Dredged soil property zoning method based on process parameters

Abstract

The invention discloses a dredging soil property zoning method based on process parameters. The method comprises the steps that construction process data are collected and preprocessed; selecting an effective construction section; selecting effective construction process parameters in the effective construction section; determining parameters of a soil property zoning basis; carrying out visualization processing on a ship moving track; visualization of the multi-dimensional process data is carried out; carrying out soil property partitioning based on visual process parameters; the reamer excavation coefficient is calibrated based on technological parameters; performing equipment performance prediction based on the soil property parameters; according to soil property partitioning based on visual process parameters, reamer excavation coefficient calibration of the process parameters and equipment property prediction based on the soil property parameters, equipment property, yield and oil consumption in the construction process in the target construction area are predicted and analyzed, and a partitioning optimization construction strategy is provided. According to the method, various technological parameters in the dredging process are comprehensively analyzed, automatic and precise partitioning of the soil texture is achieved, and the efficiency and safety of dredging operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dredging engineering, and specifically to a method for partitioning dredged soil based on process parameters. Background Art

[0002] The cutter suction dredger is the main ship in the field of dredging and reclamation. Adopting different construction processes and parameters for different soil types is beneficial to preventing phenomena such as cutter wrapping, excessive wear, and cutter tooth breakage, improving the service life of the cutter, increasing the construction output rate, and generating more economic benefits. At present, for complex dredging areas composed of multiple soil types, traditional soil partitioning methods mostly rely on manual experience or simple physical tests. Based on existing geological exploration reports, the soil in the area to be constructed is simply divided by interpolation method, which has problems such as strong subjectivity, insufficient regional division accuracy, and relatively arbitrary use of construction parameters.

[0003] When calculating the excavation output rate, the selection of the excavation coefficient according to the existing specifications is relatively arbitrary, and the difference from the actual result is large, seriously affecting the theoretical calculation of the output.

[0004] With the development of information technology, shipborne sensors are used to real-time monitor process parameters during dredging, such as cutter depth, rotation speed, load, conveying flow rate, concentration, underwater pump suction vacuum, hourly output rate, etc., and perform integration, induction, and visualization processing to accurately divide the area.

[0005] Based on the existing geological exploration report, predict the soil conditions of the lower layer during excavation, and provide optimization guidance for construction process parameters under different soil types during subsequent dredging excavation.

[0006] Therefore, a method for partitioning dredged soil based on process parameters is provided. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for partitioning dredged soil based on process parameters to overcome the existing defects. By comprehensively analyzing various process parameters during dredging, automatic and accurate partitioning of soil is achieved, and the cutter excavation coefficient is optimized according to different soil types, improving the calculation accuracy and the efficiency and safety of dredging operations.

[0008] The technical solution to achieve the above purpose is as follows:

[0009] A method for partitioning dredged soil based on process parameters, comprising:

[0010] Step S1, collecting and preprocessing construction process data;

[0011] Step S2, selecting effective construction sections;

[0012] Step S3, selecting effective construction process parameters within the effective construction sections;

[0013] Step S4, determining the parameters based on which the soil quality zoning is carried out;

[0014] Step S5, visual processing of the ship's operation trajectory;

[0015] Step S6, visualization of multi-dimensional process data;

[0016] Step S7, carrying out soil quality zoning based on the visualized process parameters;

[0017] Step S8, calibrating the cutterhead excavation coefficient based on the process parameters;

[0018] Step S9, predicting the equipment performance based on the soil quality parameters;

[0019] Step S10, predicting and analyzing the equipment performance, output, and fuel consumption during the construction process in the target construction area according to the soil quality zoning based on the visualized process parameters, the calibration of the cutterhead excavation coefficient of the process parameters, and the prediction of the equipment performance based on the soil quality parameters, and proposing a zoning optimization construction strategy;

[0020] Step S11, storing and exporting the construction strategy data.

[0021] Preferably, in the said Step S1, the construction process data includes: the cutterhead depth h, rotation speed n, load P, conveying flow velocity v 流 , concentration C, and underwater pump suction vacuum p parameters that are real-time monitored and obtained by the on-board sensors, and the data is stored in the CSV format;

[0022] In the said Step S2, the selection of the effective construction section is carried out by sorting the cutterhead depth data in descending order and drawing a bar chart, removing the data of non-construction periods, and selecting the appropriate cutterhead depth interval as the effective construction section.

[0023] Preferably, in the said Step S3, selecting the effective construction process parameters within the effective construction section, including:

[0024] According to the obtained effective construction depth, screening the longitude and latitude coordinates, cutterhead load P, rotation speed n, v 流 , concentration C, and underwater pump suction vacuum p data in the aisdata, and calculating the hourly production rate Q,

[0025] Q = v 流 ·A·C;

[0026] In the formula, A is the cross-sectional area of the pipe;

[0027] By setting the median absolute deviation value MAD of the process parameters, the value exceeding three times the converted median absolute deviation value MAD is defined as an outlier and removed from the data set. Among them, the formula for the median absolute deviation value MAD is defined as:

[0028] MAD = median(|x 1 - M|, |x 2 - M|, |x 3 - M|... |x n - M|);

[0029] Wherein, X = {x 1 , x 2 , x 3 ... x n} is a data set of a certain process parameter, and M is the median of this process parameter data set;

[0030] Set a logical index regarding time through aispdata, convert the date data into date sequence values, and then analyze the process parameter conditions in different time periods by inputting different times;

[0031] In the step S4, by comprehensively considering the pipeline transportation output and the cutter power, the transportation output a that can be generated by the unit power output of the cutter is used as an important parameter for the soil quality zoning basis, that is:

[0032] a = Q / P;

[0033] Wherein, Q is the hourly output rate and P is the cutter load;

[0034] Calculate the soil quality zoning basis parameter a at each moment in the dredging area according to the ship process parameters monitored by the index, and divide the dredging area based on this, and quantify the excavation difficulty at any position in the dredging area.

[0035] Preferably, in the step S5, by setting different logical indexes regarding time, draw the longitude and latitude trajectory map of a specific data subset to show the construction movement path conditions of the dredger in different time periods in the dredging area.

[0036] Preferably, in the step S6, the visualization of multi-dimensional process data includes:

[0037] Geographic coordinate visualization, use geoaxes to draw data points of the load during normal construction on the geographic coordinate map, and encode the color according to the load value to show the load distribution;

[0038] Data three-dimensional visualization, draw a three-dimensional scatter plot regarding longitude and latitude, load, and show the change of the load in space with longitude and latitude;

[0039] Partition visualization, according to the logical index of longitude and latitude coordinates, draw the variation of cutterhead load in different regions, and at the same time, for this data subset, automatically calculate the average value, median, and standard deviation of the cutterhead load, and display the average level and dispersion degree of process parameters within a region or a period of time;

[0040] Visualization functions for geographic coordinates, three-dimensional data, and partition data are implemented for multiple process parameters such as cutterhead rotation speed n, conveying flow velocity v 流 , concentration C, and underwater pump suction vacuum p.

[0041] Preferably, in step S7, according to the color of the cutterhead load data points in the geographic information coordinate map in the visualization of process data, the soil quality in the dredging area is divided into 6 categories: rock, hard clay, soft clay, medium-dense sand, loose sand, and silt, and the construction process parameters of similar soil quality are combined into a soil quality database.

[0042] Preferably, in step S8, according to the geological exploration report of the dredging area, take the soil quality data of a certain borehole and the corresponding process parameters during excavation here, that is,

[0043] The calculation formula for cutterhead cutting force based on cutterhead power is:

[0044]

[0045] In the formula, P is the cutterhead power, n is the cutterhead rotation speed, l is the blade length, and R is the average cutterhead diameter;

[0046] The calculation formula for cutterhead cutting force based on soil quality is:

[0047]

[0048] In the formula, τ is the shear stress, t is the cutting thickness, b is the effective width of the cutting edge, θ is the shear angle, α is the cutting edge angle, ρ 0 is the friction angle between the cutter teeth and the soil;

[0049] The excavation productivity formula of a cutter suction dredger can be expressed as:

[0050] W = 60K × D × t × v;

[0051] In the formula, K is the cutterhead excavation coefficient, D is the cutterhead forward movement distance, and v is the cutterhead transverse movement speed;

[0052] Determine the cutterhead excavation coefficient K under the rated transverse movement speed v according to the above formula, and correspond it to the soil quality grade of the partition to determine the cutterhead excavation coefficient K under this soil quality condition.

[0053] Preferably, in step S9, the equipment performance prediction based on soil quality parameters includes:

[0054] A large number of process data during excavation of different soil types are obtained according to shallow excavation, mainly including rotational speed n, load P, conveying flow velocity v 流 , concentration C and the underwater pump suction vacuum p parameters, and the standard penetration number N representing the soil bearing capacity, cohesion c of the soil strength, and internal friction angle are selected as soil parameters;

[0055] A multiple linear regression model relationship is established between the cutter head load P, the standard penetration number N, the cohesion c of the soil strength, and the internal friction angle as follows:

[0056]

[0057] In the formula, β 0 , β 1 , β 2 and β 3 are respectively the model coefficients obtained through regression analysis, and ε is the model error term, representing other random influencing factors not included in the model;

[0058] Train the multiple linear regression model, that is, solve the multiple linear regression coefficients. Using the least squares method, find a set of coefficients to minimize the sum of squared errors between the predicted values and the observed values, and represent the multiple linear regression model in matrix form:

[0059]

[0060] In the formula, Y is the vector of true monitoring values of process parameters, X is the design matrix of input soil parameters, is the regression coefficient characteristic weight matrix;

[0061] Find the optimal coefficients by solving the normal equation, that is:

[0062]

[0063] Finally, divide 20% of the overall data as the test set data to evaluate the performance of the model, and specifically calculate the error between the predicted value and the actual observed value using the mean squared error;

[0064] Train the multiple linear regression model through the above process, and complete the prediction of the cutter head rotational speed, load, conveying flow velocity, concentration, and underwater pump suction vacuum process parameters through 3 soil parameters, thereby realizing the equipment performance prediction function.

[0065] Preferably, the step S10 includes:

[0066] First, for the target construction area, determine the soil distribution in the construction area according to the geological exploration report, and divide the area using the soil zoning method based on visual process parameters;

[0067] Secondly, through the calibration method of the cutter suction coefficient of process parameters, the calibration of the cutter suction coefficient for each soil region in the target area is completed based on the measured data;

[0068] According to the equipment performance prediction method based on soil parameters, first call the distribution location, area, standard penetration blow count, cohesion and internal friction angle of each type of soil in the target area, train a multiple linear regression model, and predict the cutter rotation speed n, load P, conveying flow velocity v 流 , concentration C and underwater pump suction vacuum p in each soil region of the target area, and then predict the operation performance of the equipment in the target area;

[0069] Predict the conveying flow velocity and concentration through the trained multiple linear regression model, calculate the hourly output rate of conveying in each soil region, and realize the prediction of the output in the target area;

[0070] According to the unit time fuel consumption database under typical working conditions of different types of soil formed, predict the fuel consumption in different soil regions, and summarize it as the fuel consumption in the target construction area;

[0071] Based on the prediction and analysis results of the equipment operation performance, the output of the target area, and the fuel consumption in different soil regions, propose a zoning optimization construction strategy;

[0072] Among them,

[0073] According to the excavation productivity formula of the cutter suction dredger, calculate the excavation productivity of different soil regions at a certain cross-travel speed, and realize the prediction of the output in the target area, that is:

[0074] M = W 1 ·t 1 +W 2 ·t 2 +...+W n ·t n ;

[0075] In the formula, M is the output of the target area under the effective construction time, W n is the excavation productivity of area n, and t n is the effective construction duration of the ship in area n;

[0076] For the construction optimization of the target area output, aiming at the limited construction period and certain onshore output requirements, first determine the required soil types, and excavate the soils that meet the requirements according to the soil zoning situation;

[0077] Utilize the established multiple linear regression model of process parameters and soil parameters and the calibrated cutter suction coefficients of different soils, and according to the construction target output and construction period requirements, conduct targeted excavation of the target area and design a reasonable construction plan;

[0078] For the target construction area that has been divided, determine the output to be achieved and set the construction period for each soil type area:

[0079] t 总 ≥t 1 +t 2 +...+t n ;

[0080] According to the excavation productivity of different soil type areas, determine the difficulty of excavation, and reasonably allocate the construction time and cutterhead traverse speed for each area to meet the requirements of the construction period and output. That is, the output M of the target area:

[0081] M = 60·D·(W 1 ·t 1 ·v 1 +W 2 ·t 2 ·v 2 +...+W n ·t n ·v n );

[0082] In the formula, v n is the cutterhead traverse speed of area n.

[0083] Preferably, in step S11, the construction strategy data can output various format picture files, and at the same time summarize the typical parameter tables and time schedules during the construction excavation of different areas, and quantitatively display the construction conditions of the cutter suction dredger in different soil type areas.

[0084] The beneficial effects of the present invention are as follows: The present invention can perform multi-dimensional visualization processing on various construction process data, and propose the basis parameters for soil type zoning. Based on this, soil type zoning is carried out, the cutter excavation coefficient is calibrated, and the output of the target area and the equipment performance are predicted; for the target construction area, the equipment performance, output, fuel consumption, etc. during the construction process are predicted and analyzed; for the onshore output demand and construction period requirements, a zoning optimization construction strategy is proposed for the target area; by comprehensively analyzing various process parameters during the dredging process, automatic and accurate zoning of the soil quality is realized, and the cutter excavation coefficient is optimized according to different soil qualities, improving the calculation accuracy, as well as the efficiency and safety of the dredging operation. Brief Description of the Drawings

[0085] Figure 1 is a flowchart of a dredging soil type zoning method based on process parameters of the present invention;

[0086] Figure 2 is a schematic diagram of the cutter depth of a certain dredging area in an embodiment of the present invention;

[0087] Figure 3It is a schematic diagram of the ship's movement trajectory in a certain dredging area in the embodiment of the present invention;

[0088] Figure 4 It is a chromatic dot map of the cutterhead load in a certain dredging area in the embodiment of the present invention;

[0089] Figure 5 It is a three-dimensional scatter plot of the cutterhead load in a certain dredging area in the embodiment of the present invention;

[0090] Figure 6 It is an interpolation grid map of the cutterhead load in a certain dredging area in the embodiment of the present invention;

[0091] Figure 7 It is a time history diagram of the cutterhead rotation speed in a certain dredging area on a certain day in the embodiment of the present invention;

[0092] Figure 8 It is a time history diagram of the load in a certain dredging area on a certain day in the embodiment of the present invention;

[0093] Figure 9 It is a time history diagram of the concentration in a certain dredging area on a certain day in the embodiment of the present invention;

[0094] Figure 10 It is a time history diagram of the flow velocity in a certain dredging area on a certain day in the embodiment of the present invention;

[0095] Figure 11 It is a time history diagram of the underwater pump suction vacuum in a certain dredging area on a certain day in the embodiment of the present invention;

[0096] Figure 12 It is a time history diagram of the production rate in a certain dredging area on a certain day in the embodiment of the present invention. Specific embodiments

[0097] Next, the technical solution of the present invention will be clearly and completely described in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0098] Next, the present invention will be further described in conjunction with the accompanying drawings.

[0099] As Figure 1 shown, a dredging soil quality zoning method based on process parameters includes:

[0100] Step S1, Construction process data collection and preprocessing.

[0101] In the embodiment, the construction process data includes: the cutter depth h, rotation speed n, load P, conveying flow rate v 流 , concentration C, and underwater pump suction vacuum p parameters, which are obtained by real-time monitoring using shipborne sensors and stored in CSV format.

[0102] (1) Initialize variables: Clear unnecessary variables in the workspace, and only retain two variables, num and pdata, to avoid data conflicts.

[0103] (2) Data reading: Read numerical data and multi-type raw data from the CSV file into the num and pdata variables respectively; if the two variables do not exist, perform the reading operation; otherwise, directly use the existing data.

[0104] (3) Effective data screening: Screen out non-empty AIS data through logical indexing and store it in the aisdata (numerical data) and asispadata (multi-type raw data) variables.

[0105] Step S2, Select effective construction sections.

[0106] In the embodiment, the selection of effective construction sections is achieved by sorting the cutter depth data in descending order, plotting a bar chart, removing data during non-construction periods, and selecting an appropriate cutter depth interval as the effective construction section.

[0107] Step S3, Select effective construction process parameters within the effective construction section.

[0108] In the embodiment, the selection of effective construction process parameters within the effective construction section includes:

[0109] According to the obtained effective construction depth, screen the longitude and latitude coordinates, cutter load P, rotation speed n, conveying flow rate v 流 , concentration C, and underwater pump suction vacuum p data in aisdata, and calculate the hourly production rate Q,

[0110] Q = v 流 ·A·C;

[0111] In the formula, A is the cross-sectional area of the pipe;

[0112] By setting the median absolute deviation value MAD of the process parameters, values exceeding three times the converted median absolute deviation value MAD are defined as outliers and removed from the data set. Among them, the formula for the median absolute deviation value MAD is defined as:

[0113] MAD = median(|x 1 - M|,|x 2 - M|,|x3 -M|...|x n -M|);

[0114] Wherein, X = {x 1 , x 2 , x 3 ... x n} is a data set of a certain process parameter, and M is the median of this process parameter data set;

[0115] Set the logical index regarding time through aispdata, convert the date data into date sequence values, and then analyze the process parameter conditions in different time periods by inputting different times.

[0116] Step S4, determine the parameters for soil quality zoning.

[0117] In the embodiment, by comprehensively considering the pipeline transportation output and the cutter power, the transportation output a that can be generated by the unit power output of the cutter is used as an important parameter for soil quality zoning, that is:

[0118] a = Q / P;

[0119] Wherein, Q is the hourly output rate, and P is the cutter load;

[0120] Calculate the soil quality zoning basis parameter a at each moment in the dredging area according to the ship process parameters monitored by the index, and divide the dredging area based on this, and quantify the excavation difficulty at any position in the dredging area.

[0121] Step S5, visualization processing of the ship's operation trajectory.

[0122] In the embodiment, by setting different logical indexes regarding time, draw the longitude and latitude trajectory map of a specific data subset to show the construction movement path conditions of the dredger in different time periods in the dredging area.

[0123] Step S6, visualization of multi-dimensional process data.

[0124] In the embodiment, the visualization of multi-dimensional process data includes:

[0125] Geographical coordinate visualization, using geoaxes to draw data points of the load during normal construction on the geographical coordinate map, and encoding the color according to the load value to show the load distribution;

[0126] Data three-dimensional visualization, draw a three-dimensional scatter plot regarding longitude and latitude, load, and show the change of the load in space with longitude and latitude;

[0127] Partition visualization, according to the logical index of longitude and latitude coordinates, draw the variation of the cutterhead load in different areas. At the same time, for this data subset, automatically calculate the average value, median, and standard deviation of the cutterhead load, and display the average level and dispersion degree of the process parameters within an area or a period of time;

[0128] For example, for the cutterhead load,

[0129] Geographical coordinate visualization, a conditional command can be set to only display data with a load greater than 100kW during the visualization process, filtering out the construction state where the cutterhead is lowered but no effective soil cutting is carried out;

[0130] Data three-dimensional visualization, for the selected data, perform bicubic spline interpolation calculations on longitude, latitude, and load to generate an interpolation grid distribution map of the load, so as to more smoothly display the spatial variation of the load;

[0131] The visualization functions of geographical coordinates, three-dimensional data, and partition data are realized for multiple process parameters such as cutterhead rotation speed n, conveying flow velocity v 流 , concentration C, and underwater pump suction vacuum p; The specific implementation method is similar to that of the cutterhead load and can be used for comprehensive analysis of the ship construction state.

[0132] Step S7, perform soil quality zoning based on the visualized process parameters.

[0133] In the embodiment, according to the colors of the cutterhead load data points in the geographical information coordinate map in the visualization of the process data, the soil quality in the dredging area is divided into 6 categories: rock, hard clay, soft clay, medium-dense sand, loose sand, and silt, and the construction process parameter combinations of similar soil qualities are combined into a soil quality database.

[0134] In the embodiment, through the standard penetration test, the excavated soil quality can be more simply classified according to the number of standard penetration blows.

[0135] In the embodiment, by visually analyzing the flow velocity and concentration of the conveying process parameters in an area, the construction soil quality can be judged. Generally, the principle is that the harder the soil quality, the lower the concentration, and vice versa. The results can be cross-validated with the soil quality area division situation obtained from the cutterhead load to improve the accuracy.

[0136] In the embodiment, by visually analyzing the flow velocity and concentration of the conveying process parameters in an area, the hourly production rate of the construction ship in this area can be calculated, which is the ultimate goal of process parameter optimization. When the concentration is extremely low and the production rate is close to 0, it may be that the ship is in the state of pumping clear water, which is not normal construction.

[0137] In the embodiment, based on the parameter a for the soil quality zoning in an area, comprehensively consider the effects of the cutterhead and pipeline conveying to determine the soil quality zoning situation in the dredging area.

[0138] In the embodiment, the divided area is compared and analyzed with the existing geological exploration report for cross-verification; the ship construction state at this time is analyzed, and the process parameters are optimized to select appropriate cutterhead rotation speed and underwater pump suction vacuum degree to maximize the hourly production rate.

[0139] Step S8, calibration of the cutterhead excavation coefficient based on process parameters.

[0140] In the embodiment, according to the geological exploration report of the dredging area, the soil quality data of a certain borehole and the corresponding process parameters during excavation here are taken, that is,

[0141] The calculation formula for the cutterhead cutting force according to the cutterhead power is:

[0142]

[0143] In the formula, P is the cutterhead power, n is the cutterhead rotation speed, l is the blade length, and R is the average cutterhead diameter;

[0144] The calculation formula for the cutterhead cutting force according to the soil quality is:

[0145]

[0146] In the formula, τ is the shear stress, t is the cutting thickness, b is the effective width of the cutting edge, θ is the shear angle, α is the cutting edge angle, and ρ 0 is the friction angle between the cutter tooth and the soil;

[0147] The excavation productivity formula of the cutter suction dredger can be expressed as:

[0148] W = 60K × D × t × v;

[0149] In the formula, K is the cutterhead excavation coefficient, D is the forward movement distance of the cutterhead, and v is the transverse movement speed of the cutterhead;

[0150] According to the above formula, the cutterhead excavation coefficient K under the rated transverse movement speed v is determined, and it is corresponded with the soil quality grade of the partition to determine the cutterhead excavation coefficient K under this soil condition.

[0151] In the embodiment, the soil quality data and process parameters of multiple boreholes are taken, and the cutterhead excavation coefficient K for different soil qualities is calibrated to form a database of the cutterhead excavation coefficient K corresponding to the soil quality classification; based on this as a judgment standard, the excavation difficulty of different soil qualities in different dredging areas can be quantified according to the geological exploration data.

[0152] Step S9, prediction of equipment performance based on soil quality parameters.

[0153] In the embodiment, the prediction of equipment performance based on soil quality parameters includes:

[0154] Obtain a large amount of process data during excavation of different soil types through shallow excavation, mainly including rotational speed n, load P, conveying flow velocity v 流 , concentration C, and the parameter of the underwater pump suction vacuum p. Select the standard penetration number N representing the soil bearing capacity, the cohesion c of the soil strength, and the internal friction angle as soil parameters;

[0155] Establish the multiple linear regression model relationship between the cutterhead load P, the standard penetration number N, the cohesion c of the soil strength, and the internal friction angle as follows:

[0156]

[0157] In the formula, β 0 , β 1 , β 2 , and β 3 are the model coefficients obtained through regression analysis respectively, and ε is the model error term, representing other random influencing factors not included in the model;

[0158] Train the multiple linear regression model, that is, solve the multiple linear regression coefficients. Use the least squares method to find a set of coefficients to minimize the sum of squared errors between the predicted values and the observed values. Represent the multiple linear regression model in matrix form:

[0159]

[0160] In the formula, Y is the vector of the true monitoring values of the process parameters, X is the design matrix of the input soil parameters, is the regression coefficient characteristic weight matrix;

[0161] Find the optimal coefficients by solving the normal equation, that is:

[0162]

[0163] Finally, divide 20% of the overall data as the test set data to evaluate the performance of the model. Specifically, calculate the error between the predicted value and the actual observed value using the mean squared error;

[0164] Train the multiple linear regression model through the above process. Complete the prediction of the cutterhead rotational speed, load, conveying flow velocity, concentration, and underwater pump suction vacuum process parameters through 3 soil parameters, and then realize the equipment performance prediction function.

[0165] Step S10, predict and analyze the equipment performance, output, and fuel consumption during the construction process in the target construction area according to the soil zoning based on the visualized process parameters, the calibration of the cutter excavation coefficient of the process parameters, and the equipment performance prediction based on the soil parameters, and propose a zoning optimization construction strategy.

[0166] In the embodiment, during the construction process in the target construction area, the equipment performance, output, and fuel consumption are predicted and analyzed, and the zoning optimization construction strategy is proposed, including:

[0167] First, for the target construction area, according to the geological exploration report, determine the soil distribution in the construction area, and use the soil zoning method based on visual process parameters to divide the area;

[0168] Secondly, through the calibration method of the cutter suction coefficient of the process parameters, based on the measured data, complete the calibration of the cutter suction coefficient in each soil area in the target area;

[0169] According to the equipment performance prediction method based on soil parameters, first call the distribution position, area, standard penetration number, cohesion, and internal friction angle of each soil type in the target area, train the multiple linear regression model, and predict the cutter rotation speed n, load P, conveying flow velocity v 流 , concentration C, and underwater pump suction vacuum p in each soil area in the target area, and then perform the prediction function of the equipment operation performance in the target area;

[0170] Predict the conveying flow velocity and concentration through the trained multiple linear regression model, calculate the hourly output rate of conveying in each soil area, and realize the prediction of the output in the target area;

[0171] According to the unit time fuel consumption database under the typical working conditions of different types of soil formed, predict the fuel consumption in different soil areas, and summarize it as the fuel consumption in the target construction area;

[0172] According to the prediction and analysis results of the equipment operation performance, the output in the target area, and the fuel consumption in different soil areas, propose the zoning optimization construction strategy;

[0173] Among them,

[0174] According to the excavation productivity formula of the cutter suction dredger, calculate the excavation productivity in different soil areas at a certain cross-travel speed, and realize the prediction of the output in the target area, that is:

[0175] M = W 1 ·t 1 +W 2 ·t 2 +...+W n ·t n ;

[0176] In the formula, M is the output of the target area under the effective construction time, W n is the excavation productivity of area n, and t n is the effective construction duration of the ship in area n;

[0177] Optimization of construction for target area output. For a limited construction period and certain onshore output requirements, first determine the required soil type, and excavate the compliant soil according to the soil zoning situation.

[0178] Using the established multiple linear regression model of process parameters and soil parameters, as well as the calibrated cutter suction coefficients for different soils, conduct targeted excavation of the target area according to the construction target output and construction period requirements, and design a reasonable construction plan.

[0179] For the already divided target construction area, determine the output to be achieved, and set the construction period for each soil area:

[0180] t 总 ≥t 1 +t 2 +...+t n ;

[0181] According to the excavation productivity of different soil areas, determine the difficulty of excavation, and reasonably allocate the construction time and cutter traverse speed for each area to meet the construction period and output requirements, that is, the target area output M:

[0182] M = 60·D·(W 1 ·t 1 ·v 1 +W 2 ·t 2 ·v 2 +...+W n ·t n ·v n );

[0183] In the formula, v n is the cutter traverse speed of area n.

[0184] Step S11, store and export the construction strategy data.

[0185] In the embodiment, for the visualized graphic information, storage in multiple resolutions of 100 - 600 dpi and multiple image formats such as JPEG, PNG, BMP, etc. can be selected, which is convenient for report writing, archiving, result sharing and subsequent analysis; at the same time, summarize the typical parameter tables and schedule tables during construction excavation in different areas, and quantitatively display the construction conditions of the cutter suction dredger in different soil areas. Among them, the typical soil parameter table, the typical process parameter table for sectional dredging construction, and the sectional dredging construction schedule table are shown in Tables 1, 2, and 3 respectively.

[0186] Area Division Soil Type Standard Penetration Test Blows Cohesion Internal Friction Angle Area 1 Area 2 Area 3

[0187] Table 1

[0188]

[0189] Table 2

[0190] Area 1 Area 2 Area 3 Construction Area Area Construction Time Period Range Total Construction Duration

[0191] Table 3

[0192] The following will further elaborate on the soil zoning dredging technology and its application of the present invention in detail through a specific actual dredging project in conjunction with the accompanying drawings by way of specific embodiments:

[0193] Step S1, construction process data collection and preprocessing.

[0194] In the embodiment, the construction process data includes: the cutterhead depth h, rotational speed n, load P, conveying flow velocity v, 流 concentration C, and underwater pump suction vacuum p parameters that are monitored and obtained in real time by on-board sensors, and the data is stored in CSV format.

[0195] (1) Initialize variables: Clear unnecessary variables in the workspace, and only retain the two variables num and pdata to avoid data conflicts.

[0196] (2) Data reading: Read numerical data and multi-type raw data from the CSV file into the num and pdata variables respectively; if the two variables do not exist, perform the reading operation; otherwise, directly use the existing data.

[0197] (3) Valid data screening: Screen out non-empty AIS data through logical indexing into the aisdata (numerical data) and asispadata (multi-type raw data) variables.

[0198] Step S2, select valid construction sections.

[0199] As Figure 2 shown, sort the cutterhead depth in aisdata in descending order and draw a bar chart to visually display the distribution of the cutterhead depth.

[0200] First, remove the intervals where the cutterhead depth is negative, that is, when the cutterhead is lifted during non-construction periods. According to the trend of the bar chart, take the point where the cutterhead depth drops rapidly as the node, and refer to the existing geological exploration report to select the cutterhead depth interval of 8 - 15m as the valid construction section, and the corresponding relevant data can be used for subsequent analysis.

[0201] Step S3, select valid construction process parameters within the valid construction section.

[0202] According to the valid construction depth selected in Step 2, screen the data such as longitude and latitude coordinates, cutterhead load, rotational speed, conveying flow velocity, concentration, and underwater pump suction vacuum in aisdata, and calculate the hourly production rate Q,

[0203] Q = v流 ·A·C;

[0204] Wherein, A is the cross-sectional area of the pipe;

[0205] By setting the median absolute deviation value MAD of the process parameters, the value exceeding three times the converted median absolute deviation value MAD is defined as an outlier and removed from the data set. Among them, the formula for the median absolute deviation value MAD is defined as:

[0206] MAD = median(|x 1 - M|, |x 2 - M|, |x 3 - M|... |x n - M|);

[0207] Wherein, X = {x 1 , x 2 , x 3 ... x n} is a data set of a certain process parameter, and M is the median of this process parameter data set;

[0208] Set a logical index regarding time through aispdata, convert the date data into date sequence values, and then analyze the process parameter conditions in different time periods by inputting different times.

[0209] Step S4, determine the parameters for soil quality zoning.

[0210] In the embodiment, by comprehensively considering the pipeline transportation output and the cutter power, the transportation output a generated by the unit power output of the cutter is used as an important parameter for soil quality zoning, that is:

[0211] a = Q / P;

[0212] Wherein, Q is the hourly production rate, and P is the cutter load;

[0213] Calculate the soil quality zoning basis parameter a at each moment in the dredging area according to the ship process parameters monitored by the index, and divide the dredging area based on this to quantify the excavation difficulty at any position in the dredging area.

[0214] Step S5, visualization processing of the ship's operation trajectory.

[0215] In the embodiment, by setting different logical indexes regarding time, a longitude and latitude trajectory map of a specific data subset is drawn to show the construction movement path of the dredger in different time periods in the dredging area. As Figure 3 shown, take excavation soil samples at two position points 1 and 2 in the figure, and conduct in-situ standard penetration tests and indoor direct shear tests to quantify the strength of the excavated soil. Among them, the standard penetration blow counts and strength parameters of the sampled soil are shown in Table 4 below.

[0216] Location Point Serial Number Category Standard Penetration Test Blows <![CDATA[C(kg / cm 2 )]]> Φ(°) 1 Yellow and White Mixed Clay 21 0.29 25.58 2 White Clay 28 0.49 23.39 3 Coarse Sand 8 0 35.21

[0217] Table 4

[0218] Step S6, visualization of multi-dimensional process data.

[0219] During dredging, the cutterhead load is an important process data reflecting the hardness of the soil. By performing multi-dimensional visualization on it, the soil conditions in the dredging area can be analyzed.

[0220] (1) Geographic coordinate visualization: Use geoaxes to plot the data points of the load during normal construction on the geographic coordinate map. The color is encoded according to the load value. Remove the data with a load less than 100 kW, and filter out the construction states where the cutterhead is lowered but no effective soil cutting is performed, and display the load distribution, as Figure 4 shown.

[0221] (2) 3D data visualization: Plot a 3D scatter plot of longitude, latitude, and load to show the spatial variation of the load with longitude and latitude. Further, biharmonic spline interpolation calculations can be performed on the selected data for longitude, latitude, and load to generate an interpolation grid distribution map of the load to more smoothly display the spatial variation of the load, as Figure 5 、 6 shown.

[0222] Step S7, soil zoning based on visualized process parameters.

[0223] According to the visualization content of the cutterhead load in Step S5, the areas where position point 1 and position point 2 are located can be divided into two zones according to the color of the cutterhead load data points in the geographic information coordinate map. It can be clearly seen that the cutterhead load during excavation in the area of position point 2 is higher than that of position point 1. The excavation hardness and difficulty of white clay are greater than those of yellow-white intercalated clay. It can also be seen from the standard penetration test blow counts and direct shear strength parameters of the two types of soil that the strength of white clay is higher.

[0224] Further, according to the different soil types, it is possible to prevent the cutterhead load from being too large, causing excessive wear of the cutterhead or even breakage of the cutter teeth.

[0225] Step S8, calibration of the cutterhead excavation coefficient based on process parameters.

[0226] In this area's dredging project, the dimensions of the cutterhead of the cutter suction dredger are 1.8 m in height and 3.2 m in diameter.

[0227] In the embodiment, according to the geological exploration report of the dredging area, the soil data of a certain borehole and the corresponding process parameters during excavation here are taken, that is,

[0228] The calculation formula for the cutterhead cutting force based on the cutterhead power is as follows:

[0229]

[0230] In the formula, P is the cutterhead power, n is the cutterhead rotation speed, l is the blade length, and R is the average diameter of the cutterhead;

[0231] The calculation formula for the cutterhead cutting force based on the soil quality is as follows:

[0232]

[0233] In the formula, τ is the shear stress, t is the cutting thickness, b is the effective width of the cutting edge, θ is the shear angle, α is the cutting edge angle, and ρ 0 is the friction angle between the cutter tooth and the soil;

[0234] Based on the cutting force calculation formulas for the cutterhead power and soil quality, the cutting thickness calculation formula is derived:

[0235]

[0236] Among them, the blade length l can be taken as 0.6 - 0.9 times the cutterhead diameter, taking 2.25 m, the effective width b of the cutting edge can be taken as 0.4 times the cutterhead height, which is 0.72 m; the cutting edge angle α is taken as 45°, and ρ 0 is the friction angle between the cutter tooth and the soil, taking 20°;

[0237] The excavation productivity formula of the cutter suction dredger can be expressed as:

[0238] W = 60K × D × t × v;

[0239] In the formula, K is the cutterhead excavation coefficient, D is the forward movement distance of the cutterhead, v is the transverse movement speed of the cutterhead, the forward movement distance D generally can be taken as 0.5 - 1.0 times the cutterhead length, taking 1.35 m here;

[0240] According to the hourly production rate Q of the ship obtained by monitoring and calculation, the excavation production efficiency calculated by the empirical formula is calibrated, and the cutterhead excavation coefficient applicable to this kind of soil quality is re - determined. The excavation coefficients of the two soil qualities at two positions in the dredging area described in step S5 are calibrated respectively. The excavation coefficients of position point 1 (yellow - white mixed clay) and position point 2 (white clay) are 0.91 and 0.82 respectively.

[0241] Step S9, equipment performance prediction based on soil parameters.

[0242] Based on a large amount of process data obtained from shallow - layer excavation for different soil qualities, mainly including the rotation speed n, load P, conveying flow velocity v 流 , concentration C, and the underwater pump suction vacuum p parameters, select the standard penetration number N representing the soil bearing capacity, the cohesion c of the soil strength, and the internal friction angle As soil parameters, a multiple linear regression model is established between the cutterhead load P, the standard penetration number N, the cohesion c of the soil strength, and the internal friction angle between them;

[0243] Based on the soil parameters, the prediction of the cutterhead rotation speed n, the load P, the conveying flow velocity v 流 , the concentration C, and the underwater pump suction vacuum p process parameters is completed. At the same time, the fuel consumption per unit time under typical process parameters of different soil types is estimated.

[0244] Step S10, based on the soil zoning according to the visualized process parameters, the calibration of the cutter excavation coefficient of the process parameters, and the prediction of the equipment performance based on the soil parameters, the equipment performance, output, and fuel consumption during the construction process in the target construction area are predicted and analyzed, and a zoning optimization construction strategy is proposed.

[0245] Step S11, store and export the construction strategy data.

[0246] According to the conveying output rate, the location points 1 and 2 can be zoned. The average conveying hourly output rate in the area where the location point 1 (yellow-white mixed clay) is located can reach 5×105 m3 / h, and the area where the location point 2 (white clay) is located is only 4.3×105 m3 / h. When the conveying flow velocities in the areas of the two location points are close, the conveying concentration in the area of the denser and harder soil at the location point 2 is about 8% lower, and the construction difficulty is significantly higher.

[0247] Based on the soil zoning basis parameters determined in step S4, the influence of the soil on the cutterhead power and the conveying output can be comprehensively considered. According to the contribution of the unit cutterhead power to the conveying output rate, the construction excavation efficiency is measured, and the soil in the dredging area is divided, and the division effect is more distinct and accurate.

[0248] The present invention can also perform time history analysis on the construction process parameters for some specific time periods, analyze the influence of different soils on the specific process parameters, and select the changes in the cutterhead rotation speed n, the load P, the conveying flow velocity v 流 , the concentration C, the underwater pump suction vacuum p, and the hourly output rate Q of the soil at the location 1 within one day are as Figures 7 - 12 shown. Based on this, a process database of the soil at the location 1, that is, the yellow-white mixed clay, can be established as a reference for selecting process parameters during subsequent excavation or excavation of similar soils in other projects.

[0249] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; 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 dredged soil zoning method based on process parameters, characterized in that: include: Step S1, construction process data collection and preprocessing; Step S2, selecting an effective construction section; Step S3, selecting effective construction process parameters in the effective construction section; Step S4, determining the parameters based on which the soil is divided; Step S5, visualization of the ship's running track; Step S6, visualization of multi-dimensional process data; Step S7, performing soil zoning based on the visualized process parameters; Step S8, calibrating the reamer excavation coefficient based on the process parameters; Step S9, predicting equipment performance based on soil parameters; Step S10, predicting and analyzing the equipment performance, output, and fuel consumption during the construction process in the target construction area according to soil zoning based on visualized process parameters, reamer excavation coefficient calibration based on process parameters, and equipment performance prediction based on soil parameters, and proposing a zoning optimization construction strategy; Step S11, storing and exporting the construction strategy data.

2. The dredged soil zoning method based on process parameters according to claim 1 is characterized in that: In step S1, the construction process data includes: the reamer depth h, speed n, load P, conveying flow rate v, which are monitored and obtained in real time by the shipboard sensor. 流 , concentration C and underwater pump suction vacuum p parameters, and store the data in CSV format; In step S2, the effective construction section is selected by sorting the reamer depth data in descending order and drawing a bar chart, removing the non-construction period data, and selecting a suitable reamer depth interval as the effective construction section.

3. The dredged soil zoning method based on process parameters according to claim 2 is characterized in that: In step S3, selecting effective construction process parameters in the effective construction section includes: According to the effective construction depth, filter the longitude and latitude coordinates, reamer load P, speed n, v in aisdata 流 , concentration C and underwater pump suction vacuum p data, calculate the hourly production rate Q, Q=v 流 ·A·C; Where A is the cross-sectional area of ​​the tube; By setting the median absolute deviation value MAD of the process parameters, the values ​​that exceed three times the converted median absolute deviation value MAD are defined as outliers and removed from the data set. The median absolute deviation value MAD formula is defined as: MAD=mediam(|x1-M|,|x2-M|,|x3-M|...|x n -M|); Where, X={x1,x2,x3...x n } is a data set of a certain process parameter, and M is the median of this process parameter data set; Set the logical index about time through aispdata, and convert the date data into date sequence value, and then analyze the process parameters in different periods by inputting different times; In step S4, by comprehensively considering the pipeline transportation output and the reamer power, the transportation output a that can be generated by the reamer unit power output is used as an important parameter for soil zoning, that is: a=Q / P; Where, Q is the hourly production rate, P is the reamer load; The soil partitioning parameter a at each moment in the dredging area is calculated based on the ship process parameters monitored by the index, and the dredging area is divided accordingly, and the excavation difficulty at any position in the dredging area is quantified.

4. The dredged soil zoning method based on process parameters according to claim 3 is characterized in that: In the step S5, by setting different logical indexes related to time, a latitude and longitude trajectory diagram of a specific data subset is drawn to show the construction movement path of the dredging vessel in the dredging area at different time periods.

5. The dredged soil zoning method based on process parameters according to claim 4 is characterized in that: In step S6, the visualization of multi-dimensional process data includes: Geographic coordinate visualization, using geoaxes to draw data points of load during normal construction on a geographic coordinate map, with colors coded according to load values ​​to show load distribution; Three-dimensional data visualization, drawing a three-dimensional scatter plot of longitude, latitude and load, showing how the load changes in space with longitude and latitude; Zoning visualization: based on the logical index of longitude and latitude coordinates, the changes of reamer load in different areas are plotted. At the same time, for this data subset, the mean, median and standard deviation of the reamer load are automatically calculated to show the average level and dispersion of process parameters in a region or a period of time. Visualization of geographic coordinates, three-dimensional data, and partition data, in terms of reamer speed n, conveying flow rate v 流 , concentration C and underwater pump suction vacuum p multiple process parameters are achieved.

6. The method for zoning dredged soil based on process parameters according to claim 5 is characterized in that: In step S7, the soil in the dredging area is divided into six categories: rock, hard clay, soft clay, medium dense sand, loose sand and silt according to the color of the reamer load data point in the geographic information coordinate diagram in the visualization of the process data, and the construction process parameters of similar soils are combined into a soil database.

7. The method for zoning dredged soil based on process parameters according to claim 6 is characterized in that: In step S8, according to the geological survey report of the dredging area, the soil data of a certain borehole and the corresponding process parameters for excavation there are obtained, that is, The calculation formula for the reamer cutting force based on the reamer power is: In the formula, P is the reamer power, n is the reamer speed, l is the blade length, and R is the average reamer diameter; The calculation formula for the reamer cutting force according to the soil quality is: Where τ is the shear stress, t is the cutting thickness, b is the effective width of the cutting edge, θ is the shear angle, α is the blade angle, and ρ0 is the friction angle between the blade and the soil; The dredging productivity formula of the cutter suction dredger can be expressed as: W = 60K × D × t × v; Where K is the reamer excavation coefficient, D is the reamer forward movement distance, and v is the reamer transverse movement speed; The reamer excavation coefficient K at the rated traverse speed v is determined according to the above formula, and it is matched with the soil grade of the partition to determine the reamer excavation coefficient K under the soil condition.

8. The method for zoning dredged soil based on process parameters according to claim 7 is characterized in that: In step S9, the equipment performance prediction based on soil parameters includes: Based on shallow excavation, a large number of process data for excavation of different soil types are obtained, mainly including reamer speed n, load P, conveying flow rate v 流 , concentration C and underwater pump suction vacuum p parameters, select the standard penetration number N that represents the bearing capacity of the soil and the cohesion c and internal friction angle of the soil strength As soil quality parameters; Establish the relationship between reamer load P, standard penetration number N, cohesion c and internal friction angle of soil strength The relationship between the multiple linear regression model is as follows: In the formula, β0, β1, β2 and β3 are the model coefficients obtained through regression analysis, and ε is the model error term, which represents other random influencing factors not included in the model; Training the multiple linear regression model means solving the multiple linear regression coefficients and using the least squares method to find a set of coefficients that minimizes the sum of squared errors between the predicted values ​​and the observed values. The multiple linear regression model is expressed in matrix form: Where Y is the real monitoring value vector of process parameters, X is the input soil parameter design matrix, is the regression coefficient feature weight matrix; The optimal coefficients are found by solving the normal equations, namely: Finally, 20% of the overall data is divided as the test set data to evaluate the performance of the model. Specifically, the mean square error is used to calculate the error between the predicted value and the actual observed value. Through the above process, the multivariate linear regression model is trained, and the auger speed, load, conveying flow rate, concentration and underwater pump suction vacuum process parameters are predicted through three soil parameters, thereby realizing the equipment performance prediction function.

9. The method for zoning dredged soil based on process parameters according to claim 8, characterized in that: The step S10 comprises: Firstly, for the target construction area, the soil distribution of the construction area is determined according to the geological survey report, and the area is divided using the soil zoning method based on visualized process parameters; Secondly, the reamer excavation coefficient calibration method of process parameters is used to calibrate the reamer excavation coefficient of each soil type area in the target area based on measured data; According to the equipment performance prediction method of soil parameters, the distribution position, area, standard penetration number, cohesion and internal friction angle of each soil in the target area are first called, and the multivariate linear regression model is trained to predict the reamer speed n, load P, and conveying flow rate v of each soil area in the target area. 流 , concentration C and underwater pump suction vacuum p, and then predict the equipment operating performance in the target area; The trained multivariate linear regression model is used to predict the transport velocity and concentration, and the hourly transport yield rate of each soil type area is calculated to predict the yield of the target area. Based on the database of unit time fuel consumption under typical working conditions of different types of soil, the fuel consumption of different soil areas is predicted and summarized as the fuel consumption of the target construction area; Based on the forecast and analysis results of equipment operating performance, target area production, and fuel consumption in different soil areas, a zoning optimization construction strategy is proposed; in, According to the dredging productivity formula of the cutter suction dredger, the dredging productivity of different soil areas at a certain traverse speed is calculated to predict the output of the target area, that is: M=W1·t1+W2·t2+...+W n ·t n ; Where M is the output of the target area under the effective construction time, W n is the mining productivity of area n, t n is the effective construction time of the ship in area n; Optimize the construction of target area production. To meet the limited construction period and certain onshore production requirements, first determine the required soil type and excavate the soil that meets the requirements according to the soil zoning conditions; Using the established multivariate linear regression model of process parameters and soil parameters and the calibrated reamer excavation coefficients of different soils, targeted excavation is carried out in the target area according to the construction target output and construction period requirements, and a reasonable construction plan is designed; For the target construction area that has been divided, determine the output to be achieved and set the construction period for each soil area: t 总 ≥t1+t2+...+t n ; According to the excavation productivity of different soil areas, determine the difficulty of excavation, and reasonably allocate the construction time and traverse speed of each area to meet the construction period and output requirements, that is, the target area output M: M=60·D·(W1·t1·v1+W2·t2·v2+...+W n ·t n ·v n ); In the formula, v n is the traverse speed of the reamer in area n.

10. The method for zoning dredged soil based on process parameters according to claim 9, characterized in that: In step S11, the construction strategy data can be exported as image files in various formats, and typical parameter tables and timetables for construction and excavation in different areas can be summarized to quantitatively display the construction status of the cutter suction dredger in different soil areas.

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