CN116308183A - Intelligent design method for key indexes of hydraulic and hydroelectric engineering artificial sand stone processing system - Google Patents
Intelligent design method for key indexes of hydraulic and hydroelectric engineering artificial sand stone processing system Download PDFInfo
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- 239000004576 sand Substances 0.000 title claims abstract description 73
- 238000012545 processing Methods 0.000 title claims abstract description 62
- 238000013461 design Methods 0.000 title claims abstract description 54
- 239000004575 stone Substances 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000000513 principal component analysis Methods 0.000 claims abstract description 6
- 238000007637 random forest analysis Methods 0.000 claims abstract description 4
- 238000010276 construction Methods 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 12
- 239000011435 rock Substances 0.000 claims description 10
- 238000004519 manufacturing process Methods 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 description 8
- 239000000047 product Substances 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
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Abstract
The invention discloses an intelligent design method for key indexes of an artificial sand and stone processing system of a hydraulic and hydroelectric engineering, which is implemented by collecting key index data of the artificial sand and stone processing system of the hydraulic and hydroelectric engineering; the key indexes are used as output parameters, and the key indexes reflecting the manual sand processing system of the hydraulic and hydroelectric engineering are extracted through principal component analysis; and obtaining a key index design model of the artificial sand processing system through a random forest regression model. The invention has the advantages that in the intelligent application of the machine and the design of the artificial sand and stone system of the hydraulic and hydroelectric engineering, the key indexes of the artificial sand and stone system to be built can be rapidly designed, and the problems of excessive dependence on experience, lack of standardization, long design period and poor accuracy of the key index design of the artificial sand and stone processing system in the field of the hydraulic and hydroelectric engineering can be solved.
Description
Technical Field
The invention relates to the field of hydraulic and hydroelectric engineering, in particular to an intelligent design method for key indexes of an artificial sand stone processing system of hydraulic and hydroelectric engineering.
Background
The traditional design method of the artificial sand stone processing system in the field of water conservancy and hydropower engineering is as follows: according to the use conditions and quality requirements of the project sand aggregate, selecting a product inspection standard and a product particle size; according to the selected sand and stone stock ground conditions, material parameters, mining method and coarse crushing equipment to be selected, the total crushing ratio is calculated; determining and distributing the number of crushing sections according to the total crushing ratio and the rock characteristics; determining the opening degree of a discharge hole and the size of a sieve hole; determining a sand making process; performing overall arrangement according to the process and equipment selection conditions; and determining key indexes such as engineering floor area, engineering cost, construction period, labor force allocation and the like according to engineering arrangement.
The design method is greatly influenced by human experience when determining key indexes of the artificial sand processing system. And the whole design process needs a plurality of professionals to cooperate, and the design period is long. Meanwhile, when the design scheme is implemented in a floor mode, the engineering quality and the progress are affected due to the fact that different degrees exist between the design scheme and the actual engineering condition.
Along with the rapid development of the water conservancy and hydropower industry, a large amount of valuable engineering construction data are accumulated in the industry, and a large amount of valuable reference data are also formed in the aspect of index design of an artificial sand and stone processing system. How to combine machine intelligence, utilize a large amount of valuable reference data of current, solve the key index design of the manual grit processing system in the hydraulic and hydroelectric engineering field and excessively rely on experience, lack standardization, design cycle is long, the problem that the accuracy is poor has become the important subject in the hydraulic engineering field.
Disclosure of Invention
The invention aims to provide an intelligent design method for key indexes of an artificial sand stone processing system of a hydraulic and hydroelectric engineering.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention relates to an intelligent design method for key indexes of an artificial sand stone processing system of a hydraulic and hydroelectric engineering, which specifically comprises the following steps:
s1, collecting key index data of an artificial sand processing system of an established hydraulic and hydroelectric engineering;
s2, storing the key index data according to item characteristics in a classified mode;
s3, dividing the key index data into a training set and a testing set;
s4, taking the key indexes as output parameters, and extracting the key indexes reflecting the artificial sand and stone processing system of the hydraulic and hydroelectric engineering through principal component analysis;
s5, inputting the training set into a random forest regression model for training, and performing parameter tuning by a Bayesian optimization method to obtain a key index design model of the artificial sand processing system;
s6, verifying the key index design model of the artificial sand processing system by using a test set, and evaluating the accuracy of the key index design model of the artificial sand processing system;
s7, if the accuracy rate meets the threshold requirement, completing the design of key indexes of the artificial sand processing system to be built by using a key index design model of the artificial sand processing system; and if the accuracy rate is lower than the threshold requirement, repeating the steps S4 to S6.
Further, the key index data comprise construction sites, construction periods, mother rock lithology, production scale, breaking section number, sand and stone unit price, labor allocation, engineering cost, building area and occupied area.
Further, the key index data is obtained from an enterprise internal database, a technical examination, an consultation unit internal database, an engineering reference unit database, papers, annual reports and bidding information.
Further, the key index data of the criticizing or the key index data of the implemented project are reviewed to be optimal.
Further, the parameter tuning includes tree number, non-purity function, feature number, maximum depth of tree, early growth threshold of tree.
Further, the accuracy takes average absolute error, mean square error, root mean square error, decision coefficient, precision, accuracy and recall as evaluation standards, and the thresholds are respectively set.
The invention has the advantages that the intelligent machine application is realized in the design of the artificial sand and stone system of hydraulic and hydroelectric engineering. The prediction model of the hydraulic and hydroelectric engineering artificial sand and stone system design is formed by collecting the data of the hydraulic and hydroelectric engineering under construction and the conditions of the design indexes of the existing artificial sand and stone system, analyzing and sorting the data and the conditions of the existing artificial sand and stone system by utilizing the strong analysis and integration capability of a computer. The prediction model can be used for rapidly designing key indexes of an artificial sand stone system of a project to be built, and the problems that the key index design of the artificial sand stone processing system in the field of water conservancy and hydropower engineering is excessively dependent on experience, lack of standardization, long design period and poor accuracy can be solved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the technical solutions in the embodiments of the present invention will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: as shown in FIG. 1, the intelligent design method for key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system specifically comprises the following steps:
s1, collecting key index data of an artificial sand processing system of an established hydraulic and hydroelectric engineering;
the key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system can be obtained through an enterprise internal database, such as reports, drawings, tables and the like formed by enterprises in the hydraulic and hydroelectric engineering construction process, an internal database of technical examination and consultation units, databases of various engineering construction units, papers, annual reports, bidding information and the like disclosed on a network. The obtained data comprise construction sites, construction periods, mother rock lithology, production scale, crushing section number, sand unit price, labor force allocation, engineering cost, building area, occupied area and the like, so as to evaluate and review critical index data of the finished projects or the critical index data of the implemented projects to be optimal.
S2, storing the key index data according to item characteristics in a classified mode;
the key indexes of the obtained hydraulic and hydroelectric engineering artificial sand processing system are classified according to certain project characteristics by adopting a centralized storage device, and a database is formed in the form of standardized product units (English full name: standard Product Unit). The classification items include construction sites, construction periods, mother rock lithology, production scale, number of crushing sections, sand unit price, labor force allocation, engineering cost, construction area, occupied area and the like.
And S3, dividing the key index data into a training set and a testing set.
S4, training by using the training set. Specifically, key indexes such as construction sites, mother rock lithology and production scale are used as input parameters, construction period, number of broken sections, sand and stone unit price, labor allocation, engineering cost, building area, occupied area and the like are used as output parameters, characteristic dimension reduction is performed through principal component analysis, key indexes reflecting the manual sand and stone processing system of the water conservancy and hydropower engineering are extracted, and multiple indexes are converted into a few indexes.
The Principal Component Analysis (PCA) is essentially characterized by dimension reduction, and can convert key indexes of a plurality of artificial sand processing systems into a few comprehensive indexes, namely principal components, so as to reflect the overall requirements of the artificial sand processing systems. In the method, construction sites, mother rock lithology and production scale are used as input parameters, construction period, breaking section number, sand unit price, labor force allocation, engineering cost, building area, occupied area and the like are used as output parameters, the dimension reduction treatment is carried out through a principal component analysis method, key indexes of an artificial sand processing system of the hydraulic and hydroelectric engineering are extracted and reflected, and the data training requirement under the condition of sample data scarcity is met.
And S5, inputting the training set into a random forest regression model for training, and performing parameter tuning on parameters such as the number of trees, the impure function, the feature number, the maximum depth of the trees, the early growth threshold of the trees and the like by a Bayesian optimization method to obtain the key index design model of the artificial sand processing system.
S6, verifying the key index design model of the artificial sand processing system by using a test set, and evaluating the accuracy of the key index design model of the artificial sand processing system; inputting test set data into a key index design model of an artificial sand stone processing system, comparing data output by the model with actual data of the test set, judging the accuracy of the model, taking average absolute error, mean square error, root mean square error, decision coefficient, precision, accuracy and recall rate as evaluation standards, and setting threshold values respectively.
S7, if the accuracy rate meets the threshold requirement, completing the design of key indexes of the artificial sand processing system to be built by using a key index design model of the artificial sand processing system; and if the accuracy is lower than the threshold requirement, repeating the steps S4 to S6 until the accuracy of the key index design model of the artificial sand processing system meets the threshold requirement position.
Example 2: taking specific engineering data as an example, the reliability of the model of the invention is illustrated
The construction site for processing the artificial sand stone of a certain hydraulic and hydroelectric engineering is Shaanxi province, the lithology of the mother rock is limestone, and the production scale is 520t/h.
In the design method of the traditional artificial sand processing system, the process flow design of the artificial sand processing system is needed first. According to engineering quantity and mixing proportion data, selecting the grain size of the product after statistical analysis; determining key processes including a crushing process, a screening process, a sand making process and an aggregate shaping process; determining processing technological processes including a coarse crushing workshop, a medium crushing workshop, a fine crushing workshop, an ultrafine crushing workshop, a sand making and shaping workshop, a screening and flushing workshop and the like; then carrying out process flow balance calculation, namely comprehensively considering test data of similar rocks provided by related equipment manufacturers, and selecting granularity characteristics of crushing equipment products; calculating the processing capacity of each workshop according to the result of the flow calculation table and the total system processing capacity; and then, selecting equipment types, namely selecting the types and the quantity of equipment in a coarse crushing, medium crushing, fine crushing, sand making, shaping and screening workshop according to the process flow calculation.
The sand and stone material processing system consists of a coarse crushing workshop, a first screening workshop, a semi-finished product bin, a second screening workshop, a medium crushing workshop, an extra-large stone and large stone flushing screening workshop, a third screening workshop, an adjusting bin of the third screening workshop, a vertical shaft crushing workshop, a rod mill workshop, a finished product bin, a water supply and drainage system, a power supply system, a dust removal system, a connecting tape machine of each workshop, corresponding auxiliary facilities and the like. Therefore, after the design of the technological process of the traditional artificial sand stone processing system is completed, a design plan view is arranged according to the topography, geological data and land feature range and combining the production process and working procedures of the system and the requirements of workshops on the bearing capacity of the foundation.
Finally, according to the engineering layout plan, adding the building areas to obtain the total building area; and the required occupation area is defined according to relevant regulations and engineering experience; calculating the engineering quantity according to the system configuration; according to the cost calculation method, calculating the engineering cost; determining a construction period according to the engineering quantity and related experience; according to the labor force conditions required by each system, comprehensively producing the working system and determining the required labor force; and according to the approximate calculation quota and the planned processing technology, finally determining the comprehensive unit price of the sand and stone.
The key index design model of the artificial sand processing system obtained by the method only needs to input the construction site, the lithology of the parent rock and the production scale into the model, and the model obtains the following output data after calculation and prediction:
the construction period is as follows: for 15 months; number of crushing sections: 3 sections; grit unit price: 53 yuan/t; labor force preparation: 153 persons; engineering cost: 4855 ten thousand yuan; building area: 942m 2 The method comprises the steps of carrying out a first treatment on the surface of the Floor area: 67356m 2 。
Parameters determined by a plurality of professionals aiming at key index matching design of the hydraulic and hydroelectric engineering artificial sand processing system are consistent with parameters predicted by the model of the invention. That is, the key index of the hydraulic and hydroelectric engineering artificial sand stone processing system obtained by the method has the advantages of reliability, design period section, labor saving, no dependence on subjective experience of personnel and the like.
Claims (6)
1. The intelligent design method for the key index of the hydraulic and hydroelectric engineering artificial sand stone processing system is characterized by comprising the following steps of:
s1, collecting key index data of an artificial sand processing system of an established hydraulic and hydroelectric engineering;
s2, storing the key index data according to item characteristics in a classified mode;
s3, dividing the key index data into a training set and a testing set;
s4, taking the key indexes as output parameters, and extracting the key indexes reflecting the artificial sand and stone processing system of the hydraulic and hydroelectric engineering through principal component analysis;
s5, inputting the training set into a random forest regression model for training, and performing parameter tuning by a Bayesian optimization method to obtain a key index design model of the artificial sand processing system;
s6, verifying the key index design model of the artificial sand processing system by using a test set, and evaluating the accuracy of the key index design model of the artificial sand processing system;
s7, if the accuracy rate meets the threshold requirement, completing the design of key indexes of the artificial sand processing system to be built by using a key index design model of the artificial sand processing system; and if the accuracy rate is lower than the threshold requirement, repeating the steps S4 to S6.
2. The intelligent design method for key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system according to claim 1, which is characterized by comprising the following steps: the key index data comprise construction sites, construction periods, mother rock lithology, production scale, crushing section number, sand unit price, labor force allocation, engineering cost, building area and occupied area.
3. The intelligent design method for key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system according to claim 2, which is characterized by comprising the following steps: the key index data is obtained from an enterprise internal database, a technical examination, an consultation unit internal database, an engineering reference unit database, papers, annual reports and bidding information.
4. The intelligent design method for key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system according to claim 3, which is characterized by comprising the following steps: and the key index data of the criticizing or the key index data of the implemented project are reviewed and optimized.
5. The intelligent design method for key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system according to claim 1, which is characterized by comprising the following steps: the parameter tuning includes the number of trees, the function of the purity, the number of features, the maximum depth of the trees, and the early growth threshold of the trees.
6. The intelligent design method for key indexes of the hydraulic and hydroelectric engineering artificial sand stone processing system according to claim 1, which is characterized by comprising the following steps: the accuracy takes average absolute error, mean square error, root mean square error, decision coefficient, precision, accuracy and recall as evaluation standards, and the thresholds are respectively set.
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- 2023-03-23 CN CN202310287446.6A patent/CN116308183A/en active Pending
Patent Citations (6)
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| CN103854137A (en) * | 2014-03-14 | 2014-06-11 | 国家电网公司 | Method for evaluating economic and technical indexes of engineering project |
| CN111027117A (en) * | 2019-11-12 | 2020-04-17 | 华北水利水电大学 | BP neural network analysis method for predicting compressive strength by key index of cemented sand gravel mixture ratio |
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