Sludge solidified soil particle detection device

Abstract

The invention provides a sludge solidified soil particle detection device, and belongs to the technical field of soil solidification inspection, the sludge solidified soil particle detection device comprises a detection box body, a driving mechanism, a lifting mechanism, a detection mechanism and a sludge solidified soil particle analysis system; the detection box body is in a cuboid shape, a feeding port is formed in the top of the detection box body, and a discharging port is formed in the bottom of the detection box body. A sieve plate assembly is transversely arranged in the detection box body, and the sieve plate assembly divides the internal space of the detection box body into an upper chamber and a lower chamber; the sieve plate assembly comprises a fixed frame and a sieve plate body, the fixed frame is fixed on the inner wall of the detection box body through bolts, and the sieve plate body is mounted on the fixed frame through a frame; a plurality of detection holes are formed in the sieve plate body and are distributed in an array; the driving mechanism comprises a driving motor and an eccentric wheel, the driving motor is fixedly installed on the outer wall of the detection box body through a motor base, and the eccentric wheel is fixedly connected with an output shaft of the driving motor.

Description

Technical Field

[0001] The present invention belongs to the technical field of soil solidification inspection, and in particular, relates to a sludge solidification soil particle detection device. Background Art

[0002] Sludge is an inevitable byproduct of urban sewage treatment, and its main components include water, organic matter, inorganic minerals, etc. Traditional sludge treatment methods mainly include incineration, composting, landfill, etc., but these methods have problems such as high cost and serious environmental pollution. Therefore, in recent years, the technology of turning sludge into solidified soil has gradually emerged. The so-called sludge solidified soil refers to a new type of building material made by mixing sludge with inorganic cementitious materials such as cement and lime and solidifying it. It has high strength and good durability.

[0003] As an environmentally friendly building material, sludge-solidified soil has broad application prospects in urban infrastructure, road engineering and other fields. For example, it can be used to manufacture various prefabricated components, such as roadbed, slope protection, retaining wall, etc.; it can also be used for on-site solidification and repair of slopes and dams inside the city. Compared with traditional sand and gravel, sludge-solidified soil has the advantages of low cost and low environmental load, and is of great significance in promoting urban infrastructure construction and sludge resource utilization.

[0004] However, there are still some problems with the existing sludge solidification soil production technology. First, the current detection method mainly relies on manual sampling and manual classification, which is inefficient and difficult to achieve rapid and continuous detection of particle size distribution. Secondly, the size distribution of solidified soil particles directly affects its strength and stability, but the existing technology is difficult to comprehensively and accurately evaluate its particle characteristics. Thirdly, in the actual production process, due to the uncontrollability of factors such as raw material ratio and solidification conditions, it is difficult to ensure the uniformity of the quality of solidified soil products. Therefore, it is urgent to develop a new type of sludge solidification soil particle detection device to solve the above technical problems. Summary of the invention

[0005] The invention provides a sludge solidified soil particle detection device, which can improve the detection speed of sludge solidified soil particles.

[0006] The present invention is achieved in that:

[0007] The present invention provides a sludge solidified soil particle detection device, which comprises: a detection box, a driving mechanism, a lifting mechanism, a detection mechanism and a sludge solidified soil particle analysis system; the detection box is in a rectangular parallelepiped shape, a feed port is arranged on the top of the detection box, and a discharge port is arranged on the bottom of the detection box; a sieve plate assembly is arranged transversely inside the detection box, and the sieve plate assembly divides the internal space of the detection box into an upper chamber and a lower chamber; the sieve plate assembly comprises a fixed frame and a sieve plate body, the fixed frame is fixed to the inner wall of the detection box by bolts, and the sieve plate body is installed on the fixed frame by a frame; a plurality of detection holes are arranged on the sieve plate body, and the detection holes are distributed in an array; the driving mechanism comprises a driving motor and an eccentric wheel, the driving motor is fixedly installed on the outer wall of the detection box through a motor seat, and the eccentric wheel is fixedly connected to the output shaft of the driving motor; a shock-absorbing support assembly is arranged at the bottom of the detection box, and the shock-absorbing support assembly comprises a base and a shock-absorbing spring, the base is rectangular, and shock-absorbing springs are evenly arranged at the four corners of the base.

[0008] The technical effects of a sludge solidification soil particle detection device provided by the present invention are as follows: The present invention proposes a sludge solidification soil particle detection device, which can realize rapid and continuous detection of particle size distribution and simultaneously monitor the vibration characteristics and deformation characteristics of the particles. Compared with the prior art, the present invention has the following advantages:

[0009] 1. High degree of automation, which can realize real-time collection and analysis of particle size data, greatly improves the detection efficiency and avoids the tedious operation of manual screening.

[0010] 2. The vibration signal and deformation signal of the particles can be collected synchronously. By analyzing these data, the physical properties of the particles can be evaluated more comprehensively and accurately, providing a basis for process optimization and quality control.

[0011] 3. The built-in intelligent algorithm can automatically eliminate abnormal data and improve the reliability of the test results; at the same time, it can calculate the uniformity coefficient of particle size distribution in real time, detect quality anomalies in time, and provide feedback for production process control.

[0012] 4. With modular design, each functional unit can be flexibly combined to meet the customized requirements of different production scales and testing needs.

[0013] Therefore, the sludge-solidified soil particle detection device of the present invention can effectively solve the problems of low efficiency, incomplete detection, and difficult quality control in the prior art, and provide reliable technical support for the industrial production of sludge-solidified soil.

[0014] On the basis of the above technical solution, the sludge solidification soil particle detection device of the present invention can also be improved as follows:

[0015] Among them, the fixed frame includes a main frame and reinforcing ribs; the main frame is rectangular, and a plurality of fixing holes are evenly distributed around the main frame; the reinforcing ribs are fixed to the inner side of the main frame in a cross shape, and the reinforcing ribs and the main frame are fixedly connected by welding; the frame is fixed to the main frame by bolts.

[0016] Furthermore, the lifting mechanism includes a lifting frame, a screw rod and a handwheel; the lifting frame includes a column and a crossbeam, the columns are fixed on both sides of the detection box, and the crossbeam is connected between the two columns; the screw rod is vertically arranged inside the column, the top end of the screw rod is fixedly connected to the handwheel, and the bottom end of the screw rod is rotatably connected to the column through a bearing seat; a threaded hole cooperating with the screw rod is provided on the crossbeam, and the crossbeam forms a lifting transmission cooperation with the screw rod through the threaded hole.

[0017] Furthermore, the detection mechanism includes a weighing sensor and a strain gauge; the weighing sensor is fixedly installed in the lower chamber of the detection box, and a receiving tray is provided on the top of the weighing sensor; the strain gauge is provided on the lower surface of the sieve plate body for detecting the deformation of the sieve plate body; the sieve plate body is made of elastic steel plate; the detection aperture on the sieve plate body decreases from top to bottom; the aperture range of the detection hole is 1 mm to 50 mm; the inner wall of the detection hole is smooth and free of burrs.

[0018] Furthermore, a feed hopper is provided at the feed port of the detection box, and the feed hopper is funnel-shaped; the upper part of the feed hopper is a square opening, and the lower part is a circular outlet; the feed hopper and the detection box are connected by a flange, and a plurality of bolt holes are evenly arranged on the flange; a material blocking assembly is provided at the discharge port of the detection box; the material blocking assembly includes a baffle and an operating rod, the baffle is rotatably connected to the discharge port by a hinge, and the operating rod is fixed to one end of the baffle; the baffle is in a closed state under the action of gravity; a protective layer is provided on the inner wall of the detection box; the protective layer is made of wear-resistant rubber material; the protective layer is fixedly connected to the inner wall of the detection box by an adhesive.

[0019] Furthermore, it also includes a compensation sensor group, specifically a position sensor, a velocity sensor, an acceleration sensor, a temperature and humidity sensor, an air pressure sensor, a laser particle size analyzer, an industrial camera, a displacement sensor and a vibration sensor; wherein the position sensor adopts a photoelectric position sensor, which is installed on the inner wall of the detection box and is distributed in a circular array; the velocity sensor adopts a laser Doppler velocity sensor, which is installed on the side wall of the detection box; the acceleration sensor adopts a piezoelectric acceleration sensor, which is installed on the outer wall of the detection box; the temperature and humidity sensor and the air pressure sensor are installed inside the detection box; the laser particle size analyzer is installed on the side wall of the detection box, and a measuring window is provided between the laser particle size analyzer and the detection area; the industrial camera is installed on the top of the detection box and is aligned with the detection area; the displacement sensor adopts an eddy current displacement sensor, which is installed on the support frame of the sieve plate assembly; there are multiple vibration sensors, which are installed on each sieve plate body.

[0020] Furthermore, the sludge solidification soil particle analysis system includes a single chip microcomputer, a display, a particle size detector, a compensation sensor group, a data collector, and an alarm; the single chip microcomputer is electrically connected to the display, the particle size detector, the compensation sensor group, the data collector, and the alarm; a particle analysis module is provided in the single chip microcomputer for analyzing the size distribution of sludge solidification soil particles and the validity of the detection data; when the particle analysis module is running, it is used to perform the following steps:

[0021] S10, collecting a vibration signal from a vibration sensor within a preset time period, and determining a vibration detection window according to the vibration signal;

[0022] S20, collecting particle size data output by the particle size detector within the vibration detection window, and recording the collection time corresponding to each particle size data;

[0023] S30, calculating the time interval between two adjacent collection moments, comparing the time interval with a preset threshold, and eliminating particle size data corresponding to a time interval greater than the preset threshold;

[0024] S40, performing spectrum analysis on the vibration signal in the vibration detection window to obtain a vibration frequency distribution curve, comparing the vibration frequency distribution curve with a standard vibration curve, and determining a vibration abnormality period;

[0025] S50, eliminating the particle size data collected during the abnormal vibration period to obtain a valid particle size data sequence;

[0026] S60, classifying the effective particle size data sequence according to size, counting the number of particles in each size class, and generating a particle size distribution curve;

[0027] S70, determining whether the data volume of the effective particle size data sequence meets the statistical requirements, and when the statistical requirements are not met, sending a prompt signal through the alarm;

[0028] S80, calculating the uniformity coefficient of the particle size distribution curve, comparing the uniformity coefficient with a preset range, and when the uniformity coefficient exceeds the preset range, sending an alarm signal through the alarm device;

[0029] S90, displaying the particle size distribution curve, the uniformity coefficient, and the effectiveness evaluation of the detection result on the display.

[0030] Furthermore, the step of determining the preset threshold specifically includes:

[0031] Step 1, establishing a particle motion differential equation group, wherein the particle motion differential equation group includes a particle falling velocity equation, a particle force balance equation, a particle acceleration equation and a particle displacement equation;

[0032] Step 2: Establish a vibration influence equation group, wherein the vibration influence equation group includes a vibration displacement equation, a vibration velocity equation, a vibration acceleration equation and a vibration period equation;

[0033] Step 3: Based on the collected particle position information, particle falling speed information, vibration acceleration information and detection area environmental parameter information, the particle motion differential equation group and the vibration influence equation group are solved simultaneously to obtain a theoretical time interval distribution curve of particles passing through the detection area;

[0034] Step 4: According to the mean and standard deviation of the theoretical time interval distribution curve, determine the preset threshold as the mean plus three times the standard deviation.

[0035] Furthermore, the criterion for judging whether the data volume of the effective particle size data sequence meets the statistical requirements is that the total number of particles in the effective particle size data sequence is not less than 1000 and the continuous sampling time is not less than 300 seconds.

[0036] Furthermore, the step of determining the preset range specifically includes:

[0037] Step a, establishing a particle distribution characteristic equation group, wherein the particle distribution characteristic equation group includes a particle size distribution density equation, a particle cumulative distribution equation, a particle characteristic size equation and a particle dispersion equation;

[0038] Step b, establishing a uniformity evaluation equation group, wherein the uniformity evaluation equation group includes a uniformity coefficient calculation equation, an unevenness coefficient equation, a curvature coefficient equation and a gradient coefficient equation;

[0039] Step c, based on the collected particle size distribution information, particle morphology characteristic information and detection system operation status information, the particle distribution characteristic equation group and the uniformity evaluation equation group are substituted into the Monte Carlo simulation calculation to obtain the uniformity coefficient distribution under different working conditions;

[0040] Step d: According to the confidence interval of the uniformity coefficient distribution, determine the preset range as the 95% confidence interval of the uniformity coefficient distribution.

[0041] Compared with the prior art, the beneficial effect of the sludge solidification soil particle detection device provided by the present invention is: the present invention proposes a sludge solidification soil particle detection device, which can realize the rapid and continuous detection of particle size distribution and simultaneously monitor the vibration characteristics and deformation characteristics of the particles. Compared with the prior art, the present invention has the following advantages:

[0042] 1. High degree of automation, which can realize real-time collection and analysis of particle size data, greatly improves the detection efficiency and avoids the tedious operation of manual screening.

[0043] 2. The vibration signal and deformation signal of the particles can be collected synchronously. By analyzing these data, the physical properties of the particles can be evaluated more comprehensively and accurately, providing a basis for process optimization and quality control.

[0044] 3. The built-in intelligent algorithm can automatically eliminate abnormal data and improve the reliability of the test results; at the same time, it can calculate the uniformity coefficient of particle size distribution in real time, detect quality anomalies in time, and provide feedback for production process control.

[0045] 4. With modular design, each functional unit can be flexibly combined to meet the customized requirements of different production scales and testing needs.

[0046] Therefore, the sludge-solidified soil particle detection device of the present invention can effectively solve the problems of low efficiency, incomplete detection, and difficult quality control in the prior art, and provide reliable technical support for the industrial production of sludge-solidified soil. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0048] Figure 1 It is a schematic diagram of the internal structure of a sludge solidification soil particle detection device;

[0049] Figure 2Run steps for the particle analysis module;

[0050] Figure 3 It is the vibration signal spray spectrum analysis diagram;

[0051] Figure 4 is a particle size distribution curve;

[0052] Figure 5 It is a comparison chart of uniformity coefficient;

[0053] Figure 6 The overall architecture diagram of the analysis system;

[0054] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0055] 1. Detection box; 2. Sieve plate assembly; 21. Fixed frame; 22. Sieve plate body; 3. Shock-absorbing support assembly; 31. Base; 32. Shock-absorbing spring; 4. Driving mechanism; 41. Motor; 42. Eccentric wheel; 5. Detection mechanism; 51. Weighing sensor; 52. Strain gauge; 6. Wear-resistant rubber layer; 7. Discharge port; 71. Baffle; 72. Operating lever; 8. Auxiliary sensor; 81. Position sensor; 82. Speed ​​sensor; 83. Acceleration sensor; 84. Temperature and humidity sensor; 85. Air pressure sensor; 86. Laser particle size analyzer; 87. Industrial camera; 88. Displacement sensor; 89. Vibration sensor. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0057] like Figure 1 The figure is a schematic diagram of the structure of a sludge solidified soil particle detection device provided by the present invention, in which the device comprises: a detection box, a driving mechanism, a lifting mechanism, a detection mechanism and a sludge solidified soil particle analysis system; the detection box is in the shape of a rectangular parallelepiped, a feed inlet is arranged on the top of the detection box, and a discharge outlet is arranged on the bottom of the detection box; a sieve plate assembly is arranged transversely inside the detection box, and the sieve plate assembly divides the internal space of the detection box into an upper chamber and a lower chamber; the sieve plate assembly comprises a fixed frame and a sieve plate body, the fixed frame is fixed to the inner wall of the detection box by bolts, and the sieve plate body is mounted on the fixed frame by a frame; a plurality of detection holes are arranged on the sieve plate body, and the detection holes are distributed in an array; the driving mechanism comprises a driving motor and an eccentric wheel, the driving motor is fixedly mounted on the outer wall of the detection box through a motor seat, and the eccentric wheel is fixedly connected to the output shaft of the driving motor; a shock-absorbing support assembly is arranged at the bottom of the detection box, and the shock-absorbing support assembly comprises a base and a shock-absorbing spring, the base is rectangular, and shock-absorbing springs are evenly arranged at the four corners of the base.

[0058] Among them, in the above technical scheme, the fixed frame includes a main frame and reinforcing ribs; the main frame is rectangular, and a plurality of fixing holes are evenly distributed around the main frame; the reinforcing ribs are fixed to the inner side of the main frame in a cross shape, and the reinforcing ribs and the main frame are fixedly connected by welding; the frame is fixed to the main frame by bolts.

[0059] Furthermore, in the above technical solution, the lifting mechanism includes a lifting frame, a screw rod and a handwheel; the lifting frame includes a column and a crossbeam, the columns are fixed on both sides of the detection box, and the crossbeam is connected between the two columns; the screw rod is vertically arranged inside the column, the top end of the screw rod is fixedly connected to the handwheel, and the bottom end of the screw rod is rotatably connected to the column through a bearing seat; a threaded hole cooperating with the screw rod is provided on the crossbeam, and the crossbeam forms a lifting transmission cooperation with the screw rod through the threaded hole.

[0060] Furthermore, in the above technical scheme, the detection mechanism includes a weighing sensor and a strain gauge; the weighing sensor is fixedly installed in the lower chamber of the detection box, and a receiving tray is provided on the top of the weighing sensor; the strain gauge is provided on the lower surface of the sieve plate body for detecting the deformation of the sieve plate body; the sieve plate body is made of elastic steel plate; the detection aperture on the sieve plate body decreases from top to bottom; the aperture range of the detection hole is 1 mm to 50 mm; the inner wall of the detection hole is smooth and free of burrs.

[0061] Furthermore, in the above technical scheme, a feed hopper is provided at the feed port of the detection box, and the feed hopper is funnel-shaped; the upper part of the feed hopper is a square opening, and the lower part is a circular outlet; the feed hopper and the detection box are connected by a flange, and a plurality of bolt holes are evenly arranged on the flange; a material blocking assembly is provided at the discharge port of the detection box; the material blocking assembly includes a baffle and an operating rod, the baffle is rotatably connected to the discharge port by a hinge, and the operating rod is fixed to one end of the baffle; the baffle is in a closed state under the action of gravity; a protective layer is provided on the inner wall of the detection box; the protective layer is made of wear-resistant rubber material; the protective layer is fixedly connected to the inner wall of the detection box by an adhesive.

[0062] Furthermore, the above technical solution also includes a compensation sensor group, specifically a position sensor, a velocity sensor, an acceleration sensor, a temperature and humidity sensor, an air pressure sensor, a laser particle size analyzer, an industrial camera, a displacement sensor and a vibration sensor; wherein the position sensor adopts a photoelectric position sensor, which is installed on the inner wall of the detection box and is distributed in a circular array; the velocity sensor adopts a laser Doppler velocity sensor, which is installed on the side wall of the detection box; the acceleration sensor adopts a piezoelectric acceleration sensor, which is installed on the outer wall of the detection box; the temperature and humidity sensor and the air pressure sensor are installed inside the detection box; the laser particle size analyzer is installed on the side wall of the detection box, and a measuring window is provided between the laser particle size analyzer and the detection area; the industrial camera is installed on the top of the detection box and is aligned with the detection area; the displacement sensor adopts an eddy current displacement sensor, which is installed on the support frame of the sieve plate assembly; there are multiple vibration sensors, which are installed on each sieve plate body.

[0063] like Figure 2 As shown, in the above technical solution, the sludge solidification soil particle analysis system includes a single chip microcomputer, a display, a particle size detector, a compensation sensor group, a data collector, and an alarm; the single chip microcomputer is electrically connected to the display, the particle size detector, the compensation sensor group, the data collector, and the alarm; a particle analysis module is provided in the single chip microcomputer for analyzing the size distribution of sludge solidification soil particles and the validity of the detection data; when the particle analysis module is running, it is used to perform the following steps:

[0064] S10, collecting vibration signals from the vibration sensor within a preset time period, and determining a vibration detection window according to the vibration signals;

[0065] S20, collecting particle size data output by the particle size detector in the vibration detection window, and recording the collection time corresponding to each particle size data;

[0066] S30, calculating the time interval between two adjacent collection moments, comparing the time interval with a preset threshold, and eliminating the particle size data corresponding to the time interval greater than the preset threshold;

[0067] S40, performing spectrum analysis on the vibration signal in the vibration detection window to obtain a vibration frequency distribution curve, comparing the vibration frequency distribution curve with a standard vibration curve, and determining a vibration abnormality period;

[0068] S50, eliminating the particle size data collected during the abnormal vibration period to obtain a valid particle size data sequence;

[0069] S60, classifying the effective particle size data sequence according to size, counting the number of particles in each size class, and generating a particle size distribution curve;

[0070] S70, determining whether the data volume of the effective particle size data sequence meets the statistical requirements, and when the statistical requirements are not met, sending a prompt signal through an alarm;

[0071] S80, calculating the uniformity coefficient of the particle size distribution curve, comparing the uniformity coefficient with a preset range, and when the uniformity coefficient exceeds the preset range, sending an alarm signal through an alarm;

[0072] S90, displaying the particle size distribution curve, uniformity coefficient and effectiveness evaluation of the test result on a display.

[0073] Furthermore, in the above technical solution, the step of determining the preset threshold value specifically includes:

[0074] Step 1, establishing a particle motion differential equation group, the particle motion differential equation group includes a particle falling velocity equation, a particle force balance equation, a particle acceleration equation and a particle displacement equation;

[0075] Step 2: Establish a vibration influence equation group, which includes a vibration displacement equation, a vibration velocity equation, a vibration acceleration equation and a vibration period equation;

[0076] Step 3: Based on the collected particle position information, particle falling speed information, vibration acceleration information and detection area environmental parameter information, the particle motion differential equations and the vibration influence equations are solved simultaneously to obtain a theoretical time interval distribution curve of particles passing through the detection area;

[0077] Step 4: According to the mean and standard deviation of the theoretical time interval distribution curve, determine the preset threshold value as the mean plus three times the standard deviation.

[0078] Furthermore, in the above technical solution, the criterion for judging whether the data volume of the effective particle size data sequence meets the statistical requirements is that the total number of particles in the effective particle size data sequence is not less than 1000 and the continuous sampling time is not less than 300 seconds.

[0079] Furthermore, in the above technical solution, the step of determining the preset range specifically includes:

[0080] Step a, establishing a particle distribution characteristic equation group, the particle distribution characteristic equation group includes a particle size distribution density equation, a particle cumulative distribution equation, a particle characteristic size equation and a particle dispersion equation;

[0081] Step b, establishing a uniformity evaluation equation group, wherein the uniformity evaluation equation group includes a uniformity coefficient calculation equation, an unevenness coefficient equation, a curvature coefficient equation and a gradient coefficient equation;

[0082] Step c, based on the collected particle size distribution information, particle morphology characteristic information and detection system operation status information, the particle distribution characteristic equation group and the uniformity evaluation equation group are substituted into the Monte Carlo simulation calculation to obtain the uniformity coefficient distribution under different working conditions;

[0083] Step d: According to the confidence interval of the uniformity coefficient distribution, determine the preset range as the 95% confidence interval of the uniformity coefficient distribution.

[0084] The specific implementation of steps S10-S90 is as follows:

[0085] Step S10: collecting vibration signals of the vibration sensor within a preset time period, and determining a vibration detection window according to the vibration signals. Specifically, this step includes the following sub-steps:

[0086] Sub-step S11: using a plurality of vibration sensors (such as acceleration sensors) to collect vibration signals in the detection box, with a sampling frequency of not less than 2000 Hz.

[0087] Sub-step S12: Performing spectrum analysis on the collected vibration signal by Fast Fourier Transform (FFT) to obtain a vibration frequency spectrum.

[0088] Sub-step S13: Determine the main vibration mode of the particle screening process based on the main frequency peak and energy distribution in the vibration frequency spectrum, and set the time range of the vibration detection window based on this. The vibration detection window should be able to cover the main vibration response process of the particles passing through the sieve plate.

[0089] Step S20: Collect the particle size data output by the particle size detector within the vibration detection window, and record the collection time corresponding to each particle size data. Specifically, this step includes the following sub-steps:

[0090] Sub-step S21: using a laser particle size analyzer or a high-resolution industrial camera to collect particle size data in real time during the screening process.

[0091] Sub-step S22: Use a high-precision timestamp to record the collection time of each particle size data, and the accuracy of the timestamp is not less than 0.1ms.

[0092] Sub-step S23: In combination with the vibration detection window determined in step S13, the particle size data within the window and its corresponding collection time are extracted.

[0093] Step S30: Calculate the time interval between two adjacent acquisition moments, compare the time interval with a preset threshold, and remove the particle size data corresponding to the time interval greater than the preset threshold. Specifically, this step includes the following sub-steps:

[0094] Sub-step S31: Calculate the time interval between adjacent particles according to the collection time data recorded in step S22.

[0095] Sub-step S32: Compare the calculated time interval Δt with a preset threshold Δt0, and when Δt>Δt0, remove the particle size data corresponding to the time interval.

[0096] Sub-step S33: The method for determining the preset threshold value Δt0 is: first, establish a group of differential equations for particle motion and a group of vibration influence equations, and solve them jointly to obtain a theoretical time interval distribution curve for particles passing through the detection area; then determine Δt0 based on the mean of the theoretical distribution curve plus three times the standard deviation.

[0097] Step S40: performing spectrum analysis on the vibration signal in the vibration detection window to obtain a vibration frequency distribution curve, comparing the vibration frequency distribution curve with a standard vibration curve, and determining the abnormal vibration period.

[0098] Specifically, this step includes the following sub-steps:

[0099] Sub-step S41: Performing spectrum analysis on the vibration signal collected in step S10 using fast Fourier transform (FFT) to obtain a vibration frequency distribution curve X(f).

[0100] Sub-step S42: Compare the obtained vibration frequency distribution curve X(f) with the standard normal vibration curve X0(f) to calculate the difference between the two. When the difference exceeds a preset threshold, it is determined that there is vibration abnormality in the period.

[0101] Sub-step S43: Determine the abnormal vibration period and record its start time and end time.

[0102] Step S50: Eliminate the particle size data collected during the abnormal vibration period to obtain a valid particle size data sequence. Specifically, this step includes the following sub-steps:

[0103] Sub-step S51: according to the abnormal vibration period determined in step S40, find out the particle size data collected in the period.

[0104] Sub-step S52: In the particle size data sequence obtained in step S20, data in the vibration abnormality period are eliminated to obtain a valid particle size data sequence.

[0105] Step S60: classify the effective particle size data sequence according to size, count the number of particles in each size class, and generate a particle size distribution curve. Specifically, this step includes the following sub-steps:

[0106] Sub-step S61: According to the magnitude of the particle size data, divide it into 10-20 size grades.

[0107] Sub-step S62: Count the number of particles in each size class and calculate the percentage of each class.

[0108] Sub-step S63: Draw a particle size distribution curve according to the percentage of each size grade.

[0109] Step S70: Determine whether the data volume of the effective particle size data sequence meets the statistical requirements. If it does not meet the statistical requirements, an alarm is used to send a prompt signal. Specifically, this step includes the following sub-steps:

[0110] Sub-step S71: Calculate the total number N of particles in the effective particle size data sequence.

[0111] Sub-step S72: Determine whether N is not less than 1000 and whether the continuous sampling time is not less than 300 seconds.

[0112] Sub-step S73: When N or the sampling time does not meet the above requirements, an alarm is used to send a prompt signal to prompt that the sampling time needs to be extended or the number of sampling points needs to be increased.

[0113] Step S80: Calculate the uniformity coefficient of the particle size distribution curve, compare the uniformity coefficient with a preset range, and when the uniformity coefficient exceeds the preset range, send an alarm signal through an alarm. Specifically, this step includes the following sub-steps:

[0114] Sub-step S81: Calculate the uniformity coefficient U according to the particle size distribution curve obtained in step S60. The calculation formula is:

[0115]

[0116] Where d 10 , d 30 , d 60 are the particle sizes corresponding to 10%, 30%, and 60% on the cumulative distribution curve respectively, and λ is the weight coefficient, which takes a value of 0.5.

[0117] Sub-step S82: Compare the calculated uniformity coefficient U with the preset range [U min , U max ] for comparison. min orU>U max When an alarm occurs, an alarm signal is issued through the alarm.

[0118] Sub-step S83: Preset range [U min , U max ​] is determined as follows: first, a set of particle distribution characteristic equations and a set of uniformity evaluation equations are established, and the distribution of the uniformity coefficient under different working conditions is obtained through Monte Carlo simulation; then, the preset range is determined with a 95% confidence interval of the distribution.

[0119] Step S90: Display the particle size distribution curve, uniformity coefficient and effectiveness evaluation of the test results on the display. Specifically, this step includes the following sub-steps:

[0120] Sub-step S91: drawing the particle size distribution curve obtained in step S60 on the display.

[0121] Sub-step S92: Displaying the uniformity coefficient U calculated in step S80 on the display.

[0122] Sub-step S93: Based on the results of step S70 and step S80, an evaluation of the effectiveness of the detection result is given, that is, whether the amount of data is sufficient and whether the particle size distribution is uniform.

[0123] In summary, the specific implementation of steps S10-S90 of the sludge solidification soil particle detection device mainly includes:

[0124] 1) Collect vibration signals and determine the vibration detection window;

[0125] 2) Collecting particle size data within the vibration detection window and recording the collection time;

[0126] 3) Eliminate data from abnormal periods to obtain a valid particle size data sequence;

[0127] 4) Gradual statistical particle size distribution curve;

[0128] 5) Calculate the uniformity coefficient and compare it with the preset range;

[0129] 6) Display the test results on the display.

[0130] The implementation of these steps involves theoretical models such as particle motion differential equations, vibration influence equations, particle distribution characteristic equations, uniformity evaluation equations, and methods for obtaining relevant parameters. The determination of these models and parameters ensures the scientificity and reliability of the detection process. At the same time, each step is closely linked to each other, forming a complete set of sludge solidification soil particle detection methods.

[0131] The three-dimensional coordinate data of particles collected by the position sensor, the instantaneous velocity data of particles collected by the velocity sensor, the vibration acceleration data collected by the acceleration sensor, the ambient temperature and relative humidity data collected by the temperature and humidity sensor, and the ambient air pressure data collected by the air pressure sensor; the particle size distribution data collected by the laser particle size analyzer, the particle morphology image data collected by the high-resolution industrial camera, the sieve plate vibration displacement data collected by the displacement sensor, and the sieve plate strain data collected by the strain gauge.

[0132] 1. The particle motion differential equations are specifically expressed as follows:

[0133] Particle falling velocity equation:

[0134]

[0135] Where v is the particle falling velocity in m / s; t is the time in s; g is the gravitational acceleration, which is 9.81 m / s 2 ; μ is the fluid dynamic viscosity, in Pa·s; d is the particle diameter, in m; m is the particle mass, in kg; ρ f is the fluid density in kg / m 3 ; C d is the drag coefficient, dimensionless.

[0136] Particle force balance equation:

[0137]

[0138] In the formula, F g is gravity, unit is N; F d is the resistance, the unit is N; F b is the buoyancy, in N; F v is the vibration excitation force, the unit is N; x is the particle position, the unit is m.

[0139] Particle acceleration equation:

[0140]

[0141] Where a(t) is the particle acceleration in m / s 2 ξ(t) is a random disturbance term, which follows the normal distribution N(0,σ 2 ).

[0142] Particle displacement equation:

[0143]

[0144] Where x0 is the initial position, unit is m; τ is the integral variable.

[0145] 2. The vibration influence equations are specifically expressed as follows:

[0146] Vibration displacement equation:

[0147]

[0148] In the formula, x v (t) is the vibration displacement, in m; A0 is the initial amplitude, in m; ζ is the damping ratio, dimensionless; ω n is the natural frequency, in rad / s; ω d is the damped vibration frequency, in rad / s; φ is the phase angle, in rad; A i is the amplitude of the i-th harmonic component; ω i is the angular frequency of the i-th harmonic component.

[0149] Vibration velocity equation:

[0150]

[0151] In the formula, v v (t) is the vibration velocity in m / s.

[0152] Vibration acceleration equation:

[0153]

[0154] In the formula, a v (t) is the vibration acceleration, in m / s 2 ; η(t) is the measurement noise, which follows a normal distribution

[0155] Oscillation period equation:

[0156]

[0157] Where T(t) is the vibration period in seconds; δ(t) is the periodic fluctuation term, which obeys the uniform distribution U(-ΔT, ΔT).

[0158] 3. The particle distribution characteristic equation group is specifically expressed as follows:

[0159] Particle size distribution density equation:

[0160]

[0161] Where f(d) is the particle size distribution density function; μ is the location parameter of the lognormal distribution; σ is the scale parameter of the lognormal distribution.

[0162] Particle cumulative distribution equation:

[0163]

[0164] Where F(d) is the cumulative distribution function; γ(d) is the distribution correction term, which is used to correct the deviation between the theoretical distribution and the actual distribution.

[0165] Particle characteristic size equation:

[0166]

[0167] Where d is the characteristic dimension in m.

[0168] Particle dispersion equation:

[0169]

[0170] In the formula, S d is the dispersion, unit is m.

[0171] 4. The uniformity evaluation equation group is specifically expressed as follows:

[0172] The uniformity coefficient calculation equation is:

[0173]

[0174] Where U is the uniformity coefficient, dimensionless; d 10 , d 30 , d 60 are the particle sizes corresponding to 10%, 30%, and 60% on the cumulative distribution curve, respectively, in m; λ is the weight coefficient, and its value range is [0, 1].

[0175] The equation for the non-uniformity coefficient is:

[0176]

[0177] In the formula, C u is the non-uniformity coefficient; d i is the diameter of the ith particle, in m; is the average particle diameter, in m; n is the number of samples; κ(n) is the sample size correction function,

[0178] Curvature coefficient equation:

[0179]

[0180] In the formula, C c is the curvature coefficient; α is the curvature sensitivity coefficient, and its value range is [0.01, 0.1]; is the second derivative of the cumulative distribution function.

[0181] Gradient coefficient equation:

[0182]

[0183] In the formula, G c is the gradient coefficient; F i is the cumulative distribution value of the i-th particle size interval; d i is the representative particle size of the ith particle size interval, in m; ω i is the weight factor, β(m) is the interval number correction term, β(m) = ln(m) / m; m is the total number of particle size intervals.

[0184] The method for obtaining parameters based on the collected experimental data is as follows:

[0185] 1. Obtaining particle motion parameters:

[0186] Step 1: Use a high-speed camera system to record the falling trajectory of a single particle, with a camera frequency of no less than 1000 frames per second;

[0187] Step 2: Extract the position coordinates (x i ,y i , t i );

[0188] Step 3: Use the least squares method to fit the particle motion trajectory and obtain velocity and acceleration data;

[0189] Step 4: Calculate the drag coefficient C using the following equation d :

[0190]

[0191] In the formula, v t is the particle terminal velocity, in m / s.

[0192] 2. Vibration parameter acquisition:

[0193] Step 1: Install an acceleration sensor on the detection box with a sampling frequency of no less than 2000Hz;

[0194] Step 2: Obtain the vibration spectrum through fast Fourier transform:

[0195]

[0196] Where X(f) is the spectrum function; x(t) is the time domain signal; f is the frequency, in Hz;

[0197] Step 3: Identify the main vibration modes using the following equations:

[0198] ωn =2πf n ;

[0199]

[0200] In the formula, f n is the natural frequency, in Hz; A n , A n+1 Two consecutive amplitude peaks.

[0201] 3. Obtaining particle distribution parameters:

[0202] Step 1: Collect size data of no less than 1000 particles;

[0203] Step 2: Calculate the lognormal distribution parameters by maximum likelihood estimation:

[0204]

[0205] Step 3: Use the Kolmogorov-Smirnov test to verify the goodness of fit of the distribution:

[0206] D n =sup x |F n (x)-F(x)|;

[0207] In the formula, F n (x) is the empirical distribution function; D n is the test statistic.

[0208] 4. Obtaining uniformity evaluation parameters:

[0209] Step 1: Sort the particle samples by size from small to large;

[0210] Step 2: Calculate the particle size corresponding to the cumulative percentage:

[0211]

[0212] Where P k is the percentile; [nP k ] indicates rounding; θ is the interpolation coefficient, which is 0.5;

[0213] Step 3: Calculate the curvature coefficient by numerical integration:

[0214]

[0215] Therefore, step 3 and step c can be supplemented as follows:

[0216] Step 3: Based on the collected particle motion trajectory data and vibration acceleration data, the particle motion differential equations and the vibration influence equations are solved simultaneously to obtain a theoretical time interval distribution curve of particles passing through the detection area;

[0217] Step c: Based on the collected particle size distribution data and uniformity evaluation parameters, the particle distribution characteristic equation group and the uniformity evaluation equation group are substituted into the Monte Carlo simulation calculation to obtain the uniformity coefficient distribution under different working conditions.

[0218] The default values ​​or value ranges of all the above parameters and functions are as follows:

[0219] 1.μ: fluid dynamic viscosity, water at 20°C is 1.002×10 -3 pa·s;

[0220] 2.ρ f : Fluid density, water at 20°C is 998kg / m 3 ;

[0221] 3. σ v : Measurement noise standard deviation, value range [0.001, 0.01] m / s 2 ;

[0222] 4.ΔT: period fluctuation range, value range [0.001, 0.01]s;

[0223] 5.λ: weight coefficient, default value is 0.5;

[0224] 6.α: curvature sensitivity coefficient, default value is 0.05;

[0225] 7.n: minimum sample size 1000;

[0226] 8.m: Particle size interval number, the default is 10 to 20.

[0227] Embodiment 1:

[0228] In step S10, it is necessary to determine the vibration detection window. First, multiple acceleration sensors (such as models) are used to collect vibration signals a(t) in the detection box, with a sampling frequency f s Not less than 2000Hz. Then, the collected vibration signal is analyzed by fast Fourier transform (FFT) to obtain the vibration frequency spectrum X(f):

[0229]

[0230] Where f is the frequency in Hz. According to the main frequency peak and energy distribution in the vibration frequency spectrum X(f), the main vibration mode of the particle screening process can be determined. Assume that the natural angular frequency ω of the main vibration mode is nThe corresponding vibration period T can be calculated as:

[0231]

[0232] In the formula, δ is a random periodic fluctuation term, which obeys the uniform distribution U(-ΔT, ΔT), and ΔT = 0.005s. Taking the period T of the main vibration mode as the benchmark, the time range of the vibration detection window is set to [t s -2T,t s +2T], where t s The window start time.

[0233] In step S20, it is necessary to collect particle size data within the vibration detection window. First, a laser particle size analyzer (such as) can be used to measure the instantaneous size d(t) of the particles in real time. At the same time, a high-precision timestamp is used to record the collection time t of each particle size data, and the accuracy of the timestamp is not less than 0.1ms. In this way, the particle size data sequence (d i , t i ).

[0234] In step S30, some abnormal particle size data need to be eliminated. First, the time interval Δt between two adjacent acquisition moments is calculated. i =t i+1 -t i According to the simultaneous solution of the particle motion differential equations and the vibration influence equations, the theoretical time interval distribution p(Δt) of particles passing through the detection area can be obtained. Specifically, the particle motion differential equations include:

[0235] Particle falling velocity equation:

[0236]

[0237] Particle force balance equation:

[0238]

[0239] Particle acceleration equation:

[0240]

[0241] Particle displacement equation:

[0242]

[0243] Where v is the particle falling velocity, g is the gravitational acceleration, μ is the fluid dynamic viscosity, d is the particle diameter, m is the particle mass, and ρ is the particle velocity. f is the fluid density, C d is the drag coefficient, F g is gravity, Fd is the resistance, F b is the buoyancy, F v is the vibration excitation force, x is the particle position, and ξ(t) is the random disturbance.

[0244] The vibration effect equations include:

[0245] Vibration displacement equation:

[0246]

[0247] Vibration velocity equation:

[0248]

[0249] Vibration acceleration equation:

[0250]

[0251] Oscillation period equation:

[0252]

[0253] In the formula, x v is the vibration displacement, A0 is the initial amplitude, ζ is the damping ratio, ω n is the natural frequency, ω d is the damped vibration frequency, φ is the phase angle, A i is the amplitude of the th harmonic component, ω i is the angular frequency of the th harmonic component, v v is the vibration speed, a v is the vibration acceleration, η(t) is the measurement noise, T is the vibration period, and δ(t) is the periodic fluctuation term.

[0254] By solving these differential equations together, we can obtain the theoretical time interval distribution p(Δt) of particles passing through the detection area. Then, the calculated time interval Δt i The data point Δt0 is compared with the mean of the theoretical distribution plus three times the standard deviation as the preset threshold value, and the data point Δt is eliminated. i >Δt0. In this way, a valid particle size data series is obtained.

[0255] In step S40, the vibration signal needs to be analyzed to determine the abnormal vibration period. First, the vibration signal collected in step S10 is still analyzed by fast Fourier transform (FFT) a(t) to obtain the vibration frequency spectrum X(f). Then, the obtained vibration frequency spectrum X(f) is compared with the standard normal vibration spectrum X0(f) to calculate the difference between the two:

[0256]

[0257] Where [f1, f2] is the frequency range of the analysis. When D exceeds the preset threshold, it is determined that there is vibration abnormality in this period. Record the start time t of the vibration abnormality b and the end time t e .

[0258] In step S50, the particle size data in the abnormal vibration period needs to be eliminated. First, according to the abnormal vibration period [t b , t e ], find out the particle size data collected during this period. Then, remove the data during the vibration abnormal period from the complete particle size data sequence obtained in step S20, and obtain the effective particle size data sequence {d j}.

[0259] In step S60, it is necessary to convert the effective particle size data sequence {d j The particles are classified according to their size, and the number of particles in each size class is counted to generate a particle size distribution curve. Specifically, the particle size can be divided into m classes, and the representative size of each class is d i (i=1,2,...,m). Then the number of particles in the i-th size class is n i The percentage of the total is:

[0260]

[0261] According to the percentage of each size class p i , the particle size distribution curve can be drawn.

[0262] In step S80, the uniformity coefficient U of the particle size distribution curve needs to be calculated and compared with a preset range. The calculation formula of the uniformity coefficient U is:

[0263]

[0264] Where, d 10 , d 30 , d 60 are the particle sizes corresponding to 10%, 30%, and 60% on the cumulative distribution curve respectively, and λ is the weight coefficient, which takes a value of 0.5.

[0265] Preset range [U min , U max ] is determined by first establishing a particle distribution characteristic equation group and a uniformity evaluation equation group, and obtaining the distribution of the uniformity coefficient under different working conditions through Monte Carlo simulation; and then determining the preset range with a 95% confidence interval of the distribution. Specifically, the particle distribution characteristic equation group includes:

[0266] Particle size distribution density equation:

[0267]

[0268] Particle cumulative distribution equation:

[0269]

[0270] Particle characteristic size equation:

[0271]

[0272] Particle dispersion equation:

[0273]

[0274] In the formula, μ is the location parameter of the lognormal distribution, σ is the scale parameter of the lognormal distribution, and γ(d) is the distribution correction term. The uniformity evaluation equation group includes:

[0275] The uniformity coefficient calculation equation is:

[0276]

[0277] The equation for the non-uniformity coefficient is:

[0278]

[0279] Curvature coefficient equation:

[0280]

[0281] Gradient coefficient equation:

[0282]

[0283] In the formula, C u is the non-uniformity coefficient, is the average particle diameter, κ(n) is the sample size correction function, C c is the curvature coefficient, α is the curvature sensitivity coefficient, G c is the gradient coefficient, ω i is the weight factor, and β(m) is the interval number correction term.

[0284] Finally, the calculated uniformity coefficient U is compared with the preset range [U min , U max ] for comparison. min orU>U max When an alarm occurs, an alarm signal is issued through the alarm.

[0285] ​The sludge solidified soil particle detection device of the present invention is mainly composed of the following key components: a detection box, a driving mechanism, a lifting mechanism, a detection mechanism and a sludge solidified soil particle analysis system. These components work closely together to achieve rapid detection and comprehensive analysis of solidified soil particles.

[0286] First, the detection box adopts a rectangular structure with a sieve plate assembly inside, which divides the space into two chambers, the upper chamber is the feeding area, and the lower chamber is the detection area. There are multiple detection holes of different sizes on the sieve plate, which are used to grade and screen the particles of the feed. The drive mechanism (motor + eccentric wheel) can apply vibration to the detection box, which helps the particles pass through the sieve plate.

[0287] During the test, the solidified sludge to be tested is first poured into the feed port of the upper chamber. Under the action of gravity and vibration, the particles will fall into the lower chamber through sieve holes of different diameters according to their size. The testing mechanism is equipped with a weighing sensor, which can monitor the total weight of particles passing through the sieve plate in real time; at the same time, a strain gauge is also provided to detect the deformation of the sieve plate when it is impacted by particles.

[0288] The inner wall of the detection box is coated with a wear-resistant rubber layer to protect the internal components from mechanical wear of particles. The discharge port of the lower chamber is equipped with a baffle-type material blocking device to control the discharge of particles.

[0289] In addition, the detection device is also equipped with a variety of auxiliary sensors, including position sensors, velocity sensors, acceleration sensors, temperature and humidity sensors, air pressure sensors, etc. These sensors can comprehensively collect multi-dimensional data such as particle motion trajectory, vibration characteristics, and environmental parameters, providing a basis for subsequent intelligent analysis.

[0290] Finally, all collected data will be transmitted to the sludge solidification soil particle analysis system for processing. The system includes a single-chip microcomputer, a display, a particle size detector, a compensation sensor group, a data collector, an alarm, etc. The single-chip microcomputer has a built-in special particle analysis module that can perform the following steps:

[0291] 1. Analyze the vibration signal of the screen plate and determine the vibration detection window;

[0292] 2. Collect particle size data within the window and remove abnormal time interval data;

[0293] 3. Analyze the vibration signal, identify the abnormal vibration period, and eliminate the particle data within this period;

[0294] 4. Perform graded statistics on effective particle size data and draw a particle size distribution curve;

[0295] 5. Calculate the uniformity coefficient of particle size distribution and compare it with the preset range, and issue abnormal alarm in time.

[0296] This intelligent analysis method based on multi-source data fusion can effectively improve the reliability and accuracy of detection results and provide support for quality control of the production process.

[0297] In summary, the sludge solidified soil particle detection device of the present invention realizes comprehensive and accurate detection of solidified soil particle size, vibration characteristics, deformation characteristics, etc. through automated screening mechanism, intelligent data analysis algorithm, and coordinated work of multiple sensors.

[0298] Specifically, the principle of the present invention is:

[0299] The detection principle includes:

[0300] Screening principle: The sludge solidification soil particles are screened by using the multiple detection holes of the screen plate assembly. As the detection apertures on the screen plate gradually decrease from top to bottom, the particles are separated into different chambers according to their sizes. Smaller particles fall into the lower chamber through the holes, while larger particles are blocked by the screen plate, thus enabling the classification of particle sizes.

[0301] Weighing principle: The weighing sensor installed in the lower chamber can monitor the weight of the particles that fall into the lower chamber after being screened by the sieve plate in real time. This data can reflect the physical properties and curing effect of the sample.

[0302] Deformation detection: Strain gauges are set on the lower surface of the sieve plate body to detect the deformation of the sieve plate during the screening process. This can provide feedback on the force exerted by the particles on the sieve plate, thereby helping to analyze the characteristics of the particles.

[0303] Driving and vibration principle: The driving mechanism provides vibration through the motor and eccentric wheel, so that the particles can pass through the sieve holes more effectively during the screening process, ensuring the efficiency and accuracy of screening.

[0304] Embodiment 2:

[0305] A specific implementation scheme of a sludge solidification soil particle detection device is as follows: the detection box 1 adopts a rectangular parallelepiped structure with dimensions of 1.5 meters in length, 1 meter in width, and 1.2 meters in height. A sieve plate assembly 2 is provided inside the box to divide the space into two upper and lower chambers. The sieve plate assembly 2 includes a fixed frame 21 and a sieve plate body 22. The fixed frame 21 is fixed to the inner wall of the box by bolts, and the sieve plate body 22 is installed on the fixed frame 21 by a frame. There are 15 detection holes on the sieve plate body 22, and the apertures decrease from top to bottom, respectively, 10 mm, 8 mm, 6 mm, 4 mm, and 2 mm.

[0306] A shock-absorbing support assembly 3 is provided at the bottom of the box, including four rectangular bases 31 and shock-absorbing springs 32 installed on the bases. This design can effectively isolate the influence of external vibration on the detection process.

[0307] The driving mechanism 4 is mounted on the outer wall of the box body, and is composed of a motor 41 and an eccentric wheel 42. The motor 41 is fixed by a motor seat, and the eccentric wheel 42 is connected to the motor output shaft. The driving mechanism can apply periodic vibration to the detection box body to promote the particles to pass through the sieve plate.

[0308] The detection mechanism is arranged in the lower chamber of the box body, and includes a weighing sensor 51 and a strain gauge 52. The weighing sensor 51 is installed at the bottom of the lower chamber, and a receiving tray 511 is arranged above the weighing sensor 51 to receive particles passing through the sieve plate. The strain gauge 52 is pasted on the lower surface of the sieve plate body 22, and can detect the deformation of the sieve plate when it is impacted by particles.

[0309] The inner wall of the box is coated with a wear-resistant rubber layer 6 to protect the internal components from particle wear. The discharge port 7 is provided with a baffle 71 and an operating rod 72 to control the discharge of particles.

[0310] The detection device is also equipped with a variety of auxiliary sensors, including:

[0311] Position sensor 81: a photoelectric position sensor is installed on the inner wall of the box in a circular array to monitor the particle movement trajectory;

[0312] Speed ​​sensor 82: A laser Doppler sensor is installed on the side wall of the box to measure the instantaneous speed of particles;

[0313] Acceleration sensor 83: a piezoelectric sensor is installed on the outer wall of the box to collect vibration acceleration signals;

[0314] Temperature and humidity sensor 84 and air pressure sensor 85: installed inside the box to collect environmental parameters;

[0315] Laser particle size analyzer 86: installed on the side wall of the box, and detects particle size distribution through the measuring window;

[0316] Industrial camera 87: installed on the top of the box to take images of particle morphology;

[0317] Displacement sensor 88: an eddy current sensor is used and installed on the screen plate support frame to monitor the vibration displacement of the screen plate;

[0318] Vibration sensor 89: Multiple sensors are installed on each screen plate body to collect local vibration signals.

[0319] The data from all these sensors will be transmitted to the sludge solidification soil particle analysis system for processing and analysis.

[0320] In this embodiment, the specific steps of the detection process are as follows:

[0321] 1. Start the detection equipment to detect the temperature, humidity, air pressure and other parameters inside and outside the box and the surrounding environment. Ensure that all monitoring sensors are working properly.

[0322] 2. Open the baffle 71 of the discharge port 7 and manually pour the sludge solidified soil material to be tested into the feed port.

[0323] 3. Start the drive motor 41 to make the detection box vibrate and promote the particles to pass through the sieve plate. At the same time, each sensor starts to collect data:

[0324] Position sensor 81, velocity sensor 82, acceleration sensor 83 collect particle motion trajectory and vibration characteristic data;

[0325] Laser particle size analyzer 86 and industrial camera 87 collect particle size and morphology data;

[0326] The load cell 51 and the strain gauge 52 monitor the total weight of particles passing through the screen and the deformation of the screen.

[0327] 4. After a period of testing (such as 5 minutes), close the discharge port baffle 71, stop the motor 41, and complete the testing of the batch.

[0328] 5. The collected data are transmitted to the analysis system, and the particle analysis algorithm is executed by the microcontroller to obtain the following results:

[0329] Particle size distribution curve: Count the number of particles in each size class and draw a particle size distribution curve;

[0330] Uniformity coefficient: Calculate the uniformity coefficient U according to the cumulative distribution curve and compare it with the preset range;

[0331] Validity of test data: Analyze vibration signals, eliminate particle data during abnormal periods, and provide validity evaluation.

[0332] 6. Display the analysis results on the display and issue corresponding prompts through the alarm.

[0333] 7. Unload the samples to be tested, clean the test box, and prepare for the next batch of testing.

[0334] By implementing this detection device, comprehensive and automated detection of sludge solidified soil particle size distribution, vibration characteristics, deformation characteristics and other indicators can be achieved. Compared with traditional manual sampling detection, this device has the following advantages:

[0335] 1. The detection efficiency is significantly improved, and a comprehensive test can be completed within 5 minutes, which greatly shortens the detection cycle.

[0336] 2. The test results are more objective and reliable, and can automatically eliminate abnormal data, avoiding errors caused by manual analysis.

[0337] 3. The particle dynamic characteristics can be monitored synchronously to provide a basis for optimizing process parameters.

[0338] 4. Built-in intelligent algorithm can calculate the uniformity coefficient in real time, detect quality problems in time, and provide feedback for production process control.

[0339] Therefore, the detection device has important application value in improving production efficiency and ensuring product quality.

[0340] Example 3 is an example of a sludge solidification soil particle analysis system;

[0341] The overall architecture of the analysis system is as follows Figure 6 As shown in the figure, it mainly includes a single chip microcomputer, a display, a particle size detector, a compensation sensor group, a data collector and an alarm. Among them, the single chip microcomputer has a built-in special particle analysis module, which is responsible for executing the core algorithm of data processing and analysis.

[0342] During the detection process, the raw data collected by each sensor is first aggregated to the data collector, and then read and processed by the microcontroller in real time. The specific data analysis steps are as follows:

[0343] Step 1: Collect the vibration signal of the vibration sensor 89 within a preset time period, and determine the vibration detection window according to the vibration signal.

[0344] First, the fast Fourier transform (FFT) is used to perform spectrum analysis on the collected vibration signal a(t) to obtain the vibration frequency spectrum X(f):

[0345]

[0346] Then, according to the main frequency peak and energy distribution in the spectrum X(f), the main vibration mode of the particle screening process is determined, and the time range of the vibration detection window is set accordingly as [t s -2T,t s +2T], where t s is the starting time of the window, and T is the period of the main vibration mode.

[0347] Step 2: Collect the particle size data output by the particle size detector within the vibration detection window, and record the collection time corresponding to each data.

[0348] The data sequence collected by the particle size detector is {d i , t i}, where d i is the size of the ith particle, ti For its collection time.

[0349] Step 3: Calculate the time interval Δt between two adjacent acquisition times i =t i+1 -t i , and compare it with the preset threshold Δt0, eliminating Δt i >Data point of Δt0.

[0350] The method for determining the preset threshold Δt0 is as follows: first, establish a group of differential equations for particle motion and a group of vibration influence equations, and solve them jointly to obtain the theoretical time interval distribution p(Δt) of particles passing through the detection area; then determine Δt0 based on the mean of the theoretical distribution plus three times the standard deviation.

[0351] In this way, we get the effective particle size data series {d j}.

[0352] Step 4: Perform spectrum analysis on the vibration signal a(t) within the vibration detection window to obtain the vibration frequency distribution curve X(f), and compare it with the standard vibration curve X0(f) to determine the abnormal vibration period.

[0353] Specifically, the difference D between the two spectra is calculated:

[0354]

[0355] When D exceeds the preset threshold, it is determined that there is vibration abnormality during this period, and the start time t of the vibration abnormality is recorded. b and the end time t e .

[0356] like Figure 3 As shown in the figure, it is the corresponding vibration signal spectrum diagram, which shows two curves: the measured spectrum X(f) and the standard spectrum X0(f). The measured spectrum (solid line) shows two main frequency peaks, located near 30Hz and 60Hz respectively. The standard spectrum (dashed line) also has a similar peak distribution, but the peak at 30Hz is slightly higher and the peak at 60Hz is slightly lower. This difference reflects the abnormal vibration and can be used to determine the abnormal vibration period.

[0357] Step 5: Eliminate the particle size data collected during the abnormal vibration period to obtain a valid particle size data sequence {d j}.

[0358] Step 6: Convert the effective particle size data series {d j}Classify by size, count the number of particles in each size class, and generate a particle size distribution curve.

[0359] Specifically, the particle size can be divided into m levels, and the representative size of each level is d i (i=1,2,...,m). Then the number of particles in the i-th size class is n i The percentage of the total is:

[0360]

[0361] Based on the percentage of each size class, a particle size distribution curve can be drawn, such as Figure 4 shown.

[0362] The horizontal axis represents the particle size (in millimeters), and the vertical axis represents the percentage of particles of each size. The curve shows a major peak (at about 0.5 mm) and a minor peak (at about 1.2 mm). This distribution pattern is consistent with the particle size classification and distribution statistics described in the embodiment. The chart clearly shows the size distribution characteristics of the sludge solidification soil particles, which can be used to evaluate the uniformity and quality of the product.

[0363] Step 7: Calculate the uniformity coefficient U of the particle size distribution curve and compare it with the preset range [U min , U max ] for comparison.

[0364] The calculation formula of uniformity coefficient U is:

[0365]

[0366] Where d 10 , d 30 , d 60 are the particle sizes corresponding to 10%, 30%, and 60% on the cumulative distribution curve respectively, and λ is the weight coefficient, which takes a value of 0.5.

[0367] Preset range [U min , U max ] is determined as follows: first, a set of particle distribution characteristic equations and a set of uniformity evaluation equations are established, and the distribution of the uniformity coefficient under different working conditions is obtained through Monte Carlo simulation; then, the preset range is determined with a 95% confidence interval of the distribution.

[0368] like Figure 5 As shown in the figure, the uniformity coefficient of the sample is U = 1.32, which falls within the preset range [1.2, 1.5], indicating that the particle size distribution is relatively uniform. The figure shows four bar graphs, representing the uniformity coefficient of the sample U (1.32), the lower limit of the preset range U min 2), upper limit U max5), and an example value (1.6) that is out of range. The two blue dashed lines mark the preset range [1.2, 1.5]. The uniformity coefficient of the sample (green column) falls within this range, as shown in the example "The uniformity coefficient of the sample U = 1.32, which falls within the preset range [1.2, 1.5], indicating that the particle size distribution is relatively uniform". This figure intuitively shows how to judge whether the uniformity of the sample meets the requirements.

[0369] Step 8: Determine whether the data volume of the effective particle size data sequence meets the statistical requirements. If not, send a prompt signal through an alarm.

[0370] Specifically, the total number of particles in the valid particle size data sequence is required to be no less than 1000, and the continuous sampling time is required to be no less than 300 seconds. When the data volume is insufficient, the alarm will send out an audible and visual signal to prompt that the sampling time needs to be extended or the number of sampling points needs to be increased.

[0371] Step 9: Display the analysis results on the display, including the particle size distribution curve, uniformity coefficient and effectiveness evaluation of the test results.

[0372] Through the implementation of the above intelligent algorithm, the particle analysis system can achieve a comprehensive and accurate assessment of the particle characteristics of sludge solidification soil. Specifically:

[0373] 1. The vibration signal analysis method can automatically eliminate abnormal data and improve the reliability of the detection results.

[0374] 2. The preset threshold calculated based on the theoretical model can more accurately judge the validity of the data.

[0375] 3. Introduce uniformity evaluation indicators to monitor product quality in real time and conduct feedback control of the production process.

[0376] 4. Integrates data from multiple sensors to comprehensively analyze the size, vibration, and deformation characteristics of particles.

[0377] Compared with traditional manual sampling and testing, this analysis system has the advantages of high efficiency and reliable results, providing strong support for the intelligent control of sludge solidification soil production.

Claims

1. A sludge solidification soil particle detection device, characterized in that: include: A detection box, a driving mechanism, a lifting mechanism, a detection mechanism and a sludge solidified soil particle analysis system; the detection box is in the shape of a rectangular parallelepiped, a feed inlet is arranged on the top of the detection box, and a discharge port is arranged on the bottom of the detection box; a sieve plate assembly is arranged horizontally inside the detection box, and the sieve plate assembly divides the internal space of the detection box into an upper chamber and a lower chamber; the sieve plate assembly comprises a fixed frame and a sieve plate body, the fixed frame is fixed to the inner wall of the detection box by bolts, and the sieve plate body is installed on the fixed frame through a frame; a plurality of detection holes are arranged on the sieve plate body, and the detection holes are distributed in an array; the driving mechanism comprises a driving motor and an eccentric wheel, the driving motor is fixedly mounted on the outer wall of the detection box through a motor seat, and the eccentric wheel is fixedly connected to the output shaft of the driving motor; a shock-absorbing support assembly is arranged at the bottom of the detection box, and the shock-absorbing support assembly comprises a base and a shock-absorbing spring, the base is rectangular, and shock-absorbing springs are evenly arranged at the four corners of the base.

2. The sludge solidification soil particle detection device according to claim 1, characterized in that: The fixed frame includes a main frame and reinforcing ribs; the main frame is rectangular, and a plurality of fixing holes are evenly distributed around the main frame; the reinforcing ribs are fixed to the inner side of the main frame in a cross shape, and the reinforcing ribs and the main frame are fixedly connected by welding; the frame is fixed to the main frame by bolts.

3. The sludge solidification soil particle detection device according to claim 2, characterized in that: The lifting mechanism includes a lifting frame, a screw rod and a handwheel; the lifting frame includes a column and a crossbeam, the columns are fixed on both sides of the detection box, and the crossbeam is connected between the two columns; the screw rod is vertically arranged inside the column, the top end of the screw rod is fixedly connected to the handwheel, and the bottom end of the screw rod is rotatably connected to the column through a bearing seat; a threaded hole matching the screw rod is arranged on the crossbeam, and the crossbeam forms a lifting transmission match with the screw rod through the threaded hole.

4. The sludge solidification soil particle detection device according to claim 3, characterized in that: The detection mechanism includes a weighing sensor and a strain gauge; the weighing sensor is fixedly installed in the lower chamber of the detection box, and a receiving tray is arranged on the top of the weighing sensor; the strain gauge is arranged on the lower surface of the sieve plate body to detect the deformation of the sieve plate body; the sieve plate body is made of elastic steel plate; the detection aperture on the sieve plate body decreases from top to bottom; the aperture range of the detection hole is 1 mm to 50 mm; the inner wall of the detection hole is smooth and free of burrs.

5. The sludge solidification soil particle detection device according to claim 4, characterized in that: A feed hopper is provided at the feed inlet of the detection box, and the feed hopper is funnel-shaped; the upper part of the feed hopper is a square opening, and the lower part is a circular outlet; the feed hopper and the detection box are connected by a flange, and a plurality of bolt holes are evenly arranged on the flange; a material blocking assembly is provided at the discharge port of the detection box; the material blocking assembly includes a baffle and an operating rod, the baffle is rotatably connected to the discharge port by a hinge, and the operating rod is fixed to one end of the baffle; the baffle is in a closed state under the action of gravity; a protective layer is provided on the inner wall of the detection box; the protective layer is made of wear-resistant rubber material; the protective layer is fixedly connected to the inner wall of the detection box by an adhesive.

6. The sludge solidification soil particle detection device according to claim 5, characterized in that: It also includes a compensation sensor group, specifically a position sensor, a velocity sensor, an acceleration sensor, a temperature and humidity sensor, an air pressure sensor, a laser particle size analyzer, an industrial camera, a displacement sensor and a vibration sensor; wherein the position sensor adopts a photoelectric position sensor, which is installed on the inner wall of the detection box and is distributed in a circular array; the velocity sensor adopts a laser Doppler velocity sensor, which is installed on the side wall of the detection box; the acceleration sensor adopts a piezoelectric acceleration sensor, which is installed on the outer wall of the detection box; the temperature and humidity sensor and the air pressure sensor are installed inside the detection box; the laser particle size analyzer is installed on the side wall of the detection box, and a measuring window is provided between the laser particle size analyzer and the detection area; the industrial camera is installed on the top of the detection box and is aligned with the detection area; the displacement sensor adopts an eddy current displacement sensor, which is installed on the support frame of the sieve plate assembly; there are multiple vibration sensors, which are installed on each sieve plate body.

7. The sludge solidification soil particle detection device according to claim 6, characterized in that: The sludge solidification soil particle analysis system comprises a single chip microcomputer, a display, a particle size detector, a compensation sensor group, a data collector, and an alarm; the single chip microcomputer is electrically connected to the display, the particle size detector, the compensation sensor group, the data collector, and the alarm; a particle analysis module is provided in the single chip microcomputer for analyzing the size distribution of sludge solidification soil particles and the validity of the detection data; when the particle analysis module is running, it is used to perform the following steps: S10, collecting a vibration signal from a vibration sensor within a preset time period, and determining a vibration detection window according to the vibration signal; S20, collecting particle size data output by the particle size detector within the vibration detection window, and recording the collection time corresponding to each particle size data; S30, calculating the time interval between two adjacent collection moments, comparing the time interval with a preset threshold, and eliminating particle size data corresponding to a time interval greater than the preset threshold; S40, performing spectrum analysis on the vibration signal in the vibration detection window to obtain a vibration frequency distribution curve, comparing the vibration frequency distribution curve with a standard vibration curve, and determining a vibration abnormality period; S50, eliminating the particle size data collected during the abnormal vibration period to obtain a valid particle size data sequence; S60, classifying the effective particle size data sequence according to size, counting the number of particles in each size class, and generating a particle size distribution curve; S70, determining whether the data volume of the effective particle size data sequence meets the statistical requirements, and when the statistical requirements are not met, sending a prompt signal through the alarm; S80, calculating the uniformity coefficient of the particle size distribution curve, comparing the uniformity coefficient with a preset range, and when the uniformity coefficient exceeds the preset range, sending an alarm signal through the alarm device; S90, displaying the particle size distribution curve, the uniformity coefficient, and the effectiveness evaluation of the detection result on the display.

8. The sludge solidification soil particle detection device according to claim 7, characterized in that: The step of determining the preset threshold value specifically includes: Step 1, establishing a particle motion differential equation group, wherein the particle motion differential equation group includes a particle falling velocity equation, a particle force balance equation, a particle acceleration equation and a particle displacement equation; Step 2: Establish a vibration influence equation group, wherein the vibration influence equation group includes a vibration displacement equation, a vibration velocity equation, a vibration acceleration equation and a vibration period equation; Step 3: Based on the collected particle position information, particle falling speed information, vibration acceleration information and detection area environmental parameter information, the particle motion differential equation group and the vibration influence equation group are solved simultaneously to obtain a theoretical time interval distribution curve of particles passing through the detection area; Step 4: According to the mean and standard deviation of the theoretical time interval distribution curve, determine the preset threshold as the mean plus three times the standard deviation.

9. The sludge solidification soil particle detection device according to claim 8, characterized in that: The criterion for judging whether the data volume of the effective particle size data sequence meets the statistical requirements is that the total number of particles in the effective particle size data sequence is not less than 1000 and the continuous sampling time is not less than 300 seconds.

10. The sludge solidification soil particle detection device according to claim 9, characterized in that: The step of determining the preset range specifically includes: Step a, establishing a particle distribution characteristic equation group, wherein the particle distribution characteristic equation group includes a particle size distribution density equation, a particle cumulative distribution equation, a particle characteristic size equation and a particle dispersion equation; Step b, establishing a uniformity evaluation equation group, wherein the uniformity evaluation equation group includes a uniformity coefficient calculation equation, an unevenness coefficient equation, a curvature coefficient equation and a gradient coefficient equation; Step c, based on the collected particle size distribution information, particle morphology characteristic information and detection system operation status information, the particle distribution characteristic equation group and the uniformity evaluation equation group are substituted into the Monte Carlo simulation calculation to obtain the uniformity coefficient distribution under different working conditions; Step d: According to the confidence interval of the uniformity coefficient distribution, determine the preset range as the 95% confidence interval of the uniformity coefficient distribution.

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