CN111767602A - High arch dam progress simulation method based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of concrete pouring, aims to solve the problem of poor fitting degree between simulation and actual construction in the prior art, and provides a high arch dam progress simulation method based on the Internet of things, which comprises the following steps: step 1, initializing simulation initial conditions and simulation parameters, and establishing a simulation clock sequence; step 2, judging whether the dam is poured or not, if so, ending the simulation process, otherwise, screening the castable dam blocks meeting preset conditions from the warehouse surface, and determining the pouring sequence of each castable dam block according to the evaluation index and the index characteristic value of each castable dam block; and 3, determining a plurality of cable machine allocation schemes for pouring the castable dam blocks, selecting the cable machine allocation scheme corresponding to the minimum clock of the cable machine, and pouring the castable dam blocks according to the pouring sequence. The invention improves the fit degree of the simulation of the construction progress of the high arch dam and the actual construction.
Description
Technical Field
The invention relates to the technical field of concrete pouring, in particular to a high arch dam progress simulation method.
Background
With the deep application of the internet of things in dam construction, mass production data are obtained by installing sensing equipment on a concrete production, transportation and pouring link of concrete mixing plants, concrete transport vehicles, cable cranes, leveling machines, vibrating machines and the like, and the production rules of the concrete mixing plants, the concrete transport vehicles, the cable cranes, the leveling machines and the vibrating machines at a certain stage can be analyzed through a big data analysis method and updated in real time along with the pouring process of the dam. The Internet of things can promote the high arch dam construction progress simulation to be transformed from static state to dynamic state.
The simulation parameters of the existing dam concrete construction progress simulation method cannot be closely tracked and reflect the actual construction level. The operation parameters of the cable crane cannot truly reflect the operation habits of a cable crane driver, and the indexes of the operation efficiency of each link of the cable crane cannot truly reflect the influence of the change of the environment and seasons; in the updating process of the simulation clock, when pouring time is calculated, only the cable crane single-cycle time consumption and the bin surface operation process time consumption are generally considered, the mixing plant production efficiency and the transport vehicle concrete transport efficiency are not considered sufficiently, the bin leveling mechanical efficiency and the vibrating mechanical efficiency of bin surface operation are rated simply, and the rule of the operation efficiency cannot be reflected. In addition, in the existing simulation method, the logical relation between the cable crane and the castable bin surface is that the cable crane is selected firstly and then whether the cable crane can be placed in the bin for pouring is judged according to the position of the cable crane, so that the cable crane can come in and go out according to the actual condition that the cable crane is configured according to the castable bin surface in construction, the cable crane cannot be configured according to the comprehensive planning of a plurality of castable bins, and the utilization rate of the cable crane is improved. In the aspect of selecting the casting and building bin surface, the mutual influence between the balanced construction of a plurality of standard sections and the spatial position of the bin surface cannot be considered in the existing bin arranging method. In summary, the dam concrete construction progress simulation method in the prior art has the problem that the fitting degree between simulation and actual construction is poor.
Disclosure of Invention
The invention aims to solve the problem of poor fitting degree between simulation and actual construction in the prior art, and provides a high arch dam progress simulation method based on the Internet of things.
The technical scheme adopted by the invention for solving the technical problems is as follows: the high arch dam progress simulation method based on the Internet of things comprises the following steps:
step 1, initializing simulation initial conditions and simulation parameters, and establishing a simulation clock sequence, wherein the simulation initial conditions at least comprise dam block real-time pouring appearance, joint grouting real-time appearance, a dam block layering scheme, joint grouting partition-level grouting control parameters, mechanical equipment resources, the maximum number of bin faces and bin face lap ratio which are allowed to be poured simultaneously, and the simulation parameters at least comprise mechanical group operation parameters, bin face standby bin parameters and template parameters;
step 2, judging whether the dam is poured or not, if so, ending the simulation process, otherwise, screening the castable dam blocks meeting preset conditions from the warehouse surface, and determining the pouring sequence of each castable dam block according to the evaluation index and the index characteristic value of each castable dam block;
and 3, determining a plurality of cable machine allocation schemes for pouring the castable dam blocks, selecting the cable machine allocation scheme corresponding to the minimum clock of the cable machine, and pouring the castable dam blocks according to the pouring sequence.
Further, in step 1, the simulation clock sequence takes days as time periods and takes seconds as units, and the advancing steps of the simulation clock on the sequence are as follows:
step A, scanning all pouring machines, determining a global clock and a minimum clock of a cable crane for pouring each castable dam block, if the cable crane of the minimum clock is the cable crane in the corresponding cable crane allocation scheme, taking the minimum clock as pouring start time, and if not, taking the minimum clock of the cable crane in the corresponding cable crane allocation scheme as pouring start time;
b, judging whether the pouring starting time is effective working time or not, and if not, pushing the simulation clock to the next effective time period;
step C, calculating the pouring duration of each castable dam block, determining the pouring ending time of each castable dam block according to the pouring starting time and the pouring duration of each castable dam block, judging whether the pouring ending time is effective working time or not, if not, judging whether the corresponding castable dam block is poured or not, and if not, recalculating the pouring starting time;
d, recording the time corresponding to the pouring event of each castable dam block, advancing a simulation clock, and counting the circulating data;
and E, judging whether the dam is poured or not, if so, ending the simulation process, and otherwise, entering the step A.
Further, in step 3, the method for determining a plurality of cable crane deployment schemes for casting each castable dam block includes:
step 31, establishing a plane coordinate system by taking the cable machine main tower track as a vertical coordinate axis and taking one endpoint of the cable machine main tower track as an original point, obtaining boundary point coordinates of the surface of the storehouse to be poured, the length n of the cable machine main tower, endpoint coordinates [ (0,0), (0, R) ] of the cable machine main tower track and the minimum safe distance M of the cable machine, and determining the length range B of the surface of the storehouse which can be poured by each cable machine within preset time;
step 32, determining the total length L of the surface of the storehouse to be cast according to the boundary point coordinates of the surface of the storehouse to be castcAccording to the length range B of the pouring cabin surface of each cable crane in the preset time T and the total length L of the cabin surface to be pouredcDetermining the required number k of the cable machines, dividing the to-be-poured storehouse surface into a plurality of areas according to the required number k of the cable machines and the length range B of the corresponding cable machine pouring storehouse surface, and determining the center line position C corresponding to the length range of the corresponding cable machine pouring storehouse surfacej；
Step 33, according to the length n of the main tower of the cable crane and the end point coordinates [ (0,0), (0, R) of the track of the main tower of the cable crane]And determining the movable length range L of each cable crane by the minimum safety distance M of the adjacent cable cranesi；
Step 34, according to the central line position CjAnd the range of the movable length L of each cable craneiAnd determining a cable machine deployment scheme.
Further, in step 2, the method for determining the pouring sequence of each castable dam block includes:
step 21, setting the number of the casting warehouse surfaces as m, the number of the evaluation indexes as n, and the index characteristic value of the g-th evaluation index of the f-th casting dam block as kfgThen, we can get the index eigenvalue matrix K of m × n:
step 22, for eachIndex characteristic value k of evaluation indexfgCarrying out normalization processing to obtain a normalization value xfgAccording to the normalized value xfgNormalizing the index characteristic value matrix K to obtain a matrix X:
step 23, carrying out weighted summation on each index characteristic value of each castable dam block to obtain a comprehensive index F [ F ] of each castable dam block, wherein the calculation formula is as follows:
F[f]＝a1xf1+a2xf2+…+anxfn；
in the formula, a1、a2、…、anWeight value of each evaluation index, xf1、xf2、…、xfnNormalizing the values for each evaluation index;
the index characteristic value k for each evaluation indexfgThe normalization processing comprises the following steps:
for the evaluation index with the larger index characteristic value, the better, the index characteristic value is normalized:
for the evaluation index with the smaller index characteristic value, the better, the index characteristic value is normalized:
and 24, determining the pouring sequence of the dam blocks according to the comprehensive indexes of the pouring dam blocks.
Further, the method also comprises the following steps:
step 25, adjusting the dam block pouring sequence according to the dam block storehouse surface overlapping proportion and the standard section concrete balance principle, or adjusting the dam block pouring sequence according to the dam block storehouse surface overlapping proportion and the dam blocks of which the number is not more than the preset number continuously poured in the same standard section, wherein the standard section concrete balance principle comprises the following steps:
setting the pouring sequence of L pouring dam blocks to be adjusted, wherein the dam is provided with Q mark sections, and the concrete work amount of each mark section is W1、W2、…、WQThe current accumulated concrete engineering quantity of each standard section is w1、w2、…、wQThe concrete amount of each dam block is w1Q、w2Q、…、wLQAnd then, the engineering quantity of each standard section of concrete should meet the following requirements:
W1:W2:…:WQ≈(w1+w1Q):(w1+w1Q):…:(w1+w1Q)＝1:(2+):…:(Q+)；
in the formula, the ratio of the engineering quantities of different standard sections taking the 1 st standard section as a reference standard section is an adjustable error.
Further, the method also comprises the following steps:
and 4, judging whether an irrigation area needing joint grouting exists or not, if so, performing joint grouting operation on the irrigation area, and otherwise, returning to the step 1.
Further, the method for calculating the pouring duration of each castable dam block comprises the following steps:
if the efficiency of each link of the transport vehicle meets the first distribution mean value tqThe first variance is σqNormal distribution of (T)CA＝(tq,σq) The operation speed of each link of the cable crane conforms to the second distribution mean value voThe second variance is σoNormal distribution of VLA＝(vo,σo) The third distribution mean value of the discharging of the cable crane is txieThird difference sigmaxieNormal distribution of (T)LAX＝(txie,σxie) The cable crane waiting material meets the fourth distribution mean value of tdaFourth variance σdaNormal distribution of (T)LAD＝(tda,σda) The cable crane operation efficiency meets the fifth distribution mean value of pnnThe fifth variance is σnnNormal distribution p ofnn＝(pnn,σnn) Nn is the number of machines capable of pouring dam blocks, and the concrete is put in each binThe link time is respectively as follows:
if the number of links of the transport vehicle is q, the single cycle time of the ith transport vehicle is as follows:
in the formula, tqiIs the first distribution mean, σ, of the ith carriageqiA first variance for the ith carriage;
if the number of the links of the cable crane is o, the single cycle time of the ith cable crane is as follows:
wherein l is the number of green sheets of the castable block, s is the area of the green sheets of the castable block, voiIs the second distribution mean value of the i-th cable crane running speed, txieiThird distribution mean, t, for i cable machine unloadingdaiThe fourth distribution average value of the material waiting of the ith cable crane;
the concrete warehousing time of the ith vehicle is as follows:
and obtaining the concrete warehousing efficiency and the warehouse surface vibration efficiency, and calculating the pouring duration of each castable dam block according to the relation between the concrete warehousing efficiency and the warehouse surface vibration efficiency and on the basis of the time consumed by concrete warehousing.
Further, the calculating the pouring duration of each castable dam block according to the relation between the concrete warehousing efficiency and the warehouse surface vibration efficiency and based on the time consumed by concrete warehousing comprises:
if the concrete warehousing efficiency is less than or equal to the warehouse surface vibration efficiency, the pouring duration of the castable dam blocks is as follows:
in the formula, smBlank for m-th blank layer of pouring dam blockArea of layer, hmThe thickness of the blank layer of the mth blank layer of the castable dam block is m, 1, 2, … and l, u is the number of cable machines required by each castable dam block, VbThe volume of concrete which can be lifted by a single cable crane.
Further, calculating the pouring duration of each castable dam block according to the relation between the concrete warehousing efficiency and the warehouse surface vibration efficiency and based on the time consumed by concrete warehousing further comprises:
if the efficiency of putting the concrete into the warehouse is greater than the efficiency of vibrating the warehouse surface, the pouring duration of the castable dam is as follows:
in the formula, pmThe bin face vibration efficiency of the m-th blank layer of the castable dam block can be improved.
Further, calculating the pouring duration of each castable dam block according to the relation between the concrete warehousing efficiency and the warehouse surface vibration efficiency and based on the time consumed by concrete warehousing further comprises:
if there is l in the castable dam block1The concrete warehousing efficiency of each blank layer is less than or equal to the vibration efficiency of the warehouse surface, and l exists2The concrete efficiency of putting in storage on each base layer is greater than storehouse face vibration efficiency, and then the duration of pouring of the damming piece that can water is:
in the formula I1+l2＝l。
The invention has the beneficial effects that: according to the high arch dam progress simulation method based on the Internet of things, historically accumulated monitoring data are mined by using a big data analysis method, and the production rules of a concrete mixing plant, a concrete transport vehicle, a cable crane, a leveling machine and a vibrating machine are obtained; considering the construction equilibrium of multiple standard sections and the space interference between the bin surfaces, and optimizing a bin surface sequencing method; meanwhile, the matching logic relation between the cable crane and the bin surface is optimized, the cable crane is configured according to the requirement of the castable bin surface group, and the idle possibility of the cable crane is reduced. The traditional simulation method is promoted to change from static parameters to dynamic parameters by depending on the application result of the Internet of things, and the fit degree of the simulation of the construction progress of the high arch dam and the actual construction is improved.
Detailed Description
The following examples describe embodiments of the present invention in detail.
The invention discloses a high arch dam progress simulation method based on the Internet of things, which comprises the following steps of:
step 1, initializing simulation initial conditions and simulation parameters, and establishing a simulation clock sequence, wherein the simulation initial conditions at least comprise dam block real-time pouring appearance, joint grouting real-time appearance, a dam block layering scheme, joint grouting partition-level grouting control parameters, mechanical equipment resources, the maximum number of bin faces and bin face lap ratio which are allowed to be poured simultaneously, and the simulation parameters at least comprise mechanical group operation parameters, bin face standby bin parameters and template parameters;
before the simulation is started, the initial simulation conditions and the simulation parameters are initialized and updated, and the automatic updating of the face data is realized by acquiring and implementing the face data and grouting face data through the Internet of things. The mechanical group comprises a concrete mixing plant, a concrete transport vehicle, a cable machine, a storehouse surface leveling machine, a storehouse surface vibrating machine and the like. The GPS + RTK high-precision positioning equipment, the RFID sensing equipment, the UWB positioning equipment, the tilt angle sensor, the corner sensor, the ultrasonic/infrared depth monitoring equipment and the like are arranged on the mechanical group, so that the whole process monitoring of the production data of the mechanical group is realized, and mass production data are obtained. The operation rules of the mechanical group in the current time period can be analyzed and obtained through a big data analysis technology, and the operation rules comprise the production efficiency distribution of a mixing plant, the efficiency distribution of each link of loading, heavy truck transportation, waiting, material transferring and no-load return stroke of a transport vehicle, the efficiency distribution of each link of loading, tank lifting, heavy tank transportation, bin surface alignment, unloading and empty pipe return stroke of a cable crane, the efficiency distribution of each link of a cable crane lifting process, the efficiency distribution of a bin surface leveling machine, the efficiency distribution of a bin surface vibrating machine and the like.
Step 2, judging whether the dam is poured or not, if so, ending the simulation process, otherwise, screening the castable dam blocks meeting preset conditions from the warehouse surface, and determining the pouring sequence of each castable dam block according to the evaluation index and the index characteristic value of each castable dam block;
the preset conditions may be as follows:
(1) the dam should be within the control range of the device.
(2) The dam block should meet the requirement of inter-layer intermission time, and the distance between the current clock time and the pouring completion time of the poured dam block should not be less than the minimum intermission time.
(3) The appearance of the dam body meets the condition that the dam sections rise alternately in height in the pouring process of the dam body.
(4) The height difference of the adjacent dam sections is not larger than the allowable height difference of the adjacent dam sections.
(5) The height difference of the adjacent column blocks is larger than the product of the number of building block layers required by the cantilever support and the thickness of the building blocks.
(6) The bin face should have sufficient preparation time.
(7) The distance between the casting devices should be greater than the allowed safety distance.
(8) The dam should have sufficient base processing time.
(9) All dam segments cannot be larger than a predetermined height.
(10) The dam body rising speed process should meet the stress requirement in the construction period.
(11) The requirements of equipment pouring strength and material feeding strength of a mixing plant are met.
The method for determining the pouring sequence of the pouring dam blocks comprises the following steps:
step 21, setting the number of the casting warehouse surfaces as m, the number of the evaluation indexes as n, and the index characteristic value of the g-th evaluation index of the f-th casting dam block as kfgThen, we can get the index eigenvalue matrix K of m × n:
step 22, index characteristic value k for each evaluation indexfgCarrying out normalization processing to obtain a normalization value xfgAccording to the normalized value xfgFor the indexAnd carrying out normalization processing on the eigenvalue matrix K to obtain a matrix X:
wherein the index characteristic value k for each evaluation indexfgThe normalization processing comprises the following steps:
for the evaluation index with the larger index characteristic value, the better, the index characteristic value is normalized:
for the evaluation index with the smaller index characteristic value, the better, the index characteristic value is normalized:
step 23, carrying out weighted summation on each index characteristic value of each castable dam block to obtain a comprehensive index F [ F ] of each castable dam block, wherein the calculation formula is as follows:
F[f]＝a1xf1+a2xf2+…+anxfn；
in the formula, a1、a2、…、anWeight value of each evaluation index, xf1、xf2、…、xfnNormalizing the values for each evaluation index;
and 24, determining the pouring sequence of the dam blocks according to the comprehensive indexes of the pouring dam blocks.
When dam concrete is poured in different standard sections, a plurality of continuous dam blocks in the determined L castable dam blocks may belong to the same standard section, which is not practical in the actual multi-standard-section engineering; meanwhile, the overlapping proportion of the dam blocks which are continuously poured is too large in space, so that the pouring efficiency is not promoted and the concrete blank layer is not covered, and therefore the screened L castable dam blocks need to be reordered. Based on this, this embodiment further includes:
step 25, adjusting the dam block pouring sequence according to the dam block storehouse surface overlapping proportion and the standard section concrete balance principle, or adjusting the dam block pouring sequence according to the dam block storehouse surface overlapping proportion and the dam blocks of which the number is not more than the preset number continuously poured in the same standard section, wherein the standard section concrete balance principle comprises the following steps:
setting the pouring sequence of L pouring dam blocks to be adjusted, wherein the dam is provided with Q mark sections, and the concrete work amount of each mark section is W1、W2、…、WQThe current accumulated concrete engineering quantity of each standard section is w1、w2、…、wQThe concrete amount of each dam block is w1Q、w2Q、…、wLQAnd then, the engineering quantity of each standard section of concrete should meet the following requirements:
W1:W2:…:WQ≈(w1+w1Q):(w1+w1Q):…:(w1+w1Q)＝1:(2+):…:(Q+)；
in the formula, the ratio of the engineering quantities of different standard sections taking the 1 st standard section as a reference standard section is an adjustable error.
Meanwhile, the bin surface with the comprehensive index arranged in the front is used as a reference object, the bin surface which does not meet the overlapping proportion index is adjusted, and if the bin surface does not meet the limitation of the mutual overlapping proportion, the pouring sequence is kicked out.
And 3, determining a plurality of cable machine allocation schemes for pouring the castable dam blocks, selecting the cable machine allocation scheme corresponding to the minimum clock of the cable machine, and pouring the castable dam blocks according to the pouring sequence.
The method for determining a plurality of cable deployment scenarios for casting each castable dam block comprises the following steps:
step 31, establishing a plane coordinate system by taking the cable machine main tower track as a vertical coordinate axis and taking one endpoint of the cable machine main tower track as an original point, obtaining boundary point coordinates of the surface of the storehouse to be poured, the length n of the cable machine main tower, endpoint coordinates [ (0,0), (0, R) ] of the cable machine main tower track and the minimum safe distance M of the cable machine, and determining the length range B of the surface of the storehouse which can be poured by each cable machine within preset time;
step 32, determining the total length L of the surface of the storehouse to be cast according to the boundary point coordinates of the surface of the storehouse to be castcAccording to the length range B of the pouring cabin surface of each cable crane in the preset time T and the total length L of the cabin surface to be pouredcDetermining the required number k of the cable machines, dividing the to-be-poured storehouse surface into a plurality of areas according to the required number k of the cable machines and the length range B of the corresponding cable machine pouring storehouse surface, and determining the center line position C corresponding to the length range of the corresponding cable machine pouring storehouse surfacej；
The storehouse surface to be cast comprises a plurality of storehouse surfaces to be cast, wherein the plurality of storehouse surfaces to be cast have lap joint storehouse surfaces, and the total length L of the storehouse surfaces to be castcThe determination method comprises the following steps:
determining a minimum ordinate y from the coordinates of the boundary points of a plurality of surfaces of the storage space to be castminAnd the maximum ordinate ymaxAccording to said minimum ordinate yminAnd the maximum ordinate ymaxCalculating the total length L of the surface of the storehouse to be pouredcThe calculation formula is as follows:
Lc＝ymax-ymin；
the center line position C corresponding to the length range of the cable crane castable bin surfacejThe calculation formula of (a) is as follows:
wherein j is 1, 2, 3, … …, k.
Step 33, according to the length n of the main tower of the cable crane and the end point coordinates [ (0,0), (0, R) of the track of the main tower of the cable crane]And determining the movable length range L of each cable crane by the minimum safety distance M of the adjacent cable cranesi；
If the total number of the cable cranes is K, the movable length range L of the ith cable craneiThe calculation formula of (a) is as follows:
wherein i is 1, 2, 3, … …, K.
Step 34, according to the central line position CjAnd the range of the movable length L of each cable craneiAnd determining a cable machine deployment scheme.
Specifically, the center line position C corresponding to the length range of the castable bin surface of the 1 st cable crane is determined in sequence1The center line position C corresponding to the length range of the cable machine pouring bin surfacejThe range of the movable length of the cable machine to which the cable machine belongs, and further C is generatedjAnd LiAnd obtaining a plurality of cable deployment schemes by the matching matrix.
After the dam block is poured, entering a joint grouting step:
and 4, judging whether an irrigation area needing joint grouting exists or not, if so, performing joint grouting operation on the irrigation area, and otherwise, returning to the step 1.
In this embodiment, the simulation clock sequence takes days as a time period and takes seconds as a unit, and the advancing steps of the simulation clock on the sequence are as follows:
step A, scanning all pouring machines, determining a global clock and a minimum clock of a cable crane for pouring each castable dam block, taking the minimum clock as pouring starting time, judging whether the pouring starting time is effective working time or not, and if not, pushing a simulation clock to the next effective time period;
step B, calculating the pouring duration of each castable dam block, determining the pouring ending time of each castable dam block according to the pouring starting time and the pouring duration of each castable dam block, judging whether the pouring ending time is effective working time or not, if not, judging whether the corresponding castable dam block is poured or not, and if not, recalculating the pouring starting time;
c, recording the time corresponding to the pouring event of each castable dam block, advancing a simulation clock, and counting the circulating data;
and D, judging whether the dam is poured or not, if so, ending the simulation process, and otherwise, entering the step A.
In the high arch dam construction simulation, the pouring duration can affect the change of the state of a cable crane and the state of a dam block. The traditional simulation method does not consider the influence of the efficiency of the transport vehicle on the concrete warehousing strength, and secondly, the cable crane is assumed to be seamlessly connected to feed after returning to the feeding platform; meanwhile, the influence of the mechanical operation efficiency of the bin surface on the warehousing condition of the cable crane is not considered. Therefore, the embodiment provides a method for calculating the pouring duration of each castable dam block based on the internet of things, which comprises the following steps:
firstly, decomposing the concrete production and transportation construction process of each link of mixing plant-transport vehicle- (unloading platform) -cable machine-bin surface. The process of the concrete transport vehicle from the unloading platform, the mixing plant and the unloading platform can be divided into links of no-load return, waiting for materials in the mixing plant, charging, heavy vehicle transportation, waiting for the unloading platform and cable machine alignment charging because the transportation distance is basically unchanged; the transportation process of the cable crane from the unloading platform to the bin surface is in a changing process along with different transportation distances of pouring positions, and the process can be divided into the steps of material waiting of the unloading platform, loading, lifting acceleration, lifting deceleration, traction acceleration, traction uniform speed, traction deceleration, descending acceleration, descending uniform speed, descending deceleration, bin surface blanking and empty returning.
And the efficiency of each link can be subjected to big data analysis by combining the monitoring data based on the Internet of things, and the production rule is mined. If the efficiency of each link of the transport vehicle meets the first distribution mean value tqThe first variance is σqNormal distribution of (T)CA＝(tq,σq) The operation speed of each link of the cable crane conforms to the second distribution mean value voThe second variance is σoNormal distribution of VLA＝(vo,σo) The third distribution mean value of the discharging of the cable crane is txieThird difference sigmaxieNormal distribution of (T)LAX＝(txie,σxie) The cable crane waiting material meets the fourth distribution mean value of tdaFourth variance σdaNormal distribution of (T)LAD＝(tda,σda) The cable crane operation efficiency meets the fifth distribution mean value of pnnThe fifth variance is σnnNormal distribution p ofnn＝(pnn,σnn) Nn is the number of machines capable of pouring dam blocks, and the time of each link of putting concrete into a warehouse are respectivelyComprises the following steps:
if the number of links of the transport vehicle is q, the single cycle time of the ith transport vehicle is as follows:
in the formula, tqiIs the first distribution mean, σ, of the ith carriageqiA first variance for the ith carriage;
if the number of the links of the cable crane is o, the single cycle time of the ith cable crane is as follows:
wherein l is the number of green sheets of the castable block, s is the area of the green sheets of the castable block, voiIs the second distribution mean value of the i-th cable crane running speed, txieiThird distribution mean, t, for i cable machine unloadingdaiThe fourth distribution average value of the material waiting of the ith cable crane;
the concrete warehousing time of the ith vehicle is as follows:
obtaining concrete warehousing efficiency and warehouse surface vibration efficiency, calculating pouring duration of each castable dam block according to the relation between the concrete warehousing efficiency and the warehouse surface vibration efficiency and based on the time consumed by concrete warehousing, and specifically comprising the following steps:
1) if the efficiency of putting the concrete into the warehouse is less than or equal to the efficiency of vibrating the warehouse surface, the pouring duration of the castable dam is as follows:
in the formula, smIs the green area of the m-th green layer of the castable dam block, hmThe thickness of the blank layer of the mth blank layer of the castable dam block is m, 1, 2, … and l, u is the number of cable machines required by each castable dam block, VbThe volume of concrete which can be lifted by a single cable crane.
2) If the efficiency of putting into storage of concrete is greater than storehouse face vibration efficiency, then the duration of pouring of the damming piece that can water is:
in the formula, pmThe bin face vibration efficiency of the m-th blank layer of the castable dam block can be improved.
3) If there is a dam block1The concrete warehousing efficiency of each blank layer is less than or equal to the vibration efficiency of the warehouse surface, and l exists2The concrete efficiency of putting in storage on each base layer is greater than storehouse face vibration efficiency, and then the duration of pouring of the damming piece that can water is:
in the formula I1+l2＝l。
Claims (10)
1. The high arch dam progress simulation method based on the Internet of things is characterized by comprising the following steps:
step 1, initializing simulation initial conditions and simulation parameters, and establishing a simulation clock sequence, wherein the simulation initial conditions at least comprise dam block real-time pouring appearance, joint grouting real-time appearance, a dam block layering scheme, joint grouting partition-level grouting control parameters, mechanical equipment resources, the maximum number of bin faces and bin face lap ratio which are allowed to be poured simultaneously, and the simulation parameters at least comprise mechanical group operation parameters, bin face standby bin parameters and template parameters;
step 2, judging whether the dam is poured or not, if so, ending the simulation process, otherwise, screening the castable dam blocks meeting preset conditions from the warehouse surface, and determining the pouring sequence of each castable dam block according to the evaluation index and the index characteristic value of each castable dam block;
and 3, determining a plurality of cable machine allocation schemes for pouring the castable dam blocks, selecting the cable machine allocation scheme corresponding to the minimum clock of the cable machine, and pouring the castable dam blocks according to the pouring sequence.
2. The method for simulating the progress of the high arch dam based on the internet of things as claimed in claim 1, wherein in step 1, the sequence of the simulation clocks takes a day as a time period and takes a second as a unit, and the advancing steps of the simulation clocks on the sequence are as follows:
step A, scanning all pouring machines, determining a global clock and a minimum clock of a cable crane for pouring each castable dam block, if the cable crane of the minimum clock is the cable crane in the corresponding cable crane allocation scheme, taking the minimum clock as pouring start time, and if not, taking the minimum clock of the cable crane in the corresponding cable crane allocation scheme as pouring start time;
b, judging whether the pouring starting time is effective working time or not, and if not, pushing the simulation clock to the next effective time period;
step C, calculating the pouring duration of each castable dam block, determining the pouring ending time of each castable dam block according to the pouring starting time and the pouring duration of each castable dam block, judging whether the pouring ending time is effective working time or not, if not, judging whether the corresponding castable dam block is poured or not, and if not, recalculating the pouring starting time;
d, recording the time corresponding to the pouring event of each castable dam block, advancing a simulation clock, and counting the circulating data;
and E, judging whether the dam is poured or not, if so, ending the simulation process, and otherwise, entering the step A.
3. The internet of things-based high arch dam progress simulation method according to claim 1, wherein in step 3, the method for determining a plurality of cable crane allocation schemes for casting each castable dam block comprises:
step 31, establishing a plane coordinate system by taking the cable machine main tower track as a vertical coordinate axis and taking one endpoint of the cable machine main tower track as an original point, obtaining boundary point coordinates of the surface of the storehouse to be poured, the length n of the cable machine main tower, endpoint coordinates [ (0,0), (0, R) ] of the cable machine main tower track and the minimum safe distance M of the cable machine, and determining the length range B of the surface of the storehouse which can be poured by each cable machine within preset time;
step 32, determining the total length L of the surface of the storehouse to be cast according to the boundary point coordinates of the surface of the storehouse to be castcAccording to the length range B of the pouring cabin surface of each cable crane in the preset time T and the total length L of the cabin surface to be pouredcDetermining the required number k of the cable machines, dividing the to-be-poured storehouse surface into a plurality of areas according to the required number k of the cable machines and the length range B of the corresponding cable machine pouring storehouse surface, and determining the center line position C corresponding to the length range of the corresponding cable machine pouring storehouse surfacej；
Step 33, according to the length n of the main tower of the cable crane and the end point coordinates [ (0,0), (0, R) of the track of the main tower of the cable crane]And determining the movable length range L of each cable crane by the minimum safety distance M of the adjacent cable cranesi；
Step 34, according to the central line position CjAnd the range of the movable length L of each cable craneiAnd determining a cable machine deployment scheme.
4. The internet of things-based high arch dam progress simulation method according to claim 1, wherein in step 2, the method for determining the pouring sequence of each pourable dam block comprises:
step 21, setting the number of the casting warehouse surfaces as m, the number of the evaluation indexes as n, and the index characteristic value of the g-th evaluation index of the f-th casting dam block as kfgThen, we can get the index eigenvalue matrix K of m × n:
step 22, index characteristic value k for each evaluation indexfgCarrying out normalization processing to obtain a normalization value xfgAccording to the normalized value xfgNormalizing the index characteristic value matrix K to obtain a matrix X:
step 23, carrying out weighted summation on each index characteristic value of each castable dam block to obtain a comprehensive index F [ F ] of each castable dam block, wherein the calculation formula is as follows:
F[f]＝a1xf1+a2xf2+…+anxfn；
in the formula, a1、a2、…、anWeight value of each evaluation index, xf1、xf2、…、xfnNormalizing the values for each evaluation index;
the index characteristic value k for each evaluation indexfgThe normalization processing comprises the following steps:
for the evaluation index with the larger index characteristic value, the better, the index characteristic value is normalized:
for the evaluation index with the smaller index characteristic value, the better, the index characteristic value is normalized:
and 24, determining the pouring sequence of the dam blocks according to the comprehensive indexes of the pouring dam blocks.
5. The Internet of things-based high arch dam progress simulation method according to claim 3, further comprising:
step 25, adjusting the dam block pouring sequence according to the dam block storehouse surface overlapping proportion and the standard section concrete balance principle, or adjusting the dam block pouring sequence according to the dam block storehouse surface overlapping proportion and the dam blocks of which the number is not more than the preset number continuously poured in the same standard section, wherein the standard section concrete balance principle comprises the following steps:
the pouring sequence of the L pouring dam blocks needs to be adjustedThe dam has Q mark sections, and the concrete engineering quantity of each mark section is W1、W2、…、WQThe current accumulated concrete engineering quantity of each standard section is w1、w2、…、wQThe concrete amount of each dam block is w1Q、w2Q、…、wLQAnd then, the engineering quantity of each standard section of concrete should meet the following requirements:
W1:W2:…:WQ≈(w1+w1Q):(w1+w1Q):…:(w1+w1Q)＝1:(2+):…:(Q+)；
in the formula, the ratio of the engineering quantities of different standard sections taking the 1 st standard section as a reference standard section is an adjustable error.
6. The internet of things-based high arch dam progress simulation method according to claim 1, further comprising:
and 4, judging whether an irrigation area needing joint grouting exists or not, if so, performing joint grouting operation on the irrigation area, and otherwise, returning to the step 1.
7. The Internet of things-based high arch dam progress simulation method according to claim 2, wherein the method for calculating the pouring duration of each pourable dam block comprises:
if the efficiency of each link of the transport vehicle meets the first distribution mean value tqThe first variance is σqNormal distribution of (T)CA＝(tq,σq) The operation speed of each link of the cable crane conforms to the second distribution mean value voThe second variance is σoNormal distribution of VLA＝(vo,σo) The third distribution mean value of the discharging of the cable crane is txieThird difference sigmaxieNormal distribution of (T)LAX＝(txie,σxie) The cable crane waiting material meets the fourth distribution mean value of tdaFourth variance σdaNormal distribution of (T)LAD＝(tda,σda) The cable crane operation efficiency meets the fifth distribution mean value of pnnThe fifth variance is σnnNormal distribution ofpnn＝(pnn,σnn) And nn is the mechanical quantity of the pouring dam blocks, and the time of each link of concrete warehousing is as follows:
if the number of links of the transport vehicle is q, the single cycle time of the ith transport vehicle is as follows:
in the formula, tqiIs the first distribution mean, σ, of the ith carriageqiA first variance for the ith carriage;
if the number of the links of the cable crane is o, the single cycle time of the ith cable crane is as follows:
wherein l is the number of green sheets of the castable block, s is the area of the green sheets of the castable block, voiIs the second distribution mean value of the i-th cable crane running speed, txieiThird distribution mean, t, for i cable machine unloadingdaiThe fourth distribution average value of the material waiting of the ith cable crane;
the concrete warehousing time of the ith vehicle is as follows:
and obtaining the concrete warehousing efficiency and the warehouse surface vibration efficiency, and calculating the pouring duration of each castable dam block according to the relation between the concrete warehousing efficiency and the warehouse surface vibration efficiency and on the basis of the time consumed by concrete warehousing.
8. The internet of things-based high arch dam progress simulation method according to claim 7, wherein the calculating the pouring duration of each pourable dam block based on the concrete warehousing efficiency and the warehouse surface vibrating efficiency relationship and the time spent in concrete warehousing comprises:
if the concrete warehousing efficiency is less than or equal to the warehouse surface vibration efficiency, the pouring duration of the castable dam blocks is as follows:
in the formula, smIs the green area of the m-th green layer of the castable dam block, hmThe thickness of the blank layer of the mth blank layer of the castable dam block is m, 1, 2, … and l, u is the number of cable machines required by each castable dam block, VbThe volume of concrete which can be lifted by a single cable crane.
9. The internet of things-based high arch dam progress simulation method according to claim 8, wherein the calculating the casting duration of each castable dam block based on the concrete warehousing efficiency and the time spent on concrete warehousing further comprises:
if the efficiency of putting the concrete into the warehouse is greater than the efficiency of vibrating the warehouse surface, the pouring duration of the castable dam is as follows:
in the formula, pmThe bin face vibration efficiency of the m-th blank layer of the castable dam block can be improved.
10. The internet of things-based high arch dam progress simulation method according to claim 9, wherein the calculating the casting duration of each castable dam block based on the concrete warehousing efficiency and the time spent on concrete warehousing further comprises:
if there is l in the castable dam block1The concrete warehousing efficiency of each blank layer is less than or equal to the vibration efficiency of the warehouse surface, and l exists2The concrete efficiency of putting in storage on each base layer is greater than storehouse face vibration efficiency, and then the duration of pouring of the damming piece that can water is:
in the formula I1+l2＝l。
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