CN114580852A - Water pump breakwater clearance real-time warning system based on industry big data - Google Patents
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Abstract
The invention relates to the technical field of neural network models and artificial intelligence, in particular to a real-time reminding system for cleaning a water baffle of a water pump based on industrial big data, which is an artificial intelligence system in the production field and comprises the following components: the data acquisition module is used for respectively acquiring the flow speed and the instantaneous power of the water inlet of the water pump at the current moment and sending the acquired data to the data processing module; the data processing module is used for comparing the flow velocity of the water inlet with the theoretical flow velocity value to obtain a deviation value, calculating a theoretical correction index of the flow velocity of the water inlet according to the deviation value, and correcting the flow velocity of the water inlet by using the theoretical correction index to obtain a corrected flow velocity value; obtaining a blockage evaluation index of an inlet grid of the water pump based on the flow rate value and the instantaneous power; and the data evaluation module is used for comparing the blockage evaluation index with a set standard, and alarming when the blockage evaluation index is greater than or equal to the set standard. Namely, the invention can detect the operation of the water pump in real time and give an alarm in real time.
Description
Technical Field
The invention relates to the technical field of neural network models and artificial intelligence, in particular to a water pump water baffle cleaning real-time reminding system based on industrial big data.
Background
The grid of intaking of water pump is the first filter screen of water pump, filters the impurity in the rivers for impurity can't get into the water pump, can increase the life of water pump, also to the efficiency with higher speed that improves the water pump.
However, in the use process of the water pump, due to the use time, excessive scale can be attached to the water inlet grille along with the influence of the water quality, and the normal work of the water pump is influenced, so that the performance of a water baffle (water inlet grille) of the water pump needs to be evaluated, and the inlet grille needs to be timely treated, so that the problem of resource waste is avoided.
Disclosure of Invention
The invention aims to provide a water pump water baffle cleaning real-time reminding system based on industrial big data, which is used for solving the problem that the service life of a water pump is influenced by excessive water scales of a water inlet grid in the water pump in the prior art.
The invention discloses a water pump water baffle cleaning real-time reminding system based on industrial big data, which comprises:
the data acquisition module is used for respectively acquiring the flow rate and the instantaneous power of the water inlet of the water pump at each moment in the current time period and sending the acquired data of the flow rate and the instantaneous power to the data processing module;
the data processing module is used for comparing the flow velocity of the water inlet with the theoretical flow velocity value to obtain a deviation value, calculating a theoretical correction index of the flow velocity of the water inlet according to the deviation value, and correcting the flow velocity of the water inlet by using the theoretical correction index to obtain a corrected flow velocity value;
obtaining a blockage evaluation index of an inlet grid of the water pump based on the flow rate value and the instantaneous power, and further obtaining a blockage evaluation sequence of the current time period;
the data processing module sends the blockage evaluation index to a data evaluation module;
and the data evaluation module is used for comparing each blockage evaluation index with a set standard, and when the blockage evaluation indexes are more than or equal to the set standard and the number of the blockage evaluation indexes is more than the set number, alarming the water pump running in the current time period.
Further, the data acquisition module is also used for acquiring a vibration acceleration sequence of the water pump in the current time period.
Further, the data processing module performs density clustering on the vibration acceleration sequence of the current time period to obtain vibration acceleration levels of different categories; setting weights corresponding to different vibration acceleration levels;
and adjusting the congestion evaluation indexes by using the weights corresponding to all levels to obtain a congestion evaluation sequence corresponding to the current time period.
Further, the vibration acceleration level is four levels; wherein the weight corresponding to the first level is 0.4, the weight corresponding to the second level is 0.3, the weight corresponding to the third level is 0.2, and the weight corresponding to the fourth level is 0.1.
Further, the data processing module is also used for predicting the performance of the water pump:
and inputting the blocking evaluation sequence corresponding to the current time period into the trained TCN network model, and outputting the predicted blocking evaluation sequence of the next time period.
Further, the loss function of the TCN network model is:
calculating an overall difference index of the occlusion evaluations in any occlusion evaluation sequence,
and calculating to obtain a new loss function by taking the overall difference index as a correction value of the mean square error loss function.
Further, the overall difference index is:
wherein, ciIs the overall difference index u of the blockage evaluation at the ith moment of the current time periodiAnd U is a blockage evaluation sequence at the ith moment in the current time period, and the D () function represents a difference function.
The invention has the beneficial effects that:
the scheme of the invention is based on an intelligent system based on the production field, and starts from the blockage influence of an inlet grating, three industrial big data information of water flow speed of a water inlet, instantaneous power of a water pump and vibration acceleration of a motor of the water pump are obtained, a simple blockage evaluation is obtained by using the vibration acceleration of the water pump and the change of the instantaneous power of the water pump in the working process of the water pump, and then the change condition of the vibration acceleration is corrected, so that the problem that whether the inlet grating of the water pump influences the normal working of the water pump can be accurately determined.
Meanwhile, the neural network model is adopted, and the blocking condition of the water inlet grille of the water pump is predicted based on the problem of water quality, so that the water pump is convenient to overhaul subsequently.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a structural block diagram of a water pump water baffle cleaning real-time reminding system based on industrial big data.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the embodiments, structures, features and effects thereof according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples.
The present invention is directed to an assessment of the impact of the water pump inlet grill, which is actually a water baffle that filters impurities from the water, on the proper operation of the water pump.
The invention relates to a water pump water baffle cleaning real-time reminding system based on industrial big data, which is a detection device actually arranged on a water pump, and comprises a data acquisition module, a data processing module and a data evaluation module as shown in figure 1; the data acquisition module is used for collecting the collected relevant industrial big data of the water pump, sending the data to the data processing module for analysis, sending the analysis result to the data evaluation module, obtaining the final monitoring result and realizing the real-time alarm of the work of the water pump.
Specifically, the data acquisition module in this embodiment is configured to respectively acquire a water inlet flow rate and an instantaneous power of the water pump at a current time, and send acquired data to the data processing module;
the water inlet velocity of flow among the above-mentioned is through the water inlet installation current meter at the water pump for monitor the water velocity of water inlet size, obtains water inlet velocity of flow sequence V ═ V1,..,vi}。
It should be noted that, because the flow rate of the water inlet is directly influenced by the water permeability of the grating, when the impurity accumulation degree on the grating is larger, the blocking degree of the grating is higher, and the water flow speed is slower, the flow rate of the water inlet measured in real time can directly indicate the blocking degree of the water inlet grating.
The instantaneous power of the water pump is directly collected from the terminal of the water pump motor.
It should be noted that when the water inlet grille of the water pump is blocked more, the water flow can extrude and crack a part of impurities into the water pump, which may cause mechanical blockage of the water pump, so that the instantaneous power of the water pump is too high; it also results in an increase in the quality of the water flow in the pump, which increases the power of the pump.
The data processing module is used for comparing the flow velocity of the water inlet with the theoretical flow velocity value to obtain a deviation value, calculating a theoretical correction index of the flow velocity of the water inlet according to the deviation value, and correcting the flow velocity of the water inlet by using the theoretical correction index to obtain a corrected flow velocity value;
obtaining a blockage evaluation index of an inlet grid of the water pump based on the flow rate value and the instantaneous power; the data processing module sends the blockage evaluation index to a data evaluation module;
the theoretical flow velocity value is the corresponding flow velocity value when the water pump works normally and the inlet grille is scale-free, and the theoretical flow velocity value can be recorded through testing when the water pump leaves a factory.
Of course, the theoretical flow velocity value may also be obtained by fitting the flow velocity value according to the intake flow velocity value in a period of time in the collected historical data to obtain a trend function, and specifically, performing partial linear analysis on the water intake flow velocity sequence V:
x＝{1,…,i},V＝{v1,..,vi}
wherein k is the slope of the change straight line of the water inlet flow velocity sequence V, and k is less than 0, which represents that the water inlet flow velocity sequence V is on the time lineiIs time-inversely related.
Based on the obtained fitted straight line slope k, solving an intercept b by using a undetermined coefficient method; the fitted straight-line function representation y is obtained as kx + b.
Based on the obtained fitting function, by substituting x ═ {1, …, i }, y ═ y can be obtained1,…,yi}。
Loss_i＝|vi-yi|
The loss function is the absolute value of the difference between the actual value and the fitted straight line; the closer the Loss _ i is to 0, the higher the degree of fit proves, and vice versa.
And (3) carrying out normalization on Loss, and calculating a theoretical correction index:
wherein, ciTheory of water velocity at time iThe correction value is calculated.
The corrected flow rate value is then:
di＝ci*vi
it should be noted that the blockage of the water inlet grille is inversely related to the water inlet flow rate, i.e. the higher the blockage, the lower the water flow rate. Such a correction may express a blockage of the water inlet grille.
Wherein the blockage is evaluated as:
wherein (max (S) -mean (S)) is the extreme fluctuation evaluation at time i; the larger the difference, the larger the extreme fluctuation; e.g. of the type-STD(S)Is a stability evaluation of the sequence of new instantaneous powers at the current time; the greater the value, the greater the stability.
Thus, a simple water pump blockage evaluation sequence R ═ { R ═ R is obtained1,…,ri}。
And the data evaluation module is used for comparing each blockage evaluation index with a set standard, and when the blockage evaluation indexes are more than or equal to the set standard and the number of the blockage evaluation indexes is more than the set number, alarming the water pump running in the current time period.
The set number in the embodiment is determined according to the number of the acquired moments in the current time period, and is used for analyzing the blockage evaluation index so as to eliminate the accidental events in operation.
Wherein the set standard is the normal water pump performance when the water pump is in normal operation.
Further, the data acquisition module is also used for acquiring a vibration acceleration sequence of the water pump in the current time period. And the vibration acceleration sequence of the water pump in the current time period is acquired.
The data processing module carries out density clustering on the vibration acceleration sequence of the current time period to obtain vibration acceleration levels of different categories; setting weights corresponding to different vibration acceleration levels; and adjusting the congestion evaluation indexes by using the weights corresponding to all levels to obtain a congestion evaluation sequence corresponding to the current time period.
The density clusters in this embodiment are K-means, of which the categories are 4, and the mathematical basis for K-means is to make a difference along the time axis.
Specifically, in the present embodiment,:
a first cluster, considered as first gear acceleration;
a second cluster, considered second gear acceleration;
a third cluster, considered third gear acceleration;
and the fourth cluster, which is considered to be the fourth gear acceleration.
It should be noted that when the water inlet grille of the water pump is seriously blocked and the power is continuously increased, the pressure difference between two sides of the grille is overlarge, so that a part of impurities are crushed by the pressure, and enter the water pump along with the water flow through the grille, so that the impeller of the water pump is unevenly collided by the impurities, and the motor of the water pump generates corresponding vibration, and therefore, the evaluation at an extreme moment can be obtained by using the change of the vibration acceleration.
Secondly, based on the above, in this embodiment, the clustering result is subjected to weight distribution:
the weight corresponding to the first gear acceleration is c1＝0.4。
The weight corresponding to the acceleration of the second gear is c2＝0.3。
The weight corresponding to the acceleration of the third gear is c3＝0.2。
The weight corresponding to the fourth gear acceleration is c4＝0.1。
The final evaluation value of the blockage in the above is:
ui＝ri*cj
wherein r isiAs an occlusion evaluation value, cjIs the weight of the jth acceleration level.
In the above, uiThe method is the final evaluation of the water pump grid blockage at the moment i; the higher the value, the lower the degree of clogging.
And finally, comparing the blockage evaluation value with a set threshold value, and when the blockage evaluation value of the water inlet grille is smaller than the threshold value u, considering that the water pump is seriously blocked at the moment.
Further, the data evaluation module is also used for predicting the performance of the water pump:
and inputting the blocking evaluation sequence corresponding to the current time period into the trained TCN network model, and outputting the predicted blocking evaluation sequence of the next time period.
Wherein, the loss function of the TCN network model is as follows:
calculating an overall difference index of the occlusion evaluation in any occlusion evaluation sequence,
and taking the integral difference index as a correction value of the mean square error loss function, and calculating to obtain a new loss function:
wherein C is the overall difference index after normalization as loss weight, loss is the loss function of the sample,
to predict a sample, yiIs a feature sample.
The purpose of the loss function is to ensure the convergence of the loss function, so that loss becomes smaller through continuous training and the predicted trend is accurate.
The overall difference index among the above is:
wherein, ciIs an overall difference index at the ith time, uiAnd U is a blockage evaluation sequence and the D () function represents a difference function.
It should be noted that the training process for the TCN network is prior art and will not be described in detail here.
The scheme of the invention starts from the blocking influence of the grating, obtains three pieces of information of the water flow speed of the water inlet, the instantaneous power of the water pump and the vibration acceleration of the motor of the water pump, obtains a simple blocking evaluation by using the vibration acceleration of the water pump and the change of the instantaneous power of the water pump in the working process of the water pump, and corrects the blocking evaluation according to the change condition of the vibration acceleration, so that the extreme condition can be accurately found.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. Water pump breakwater clearance real-time warning system based on industry big data, its characterized in that includes:
the data acquisition module is used for respectively acquiring the flow rate and the instantaneous power of the water inlet of the water pump at each moment in the current time period and sending the acquired data of the flow rate and the instantaneous power to the data processing module;
the data processing module is used for comparing the flow velocity of the water inlet with the theoretical flow velocity value to obtain a deviation value, calculating a theoretical correction index of the flow velocity of the water inlet according to the deviation value, and correcting the flow velocity of the water inlet by using the theoretical correction index to obtain a corrected flow velocity value;
obtaining a blockage evaluation index of an inlet grid of the water pump based on the flow rate value and the instantaneous power, and further obtaining a blockage evaluation sequence of the current time period;
the data processing module sends the blockage evaluation index to a data evaluation module;
and the data evaluation module is used for comparing each blockage evaluation index with a set standard, and when the blockage evaluation indexes are more than or equal to the set standard and the number of the blockage evaluation indexes is more than the set number, alarming the water pump running in the current time period.
2. The system for real-time reminding of cleaning of the water baffle of the water pump based on the industrial big data as claimed in claim 1, wherein the data acquisition module is further configured to acquire a vibration acceleration sequence of the water pump at a current time period.
3. The system for real-time reminding of cleaning of the water baffle of the water pump based on the industrial big data as claimed in claim 2, wherein the data processing module performs density clustering on the vibration acceleration sequence of the current time period to obtain vibration acceleration levels of different categories; setting weights corresponding to different vibration acceleration levels;
and adjusting the congestion evaluation indexes by using the weights corresponding to all levels to obtain a congestion evaluation sequence corresponding to the current time period.
4. The real-time reminding system for cleaning the water baffle of the water pump based on the industrial big data as claimed in claim 3, wherein the vibration acceleration level is four levels; wherein the weight corresponding to the first level is 0.4, the weight corresponding to the second level is 0.3, the weight corresponding to the third level is 0.2, and the weight corresponding to the fourth level is 0.1.
5. The system for real-time reminding of cleaning of the water baffle of the water pump based on the industrial big data as claimed in claim 3, wherein the data evaluation module is further used for the step of predicting the performance of the water pump:
and inputting the blocking evaluation sequence corresponding to the current time period into the trained TCN network model, and outputting the predicted blocking evaluation sequence of the next time period.
6. The industrial big data-based real-time water pump water baffle cleaning reminding system as claimed in claim 5, wherein the loss function of the TCN network model is as follows:
calculating an overall difference index of the occlusion evaluations in any occlusion evaluation sequence,
and calculating to obtain a new loss function by taking the overall difference index as a correction value of the mean square error loss function.
7. The real-time reminding system for cleaning the water baffle of the water pump based on the industrial big data as claimed in claim 6 is characterized in that the overall difference index is as follows:
wherein, ciIs an overall difference index at the ith time, uiAnd U is a blockage evaluation sequence and the D () function represents a difference function.
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