A method for predicting the position density of the cutter suction port of a cutter suction dredger

Abstract

The present invention relates to a method for predicting the density of the suction port position of a cutter suction dredger, and belongs to the field of engineering ship automation systems. The method first collects the suction vacuum value, density value and flow rate value in real time, and processes and stores the data in real time; then, the suction vacuum value is time-shifted by using the length and flow rate of the pipeline from the suction vacuum sensor to the density meter, so that it matches the density value in time dimension, and after the matching is successful, the suction vacuum value and the density value are respectively extracted and stored as characteristic values ​​by using the weighted average algorithm; then, a fitting operation is performed on the characteristic statistical data; a fitting equation is obtained by using the fitting operation result, and the density value corresponding to the position of the cutter suction port is predicted in advance by using the suction vacuum value obtained in real time; and at the same time, the fitting equation is fitted and corrected in real time. The present invention can significantly improve the control accuracy of the dredging construction of the cutter suction dredger by accurately predicting the position of the cutter suction port of the cutter suction dredger in real time, thereby ensuring the efficiency of the dredging construction.

Description

Technical Field

[0001] The invention belongs to the technical field of engineering ship automation, and in particular relates to a method for predicting the position density of a cutter suction port of a cutter suction dredger. Background Art

[0002] Mud density is a crucial process parameter for dredging construction vessels, which directly reflects the construction efficiency of the vessel and is also a particularly important reference parameter for dredging construction personnel. If the mud density is too low, it is necessary to increase the mud density in a timely manner by increasing the traverse speed, increasing the mud pump speed, and increasing the excavation depth, thereby improving production efficiency and output; if the mud density is too high, it may cause pipe blockage, excessive mud pump power, and other problems, and it is necessary to take timely measures such as reducing the traverse speed and reducing the excavation depth.

[0003] However, due to the limitations of the installation environment, pipeline layout, pump room space and other conditions of the density meter itself, the density meter is usually installed in a pipeline with a length of more than 60 meters from the auger suction port. For large-scale cutter suction dredgers, the distance will be longer. The mud flow rate of the cutter suction dredger under normal construction is about 5m / s, so it takes more than 12 seconds to obtain the mud density at the auger suction port, resulting in the construction personnel being unable to obtain the mud density at the auger suction port in a timely and accurate manner, which in turn causes the operation of the dredging construction personnel to be relatively delayed, affecting the construction efficiency of the ship. Especially in the process of automatic dredging or unmanned dredging by the cutter suction dredger, if the automatic operation can be performed according to the predicted mud density data, the dredging construction efficiency and control accuracy can be significantly improved.

[0004] Although the suction vacuum sensor can be installed near the suction port of the reamer, and there is a certain correlation between the suction vacuum and the mud density, the suction vacuum and the mud density are not in a linear relationship. Construction personnel cannot intuitively judge the mud density at the current reamer position through the suction vacuum data.

[0005] Therefore, in order to solve the above problems, it is urgent to develop a method for predicting the position density of the suction port of the cutter suction dredger to improve the operability and control accuracy of construction personnel and further improve the dredging construction efficiency. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a method for predicting the density at the suction port of a cutter suction dredger. The method collects the suction vacuum, density and flow rate data measured by a suction vacuum sensor, a density meter and a flow meter installed near the density meter at the suction port of the cutter in real time, and processes and stores the data in real time; then, the suction vacuum value is time-shifted using the length and flow rate of the pipeline from the suction vacuum sensor to the density meter to match the time dimension with the density value, and characteristic values ​​are extracted respectively; finally, a fitting operation is performed on the characteristic statistical data; after completing the above data fitting, a fitting equation is obtained using the fitting operation result, and a fitting correction is performed in real time; the density value corresponding to the suction port of the cutter is predicted by substituting the suction vacuum value obtained in real time into the fitting equation.

[0007] The present invention is implemented in this way: a method for predicting the position density of the suction port of a cutter suction dredger comprises the following steps:

[0008] S1, real-time collection of suction vacuum value, density value and flow rate value, and real-time filtering and storage of the data;

[0009] S2, using the pipeline length and flow rate value from the suction vacuum value collection position to the density value collection position to perform time shift operation on the suction vacuum value, so that it is matched with the density value in time dimension, and after the match is successful, the weighted average algorithm is used to extract the suction vacuum value and the density value as feature values ​​for storage;

[0010] S3, performing fitting operation on the statistical data of the suction vacuum and density as characteristic values;

[0011] S4. A fitting equation is obtained by fitting operation, and the suction vacuum value obtained in real time is input into the fitting equation to obtain the current predicted density value; meanwhile, the fitting equation is corrected in real time.

[0012] Preferably, in step S1, the real-time collection is completed by a data collection module of the ship's dredging integrated monitoring system.

[0013] Preferably, in step S1, the real-time filtering processing refers to using a Gaussian filtering algorithm to process data, and preset boundary values, including the maximum suction vacuum value, the minimum suction vacuum value, the maximum density value, the minimum density value, the maximum flow rate value and the minimum flow rate value. When the data exceeds the maximum and minimum boundary value ranges, it is judged as abnormal data and is directly discarded.

[0014] Preferably, in step S1, the suction vacuum value is obtained by collecting data from a suction vacuum sensor installed near the suction port of the reamer, the density value is obtained by collecting data from a density meter installed on the ship's mud delivery pipeline, and the flow rate value is obtained by collecting data from a flow meter installed near the density meter.

[0015] Further preferably, the suction vacuum sensor installed near the suction port of the reamer means that the suction vacuum sensor is installed within 1 meter from the suction port of the reamer.

[0016] Further preferably, the flow meter installed close to the density meter means that the density meter is directly nested outside the flow meter for installation; or, the installation distance between the density meter and the flow meter is within 1 meter.

[0017] Preferably, in step S2, the time-shift operation refers to first calculating the time required for the mud to flow from the suction vacuum value collection position to the density value collection position through the pipeline length and the stored flow rate data, and then extracting the corresponding suction vacuum value from the stored suction vacuum values.

[0018] Further preferably, the specific calculation process of the time-shift operation is as follows: first, starting from a certain current time point, traverse the stored flow velocity data, use formula (1) to calculate the mud flow distance, and then use formula (2) to subtract the pipeline length from the mud flow distance calculated by formula (1). When the deviation value is less than 1m, stop traversing, the calculation is completed, and the suction vacuum value corresponding to the time point is extracted.

[0019] (1)

[0020] (2)

[0021] Where: is the mud flow distance calculated by the velocity data points and the corresponding time intervals, is the stored flow rate; The time interval for storing flow rate; is the pipeline length, is the difference between pipeline length and mud flow distance.

[0022] Preferably, in step S2, the characteristic value refers to the density value at a current time point and the suction vacuum value obtained after time shift operation, and the suction vacuum value and density value finally obtained after calculation by weighted average algorithm.

[0023] Further preferably, in step S2, in the weighted average algorithm, the closer the feature value storage is to a current time point, the higher the weight it occupies.

[0024] Further preferably, the number of identical characteristic values ​​at different times participating in the weighted calculation is preset by the dredging integrated monitoring system of the ship.

[0025] Preferably, in step S3, the fitting operation refers to using the suction vacuum value and density value extracted by the weighted average algorithm, performing a fitting operation by a least squares polynomial fitting algorithm, and calculating the coefficients of the fitting equation to finally obtain a polynomial curve fitting equation.

[0026] Further preferably, the process of calculating the coefficients of the fitting equation by the least squares polynomial fitting algorithm is as follows: let the polynomial curve fitting equation be formula (3), and use formula (4) to calculate the square sum of each feature point to formula (3): R 2 To minimize the sum of squares, first calculate the right side of formula (4) k i The partial derivatives are then obtained, and the equations after partial derivatives are simplified and organized into a matrix equation formula (5). The coefficients of the fitting equation are then obtained through the matrix equation, and finally the polynomial curve fitting equation is obtained.

[0027] (3)

[0028] (4)

[0029] (5)

[0030] Where: is the polynomial curve fitting equation; are the coefficients of the fitting equation; is the order of the fitting equation; is the number of feature points; is the suction vacuum value; is the suction vacuum value at different characteristic points; is the density value of different feature points; It is 1, 2, 3, …n.

[0031] Preferably, in step S3, the fitting correction refers to re-performing the fitting operation each time after completing data collection and successfully extracting characteristic values.

[0032] The advantages and positive effects of the present invention are:

[0033] The present invention utilizes suction vacuum, density, flow rate and combines the data of pipeline length from the installation position of suction vacuum sensor to the installation position of density meter, number of characteristic points, boundary value, etc., adopts time-shift operation and prediction model algorithm, and comprehensively utilizes computer, network, sensor and automation technologies, so as to solve the problem that dredging construction personnel cannot accurately and timely obtain the mud density at the auger suction port position during manual operation or automatic dredging process, realizes rapid and accurate prediction of the density at the auger suction port position of cutter suction dredger, greatly improves the real-time performance and control accuracy of dredging construction operation, and thus improves the construction efficiency of cutter suction dredger. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of the installation position of the suction port of the cutter of a cutter suction dredger provided in an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of time-shift calculation of a method for predicting the position density of a cutter suction port of a cutter suction dredger provided in an embodiment of the present invention;

[0036] Figure 3 A flow chart of a method for predicting the position density of a cutter suction port of a cutter suction dredger provided in an embodiment of the present invention;

[0037] Figure 4 A fitting curve diagram of a method for predicting the position density of a cutter suction port of a cutter suction dredger provided in an embodiment of the present invention.

[0038] In the figure: 1. Suction vacuum sensor; 2. Density meter; 3. Flow meter; 4. Reamer suction port. DETAILED DESCRIPTION

[0039] In order to further understand the content, characteristics and effects of the present invention, the following examples are given, and the present invention is described in detail below in conjunction with specific embodiments and drawings. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be pointed out that for those of ordinary skill in the art, several changes and improvements can be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0040] The embodiment of the present invention provides a method for predicting the position density of the suction port of a cutter suction dredger, and the required equipment and installation position are as follows: Figure 1 As shown, the device includes: an inhalation vacuum sensor 1, a density meter 2 and a flow meter 3, wherein the inhalation vacuum sensor 1 adopts an isolated intelligent pressure transmitter, the density meter 2 adopts a radioactive density meter or an electrical density meter, and the flow meter 3 adopts an electromagnetic flow meter.

[0041] The suction vacuum sensor 1 is installed within one meter from the reamer suction port 4, the flow meter 3 is installed on the ship's mud pipeline in the form of a short section, and the density meter 2 is directly nested outside the flow meter for installation, or the installation distance between the flow meter and the density meter is within 1 meter.

[0042] like Figure 3 As shown, a method for predicting the position density of the cutter suction port of a cutter suction dredger provided by an embodiment of the present invention is implemented in the following steps:

[0043] S1. Collect the suction vacuum value, density value and flow rate value in real time, and filter and store the data in real time.

[0044] The suction vacuum value is obtained by collecting data from a suction vacuum sensor, the density value is obtained by collecting data from a density meter, and the flow rate value is obtained by collecting data from a flow meter.

[0045] The real-time data collection is completed through the data collection module of the ship's dredging integrated monitoring system.

[0046] The real-time filtering process refers to the use of Gaussian filtering algorithm to process data, and preset boundary values, including the minimum suction vacuum value, the maximum suction vacuum value, the minimum density value, the maximum density value, the minimum flow rate value and the maximum flow rate value. When the data exceeds the maximum and minimum boundary value range, it is judged as abnormal data and directly discarded. In this example, the minimum suction vacuum value is set to -10bar, the maximum suction vacuum value is set to 0bar; the minimum density value is set to 1000kg / m 3 , the maximum density is set to 1800kg / m 3 ; The minimum flow velocity is set to 0m / s and the maximum flow velocity is set to 10m / s.

[0047] S2. Use the length of the pipeline from the installation position of the suction vacuum sensor to the installation position of the density meter and the flow rate value to perform a time shift operation on the suction vacuum value to match it with the density value in the time dimension. After the match is successful, use the weighted average algorithm to extract the suction vacuum value and the density value as feature values ​​for storage. If there is no match, repeat step S1.

[0048] The time-shift operation refers to first calculating the time required for the mud to flow from the suction vacuum sensor installation position to the density meter installation position through the pipeline length and the stored flow rate data, and then extracting the corresponding suction vacuum value from the stored suction vacuum values ​​(that is, if the time required for the mud to flow from the suction vacuum sensor installation position to the density meter installation position is Δt, then the density value at time t corresponds to the suction vacuum value at time t-Δt).

[0049] The specific calculation process of the time-shift operation is as follows: first, starting from a certain current time point, traverse the stored flow velocity data, use formula (1) to calculate the mud flow distance, and then use formula (2) to subtract the pipeline length from the mud flow distance calculated by formula (1). When the deviation value is less than 1m, stop traversing, the calculation is completed, and the suction vacuum value corresponding to the time point is extracted.

[0050] (1)

[0051] (2)

[0052] Where: is the mud flow distance calculated by the velocity data points and the corresponding time intervals; is the stored flow rate; The time interval for storing flow rate; is the pipeline length, that is, the actual pipeline length; It is the difference between the actual pipeline length and the mud flow distance.

[0053] Pipeline length Figure 1 and Figure 2 The figure shows the pipe length from the installation position of the suction vacuum sensor to the installation position of the density meter.

[0054] The characteristic value refers to the density value at a certain point in time and the suction vacuum value obtained after the time shift operation, and the suction vacuum value and density value finally obtained after calculation by the weighted average algorithm. Among them, in the weighted average algorithm, the closer the characteristic value storage is to the current point in time, the higher the weight it occupies. The number of the same characteristic values ​​at different times participating in the weighted calculation is preset by the ship's dredging integrated monitoring system. In this example, the number of characteristic values ​​is set to 10.

[0055] S3. Perform fitting calculation on the statistical data of the suction vacuum and density as characteristic values.

[0056] The fitting operation refers to using the suction vacuum value and density value extracted by the weighted average algorithm, performing a fitting operation through a least squares polynomial fitting algorithm, and calculating the coefficients of the fitting equation to finally obtain a polynomial curve fitting equation.

[0057] The process of calculating the coefficients of the fitting equation by the least squares polynomial fitting algorithm is as follows: Assume that the polynomial curve fitting equation is as shown in formula (3), and use formula (4) to calculate the square sum of each feature point to formula (3): R 2 To minimize the sum of squares, first calculate the right side of formula (4) k iPartial derivatives are obtained, and then the equations after partial derivatives are simplified and organized into a matrix equation formula (5). The coefficients of the fitting equation are then obtained through the matrix equation, and finally the polynomial curve fitting equation is obtained.

[0058] (8)

[0059] (9)

[0060] (10)

[0061] Where: is the polynomial curve fitting equation; are the coefficients of the fitting equation; is the order of the fitting equation; is the number of feature points; is the suction vacuum value; is the suction vacuum value at different characteristic points; is the density value of different feature points; It is 1, 2, 3, …n.

[0062] The fitting correction refers to re-performing the fitting operation each time after completing data collection and successfully extracting characteristic values.

[0063] The fitting curve diagram of the position density prediction method of the cutter suction port of the cutter suction dredger in this example is as follows: Figure 4 shown.

[0064] S4. A fitting equation is obtained by fitting operation, and the suction vacuum value obtained in real time is input into the fitting equation to obtain the current predicted density value; meanwhile, the fitting equation is corrected in real time.

[0065] The present invention solves to a great extent the problem that dredging construction personnel cannot accurately and timely obtain the mud density at the auger suction port position during manual operation or automatic dredging, and realizes rapid and accurate prediction of the density at the auger suction port position of the cutter suction dredger, which greatly improves the real-time performance and control accuracy of dredging construction control, thereby improving the construction efficiency of the cutter suction dredger.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents, and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the position density of the cutter suction port of a cutter suction dredger, characterized in that: The steps include: S1, real-time collection of suction vacuum value, density value and flow rate value, and real-time filtering and storage of the data; S2, using the pipeline length and flow rate value from the suction vacuum value collection position to the density value collection position to perform time shift operation on the suction vacuum value, so that it is matched with the density value in time dimension, and after the match is successful, the weighted average algorithm is used to extract the suction vacuum value and the density value as feature values ​​for storage; The time shift calculation means first calculating the time required for the mud to flow from the suction vacuum value collection position to the density value collection position through the pipeline length and the stored flow rate data, and then extracting the corresponding suction vacuum value from the stored suction vacuum values; The specific calculation process of the time shift operation is as follows: first, starting from a certain current time point, traverse the stored flow velocity data, use formula (1) to calculate the mud flow distance, and then use formula (2) to subtract the pipeline length from the mud flow distance calculated by formula (1). When the deviation value is less than 1m, stop traversing, the calculation is completed, and the suction vacuum value corresponding to the time point is extracted; （1） （2） Where: is the mud flow distance calculated by the velocity data points and the corresponding time intervals; is the stored flow rate; The time interval for storing flow rate; is the pipeline length; is the difference between the pipeline length and the mud flow distance; S3, performing fitting operation on the statistical data of the suction vacuum and density as characteristic values; S4. A fitting equation is obtained by fitting operation, and the suction vacuum value obtained in real time is input into the fitting equation to obtain the current predicted density value; meanwhile, the fitting equation is corrected in real time.

2. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 1, characterized in that: In step S1, the real-time collection is completed by a data collection module of the dredging integrated monitoring system of the ship; The real-time filtering process refers to using a Gaussian filtering algorithm to process data, and preset boundary values, including the maximum suction vacuum value, the minimum suction vacuum value, the maximum density value, the minimum density value, the maximum flow rate value, and the minimum flow rate value. When the data exceeds the maximum and minimum boundary value ranges, it is judged as abnormal data and directly discarded; The suction vacuum value is obtained by collecting data from a suction vacuum sensor installed near the suction port of the reamer, the density value is obtained by collecting data from a density meter installed on the ship's mud delivery pipeline, and the flow rate value is obtained by collecting data from a flow meter installed near the density meter.

3. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 2, characterized in that: The suction vacuum sensor installed near the suction port of the reamer means that the suction vacuum sensor is installed within 1 meter from the suction port of the reamer; The flow meter installed close to the density meter means that the density meter is directly nested outside the flow meter for installation; or, the installation distance between the density meter and the flow meter is within 1 meter.

4. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 1, characterized in that: In step S2, the characteristic value refers to the density value at a certain time point and the suction vacuum value obtained after time shift operation, and the suction vacuum value and density value finally obtained after calculation by weighted average algorithm.

5. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 4, characterized in that: In step S2, in the weighted average algorithm, the closer the feature value storage is to the current time point, the higher the weight it occupies; The number of identical characteristic values ​​at different times participating in the weighted calculation is preset by the ship's dredging integrated monitoring system.

6. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 1, characterized in that: In step S3, the fitting operation refers to using the suction vacuum value and density value extracted by the weighted average algorithm to perform a fitting operation through a least squares polynomial fitting algorithm, and calculating the coefficients of the fitting equation to finally obtain a polynomial curve fitting equation.

7. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 6, characterized in that: The process of calculating the coefficients of the fitting equation by the least squares polynomial fitting algorithm is as follows: Let the polynomial curve fitting equation be formula (3), and use formula (4) to calculate the sum of the squares of each feature point to formula (3): R 2 To minimize the sum of squares, first calculate the right side of formula (4) k i The partial derivatives are then simplified and organized into a matrix equation (5). The coefficients of the fitting equation are then obtained through the matrix equation, and finally the polynomial curve fitting equation is obtained. （3） （4） （5） Where: is the polynomial curve fitting equation; are the coefficients of the fitting equation; is the order of the fitting equation; is the number of feature points; is the suction vacuum value; is the suction vacuum value at different characteristic points; is the density value of different feature points; It is 1, 2, 3, …n.

8. The method for predicting the position density of the cutter suction port of a cutter suction dredger according to claim 1, characterized in that: In step S4, the fitting correction refers to re-performing the fitting operation each time after completing data collection and successfully extracting characteristic values.

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