Slurry preparation process for karst foundation grouting

Abstract

The invention belongs to the technical field of foundation slurry preparation, and provides a slurry preparation process for karst foundation grouting, which comprises the following steps: calculating an actual sieving rate, comparing the actual sieving rate with an expected sieving rate, evaluating whether the quality of sieved bentonite reaches the standard or not, and if not, judging whether the quality of the sieved bentonite reaches the standard or not. The method comprises the following steps: performing particle size analysis treatment on bentonite retained on a screen to obtain a particle size uniformity value, reflecting the particle size uniformity degree of the bentonite retained on the screen through the particle size uniformity value, and comparing the particle size uniformity value with a particle size uniformity threshold value to obtain that the actual screening rate does not reach the standard due to non-uniform particle size.

Description

Technical Field

[0001] The invention belongs to the technical field of foundation mud preparation, in particular to a mud preparation process for grouting of karst foundation. Background Art

[0002] Bentonite is a natural mineral material with excellent water absorption and swelling properties and colloidal dispersion properties. In grouting mud, bentonite plays a role in increasing mud viscosity, improving mud 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 the grouting process for mud particle size.

[0003] In the prior art, when bentonite powder is screened, if the screening efficiency is low, the screen will be replaced. However, the potential risk is that the bentonite powder particles are large, which affects the quality of the grouting mud. Therefore, in this application, under such conditions of low screening efficiency, the particle size uniformity of the retained bentonite on the screen is analyzed. If the particle size of the retained bentonite on the screen is uneven, the correlation between the particle size of the bentonite and the grinding pressure of the grinder in multiple historical cycles is analyzed. If the correlation is large, the pressure fluctuation degree of the grinder during the grinding cycle is analyzed. If the pressure fluctuation degree 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 of the grinder, the problem of uneven bentonite particle size after grinding due to large grinding pressure fluctuation, which in turn leads to a low actual screening rate of the bentonite after grinding, is solved.

[0004] To this end, the present invention provides a slurry preparation process for karst foundation grouting. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] A slurry preparation process for grouting in karst foundation, comprising:

[0008] Step 1: Screen the ground bentonite block and obtain the retention data of the bentonite retained on the sieve. Analyze and process the retention data to obtain the actual screening rate. Compare the actual screening rate with the expected screening rate. The process is as follows:

[0009] If the actual screening rate is greater than or equal to the expected screening rate, a screening standard reaching signal is generated;

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

[0011] Step 2: If a sieving analysis signal is generated, a particle size analysis is performed on the bentonite retained on the sieve to obtain particle size analysis data, and the particle size analysis data is analyzed and processed to obtain a particle size unevenness signal;

[0012] Step 3: Based on the adjustment optimization signal, the grinding pressure data of the grinder in the grinding cycle is obtained, and the grinding pressure data is analyzed and processed to obtain a pressure fluctuation value, and based on the pressure fluctuation value, an analysis is performed to obtain a correlation analysis signal;

[0013] Step 4: Based on the correlation analysis signal, the grinding force data of the grinder is compared and analyzed with the particle size analysis data of bentonite in multiple historical periods to obtain a correlation analysis value, and analysis and processing are performed based on the correlation analysis value to obtain an adjustment optimization signal;

[0014] Step 5: Based on the adjustment optimization signal, the adjustment coefficient is obtained, and the grinding pressure of the grinder is adjusted and optimized to solve the problem of low actual screening rate caused by uneven particle size of bentonite after grinding due to uneven grinding force of the grinder.

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

[0016] Obtain the mass retained on the screen and mark it as M Z , the retention mass M retained on the screen Z Substituting into the formula: The actual screening rate δ is calculated, where M represents the total mass of the bentonite block after grinding.

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

[0018] Scanning the bentonite on the sieve with an image scanner to obtain a scanned image, and analyzing and processing the scanned image with an image analysis method to obtain particle size analysis data, wherein the particle size analysis data includes a particle size value;

[0019] Based on the scanned image, the sieve retention area in the scanned image is divided to obtain analysis sub-areas, the particle size values ​​in the analysis sub-areas are obtained, and the particle size values ​​in the analysis sub-areas are added and averaged to obtain the sub-area particle value;

[0020] Subtract the sub-region particle value from the sieved particle value to obtain the sub-region deviation value;

[0021] The sub-region deviation values ​​corresponding to all the analyzed sub-regions are summed and averaged to obtain the retention deviation value;

[0022] Combining all analysis sub-regions within the retention region of the screen to obtain a plurality of retention analysis groups, wherein a retention analysis group is formed by combining two different analysis sub-regions;

[0023] A retention analysis group is randomly selected, and the sub-region deviation values ​​corresponding to two different analysis sub-regions in the retention analysis group are subtracted, and the absolute value is taken to obtain the comparison deviation value;

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

[0025] The retention deviation value and the comparison degree value are added and summed 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] As a further solution of the present invention, the grinding force data of the grinder is compared and analyzed with the particle size analysis data of the bentonite, and the process is as follows:

[0027] Obtaining grinding pressure data through a grinding log of the grinding machine during a grinding cycle, wherein the grinding pressure data includes a current pressure value, dividing the grinding cycle into a plurality of grinding time periods, and obtaining the current pressure value within the grinding time period;

[0028] A two-dimensional coordinate system is established, with the X-axis representing time and the Y-axis representing pressure. The current pressure values ​​in all grinding periods are substituted into the two-dimensional coordinate system and connected using curves to obtain the current pressure change curve.

[0029] As a further solution of the present invention, the method of acquiring the correlation analysis signal is:

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

[0031] Add all unit change values ​​and take the average to get the fluctuation degree value;

[0032] Subtract the horizontal coordinates of adjacent peak points and trough points, take the absolute value, and get the change interval duration;

[0033] Add up all the change intervals and take the average to get the fluctuation interval value;

[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, and if the pressure fluctuation value is greater than the pressure fluctuation threshold, a correlation analysis signal is generated.

[0036] As a further solution of the present invention, the grinding force data of the grinder is compared and analyzed with the particle size analysis data of bentonite. The comparison and analysis process is as follows:

[0037] Select any one historical period from multiple historical periods for analysis. The process is as follows:

[0038] The grinding force data of the grinder in the historical period is obtained through the historical grinding pressure report, wherein the grinding force data includes the grinding pressure value, and 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 ​​in the historical period are sorted and integrated according to the time sequence of the historical 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 according to the time sequence of the historical periods to obtain a pressure analysis group, wherein the pressure analysis group is composed of the elements in the two historical grinding tables Q;

[0041] Subtract the two elements in the pressure analysis group to obtain the pressure change value for the period;

[0042] Integrate multiple pressure analysis groups into a set F = {f1, f2, f3, ..., f j};

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

[0044] Based on the historical granularity table P, extract the elements in the historical granularity table P according to the time sequence of the historical period to obtain a granularity analysis group, wherein the granularity analysis group is composed of the elements in the two historical granularity tables Q;

[0045] Subtract the two elements in the particle size analysis group to obtain the particle size change value for the time period;

[0046] Integrate multiple particle size analysis groups into a set R = {r1, r2, r3, ..., r m}.

[0047] As a further solution of the present invention, the method of obtaining the associated stability value and the periodic trend ratio is:

[0048] Extract f1 from set F and r1 from set R, and combine f1 and r1 into an association analysis group until f1 in set F is extracted.j With the set R m Until a correlation analysis is formed;

[0049] In the association analysis group, the positivity of the elements in set F is compared with the positivity of the elements in set R. The process is as follows:

[0050] If f1 in set F and r1 in set R have the same positive and negative properties, they are the same trend analysis group;

[0051] If the positive and negative values ​​of f1 in set F and r1 in set R are different, they are non-identical trend analysis groups;

[0052] Count the number of analysis groups with the same trend, calculate the ratio of the number of analysis groups with the same trend to the total number of associated analysis groups, and get the proportion of the same trend;

[0053] The corresponding trend proportions of multiple historical periods are summed up and averaged to obtain the period trend proportion, which is marked as G. q ;

[0054] In the same trend analysis group, the ratio of the pressure change value of the period to the particle size change value of the period is calculated to obtain the unit correlation value;

[0055] Subtract the adjacent unit correlation values, take the absolute value, and obtain the unit correlation difference;

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

[0057] Subtract the historical correlation values ​​corresponding to adjacent historical periods, take the absolute value, and obtain the historical correlation difference;

[0058] All historical correlation differences are added together and averaged to obtain the correlation stability value, which is marked as G. w .

[0059] As a further solution of the present invention, the method of obtaining the association analysis value is:

[0060] The proportion of the cycle trend is G q Associated with stability value G w Substituting into the formula: The correlation analysis value ZL is calculated, where α and β represent preset proportional coefficients.

[0061] As a further solution of the present invention, the acquisition method of the adjustment optimization signal is as follows:

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

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

[0064] Add and average all the unit correlation values ​​in the historical period to obtain the period correlation value;

[0065] Add up the cycle correlation values ​​corresponding to all historical cycles and take the average to get the adjustment coefficient, which is marked as K;

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

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

[0068] (1) The present invention calculates the actual screening rate, compares the actual screening rate with the expected screening rate, and evaluates whether the quality of the screened bentonite meets the standard. If it does not meet the standard, the bentonite retained on the sieve is subjected to a particle size analysis to obtain a particle size uniformity value, thereby reflecting the particle size uniformity of the bentonite retained on the sieve through the particle size uniformity value, and compares the particle size uniformity value with a particle size uniformity threshold value to obtain whether the actual screening rate does not meet the standard due to uneven particle size;

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

[0070] (3) The present 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 there is a large fluctuation in the grinding pressure during the grinding process of the grinder, the required adjustment amount of the grinding pressure is calculated using a formula through the adjustment coefficient, thereby solving the problem of uneven bentonite particle size after grinding due to large fluctuations in grinding pressure, which in turn leads to a low actual screening rate of the bentonite after grinding. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The present invention will be further described below in conjunction with the accompanying drawings.

[0072] Figure 1 It is a flow chart of screening efficiency evaluation and analysis of a slurry preparation process for karst foundation grouting according to the present invention;

[0073] Figure 2It is a flow chart for analyzing the causes of uneven particle size in a slurry preparation process for grouting in karst foundations according to the present invention;

[0074] Figure 3 The invention discloses a screening efficiency optimization flow chart of a mud preparation process for karst foundation grouting. DETAILED DESCRIPTION

[0075] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0076] Example 1

[0077] The slurry preparation process for grouting in karst foundation described in the embodiment of the present invention specifically comprises the following steps:

[0078] S1, removing water from 50-70 parts of bentonite by freeze drying, crushing the freeze-dried bentonite, grinding the crushed block bentonite, sieving, and taking the sieved bentonite powder;

[0079] S2, adding 15-20 parts of sodium sulfate and 10-15 parts of carboxymethyl cellulose to bentonite powder, stirring evenly at a temperature of 40-50° C., stirring while heating, stirring for 1-2 hours when the temperature reaches 40-50° C., and then keeping warm for 1-2 hours after stirring evenly to obtain sodium-containing carboxymethyl cellulose-bentonite;

[0080] S3, adding 15-20 parts of graphene oxide to the sodium-treated carboxymethyl cellulose-bentonite, stirring by ultrasonic for 1-2 hours, stirring evenly, and obtaining a preliminary slurry;

[0081] S4, adding 8 to 12 parts of nano titanium dioxide to the preliminary slurry, firstly dispersing the nano titanium dioxide with 8 to 12 parts of deionized water in ultrasound, then adding it to the preliminary slurry, then stirring at a high temperature of 200±20°C for 3 to 5 hours, stirring evenly and keeping warm for 1 to 2 hours to obtain grouting slurry;

[0082] S5. Perform performance testing on the grouting slurry to determine the performance of the grouting slurry.

[0083] Example 2

[0084] like Figure 1 - Figure 2 As shown, based on Example 1, a slurry preparation process for grouting in karst foundation according to an embodiment of the present invention comprises:

[0085] Step 1: Screening the ground bentonite block, and obtaining retention data of the bentonite retained on the sieve, wherein the retention data includes the retention mass, analyzing and processing the retention data to obtain an actual screening rate, comparing the actual screening rate with the expected screening rate, and generating a screening analysis signal if the actual screening rate is less than the expected screening rate;

[0086] In some examples, the retained mass on the screen is obtained and labeled as M Z , the retention mass M retained on the screen Z Substituting into the formula: The actual screening rate δ is calculated, where M represents the total mass of the bentonite block after grinding;

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

[0088] If the actual screening rate is greater than or equal to the expected screening rate, it means that the quality of the bentonite actually screened meets the standard during the screening process, and a screening standard-reaching signal is generated;

[0089] If the actual screening rate is less than the expected screening rate, it means that the quality of the bentonite actually screened does not meet the standard during the screening process, and a screening analysis signal is generated;

[0090] Step 2: Based on the sieving analysis signal, the bentonite retained on the sieve is subjected to particle size analysis to obtain particle size analysis data, the particle size analysis data is analyzed and processed, and a particle size uniformity value is output and compared with a particle size uniformity threshold value. If the particle size uniformity value is greater than the particle size uniformity threshold value, a particle size unevenness signal is generated;

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

[0092] Based on the scanned image, the sieve retention area in the scanned image is divided to obtain analysis sub-areas, the particle size values ​​in the analysis sub-areas are obtained, and the particle size values ​​in the analysis sub-areas are added and averaged to obtain the sub-area particle value;

[0093] It should be noted that the screen retention area in the scanned image is divided in equal areas, so the areas of the analyzed sub-areas are equal;

[0094] Subtract the sub-region particle value from the sieved particle value to obtain the sub-region deviation value;

[0095] The sub-region deviation values ​​corresponding to all the analyzed sub-regions are summed and averaged to obtain the retention deviation value;

[0096] It should be noted that the sieved particle value is obtained by adding and averaging the sieved bentonite particle size values;

[0097] Combining all analysis sub-regions within the retention region of the screen to obtain a plurality of retention analysis groups, wherein a retention analysis group is formed by combining two different analysis sub-regions;

[0098] A retention analysis group is randomly selected, and the sub-region deviation values ​​corresponding to two different analysis sub-regions in the retention analysis group are subtracted, and the absolute value is taken to obtain the comparison deviation value;

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

[0100] Exemplarily, for example, the retention area of ​​the sieve is divided into three analysis sub-areas of equal area, namely, analysis sub-area A, analysis sub-area B and analysis sub-area C, wherein the sub-area particle value corresponding to the analysis sub-area A is -100 mesh, the sub-area particle value corresponding to the analysis sub-area B is -150 mesh, the sub-area particle value corresponding to the analysis sub-area C is -120 mesh, and the sieved particle value is -200 mesh;

[0101] Subtract the sub-region particle value of -100 mesh corresponding to the A analysis sub-region from the sieved particle value of -200 mesh to obtain a sub-region deviation value of 100 mesh, subtract the sub-region particle value of -150 mesh corresponding to the B analysis sub-region from the sieved particle value of -200 mesh to obtain a sub-region deviation value of 50 mesh, and subtract the sub-region particle value of -120 mesh corresponding to the C analysis sub-region from the sieved particle value of -200 mesh to obtain a sub-region deviation value of 80 mesh;

[0102] The A analysis sub-region, the B analysis sub-region, and the C analysis sub-region were combined to obtain three retention analysis groups, namely, the AB retention analysis group, the AC retention analysis group, and the BC retention analysis group;

[0103] Based on the AB retention analysis group, the subregion deviation value corresponding to the A analysis subregion in the AB retention analysis group is subtracted from the subregion deviation value corresponding to the B analysis subregion, and the absolute value is taken to obtain a comparison deviation value of 50 mesh; based on the AC retention analysis group, the subregion deviation value corresponding to the A analysis subregion in the AC retention analysis group is subtracted from the subregion deviation value corresponding to the C analysis subregion, and the absolute value is taken to obtain a comparison deviation value of 20 mesh; based on the AB retention analysis group, the subregion deviation value corresponding to the B analysis subregion in the BC retention analysis group is subtracted from the subregion deviation value corresponding to the C analysis subregion, and the absolute value is taken to obtain a comparison deviation value of 30 mesh;

[0104] The comparison deviation value of 50 mesh corresponding to the AB retention analysis group, the comparison deviation value of 20 mesh corresponding to the AC retention analysis group, and the comparison deviation value of 30 mesh corresponding to the BC retention analysis group were added and averaged to obtain a comparison degree value of 33.333 mesh (rounded to three decimal places);

[0105] The retention deviation value and the comparison degree value are added and summed to obtain the particle size uniformity value;

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

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

[0108] If the particle size uniformity value is greater than the particle size uniformity threshold, it means that the bentonite particles retained on the sieve are relatively uneven, and a particle size unevenness signal is generated;

[0109] If the particle size uniformity value is less than or equal to the particle size uniformity threshold, it means that the bentonite particles retained on the sieve are relatively uniform, and a particle size uniformity signal is generated;

[0110] The specific implementation scheme of the embodiment of the present invention is: by calculating the actual screening rate, the actual screening rate is compared with the expected screening rate, and whether the quality of the screened bentonite is up to standard is evaluated. If it is not up to standard, the bentonite retained on the sieve is subjected to particle size analysis to obtain a particle size uniformity value, thereby reflecting the particle size uniformity of the bentonite retained on the sieve through the particle size uniformity value, and comparing the particle size uniformity value with the particle size uniformity threshold value, it is obtained that the actual screening rate does not meet the standard due to uneven particle size.

[0111] Example 3

[0112] like Figure 1 - Figure 2 As shown, in Example 1, 50 to 70 parts of bentonite are freeze-dried to remove water from the bentonite, the freeze-dried bentonite is crushed, the crushed block bentonite is ground, and sieved. Therefore, it can be easily thought that the bentonite particles retained on the sieve are uneven during sieving. This is because when the crushed block bentonite is ground, the pressure of the grinder affects the uneven bentonite particles. A slurry preparation process for karst foundation grouting according to an embodiment of the present invention includes:

[0113] Step 3: Based on the uneven particle size signal, the grinding pressure data of the grinder during the grinding cycle is obtained, and the grinding pressure data is analyzed and processed to obtain a pressure fluctuation value, and compared with the pressure fluctuation threshold. If the pressure fluctuation value is greater than the pressure fluctuation threshold, a correlation analysis signal is generated;

[0114] In some embodiments, the grinding pressure data is obtained through a grinding log of the grinding machine during a grinding cycle, wherein the grinding pressure data includes a current pressure value, the grinding cycle is divided into a number of grinding time periods, and the current pressure value within the grinding time period is obtained;

[0115] It should be noted that the grinding cycle division is to divide the grinding cycle into equal time intervals, and the duration of each grinding period is the same;

[0116] A two-dimensional coordinate system is established, with the X-axis representing time and the Y-axis representing pressure. The current pressure values ​​in all grinding periods are substituted into the two-dimensional coordinate system and connected using curves to obtain the current pressure change curve.

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

[0118] Add all unit change values ​​and take the average to get the fluctuation degree value;

[0119] Subtract the horizontal coordinates of adjacent peak points and trough points, take the absolute value, and get the change interval duration;

[0120] Add up all the change intervals and take the average to get the fluctuation interval value;

[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 value as follows:

[0123] If the pressure fluctuation value is greater than the pressure fluctuation threshold, it means that the grinding pressure of the grinder fluctuates greatly during the grinding cycle, and a correlation analysis signal is generated;

[0124] If the pressure fluctuation value is less than or equal to the pressure fluctuation threshold, it means that the grinding pressure of the grinder fluctuates slightly during the grinding cycle, and a related non-analytical signal is generated;

[0125] Step 4: Based on the correlation analysis signal, the grinding force data of the grinder is compared and analyzed with the particle size analysis data of the bentonite in multiple historical periods to obtain a correlation analysis value, and 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, a pressure adjustment signal is generated;

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

[0127] In some embodiments, a historical period is randomly selected from multiple historical periods for analysis, and the process is as follows:

[0128] The grinding force data of the grinder in the historical period is obtained through the historical grinding pressure report, wherein the grinding force data includes the grinding pressure value, and 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 way the historical cycles are divided is the same as the way the grinding cycles are divided;

[0130] The grinding pressure values ​​in the historical period are sorted and integrated according to the time sequence of the historical period to obtain the historical grinding table Q = {y1, y2, y3, ..., y n}, for example, y1 represents the value of the grinding pressure value corresponding to the first historical period in the historical cycle, y n It represents the value of the grinding pressure value corresponding to the nth historical period in the historical cycle, and n represents the total number of historical periods in the historical cycle;

[0131] Based on the historical grinding table Q, the elements in the historical grinding table Q are extracted according to the time sequence of the historical periods to obtain a pressure analysis group, wherein the pressure analysis group is composed of the elements in two historical grinding tables Q, for example, the pressure analysis group can be {y1, y2}, {y2, y3} or {y3, y4}, etc., wherein {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] Subtract the two elements in the pressure analysis group to obtain the pressure change value for the period;

[0133] Integrate multiple pressure analysis groups into a set F = {f1, f2, f3, ..., f j}, for example, f1 is the pressure change value of the period corresponding to the first pressure analysis group, f j is the pressure change value of the period corresponding to the j-th pressure analysis group, and j represents the total number of pressure analysis groups;

[0134] The particle size values ​​in the historical period are sorted and integrated according to the time sequence of the historical period to obtain the historical particle size table P = {X1, X2, X3, ..., X c For example, X1 represents the value of the particle size corresponding to the first historical period in the historical cycle, X cIt represents the value of the particle size corresponding to the cth historical period in the historical cycle, where c represents the total number of historical periods in the historical cycle;

[0135] Based on the historical granularity table P, the elements in the historical granularity table P are extracted according to the time sequence of the historical period to obtain a granularity analysis group, wherein the granularity analysis group is composed of elements in two historical granularity tables Q, for example, the granularity analysis group can be {X1, X2}, {X2, X3} or {X3, X4}, etc., wherein {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] Subtract the two elements in the particle size analysis group to obtain the particle size change value for the time period;

[0137] Integrate multiple particle size analysis groups into a set R = {r1, r2, r3, ..., r m}, for example, r1 is the particle size change value of the first particle size analysis group corresponding to the time period, r m It is represented as the particle size change value of the time period corresponding to the mth particle size analysis group, and m represents the total number of particle size analysis groups;

[0138] For example, extract f1 from set F, extract r1 from set R, and form a correlation analysis group with f1 and r1 until f1 in set F is j With the set R m Until a correlation analysis is formed;

[0139] Exemplarily, within the association analysis group, the positivity of the elements in set F is compared with the positivity of the elements in set R, for example:

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

[0141] If f1 in set F and r1 in set R have the same positive and negative properties, they are the same trend analysis group;

[0142] If the positive and negative values ​​of f1 in set F and r1 in set R are different, they are non-identical trend analysis groups;

[0143] Count the number of analysis groups with the same trend, calculate the ratio of the number of analysis groups with the same trend to the total number of associated analysis groups, and get the proportion of the same trend;

[0144] The corresponding trend proportions of multiple historical periods are summed up and averaged to obtain the period trend proportion, which is marked as G. q ;

[0145] In the same trend analysis group, the ratio of the pressure change value of the period to the particle size change value of the period is calculated to obtain the unit correlation value;

[0146] Subtract the adjacent unit correlation values, take the absolute value, and obtain the unit correlation difference;

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

[0148] Subtract the historical correlation values ​​corresponding to adjacent historical periods, take the absolute value, and obtain the historical correlation difference;

[0149] All historical correlation differences are added together and averaged to obtain the correlation stability value, which is marked as G. w ;

[0150] The proportion of the cycle trend is G q Associated with stability value G w Substituting into the formula: The correlation analysis value ZL is calculated, where α and β represent preset proportional coefficients, and α is 1.355 and β is 2.154;

[0151] It can be understood that the meaning of the correlation analysis value is: evaluating the correlation stability between the grinding force of the grinder (especially the change of grinding pressure) and the change of bentonite particle size in multiple historical periods. Specifically, if this value is larger, it means that the correlation between the change of the grinding force of the grinder and the change of bentonite particle size in multiple historical periods is relatively stable. If this value is smaller, it means that the correlation between the change of the grinding force of the grinder and the change of bentonite particle size in multiple historical periods is relatively unstable.

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

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

[0154] If the correlation analysis value is less than the correlation analysis threshold, it means that in multiple historical periods, the correlation between the grinding force change of the grinder and the particle size change of the bentonite is relatively unstable, and a non-adjustment optimization signal is generated. When a non-adjustment optimization signal is generated, it includes but is not limited to performing a cause analysis on the grinding process or other processes that affect the particle size change of the bentonite;

[0155] The specific implementation method of the embodiment of the present invention is: by determining whether the grinding pressure fluctuation phenomenon occurs, if the grinding pressure fluctuation phenomenon occurs, analyzing the correlation between the particle size of the bentonite and the grinding pressure of the grinder in multiple historical cycles, if the correlation is large, it is determined that the reason for the uneven bentonite particle size caused by the current grinding is the grinding machine pressure fluctuation.

[0156] Example 4

[0157] like Figure 1 - Figure 2 As shown, based on Example 1, Example 2, and Example 3, a slurry preparation process for karst foundation grouting described in an embodiment of the present invention includes:

[0158] Step 5: Based on the adjustment optimization signal, the adjustment coefficient is obtained, and the grinding pressure of the grinder is adjusted and optimized to solve the problem of low actual screening rate caused by uneven particle size of bentonite after grinding due to uneven grinding force of the grinder;

[0159] In some embodiments, all unit association values ​​in the historical period are added and averaged to obtain the period association value;

[0160] Add up the cycle correlation values ​​corresponding to all historical cycles and take the average to get the adjustment coefficient, which is marked as K;

[0161] Substitute the adjustment factor K into the formula: F t =F d × K, calculate the required adjustment amount F of grinding pressure t , where F d Indicated as current grinding pressure;

[0162] The specific implementation scheme of the embodiment of the present invention is: 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, when the grinding pressure of the grinder fluctuates greatly during the grinding process, the adjustment coefficient is used to calculate the required adjustment amount of the grinding pressure using a formula, thereby solving the problem of uneven bentonite particle size after grinding due to large fluctuations in grinding pressure, which in turn leads to a low actual screening rate of the bentonite after grinding.

[0163] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A slurry preparation process for grouting in karst foundation, characterized in that: include: Step 1: Screen the ground bentonite block and obtain the retention data of the bentonite retained on the sieve. Analyze and process the retention data to obtain the actual screening rate. Compare the actual screening rate with the expected screening rate. The process is as follows: If the actual screening rate is greater than or equal to the expected screening rate, a screening standard reaching signal is generated; If the actual screening rate is less than the expected screening rate, a screening analysis signal is generated; Step 2: If a sieving analysis signal is generated, a particle size analysis is performed on the bentonite retained on the sieve to obtain particle size analysis data, and the particle size analysis data is analyzed and processed to obtain a particle size unevenness signal; Step 3: Based on the adjustment optimization signal, the grinding pressure data of the grinder in the grinding cycle is obtained, and the grinding pressure data is analyzed and processed to obtain a pressure fluctuation value, and based on the pressure fluctuation value, an analysis is performed to obtain a correlation analysis signal; Step 4: Based on the correlation analysis signal, the grinding force data of the grinder is compared and analyzed with the particle size analysis data of bentonite in multiple historical periods to obtain a correlation analysis value, and analysis and processing are performed based on the correlation analysis value to obtain an adjustment optimization signal; Step 5: Based on the adjustment optimization signal, the adjustment coefficient is obtained, and the grinding pressure of the grinder is adjusted and optimized to solve the problem of low actual screening rate caused by uneven particle size of bentonite after grinding due to uneven grinding force of the grinder.

2. A slurry preparation process for karst foundation grouting according to claim 1, characterized in that: The process of retention data analysis and processing is as follows: Obtain the mass retained on the screen and mark it as M Z , the retention mass M retained on the screen Z Substituting into the formula: The actual screening rate δ is calculated, where M represents the total mass of the bentonite block after grinding.

3. The slurry preparation process for grouting in karst foundation according to claim 1, characterized in that: The process of particle size analysis data analysis and processing is as follows: Scanning the bentonite on the sieve with an image scanner to obtain a scanned image, and analyzing and processing the scanned image with an image analysis method to obtain particle size analysis data, wherein the particle size analysis data includes a particle size value; Based on the scanned image, the sieve retention area in the scanned image is divided to obtain analysis sub-areas, the particle size values ​​in the analysis sub-areas are obtained, and the particle size values ​​in the analysis sub-areas are added and averaged to obtain the sub-area particle value; Subtract the sub-region particle value from the sieved particle value to obtain the sub-region deviation value; The sub-region deviation values ​​corresponding to all the analyzed sub-regions are summed and averaged to obtain the retention deviation value; Combining all analysis sub-regions within the retention region of the screen to obtain a plurality of retention analysis groups, wherein a retention analysis group is formed by combining two different analysis sub-regions; A retention analysis group is randomly selected, and the sub-region deviation values ​​corresponding to two different analysis sub-regions in the retention analysis group are subtracted, and the absolute value is taken to obtain the comparison deviation value; The comparison deviation values ​​corresponding to all retention analysis groups are added and averaged to obtain the comparison degree value; The retention deviation value and the comparison degree value are added and summed 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 slurry preparation process for grouting in karst foundation according to claim 1, characterized in that: The grinding force data of the grinder is compared with the particle size analysis data of bentonite. The process is as follows: Obtaining grinding pressure data through a grinding log of the grinding machine during a grinding cycle, wherein the grinding pressure data includes a current pressure value, dividing the grinding cycle into a plurality of grinding time periods, and obtaining the current pressure value within the grinding time period; A two-dimensional coordinate system is established, with the X-axis representing time and the Y-axis representing pressure. The current pressure values ​​in all grinding periods are substituted into the two-dimensional coordinate system and connected using curves to obtain the current pressure change curve.

5. A slurry preparation process for grouting in karst foundation according to claim 4, characterized in that: The method of obtaining the correlation analysis signal is as follows: Obtain the peak points and trough points of the current pressure change curve, and subtract the ordinates of adjacent peak points and trough points to obtain the unit change value; Add all unit change values ​​and take the average to get the fluctuation degree value; Subtract the horizontal coordinates of adjacent peak points and trough points, take the absolute value, and get the change interval duration; Add up all the change intervals and take the average to get the fluctuation interval value; 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, and if the pressure fluctuation value is greater than the pressure fluctuation threshold, a correlation analysis signal is generated.

6. The slurry preparation process for grouting in karst foundation according to claim 1, characterized in that: The grinding force data of the grinder is compared with the particle size analysis data of bentonite. The comparison and analysis process is as follows: Select any one historical period from multiple historical periods for analysis. The process is as follows: The grinding force data of the grinder in the historical period is obtained through the historical grinding pressure report, wherein the grinding force data includes the grinding pressure value, and 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 ​​in the historical period are sorted and integrated according to the time sequence of the historical period to obtain the historical grinding table Q = {y1, y2, y3, ..., y n }; Based on the historical grinding table Q, the elements in the historical grinding table Q are extracted according to the time sequence of the historical periods to obtain a pressure analysis group, wherein the pressure analysis group is composed of the elements in the two historical grinding tables Q; Subtract the two elements in the pressure analysis group to obtain the pressure change value for the period; Integrate multiple pressure analysis groups into a set F = {f1, f2, f3, ..., f j }; The particle size values ​​in the historical period are sorted and integrated according to the time sequence of the historical period to obtain the historical particle size table P = {X1, X2, X3, ..., X c }; Based on the historical granularity table P, extract the elements in the historical granularity table P according to the time sequence of the historical period to obtain a granularity analysis group, wherein the granularity analysis group is composed of the elements in the two historical granularity tables Q; Subtract the two elements in the particle size analysis group to obtain the particle size change value for the time period; Integrate multiple particle size analysis groups into a set R = {r1, r2, r3, ..., r m }.

7. A slurry preparation process for grouting in karst foundation according to claim 6, characterized in that: The method of obtaining the correlation stability value and the periodic trend ratio is as follows: Extract f1 from set F and r1 from set R, and combine f1 and r1 into an association analysis group until f1 in set F is extracted. j With the set R m Until a correlation analysis is formed; In the association analysis group, the positivity of the elements in set F is compared with the positivity of the elements in set R. The process is as follows: If f1 in set F and r1 in set R have the same positive and negative properties, they are the same trend analysis group; If the positive and negative values ​​of f1 in set F and r1 in set R are different, they are non-identical trend analysis groups; Count the number of analysis groups with the same trend, calculate the ratio of the number of analysis groups with the same trend to the total number of associated analysis groups, and get the proportion of the same trend; The corresponding trend proportions of multiple historical periods are summed up and averaged to obtain the period trend proportion, which is marked as G. q ; In the same trend analysis group, the ratio of the pressure change value of the period to the particle size change value of the period is calculated to obtain the unit correlation value; Subtract the adjacent unit correlation values, take the absolute value, and obtain the unit correlation difference; Calculate the variance of all unit correlation differences to obtain the historical correlation value; Subtract the historical correlation values ​​corresponding to adjacent historical periods, take the absolute value, and obtain the historical correlation difference; All historical correlation differences are added together and averaged to obtain the correlation stability value, which is marked as G. w .

8. The slurry preparation process for grouting in karst foundation according to claim 7, characterized in that: The association analysis value is obtained as follows: The proportion of the cycle trend is G q Associated with stability value G w Substituting into the formula: The correlation analysis value ZL is calculated, where α and β represent preset proportional coefficients.

9. A slurry preparation process for grouting in karst foundation according to claim 8, characterized in that: The method for obtaining the adjustment optimization signal is as follows: The correlation analysis value is compared with the correlation analysis threshold, and if the correlation analysis value is greater than or equal to the correlation analysis threshold, an adjustment optimization signal is generated.

10. The slurry preparation process for karst foundation grouting according to claim 1, characterized in that: The grinding pressure adjustment optimization process of the grinder is as follows: Add and average all the unit correlation values ​​in the historical period to obtain the period correlation value; Add up the cycle correlation values ​​corresponding to all historical cycles and take the average to get the adjustment coefficient, which is marked as K; Substitute the adjustment factor K into the formula: F t =F d ×K, calculate the required adjustment amount F of grinding pressure t , where F d Indicates the current grinding pressure.

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