A mud preparation process for karst foundation grouting

Abstract

The present application belongs to the technical field of foundation mud preparation, and provides a mud preparation process for karst foundation grouting, wherein the actual screening rate is calculated, the actual screening rate is compared with the expected screening rate, and whether the quality of the screened bentonite meets the standard is evaluated, if not, the particle size analysis treatment is performed on the bentonite retained on the screen to obtain the particle size uniformity value, so that the particle size uniformity degree of the bentonite retained on the screen is reflected through the particle size uniformity value, and the particle size uniformity value is compared with the particle size uniformity threshold value to obtain the reason that the actual screening rate does not meet the standard, i.e. the particle size is not uniform.

Description

Technical Field

[0001] This invention belongs to the field of foundation mud preparation technology, specifically a mud preparation process for karst foundation grouting. Background Technology

[0002] Bentonite is a natural mineral material with excellent water absorption and swelling properties and colloidal dispersion properties. In grouting slurry, bentonite plays a role in increasing slurry viscosity, improving slurry fluidity and suspension stability. Therefore, by controlling the sieve aperture size, the distribution of bentonite powder can be adjusted to meet the specific requirements of grouting process for slurry particle size.

[0003] In existing technologies, when sieving bentonite powder, if the sieving efficiency is low, the sieve screen is typically replaced. However, a potential risk is that the bentonite powder particles are large, affecting the quality of the grouting slurry. Therefore, this application analyzes the particle size uniformity of the bentonite retained on the sieve screen in cases of low sieving efficiency. If the particle size of the retained bentonite on the sieve screen is uneven, the correlation between the bentonite particle size and the grinding pressure of the grinder over multiple historical periods is analyzed. If the correlation is high, the degree of pressure fluctuation of the grinder during the grinding period is analyzed. If the pressure fluctuation of the grinder is large, it is determined that the cause of the uneven bentonite particle size is the pressure fluctuation of the grinder. Therefore, by adjusting the grinder pressure and reducing the pressure fluctuation, the problem of uneven bentonite particle size after grinding caused by large grinding pressure fluctuations, resulting in a low actual sieving rate of the ground bentonite, is solved.

[0004] Therefore, the present invention provides a mud preparation process for grouting in karst foundations. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] A mud preparation process for grouting in karst foundations includes:

[0008] Step 1: The ground lumpy bentonite is sieved, and the retention data of bentonite on the sieve is obtained. The retention data is analyzed to obtain the actual sieve passing rate. The actual sieve passing rate is compared with the expected sieve passing rate. The process is as follows:

[0009] If the actual screening rate is greater than or equal to the expected screening rate, a screening pass signal is generated.

[0010] If the actual sieving rate is less than the expected sieving rate, a sieving analysis signal is generated.

[0011] Step 2: If a sieve analysis signal is generated, then the bentonite retained on the sieve is subjected to particle size analysis to obtain particle size analysis data. The particle size analysis data is then analyzed and processed to obtain a particle size unevenness signal.

[0012] Step 3: Based on the adjustment and optimization signal, obtain the grinding pressure data of the grinding machine during the grinding cycle, analyze and process the grinding pressure data to obtain the pressure fluctuation value, and analyze and process the pressure fluctuation value to obtain the correlation analysis signal.

[0013] Step 4: Based on the correlation analysis signal, compare and analyze the grinding force data of the grinding mill with the particle size analysis data of bentonite over multiple historical periods to obtain the correlation analysis value. Then, analyze and process the correlation analysis value to obtain the adjustment and optimization signal.

[0014] Step 5: Based on the adjustment and optimization signal, obtain the adjustment coefficient, adjust and optimize the grinding pressure of the grinder, and solve the problem of low actual sieving rate caused by uneven grinding force of the grinder, which leads to uneven particle size of the bentonite after grinding.

[0015] As a further aspect of the present invention, the process of analyzing and processing the retained data is as follows:

[0016] Obtain the mass retained on the screen and label it as M. Z The retained mass M on the screen Z Substitute into the formula: The actual sieve passing rate δ is calculated, where M represents the total mass of the blocky bentonite after grinding.

[0017] As a further aspect of the present invention, the process of granularity analysis data analysis and processing is as follows:

[0018] The bentonite on the screen is scanned by an image scanner to obtain the scanned image. The scanned image is then analyzed and processed using image analysis methods to obtain particle size analysis data, which includes particle size values.

[0019] Based on the scanned image, the area where the screen is retained within the scanned image is divided into sub-regions for analysis. The particle size values ​​within each sub-region are obtained, and the sum of the particle size values ​​within each sub-region is taken as the average value to obtain the particle value of the sub-region.

[0020] The sub-region deviation value is obtained by subtracting the particle value of the sub-region from the sieved particle value.

[0021] The retention bias value is obtained by summing the corresponding sub-region deviation values ​​of all analysis sub-regions and taking the average value.

[0022] All analytical sub-regions within the sieve retention area are combined to obtain multiple retention analysis groups, where each retention analysis group is composed of two different analytical sub-regions.

[0023] Extract any stagnation analysis group, subtract the corresponding sub-region deviation values ​​of two different analysis sub-regions within the stagnation analysis group, and take the absolute value to obtain the comparison deviation value;

[0024] The comparison deviation values ​​of all the retention analysis groups are summed and averaged to obtain the comparison degree value;

[0025] The retention deviation value and the comparison degree value are added together to obtain the particle size uniformity value. If the particle size uniformity value is greater than the particle size uniformity threshold, a particle size unevenness signal is generated.

[0026] A further aspect of this invention is as follows: the grinding force data of the grinding mill is compared and analyzed with the particle size analysis data of bentonite, and the process is as follows:

[0027] Grinding pressure data is obtained from the grinding log of the grinding machine during the grinding cycle. The grinding pressure data includes the current pressure value. The grinding cycle is divided into several grinding periods, and the current pressure value within each grinding period is obtained.

[0028] Establish a two-dimensional coordinate system with time on the X-axis and pressure on the Y-axis. Substitute the current pressure values ​​for all grinding periods into the two-dimensional coordinate system and connect them using curves to obtain the current pressure change curve.

[0029] As a further aspect of the present invention, the method for obtaining the correlation analysis signal is as follows:

[0030] Obtain the peak and trough points of the current pressure change curve, and subtract the ordinates of adjacent peak and trough points to obtain the unit change value;

[0031] The fluctuation level is obtained by summing all the unit change values ​​and taking the average.

[0032] The difference between the x-coordinates of adjacent peaks and troughs is taken as the absolute value to obtain the duration of the change interval.

[0033] The fluctuation interval value is obtained by summing all the variation intervals and taking the average.

[0034] The pressure fluctuation value is obtained by calculating the ratio of the fluctuation degree value to the fluctuation interval value.

[0035] The pressure fluctuation value is compared with the pressure fluctuation threshold. If the pressure fluctuation value is greater than the pressure fluctuation threshold, a correlation analysis signal is generated.

[0036] A further aspect of this invention is as follows: The grinding force data of the grinding mill is compared and analyzed with the particle size analysis data of bentonite. The comparison and analysis process is as follows:

[0037] The analysis process involves randomly selecting one historical period from multiple historical periods, as follows:

[0038] The grinding force data of the grinding machine in the historical period is obtained by using historical grinding pressure reports. The grinding force data includes grinding pressure values. The historical period is divided into several historical time periods, and the grinding pressure values ​​in the historical time periods are obtained.

[0039] The grinding pressure values ​​within the historical time period are sorted and integrated according to the chronological order of the historical time period to obtain the historical grinding table Q = {y1, y2, y3, ..., y...} n};

[0040] Based on the historical grinding table Q, the elements in the historical grinding table Q are extracted in chronological order of the historical time period to obtain the pressure analysis group, which is composed of elements from two historical grinding tables Q.

[0041] The difference between two elements in the pressure analysis group is used to obtain the pressure change value over time.

[0042] Multiple stress analysis groups are integrated into a set F = {f1, f2, f3, ..., f...} according to their order. j};

[0043] The particle size values ​​within a historical period are sorted and integrated according to the chronological order of the historical period to obtain the historical granularity table P = {X1, X2, X3, ..., X...}. c};

[0044] Based on the historical granularity table P, the elements in the historical granularity table P are extracted in chronological order of historical periods to obtain the granularity analysis group. The granularity analysis group is composed of elements from two historical granularity tables Q.

[0045] The difference between two elements in the particle size analysis group is used to obtain the particle size change value over time.

[0046] Multiple granularity analysis groups are integrated into a set R = {r1, r2, r3, ..., r...} according to the order of the granularity analysis groups. m}

[0047] A further aspect of this invention is that the method for obtaining the correlation stability value and the proportion of cyclical trends is as follows:

[0048] Extract f1 from set F and r1 from set R. Group f1 and r1 into an association analysis group, until all f1 in set F is included.j With set R, r m Until a correlation analysis is completed;

[0049] Within the association analysis group, the sign of elements in set F is compared with the sign of elements in set R, as follows:

[0050] If f1 in set F and r1 in set R have the same sign, then they are a group with the same trend.

[0051] If f1 in set F and r1 in set R have different signs, then they are non-same trend analysis groups;

[0052] The number of groups with the same trend is counted, and the ratio of the number of groups with the same trend to the total number of groups with the same trend is calculated to obtain the proportion of groups with the same trend.

[0053] The summation and averaging of the corresponding proportions of the same trend across multiple historical periods yields the periodic trend proportion, which is denoted as G. q ;

[0054] Within the same trend analysis group, the ratio of the time period pressure change value to the time period granularity change value is calculated to obtain the unit correlation value;

[0055] The difference between adjacent cell correlation values ​​is calculated, and the absolute value is taken to obtain the cell correlation difference.

[0056] Calculate the variance of all unit correlation differences to obtain historical correlation values;

[0057] The difference between the corresponding historical correlation values ​​of adjacent historical periods is obtained by taking the absolute value.

[0058] The mean of the sums of all historical correlation differences is used to obtain the stable correlation value, which is denoted as G. w .

[0059] As a further aspect of the present invention, the method for obtaining the correlation analysis value is as follows:

[0060] Let the proportion of the cyclical trend be G. q With correlation stable value G w Substitute into the formula: The correlation analysis value ZL is calculated, where α and β represent preset proportional coefficients.

[0061] A further aspect of this invention is: adjusting and optimizing the signal acquisition method as follows:

[0062] The correlation analysis value is compared with the correlation analysis threshold. If the correlation analysis value is greater than or equal to the correlation analysis threshold, an adjustment and optimization signal is generated.

[0063] As a further aspect of the present invention, the grinding pressure adjustment and optimization process of the grinding machine is as follows:

[0064] The periodic correlation value is obtained by summing all the unit correlation values ​​within the historical period and taking the average.

[0065] The adjustment coefficient is obtained by summing the corresponding periodic correlation values ​​of all historical periods and taking the average value, and is denoted as K.

[0066] Substitute the adjustment factor K into the formula: F t =F d ×K, calculate the required adjustment amount F for grinding pressure. t , of which F d This represents the current grinding pressure.

[0067] The beneficial effects of this invention are as follows:

[0068] (1) This invention calculates the actual sieving rate, compares the actual sieving rate with the expected sieving rate, and evaluates whether the quality of the sieved bentonite meets the standard. If it does not meet the standard, the bentonite retained on the screen is subjected to particle size analysis to obtain the particle size uniformity value. The particle size uniformity value reflects the uniformity of the bentonite particles retained on the screen. The particle size uniformity value is compared with the particle size uniformity threshold to find that the failure of the actual sieving rate to meet the standard is due to the uneven particle size.

[0069] (2) The present invention determines whether there is a grinding pressure fluctuation phenomenon. If there is a grinding pressure fluctuation phenomenon, the correlation between the particle size of bentonite and the grinding pressure of the grinding machine in multiple historical periods is analyzed. If the correlation is large, it is determined that the reason for the uneven particle size of bentonite caused by the current grinding is the grinding machine pressure fluctuation.

[0070] (3) This invention obtains an adjustment coefficient by analyzing the correlation between the particle size of bentonite and the grinding pressure of the grinder in multiple historical periods. Based on the adjustment coefficient, if the grinding pressure fluctuates greatly during the grinding process, the adjustment amount required for the grinding pressure is calculated by the formula using the adjustment coefficient. This solves the problem that the particle size of the bentonite after grinding is uneven due to the large fluctuation of the grinding pressure, which in turn leads to a low actual sieve pass rate of the ground bentonite. Attached Figure Description

[0071] The invention will now be further described with reference to the accompanying drawings.

[0072] Figure 1 This is a flowchart of the sieving efficiency evaluation and analysis of a mud preparation process for karst foundation grouting according to the present invention.

[0073] Figure 2This is a flowchart analyzing the causes of particle size unevenness in a mud preparation process for karst foundation grouting according to the present invention.

[0074] Figure 3 This is a flowchart of the optimized screening efficiency of a mud preparation process for grouting in karst foundations according to the present invention. Detailed Implementation

[0075] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0076] Example 1

[0077] The mud preparation process for grouting in karst foundations described in this embodiment of the invention specifically includes:

[0078] S1. Remove the water content in 50-70 parts of bentonite by freeze drying, crush the freeze-dried bentonite, grind the crushed block bentonite, sieve it, and take the sieved bentonite powder.

[0079] S2. Add 15-20 parts of sodium sulfate and 10-15 parts of carboxymethyl cellulose to bentonite powder, stir evenly at a temperature of 40-50℃, stirring while heating, and stir for 1-2 hours after reaching the temperature of 40-50℃. After stirring evenly, keep warm for 1-2 hours to obtain sodium-modified carboxymethyl cellulose-bentonite.

[0080] S3. Add 15-20 parts of graphene oxide to the sodium-treated carboxymethyl cellulose-bentonite, and ultrasonically stir for 1-2 hours until uniform to obtain a preliminary mud slurry.

[0081] S4. Add 8-12 parts of nano titanium dioxide to the preliminary mud slurry. First, disperse the nano titanium dioxide with 8-12 parts of deionized water in an ultrasonic bath, and then add it to the preliminary mud slurry. Then stir at a high temperature of 200±20℃ for 3-5 hours. After stirring evenly, keep warm for 1-2 hours to obtain the grouting mud slurry.

[0082] S5. Conduct performance testing on the grouting slurry to determine its properties.

[0083] Example 2

[0084] like Figure 1 - Figure 2 As shown in Example 1, the mud preparation process for grouting in karst foundations according to this embodiment of the invention includes:

[0085] Step 1: The ground block bentonite is sieved and the retention data of bentonite retained on the sieve is obtained. The retention data includes the retention mass. The retention data is analyzed and processed to obtain the actual sieve rate. The actual sieve rate is compared with the expected sieve rate. If the actual sieve rate is less than the expected sieve rate, a sieve analysis signal is generated.

[0086] In some instances, the mass retained on the screen is obtained and labeled as M. Z The retained mass M on the screen Z Substitute into the formula: The actual sieve passing rate δ is calculated, where M represents the total mass of the ground block bentonite.

[0087] The actual screening rate is compared with the expected screening rate, as follows:

[0088] If the actual sieving rate is greater than or equal to the expected sieving rate, it means that the quality of the bentonite actually sieved meets the standards during the sieving process, and a sieving compliance signal is generated.

[0089] If the actual sieving rate is less than the expected sieving rate, it means that the quality of the bentonite actually sieved did not meet the standard during the sieving process, and a sieving analysis signal is generated.

[0090] Step 2: Based on the sieving analysis signal, perform particle size analysis on the bentonite retained on the sieve to obtain particle size analysis data. Analyze and process the particle size analysis data to output the particle size uniformity value and compare it with the particle size uniformity threshold. If the particle size uniformity value is greater than the particle size uniformity threshold, a particle size non-uniformity signal is generated.

[0091] In some embodiments, the bentonite on the screen is scanned by an image scanner to obtain a scanned image, and the scanned image is analyzed and processed using an image analysis method to obtain particle size analysis data, wherein the particle size analysis data includes particle size values.

[0092] Based on the scanned image, the area where the screen is retained within the scanned image is divided into sub-regions for analysis. The particle size values ​​within each sub-region are obtained, and the sum of the particle size values ​​within each sub-region is calculated and the average is taken to obtain the particle value of the sub-region.

[0093] It should be noted that the areas where the screen remains in the scanned image are divided into equal areas; therefore, the areas of the sub-regions to be analyzed are equal.

[0094] The sub-region deviation value is obtained by subtracting the particle value of the sub-region from the sieved particle value.

[0095] The retention bias value is obtained by summing the corresponding sub-region deviation values ​​of all analysis sub-regions and taking the average value.

[0096] It should be noted that the particle size distribution is obtained by summing the particle size values ​​of the sieved bentonite particles and taking the average value.

[0097] All analytical sub-regions within the sieve retention area are combined to obtain multiple retention analysis groups, where each retention analysis group is composed of two different analytical sub-regions.

[0098] Extract any stagnation analysis group, subtract the corresponding sub-region deviation values ​​of two different analysis sub-regions within the stagnation analysis group, and take the absolute value to obtain the comparison deviation value;

[0099] The comparison deviation values ​​of all the retention analysis groups are summed and averaged to obtain the comparison degree value;

[0100] For example, the sieve retention area is divided into three analytical sub-regions of equal area, namely analytical sub-region A, analytical sub-region B and analytical sub-region C. The particle value of the sub-region corresponding to analytical sub-region A is -100 mesh, the particle value of the sub-region corresponding to analytical sub-region B is -150 mesh and the particle value of the sub-region corresponding to analytical sub-region C is -120 mesh, and the sieved particle value is -200 mesh.

[0101] The sub-region deviation value of 100 mesh is obtained by subtracting the particle value of -100 mesh from the sieved particle value of -200 mesh corresponding to sub-region A. The sub-region deviation value of 50 mesh is obtained by subtracting the particle value of -150 mesh from the sieved particle value of -200 mesh corresponding to sub-region B. The sub-region deviation value of 80 mesh is obtained by subtracting the particle value of -120 mesh from the sieved particle value of -200 mesh corresponding to sub-region C.

[0102] Combining sub-regions A, B, and C yields three retention analysis groups: AB retention analysis group, AC retention analysis group, and BC retention analysis group.

[0103] Based on the AB retention analysis group, the difference between the sub-region deviation values ​​corresponding to the sub-regions of analysis A and analysis B within the AB retention analysis group is calculated, and the absolute value is taken to obtain a comparison deviation value of 50 mesh. Based on the AC retention analysis group, the difference between the sub-region deviation values ​​corresponding to the sub-regions of analysis A and analysis C within the AC retention analysis group is calculated, and the absolute value is taken to obtain a comparison deviation value of 20 mesh. Based on the AB retention analysis group, the difference between the sub-region deviation values ​​corresponding to the sub-regions of analysis B and analysis C within the BC retention analysis group is calculated, and the absolute value is taken to obtain a comparison deviation value of 30 mesh.

[0104] The comparison deviation values ​​of 50 mesh corresponding to the AB retention analysis group, 20 mesh corresponding to the AC retention analysis group, and 30 mesh corresponding to the BC retention analysis group are added together and the average value is taken to obtain the comparison degree value of 33.333 mesh (rounded to three decimal places).

[0105] The particle size uniformity value is obtained by summing the retention deviation value and the comparison degree value.

[0106] It is understandable that the particle size uniformity value means that it is calculated by the retention deviation value and the comparison degree value. The retention deviation value reflects the degree of deviation between the overall bentonite particle size retained on the screen and the particle size of the sieved particles, while the comparison degree value reflects the degree of deviation between the bentonite particle size at different positions on the screen. Therefore, it comprehensively reflects the uniformity of the bentonite particle size retained on the screen.

[0107] The particle size uniformity value is compared with the particle size uniformity threshold, as follows:

[0108] If the particle size uniformity value is greater than the particle size uniformity threshold, it indicates that the bentonite particles retained on the screen are relatively uneven in size, generating a particle size unevenness signal.

[0109] If the particle size uniformity value is less than or equal to the particle size uniformity threshold, it indicates that the bentonite particles retained on the screen are relatively uniform in size, generating a particle size uniformity signal.

[0110] The specific implementation scheme of this invention is as follows: by calculating the actual sieving rate, comparing the actual sieving rate with the expected sieving rate, and evaluating whether the quality of the sieved bentonite meets the standard, if it does not meet the standard, the bentonite retained on the screen is subjected to particle size analysis to obtain a particle size uniformity value. The particle size uniformity value reflects the uniformity of the bentonite particles retained on the screen, and the particle size uniformity value is compared with the particle size uniformity threshold to find that the failure of the actual sieving rate to meet the standard is due to the uneven particle size.

[0111] Example 3

[0112] like Figure 1 - Figure 2 As shown in Example 1, 50-70 parts of bentonite were freeze-dried to remove water. The freeze-dried bentonite was then crushed, and the crushed lumps were ground and sieved. Therefore, it is easy to imagine that the uneven particle size of the bentonite retained on the sieve during sieving is due to the pressure of the grinder affecting the particle size uniformity during the grinding of the crushed lumps. The mud preparation process for grouting in karst foundations described in this embodiment of the invention includes:

[0113] Step 3: Based on the particle size unevenness signal, obtain the grinding pressure data of the grinder during the grinding cycle, analyze and process the grinding pressure data to obtain the pressure fluctuation value, and compare it with the pressure fluctuation threshold. If the pressure fluctuation value is greater than the pressure fluctuation threshold, generate a correlation analysis signal.

[0114] In some embodiments, grinding pressure data is obtained from the grinding log of the grinder during the grinding cycle. The grinding pressure data includes the current pressure value. The grinding cycle is divided into several grinding periods, and the current pressure value within each grinding period is obtained.

[0115] It should be noted that the grinding cycle is divided into equal time intervals, and the duration of each grinding period is the same.

[0116] Establish a two-dimensional coordinate system with time on the X-axis and pressure on the Y-axis. Substitute the current pressure values ​​for all grinding periods into the two-dimensional coordinate system and connect them using curves to obtain the current pressure change curve.

[0117] Obtain the peak and trough points of the current pressure change curve, and subtract the ordinates of adjacent peak and trough points to obtain the unit change value;

[0118] The fluctuation level is obtained by summing all the unit change values ​​and taking the average.

[0119] The difference between the x-coordinates of adjacent peaks and troughs is taken as the absolute value to obtain the duration of the change interval.

[0120] The fluctuation interval value is obtained by summing all the variation intervals and taking the average.

[0121] The pressure fluctuation value is obtained by calculating the ratio of the fluctuation degree value to the fluctuation interval value.

[0122] The pressure fluctuation value is compared with the pressure fluctuation threshold, as follows:

[0123] If the pressure fluctuation value is greater than the pressure fluctuation threshold, it indicates that the grinding pressure of the grinder fluctuates greatly during the grinding cycle, generating a correlation analysis signal.

[0124] If the pressure fluctuation value is less than or equal to the pressure fluctuation threshold, it indicates that the grinding pressure of the grinder fluctuates little during the grinding cycle, generating a correlated non-analytical signal.

[0125] Step 4: Based on the correlation analysis signal, compare and analyze the grinding force data of the grinding mill with the particle size analysis data of bentonite over multiple historical periods to obtain the correlation analysis value, and compare it with the correlation analysis threshold. If the correlation analysis value is greater than or equal to the correlation analysis threshold, a pressure adjustment signal is generated.

[0126] It should be noted that multiple historical cycles have the same duration;

[0127] In some embodiments, an arbitrary historical period is selected from multiple historical periods for analysis, as follows:

[0128] The grinding force data of the grinding machine in the historical period is obtained by using historical grinding pressure reports. The grinding force data includes grinding pressure values. The historical period is divided into several historical time periods, and the grinding pressure values ​​in the historical time periods are obtained.

[0129] It should be noted that the method of dividing the historical cycle is the same as the method of dividing the grinding cycle;

[0130] The grinding pressure values ​​within the historical time period are sorted and integrated according to the chronological order of the historical time period to obtain the historical grinding table Q = {y1, y2, y3, ..., y...} n For example, y1 represents the grinding pressure value corresponding to the first historical period within the historical cycle, y n This represents the grinding pressure value corresponding to the nth historical period within the historical cycle, where n represents the total number of historical periods within the historical cycle.

[0131] Based on the historical grinding table Q, elements in the historical grinding table Q are extracted in chronological order of historical periods to obtain pressure analysis groups. Each pressure analysis group consists of elements from two historical grinding tables Q. For example, the pressure analysis group can be {y1, y2}, {y2, y3}, or {y3, y4}, etc., where {y1, y2} is the first pressure analysis group, {y2, y3} is the second pressure analysis group, and {y3, y4} is the third pressure analysis group.

[0132] The difference between two elements in the pressure analysis group is used to obtain the pressure change value over time.

[0133] Multiple stress analysis groups are integrated into a set F = {f1, f2, f3, ..., f...} according to their order. j For example, f1 is the pressure change value corresponding to the time period of the first pressure analysis group, f j Let J represent the pressure change value for the time period corresponding to the j-th pressure analysis group, where j represents the total number of pressure analysis groups.

[0134] The particle size values ​​within a historical period are sorted and integrated according to the chronological order of the historical period to obtain the historical granularity table P = {X1, X2, X3, ..., X...}. c For example, X1 represents the particle size value corresponding to the first historical period within the historical cycle, X cThis represents the particle size value corresponding to the c-th historical period within the historical cycle, where c represents the total number of historical periods within the historical cycle.

[0135] Based on the historical granularity table P, elements in the historical granularity table P are extracted according to the time sequence of historical periods to obtain granularity analysis groups. Each granularity analysis group is composed of elements from two historical granularity tables Q. For example, the granularity analysis group can be {X1, X2}, {X2, X3}, or {X3, X4}, etc., where {X1, X2} is the first granularity analysis group, {X2, X3} is the second granularity analysis group, and {X3, X4} is the third granularity analysis group.

[0136] The difference between two elements in the particle size analysis group is used to obtain the particle size change value over time.

[0137] Multiple granularity analysis groups are integrated into a set R = {r1, r2, r3, ..., r...} according to the order of the granularity analysis groups. m For example, r1 is the particle size change value corresponding to the time period of the first particle size analysis group, r m This represents the particle size change value corresponding to the m-th particle size analysis group during the corresponding time period, where m represents the total number of particle size analysis groups.

[0138] For example, f1 is extracted from set F and r1 is extracted from set R. f1 and r1 are grouped into an association analysis group, until f1 in set F is included. j With set R, r m Until a correlation analysis is completed;

[0139] For example, within an association analysis group, the sign of elements in set F is compared with the sign of elements in set R, for instance:

[0140] The process of comparing f1 in set F with r1 in set R is as follows:

[0141] If f1 in set F and r1 in set R have the same sign, then they are a group with the same trend.

[0142] If f1 in set F and r1 in set R have different signs, then they are non-same trend analysis groups;

[0143] The number of groups with the same trend is counted, and the ratio of the number of groups with the same trend to the total number of groups with the same trend is calculated to obtain the proportion of groups with the same trend.

[0144] The summation and averaging of the corresponding proportions of the same trend across multiple historical periods yields the periodic trend proportion, which is denoted as G. q ;

[0145] Within the same trend analysis group, the ratio of the time period pressure change value to the time period granularity change value is calculated to obtain the unit correlation value;

[0146] The difference between adjacent cell correlation values ​​is calculated, and the absolute value is taken to obtain the cell correlation difference.

[0147] Calculate the variance of all unit correlation differences to obtain historical correlation values;

[0148] The difference between the corresponding historical correlation values ​​of adjacent historical periods is obtained by taking the absolute value.

[0149] The mean of the sums of all historical correlation differences is used to obtain the stable correlation value, which is denoted as G. w ;

[0150] Let the proportion of the cyclical trend be G. q With correlation stable value G w Substitute into the formula: The correlation analysis value ZL is calculated, where α and β represent preset proportional coefficients, and α takes the value of 1.355 and β takes the value of 2.154.

[0151] Understandably, the meaning of the correlation analysis value is to assess the stability of the correlation between the grinding force of the grinder (especially the change in grinding pressure) and the particle size change of bentonite over multiple historical periods. Specifically, the larger the value, the more stable the correlation between the change in the grinding force of the grinder and the particle size change of bentonite over multiple historical periods; the smaller the value, the less stable the correlation between the change in the grinding force of the grinder and the particle size change of bentonite over multiple historical periods.

[0152] The correlation analysis value is compared with the correlation analysis threshold, as follows:

[0153] If the correlation analysis value is greater than or equal to the correlation analysis threshold, it indicates that the correlation between the grinding force of the grinder and the particle size of bentonite is relatively stable over multiple historical periods, generating an adjustment and optimization signal.

[0154] If the correlation analysis value is less than the correlation analysis threshold, it indicates that the correlation between the grinding force of the grinder and the particle size change of bentonite is unstable over multiple historical periods, generating a non-adjusted optimization signal. When a non-adjusted optimization signal is generated, it includes, but is not limited to, analyzing the causes of the grinding process or other processes that affect the particle size change of bentonite.

[0155] The specific implementation method of this invention is as follows: by determining whether there is a grinding pressure fluctuation, if there is a grinding pressure fluctuation, the correlation between the particle size of bentonite and the grinding pressure of the grinder in multiple historical periods is analyzed. If the correlation is large, it is determined that the cause of the uneven particle size of bentonite in the current grinding is the grinding pressure fluctuation.

[0156] Example 4

[0157] like Figure 1 - Figure 2 As shown, based on Examples 1, 2, and 3, the mud preparation process for grouting in karst foundations described in this embodiment of the invention includes:

[0158] Step 5: Based on the adjustment and optimization signal, obtain the adjustment coefficient, adjust and optimize the grinding pressure of the grinder, and solve the problem of low actual sieving rate caused by uneven grinding force of the grinder, which leads to uneven particle size of the bentonite after grinding.

[0159] In some embodiments, the summation of all unit correlation values ​​within a historical period is taken as the average to obtain the period correlation value;

[0160] The adjustment coefficient is obtained by summing the corresponding periodic correlation values ​​of all historical periods and taking the average value, and is denoted as K.

[0161] Substitute the adjustment factor K into the formula: F t =F d ×K, calculate the required adjustment amount F for grinding pressure. t , of which F d This is expressed as the current grinding pressure;

[0162] The specific implementation scheme of this invention is as follows: by analyzing the correlation between the particle size of bentonite and the grinding pressure of the grinder in multiple historical periods, an adjustment coefficient is obtained. Based on the adjustment coefficient, if the grinding pressure fluctuates greatly during the grinding process, the adjustment amount required for the grinding pressure is calculated using the formula based on the adjustment coefficient. This solves the problem that the particle size of the bentonite after grinding is uneven due to the large fluctuation of the grinding pressure, which in turn leads to a low actual sieving rate of the ground bentonite.

[0163] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A mud preparation process for grouting in karst foundations, characterized in that: include: Step 1: The ground lumpy bentonite is sieved, and the retention data of bentonite on the sieve is obtained. The retention data is analyzed to obtain the actual sieve passing rate. The actual sieve passing rate is compared with the expected sieve passing rate. The process is as follows: If the actual screening rate is greater than or equal to the expected screening rate, a screening pass signal is generated. If the actual sieving rate is less than the expected sieving rate, a sieving analysis signal is generated. Step 2: If a sieve analysis signal is generated, the bentonite retained on the sieve is subjected to particle size analysis to obtain particle size analysis data. The particle size analysis data is then analyzed and processed to obtain a particle size unevenness signal. Step 3: Based on the particle size unevenness signal, obtain the grinding pressure data of the grinder during the grinding cycle, analyze and process the grinding pressure data to obtain the pressure fluctuation value, and analyze and process the pressure fluctuation value to obtain the correlation analysis signal. Step 4: Based on the correlation analysis signal, compare and analyze the grinding force data of the grinding mill with the particle size analysis data of bentonite over multiple historical periods to obtain the correlation analysis value. Then, analyze and process the correlation analysis value to obtain the adjustment and optimization signal. Step 5: Based on the adjustment and optimization signal, obtain the adjustment coefficient, adjust and optimize the grinding pressure of the grinder, and solve the problem of low actual sieving rate caused by uneven grinding force of the grinder, which leads to uneven particle size of the bentonite after grinding.

2. The mud preparation process for grouting in karst foundations according to claim 1, characterized in that: The process of analyzing and processing stagnant data is as follows: Obtain the mass retained on the screen and mark it. The retained mass on the screen Substitute into the formula: The actual sieving rate was calculated. ,in, This represents the total mass of the blocky bentonite after grinding.

3. The mud preparation process for grouting in karst foundations according to claim 1, characterized in that: The process of granularity analysis data analysis and processing is as follows: The bentonite on the screen is scanned by an image scanner to obtain the scanned image. The scanned image is then analyzed and processed using image analysis methods to obtain particle size analysis data, which includes particle size values. Based on the scanned image, the area where the screen is retained within the scanned image is divided into sub-regions for analysis. The particle size values ​​within each sub-region are obtained, and the sum of the particle size values ​​within each sub-region is calculated and the average is taken to obtain the particle value of the sub-region. The sub-region deviation value is obtained by subtracting the particle value of the sub-region from the sieved particle value. The retention bias value is obtained by summing the corresponding sub-region deviation values ​​of all analysis sub-regions and taking the average value. All analytical sub-regions within the sieve retention area are combined to obtain multiple retention analysis groups, where each retention analysis group is composed of two different analytical sub-regions. Extract any stagnation analysis group, subtract the corresponding sub-region deviation values ​​of two different analysis sub-regions within the stagnation analysis group, and take the absolute value to obtain the comparison deviation value; The comparison deviation values ​​of all the retention analysis groups are summed and averaged to obtain the comparison degree value; The retention deviation value and the comparison degree value are added together to obtain the particle size uniformity value. If the particle size uniformity value is greater than the particle size uniformity threshold, a particle size unevenness signal is generated.

4. The mud preparation process for grouting in karst foundations according to claim 1, characterized in that: The process of analyzing and processing grinding pressure data is as follows: Grinding pressure data is obtained from the grinding log of the grinding machine during the grinding cycle. The grinding pressure data includes the current pressure value. The grinding cycle is divided into several grinding periods, and the current pressure value within each grinding period is obtained. Establish a two-dimensional coordinate system with time on the X-axis and pressure on the Y-axis. Substitute the current pressure values ​​for all grinding periods into the two-dimensional coordinate system and connect them using curves to obtain the current pressure change curve.

5. The mud preparation process for grouting in karst foundations according to claim 4, characterized in that: The method for obtaining correlation analysis signals is as follows: Obtain the peak and trough points of the current pressure change curve, and subtract the ordinates of adjacent peak and trough points to obtain the unit change value; The fluctuation level is obtained by summing all the unit change values ​​and taking the average. The difference between the x-coordinates of adjacent peaks and troughs is taken as the absolute value to obtain the duration of the change interval. The fluctuation interval value is obtained by summing all the variation intervals and taking the average. The pressure fluctuation value is obtained by calculating the ratio of the fluctuation degree value to the fluctuation interval value. The pressure fluctuation value is compared with the pressure fluctuation threshold. If the pressure fluctuation value is greater than the pressure fluctuation threshold, a correlation analysis signal is generated.

6. The mud preparation process for grouting in karst foundations according to claim 1, characterized in that: The grinding force data of the grinding mill was compared and analyzed with the particle size analysis data of bentonite. The comparison and analysis process is as follows: The analysis process involves randomly selecting one historical period from multiple historical periods, as follows: The grinding force data of the grinding machine in the historical period is obtained by using historical grinding pressure reports. The grinding force data includes grinding pressure values. The historical period is divided into several historical time periods, and the grinding pressure values ​​in the historical time periods are obtained. The grinding pressure values ​​within the historical time period are sorted and integrated according to the chronological order of the historical time period to obtain the historical grinding table Q={ , , ... }; Based on the historical grinding table Q, the elements in the historical grinding table Q are extracted in chronological order of the historical time period to obtain the pressure analysis group, which is composed of elements from two historical grinding tables Q. The difference between two elements in the pressure analysis group is used to obtain the pressure change value over time. Multiple stress analysis groups are integrated into a set F={ according to the order of stress analysis groups. , , ... }; The particle size values ​​within a historical period are sorted and integrated according to the chronological order of the historical period to obtain the historical granularity table P={ , , ... }; Based on the historical granularity table P, the elements in the historical granularity table P are extracted in chronological order of historical periods to obtain the granularity analysis group. The granularity analysis group is composed of elements from two historical granularity tables Q. The difference between two elements in the particle size analysis group is used to obtain the particle size change value over time. Multiple granularity analysis groups are integrated into a set R={ according to the order of the granularity analysis groups. , , ... } 7. The mud preparation process for grouting in karst foundations according to claim 6, characterized in that: The method for obtaining association analysis values ​​is as follows: Extract from set F Extract from set R ,Will and Form an association analysis group until set F is included. With set R Until a correlation analysis is completed; Within the association analysis group, the sign of elements in set F is compared with the sign of elements in set R, as follows: If set F contains With set R If the positive and negative values ​​are the same, they are considered as a group with the same trend. If set F contains With set R If the positive and negative values ​​are different, they are considered as different trend analysis groups; By counting the number of trend-following analysis groups and calculating the ratio of the number of trend-following analysis groups to the total number of correlation analysis groups, the proportion of trend-following groups can be obtained. The summation and averaging of the corresponding proportions of the same trend across multiple historical periods yields the periodic trend proportion, which is then labeled as follows. ; Within the same trend analysis group, the unit correlation value can be obtained by calculating the ratio between the value of pressure change over a period and the value of granularity change over a period. The difference between adjacent cell correlation values ​​is calculated, and the absolute value is taken to obtain the cell correlation difference. Calculate the variance of all unit correlation differences to obtain historical correlation values; The difference between the corresponding historical correlation values ​​of adjacent historical periods is obtained by taking the absolute value. Sum all historical correlation differences, take the average, and obtain the stable correlation value, which is then labeled as... ; The proportion of cyclical trends is Related stable values Substituting into the formula: Z The correlation analysis value was calculated. ,in, , This is represented as a preset proportional coefficient.

8. The mud preparation process for grouting in karst foundations according to claim 7, characterized in that: The method for acquiring the optimized signal is as follows: The correlation analysis value is compared with the correlation analysis threshold. If the correlation analysis value is greater than or equal to the correlation analysis threshold, an adjustment and optimization signal is generated.

9. The mud preparation process for grouting in karst foundations according to claim 1, characterized in that: The process of adjusting and optimizing the grinding pressure of the grinder is as follows: The periodic correlation value is obtained by summing all the unit correlation values ​​within the historical period and taking the average. The adjustment coefficient is obtained by summing the corresponding periodic correlation values ​​of all historical periods and taking the average value, and is denoted as K. Substitute the adjustment factor K into the formula: The required adjustment amount of grinding pressure is calculated. ,in, This represents the current grinding pressure.

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