CN117927459A - Grouting pump grouting flow optimization control method - Google Patents
Grouting pump grouting flow optimization control method Download PDFInfo
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Abstract
The invention relates to the technical field of grouting flow data transmission of grouting pumps, in particular to an optimization control method for grouting flow of a grouting pump. The method comprises the steps of obtaining grouting flow data; acquiring grouting data components after iterative decomposition; the grouting data characteristics of the data components are obtained according to the frequency change and the intensity of the data points in the data components, grouting optimization data is obtained according to the characteristic difference between the data components and the data stability degree between the reconstructed flow data, and the information conservation degree of the data components is obtained through the frequency information of the data components and the whole data components; and analyzing the prominence of each data point in the grouting data component to obtain the transmission priority of the data point, further obtaining a sampling data point and controlling the flow of the grouting pump. According to the invention, by correcting the sampling rate, more proper sampling data points are obtained, the instantaneity and the data readability in the data transmission process are ensured, and the flow control of the grouting pump is more accurate and timely.
Description
Technical Field
The invention relates to the technical field of grouting flow data transmission of grouting pumps, in particular to an optimization control method for grouting flow of a grouting pump.
Background
In the fields of underground engineering, building structure reinforcement and the like, a plurality of automatic devices exist in an underground environment, and the automatic devices play an important role in monitoring the underground environment and the devices; and the automation equipment in the underground environment transmits the monitoring data of the environment or equipment to a control center or a receiving device on the ground in a wireless transmission mode, so that the control system can analyze and store the real-time monitoring data conveniently. With the development of the internet of things technology, an automatic monitoring technology is widely applied to the field of engineering detection and control.
In the prior art, a PLC (programmable logic controller) is often used for realizing the remote operation of equipment, and the flow in the grouting process of a grouting pump is regulated and controlled. However, the premise of realizing remote control is that real-time flow data in the working process of the grouting pump needs to be obtained, and the control system needs to acquire and store the real-time data. In the process, grouting flow data are required to be transmitted from monitoring equipment to a control system, in the process of grouting flow data transmission, the accuracy and the reliability of the data are affected by the sampling rate, the risk of data loss is high in a large amount of data in the transmission process, and a large bandwidth is required to support data transmission, so that the delay of data transmission is caused in the transmission process, and the instantaneity and the readability of the data in the data transmission process are affected.
Disclosure of Invention
In order to solve the technical problems that in the grouting flow data transmission process, the sampling rate influences the instantaneity and the data readability in the data transmission process, and further influences the accuracy and timeliness of grouting pump flow control, the invention aims to provide a grouting pump grouting flow optimization control method, and the adopted technical scheme is as follows:
A grouting pump grouting flow optimization control method, the method comprising:
Acquiring grouting flow data;
Performing iterative decomposition on the grouting flow data to obtain all grouting data components in each iterative decomposition process and reconstructed flow data obtained by reconstructing the grouting data; obtaining grouting data characteristics of each grouting data component according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component; according to the difference between grouting data characteristics of any two grouting data components in each iterative decomposition process and the data stability degree between the reconstructed flow data after the adjacent iterative decomposition process, grouting optimization data of all grouting data components after iterative decomposition are obtained;
Obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component in the grouting optimization data and the grouting optimization data; obtaining the transmission priority of each data point in each grouting data component according to the data prominence degree of the data point in each grouting data component in the whole grouting data component and the information conservation degree of each grouting data component in the grouting optimization data;
obtaining sampled data points according to the transmission priority of each data point;
and controlling grouting flow of the grouting pump according to the sampling data points.
Further, the method for acquiring grouting data characteristics comprises the following steps:
Acquiring a primary frequency in each grouting data component;
Averaging the time difference between every two adjacent data points of the grouting data component to obtain the frequency change characteristic of the data points in the grouting data component;
averaging the intensity values of all data points in the grouting data component spectrogram to obtain the frequency intensity characteristics of the data points in the grouting data component;
taking the product of the primary frequency, the frequency variation characteristic and the frequency intensity characteristic as a grouting data characteristic of each grouting data component.
Further, the method for acquiring grouting optimization data comprises the following steps:
According to the grouting data characteristic difference value between any two grouting data components in each iterative decomposition process, obtaining the characteristic distribution discrete degree of the grouting data components in each iterative decomposition process;
for each iterative decomposition process, taking the reciprocal of the mean square error between the reconstructed flow data in the previous iterative decomposition process as the data stability degree between grouting flow data in each iterative decomposition process;
Taking the product of the characteristic distribution discrete degree and the data stability degree as an iterative objective function of grouting flow data after iterative decomposition; and taking the reconstructed flow data after the iterative decomposition process corresponding to the maximum value of the iterative objective function is reconstructed as grouting optimization data.
Further, the method for acquiring the discrete degree of the feature distribution comprises the following steps:
Obtaining all grouting data sets consisting of any two grouting data components after each iterative decomposition process; calculating grouting data characteristic difference values between any two grouting data components in each grouting data group; and taking the variance of the characteristic difference values of all grouting data as the characteristic distribution discrete degree of the grouting data components in each iterative decomposition process.
Further, the method for acquiring the information retention of each grouting data component comprises the following steps:
Obtaining shape parameters of the grouting optimization data and the grouting data components according to the spectrogram shape characteristics of the grouting optimization data and the grouting data components;
acquiring the information conservation degree according to an information conservation degree calculation formula, wherein the information conservation degree calculation formula is as follows:
; in the/> Representing the/>, within grouting optimization dataInformation retention of individual grouting data components; /(I)Represents the/>The/>, of the individual grouting data componentsFrequency amplitude of data points; /(I)Representing the/>, within grouting optimization dataFrequency amplitude of data points; /(I)Representing the/>, within grouting optimization dataThe number of data points for each grouting data component; /(I)Representing a number of data points within the grouting optimization data; /(I)Representing the/>, within grouting optimization dataShape parameters of the individual grouting data components; /(I)And the shape parameters of the grouting optimization data are represented.
Further, the method for obtaining the shape parameter includes:
Taking each grouting data component in the grouting optimization data as data to be processed; fitting each data point in a spectrogram of one piece of data to be processed to obtain the outline length of a fitting line and the area of the spectrogram; and taking the ratio of the contour length of the fitting line to the area of the spectrogram as the shape parameter of the data to be processed.
Further, the method for acquiring the transmission priority comprises the following steps:
Optionally selecting a data point in each grouting data component as a target data point; obtaining the data difference degree of each data point in each grouting data component according to the data difference between the flow amplitude of the target data point and the overall flow amplitude in the grouting data component to which the target data point belongs;
Taking the ratio of the total number of all data points with the same flow amplitude as the target data point and the total number of the data points in the grouting data component as the data similarity of the target data point; taking the reciprocal of the similarity degree of the data as the data distribution characteristic of the target data point; changing target data points to obtain data distribution characteristics of each data point in each grouting data component;
Taking the product of the data difference degree of each data point in each grouting data component and the data distribution characteristic as the data prominence degree of each data point;
and normalizing the product between the information conservation degree of each grouting data component and the data prominence degree of each data point in each grouting data component to obtain the transmission priority of each data point in each grouting data component.
Further, the method for acquiring the data difference degree comprises the following steps:
acquiring flow thresholds in all grouting data components in grouting optimization data;
calculating the difference between the flow amplitude of the target data point in each grouting data component and the flow average value of all the data points as a first flow difference; calculating a difference between the flow magnitude of the target data point in each grouting data component and the flow threshold in each grouting data component as a second flow difference;
Taking the ratio between the first flow difference and the second flow difference as the data difference degree of the target data point in each grouting data component;
And changing the target data points to obtain the data difference degree of each data point in each grouting data component.
Further, the method for acquiring the sampled data points comprises the following steps:
Presetting a first threshold, and taking the data points with the transmission priority higher than the first threshold as initial sampling data points in the grouting optimization data;
Taking all sampling frequencies which exceed half of the original data sampling frequency as initial sampling frequencies; taking the initial sampling frequency comprising the maximum number of initial sampling data points as a corrected sampling rate;
and obtaining all sampling data points by using the corrected sampling rate.
Further, controlling the grouting flow of the grouting pump according to the sampling data points comprises:
compressing the sampling data points to obtain grouting compression data;
transmitting and decoding the grouting compressed data, and analyzing and storing the decoded data;
and adjusting and controlling the grouting flow of the grouting pump according to the change of the real-time flow data and the change of the underground environment.
The invention has the following beneficial effects:
in order to avoid influencing the real-time performance and the readability of grouting flow data in the data transmission process, the grouting flow data are subjected to iterative decomposition, and all grouting data components in each iterative decomposition process are obtained; because the grouting data components decomposed in different iterative decomposition processes are different, in order to intuitively embody the characteristic difference between the grouting data components, the grouting data characteristics of each grouting data component are obtained according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component; because in the ideal iterative decomposition result, the difference of different degrees exists in each grouting data component and the data change after iteration tends to be stable, in order to select the optimal iterative decomposition result, the optimal iterative decomposition result is obtained according to the characteristic difference between the grouting data components and the data stability degree between the reconstructed flow data in the iterative decomposition process, namely the grouting optimization data reconstructed by all grouting data components after iterative decomposition; obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component and the grouting optimization data in the grouting optimization data, wherein the information conservation degree reflects the importance degree of different grouting data components on the whole grouting optimization data; according to the data distribution characteristics of each data point in each grouting data component in the grouting optimization data in the whole grouting data component and the information conservation degree of each grouting data component, the transmission priority of each data point in each grouting data component is obtained, and the importance of each data point in each grouting data component in the later data sampling is reflected; obtaining sampling data points according to the transmission priority of each data point; and controlling grouting flow of the grouting pump according to the sampling data points. According to the invention, by correcting the sampling rate, more proper sampling data points are obtained, the instantaneity and the data readability in the data transmission process are ensured, and the flow control of the grouting pump is more accurate and timely.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for optimizing and controlling grouting flow of a grouting pump according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following description refers to the specific implementation, structure, characteristics and effects of an optimized control method for grouting pump grouting flow according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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 specifically describes a specific scheme of the grouting pump grouting flow optimization control method provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a method for optimizing and controlling grouting flow of a grouting pump according to an embodiment of the invention is shown, and the method includes:
Step S1: and acquiring grouting flow data.
The embodiment of the invention provides a grouting flow optimization control method of a grouting pump, which aims at controlling the grouting flow of the grouting pump and firstly obtains grouting flow data. In one embodiment of the invention, a flow sensor is arranged in a grouting pump system and is responsible for collecting grouting flow, the grouting pump system is generally provided with a data acquisition unit and is responsible for receiving an electric signal from the sensor and converting the electric signal through an analog-to-digital converter (ADC) or other hardware to obtain an analog-to-digital signal of the grouting flow; the data acquisition unit converts the output of the flow sensor into grouting flow data.
In other embodiments of the present invention, grouting flow data may be obtained by other prior art, which is not limited and described herein.
Step S2: performing iterative decomposition on grouting flow data to obtain all grouting data components in each iterative decomposition process and reconstructed flow data obtained by reconstructing grouting data; according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component, acquiring grouting data characteristics of each grouting data component; and obtaining grouting optimization data reconstructed by all grouting data components after iterative decomposition according to the difference between grouting data characteristics of any two grouting data components in each iterative decomposition process and the data stability degree between the reconstructed flow data after the adjacent iterative decomposition process.
The data transmission flow mainly comprises the steps of data sampling, analog-to-digital conversion, encoding, transmission, decoding and the like, and the real-time performance and the readability of grouting flow data in the data transmission process can be greatly influenced by the data sampling. In actual situations, because the grouting flow data contains more data components, the correction of the sampling rate is greatly plagued, so that the real-time grouting flow data needs to be preprocessed, and the subsequent sampling rate correction is carried out through the preprocessing result. In the embodiment of the invention, the grouting flow data is subjected to iterative decomposition to obtain all grouting data components in each iterative decomposition process, and the optimal iterative decomposition result is selected from all iterative decomposition results to be used as the preprocessed grouting optimization data.
In the embodiment of the invention, the VMD decomposition algorithm is utilized to carry out iterative decomposition on grouting flow data, the iteration step length is set as the component number, the maximum iteration number is set as 10, and all grouting data components decomposed in each iterative decomposition process are obtained. It should be noted that the VMD decomposition algorithm is a technical means well known to those skilled in the art, and will not be described herein.
In order to intuitively embody the characteristic difference between grouting data components, in the embodiment of the invention, the grouting data characteristics of each grouting data component are obtained according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component.
Preferably, in one embodiment of the present invention, the method for acquiring grouting data features includes:
Since the primary frequencies in the grouting data components generate harmonics, and the harmonic frequencies are integer multiples of the primary frequencies, the primary frequencies in each grouting data component can be obtained from a spectrogram of the grouting data components; averaging the time difference between every two adjacent data points of the grouting data component to obtain the frequency change characteristic of the data points in the grouting data component, wherein the smaller the average value is, the larger the data change rate in the grouting data component is, and the higher the frequency of the data points in the grouting data component is; because the intensity values of the frequencies in different grouting data components also have certain characteristics, the intensity values of all data points in a grouting data component spectrogram are averaged to obtain the frequency intensity characteristics of the data points in the grouting data component; taking the product of the main frequency, the frequency variation characteristic and the frequency intensity characteristic as the grouting data characteristic of each grouting data component. In one embodiment of the invention, the time difference between every two adjacent data points in the grouting data component is obtained, and the calculation formula of the grouting data characteristics is as follows:
In the method, in the process of the invention, Represents the/>Grouting data characteristics of the individual grouting data components; /(I)Represents the/>The primary frequency of the individual grouting data components; /(I)Represents the/>The number of time differences in the individual grouting data components; /(I)Represents the/>Sequence number of time difference in each grouting data component; /(I)Represents the/>The/>, of the individual grouting data componentsTime of data point; /(I)Represents the/>The/>, of the individual grouting data componentsTime of data point; /(I)Represents the/>The number of data points in the individual grouting data component spectrograms; /(I)Represents the/>Serial numbers of data points in the frequency spectrograms of the grouting data components; /(I)Represents the/>No./>, in individual grouting data component spectrogramsIntensity values for data points.
In the grouting data characteristic calculation formula, the data characteristic of each grouting data component in grouting flow data is different,Represents the/>Frequency variation characteristics of individual grouting data components,/>Represents the/>Frequency intensity characteristics of individual grouting data components; will/>The product of the principal frequency, frequency variation characteristic and frequency intensity characteristic of each grouting data component is taken as the/>Data characteristics of the individual grouting data components; and carrying out different degrees of feature description on each grouting data component in the grouting flow data to obtain grouting data features of each grouting data component in the grouting flow data.
In order to select an optimal iterative decomposition result, in one embodiment of the invention, grouting optimization data after iterative decomposition is obtained according to the difference between grouting data characteristics of any two grouting data components in each iterative decomposition process and the data stability degree between grouting flow data in two adjacent iterative decomposition processes.
Preferably, in one embodiment of the present invention, the method for acquiring grouting optimization data includes:
According to the grouting data characteristic difference value between any two grouting data components in each iterative decomposition process, obtaining the characteristic distribution discrete degree of the grouting data components in each iterative decomposition process; taking the reciprocal of the mean square error between the reconstructed flow data in each iteration decomposition process and the reconstructed flow data in the previous adjacent iteration decomposition process as the data stability degree between grouting flow data in the adjacent iteration decomposition processes; taking the product of the discrete degree of the characteristic distribution and the stability degree of the data as an iterative objective function of grouting flow data after iterative decomposition; and taking the reconstructed flow data after the iterative decomposition process corresponding to the maximum value of the iterative objective function is reconstructed as grouting optimization data. In one embodiment of the present invention, the iterative objective function calculation formula is as follows:
In the method, in the process of the invention, Represents grouting flow data/>An iterative objective function after the iterative decomposition process is performed for the times; /(I)Represents grouting flow data/>The characteristic distribution discrete degree after the secondary iterative decomposition process; /(I)Represents the/>Reconstruction of the/>, in the flow data after the iterative decomposition processData values for data points; /(I)Represents the/>Reconstruction of the/>, in the flow data after the iterative decomposition processData values for data points; /(I)Representing a mean square error function.
In the iterative objective function calculation formula,The larger the grouting flow data is, the/>The more obvious the characteristic difference of grouting data among all grouting data components is after the iterative decomposition process, the more conforming to the expected value of the data processing result in the grouting flow data decomposition process is; in order to avoid the situation of excessive decomposition of grouting flow data, the iterative decomposition process needs to be limited,/>, andRepresents the/>Reconstructing flow data and/>, after the iterative decomposition processReconstructing the mean square error between the flow data after the iterative decomposition process; the smaller the mean square error, the description of the/>Grouting data components after the iterative decomposition process are compared with those of the first/>The more stable the grouting data composition changes after the iterative decomposition process, at this time the/>The closer the grouting data component is to the ideal decomposition result after the iterative decomposition process, at this time/>The larger; will/>As/>And when the iterative objective function reaches the maximum, the optimal reconstruction flow data can be obtained at the moment and used as grouting optimization data.
Preferably, in one embodiment of the present invention, the method for acquiring the discrete degree of the feature distribution includes:
Obtaining all grouting data sets consisting of any two grouting data components after each iterative decomposition process; calculating grouting data characteristic difference values between two grouting data components in each grouting data group; and taking the variance of the characteristic difference values of all grouting data as the characteristic distribution discrete degree of the grouting data components in each iterative decomposition process. In one embodiment of the present invention, the discrete degree calculation formula of the feature distribution is as follows:
In the method, in the process of the invention, The characteristic distribution discrete degree of the grouting data components after each iterative decomposition process is represented; /(I)Representing the number of grouting data sets after each iterative decomposition process; /(I)The serial number of the grouting data set after each iterative decomposition process is represented; /(I)Represents the/>, after each iterative decomposition processGrouting data characteristic difference values between two grouting data components in each grouting data group; The average value of the characteristic difference values of grouting data in all grouting data groups after each iterative decomposition process is shown.
In the characteristic distribution discrete degree calculation formula, when the variance of the characteristic difference value of grouting data between two grouting data components in the grouting data groups is larger, the characteristic difference value of grouting data of the two grouting data components in all grouting data groups is more discrete, at the moment, a larger difference exists between all grouting data components after the iterative decomposition process, and at the moment, the data processing result obtained in the iterative decomposition process accords with the expected value of the iterative decomposition.
Step S3: obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component and the grouting optimization data in the grouting optimization data; and obtaining the transmission priority of each data point in each grouting data component according to the data prominence degree of the data point in each grouting data component in the whole grouting data component and the information conservation degree of each grouting data component in the grouting optimization data.
Because the data points in the grouting optimization data are more, the data components are more complex, important data points in the grouting optimization data cannot be effectively acquired, and larger uncertainty can occur when the data is sampled in the subsequent data transmission process, so that the data components in the grouting optimization data are required to be separated. Since different grouting data components have different importance degrees on the whole grouting optimization data, the important grouting data components in the data transmission process often have higher expected values, and hopefully can retain as much grouting data information as possible, while in practical situations, the grouting data components with similar strength and shape to the grouting optimization data often have more importance on the whole grouting optimization data, so in the embodiment of the invention, the information conservation degree of each grouting data component is obtained according to the frequency strength similarity degree and the frequency shape similarity degree between each grouting data component and the grouting optimization data in the grouting optimization data.
Preferably, in one embodiment of the present invention, the method for acquiring the information retention degree of each grouting data component includes:
According to the spectrogram shape characteristics of the grouting optimization data and the grouting data components, obtaining shape parameters of the grouting optimization data and the grouting data components;
obtaining the information conservation degree according to an information conservation degree calculation formula, wherein the information conservation degree calculation formula is as follows:
In the method, in the process of the invention, Representing the/>, within grouting optimization dataInformation retention of individual grouting data components; /(I)Represents the/>The/>, of the individual grouting data componentsFrequency amplitude of data points; /(I)Representing the/>, within grouting optimization dataFrequency amplitude of data points; /(I)Representing the/>, within grouting optimization dataThe number of data points for each grouting data component; /(I)Representing the/>, within grouting optimization dataData point sequence numbers for the individual grouting data components; /(I)Representing a number of data points within the grouting optimization data; /(I)Representing data point sequence numbers within the grouting optimization data; /(I)Representing the/>, within grouting optimization dataShape parameters of the individual grouting data components; /(I)And the shape parameters of the grouting optimization data are represented.
In the information retention formula of the calculation formula,Represents the/>The greater the ratio between the intensity values of all data points in the individual grouting data components and the intensity values of all data points in the grouting optimization data, the greater the ratio representing the/>The higher the data intensity is in each grouting data component, the higher the similarity between the data intensity and the grouting optimization data is, namely the first/>The higher the contribution degree of each grouting data component to the grouting optimization data is, the more/>, in the grouting optimization dataThe higher the information retention of the individual grouting data components; /(I)Expressed in the frequency domain as pair/>The smaller the difference between the shape parameters of the individual grouting data components and the shape parameters of the grouting optimization data, the description of the/>The more similar the frequency components between the individual grouting data components and the grouting optimization data, the/>The higher the importance of each grouting data component to the grouting optimization data, the more/>, in the grouting optimization dataThe higher the information retention of the individual grouting data components.
Preferably, in one embodiment of the present invention, the method for acquiring a shape parameter includes:
Since the more important grouting data components for the grouting optimization data should be similar in frequency components, the shape features of the spectrograms of the two should be similar; so each grouting data component in the grouting optimization data is used as data to be processed; fitting each data point in a spectrogram of the data to be processed to obtain the outline length of the fitting line and the area of the spectrogram; and taking the ratio of the contour length of the fitting line to the spectrogram area as the shape parameter of the data to be processed.
Since different grouting data components are of different importance to the grouting optimization data, and there are more outlier data points in the important grouting data components, while there are also some relatively unimportant data points, it is necessary to analyze the importance of each data point in each grouting data component at the time of the subsequent data sampling. Because the data distribution and the data amplitude of the abnormal data points in the grouting data component are often greatly different from those of other data points, in the embodiment of the invention, the transmission priority of each data point in each grouting data component is obtained according to the data distribution characteristic and the data difference degree of each data point in each grouting data component in the grouting optimization data in the whole grouting data component and the information conservation degree of each grouting data component.
Preferably, in one embodiment of the present invention, the method for acquiring transmission priority includes:
Acquiring flow thresholds in all grouting data components in grouting optimization data; optionally selecting a data point in each grouting data component as a target data point; obtaining the data difference degree of each data point in each grouting data component according to the data difference between the flow amplitude of the target data point and the overall flow amplitude in the grouting data component to which the target data point belongs; taking the ratio of the total number of all data points with the same flow amplitude as the target data point and the total number of the data points in the grouting data component as the data similarity degree of the target data point; taking the reciprocal of the similarity degree of the data as the data distribution characteristic of the target data point; changing target data points to obtain data distribution characteristics of each data point in each grouting data component; taking the product of the data difference degree of each data point in each grouting data component and the data distribution characteristic as the data prominence degree of each data point; and normalizing the product between the information conservation degree of each grouting data component and the data prominence degree of each data point in each grouting data component to obtain the transmission priority of each data point in each grouting data component. In one embodiment of the present invention, the transmission priority calculation formula is as follows:
In the method, in the process of the invention, Representing the/>, in grouting optimization dataFirst/>, within individual grouting data componentsTransmission priority of data points; Representing the/>, in grouting optimization data Information retention of individual grouting data components; /(I)Representing the/>, in grouting optimization dataThe number of data points within the individual grouting data components; /(I)Representing the/>, in grouting optimization dataWithin the individual grouting data components, and/>The number of data points with the same data point flow amplitude; /(I)Representing the/>, in grouting optimization dataFirst/>, within individual grouting data componentsThe degree of data difference for the data points; /(I)The hyperbolic tangent function is shown, in order to normalize the bracketed content.
In the transmission priority calculation formula,The larger the description of the/>, the more/>, in the grouting optimization dataWithin the individual grouting data componentsData points are compared with the first >The higher the degree of protrusion of the entirety of the individual grouting data components, at this time, the/>The higher the data point anomaly, the more should be transmitted preferentially; /(I)The larger the description of the first/>The individual grouting data components should retain more data information during data sampling, at which point the/>The transmission priority of all data points in each grouting data component is higher; /(I)The smaller the description is at the/>And (h) in the individual grouting data componentsThe smaller the number of data points with the same data point flow amplitude, the more/>The smaller the ratio of total number of individual grouting data component data points, the greater the/>The larger the (th)/>Data points are compared with the first >The higher the degree of protrusion of other data points within the individual grouting data components, the/>The higher the transmission priority of the data points.
Preferably, in one embodiment of the present invention, the method for acquiring the degree of data difference includes:
Acquiring flow thresholds in all grouting data components in grouting optimization data; calculating the difference between the flow amplitude of the target data point in each grouting data component and the flow average value of all the data points as a first flow difference; calculating a difference between the flow magnitude of the target data point in each grouting data component and the flow threshold in each grouting data component as a second flow difference; taking the ratio between the first flow difference and the second flow difference as the data difference degree of the target data point in each grouting data component; and changing the target data points to obtain the data difference degree of each data point in each grouting data component. In one embodiment of the present invention, the data difference degree calculation formula is as follows:
In the method, in the process of the invention, Representing the/>, in grouting optimization dataFirst/>, within individual grouting data componentsThe degree of data difference for the data points; /(I)Represents the/>First/>, within individual grouting data componentsFlow magnitude of data points; /(I)Represents the/>The flow amplitude average of the data points in the grouting data components; /(I)Represents the/>Individual grouting data components the flow threshold of the grouting optimization data.
In the data difference degree calculation formula, the firstFlow amplitude and the/>, of data pointsThe larger the difference between the overall flow amplitude averages of the individual grouting data components, the description of the/>Data points and/>The greater the difference in other data points within the individual grouting data components, the greater the difference in the number/>The greater the degree of data anomaly for a data point, the greater the degree of data anomaly for a data pointThe higher the degree of anomaly of the data points; first/>The smaller the difference between the flow amplitude of the data point and the flow threshold of the grouting optimization data, the description of the/>Data points at/>The more prominent is the individual grouting data components, at this point/>The greater the degree of data anomaly for a data point, the greater the degree of data anomaly for a data pointThe higher the degree of anomaly of the data points.
Step S4: based on the transmission priority of each data point, a sampled data point is obtained.
Preferably, in one embodiment of the present invention, the method for acquiring sampled data points includes:
Presetting a first threshold value, and taking data points with transmission priority higher than the first threshold value as initial sampling data points in grouting optimization data; taking all sampling frequencies which exceed half of the original data sampling frequency as initial sampling frequencies; taking an initial sampling frequency including the maximum number of initial sampling data points as a corrected sampling rate; all sampled data points are obtained using the modified sampling rate. In one embodiment of the invention, the first threshold is set to 0.5. It should be noted that, in other embodiments of the present invention, the first threshold may be set by an operator, and the method for obtaining all the sampled data points by using the corrected sampling rate is a technical means known to those skilled in the art, which is not limited and described herein.
Step S5: and controlling grouting flow of the grouting pump according to the sampling data points.
Preferably, in one embodiment of the present invention, controlling the grouting flow of the grouting pump according to the sampled data points includes:
Compressing the sampling data points by using a Huffman coding algorithm to obtain grouting compressed data; transmitting grouting compressed data according to a communication protocol; the PLC remote control center decodes the received grouting compressed data and analyzes and stores the decoded data; and adjusting and controlling the grouting flow of the grouting pump according to the change of the real-time flow data and the change of the underground environment. In one embodiment of the present invention, the huffman coding algorithm is a technical means well known to those skilled in the art, which is not described herein, and in other embodiments of the present invention, other data compression algorithms may be used to compress the sampled data points, which is not limited herein.
So far, the grouting flow optimization control of the grouting pump is completed.
In summary, the invention obtains all grouting data components in each iterative decomposition process; according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component, acquiring grouting data characteristics of each grouting data component; according to the characteristic difference between grouting data components and the data stability degree between reconstructed flow data in the iterative decomposition process, an optimal iterative decomposition result is obtained, namely grouting optimization data reconstructed by all grouting data components after iterative decomposition; obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component and the grouting optimization data in the grouting optimization data; according to the data distribution characteristics and the data difference degree of each data point in each grouting data component in the grouting optimization data in the whole grouting data component and the information conservation degree of each grouting data component, the transmission priority of each data point in each grouting data component is obtained; obtaining sampling data points according to the transmission priority of each data point; and controlling grouting flow of the grouting pump according to the sampling data points. According to the invention, by correcting the sampling rate, more proper sampling data points are obtained, the instantaneity and the data readability in the data transmission process are ensured, and the flow control of the grouting pump is more accurate and timely.
An embodiment of a grouting flow data sampling method of a grouting pump is provided:
In the prior art, grouting flow data needs to be sampled in the process of being transmitted from monitoring equipment to a control system, the accuracy and the readability of the data are affected by the sampling rate, the risk of losing unsuitable sampling data in the process of transmission is high, and the technical problem of data transmission delay can be caused. In order to solve the technical problem, the embodiment of the invention provides a grouting flow data sampling method of a grouting pump.
Step S1: and acquiring grouting flow data.
Step S2: performing iterative decomposition on grouting flow data to obtain all grouting data components in each iterative decomposition process; according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component, acquiring grouting data characteristics of each grouting data component; and obtaining grouting optimization data of all grouting data components after iterative decomposition according to the data stability degree between the difference between grouting data characteristics of any two grouting data components in each iterative decomposition process and the reconstructed flow data formed by all grouting data components after two adjacent iterative decomposition processes.
Step S3: obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component and the grouting optimization data in the grouting optimization data; and obtaining the transmission priority of each data point in each grouting data component according to the data prominence degree of the data point in each grouting data component in the whole grouting data component and the information conservation degree of each grouting data component in the grouting optimization data.
Step S4: based on the transmission priority of each data point, a sampled data point is obtained.
Because the specific implementation process of steps S1 to S4 is already described in detail in the above-mentioned method for optimizing and controlling the grouting flow of the grouting pump, the detailed description is omitted.
The technical effect of this embodiment is: in order to avoid influencing the real-time performance and the readability of grouting flow data in the data transmission process, the embodiment carries out iterative decomposition on the grouting flow data to obtain all grouting data components in each iterative decomposition process; because the grouting data components decomposed in different iterative decomposition processes are different, in order to intuitively embody the characteristic difference between the grouting data components, the grouting data characteristics of each grouting data component are obtained according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component; because in the ideal iterative decomposition result, the difference of different degrees exists in each grouting data component and the data change after iteration tends to be stable, in order to select the optimal iterative decomposition result, the optimal iterative decomposition result is obtained according to the characteristic difference between the grouting data components and the data stability degree between the reconstructed flow data in the iterative decomposition process, namely, the reconstructed grouting optimization data of all grouting data components; obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component and the grouting optimization data in the grouting optimization data, wherein the information conservation degree reflects the importance degree of different grouting data components on the whole grouting optimization data; according to the data prominence degree of each data point in each grouting data component in grouting optimization data in the whole grouting data component and the information conservation degree of each grouting data component, the transmission priority of each data point in each grouting data component is obtained, and the importance of each data point in each grouting data component in the later data sampling is reflected; sampling data is obtained according to the transmission priority of each data point. According to the embodiment, by correcting the sampling rate, more proper sampling data points are obtained, and the instantaneity and the data readability in the data transmission process are ensured.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. The grouting pump grouting flow optimization control method is characterized by comprising the following steps of:
Acquiring grouting flow data;
Performing iterative decomposition on the grouting flow data to obtain all grouting data components in each iterative decomposition process and reconstructed flow data obtained by reconstructing the grouting data; obtaining grouting data characteristics of each grouting data component according to the frequency change characteristics and the frequency intensity characteristics of all data points in each grouting data component; according to the difference between grouting data characteristics of any two grouting data components in each iterative decomposition process and the data stability degree between the reconstructed flow data after the adjacent iterative decomposition process, grouting optimization data of all grouting data components after iterative decomposition are obtained;
Obtaining the information conservation degree of each grouting data component according to the frequency information similarity degree between each grouting data component in the grouting optimization data and the grouting optimization data; obtaining the transmission priority of each data point in each grouting data component according to the data prominence degree of the data point in each grouting data component in the whole grouting data component and the information conservation degree of each grouting data component in the grouting optimization data;
obtaining sampled data points according to the transmission priority of each data point;
and controlling grouting flow of the grouting pump according to the sampling data points.
2. The method for optimizing and controlling the grouting flow of the grouting pump according to claim 1, wherein the method for acquiring the grouting data characteristics comprises the following steps:
Acquiring a primary frequency in each grouting data component;
Averaging the time difference between every two adjacent data points of the grouting data component to obtain the frequency change characteristic of the data points in the grouting data component;
averaging the intensity values of all data points in the grouting data component spectrogram to obtain the frequency intensity characteristics of the data points in the grouting data component;
taking the product of the primary frequency, the frequency variation characteristic and the frequency intensity characteristic as a grouting data characteristic of each grouting data component.
3. The grouting pump grouting flow optimization control method according to claim 1, wherein the grouting optimization data acquisition method comprises the following steps:
According to the grouting data characteristic difference value between any two grouting data components in each iterative decomposition process, obtaining the characteristic distribution discrete degree of the grouting data components in each iterative decomposition process;
for each iterative decomposition process, taking the reciprocal of the mean square error between the reconstructed flow data in the previous iterative decomposition process as the data stability degree between grouting flow data in each iterative decomposition process;
Taking the product of the characteristic distribution discrete degree and the data stability degree as an iterative objective function of grouting flow data after iterative decomposition; and taking the reconstructed flow data after the iterative decomposition process corresponding to the maximum value of the iterative objective function is reconstructed as grouting optimization data.
4. The method for optimizing control of grouting flow of a grouting pump according to claim 3, wherein the method for obtaining the discrete degree of the characteristic distribution comprises the steps of:
Obtaining all grouting data sets consisting of any two grouting data components after each iterative decomposition process; calculating grouting data characteristic difference values between any two grouting data components in each grouting data group; and taking the variance of the characteristic difference values of all grouting data as the characteristic distribution discrete degree of the grouting data components in each iterative decomposition process.
5. The method for optimizing control of a grouting flow of a grouting pump according to claim 1, wherein the method for obtaining the information retention degree of each grouting data component comprises:
Obtaining shape parameters of the grouting optimization data and the grouting data components according to the spectrogram shape characteristics of the grouting optimization data and the grouting data components;
acquiring the information conservation degree according to an information conservation degree calculation formula, wherein the information conservation degree calculation formula is as follows:
; in the/> Representing the/>, within grouting optimization dataInformation retention of individual grouting data components; /(I)Represents the/>The/>, of the individual grouting data componentsFrequency amplitude of data points; /(I)Representing the/>, within grouting optimization dataFrequency amplitude of data points; /(I)Representing the/>, within grouting optimization dataThe number of data points for each grouting data component; /(I)Representing a number of data points within the grouting optimization data; /(I)Representing the/>, within grouting optimization dataShape parameters of the individual grouting data components; /(I)And the shape parameters of the grouting optimization data are represented.
6. The method for optimizing control of a grouting pump grouting flow according to claim 5, wherein the method for obtaining the shape parameter comprises:
Taking each grouting data component in the grouting optimization data as data to be processed; fitting each data point in a spectrogram of one piece of data to be processed to obtain the outline length of a fitting line and the area of the spectrogram; and taking the ratio of the contour length of the fitting line to the area of the spectrogram as the shape parameter of the data to be processed.
7. The method for optimizing control of grouting flow of a grouting pump according to claim 1, wherein the method for acquiring the transmission priority comprises the steps of:
Optionally selecting a data point in each grouting data component as a target data point; obtaining the data difference degree of each data point in each grouting data component according to the data difference between the flow amplitude of the target data point and the overall flow amplitude in the grouting data component to which the target data point belongs;
Taking the ratio of the total number of all data points with the same flow amplitude as the target data point and the total number of the data points in the grouting data component as the data similarity of the target data point; taking the reciprocal of the similarity degree of the data as the data distribution characteristic of the target data point; changing target data points to obtain data distribution characteristics of each data point in each grouting data component;
Taking the product of the data difference degree of each data point in each grouting data component and the data distribution characteristic as the data prominence degree of each data point;
and normalizing the product between the information conservation degree of each grouting data component and the data prominence degree of each data point in each grouting data component to obtain the transmission priority of each data point in each grouting data component.
8. The method for optimizing control of grouting flow of a grouting pump according to claim 7, wherein the method for obtaining the degree of difference in data comprises:
acquiring flow thresholds in all grouting data components in grouting optimization data;
calculating the difference between the flow amplitude of the target data point in each grouting data component and the flow average value of all the data points as a first flow difference; calculating a difference between the flow magnitude of the target data point in each grouting data component and the flow threshold in each grouting data component as a second flow difference;
Taking the ratio between the first flow difference and the second flow difference as the data difference degree of the target data point in each grouting data component;
And changing the target data points to obtain the data difference degree of each data point in each grouting data component.
9. The method for optimizing control of grouting flow of a grouting pump according to claim 1, wherein the method for acquiring sampled data points comprises:
Presetting a first threshold, and taking the data points with the transmission priority higher than the first threshold as initial sampling data points in the grouting optimization data;
Taking all sampling frequencies which exceed half of the original data sampling frequency as initial sampling frequencies; taking the initial sampling frequency comprising the maximum number of initial sampling data points as a corrected sampling rate;
and obtaining all sampling data points by using the corrected sampling rate.
10. The method for optimizing control of a grouting pump grouting flow according to claim 1, wherein controlling the grouting pump grouting flow according to the sampled data points comprises:
compressing the sampling data points to obtain grouting compression data;
transmitting and decoding the grouting compressed data, and analyzing and storing the decoded data;
and adjusting and controlling the grouting flow of the grouting pump according to the change of the real-time flow data and the change of the underground environment.
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