CN106296474B - Loader operation condition difficulty degree identification method - Google Patents
Loader operation condition difficulty degree identification method Download PDFInfo
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- CN106296474B CN106296474B CN201610585057.1A CN201610585057A CN106296474B CN 106296474 B CN106296474 B CN 106296474B CN 201610585057 A CN201610585057 A CN 201610585057A CN 106296474 B CN106296474 B CN 106296474B
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Abstract
The invention relates to a loader operation condition difficulty degree identification method, which takes an excavation operation section extracted from an operation section as a main research object, and identifies the operation condition to finally obtain an operation condition difficulty degree value. The difficulty degree of the working condition is identified, so that the power output mode of the diesel engine is controlled, and the distribution according to the requirement is realized; meanwhile, the method is used as a judgment basis for an intelligent gear-shifting control strategy, has important significance for intelligent gear shifting, power mode control, improvement of the operation performance and the like of the engineering vehicle, and is beneficial to improvement of the operation performance, energy conservation and emission reduction; meanwhile, the variable power adjustment is realized by utilizing the difficulty degree of the working condition, the application range of the engineering machinery is improved, one machine can be used for various working media, the real multiple purposes of one machine are realized, the machine can be simultaneously used for working occasions of various different media, and the working performance and the intelligent level of the machine are improved.
Description
Technical Field
The invention relates to a technology for identifying the difficulty of the working condition of an engineering vehicle, in particular to a method for identifying the difficulty of the working condition of a loader.
Background
With the rapid promotion of large-scale national engineering construction such as mining, building, water conservancy and hydropower, expressway networks, highway networks, south-to-north water transfer and the like in China, the yield, the sales volume and the holding capacity of engineering vehicles are rapidly improved, and the development of engineering machinery is abnormally rapid. More than 95% of products of the engineering machinery adopt hydraulic transmission so as to obtain the requirements of large torque and large inertia load, and the technical problem to be solved at present is urgent to ensure the reliable and efficient operation of the engineering machinery due to the severe operation environment, complex and changeable operation working conditions and the continuous improvement of the automation and informatization degrees of equipment. In the operation process of the engineering machinery, the operation performance is not only influenced by the performance of the system, but also influenced by the operation working condition to a great extent, so that the identification of the difficulty degree of the operation working condition plays an extremely important role in improving the operation performance and the intelligent degree of the engineering machinery.
The engineering vehicle is an engineering machine mainly based on operation, different operation working conditions have great influence on the fuel economy of the vehicle, particularly the materials with high compactness such as original soil, iron ore and the like are shoveled, and corresponding gear shifting control strategies and operation power modes are required to be selected for different operation working conditions. Therefore, how to effectively identify the difficulty degree of the working condition of the engineering machinery has important significance for improving the operability and the intelligent performance of the engineering machinery.
Taking a typical prototype loader in an engineering vehicle as an example, the loader is a large, medium and small multipurpose high-efficiency engineering machine mainly used for loading and unloading comprehensive materials such as soil, gravel, coal and the like, is suitable for operations such as mines, ports, capital construction, road construction and the like, and is widely applied to working conditions such as factories, stations, wharfs, goods yards, warehouses and the like. For ore with higher density, solid original image or loose object with lower density such as soil, coke, etc., the selection of the loader is also greatly different due to different working conditions. For materials with larger density, such as firm raw soil, ore and the like, because the requirement on the traction force is higher, products with lower working speed and larger digging force and traction force are selected to ensure normal use. Because the loose materials have low requirements on the traction force of the loader, products with higher running speed can be selected to obtain higher working efficiency. The working media are different, the difficulty degree of the working conditions is also different, and the engineering machinery enterprises can only release special products aiming at a certain specific working medium due to the fact that the working conditions cannot be identified. If the universal heavy industry meets the requirements of users under different working conditions and meets the requirements of coal charging operation, the universal heavy industry particularly provides different working devices such as a bucket special for coal mines, a bucket special for coke, a rock bucket, a wood grabbing device and a snow pushing device, and is matched with loaders of different models, so that the multifunctional coal loader is multipurpose.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the loader operation condition difficulty degree identification method which can improve the application range of engineering machinery, realize multiple purposes of one machine, can be simultaneously used in working occasions with various different media and improve the operation performance and the intelligent level of the loader.
The technical scheme of the invention is as follows:
a method for identifying the difficulty level of the operating condition of a loader comprises the following steps:
1) acquiring signals of the pressure of a large cavity of a movable arm and the pressure of a large cavity of a rotating bucket from a loader, and extracting the working period of the signals of the pressure of the large cavity of the movable arm and the pressure of the large cavity of the rotating bucket;
2) extracting an excavation operation section according to the obtained operation period;
3) the method comprises the steps of obtaining the excavation time length, the excavation time length change rate, the movable arm large cavity pressure maximum value and the movable arm large cavity pressure maximum value change rate of movable arm large cavity pressure of an excavation operation section, and further obtaining an operation working condition difficulty degree index according to a preset rule.
Preferably, in the excavation operation section, a minimum value point before the pressure of the large cavity of the movable arm is contacted with the material is defined as an initial time point of the excavation operation section; the first maximum point of the pressure of the big cavity of the rotary bucket is defined as the end time of the spading operation section.
Preferably, in the step 3), a fuzzy logic C-means clustering algorithm is adopted for the pressure of the large cavity of the movable arm in the excavation operation section to perform clustering analysis on the excavation time length, the change rate of the excavation time length, the maximum value of the pressure of the large cavity of the movable arm and the change rate of the maximum value of the pressure of the large cavity of the movable arm.
Preferably, the time length sequence of the excavation work segment is T ═ T (T)1,t2,...,ti,...,tn-1,tn) Calculating to obtain the mining time length t according to a fuzzy logic C mean value clustering algorithmFCMThe rate of change u of the length of excavation time of each segmenttThe following were used:
rate of change u according to length of excavation timetCalculating the cluster center value uFCMAnd will uFCMAs an evaluation of the rate of change of the length of excavation time.
Preferably, only one excavation operation is performed in one excavation time period, and second-order parabolic fitting is performed on the pressure curves of the large cavities of the movable arms of all the excavation operation periods, wherein the fitting function is as follows: p ═ a + bt + ct2；
Obtaining the motion according to the fitted functionMaximum value p of pressure of large cavity of movable armmaxAnd the time t elapsed after the maximum value is reached, the calculation formula of the maximum value change rate of the pressure of the large cavity of the movable arm is as follows:
wherein u ispIs the maximum change rate of the pressure of the large cavity of the movable arm, piFor a certain value of the pressure of the big cavity of the movable arm, pmaxThe maximum value of the pressure of the large cavity of the movable arm.
Preferably, the excavation time length, the rate of change in the excavation time length, the maximum boom large chamber pressure value, and the rate of change in the maximum boom large chamber pressure value are mapped to a radar map of a unit circle, and these values are subjected to forward normalization processing.
Preferably, after a forward normalized value is obtained, the value is represented by a radar chart; and calculating the area contained in the radar map of each operation condition, and defining the ratio of each radar map to the unit circle area as an operation difficulty value to obtain an operation condition difficulty index.
The invention has the following beneficial effects:
according to the loader working condition difficulty degree identification method, the excavation working section extracted from the working section is used as a main research object, the working condition is identified, and finally the working condition difficulty degree value is obtained. The difficulty degree of the working condition is identified, so that the power output mode of the diesel engine is controlled, and the distribution according to the requirement is realized; meanwhile, the method is used as a judgment basis for an intelligent gear-shifting control strategy, has important significance for intelligent gear shifting, power mode control, improvement of the operation performance and the like of the engineering vehicle, and is beneficial to improvement of the operation performance, energy conservation and emission reduction; meanwhile, the variable power adjustment is realized by utilizing the difficulty degree of the working condition, the application range of the engineering machinery is improved, one machine can be used for various working media, the real multiple purposes of one machine are realized, the machine can be simultaneously used for working occasions of various different media, and the working performance and the intelligent level of the machine are improved.
Drawings
FIG. 1 is a basic framework of the identification method of the present invention;
FIG. 2 is a schematic view of the evaluation index of the working condition of the present invention;
fig. 3 is a flow chart of the identification method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a loader working condition difficulty degree identification method for solving the technical blank that the loader working condition difficulty degree cannot be identified in the prior art, which is used for improving the application range of engineering machinery, realizing multiple purposes of one machine, being simultaneously used in working occasions of various different media and improving the working performance and the intelligent level of the machine.
The method of the invention, as shown in figure 1, mainly comprises the following steps:
1) acquiring signals of the pressure of a large cavity of a movable arm and the pressure of a large cavity of a rotating bucket from a loader, and extracting the working period of the signals of the pressure of the large cavity of the movable arm and the pressure of the large cavity of the rotating bucket;
2) extracting an excavation operation section according to the obtained operation period;
3) the method comprises the steps of obtaining the excavation time length, the excavation time length change rate, the movable arm large cavity pressure maximum value and the movable arm large cavity pressure maximum value change rate of movable arm large cavity pressure of an excavation operation section, and further obtaining an operation working condition difficulty degree index according to a preset rule.
Definition of excavation operation section: the initial time of the excavation operation section is that when the bucket contacts with materials, the pressure of a large cavity of a movable arm begins to increase violently, and the pressure of the large cavity of the movable arm has a minimum extreme point before the pressure changes, because the bucket is placed on the ground before excavation, and the pressure value of the large cavity of the movable arm is smaller than that during normal running at the moment, the minimum extreme point before the pressure of the large cavity of the movable arm changes violently is defined as the initial time point of the excavation operation section; when the end time of the excavation operation section is that the bucket is filled with materials and is separated from the working face, the pressure of the large cavity of the rotary bucket generally accompanies the bucket collection action (generally 1-2 times), a maximum value appears when the bucket is collected every time, the pressure of the large cavity of the rotary bucket is stably reduced after the bucket collection is finished, and the first maximum value point of the pressure of the large cavity of the rotary bucket is defined as the end time of the excavation operation section.
The following characteristics can be obtained by analyzing the excavation operation section:
(1) different materials have different time lengths of single excavation operation and different change rates of the time lengths of the excavation operations due to different states, compactness and the like. For example, when fine sand is compared with iron ore, but the fine sand belongs to a discrete particle state, the fine sand has high compactness, larger shape and high tunneling difficulty with the iron ore, so that each time of single excavation operation is more consumed, and the change rate of the time length of each excavation operation is larger.
(2) Different materials have different compactness, so that the maximum pressure value of a movable arm large cavity of the excavator is different when the bucket is fully shoveled in a single excavating operation. For example, compared with iron ore, the density of the iron ore is obviously higher than that of the fine sand, so that the maximum pressure value of the movable arm large cavity is definitely higher when the bucket is full.
(3) Different materials, because excavate the great back of the degree of difficulty, often appear the condition that can't shovel full fill, to the great iron ore of closely knit degree, former soil etc. the probability that can accomplish full fill is very little, especially iron ore, half fill just is equivalent with the weight of fine sand one fill.
(4) Different drivers have different operation habits and operation experiences, so that the shoveling degree, the digging speed and the like in the test process also influence the time length of the digging operation section, the change rate of the time length and the like.
Based on the above characteristics, the invention provides an evaluation index of the working condition, as shown in fig. 2. The complexity of the working condition is measured by the difficulty of the operation, and the degree is rated by the percentage value of 0-100%. The operation condition evaluation mainly comprises a time index and a pressure index. Wherein the time index comprises a time length and a time change rate; the pressure indicator includes a pressure extremum and a pressure rate of change. The main consideration object of the time index and the pressure index is a pressure signal of a large cavity of a movable arm of an excavation operation section, and the pressure signal is specifically represented as the time length of the excavation operation section and the change rate of the excavation operation time; and (4) excavating the maximum value of the pressure of the large cavity of the movable arm at the working section and the pressure change rate in the process of reaching the maximum value.
As shown in fig. 3, the method of the present invention first performs a working cycle extraction on the pressure signal, further performs a working segment extraction after obtaining the working cycle, obtains an excavation working segment signal, and then performs an analysis and identification, specifically as follows:
and performing cluster analysis on the excavation time length, the excavation time length change rate, the maximum value of the pressure of the large cavity of the movable arm and the change rate of the maximum value of the pressure of the large cavity of the movable arm by adopting a fuzzy logic C mean value cluster algorithm on the pressure of the large cavity of the movable arm at the excavation operation section.
Let the time length sequence of the digging operation section be T ═ T (T)1,t2,...,ti,...,tn-1,tn) Calculating to obtain the mining time length t according to a fuzzy logic C mean value clustering algorithmFCMThe rate of change u of the length of excavation time of each segmenttThe following were used:
rate of change u according to length of excavation timetCalculating the cluster center value uFCMAnd will uFCMAs an evaluation of the rate of change of the length of excavation time.
For the convenience of analysis, it is set that only one excavation operation is completed in one excavation time period, namely one shovel is completed, and second-order parabolic fitting is carried out on the pressure curves of the large cavities of the movable arms of all excavation operation sections, wherein the fitting function is as follows: p ═ a + bt + ct2；
Obtaining a maximum value p of the pressure of the large cavity of the movable arm according to the function obtained by fittingmaxAnd the time t elapsed after the maximum value is reached, the calculation formula of the maximum value change rate of the pressure of the large cavity of the movable arm is as follows:
wherein u ispIs the maximum change rate of the pressure of the large cavity of the movable arm, piFor a certain value of the pressure of the big cavity of the movable arm, pmaxThe maximum value of the pressure of the large cavity of the movable arm.
According to analysis of the obtained excavation time length, the excavation time length change rate, the maximum value of the pressure of the large cavity of the movable arm and the maximum value change rate of the pressure of the large cavity of the movable arm, the three parameters of the excavation time length, the excavation time length change rate, the maximum value of the pressure of the large cavity of the movable arm and the like are found to be in a direct proportion relation to the measurement of the difficulty degree of the operation, and the maximum value change rate of the pressure of the large cavity of the movable arm is in an inverse proportion. In order to unify the relationship, the excavation time length change rate, the maximum value of the pressure of the large cavity of the movable arm and the change rate of the maximum value of the pressure of the large cavity of the movable arm are mapped to a radar map of a unit circle, and the values are subjected to forward normalization processing.
In this embodiment, assuming that the maximum value of the time length is 20s, the time length greater than or equal to this value is represented by 1, and the value obtained by dividing the time length smaller than this value by this maximum value is taken as the normalized value; the time change rate accords with the normalization value and is not processed; since the maximum pressure of the large cavity of the movable arm is 20Mpa, dividing the maximum pressure value by the maximum pressure value to be used as a normalization value; and dividing the pressure change rate by the maximum pressure value to obtain the reciprocal, setting the maximum reciprocal value to be 3, using 1 when the maximum reciprocal value is greater than 3 to represent the normalized value, and using the processed value after 3 when the maximum reciprocal value is less than 3 as the normalized value. After unification, the evaluation influence trend of all standard characteristic values on the operation difficulty degree is consistent.
After the forward normalized values are obtained, the values are represented by a radar chart. In order to further calculate the operation difficulty value, the operation difficulty degree index is obtained by calculating the area included by the radar map of each operation working condition and defining the ratio of each radar map to the unit circle area as the operation difficulty value.
The above examples are provided only for illustrating the present invention and are not intended to limit the present invention. Changes, modifications, etc. to the above-described embodiments are intended to fall within the scope of the claims of the present invention as long as they are in accordance with the technical spirit of the present invention.
Claims (4)
1. A method for identifying the difficulty level of the operating condition of a loader is characterized by comprising the following steps:
1) acquiring signals of the pressure of a large cavity of a movable arm and the pressure of a large cavity of a rotating bucket from a loader, and extracting the working period of the signals of the pressure of the large cavity of the movable arm and the pressure of the large cavity of the rotating bucket;
2) extracting an excavation operation section according to the obtained operation period;
3) acquiring the excavation time length, the excavation time length change rate, the movable arm large cavity pressure maximum value and the movable arm large cavity pressure maximum value change rate of an excavation operation section, further acquiring an operation working condition difficulty degree index according to a preset rule, measuring the complexity degree of an operation working condition through the operation difficulty degree index, and grading by using a percentage value of 0-100%;
in the step 3), carrying out cluster analysis on the excavation time length, the excavation time length change rate, the maximum value of the movable arm large cavity pressure and the maximum value change rate of the movable arm large cavity pressure on the movable arm large cavity pressure of the excavation operation section by adopting a fuzzy logic C mean value clustering algorithm; mapping the excavation time length, the excavation time length change rate, the maximum value of the pressure of the large cavity of the movable arm and the change rate of the maximum value of the pressure of the large cavity of the movable arm to a radar map of a unit circle to obtain a forward normalization value; after a forward normalization value is obtained, representing by using a radar map; calculating the area included by the radar map of each operation condition, and defining the ratio of each radar map to the unit circle area as an operation difficulty value to obtain an operation condition difficulty index;
based on the identification of the difficulty degree of the working condition, the power output mode is controlled to realize the distribution according to the requirement; and meanwhile, the method is used as a judgment basis for an intelligent gear shifting control strategy.
2. The method for identifying the difficulty level of the working condition of the loader according to claim 1, wherein in the excavation working section, a minimum value point before the pressure of the large cavity of the movable arm contacts the material is defined as a starting time point of the excavation working section; the first maximum point of the pressure of the big cavity of the rotary bucket is defined as the end time point of the spading operation section.
3. Loader work condition according to claim 1The difficulty level identification method is characterized in that the time length sequence of the excavation operation section is set as T ═ T (T)1,t2,...,ti,...,tn-1,tn) Calculating to obtain the mining time length t according to a fuzzy logic C mean value clustering algorithmFCMThe rate of change u of the length of excavation time of each segmenttThe following were used:
rate of change u according to length of excavation timetCalculating the cluster center value uFCMAnd will uFCMAs an evaluation of the rate of change of the length of excavation time.
4. The method for identifying the difficulty level of the working condition of the loader according to claim 1, wherein only one excavation operation is performed in one excavation time period, and second-order parabolic fitting is performed on the pressure curve of the large cavity of the movable arm in all the excavation operation periods, wherein the fitting function is as follows: p ═ a + bt + ct2；
Obtaining the maximum value p of the pressure of the large cavity of the movable arm according to the function obtained by fittingmaxAnd the time t elapsed after the maximum value is reached, the calculation formula of the maximum value change rate of the pressure of the large cavity of the movable arm is as follows:
wherein u ispIs the maximum change rate of the pressure of the large cavity of the movable arm, piFor a certain value of the pressure of the big cavity of the movable arm, pmaxThe maximum value of the pressure of the large cavity of the movable arm.
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| CN201610585057.1A CN106296474B (en) | 2016-07-22 | 2016-07-22 | Loader operation condition difficulty degree identification method |
| US16/067,740 US10633823B2 (en) | 2016-07-22 | 2017-07-03 | Method of identifying a difficulty level of an operating condition of a loader |
| PCT/CN2017/091433 WO2018014718A1 (en) | 2016-07-22 | 2017-07-03 | Method for recognizing difficulty level of working condition of loading machine |
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| CN201610585057.1A CN106296474B (en) | 2016-07-22 | 2016-07-22 | Loader operation condition difficulty degree identification method |
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| WO2018014718A1 (en) * | 2016-07-22 | 2018-01-25 | 厦门大学 | Method for recognizing difficulty level of working condition of loading machine |
| CN108507510A (en) * | 2018-04-02 | 2018-09-07 | 广东劲胜智能集团股份有限公司 | A kind of method and apparatus measuring different amorphous precision part molding difficulty differences |
| CN111461568B (en) * | 2020-04-14 | 2023-05-02 | 西安热工研究院有限公司 | Method for evaluating performances of strip mine trucks |
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| FI122885B (en) * | 2005-05-30 | 2012-08-31 | John Deere Forestry Oy | System for measuring the efficiency of a forest machine |
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| CN101806079A (en) * | 2010-04-22 | 2010-08-18 | 浙江大学 | System for automatically identifying load of excavator |
| CN201649154U (en) * | 2010-04-22 | 2010-11-24 | 浙江大学 | Load automatic identification system for digging machine |
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