Device and testing method for rapidly detecting water content of tailings

Abstract

The application discloses a device and a testing method for rapidly detecting the water content of tailings, and belongs to the technical field of detection, and comprises the following steps: vibrating and compacting a tailing sample into a test mold, connecting a test circuit to obtain a resistance value, and calculating the measured water content through a water content-resistance fitting formula; simultaneously 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, tailing compactness and a mass proportion of coarse particles; and combining a test duration, determining the reliability of the result through a confidence model, and performing grading record, early warning or retesting. The method is suitable for rapid monitoring of a tailing pond site.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of detection, and particularly relates to a device for rapidly detecting the water content of tailings and a testing method. BACKGROUND

[0002] The water content of tailings is a key indicator of mine safety production, and directly affects the stability of tailings reservoirs and disaster prevention. Traditional detection methods have problems such as poor timeliness and complex operation, and there is an urgent need to develop rapid and accurate on-site detection technology.

[0003] The existing technology mainly relies on the drying and weighing method, which requires cumbersome steps such as sampling, drying, and weighing, and takes a long time of up to several hours. Some use resistance method or nuclear moisture meter, although the detection time is shortened, but it is easily disturbed by factors such as tailings particle distribution, compactness, environmental temperature and humidity, and electrode contact state, resulting in significant measurement error, lack of confidence evaluation system, and inability to identify abnormal data, which is prone to misjudgment. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a device for rapidly detecting the water content of tailings and a testing method, which solves the above problems.

[0005] To achieve the above purpose, the application realizes the following technical scheme: a testing method for rapidly detecting the water content of tailings, comprising the following steps:

[0006] After the tailings sample is loaded into the test mold and vibrated, the test circuit with a voltmeter and an ammeter is connected, the resistance value of the tailings sample is obtained, and the measured water content is obtained by inputting the water content-resistance fitting formula;

[0007] Based on the length of the tailings sample and the cross-sectional area of the tailings sample, a tailings state model is constructed to output a tailings state coefficient;

[0008] Based on the test environment temperature and the test environment humidity, an environment state model is constructed to output an environment state coefficient;

[0009] Based on the electrode parallelism, electrode axis distance (the axis distance between the two electrodes), and electrode and tailings sample contact pressure of the test circuit, an electrode field model is constructed to output an electrode field coefficient;

[0010] Based on the voltage fluctuation amplitude and the current fluctuation amplitude, an electric field model is constructed to output an electric field coefficient;

[0011] Based on the electrode field coefficient and the electric field coefficient, a test stability model is constructed to output a test stability coefficient;

[0012] A density-particle adaptation model is constructed based on the environment state coefficient, the test stability coefficient, the tailing state coefficient, the tailing sample density, and the mass proportion of coarse particles (coarse particles refer to particle sizes between 0.5 mm and 1 mm) to output a density-particle adaptation degree.

[0013] 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.

[0014] Based on the above technical solutions, the application further provides the following optional technical solutions.

[0015] Further technical solutions: the step of constructing a detection confidence model based on the density-particle adaptation degree and the test duration to judge the confidence of the detection result is:

[0016] The difference between the test duration and the optimal test duration is processed by ratio processing and square processing to obtain a test duration index;

[0017] The current test duration index and the current density-particle adaptation degree are introduced into the detection confidence model to obtain a current detection confidence, and the detection confidence model is represented as:

[0018] ;

[0019] wherein, the current detection confidence is represented as, the current test duration index is represented as, the current density-particle adaptation degree is represented as, the time sensitivity coefficient is represented as, and the greater the value, the more reliable the detection result;

[0020] The current detection confidence is compared with a corresponding threshold value:

[0021] If , the detection result is highly confident, and a routine record is made at this time;

[0022] If , the detection result is moderately confident, and a warning is noted at this time;

[0023] If , the detection result is not reliable, and forced retesting is performed.

[0024] Further technical solutions: the step of constructing a density-particle adaptation model based on the environment state coefficient, the test stability coefficient, the tailing sample density, and the mass proportion of coarse particles (coarse particles refer to particle sizes between 0.5 mm and 1 mm) to output a density-particle adaptation degree is:

[0025] The absolute difference value of the sample compactness and the optimal compactness is processed by ratio with the optimal compactness to obtain a compactness index;

[0026] The absolute difference value of the coarse particle mass proportion and the optimal coarse particle mass proportion is processed by ratio with the optimal particle mass proportion to obtain a coarse particle mass proportion index;

[0027] The current environment state coefficient, the current test stability coefficient, the current compactness index and the coarse particle mass proportion index are introduced into a density-particle size adaptation model to obtain a current density-particle size adaptation degree, and the density-particle size adaptation model is represented as:

[0028] ;

[0029] Wherein, The current density-particle size adaptation degree is represented as, The current environment state coefficient is represented as, The current test stability coefficient is represented as, The current tailing state coefficient is represented as, The current compactness index is represented as, The current coarse particle mass proportion index is represented as, And the greater the value is, the more ideal the tailing state is.

[0030] Further technical solutions: the test stability model is represented as:

[0031] ;

[0032] Wherein, The current test stability coefficient is represented as, The current electrode field coefficient is represented as, The current electric field coefficient is represented as, And the greater the value is, the more stable the overall test system is.

[0033] Further technical solutions: the step of constructing an electric field model based on the voltage fluctuation amplitude and the current fluctuation amplitude to output the electric field coefficient is:

[0034] Collecting the voltage values in the test period and constructing a voltage time series data set ;

[0035] The is introduced into the formula to obtain the voltage fluctuation amplitude, wherein The sampling number is represented as, The voltage mean value in the sampling period is represented as;

[0036] Collecting the current values in the test period and constructing a current time series data set ;

[0037] The The formula is introduced The current fluctuation amplitude is obtained, wherein The number of samples is represented by The current mean value in the sampling period is represented by

[0038] The voltage fluctuation amplitude is divided by the nominal output voltage set value of the constant voltage DC power supply to obtain the voltage fluctuation coefficient, and the current fluctuation amplitude is divided by the current mean value to obtain the current fluctuation coefficient.

[0039] The current voltage fluctuation coefficient and the current current fluctuation coefficient are introduced into the constructed electric field model to output the current electric field coefficient, and the electric field model is represented as:

[0040] ;

[0041] Wherein, The current electric field coefficient is represented by The current voltage fluctuation coefficient is represented by The current current fluctuation coefficient is represented by The voltage fluctuation sensitivity coefficient is represented by The current fluctuation sensitivity coefficient is represented by The greater the value, the more stable the electric field.

[0042] Further technical solutions: based on the electrode parallelism of the test circuit, the electrode axis distance (the axis distance between two electrodes) and the contact pressure of the electrode and the tailing sample, the steps of constructing the electrode field model to output the electrode field coefficient are:

[0043] The maximum-minimum normalization method is used to process the electrode axis distance and the electrode parallelism to obtain the electrode axis distance index and the electrode parallelism index.

[0044] The absolute difference between the contact pressure and the reference contact pressure is divided by the reference contact pressure to obtain the contact pressure relative deviation index.

[0045] The current electrode axis distance index, the current electrode parallelism index and the contact pressure relative deviation index are introduced into the electrode field model to output the current electrode field coefficient, and the electrode field model is represented as:

[0046] ;

[0047] Wherein, The current electrode field coefficient is represented by The current electrode axis distance index is represented by The current electrode parallelism index is represented by The current contact pressure relative deviation index is represented by The axis distance sensitivity coefficient is represented by represents a parallelism sensitivity coefficient, represents a pressure deviation sensitivity coefficient, the and the greater the value, the more ideal the electrode system.

[0048] Further technical solutions: 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:

[0049] The absolute difference between the environment temperature and the reference temperature is processed by ratio processing with the reference temperature, and then squared to obtain a temperature index;

[0050] The absolute difference between the environment humidity and the reference humidity is processed by ratio processing with the reference humidity, and then squared to obtain a humidity index;

[0051] The current temperature index and the humidity index are introduced into the environment state model to output a current environment state coefficient, and the environment state model is represented as:

[0052] ;

[0053] wherein, represents the current environment state coefficient, represents the current temperature index, represents the current humidity index, represents a temperature sensitivity coefficient, represents a humidity sensitivity coefficient, the and the greater the value, the more ideal the detection environment.

[0054] Further technical solutions: the step of constructing a tailings state model based on the tailings sample length and the tailings sample cross-sectional area to output a tailings state coefficient is:

[0055] The absolute difference between the tailings sample length and the standard length is processed by ratio processing with the standard length to obtain a length relative deviation index;

[0056] The absolute difference between the tailings sample cross-sectional area and the standard cross-sectional area is processed by ratio processing with the standard cross-sectional area to obtain a cross-sectional area relative deviation index;

[0057] The current length relative deviation index and the current cross-sectional area relative deviation index are introduced into the tailings state model to output a current tailings state coefficient, and the tailings state model is represented as:

[0058] ;

[0059] 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 sensitive coefficient, represents the area deviation sensitive coefficient, and The greater the value is, the more ideal the tailing sample size is.

[0060] Further technical solutions: the water content-resistance fitting formula is represented as:

[0061] ;

[0062] wherein, represents the dimensionless resistance value, represents the scale coefficient, represents the exponential coefficient, represents the water content.

[0063] A device and a testing method for rapidly detecting the water content of tailings, which adopt the above-mentioned testing method for rapidly detecting the water content of tailings, and comprise a testing circuit, wherein the testing 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 electrodes in a test mold through the ammeter, the voltmeter is connected in parallel with the test mold, the test mold comprises a box body, a side plate and the electrodes, the side plate is in sliding fit with the box body and is fixedly connected with an output shaft of a linear motion piece A which is detachably connected to the constant-voltage direct-current power supply, the electrodes are connected with a pushing mechanism installed on the side plate, and the pushing mechanism comprises a bracket, a pressure sensor and a linear motion piece B, the bracket is fixedly installed on the side plate, one end of the pressure sensor is fixedly connected with the electrodes, and the other end of the pressure sensor is fixedly connected with an output shaft of the linear motion piece B which is detachably installed on the bracket.

[0064] The present application provides a device and a testing method for rapidly detecting the water content of tailings, which have the following beneficial effects compared with the prior art:

[0065] The present application calculates the measured water content through the water content-resistance fitting formula, synchronously constructs a tailing state model, an environment state model, an electrode field model and an electric field model, outputs tailing state coefficients, environment state coefficients, electrode field coefficients and electric field coefficients, further obtains a test stability coefficient based on the electric field coefficients and the electrode field coefficients, and generates a density-particle adaptation degree based on the tailing state coefficients, the environment state coefficients, the test stability coefficient, the tailing density and the mass proportion of coarse particles through a density-particle adaptation model, so as to determine the reliability of the result through a confidence model by combining the test duration, record, warn or retest in stages, and eliminate interference systematically to ensure data reliability. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 The present application is a flowchart.

[0067] Figure 2 This is a schematic diagram of the test circuit in this invention.

[0068] Figure 3 This is a schematic diagram of the structure of the prototype mold in this invention.

[0069] Figure 4 For the present invention Figure 3 Enlarged schematic diagram of part A in the diagram.

[0070] 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

[0071] 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.

[0072] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0073] 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:

[0074] 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.

[0075] 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.

[0076] An environmental state model is constructed based on the test environment temperature and humidity, and the environmental state coefficients are output.

[0077] 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.

[0078] An electric field model is constructed based on the voltage fluctuation amplitude and the current fluctuation amplitude, and the electric field coefficients are output.

[0079] A test stability model is constructed based on the electrode field coefficient and the electric field coefficient, and the test stability coefficient is output.

[0080] 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.

[0081] A detection confidence model is constructed based on density-particle fit and test duration to determine the confidence of the detection results.

[0082] 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.

[0083] Preferably, the moisture content-resistivity fitting formula is expressed as:

[0084] ;

[0085] in, This represents the dimensionless resistance value. Represents the scaling factor. Represents the exponential coefficient. Indicates moisture content.

[0086] 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.

[0087] 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.

[0088] wherein the water content refers to the mass proportion of water in the tailings sample to be measured, which can be obtained by the drying and weighing method to establish a correlation with the resistance value.

[0089] Specifically, by establishing a nonlinear exponential function model, the resistance value is directly correlated with the water content, solving the problem that the traditional linear model cannot adapt to the characteristics of different tailings materials. In the implementation process, first, the measured resistance value is dimensionless processed to eliminate the influence of electrode contact resistance and geometric size; then the model baseline is adjusted by the scale coefficient to match the intrinsic conductivity characteristics of different mineral compositions; further, the exponential coefficient is used to capture the nonlinear response of the conduction path caused by the change of water content, such as the resistance mutation phenomenon of fine particle tailings caused by capillary action. The values of and are determined through calibration experiments, so that the model can adapt to the physical characteristics of specific tailings types.

[0090] In some specific embodiments, the calibration experiment can include the following steps: selecting tailings samples with known water content, measuring their resistance value and calculating the dimensionless resistance value ; using nonlinear regression to fit the optimal solution of and ; verifying the error range of the model predicted water content and the measured value by drying method.

[0091] Preferably, the step of constructing a tailings state model based on the length of the tailings sample and the cross-sectional area of the tailings sample to output a tailings state coefficient is:

[0092] The absolute difference between the length of the tailings sample and the standard length is processed by ratio with the standard length to obtain the length relative deviation index;

[0093] The absolute difference between the cross-sectional area of the tailings sample and the standard cross-sectional area is processed by ratio with the standard cross-sectional area to obtain the cross-sectional area relative deviation index;

[0094] The current length relative deviation index and the current cross-sectional area relative deviation index are introduced into the tailings state model to output the current tailings state coefficient, and the tailings state model is represented as:

[0095] ;

[0096] 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 sensitive coefficient, represents the area deviation sensitive coefficient, and the And the greater the value, the more ideal the tailings sample size.

[0097] Wherein, the length relative deviation index is used to quantify the influence of the sample longitudinal size deviation on the electric field distribution. The cross-sectional area relative deviation index is used to characterize the deviation of the current density distribution caused by the abnormal sample cross-sectional size. The sensitivity coefficient And Refers to the weight parameter calibrated in the model in advance, which can be determined by experience or by experimental data regression analysis, and is used to adjust the nonlinear influence relationship between length deviation and area deviation on the state coefficient.

[0098] Specifically, when the tailings sample is loaded into the mold, the actual length and cross-sectional area data of the sample are obtained by the measuring device. When calculating the length relative deviation index, the absolute value of the difference between the measured length and the standard length is divided by the standard length, for example, the standard length can be pre-set to 20 cm, if the measured length is 21 cm, the deviation index is 0.05. When calculating the cross-sectional area relative deviation index, if the standard cross-sectional area is 50 cm 2 And the measured value is 48 cm 2 , the deviation index is 0.04. The model can strengthen the influence of cross-sectional area change on the state coefficient through the square term of 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, which leads to the accelerated decline of the state coefficient, thus more sensitively reflecting the influence of cross-sectional area abnormality on resistance measurement.

[0099] Through the above technical solutions, the application can automatically identify the size deviation of the tailings sample in the length and cross-sectional area dimensions, dynamically adjust the state coefficient through the index model, and effectively suppress the abnormal electric field distribution caused by the deviation of the sample geometric parameters from the standard condition. When the sample size deviation exceeds the allowed range, the state coefficient is significantly reduced and triggers the warning mechanism of the subsequent detection confidence model, avoiding misjudgment of the size error as a change in moisture content, and improving the anti-interference ability of the resistance method detection result.

[0100] Preferably, the step of constructing an environmental state model based on the test environment temperature and the test environment humidity to output an environmental state coefficient is:

[0101] The absolute difference between the environmental temperature and the reference temperature is processed by ratio processing with the reference temperature, and then squared to obtain a temperature index;

[0102] The absolute difference between the environmental humidity and the reference humidity is processed by ratio processing with the reference humidity, and then squared to obtain a humidity index;

[0103] The current temperature index and humidity index are introduced into the environmental state model to output the current environmental state coefficient, and the environmental state model is represented as:

[0104] ;

[0105] wherein, represents the current temperature index, represents the current temperature index, represents the current temperature index, represents the temperature sensitive coefficient, represents the humidity sensitive coefficient, and the and the greater the value, the more ideal the detection environment.

[0106] wherein, the reference temperature refers to the pre-set ideal detection environment temperature value, which can specifically adopt the laboratory standard temperature 25℃ as the reference value, for eliminating the influence of temperature deviation on resistance measurement. The reference humidity refers to the pre-set ideal detection environment humidity value, which can specifically adopt the relative humidity 50% as the reference value, for eliminating the interference of humidity change on the conductive characteristics of the sample. The temperature sensitive coefficient refers to the weight parameter of the influence of temperature deviation on the detection system, which can specifically be determined by experience calibration or calibration test, for example, taking a value in the range of 0.05~0.2, for amplifying the sensitivity of temperature anomaly to the model. The humidity sensitive coefficient refers to the weight parameter of the influence of humidity deviation on the detection system, which can specifically be determined by experience calibration or humidity gradient test, for example, taking a value in the range of 0.03~0.15, for quantifying the attenuation effect of humidity fluctuation on the detection result.

[0107] Specifically, when the environmental temperature deviates from the reference temperature, the absolute difference is calculated and normalized square processing is performed, which can amplify the nonlinear influence of temperature anomaly on the detection system. 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 environmental humidity deviates from the reference humidity, the humidity index reflects the square relationship of humidity deviation through the same algorithm. After substituting the two indexes into the environmental state model, the and terms in the denominator structure will produce a multiplication amplification effect, so that the environmental state coefficient presents a decay characteristic with the increase of temperature and humidity deviation. As the input parameter of the subsequent density-particle fitting model, the coefficient can dynamically adjust the weight distribution of environmental factors on the detection confidence.

[0108] Preferably, the step of constructing an electrode field model based on the parallelism of the test circuit electrodes, the electrode axis distance (the axis distance between the two electrodes), and the contact pressure of the electrodes and the tailing sample to output the electrode field coefficient is:

[0109] The maximum-minimum normalization method is adopted to process the electrode axis distance and the electrode parallelism to obtain the electrode axis distance index and the electrode parallelism index.

[0110] The absolute difference between the contact pressure and the reference contact pressure is processed by ratio to the reference contact pressure to obtain a contact pressure relative deviation index;

[0111] 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 expressed as:

[0112]

[0113] wherein, represents the current electrode field coefficient, represents the current electrode axial distance index, represents the current electrode parallelism index, represents the current contact pressure relative deviation index, represents the axial distance sensitivity coefficient, represents the parallelism sensitivity coefficient, represents the pressure deviation sensitivity coefficient, and the The greater the value is, the more ideal the electrode system is.

[0114] The electrode axial distance index can be specifically realized by linear mapping of the measured axial distance to the standard axial distance range. The electrode parallelism index refers to the inclination angle between the electrodes obtained by an angle measuring device, which is converted into a standardized deviation parameter by a normalization method, and can be specifically realized by a laser range finder or an optical sensor, and is used to quantify the interference degree of the parallelism deviation between the electrodes on the electric field distribution. The contact pressure relative deviation index refers to the actual pressure value of the contact surface between the electrode and the tailing sample measured by a pressure sensor, and the deviation is calculated with the preset reference pressure, which can be specifically realized by a piezoelectric sensor or a strain gauge, and is used to reflect the resistance measurement error risk caused by the fluctuation of the contact pressure.

[0115] Specifically, the electrode axial distance and parallelism are converted into comparable index parameters by normalization processing, eliminating the interference of dimensional differences on model operation. The contact pressure deviation is calculated by relative ratio, which directly reflects the deviation degree of the actual pressure from the ideal state. When the three types of indexes are input into the index function model, the axial distance deviation affects the coefficient in the form of a linear term, the parallelism deviation strengthens the sensitivity of the angle deviation in the form of a square term, and the contact pressure deviation reflects the influence of continuous pressure fluctuation in the form of a linear term. By adjusting the weight of different parameters through the sensitivity coefficient, the electrode field coefficient can dynamically represent the comprehensive state of the electrode system. When the axial distance exceeds the standard range, the parallelism decreases, or the contact pressure is unstable, the negative exponential term in the index function increases, resulting in an exponential decline in the electrode field coefficient, thereby directly reflecting the negative impact of the electrode system on the test results.

[0116] ​Preferably, the step of constructing the electric field model outputting the electric field coefficient based on the voltage fluctuation amplitude and the current fluctuation amplitude is:

[0117] Collecting the voltage values in the test period and constructing the voltage time series dataset ;

[0118] The is introduced into the formula to obtain the voltage fluctuation amplitude, wherein represents the number of samples, represents the voltage mean value in the sampling period;

[0119] Collecting the current values in the test period and constructing the current time series dataset ;

[0120] The is introduced into the formula to obtain the current fluctuation amplitude, wherein represents the number of samples, represents the current mean value in the sampling period;

[0121] The voltage fluctuation amplitude is processed by ratio with the nominal output voltage set value of the constant voltage DC power supply to obtain the voltage fluctuation coefficient, and the current fluctuation amplitude is processed by ratio with the current mean value to obtain the current fluctuation coefficient;

[0122] The current voltage fluctuation coefficient and the current current fluctuation coefficient are introduced into the constructed electric field model to output the current electric field coefficient, and the electric field model is represented as:

[0123] ;

[0124] Among them, represents the current electric field coefficient, represents the current voltage fluctuation coefficient, represents the current current fluctuation coefficient, represents the voltage fluctuation sensitive coefficient, represents the current fluctuation sensitive coefficient, and the The greater the value, the more stable the electric field.

[0125] Specifically, the voltage and current data are continuously collected to form a time series dataset during the test period, and the fluctuation amplitudes of voltage and current are quantified by standard deviation calculation respectively. The ratio of voltage fluctuation amplitude to nominal output voltage set value converts the voltage fluctuation into a relative value, eliminating the influence of different power output magnitudes on the evaluation results; the ratio of current fluctuation amplitude to current average 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. The coefficient shows an exponential decay trend with the increase of the fluctuation coefficient, which intuitively reflects the negative correlation between electric field stability and parameter fluctuation. The model adjusts the contribution weight of voltage and current fluctuation to stability through the sensitive coefficient, and realizes comprehensive evaluation under the coupling action of multiple parameters.

[0126] Through the above technical solutions, the application effectively solves the measurement error problem caused by electric field fluctuation in resistance method detection. By dynamically monitoring the fluctuation characteristics of voltage and current, a multi-parameter fusion electric field stability evaluation mechanism is established, which significantly improves the reliability and anti-interference ability of the water cut detection result. The scheme can timely identify abnormal electric field fluctuation in the test process, provide quantitative stability index for the detection result, and avoid the risk of misjudgment caused by unstable electric field.

[0127] Preferably, the test stability model is represented as:

[0128] ;

[0129] Wherein, represents the current test stability coefficient, represents the current electrode field coefficient, represents the current electric field coefficient, and the The greater the value, the more stable the overall test system.

[0130] Preferably, the density-particle adaptation model is constructed based on the environmental state coefficient, the test stability coefficient, the tailings sample density, and the tailings coarse particle mass ratio (coarse particles refer to particle sizes between 0.5mm-1mm) to output the density-particle adaptation degree.

[0131] The absolute difference between the tailings sample density and the optimal density is processed by ratio to the optimal density to obtain the density index;

[0132] The absolute difference between the coarse particle mass ratio and the optimal coarse particle mass ratio is processed by ratio to the optimal particle mass ratio to obtain the coarse particle mass ratio index;

[0133] The current environment state coefficient, the current test stability coefficient, and the current density index and coarse particle mass proportion index are introduced into the density-particle size adaptation model to obtain a current density-particle size adaptation degree, and the density-particle size adaptation model is represented as:

[0134]

[0135] wherein, represents the current density-particle size adaptation degree, represents the current environment state coefficient, represents the current test stability coefficient, represents the current tailing state coefficient, represents the current density index, represents the current coarse particle mass proportion index, and the greater the value, the more ideal the tailing state.

[0136] The density-particle size adaptation model is used to comprehensively evaluate the matching degree of the tailing state and the ideal detection condition. The density index is used to quantify the negative impact of the compaction degree on the resistance measurement. The coarse particle mass proportion index is used to reflect the interference degree of the particle distribution uniformity on the current path. The environment state coefficient is used to represent the interference level of the external environment on the detection system. The test stability coefficient is used to reflect the influence of the electrode contact state and the electric field fluctuation on the detection result.

[0137] Specifically, by calculating the density index and the coarse particle mass proportion index, the deviation of the tailing compaction degree and the particle distribution is quantified, respectively, and the two indexes are used as the attenuation term of the exponential function to realize the nonlinear suppression of the deviation. At the same time, the environment state coefficient is used as the correction factor of the external environment interference, the test stability coefficient is used as the correction factor of the system stability, and the tailing state coefficient is used as the correction factor of the sample size, and the multi-dimensional parameter coupling calculation is realized through the product form. The model unifies the static deviation of the compaction degree and the particle distribution, the dynamic fluctuation of the environment and the system, and when the compaction degree deviates from the ideal value or the coarse particle distribution is uneven, the attenuation term in the exponential function will reduce the adaptation degree value, thereby objectively reflecting the deterioration degree of the detection condition. For example, when the coarse particle proportion is too high, resulting in poor particle gradation, the coarse particle mass proportion index increases, resulting in an increase in the term in the exponential function, and the adaptation degree exponentially decreases, which accurately characterizes the influence of the particle distribution on the credibility of the detection result.

[0138] Preferably, the step of constructing a detection confidence model based on the density-particle size adaptation degree and the test duration to judge the detection result confidence is:

[0139] ​​The difference between the test duration and the optimal test duration is squared after being divided by the optimal test duration to obtain a test duration index;

[0140] The current test duration index and the current density-particle fitness are introduced into a detection confidence model to obtain a current detection confidence, which is expressed as:

[0141] ;

[0142] wherein, represents the current detection confidence, represents the current test duration index, represents the current density-particle fitness, represents a time-sensitive coefficient, which and the greater the value, the more reliable the detection result;

[0143] The current detection confidence is compared with a corresponding threshold value:

[0144] If , the detection result is highly confident, and a regular record is made at this time;

[0145] If , the detection result is moderately confident, and a warning is noted at this time;

[0146] If , the detection result is not reliable, and forced retesting is performed.

[0147] The test duration index is used to quantify the influence of time deviation on the detection result. The density-particle fitness is an index reflecting the fitness of the compact state of the tailings sample and the particle distribution, and is used to represent the fitness of the tailings state to the detection. The time-sensitive coefficient is used to control the punishment intensity of the time deviation from the optimal value. The three-level threshold value judgment mechanism refers to dividing the confidence into 0.8 and 0.6 critical values, which correspond to regular record, warning prompt and forced retesting respectively, and is used to realize the hierarchical control of the detection result.

[0148] Specifically, the test duration index is calculated to strengthen the nonlinear influence of time deviation. When the test duration deviates from the optimal value, the square operation amplifies its negative effect on the confidence. The density-particle fitness, as a basic parameter, reflects the fitness of the tailings state to the detection. The higher the fitness, the higher the initial confidence. The exponential function model dynamically relates the fitness and the time deviation. When the time deviation increases, the confidence decays exponentially. The three-level threshold value division establishes clear abnormal recognition rules. When the confidence is lower than 0.6, forced retesting is triggered, avoiding the misjudgment risk caused by a single threshold value in traditional methods.

[0149] Please refer to Figure 2、 Figure 3 and Figure 4 As an embodiment of the present application, a device for rapidly detecting water content of tailings adopts the above-mentioned method for rapidly detecting water content of tailings, and comprises a test circuit, wherein the test circuit comprises 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 electrodes in a test mold through the ammeter 2, the voltmeter 3 is connected in parallel with the test mold, the test mold comprises a box body 401, side plates 402 and electrodes 404, the side plates 402 are slidingly matched with the box body 401 and are fixedly connected with an output shaft of a linear motion member A 403 which is detachably connected to the constant-voltage DC power supply 1, the electrodes 404 are connected with a pushing mechanism 405 which is installed on the side plates 402, the pushing mechanism 405 comprises a bracket 4051, a pressure sensor 4052 and a linear motion member B 4053, the bracket 4051 is fixedly installed on the side plates 402, one end of the pressure sensor 4052 is fixedly connected with the electrodes 404, and the other end of the pressure sensor 4052 is fixedly connected with an output shaft of the linear motion member B 4053 which is detachably installed on the bracket 4051.

[0150] In the embodiment of the present application, the linear motion member A 403 is started to push the side plates 402 to slide relative to the box body 401, that is, to promote relative movement of the two side plates 402 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 body 401 and the two side plates 402 and is subjected to vibration treatment, at this time, the linear motion member B 4053 is started to push the electrodes 404 to move linearly to press against the tailings sample and the pressure sensor 4052 is started to monitor the contact pressure of the electrodes 404 and the tailings sample 5 in real time, and the ammeter 2 is started to promote the test circuit to be powered on to test the water content of the tailings sample.

[0151] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0152] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application 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.

Images