converts the voltage fluctuation into a relative value, eliminating the influence of different power supply output levels on the evaluation results. The ratio of the current fluctuation amplitude to the mean current converts the current fluctuation into a relative deviation, reflecting the stability of the current signal. The two fluctuation coefficients are input into the electric field model, and the electric field coefficient is calculated through a nonlinear function relationship. This coefficient decreases exponentially with the increase of the fluctuation coefficient, intuitively reflecting the negative correlation between electric field stability and parameter fluctuations. The model adjusts the contribution weight of voltage and current fluctuations to stability through a sensitivity coefficient, achieving a comprehensive evaluation under the coupling effect of multiple parameters.

[0043] Through the above technical solution, this application effectively solves the measurement error problem caused by electric field fluctuations in resistance method detection. By dynamically monitoring the fluctuation characteristics of voltage and current, a multi-parameter fusion electric field stability assessment mechanism is established, significantly improving the reliability and anti-interference ability of moisture content detection results. This solution can promptly identify abnormal electric field fluctuations during the testing process, providing quantitative stability indicators for the detection results and avoiding the risk of misjudgment due to electric field instability.

[0044] Preferably, the test stability model is expressed as: ; in, This represents the current stability coefficient during testing. Indicates the current electrode field coefficient. Indicates the current electric field coefficient, the Furthermore, the larger the value, the more stable the overall testing system.

[0045] Preferably, the steps for constructing a density-particle fit model and outputting density-particle fit based on the environmental state coefficient, test stability coefficient, tailings sample density, and the proportion of coarse particles in the tailings (coarse particles refer to particles with a diameter between 0.5 mm and 1 mm) are as follows: The density index is obtained by processing the ratio of the absolute difference between the density of the tailings sample and the optimum density to the optimum density. The coarse particle mass ratio index is obtained by taking the ratio of the absolute difference between the coarse particle mass ratio and the optimal coarse particle mass ratio to the optimal particle mass ratio. The current environmental state coefficient, current test stability coefficient, current density index, and coarse particle mass ratio index are imported into the density-particle size fit model to obtain the current density-particle size fit. The density-particle size fit model is expressed as follows: ; in, This indicates the current density-particle size fit. This represents the current environmental state coefficient. This represents the current stability coefficient during testing. This represents the current tailings state coefficient. This indicates the current density index. This represents the current coarse particle mass percentage index, the... Furthermore, the higher the value, the more ideal the tailings condition.

[0046] The density-particle matching model is used to comprehensively evaluate the degree of matching between the tailings condition and ideal detection conditions. The density index is used to quantify the negative impact of density on resistance measurement. The coarse particle mass ratio index is used to reflect the degree of interference of particle distribution uniformity on the current path. The environmental state coefficient is used to characterize the level of interference of the external environment on the detection system. The test stability coefficient is used to reflect the influence of electrode contact state and electric field fluctuations on the detection results.

[0047] Specifically, by calculating the density index and the coarse particle mass ratio index, the deviations of tailings density and particle distribution are quantified, respectively. These two indices are then used as attenuation terms in the exponential function to achieve nonlinear suppression of the deviations. Simultaneously, the environmental state coefficient is used as a correction factor for external environmental disturbances, the test stability coefficient as a correction factor for system stability, and the tailings state coefficient as a correction factor for sample size. These multi-dimensional parameters are coupled and calculated through a product. This model unifies the static deviations of density and particle distribution with the dynamic fluctuations of the environment and system. When the density deviates from the ideal value or the coarse particle distribution is uneven, the attenuation term in the exponential function reduces the fit value, thus objectively reflecting the degree of deterioration of the detection conditions. For example, when the proportion of coarse particles is too high, leading to poor particle gradation, the coarse particle mass ratio index increases, causing the attenuation term in the exponential function to decrease. Item size increases, fit It exhibits an exponential decrease, accurately characterizing the impact of particle distribution on the reliability of detection results.

[0048] Preferably, the steps for determining the confidence level of the detection results by constructing a detection confidence model based on density-particle fit and testing duration are as follows: The test duration index is obtained by squaring the ratio of the difference between the test duration and the optimal test duration to the optimal test duration. The current test duration index and the current density-particle fit are imported into the detection confidence model to obtain the current detection confidence level. The detection confidence model is expressed as follows: ; in, Indicates the current detection confidence level. This indicates the current test duration index. This indicates the current density-particle fit. Represents the time sensitivity coefficient, the Furthermore, the higher the value, the more reliable the test results; Compare the current detection confidence level with the corresponding threshold: like If the result is high confidence, then routine recording should be performed. like If the result is within the range of 0, the test result is of moderate confidence, and a warning should be noted. like If the test result is unreliable, a retest will be mandatory.

[0049] Among them, the test duration index is used to quantify the impact of time deviation on the test results. Density-particle fit is a fitting index that reflects the compactness of the tailings sample and the particle distribution, used to characterize the suitability of the tailings state for detection. This is the time sensitivity coefficient, used to control the penalty intensity for deviations from the optimal value. The three-level threshold judgment mechanism divides the confidence level into two critical values, 0.8 and 0.6, corresponding to three processing methods: routine recording, early warning prompts, and forced retesting, respectively, to achieve hierarchical control of detection results.

[0050] Specifically, the nonlinear effect of time deviation is amplified by calculating the test duration exponentially. When the test duration deviates from the optimal value, the square operation amplifies its negative impact on confidence. Density-particle fit is used as a basic parameter to reflect the degree of fit between the tailings state and the detection; the higher the fit, the higher the initial confidence. The exponential function model dynamically correlates the fit with the time deviation; as the time deviation increases, the confidence decays exponentially. A three-level threshold division establishes clear anomaly identification rules. When the confidence falls below 0.6, a forced retest is triggered, avoiding the risk of misjudgment caused by a single threshold in traditional methods.

[0051] Please see Figure 2 , Figure 3 as well as Figure 4As an embodiment of the present invention, a device for rapidly detecting the moisture content of tailings employs the aforementioned method for rapidly detecting the moisture content of tailings, including a test circuit. The test circuit includes a constant voltage DC power supply 1, an ammeter 2, and a voltmeter 3. The constant voltage DC power supply 1 is connected in series with the electrodes in the test mold via the ammeter 2, and the voltmeter 3 is connected in parallel with the test mold. The test mold includes a housing 401, a side plate 402, and electrodes 404. The side plate 402 is slidably fitted with the housing 401 and detachably connected to the constant voltage DC power supply 1. The output shaft of the linear motion component A403 is fixedly connected. The electrode 404 is connected to the push mechanism 405 mounted on the side plate 402. The push mechanism 405 includes a bracket 4051, a pressure sensor 4052, and a linear motion component B4053. The bracket 4051 is fixedly mounted on the side plate 402. One end of the pressure sensor 4052 is fixedly connected to the electrode 404, and the other end of the pressure sensor 4052 is fixedly connected to the output shaft of the linear motion component B4053, which is detachably mounted on the bracket 4051.

[0052] In this embodiment of the invention, the linear motion component A403 is activated to push the side plate 402 to slide relative to the box 401, that is, to cause the two side plates 402 to move relative to each other until the distance between the two side plates 402 reaches a preset distance. At this time, the tailings sample 5 is placed in the cavity formed by the box 401 and the two side plates 402 and subjected to vibration treatment. Then, the linear motion component B4053 is activated to push the electrode 404 to move linearly to press against the tailings sample and the pressure sensor 4052 is used to monitor the contact pressure between the electrode 404 and the tailings sample 5 in real time. The ammeter 2 is activated to energize the test circuit and test the moisture content of the tailings sample.

[0053] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A test method for rapid detection of moisture content of tailings, characterized by, The method comprises the following steps: The tailings sample is loaded into a test mold for vibration treatment, and a test circuit with a voltmeter and an ammeter is connected to obtain the resistance value of the tailings sample and import it into a water content-resistance fitting formula to obtain the measured water content; A tailings state model is constructed based on the length of the tailings sample and the cross-sectional area of the tailings sample to output a tailings state coefficient; An environment state model is constructed based on the test environment temperature and the test environment humidity to output an environment state coefficient; An electrode field model is constructed based on the electrode parallelism, electrode axial distance and electrode contact pressure with the tailings sample of the test circuit to output an electrode field coefficient; An electric field model is constructed based on the voltage fluctuation amplitude and the current fluctuation amplitude to output an electric field coefficient; A test stability model is constructed based on the electrode field coefficient and the electric field coefficient to output a test stability coefficient; A density-particle adaptation model is constructed based on the environment state coefficient, the test stability coefficient, the tailings state coefficient, the tailings sample density and the mass proportion of coarse particles of the tailings to output a density-particle adaptation degree; A detection confidence model is constructed based on the density-particle adaptation degree and the test duration to judge the confidence of the detection result.

2. The test method for rapid detection of moisture content of tailings according to claim 1, characterized in that, The step of constructing the detection confidence model based on the density-particle adaptation degree and the test duration to judge the confidence of the detection result is: The difference between the test duration and the optimal test duration is processed by ratio processing and square processing with the optimal test duration to obtain a test duration index; The current test duration index and the current density-particle adaptation degree are imported into the detection confidence model to obtain the current detection confidence, and the detection confidence model is represented as: ； wherein, represents the current detection confidence, represents the current test duration index, represents the current density-particle fitness, represents the time sensitivity coefficient, said and the greater the value the more reliable the detection result; The current detection confidence is compared with the corresponding threshold value: If then the detection result is highly confident, and in this case the regular record is made; If then the test result is moderately confident, in which case a note is raised as a warning; If then the detection result is not reliable and a retest is forced.

3. The test method for rapid detection of moisture content of tailings according to claim 2, characterized in that, The step of constructing the density-particle adaptation model based on the environment state coefficient, the test stability coefficient, the tailings sample density and the mass proportion of coarse particles of the tailings to output the density-particle adaptation degree is: The absolute difference between the tailings sample density and the optimal density is processed by ratio processing with the optimal density to obtain a density index; The absolute difference between the mass proportion of coarse particles and the optimal mass proportion of coarse particles is processed by ratio processing with the optimal mass proportion of coarse particles to obtain a coarse particle mass proportion index; The current environment state coefficient, the current test stability coefficient, the current density index and the coarse particle mass proportion index are imported into the density-particle adaptation model to obtain the current density-particle adaptation degree, and the density-particle adaptation model is represented as: ； wherein, represents the current density-particle size fitness, represents the current environment state coefficient, represents the current test stability coefficient, represents the current tailings state coefficient, represents the current compactness index, represents the current coarse particle mass proportion index, the and the greater the value, the more ideal the tailings state.

4. The test method for rapidly detecting the moisture content of tailings according to claim 3, characterized in that, The test stability model is represented as: ； wherein, represents the current test stability coefficient, represents the current electrode field coefficient, represents the current electric field coefficient, which and the greater the value the more stable the overall test system.

5. The test method for rapid detection of moisture content of tailings according to claim 4, characterized in that, The step of constructing the electric field model based on the voltage fluctuation amplitude and the current fluctuation amplitude to output the electric field coefficient is: The voltage fluctuation amplitude is processed by ratio processing with the nominal output voltage set value of the constant voltage DC power supply to obtain a voltage fluctuation coefficient, and the current fluctuation amplitude is processed by ratio processing with the current average value to obtain a current fluctuation coefficient; The current voltage fluctuation coefficient and the current current fluctuation coefficient are imported into the constructed electric field model to output the current electric field coefficient, and the electric field model is represented as: ； wherein, represents the current electric field coefficient, represents the current voltage fluctuation coefficient, represents the current current fluctuation coefficient, represents the voltage fluctuation sensitivity coefficient, represents the current fluctuation sensitivity coefficient, said and the greater the value the more stable the electric field.

6. The test method for rapid detection of moisture content of tailings according to claim 4, characterized in that, The step of constructing the electrode field model based on the electrode parallelism, electrode axial distance and electrode contact pressure with the tailings sample of the test circuit to output the electrode field coefficient is: The electrode axial distance and the electrode parallelism are processed by maximum-minimum normalization method to obtain an electrode axial distance index and an electrode parallelism index; An absolute difference between the contact pressure and the reference contact pressure is processed by ratio with the reference contact pressure to obtain a contact pressure relative deviation index; The current electrode axial distance index, the current electrode parallelism index, and the contact pressure relative deviation index are introduced into an electrode field model to output a current electrode field coefficient, and the electrode field model is represented as: ； wherein, represents the current electrode field coefficient, represents the current electrode axis distance index, represents the current electrode parallelism index, represents the current contact pressure relative deviation index, represents the axis distance sensitivity coefficient, represents the parallelism sensitivity coefficient, represents the pressure deviation sensitivity coefficient, said and the greater the value the more ideal the electrode system.

7. The test method for rapid detection of moisture content of tailings according to claim 3, characterized in that, The step of constructing an environment state model based on the test environment temperature and the test environment humidity to output an environment state coefficient is: An absolute difference between the environment temperature and the reference temperature is processed by ratio with the reference temperature, and then squared to obtain a temperature index; An absolute difference between the environment humidity and the reference humidity is processed by ratio with the reference humidity, and then squared to obtain a humidity index; The current temperature index and the humidity index are introduced into an environment state model to output a current environment state coefficient, and the environment state model is represented as: ； wherein, represents the current environmental state coefficient, represents the current temperature index, represents the current humidity index, represents the temperature sensitivity coefficient, represents the humidity sensitivity coefficient, said and the greater the value the more ideal the detection environment.

8. The test method for quickly detecting the water content of tailings according to claim 3, characterized in that, The step of constructing a tailing state model based on the tailing sample length and the tailing sample cross-sectional area to output a tailing state coefficient is: An absolute difference between the tailing sample length and the standard length is processed by ratio with the standard length to obtain a length relative deviation index; An absolute difference between the tailing sample cross-sectional area and the standard cross-sectional area is processed by ratio with the standard cross-sectional area to obtain a cross-sectional area relative deviation index; The current length relative deviation index and the current cross-sectional area relative deviation index are introduced into a tailing state model to output a current tailing state coefficient, and the tailing state model is represented as: ； wherein, represents the current tailings state coefficient, represents the current length relative deviation index, represents the current cross-sectional area relative deviation index, represents the length deviation sensitivity coefficient, represents the area deviation sensitivity coefficient, and and the greater the value, the more ideal the tailings sample size.

9. The test method for rapid detection of moisture content of tailings according to claim 1, characterized in that, The moisture content-resistance fitting formula is represented as: ； wherein, represents a dimensionless resistance value, represents a scale factor, represents an exponential factor, represents a water cut.

10. A device for rapidly detecting the moisture content of tailings, using the test method for rapidly detecting the moisture content of tailings according to any one of claims 1 to 9, characterized in that, The test circuit comprises a constant-voltage direct-current power supply, an ammeter, and a voltmeter. The constant-voltage direct-current power supply is connected in series with an electrode in a test mold through the ammeter, and the voltmeter is connected in parallel with the test mold. The test mold comprises a box body, a side plate, and an electrode. The side plate is in sliding fit with the box body and is fixedly connected with an output shaft of a linear motion part A which is detachably connected to the constant-voltage direct-current power supply. The electrode is connected with a pushing mechanism which is installed on the side plate. The pushing mechanism comprises a bracket, a pressure sensor, and a linear motion part B. One end of the pressure sensor is fixedly connected with the electrode, and the other end of the pressure sensor is fixedly connected with an output shaft of the linear motion part B which is detachably installed on the bracket.

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