CN113570160A - Service life prediction method and device for sand pump of sand mixing truck - Google Patents
Service life prediction method and device for sand pump of sand mixing truck Download PDFInfo
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- 239000004576 sand Substances 0.000 title claims abstract description 495
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000006073 displacement reaction Methods 0.000 claims description 25
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- 238000012549 training Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 238000011897 real-time detection Methods 0.000 abstract description 3
- 238000005299 abrasion Methods 0.000 description 11
- 238000001514 detection method Methods 0.000 description 4
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- 238000004364 calculation method Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
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- 239000012535 impurity Substances 0.000 description 1
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Abstract
The invention relates to the technical field of sand pumps, in particular to a service life prediction method and device of a sand pump of a sand mixing truck. The service life prediction method of the sand pump of the sand mixing truck comprises the following steps: inputting real-time working condition data of a sand pump into a preset sand pump PWM value prediction model to obtain a real-time prediction PWM value of the sand pump; acquiring an index of the sand pump according to the real-time predicted PWM value of the sand pump; and determining the service life of the sand pump according to the index of the sand pump. The real-time working condition data of the sand pump are input into the PWM value prediction model to obtain the real-time prediction PWM value of the sand pump, so that the obtaining process of the real-time prediction PWM value of the sand pump is simplified. And calculating to obtain the index of the sand pump according to the real-time predicted PWM value of the sand pump, and determining the service life of the sand pump according to the index of the sand pump. From this, at the fracturing blender truck operation in-process, carry out real-time detection through the operating mode data to the sand pump, can accurately confirm the life-span of sand pump, the constructor of being convenient for arranges work to in time accomplish the operation.
Description
Technical Field
The invention relates to the technical field of sand pumps, in particular to a service life prediction method and device of a sand pump of a sand mixing truck.
Background
The sand pump is a core component of the sand mixing truck, is a main conveying device for conveying liquid media with solid impurities, and is easily impacted by sand moving at high speed to cause abrasion. The sand pump can be degraded or even can not work due to the serious abrasion degree of the sand pump.
In the present fracturing blender truck equipment, the sand pump only can replace when failing completely and hindering normal construction, just can discover the wearing and tearing condition and the maintenance of sand pump when artifical periodic disassembly inspection sand pump simultaneously. When the sand pump is invalid, the sand pump can not be effectively maintained due to larger abrasion in the using process, and the damage caused is larger. The detection sand pump is manually and periodically disassembled, and when the interval time is long, the abrasion condition of the sand pump cannot be monitored in time; when the interval time is shorter, all need carry out the dismouting to the sand pump at every turn, the time that consumes is longer, is unfavorable for the continuation use of sand pump.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a service life prediction method of a sand pump of a sand mixing truck, which comprises the following steps:
inputting real-time working condition data of a sand pump into a preset sand pump PWM value prediction model to obtain a real-time prediction PWM value of the sand pump;
acquiring an index of the sand pump according to the real-time predicted PWM value of the sand pump;
and determining the service life of the sand pump according to the index of the sand pump.
Optionally, the method for constructing the sand pump PWM value prediction model includes:
acquiring historical working condition data of the sand pump and a corresponding historical PWM (pulse width modulation) value of the sand pump as training data;
and training a preset neural network according to the training data, and establishing the sand pump PWM value prediction model according to the trained neural network.
Optionally, the indication of the sand pump comprises performance of the sand pump, a maximum displacement available for the sand pump, and a remaining available time for the sand pump.
Optionally, the determining the life of the sand pump according to the index of the sand pump comprises:
obtaining a construction process of the sand pump;
and judging whether the performance of the sand pump, the available maximum discharge capacity of the sand pump and the remaining available time of the sand pump all meet the construction process of the sand pump so as to determine the service life of the sand pump.
Optionally, the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump includes:
and acquiring the operation data of the sand pump, analyzing the trend of the predicted PWM value of the sand pump and the discharge capacity of the sand pump, the trend of the actual PWM value of the sand pump and the discharge capacity of the sand pump in the process from the minimum PWM value of the sand pump to the maximum PWM value of the sand pump, and determining the available maximum discharge capacity of the sand pump according to the actual PWM value of the sand pump.
Optionally, the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
the method comprises the steps of obtaining operation data of the sand pump, analyzing the performance of the sand pump and the trend of the service time of the sand pump in the whole time period from 100% to 0% of the performance of the sand pump, and determining the remaining available time of the sand pump according to the service time of the sand pump.
Optionally, the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
acquiring an actual PWM value of the sand pump and a maximum PWM value of the sand pump;
substituting the real-time predicted PWM value of the sand pump, the actual PWM value of the sand pump and the maximum PWM value of the sand pump into a first formula to obtain the real-time performance of the sand pump;
the first formula is:
optionally, the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
acquiring a minimum PWM value of the sand pump;
substituting the actual PWM value of the sand pump, the maximum PWM value of the sand pump and the minimum PWM value of the sand pump into a second formula to obtain the real-time available maximum discharge capacity of the sand pump;
the second formula is:
optionally, the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
substituting the real-time performance of the sand pump into a third formula to obtain the real-time remaining usable time of the sand pump;
the third formula is:
wherein T is AZ + C, T is the real-time remaining usable time of the sand pump, Z is the real-time performance of the sand pump, A is the usable time when the sand pump is not worn, and C is a correction constant.
The service life prediction method of the sand mixing truck sand pump has the technical effects that: the real-time working condition data of the sand pump are input into the PWM value prediction model to obtain the real-time prediction PWM value of the sand pump, so that the obtaining process of the real-time prediction PWM value of the sand pump is simplified. And calculating to obtain the index of the sand pump according to the real-time predicted PWM value of the sand pump, and determining the service life of the sand pump according to the index of the sand pump. From this, at the fracturing blender truck operation in-process, carry out real-time detection through the operating mode data to the sand pump, can accurately confirm the life-span of sand pump, the constructor of being convenient for arranges work to in time accomplish the operation.
The invention also provides a service life prediction device of the sand mixing truck sand pump, which comprises a computer readable storage medium and a processor, wherein the computer readable storage medium is used for storing a computer program, and the computer program is read by the processor and runs to realize the service life prediction method of the sand mixing truck sand pump.
The service life prediction device of the sand mixing truck sand pump is used for realizing the service life prediction method of the sand mixing truck sand pump, so that the technical effects are the same, and the description is omitted.
Drawings
FIG. 1 is a flow chart of a method for predicting the life of a sand pump of a sand mixing truck according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for establishing a model for predicting the PWM value of a sand pump of a sand mixing truck according to an embodiment of the present invention;
FIG. 3 is a trend analysis plot of predicted PWM current values, actual PWM current values, and sand pump displacement for an embodiment of the present invention;
FIG. 4 is a graph illustrating a trend analysis of sand pump performance and sand pump service life in accordance with an embodiment of the present invention.
Detailed Description
The foregoing objects, features and advantages of the invention will be apparent from the following more particular description of the invention, as illustrated in the accompanying drawings.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
Moreover, although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, an embodiment of the present invention provides a method for predicting a service life of a sand pump of a sand mixing truck, including:
s1: inputting real-time working condition data of the sand pump into a preset sand pump PWM value prediction model to obtain a real-time prediction PWM value of the sand pump;
s2: acquiring an index of the sand pump according to the real-time prediction PWM value of the sand pump;
s3: and determining the service life of the sand pump according to the index of the sand pump.
When the sand pump of the sand mixing truck is in the operation process and the abrasion degree of the sand pump is serious, the sand pump is difficult to continue to work. In the prior art, the detection on whether the sand pump can continue to work or not and the duration of continuous work of the sand pump require that a constructor unpacks the sand pump apart, observe the sand pump through a visual inspection means and judge the sand pump according to experience. Therefore, the detection means is complex, and the accuracy judgment deviation of the abrasion of the sand pump is large. Meanwhile, the constructor is difficult to judge whether the sand pump can continue to work or not and the continuous duration of the continuous work only through visual inspection.
In the present embodiment, the PWM value of the sand pump is the PWM control current value of the sand pump, specifically, the PWM value is the current value of the oil pump or the motor that powers the sand pump. And establishing a relation between the service life of the sand pump and the indexes of the sand pump. And inputting the real-time working condition data of the sand pump into the PWM value prediction model to obtain the real-time predicted PWM value of the sand pump. And then, calculating to obtain the index of the sand pump according to the real-time predicted PWM value of the sand pump. And finally, determining the service life of the sand pump according to the relation between the service life of the sand pump and the index of the sand pump.
In conclusion, the real-time working condition data of the sand pump is input into the PWM value prediction model to obtain the real-time prediction PWM value of the sand pump, and the obtaining process of the real-time prediction PWM value of the sand pump is simplified. And calculating to obtain the index of the sand pump according to the real-time predicted PWM value of the sand pump, and determining the service life of the sand pump according to the index of the sand pump. From this, at the fracturing blender truck operation in-process, carry out real-time detection through the operating mode data to the sand pump, can accurately confirm the life-span of sand pump, the constructor of being convenient for arranges work to in time accomplish the operation.
Optionally, as shown in fig. 2, the method for constructing the sand pump PWM value prediction model includes:
s4: acquiring historical working condition data of the sand pump and a historical PWM value of the corresponding sand pump as training data;
s5: and training a preset neural network according to the training data, and establishing a sand pump PWM value prediction model according to the trained neural network.
Specifically, according to the correlation analysis, the influence factors affecting the PWM value of the sand pump are: the chassis engine speed, the discharge displacement, the sand ratio and the discharge pressure can be used as the working condition data of the sand pump. In other words, in the process of training the neural network, historical working condition data of the sand pump is used as input, and a historical PWM value of the corresponding sand pump is used as a label until the neural network meets the training requirement. In addition, preferably, the historical operating condition data can be corresponding data when the sand pump performance is best, for example, operating condition data when the sand pump is not worn and a corresponding sand pump PWM value are selected as training data. In actual operation, the chassis engine speed, the discharge displacement, the sand ratio and the discharge pressure of the sand pump at different PWM values can be collected from a new sand mixing truck as training data.
In the embodiment, the rotation speed, the discharge displacement, the sand ratio and the discharge pressure of the chassis engine of the sand pump at different PWM values are collected to serve as working condition data, training data of a sand pump PWM value prediction model are constructed according to the rotation speed, the discharge displacement, the sand ratio and the discharge pressure of the chassis engine of the sand pump at different PWM values, the accuracy of the prediction of the PWM value of the sand pump can be improved, and therefore the service life of the sand pump can be effectively predicted according to the PWM prediction model.
Optionally, the indicators of the sand pump include performance of the sand pump, a maximum displacement available for the sand pump, and a remaining available time for the sand pump.
In the present embodiment, the index of the sand pump is quantified, specifically, the index of the sand pump is set to three parts, the performance of the sand pump, the available maximum displacement of the sand pump, and the remaining available time of the sand pump. Wherein, the performance of the sand pump can be used for representing the abrasion degree of the sand pump; specifically, when the performance of the sand pump is 100%, it indicates that the sand pump does not wear, i.e., the smaller the performance of the sand pump, the greater the degree of wear of the sand pump. The available maximum displacement of the sand pump can be used for representing the maximum displacement which can be achieved by the sand pump under the real-time performance condition; specifically, in the construction process, the discharge capacity of the sand pump may be increased to meet the construction process, whether the sand pump can continue to increase the discharge capacity to meet the construction requirement or not under the current real-time performance condition of the sand pump can be judged according to the available maximum discharge capacity of the sand pump, if yes, the sand pump can continue to be used is indicated, and if not, the sand pump needs to be replaced or maintained. The remaining available time of the sand pump can be used for representing the time that the sand pump can continue to be constructed under the condition of the real-time performance of the current sand pump; specifically, when the remaining usable time of the sand pump is not 0, it indicates that the sand pump is not completely damaged and can be used continuously, and when the remaining usable time of the sand pump is 0, it indicates that the sand pump is completely damaged and can not be used continuously.
Optionally, determining the life of the sand pump according to the index of the sand pump comprises:
obtaining a construction process of the sand pump;
and judging whether the performance of the sand pump, the available maximum discharge capacity of the sand pump and the remaining available time of the sand pump all meet the construction process of the sand pump or not so as to determine the service life of the sand pump.
In this embodiment, when a constructor determines the life of a sand pump using a single index, for example, the life of the sand pump is determined only according to the remaining available time of the sand pump, the performance of the sand pump and the maximum discharge capacity available for the sand pump may be difficult to satisfy the construction process, and thus the accuracy is poor, while at the same time, determining the life of the sand pump according to the performance of the sand pump, the maximum discharge capacity available for the sand pump, and the remaining available time of the sand pump may make the sand pump satisfy the construction process, and thus the accuracy is high.
Optionally, as shown in fig. 3, obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
the method comprises the steps of obtaining operation data of the sand pump, analyzing the trend of a prediction PWM value of the sand pump and the discharge capacity of the sand pump, analyzing the trend of an actual PWM value of the sand pump and the trend of the discharge capacity of the sand pump in the process from the minimum PWM value of the sand pump to the maximum PWM value of the sand pump, and determining the available maximum discharge capacity of the sand pump according to the actual PWM value of the sand pump.
In the present embodiment, the first coordinate system is established with the discharge capacity of the sand pump as the horizontal axis and the PWM value of the sand pump as the vertical axis. And recording the discharge capacity of the sand pump, and the actual PWM value of the sand pump and the predicted PWM value of the sand pump corresponding to the discharge capacity of the sand pump in the running process of the sand pump. Specifically, when the displacement of the sand pump is 14 square, the predicted PWM value of the sand pump corresponding to the displacement of the sand pump is 650mA, and the actual PWM value of the sand pump corresponding to the displacement of the sand pump is 800mA, which may be recorded as (14,650) and (14, 800). And two line graphs are drawn on a second coordinate system by acquiring data of the sand pump in the operation process and according to the corresponding relation between the actual PWM value of the sand pump and the discharge capacity of the sand pump and the predicted PWM value of the sand pump and the discharge capacity of the sand pump. Therefore, according to the predicted PWM value of the sand pump and the trend graph of the discharge capacity of the sand pump, the first discharge capacity of the sand pump can be obtained through the real-time predicted PWM value of the sand pump; the second displacement of the sand pump can be obtained from the maximum PWM value of the sand pump according to the actual PWM value of the sand pump and the trend graph of the displacement of the sand pump.
When the first displacement is larger than the second displacement, the displacement of the sand pump is difficult to meet the construction process, and the sand pump needs to be replaced; and when the first displacement is less than or equal to the second displacement, the displacement of the sand pump can meet the construction process, and the sand pump can be continuously used.
Optionally, as shown in fig. 4, obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
and acquiring the operation data of the sand pump, analyzing the performance of the sand pump and the trend of the service time of the sand pump in the whole time period from 100% to 0% of the performance of the sand pump, and determining the remaining available time of the sand pump according to the service time of the sand pump.
In this embodiment, the second coordinate system is established with the performance of the sand pump as the vertical axis and the service time of the sand pump as the horizontal axis. During the operation of the sand pump, the service time of the sand pump and the performance of the sand pump corresponding to the service time of the sand pump are recorded, and specifically, when the service time of the sand pump is 0, the performance of the sand pump corresponding to the service time is 100%, which can be recorded as (0, 100%). And drawing a line graph on the second coordinate system by acquiring data of the sand pump in the operation process and according to the corresponding relation between the service time of the sand pump and the performance of the sand pump. Therefore, according to the trend of the line graph, the service time of the sand pump and the performance of the corresponding sand pump can be predicted. According to the trend that the performance index of the sand pump is reduced, after the sand pump runs for a period of time and data are accumulated, the time required by the sand pump to reach 20% (needing repeated correction) performance, namely the service time of the sand pump, can be predicted, and finally the remaining usable time of the sand pump is obtained, so that the service life of the sand pump can be predicted.
Optionally, the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump includes:
acquiring an actual PWM value of the sand pump and a maximum PWM value of the sand pump;
and substituting the real-time predicted PWM value of the sand pump, the actual PWM value of the sand pump and the maximum PWM value of the sand pump into a first formula to obtain the real-time performance of the sand pump.
In this embodiment, the first formula may be:
therefore, the real-time performance of the sand pump can be calculated and obtained according to the predicted PWM current value, and when the predicted PWM current value is larger than or equal to the actual PWM current value, the obtained performance of the sand pump is larger than or equal to 100%, namely the sand pump is not worn at the moment and can be continuously used; when the predicted PWM current value is smaller than the actual PWM current value, the performance index of the obtained sand pump is smaller than 100%, namely that the sand pump is worn at the moment and whether a constructor is needed to be used for determining by combining a corresponding construction process; when the performance of the sand pump is 0, the sand pump is completely damaged, and the sand pump needs to be replaced or maintained. Therefore, the abrasion degree of the sand pump can be directly judged by analyzing the performance of the sand pump, the abrasion degree of the sand pump is not required to be checked after the sand pump is disassembled, and the detection process of the abrasion degree of the sand pump is simplified; meanwhile, the working condition data of the sand pump is detected in real time, the real-time prediction PWM value of the sand pump can be obtained through the PWM value prediction model of the sand pump of the sand mixing truck, the real-time prediction PWM value of the sand pump is substituted into the first formula to obtain the real-time performance of the sand pump, the real-time abrasion degree of the sand pump can be determined, and the sand pump can be maintained or replaced in time. In addition, the performance accuracy of the sand pump obtained through calculation is high, and the accuracy of service life prediction of the sand pump is improved.
Optionally, obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
acquiring a minimum PWM value of the sand pump;
and substituting the actual PWM value of the sand pump, the maximum PWM value of the sand pump and the minimum PWM value of the sand pump into a second formula to obtain the real-time available maximum discharge capacity of the sand pump.
In this embodiment, the second formula may be:
under different construction process conditions, the required discharge capacity of the sand pump is different, and when the current discharge capacity of the sand pump is small, the discharge capacity may need to be increased. According to the second formula, the available maximum displacement of the sand pump can be calculated according to the actual PWM current value of the sand pump. The available maximum discharge capacity of the sand pump represents the maximum discharge capacity which can be reached by the sand pump when the PWM current value of the sand pump is adjusted to the maximum under the current working condition. Specifically, when the available maximum displacement is greater than or equal to the required displacement, the sand pump meets the requirements of the construction process; when the available maximum discharge capacity is less than the required discharge capacity, the sand pump does not meet the requirements of the construction process. Therefore, the available maximum discharge capacity of the sand pump can be calculated and obtained through the actual PWM value, the obtaining process of the available maximum discharge capacity of the sand pump is simplified, the accuracy of the obtained available maximum discharge capacity of the sand pump is improved, and the accuracy of service life prediction of the sand pump is further improved.
Optionally, obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further includes:
and substituting the real-time performance of the sand pump into a third formula to obtain the real-time remaining usable time of the sand pump.
In this embodiment, the third formula may be:
t is the real-time remaining usable time of the sand pump, Z is the real-time performance of the sand pump, a is the usable time when the sand pump is not worn, and C is a correction constant.
According to the third formula, the real-time remaining usable time of the sand pump can be calculated according to the real-time performance of the sand pump. Wherein, the remaining usable time of the sand pump represents the remaining time in the process of the performance of the sand pump from 100% to 0. Therefore, the real-time residual available time of the sand pump, namely the service life of the sand pump, can be directly obtained by substituting the real-time performance of the sand pump into the third formula. Therefore, the service life prediction process of the sand pump can be simplified, and the service life prediction accuracy of the sand pump is further improved.
Another embodiment of the present invention provides a sand mixing truck sand pump life prediction device, which includes a computer readable storage medium storing a computer program and a processor, wherein when the computer program is read and executed by the processor, the sand mixing truck sand pump life prediction method is implemented.
In this embodiment, the readable storage medium and the processor may be configured as a monitoring software, that is, the monitoring software has both functions of the storage medium and the processing program. Storing a PWM value prediction model of the sand pump as a pb format file, and loading the pb format file into monitoring software; meanwhile, the service life prediction device of the sand mixing truck also comprises a controller, so as to read the real-time working condition data of the sand pump, input the real-time working condition data of the sand pump into monitoring software, and obtain the predicted PWM value of the sand pump according to the PWM value prediction model of the sand pump; meanwhile, the service life prediction device of the sand pump of the sand mixing truck also comprises a sensor, the sensor can detect the actual PWM value of the sand pump, and the predicted PWM value of the sand pump and the actual PWM value of the sand pump are substituted into monitoring software, so that the real-time performance of the sand pump, the real-time available maximum discharge capacity of the sand pump and the real-time remaining available time of the sand pump can be obtained; in addition, the service life prediction device of the sand pump of the sand mixing truck further comprises a human-computer interface, so that the indexes of the three sand pumps, namely the real-time performance of the sand pump, the real-time available maximum discharge capacity of the sand pump and the real-time remaining available time of the sand pump, can be observed and analyzed conveniently. Meanwhile, in the operation process of the sand pump, the working condition data of the sand pump is accumulated continuously, so that the real-time performance of the sand pump, the real-time available maximum discharge capacity of the sand pump and the real-time remaining available time of the sand pump can be corrected, and the accuracy of service life prediction of the sand pump is improved.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. A service life prediction method of a sand pump of a sand mixing truck is characterized by comprising the following steps:
inputting real-time working condition data of a sand pump into a preset sand pump PWM value prediction model to obtain a real-time prediction PWM value of the sand pump;
acquiring an index of the sand pump according to the real-time predicted PWM value of the sand pump;
and determining the service life of the sand pump according to the index of the sand pump.
2. The method for predicting the service life of the sand pump of the sand mixing truck as claimed in claim 1, wherein the method for constructing the sand pump PWM value prediction model comprises the following steps:
acquiring historical working condition data of the sand pump and a corresponding historical PWM (pulse width modulation) value of the sand pump as training data;
and training a preset neural network according to the training data, and establishing the sand pump PWM value prediction model according to the trained neural network.
3. The method of predicting the life of a sand pump of a sand mixing truck as recited in claim 1, wherein the indicators of the sand pump include performance of the sand pump, a maximum displacement available for the sand pump, and a remaining available time of the sand pump.
4. The method of predicting the life of a sand pump of a sand mix truck as recited in claim 3, wherein said determining the life of the sand pump based on the sand pump index comprises:
obtaining a construction process of the sand pump;
and judging whether the performance of the sand pump, the available maximum discharge capacity of the sand pump and the remaining available time of the sand pump all meet the construction process of the sand pump so as to determine the service life of the sand pump.
5. The method for predicting the service life of the sand pump of the sand mixing truck as claimed in claim 3, wherein the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump comprises:
and acquiring the operation data of the sand pump, analyzing the trend of the predicted PWM value of the sand pump and the discharge capacity of the sand pump, the trend of the actual PWM value of the sand pump and the discharge capacity of the sand pump in the process from the minimum PWM value of the sand pump to the maximum PWM value of the sand pump, and determining the available maximum discharge capacity of the sand pump according to the actual PWM value of the sand pump.
6. The method for predicting the service life of a sand pump of a sand mixing truck as recited in claim 3, wherein the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further comprises:
the method comprises the steps of obtaining operation data of the sand pump, analyzing the performance of the sand pump and the trend of the service time of the sand pump in the whole time period from 100% to 0% of the performance of the sand pump, and determining the remaining available time of the sand pump according to the service time of the sand pump.
7. The method for predicting the service life of a sand pump of a sand mixing truck as recited in claim 3, wherein the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further comprises:
acquiring an actual PWM value of the sand pump and a maximum PWM value of the sand pump;
substituting the real-time predicted PWM value of the sand pump, the actual PWM value of the sand pump and the maximum PWM value of the sand pump into a first formula to obtain the real-time performance of the sand pump;
the first formula is:
8. the method for predicting the service life of a sand pump of a sand mixing truck as recited in claim 6, wherein the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further comprises:
acquiring a minimum PWM value of the sand pump;
substituting the actual PWM value of the sand pump, the maximum PWM value of the sand pump and the minimum PWM value of the sand pump into a second formula to obtain the real-time available maximum discharge capacity of the sand pump;
the second formula is:
9. the method for predicting the service life of a sand pump of a sand mixing truck as recited in claim 6, wherein the obtaining the index of the sand pump according to the real-time predicted PWM value of the sand pump further comprises:
substituting the real-time performance of the sand pump into a third formula to obtain the real-time remaining usable time of the sand pump;
the third formula is:
T＝AZ+C，
wherein T is the real-time remaining usable time of the sand pump, Z is the real-time performance of the sand pump, A is the usable time when the sand pump is not worn, and C is a correction constant.
10. A sand mixing truck sand pump life prediction device comprising a computer readable storage medium storing a computer program and a processor, the computer program being read and executed by the processor to implement the sand mixing truck sand pump life prediction method according to any one of claims 1 to 9.
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