CN109284858B - Reinforcing steel bar blanking optimization method and device and storage equipment - Google Patents
Reinforcing steel bar blanking optimization method and device and storage equipment Download PDFInfo
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Abstract
The invention discloses a method, a device and a storage device for steel bar blanking optimization, wherein the method comprises the following steps: screening an entire steel bar optimized combination scheme, screening a residual steel bar to be blanked optimized combination scheme, node optimization and material local optimization. The invention can make the waste rate of the steel bar less than 1% through the optimized combination of the whole steel bar, the optimized combination of the length of the residual steel bar, the local optimization and the node number optimization after the combination, and compared with the material waste rate of the existing commercial software, the material waste rate of the invention is reduced by 4 percentage points. Meanwhile, the labor cost is reduced by superposition, and the economic benefit is very obvious.
Description
Technical Field
The invention relates to a method and a device for optimizing steel bar blanking and storage equipment, and belongs to the technical field of civil engineering.
Background
After various civil engineering construction projects are researched and researched, workers generally adopt an empirical method to carry out steel bar blanking or utilize the existing commercial software to sample steel bars, but the blanking method generally has a large steel bar waste rate which is generally more than or equal to 5%. In a typical 10 ten thousand property project, the total amount of funds wasted is:
100000 (square) 50kg (steel amount/square)/1000 (converted to ton) 4500 (reinforcing steel market price) 5% (wave rate) 112.5 ten thousand yuan
Therefore, the optimization method of the steel bar blanking with lower waste rate is provided, and the economic and social benefits are very obvious.
The optimized blanking of the reinforcing steel bars is a one-dimensional optimized combination problem, and the optimized blanking of the reinforcing steel bars is researched by adopting a simulated annealing algorithm, a genetic algorithm and the like, but the methods are still in the theoretical research level, and a plurality of problems still exist when the method is applied to engineering practice. The subsequent research discusses the problem of steel bar optimized blanking by simplification and adopting a linear programming algorithm, in fact, steel bar optimized blanking in actual engineering is not a pure linear problem, a solving model meeting engineering requirements comprises a plurality of nonlinear constraints, and the whole solving model is a nonlinear model. Solving the nonlinear problem with the linear model results in errors or even errors between the optimization results and the actual engineering requirements.
The chinese patent database of 2012, 1-18 th discloses a patent name: the intelligent steel bar screening and blanking method (with the publication number of CN102322151A) comprises the following steps: screening a large number of steel bar blanking combinations to be processed by dynamically setting screening conditions, comparing screened preliminary steel bar blanking combination schemes, adjusting and optimizing the combinations, and determining a final steel bar blanking combination scheme. However, the method simulates an empirical screening technology, the foundation of which is not a well-known operational research optimization theory, and a local optimal solution may be found by the method, but a global optimal solution is not obtained, or a final reinforcing steel bar blanking combination scheme is not an optimal scheme. In addition, the method does not consider labor cost, in fact, the labor cost is not a negligible project cost, and when the labor cost such as the steel bar cutting times and the welding joints is reduced, the material waste is generally increased, namely, the labor cost is in inverse proportion to the material waste rate.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method, a device and storage equipment for steel bar blanking optimization, which can save material cost and reduce labor cost.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a steel bar blanking optimization method comprises the following steps:
comparing the length of the steel bars to be blanked with the length of the raw material steel bars one by one, and directly blanking the raw material steel bars meeting the screening condition by taking the steel bar waste rate smaller than a set threshold value and the remaining steel bar length smaller than 35d as the screening condition; wherein: the length of the residual steel bar is equal to the length of the steel bar to be blanked, the length of the raw material steel bar and the node number of the steel bar, d represents the diameter of the raw material steel bar, and s represents the loss of a steel bar joint;
for the rest reinforcing steel bars to be blanked, circularly selecting a group of reinforcing steel bars from the rest raw material reinforcing steel bars as a whole reinforcing steel bar blanking combination by taking the length of the rest reinforcing steel bars greater than or equal to the length of the minimum raw material reinforcing steel bar as a screening condition;
the method comprises the following steps of (1) screening out an optimal whole steel bar blanking combination by taking the minimum total number of steel bars after a plurality of raw material steel bars are combined as a target and considering the loss of a steel bar joint;
aiming at the minimum waste rate of the steel bars, establishing a nonlinear lingo model by using the combination of two sections of steel bars as a hard constraint at most for the length of each residual steel bar, and solving a global optimal solution;
and further carrying out node optimization and material local optimization on the global optimal solution to obtain a final steel bar blanking list and a steel bar cutting method.
Further, the optimal scheme for screening the whole steel bar blanking combination comprises the following steps:
for the whole steel bar blanking combination circularly selected from the length sequence of the raw material steel bars, if the length of the remaining steel bars after the blanking of the whole steel bar combination is more than or equal to (35d + s), storing the length data of the remaining steel bars and the blanking combination data of the selected whole steel bars;
if the length of the residual steel bar after the blanking of the whole steel bar combination is less than (35d + s), further judging whether the steel bar waste rate is less than a set threshold value: if the steel bar waste rate is less than a set threshold value, storing the length data of the steel bars to be blanked, the blanking combination data of the whole steel bars and the length data of the residual steel bars;
if the steel bar waste rate is larger than or equal to a set threshold value, deleting the data with the minimum length of the raw material steel bars from the whole steel bar blanking combination, recalculating and storing the length data of the residual steel bars, and simultaneously storing the recombined whole steel bar blanking combination data;
finally, the optimal scheme of the whole steel bar blanking combination is obtained.
Further, the establishing of the nonlinear lingo model comprises the following steps:
aiming at the minimum waste rate of the steel bars, and taking the maximum length of each residual steel bar as a hard constraint by combining two sections of steel bars, compiling a nonlinear lingo model:
xijnot less than 35d + s or xij＝0
Wherein: x is the number ofi，jRepresenting the length of the jth section of the steel bar forming the ith residual steel bar length divided by the length of the jth raw material steel bar; a. thejRepresenting the length of the jth raw material reinforcing steel bar; b (i) the length of the ith steel bar to be blanked is represented; m represents the total number of raw material reinforcing steel bars; n represents the total number of the reinforcing steel bars to be blanked; d represents the diameter of the raw material steel bar; s represents a rebar junction loss.
Further, the node optimization comprises the following steps:
reading a result file output by the nonlinear lingo model, and acquiring an optimization result matrix of the length of the residual steel bars and a steel bar cutting method matrix;
assuming that each row of the optimization result matrix is blanking combination data of the length of the residual steel bar, and each column of the matrix of the steel bar cutting method is data for performing combination cutting on the whole steel bar according to the blanking combination data of the length of the residual steel bar;
reading each row of the optimization result matrix of the length of the residual steel bars one by one, and judging whether the row comprises two pieces of non-whole steel bar length data: if not, reading the next row; if yes, taking out the length data of the two non-whole reinforcing steel bars, comparing the length data with each column of the matrix of the reinforcing steel bar cutting method, and judging whether the two data exist in a certain column at the same time: if the two data are not simultaneously existed, the optimization result matrix is circulated to the next row, and if the two data are simultaneously existed, the sum of the two data is respectively used for replacing the corresponding data in the optimization result matrix and the corresponding data in the matrix column of the steel bar cutting method until the optimization result matrix of the length of the residual steel bar is traversed, and the optimization result matrix of the length of the residual steel bar after node optimization and the steel bar cutting method matrix after node optimization are obtained.
Further, the local optimization of the material comprises the following steps:
reading a result file output by the nonlinear lingo model, and acquiring an optimization result matrix of the length of the residual steel bars and a steel bar cutting method matrix;
reading the length of the rest raw material steel bars, counting the length types and the number of the steel bars, and establishing a steel bar length type matrix;
assuming that each column of the matrix of the steel bar cutting method is data for performing combined cutting on the whole steel bar according to the blanking combined data of the length of the residual steel bar;
reading each row of the matrix of the steel bar cutting method one by one, and solving the sum of the lengths of the cut steel bars of the row;
the sum of the lengths of the cut steel bars in each row is merged into a steel bar length type matrix to form a new matrix, and then the new matrix is arranged in a descending order according to the lengths of the steel bars;
and (3) solving the index of the sum of the lengths of the rows of the cut steel bars in the new matrix, and judging whether the index is in the last row of the new matrix or not: if so, reading the next column of the matrix of the steel bar cutting method, and continuing the next cycle until traversing the matrix of the steel bar cutting method; if not, the next index corresponding numerical value of the index is taken out to replace the first value of the matrix column vector of the steel bar cutting method, namely the length of the cut raw material steel bar.
Further, before establishing the nonlinear lingo model, the following steps should be performed:
storing the length of the raw material steel bar and the length of the residual steel bar into two rows in the same excel file, and respectively arranging the lengths in a descending order;
the excel file with the length of the raw material steel bar and the length of the residual steel bar is cut into a plurality of subfiles, and the requirements are as follows: each subfile comprises 50-60 pieces of residual steel bar length data, and the difference between the total length of the original material steel bars and the total length of the residual steel bars in each subfile is less than or equal to 10.
Further, before comparing the length of the steel bar to be blanked with the length of the raw material steel bar one by one, the length of the steel bar to be blanked and the length of the raw material steel bar should be respectively arranged according to the length descending order.
And further, circularly selecting a group of data from the length sequence of the raw material steel bars according to the length of the raw material steel bars in a reverse order by taking the length of the residual steel bars more than or equal to the length of the minimum raw material steel bars as a screening condition to serve as the whole steel bar blanking combination.
The present invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the steps of any of the foregoing method of rebar optimization.
Compared with the prior art, the invention has the following beneficial effects:
simulating an artificial blanking method, and extracting the optimal combination of the whole steel bar by adopting Matlab; the large matrix is cut into a plurality of sub-matrixes, so that the calculation efficiency is improved; establishing an operation research optimization model, solving the optimal solution of the residual steel bars, and obtaining a steel bar cutting method and a residual steel bar blanking method; the steel bar cutting method and the residual steel bar blanking method are optimized, and the number of steel bar welding nodes is reduced; the steel bar blanking method not only controls the material consumption to the maximum extent and reduces the material waste, but also reduces the cutting times and the number of welding nodes and optimizes the number of welding nodes by controlling the total number of the steel bars, thereby greatly reducing the labor cost.
Drawings
FIG. 1 is a flow chart of the method for screening the optimized combination scheme of the whole steel bar;
FIG. 2 is a flow chart of the optimal combination scheme for screening the length of the remaining steel bars;
FIG. 3 is a flow chart of node optimization and material local optimization.
Detailed Description
The invention provides a method for optimizing material and labor cost of steel bar blanking, which mainly comprises the following steps: screening an entire steel bar optimization combination scheme, screening a residual steel bar length optimization combination scheme, and performing node optimization and material local optimization.
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, it is a flow chart of screening the whole steel bar optimization combination scheme, including the following steps:
the method comprises the following steps: the data is read and named. The method comprises the steps of collecting the length of a raw material steel bar and the length of a steel bar to be blanked, and storing the lengths in two rows in the same excel respectively. In order to reduce the steel bar joints, steel bars with longer length are selected as much as possible in the whole steel bar optimization combination, and before optimization, the lengths of the raw material steel bars and the lengths of the steel bars to be blanked are respectively arranged in a descending order according to the respective lengths. For convenience of description, the length row of the reinforcing bars as the raw material is named as a row a, and the length row of the reinforcing bars to be blanked is named as a row B.
Step two: and (5) performing preliminary optimization. Selecting one data from the column B, comparing the data with the data in the column A one by one, if the blanking condition that the waste rate is less than a set threshold value and the length of the residual steel bar is less than 35d is met, directly blanking, storing the length of the residual steel bar and the length of the whole steel bar, deleting the length data of the raw material steel bar in the column A, deleting the length data of the steel bar to be blanked in the column B, and continuing the next step until the column B is traversed. The set threshold should be no greater than 6%, with a preferred set threshold of 4%.
Step three: and judging whether to continue optimization. Judging whether the variable for storing the steel bars to be blanked is empty, if so, indicating that all the steel bars to be blanked are all blanked in an optimized mode, if not, moving the pointer to the beginning of the column B, and entering the next step;
step four: setting the screening conditions of the whole steel bar blanking combination, which specifically comprises the following steps: the length of the residual steel bar is more than or equal to the minimum value of the length of the raw material steel bar. When the length of the residual steel bars is larger than or equal to the minimum value of the lengths of the raw material steel bars, the fact that a whole steel bar can be screened out from the residual raw material steel bars to combine the steel bars to be blanked is shown.
Step five: and extracting the whole steel bar blanking combination. Reading length data of a steel bar to be blanked in the column B, circularly reading length data of a raw material steel bar from the column A, storing the length data of the raw material steel bar in a whole steel bar blanking combination, judging whether the screening condition set in the step four is met, and if the screening condition is met, continuously reading the length data of the raw material steel bar; and if not, stopping adding data into the whole steel bar blanking combination, and entering the next step.
Step six: and calculating the length of the residual steel bars after optimized combination according to the whole steel bars, if the length of the residual steel bars is more than or equal to (35d + s), storing the length of the residual steel bars and the blanking combination of the whole steel bars, and deleting all raw material steel bar length data contained in the blanking combination of the whole steel bars from the column A. Wherein: s is the bar joint loss and d is the raw material bar diameter.
If the length of the residual steel bar is less than 35d + s and the waste rate of the steel bar is less than a set threshold value, the length of the residual steel bar, the length of the steel bar to be blanked and the blanking combination result of the whole steel bar are stored after the steel bar to be blanked is optimized.
And if the length of the residual steel bar is less than 35d + s and the waste rate of the steel bar is greater than a set threshold value, deleting a shortest raw material steel bar from the whole steel bar blanking combination, recalculating the length of the residual steel bar, and storing the whole steel bar blanking combination result and the length of the residual steel bar.
And after traversing the column B, finishing the optimization of the blanking combination of the whole reinforcing steel bar, and finally obtaining the optimal scheme of the blanking combination of the whole reinforcing steel bar.
As shown in table 1, for an example of the whole steel bar blanking combination, the standard length of the raw material steel bar is 12 meters and 9 meters, and taking the length of the steel bar to be blanked as 27.02 meters, a 12-meter raw material steel bar and a 9-meter raw material steel bar can be combined, and assuming that the loss of the steel bar joint is 0.06 meter, the remaining length of the steel bar is 27.02-12-9+2 × 0.06-6.14 meters.
TABLE 1 Whole steel bar blanking assembly
| Length of steel bar to be blanked | Raw material reinforcing steel bar | Raw material reinforcing steel bar | Length of residual reinforcing bar |
| 27.02 | 12 | 9 | 6.14 |
| 20.58 | 9 | 9 | 2.64 |
| 19.98 | 12 | 0 | 8.04 |
As shown in fig. 2, it is a flow chart of the optimal combination scheme for screening the length of the remaining steel bars, and the method includes the following steps:
step seven: the remaining bar length data and the remaining raw material bar length data are saved to E, F columns of the same excel file, and E, F columns are arranged in descending order, respectively.
Step eight: and segmenting the excel file into a plurality of subfiles. E. The F column usually has thousands of lines, in order to avoid calculating 'crash', the excel file is usually divided into a plurality of subfiles before optimization, each subfile generally comprises 50-60 residual steel bar length data, and the calculation efficiency is highest at the moment. In order to ensure that there is a solution in the lingo optimization process and the material waste is minimum, the difference between the total length of the original material steel bars in each subfile and the total length of the remaining steel bars is required to be less than or equal to 10. According to the two standards, the excel file is divided and stored in each subfile, and the length of the raw material steel bar and the length of the residual steel bar are respectively stored in G, H columns of each subfile.
Step nine: and establishing an optimization model. Aiming at the minimum waste rate of the steel bars, and taking the combination of 2 sections of raw material steel bars as a maximum for each residual steel bar as a hard constraint, compiling a steel bar optimized nonlinear lingo model, which is concretely as follows:
xijnot less than 35d + s or xij＝0
Wherein: x is the number ofi，jRepresenting the length of the jth section of the steel bar forming the ith residual steel bar length divided by the length of the jth raw material steel bar; a. thejRepresenting the length of the jth raw material reinforcing steel bar; b (i) the length of the ith steel bar to be blanked is represented; m represents the total number of raw material reinforcing steel bars; n represents the total number of the reinforcing steel bars to be blanked; d represents the diameter of the raw material steel bar; s represents a rebar junction loss.
Step ten: and respectively optimizing data in the subfiles by adopting a nonlinear lingo model, and respectively storing the data in the corresponding result files in a txt format.
As shown in fig. 3, it is a flow chart of node optimization and material local optimization, which includes the following steps:
step eleven: a program is compiled to read all txt result files, and an optimization result matrix of the length of the residual steel bars and a steel bar cutting method matrix are extracted; and reading the whole steel bar blanking combination result file, obtaining the whole steel bar blanking combination, and respectively arranging the two matrixes in a descending order according to the lengths of the remaining steel bars.
Taking table 1 as an example, an example of an optimization result matrix of the remaining bar lengths and a corresponding bar cutting method matrix is given below:
TABLE 2 optimization result matrix of the remaining reinforcement lengths
| Length of residual reinforcing bar | Section 1 | Section 2 | Section 3 |
| 6.14 | 2.5 | 0 | 3.7 |
| 2.64 | 1.2 | 1.5 | 0 |
| 8.04 | 2.5 | 1 | 4.6 |
TABLE 3 reinforcing bar cutting method matrix
| Whole steel bar | 9 | 4.51 | 12 | 4.51 |
| Section 1 | 2.5 | 0 | 3.7 | 2 |
| Section 2 | 3.7 | 1.5 | 0 | 1.2 |
| Section 3 | 2.5 | 1 | 4.6 | 1.2 |
Step twelve: and (6) node optimization.
Assuming that each row of the optimization result matrix is blanking combination data of the length of the residual steel bar, and each column of the matrix of the steel bar cutting method is data for performing combination cutting on the whole steel bar according to the blanking combination data of the length of the residual steel bar;
reading each row of the optimization result matrix of the lengths of the residual steel bars one by one, judging whether the row comprises two pieces of non-whole steel bar length data, and reading the next row if the row does not comprise the two pieces of non-whole steel bar length data; if yes, the two data are taken out and compared with each column of the matrix of the steel bar cutting method, whether the two data exist in a certain column or not is judged, if not, the steel bar blanking list is circulated to the next row, and if yes, the sum of the two numbers is used for replacing the corresponding data in the steel bar blanking clear row and the corresponding data in the matrix of the steel bar cutting method respectively. And traversing the optimization result matrix of the length of the residual steel bars to obtain the optimization result matrix of the length of the residual steel bars and the matrix of the steel bar cutting method. The operation can fully reduce the total number of the steel bars, thereby reducing the cutting times and the welding times, reducing the damage of the welding joint, saving the material cost and the labor cost.
Step thirteen: further material local optimization: reading the length of the rest raw material steel bars, counting the length types and the number of the steel bars, and establishing a steel bar length type matrix; as shown in table 4, is an example of a rebar length category matrix:
TABLE 4 reinforcing bar length kind matrix
| Length of raw material reinforcing bar | Root number of |
| 20.59 | 143 |
| 12 | 250 |
| 9 | 390 |
| 4.51 | 3 |
Assuming that each column of the matrix of the steel bar cutting method is data for performing combined cutting on the whole steel bar according to the blanking combined data of the length of the residual steel bar;
reading each row of the matrix of the steel bar cutting method one by one, and solving the sum of the lengths of the cut steel bars of the row;
the sum of the lengths of the rows of cut steel bars is merged into a steel bar length type matrix to form a new matrix, and then the new matrix is arranged in a descending order according to the lengths of the rest raw material steel bars;
as shown in table 5, the total length of the cut bars in the bar cutting method matrix provided in table 3 is incorporated into a new matrix formed by the bar length faithful matrix provided in table 4:
table 5 new matrix formed after incorporating the sum of lengths
| Length of raw material reinforcing bar | Root number of |
| 20.59 | 143 |
| 12 | 250 |
| 9 | 390 |
| 8.7 | 1 |
| 4.51 | 3 |
And (3) solving the index of the sum of the lengths of the rows of the cut steel bars in the new matrix, and judging whether the index is in the last row of the new matrix or not: if so, reading the next column of the matrix of the steel bar cutting method, and continuing the next cycle until traversing the matrix of the steel bar cutting method; if not, the next index corresponding numerical value of the index is taken out to replace the first value of the matrix column vector of the steel bar cutting method, namely the length of the cut raw material steel bar.
The local optimization of the material can reduce the use of the material and achieve the aim of saving the material.
Fourteen steps: and acquiring a steel bar blanking list. And merging the whole steel bar blanking combination matrix and the optimization result matrix of the length of the residual steel bars according to the length data of the residual steel bars to obtain a steel bar blanking list.
The invention is further described with reference to specific examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The specific embodiment is as follows:
the embodiment of the present invention will be described in further detail by taking the middle iron item as an example. This project raw and other materials reinforcing bar diameter is 30mm, and raw and other materials reinforcing bar: 957 reinforcing steel bars with the length of 20.59m, 1000 reinforcing steel bars with the length of 12m and 9m and 1957 reinforcing steel bars with the length of 4.51m are respectively arranged on the reinforcing steel bars with the length of 4864; 20.59m is the whole customized rebar length, 12m, 9m are standard rebar lengths, 4.51m is the remaining rebar length of other projects, treat blanking reinforcing bar 112 kind and totally 3087, including the following step specifically:
the excel file storing the raw Material steel bars and the steel bars to be blanked is read through the matlab, the raw Material steel bar column vectors and the steel bar column vectors to be blanked are stored in the variable Material and the Sample respectively, and the Material and the Sample are arranged according to the respective descending order of the length.
The set blanking conditions are as follows: the wave rate is less than 4%, and the length of the residual steel bar is less than 35 d. And d is the diameter of the steel bar. The length of the residual steel bar is equal to the length of the steel bar to be blanked, the length of the raw material steel bar and the node number of the steel bar are equal to 0.06; the waste rate is the percentage of the length of the residual reinforcing steel bar divided by the length of the reinforcing steel bar to be blanked.
Taking out a number from the Sample, circulating the Material, comparing the Material with the number, judging whether the preset blanking condition is met, if so, directly blanking, saving the length of the residual steel bar by a negative value as a mark of the blanked steel bar, saving the whole steel bar and the steel bar cutting method, and if not, entering the next circulation. After traversing Sample, the optimization of the round is finished, and partial optimization results are shown in the following table.
Table 6 partial optimization results
In this example, 11.87m of the steel bar to be blanked is taken out of Sample, and compared with Material, the result shows that the residual length of the steel bar is 0.13m, when the residual length is less than 35d, 1.05, and the waste rate is 0.13/11.87, 1.1% < 4%, and the steel bar is directly stored.
And judging whether the steel bar to be blanked is optimized or not. And judging whether the variable for storing the length of the steel bar to be optimized is empty, if so, indicating that all the steel bars are optimized, finishing the optimization, and if not, moving the pointer to the beginning of the Sample column.
Setting and extracting the whole steel bar blanking combination conditions, and setting the screening conditions as follows: the remaining length of the reinforcing bar is greater than the minimum length of the reinforcing bar of the raw material. The minimum length of the raw material rebar in this example is 4.51 m.
And circularly selecting the steel bars from the column vector Material, calculating the length of the corresponding residual steel bar when each selected steel bar enters the length combination of the whole steel bar under the condition of considering the loss of the steel bar joint of 0.06m, judging whether the length of the residual steel bar meets the screening condition, if so, continuously screening the steel bars to enter the length combination of the whole steel bar, otherwise, stopping screening, and possibly setting the current combination to be the optimal length combination of the whole steel bar.
And when the length of the residual steel bar is more than or equal to 35d + s, the length of the whole steel bar is stored and combined in sections 1 to 10, and the length of the residual steel bar is stored in section 11 for orderly optimization. And simultaneously deleting the corresponding steel bars in the combination in the raw Material queue Material.
In this example, the remaining bar length is 55.63-2 20.59-12+3 0.06-2.63 >35d + s-1.11, and the corresponding values are stored, as shown in table 1-1.
TABLE 1-1 optimized combination of whole steel bars
And when the length of the residual steel bar is less than 35d + s, if the length of the residual steel bar can ensure that the waste rate is below 4%, blanking of the steel bar is completed, and the length combination of the whole steel bar and the length of the residual steel bar are stored.
And after the optimization is completed, arranging the whole steel bar blanking combination result file according to the steel bar residual length sequence in a descending order.
In this example, if the remaining rebars are directly optimized by lingo, the total number of variables 3033, 1786, 5416938, the constraint number 5426576, and the computer memory and CPU capacity are not sufficient. Before optimization, the file needs to be split into 50 sub-files, each sub-file contains 57 raw material steel bars and about 17 residual steel bars.
Establishing a computational mathematical model, and compiling a lingo program, wherein the specific partial statements are as follows:
min＝@sum(RebarSet(j)：(A(j)＊(1-T(j))))；
@for(Rebarset2(i)：@sum(Rebarset(j)：X(i，j)＊A(j))
-(@sum(Rebarset(j)：@sign(X(i，j)))-1)＊0.06＝B(i))：
@for(Rebarset2(i)：@sum(Rebarset(j)：@sign(X(i，j)))＜＝2)；
@for(RebarSet(j)：@sum(RebarSet2(i)：X(i，j))＝T(j))；
！@for(Rebarset(j)：@sum(Rebarset2(i)：@sign(X(i，j)))＜＝3)；
@for(links(i，j)：@semic(Xb(i，j)，X(i，j)，1))；
@for(Rebarset(j)：@BND(0，T(j)，1))；
after optimizing all the subfiles, saving the subfiles to a txt file, wherein the total number of the files is 50.
And combining all the optimization results to obtain the optimization results of the residual steel bars and the cutting method. Such as: the remaining bar 6.62m is welded from two lengths of 4.15m and 2.53m bar, taking into account the 0.06m weld joint loss. In tables 1-2, the entire rebar 20.59m was cut into 3 sections of 5.48m and 1 section of 4.15 m.
TABLE 1-2 optimization result matrix of residual steel bars
TABLE 1-3 preliminary Rebar cutting method
| Whole steel bar | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 |
| Section 1 | 5.48 | 5.48 | 5.48 | 5.48 | 5.48 | 5.48 | 5.35 |
| Section 2 | 5.48 | 5.48 | 3.83 | 3.05 | 5.48 | 5.48 | 5.35 |
| Section 3 | 5.48 | 3.5 | 3.2 | 1.98 | 3.63 | 3.75 | 3.89 |
| Section 4 | 4.15 | 2.53 | 2.08 | 1.2 | 1.2 | 3.48 | 1.2 |
| Section 5 | 0 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 |
| Section 6 | 0 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 |
| Section 7 | 0 | 1.2 | 1.2 | 1.2 | 1.2 | 0 | 1.2 |
| Section 8 | 0 | 0 | 1.2 | 1.2 | 1.2 | 0 | 1.2 |
| Section 9 | 0 | 0 | 1.2 | 1.2 | 0 | 0 | 0 |
| Section 10 | 0 | 0 | 0 | 1.2 | 0 | 0 | 0 |
| Section 11 | 0 | 0 | 0 | 1.2 | 0 | 0 | 0 |
And after the nodes are optimized, obtaining a new optimization result of the length of the residual steel bar and a cutting method. The method comprises the following specific steps: reading each row in table 1-2 in a loop, if each row has only one number, indicating only one section of rebar, without necessarily optimizing, and continuing to read the next column if there are two data, such as: 5.48, 1.2 of the third column. Each column is searched in tables 1-3 and if the two numbers appear in the same column, the welded length of the two bars is calculated, and it is apparent that 5.48 and 1.2 appear in the second column of tables 1-3 at the same time, and the welded length of the two bars is 5.48+ 1.2-0.06-6.62. Tables 1-4 and 1-5 were obtained by replacing 5.48 with 6.62 and 1.2 with 0 in tables 1-2 and 1-3, respectively. In the process, obviously, one steel bar is reduced, one node is reduced, and the sequential method is adopted, in this example, 1835 welding nodes are welded before optimization, and 808 welding nodes are welded after optimization, so that 1027 welding nodes are reduced, and the node optimization rate is 56%.
Table 1-4 optimization result matrix of residual steel bars after node optimization
TABLE 1-5 method for cutting reinforcing bar
| Whole steel bar | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 |
| Section 1 | 5.48 | 6.62 | 6.62 | 6.62 | 6.62 | 6.62 | 6.49 |
| Section 2 | 5.48 | 6.62 | 3.83 | 3.05 | 6.62 | 6.62 | 6.49 |
| Section 3 | 5.48 | 4.64 | 3.2 | 1.98 | 3.63 | 3.75 | 3.89 |
| Section 4 | 4.15 | 2.53 | 2.08 | 0 | 0 | 3.48 | 0 |
| Section 5 | 0 | 0 | 0 | 1.2 | 0 | 0 | 0 |
| Section 6 | 0 | 0 | 1.2 | 1.2 | 1.2 | 0 | 1.2 |
| Section 7 | 0 | 0 | 1.2 | 1.2 | 1.2 | 0 | 1.2 |
| Section 8 | 0 | 0 | 1.2 | 1.2 | 1.2 | 0 | 1.2 |
| Section 9 | 0 | 0 | 1.2 | 1.2 | 0 | 0 | 0 |
| Section 10 | 0 | 0 | 0 | 1.2 | 0 | 0 | 0 |
| Section 11 | 0 | 0 | 0 | 1.2 | 0 | 0 | 0 |
And further combining the blanking combination data of the whole steel bar to obtain a preliminary steel bar blanking list.
Tables 1-6 are blanking lists of a portion of the bars, the first column of data representing the length of the bars to be blanked, the other columns representing lengths of bars combined to the length of the bars to be blanked, taking into account the 0.06m welded joint loss per joint. Such as: 203.33 ═ 20.59 × 9+12+4.15+2.53- (12-1) × 0.06.
Tables 1 to 5 show a method for cutting a portion of a reinforcing bar, wherein the first row of data represents the length of the entire bar of the raw material, and the other rows represent the lengths of the bars corresponding to the respective sections, for example: the first row and the third column, 20.59, are cut into 4 sections of 2 steel bars, 6.62, 1 steel bar, 4.64 and 1 steel bar, 2.53. 2 of these 6.62 bars were welded in section 11 of the 2 31.6m bars of tables 1-6.
Calculating the total steel bar waste rate, which comprises the following steps: all the steel bars in the whole steel bar length combination are utilized by the whole steel bar, no waste exists, and the waste is mainly caused by cutting method matrixes, such as: in the second bar (in the third column) of tables 1-5, the amount of waste is 20.59-2 x 6.62-4.64-2.53-0.18. And superposing the cutting waste amount of each reinforcing steel bar, namely the total waste amount, and dividing the total length of all the reinforcing steel bars to be blanked by the total waste amount to obtain the total waste rate. In the example, the total waste amount is 336.07m, the total length of the steel bars to be blanked is 36482.85m, and the total waste rate is 0.92%.
TABLE 1-6 Blanking List of reinforcing bars
| Steel bar to be blanked | Section 1 | Section 2 | Section 3 | Section 4 | Section 5 | Section 6 | Section 7 | Section 8 | Section 9 | Section 10 | Section 11 | Section 12 |
| 203.33 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 12 | 4.15 | 2.53 |
| 200.33 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 20.59 | 9 | 3.75 | 2.93 |
| 55.63 | 20.59 | 20.59 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2.63 | 0 |
| 31.6 | 20.59 | 4.51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.62 | 0 |
| 31.6 | 20.59 | 4.51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.62 | 0 |
| 30.5 | 12 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.62 | 0 |
| 30.5 | 12 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.62 | 0 |
| 27.5 | 12 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.62 | 0 |
| 27.5 | 12 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.62 | 0 |
| 22.52 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4.64 | 0 |
| 24.37 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.49 | 0 |
| 24.37 | 9 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.49 | 0 |
According to the invention, through the methods of simulating manual blanking, lingo modeling optimization, cutting method local optimization, reinforcing steel bar node optimization and the like, the material waste rate can be controlled to be below 1%, compared with the waste rate of 5% of the existing commercial software in other technologies, the optimization precision is improved by 4%, the calculation is carried out by using a general 10-ten-thousand-square project, and the material cost can be saved by about 90 thousands. In addition, when the material cost is controlled, the number of the steel bar welding nodes and the number of cutting times are optimized by adding the constraint that the number of the steel bar cutting nodes is not more than 2 at most in the lingo modeling, so that the labor cost of steel bar processing is reduced.
The invention also provides a steel bar blanking optimization device, which comprises:
a processor adapted to implement instructions; and
a storage device adapted to store a plurality of instructions adapted to be loaded by a processor and to perform the steps of any of the foregoing rebar optimization methods.
The present invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the steps of any of the foregoing method of rebar optimization.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. The steel bar blanking optimization method is characterized by comprising the following steps:
comparing the length of the steel bars to be blanked with the length of the raw material steel bars one by one, and directly blanking the raw material steel bars meeting the screening condition by taking the steel bar waste rate smaller than a set threshold value and the remaining steel bar length smaller than 35d as the screening condition; wherein: the length of the residual steel bar = the length of the steel bar to be blanked-the length of the raw material steel bar + the node number of the steel barsAnd d represents the diameter of the raw material reinforcing steel bar,srepresents a loss of the rebar junction;
for the rest reinforcing steel bars to be blanked, circularly selecting a group of reinforcing steel bars from the rest raw material reinforcing steel bars as a whole reinforcing steel bar blanking combination by taking the length of the rest reinforcing steel bars greater than or equal to the length of the minimum raw material reinforcing steel bar as a screening condition;
the method comprises the following steps of (1) screening out an optimal whole steel bar blanking combination by taking the minimum total number of steel bars after a plurality of raw material steel bars are combined as a target and considering the loss of a steel bar joint;
aiming at the minimum waste rate of the steel bars, establishing a nonlinear lingo model by using the combination of two sections of steel bars as a hard constraint at most for the length of each residual steel bar, and solving a global optimal solution;
further performing node optimization and material local optimization on the global optimal solution to obtain a final steel bar blanking list and a steel bar cutting method;
the optimal scheme for screening the whole steel bar blanking combination comprises the following steps:
for the whole steel bar blanking combination circularly selected from the length sequence of the raw material steel bars, if the length of the remaining steel bars after the blanking of the whole steel bar combination is more than or equal to (35d + s), storing the length data of the remaining steel bars and the blanking combination data of the selected whole steel bars;
if the length of the residual steel bar after the blanking of the whole steel bar combination is less than (35d + s), further judging whether the steel bar waste rate is less than a set threshold value: if the steel bar waste rate is less than a set threshold value, storing the length data of the steel bars to be blanked, the blanking combination data of the whole steel bars and the length data of the residual steel bars;
if the steel bar waste rate is larger than or equal to a set threshold value, deleting the data with the minimum length of the raw material steel bars from the whole steel bar blanking combination, recalculating and storing the length data of the residual steel bars, and simultaneously storing the recombined whole steel bar blanking combination data;
finally, obtaining the optimal scheme of the whole steel bar blanking combination;
the node optimization comprises the following steps:
reading a result file output by the nonlinear lingo model, and acquiring an optimization result matrix of the length of the residual steel bars and a steel bar cutting method matrix;
assuming that each row of the optimization result matrix is blanking combination data of the length of the residual steel bar, and each column of the matrix of the steel bar cutting method is data for performing combination cutting on the whole steel bar according to the blanking combination data of the length of the residual steel bar;
reading each row of the optimization result matrix of the length of the residual steel bars one by one, and judging whether the row comprises two pieces of non-whole steel bar length data: if not, reading the next row; if yes, taking out the length data of the two non-whole reinforcing steel bars, comparing the length data with each column of the matrix of the reinforcing steel bar cutting method, and judging whether the two data exist in a certain column at the same time: if the two data are not simultaneously existed, the optimization result matrix is circulated to the next row, if the two data are simultaneously existed, the sum of the two data is respectively used for replacing the corresponding data in the optimization result matrix and the corresponding data in the matrix column of the steel bar cutting method until the optimization result matrix of the length of the residual steel bar is traversed, and the optimization result matrix of the length of the residual steel bar after node optimization and the steel bar cutting method matrix after node optimization are obtained;
the local optimization of the material comprises the following steps:
reading a result file output by the nonlinear lingo model, and acquiring an optimization result matrix of the length of the residual steel bars and a steel bar cutting method matrix;
reading the length of the rest raw material steel bars, counting the length types and the number of the steel bars, and establishing a steel bar length type matrix;
assuming that each column of the matrix of the steel bar cutting method is data for performing combined cutting on the whole steel bar according to the blanking combined data of the length of the residual steel bar;
reading each row of the matrix of the steel bar cutting method one by one, and solving the sum of the lengths of the cut steel bars of the row;
the sum of the lengths of the cut steel bars in each row is merged into a steel bar length type matrix to form a new matrix, and then the new matrix is arranged in a descending order according to the lengths of the steel bars;
and (3) solving the index of the sum of the lengths of the rows of the cut steel bars in the new matrix, and judging whether the index is in the last row of the new matrix or not: if so, reading the next column of the matrix of the steel bar cutting method, and continuing the next cycle until traversing the matrix of the steel bar cutting method; if not, the next index corresponding numerical value of the index is taken out to replace the first value of the matrix column vector of the steel bar cutting method, namely the length of the cut raw material steel bar.
2. The steel bar blanking optimization method according to claim 1, wherein the establishing of the nonlinear lingo model comprises the following steps:
aiming at the minimum waste rate of the steel bars, and taking the maximum length of each residual steel bar as a hard constraint by combining two sections of steel bars, compiling a nonlinear lingo model:
wherein:x i,j is expressed as constituting theiThe length of the residual steel barjDividing the length of the section of the reinforcing steel bar by the length of the jth raw material reinforcing steel bar;A j is shown asjThe length of the raw material steel bar;B(i)is shown asiThe length of the steel bar to be blanked is determined;mrepresenting the total number of raw material steel bars;nrepresenting the total number of the steel bars to be blanked;drepresenting the diameter of the raw material steel bar;sindicating a rebar splice loss.
3. The steel bar blanking optimization method according to claim 1, wherein the following steps are performed before the nonlinear lingo model is established:
storing the length of the raw material steel bar and the length of the residual steel bar into two rows in the same excel file, and respectively arranging the lengths in a descending order;
the excel file with the length of the raw material steel bar and the length of the residual steel bar is cut into a plurality of subfiles, and the requirements are as follows: each subfile comprises 50-60 pieces of residual steel bar length data, and the difference between the total length of the original material steel bars and the total length of the residual steel bars in each subfile is less than or equal to 10.
4. The steel bar blanking optimization method according to claim 1, wherein the lengths of the steel bars to be blanked and the length of the raw material steel bars are respectively arranged in descending length order before comparing the lengths of the steel bars to be blanked and the lengths of the raw material steel bars one by one.
5. The steel bar blanking optimization method according to claim 1, wherein a group of data is circularly selected from the length sequence of the raw material steel bars according to the length of the raw material steel bars in a reverse order by taking the length of the residual steel bars greater than or equal to the length of the minimum raw material steel bars as a screening condition to serve as the whole steel bar blanking combination.
6. The utility model provides a reinforcing bar unloading optimizing apparatus which characterized in that includes:
a processor adapted to implement instructions; and
a storage device adapted to store a plurality of instructions adapted to be loaded by a processor and to perform the steps of any of claims 1-5.
7. A memory device having stored therein a plurality of instructions adapted to be loaded by a processor and to carry out the steps of any of claims 1-5.
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| CN109732021B (en) * | 2019-03-19 | 2020-05-19 | 湖北文理学院 | Steel bar blanking method |
| CN110206354B (en) * | 2019-06-13 | 2020-12-18 | 湖北亘华工科有限公司 | Lifting type intelligent parking equipment |
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| CN111636735B (en) * | 2020-05-27 | 2022-03-08 | 无锡科技职业学院 | Intelligent stereo garage management system |
| CN112096152B (en) * | 2020-09-21 | 2021-12-07 | 淮北创谷科技有限公司 | Intelligent parking lot |
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| CN112983075B (en) * | 2021-02-25 | 2022-05-06 | 常州工学院 | Multilayer multi-ring disc type stereo parking garage |
| CN113761635B (en) * | 2021-09-18 | 2024-04-05 | 广联达科技股份有限公司 | Method for determining processing shape of steel bar and method for processing steel bar |
| CN113806690B (en) * | 2021-09-28 | 2024-03-19 | 西安理工大学 | Intelligent optimization method, system and storable medium for steel bar blanking |
| EP4194644A4 (en) * | 2021-10-11 | 2023-11-22 | Pathway Automatic Parking (Beijing) Co., Ltd. | Three-dimensional parking system |
| CN113719170B (en) * | 2021-10-11 | 2023-03-24 | 坪安坪泊(北京)科技有限公司 | Three-dimensional parking system |
| CN113882727A (en) * | 2021-10-25 | 2022-01-04 | 华南理工大学 | Lifting type automatic wireless charging three-dimensional parking system and working method |
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| CN118244684A (en) * | 2024-05-28 | 2024-06-25 | 山东博硕自动化技术有限公司 | Comprehensive automatic batching simulation device and control method thereof |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002036177A (en) * | 2000-05-26 | 2002-02-05 | Gerber Technol Inc | Device and method for cutting out combination of sheet members |
| CN101739606A (en) * | 2008-11-19 | 2010-06-16 | 北京理工大学 | Raw material-saving one-dimensional stock-cutting method |
| CN102322151A (en) * | 2011-05-26 | 2012-01-18 | 中国建筑标准设计研究院 | Intelligent steel bar screening and blanking optimization method |
| CN103455848A (en) * | 2013-08-05 | 2013-12-18 | 浙江大学城市学院 | Method for optimizing and cutting rolled steel |
| CN105488301A (en) * | 2015-12-24 | 2016-04-13 | 上海市基础工程集团有限公司 | Three-dimensional reinforcing steel bar lofting method |
| CN107729662A (en) * | 2017-10-23 | 2018-02-23 | 朱海涛 | Steel Reinforcing Bar Material matching process based on BIM technology |
| CN107862117A (en) * | 2017-10-27 | 2018-03-30 | 广东星层建筑科技股份有限公司 | A kind of 3D solid reinforcing bar generation method and equipment based on BIM |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BE667357A (en) * | 1964-07-29 | |||
| JP4941053B2 (en) * | 2007-03-29 | 2012-05-30 | Ｊｆｅスチール株式会社 | Steel product production planning method and manufacturing method |
| CN203201208U (en) * | 2013-01-31 | 2013-09-18 | 中冶南方工程技术有限公司 | Novel lifting, rotating and horizontally moving stereo garage |
| CN104929399B (en) * | 2015-06-24 | 2017-07-14 | 黄河科技学院 | Automatic rotary multilayer parking system |
| CN105332510A (en) * | 2015-11-12 | 2016-02-17 | 徐仁贵 | Construction method of complicated beam column joint core area |
| CN205153653U (en) * | 2015-11-27 | 2016-04-13 | 黄延庆 | Multilayer garage parking |
| CN211081183U (en) * | 2018-04-26 | 2020-07-24 | 湖北文理学院 | Three-dimensional parking garage |
-
2018
- 2018-04-26 CN CN201810383424.9A patent/CN108360890A/en active Pending
- 2018-08-06 CN CN201810885695.4A patent/CN108999447A/en active Pending
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002036177A (en) * | 2000-05-26 | 2002-02-05 | Gerber Technol Inc | Device and method for cutting out combination of sheet members |
| CN101739606A (en) * | 2008-11-19 | 2010-06-16 | 北京理工大学 | Raw material-saving one-dimensional stock-cutting method |
| CN102322151A (en) * | 2011-05-26 | 2012-01-18 | 中国建筑标准设计研究院 | Intelligent steel bar screening and blanking optimization method |
| CN103455848A (en) * | 2013-08-05 | 2013-12-18 | 浙江大学城市学院 | Method for optimizing and cutting rolled steel |
| CN105488301A (en) * | 2015-12-24 | 2016-04-13 | 上海市基础工程集团有限公司 | Three-dimensional reinforcing steel bar lofting method |
| CN107729662A (en) * | 2017-10-23 | 2018-02-23 | 朱海涛 | Steel Reinforcing Bar Material matching process based on BIM technology |
| CN107862117A (en) * | 2017-10-27 | 2018-03-30 | 广东星层建筑科技股份有限公司 | A kind of 3D solid reinforcing bar generation method and equipment based on BIM |
Non-Patent Citations (2)
| Title |
|---|
| 基于MATLAB的钢筋下料优化算法;漏家俊;《建筑施工》;20180225(第02期);全文 * |
| 钢筋下料问题的数学规划模型;张玉岩等;《渤海大学学报(自然科学版)》;20171215(第04期);全文 * |
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| CN109376438B (en) | 2023-02-03 |
| CN108360890A (en) | 2018-08-03 |
| CN109284858A (en) | 2019-01-29 |
| CN109376438A (en) | 2019-02-22 |
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