WO2022257661A1

WO2022257661A1 - Data processing method of dredging and transport system on long-distance pipeline transport site

Info

Publication number: WO2022257661A1
Application number: PCT/CN2022/091219
Authority: WO
Inventors: 王费新; 张晴波; 周忠玮; 程书凤; 江帅; 树伟; 刘功勋; 冒小丹; 张忱; 袁超哲; 尹立明
Original assignee: 中交疏浚技术装备国家工程研究中心有限公司
Priority date: 2021-06-10
Filing date: 2022-05-06
Publication date: 2022-12-15
Also published as: CN113483805B; CN113483805A

Abstract

A data processing method of a dredging and transport system on a long-distance pipeline transport site. The data comprises flow velocity data, pressure data, and concentration data, and the flow velocity data and pressure data are obtained by existing methods. The method comprises: first, selecting the starting point of the entire pipeline as a monitoring point, obtaining a flow velocity value of a sediment fluid at the monitoring point as a common velocity value of the entire pipeline comprising a downstream target pipe section to be tested at the same time point, obtaining a concentration value at the monitoring point, deducing the concentration distribution in the entire pipeline at any time point according to the concentration value of the monitoring point changing over time, matching the pressure difference of the target pipe section, and constructing data sets; next, cleaning the data sets; and then, using the data sets as two-dimensional spatial data, and sorting, grouping, and averaging same according to their size so as to weaken the centrality of the data. The processed data set is representative and authentic, thereby providing reliable engineering data for subsequent research.

Description

A data processing method for long-distance pipeline transportation on-site dredging transportation system

technical field

The invention belongs to the technical field of dredging engineering.

Background technique

In dredging engineering, the main form of sediment transportation is hydraulic transportation, especially long-distance pipeline transportation of solid-liquid mixed multiphase flow, and multi-pump systems are often used. Pipeline transportation has a great influence on the efficiency of dredging construction and consumes a lot of energy. For example, the energy consumption of pipeline transportation accounts for more than 80% of the total energy consumption in the construction of cutter suction dredgers. If the slurry flow rate is too fast, friction will be increased and power will be wasted. If the flow rate is too slow, sediment will be deposited, leading to a series of problems such as pipe blockage and pipe burst. Realizing efficient, stable and safe hydraulic transportation is the goal that has been pursued all along.

Considering the fluctuation of the actual operation data of the dredging pipeline over time and along the distribution, the conventional on-site test data usually adopts the processing scheme of averaging by period (such as 1 hour, 1 day, etc.), so that the processed data can be more accurate in terms of flow velocity and concentration distribution. It tends to be concentrated and cannot effectively reflect the actual conveying characteristics. The data obtained on site is different from the stable and controllable data in the laboratory. It is highly volatile and complex, and the real-time changing flow rate, concentration and pressure difference data are interrelated.

These data are the basis for further research on the pipeline transportation mechanism, perfecting the transportation theory, and establishing the theoretical model of transportation, and the field test data can only be effectively used after being processed by scientific methods. For example, over the years, experts in related fields at home and abroad have He scholars have carried out a lot of tests and researches on the friction loss and critical velocity of pipeline hydraulic transportation. Most of the early studies were from the macro level, based on certain theoretical assumptions and indoor small-diameter pipeline transportation tests or on-site small and medium-sized pipes. In the dredging industry and related fields, the research results of Durand, Newitt, Wasp, Fei Xiangjun, Wang Shaozhou, etc. are represented. These calculation models are used in the performance analysis and calculation of dredging and conveying systems under modern large-diameter pipelines, high-concentration transportation, coarse particles or complex soil conditions, and there may be large deviations. Reasonably processed field data can better modify and optimize these empirical models, and even propose a more reasonable calculation model.

To sum up, it is necessary to propose a scientific and feasible processing method for on-site data transmission. Considering that the concentration sensor is expensive, it is difficult to arrange and install it on the water surface pipeline. Therefore, the cost and difficulty of collecting effective data through the deployment of a large number of physical concentration sensors are very high, and it is not easy to promote and apply in engineering.

Contents of the invention

In view of the above problems, the object of the present invention is to disclose a set of data processing methods, through which the basic pipeline data can be obtained, which can effectively replace the dense physical concentration sensors to obtain the basic pipeline data, which is used for engineering applications and dredging pipeline characteristics research. The present invention considers adopting the processing method of classifying and averaging according to the flow velocity and concentration. Firstly, the measured flow velocity, concentration, and pressure data are pre-processed, and then combined with the layout of pipelines and measuring points, the real-time concentration of the test pipe section is deduced under reasonable assumptions. data. Subsequently, the test working condition data combinations (flow rate, concentration and pressure difference) are classified and sorted according to flow rate and concentration, and divided according to a certain number (generally not less than 500 data combinations). Finally, take the average value of each divided data group to form the final test condition data group. Through the above processing methods, the test results under a wider flow rate and concentration distribution in actual construction can be obtained, and the deviation of test results caused by a small number of data and various abnormal conditions can be reduced by averaging. Further application is to apply the data and method, which can be used to study the mechanism of pipeline transportation, improve the transportation theory, and establish the theoretical model of transportation.

In order to achieve the above object, the present invention needs to protect the following technical solutions:

Summary technical solution:

A data processing method for long-distance pipeline transportation on-site dredging and transportation system, the data includes flow velocity data, pressure data, and concentration data; the flow velocity data and pressure data are obtained through existing methods, and it is characterized in that, first, the entire transportation pipeline is selected The initial point of the monitoring point is used as the monitoring point, and the flow velocity value of the sediment fluid at the monitoring point is regarded as the common flow velocity value of the entire downstream pipeline (including the target pipe section) at the same time, and the concentration value at the monitoring point is obtained. The time-varying concentration value deduces the concentration distribution on the entire pipeline at any time, matches the pressure difference of the target pipe section, and constructs a data group; then, cleans the data group; then, uses the data group as two-dimensional spatial data, and separates them according to their size Sorting, grouping, averaging, weakening the concentration of data. The data group processed by the method of the invention is representative and authentic, and provides reliable engineering data for follow-up research.

Compared with the prior art, the innovation and advantage of the present invention are: a long-distance pipeline transportation field data processing method and its application are established, and the data can be classified and averaged according to the flow rate and concentration to form the final test condition data It can obtain the test results under a wide range of flow velocity and concentration distribution in actual construction, and reduce the test deviation of a small number of data and the deviation of test results caused by various abnormal situations on average; it can be used to study the pipeline transportation mechanism, improve the transportation theory, and establish the transportation Work such as theoretical models, such as studying the matching between different friction empirical formulas and the transportation project, and revising and optimizing them, and even proposing new calculation formulas, which can provide more accurate calculation results in related calculations, and have practical significance. Engineering significance.

Description of drawings

Fig. 1 shows the flow chart of long-distance pipeline transportation field data processing and empirical formula correction method;

Figure 2 shows the concentration distribution diagram on the pipeline at a certain moment;

Figure 3 shows a two-dimensional schematic diagram of data classification;

Figure 4 shows the comparison between the friction loss calculated by the common formula and the measured value of a steel pipe section;

Figure 5 shows the comparison between the friction loss calculated by the new formula and the measured value of a certain steel pipe section.

Detailed ways

In order to further understand the invention content, characteristics and effects of the present invention, a certain construction condition of a certain cutter suction dredger is hereby taken as an embodiment, and the present invention is described in detail as follows in conjunction with the accompanying drawings:

This method is not limited to the pipeline transportation of the cutter suction dredger in this embodiment, and is also applicable to other multi-pump-pipeline solid-liquid two-phase flow transportation processes. In the following description, many specific details are set forth in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described here, so the protection scope of the present invention is not limited by the specific embodiments disclosed below. limits;

The present invention proposes a long-distance pipeline transportation on-site data processing method and its application. The flow chart is shown in Figure 1. The engineering condition used in this embodiment is pipeline transportation by a cutter suction dredger, which specifically includes the following steps:

S1: Long-distance pipeline transportation construction data collection, provided to S2:

S1-1: Collect static data of pipeline transportation, that is, data that does not change with time within a certain period of time. These data often do not change in the short term during engineering construction, such as the length, diameter, type, roughness, and sediment transport of pipelines Particle size, bulk density, and excavation depth, elevation, etc., these data will be used in the following data processing and application examples;

S1-2: Collect dynamic data of pipeline transportation, that is, data that changes in real time over time, mainly including concentration at the starting point of transportation, flow velocity, and pipeline pressure at the test target section. These three physical quantities are also the most important physical quantities for long-distance pipeline transportation in dredging projects , is a key parameter for analysis and research. The following is mainly for the processing and analysis of dynamic data. When studying pipeline transportation problems, the pressure on a single pipeline section is of little significance. It is often necessary to analyze the degree of pressure loss after the fluid flows through a section of pipeline, that is, the pressure difference between the head and tail of this section of pipeline. Therefore, in actual measurement, this Absolute pressure values at the start and end positions of the segment pipeline (test target segment).

S2: Pre-processing of long-distance pipeline transmission data, mainly for the above-mentioned dynamic data processing:

S2-1: Pre-processing of flow rate data, obtaining the accurate value of flow rate by color tracing method, and calibrating the flow rate sensor; in the construction operation, there are individual moments or individual extremely short-term data in the signal collection on the cutter suction dredger In the case of loss, the data change is often not sudden, so it can be corrected by the data measured before and after;

S2-2: Preprocessing of concentration data. In the same way, firstly, the missing concentration data needs to be corrected, and secondly, the wet square concentration (also known as the ship display concentration) measured by the sensor is converted into the particle volume concentration;

The wet square concentration: this is a professional term for pipeline transportation in dredging engineering; when the dredger is digging the soil, the undisturbed soil is loose, and the concentration directly measured at this time is the wet square concentration, and its value is larger than the particle volume concentration .

S2-3: Preprocessing of pressure data. Calculate the pressure difference between the start position and the end position of the measurement target segment, and record missing or obviously wrong data as NaN;

S2-4: Clock matching. Due to various reasons, the time of the installed sensor sometimes shows that the recorded time is inconsistent, and clock matching is required.

S3: Deduction calculation of concentration distribution:

It is impossible to install a concentration meter on the entire pipeline for on-site measurement. The cost and installation difficulty are very high, and it is not realistic. It is basically the same in all sections of the entire pipeline, but affected by many factors such as dredger operation mode, underwater topography, and soil conditions, the concentration change is more complicated, and its distribution in the entire pipeline is also very uneven. The concentration obtained at a single measurement point on the ship is deduced to the spatial distribution of the entire pipeline to obtain the concentration distribution of the entire pipeline.

In order to be safe in engineering, the slurry flow rate is often controlled in the range higher than the critical flow rate to reduce sediment deposition and prevent pipe plugging. Therefore, the present invention assumes that the concentration value of the slurry does not change with the change of the spatial position during the conveying process. Taking the slurry element at the suction port of the pipeline as an example at a certain moment, the particle volume concentration of the slurry element is C _vd , since the slurry flow rate V changes with time, it can be considered as a function of time t, so The moving distance of the slurry micro-element with concentration value C _vd is different in different time. After Δt time, the slurry is located at a distance from the starting point x, which can be expressed as:

For this slurry element, the distance from the starting point at any time can be calculated by integration. Similarly, for any slurry element, its moving distance can be calculated by this integration method. In this way, the distance on the entire pipeline can be obtained. The concentration distribution of the slurry at any time is used to provide to step S4. The invention discretizes the time in seconds, and tracks every moment, every concentration value and its displacement distance on the pipeline. Therefore, the interval distance between the traced concentration values is different, and the concentration at the interval length between two concentrations is obtained by interpolation. The present invention takes the concentration and flow velocity measurement position as the initial zero point of the pipeline, and calculates the concentration distribution of the whole pipeline at different times sequentially based on the concentration and flow velocity measurement values measured at this point. In this embodiment, the data within a certain 25000s is selected for analysis. Taking the 14000s time of this period as an example, the calculated concentration distribution results are shown in FIG. 3 .

S4: Calculate the average concentration of the target pipe segment

In S3, the concentration distribution on the entire pipeline at different times has been calculated. The target pipeline section is a small section of the entire pipeline. Since the data is discretized, the average concentration of the target pipeline section needs to be calculated by numerical integration. In this way, the corresponding average concentration on the target pipe section can be calculated at all times

It is worth noting that, at any time in the target pipe section studied in this embodiment, the average concentration on the pipe section

Together with the three physical quantities of mud flow rate V and pressure difference P between the head and the tail of the pipeline, a data set is formed

The data in the data group are interrelated and correspond to each other (the data processing in the following is all performed on the data group).

S5: Obtain (flow rate-average concentration-pressure difference) one-to-one corresponding data set α, which is further processed by S6:

The average concentration at all times calculated on the target pipe section in S4

The data groups composed of the flow velocity V and the pressure difference P corresponding to each moment are collected together to form a data group set α, assuming that α has n data groups, it can be expressed as

S6: Data screening and elimination:

The data set obtained in S5 is not a completely ideal data set. There are many "problem data" that will affect the follow-up research. The obtained data group is screened and screened. If there is a problem with any data in the data group, or there is a contradiction between the data in the data group, the entire data group needs to be removed from the data group set. The removed data group set is β, Suppose there are m data groups, then

There may be various problems in the data measured on site. Screening and eliminating these problem data can improve the accuracy and validity of the data. The following is an example of "problem data":

For the data set with low concentration and large pressure difference, the cutter head of the dredging ship needs to make a U-turn when it swings to the edge. At this time, the soil is no longer cut. The concentration is very small, and even many data are close to zero. The pressure difference value should also be very small, but sometimes the measured pressure difference is still not small. This is due to the sensor signal problem, and the data collected in the project will not decrease instantaneously;

For the data group with high concentration and normal pressure difference, the concentration meter has a range limit. When the actual concentration exceeds the maximum range of the concentration meter, the concentration meter only records the maximum value. At this time, the pressure difference measurement is accurate, which is also a kind of "Question Data";

In the previous calculation process, some pressure difference data were set as NaN, which is problem data, and its corresponding data group should also be eliminated in this link.

S7: Data post-processing:

Conventional on-site test data mostly adopts a time-period average (such as 1 hour, 1 day, etc.) processing scheme, which makes the processed data tend to be concentrated in flow rate and concentration distribution, and cannot effectively reflect the actual transportation characteristics.

The present invention's coping strategy: sort and classify the flow rate and concentration, and take the average of the data in the same category, weaken the data error, weaken the data concentration, and make the new data group after processing more representative and authentic. Research provides solid engineering data.

The specific processing method is as follows:

S7-1: Sort the m data groups in the data group set β according to the speed in the data group, and keep the corresponding relationship between the other two physical quantities and speeds. After sorting, each i data is a large category , classified in turn, provided to step S7-2;

S7-2: In each category, sort the data groups according to the concentration in the data groups, and take each j data group as a sub-category, classify them sequentially, and provide them to step S7-3;

S7-3: In this way, the huge data set is divided into two-dimensional space with the speed as the abscissa and the concentration as the ordinate. For easy understanding, draw Figure 3 for illustration:

Assuming that there are 98,000 data groups in the set β, sorted by speed, from small to large, every 7,000 data groups are a large category, there are a total of 14 major categories, which are divided into 14 columns in the coordinate space of Figure 3 , because in actual construction, the slurry flow velocity is more concentrated near the flow velocity of 5m/s, so the velocity range corresponding to the flow velocity close to 5m/s is small; similarly, for the data groups in the same category, the concentration After sorting, from small to large, every 500 data groups is a sub-category, and there are 14 sub-categories in the same large category, which are divided into 14 horizontal columns in the space in Figure 3. The vertical columns and horizontal columns are The 98,000 data groups are grouped into 196 sub-categories, that is to say, each grid in Figure 3 is equivalent to a sub-category, and each sub-category has 500 data groups. We think that in this sub-category Data sets have similar properties and reflect similar physical phenomena. Then, take the average of the data sets in the same category, that is, take the average of the three physical quantities of the flow velocity, the average concentration of the pipeline, and the pressure difference between the head and the tail of the pipeline, and a new processed data set can be obtained

S7-4: Merge these data groups into a set γ, as the final data group set, if γ has k data groups, it can be expressed as

To sum up, the principle of the technical solution of the present invention: select a certain monitoring point through mathematical means, and deduce the concentration value of the monitoring point changing with time to the concentration distribution on the entire delivery pipeline at any time, so as to provide convenience for the pressure difference matching of the target pipe section , to construct a data group; according to the construction characteristics of dredging ships and related theories, analyze and identify the existing "problem data", and clean the data group, which improves the reliability of the final data group; Its size is sorted, grouped, and averaged separately, which weakens the concentration of data, makes the new processed data group more representative and authentic, and provides reliable engineering data for follow-up research. It is worth noting that the processing method of the technical solution of the present invention has strict requirements on the processing flow, and the order cannot be reversed. Elimination is carried out in this process, which is a set of interlocking and complete data processing methods. Leaving aside any link, it will affect the reliability of the final data set.

Example 2

This embodiment 2 carries out data analysis and application based on embodiment 1, which is regarded as S8:

The data provided by step S7 can be applied to the research of the pipeline transportation mechanism, the improvement of the transportation theory, the establishment of the transportation theory model and so on.

In this embodiment, the establishment of a new friction calculation formula is used as an application example for illustration. Regarding the calculation of friction resistance, most researchers have established empirical or semi-empirical and semi-theoretical calculation models based on certain theoretical assumptions and indoor small-diameter pipeline transportation tests or on-site small-diameter pipeline transportation test results. , represented by the research results of Durand, Newitt, Wasp, Fei Xiangjun, Wang Shaozhou, etc. These calculation models are used in the performance analysis and calculation of dredging and conveying systems under modern large-diameter pipelines, high-concentration transportation, coarse particles or complex soil conditions, and there may be large deviations. The field data after reasonable processing can better modify and optimize these empirical models, and even propose a more reasonable calculation model, which is the main problem to be solved in this embodiment.

In this embodiment, the three friction empirical formulas of Durand's formula, Jufin's formula and Fei Xiangjun's formula are firstly applied to the transportation project to calculate the frictional resistance, as shown in Figure 4, the abscissa is the field data acquired by S1-S7, namely The actual measured value, the ordinate is the theoretical value calculated by the empirical formula, the closer these scattered points are to the middle straight line, the higher the matching degree between the theoretical calculation value and the field measured value is, obviously, the matching degree between the calculation results of these empirical formulas and the project is better Difference. If it can be corrected, optimized, or even put forward a new calculation formula, more accurate calculation results can be provided in related calculations, which has practical engineering significance. For this reason, the present invention has done following processing:

The existing Fei Xiangjun formula divides friction into two parts: carrier friction and bed friction.

In formula (2), I _m is the friction loss of the transported slurry (mH ₂ O/m); α is the correction coefficient related to the relative viscosity coefficient of the slurry; λ is the resistance coefficient along the pipeline when transporting clean water; V g is the acceleration of gravity (m/s ² ); D is the inner diameter of the pipeline (m); γ _m is the bulk density of the slurry (t/m ³ ); γ _w is the bulk density of the transport medium, the The embodiment is mainly sea water, which is 1.025t/ ^m3 ; γ _s is the bulk density of the solid material to be transported, and the working condition used in this embodiment is mainly medium and coarse sand, which is 2.65t/ ^m3 ; K _m is the test coefficient; μ _s is the friction Coefficient, generally taken as 0.44; C _vd is the volume concentration of solid particles in the slurry, V _c is the critical velocity (m/s), and is calculated using the standard formula (JTS 181-5-2012); V _ss is the sedimentation velocity of sediment particles In this working condition, in the working condition of this embodiment, the conveying medium is medium-coarse sand. After testing, the median particle size _d50 is 0.7mm. In this embodiment, the Wushui formula is used for calculation, see formula (3).

In the formula, v is the hydrodynamic viscosity coefficient, which is taken as 10 ⁶ m ² /s.

It is well known in the art that Fei Xiangjun's formula is more suitable for pipeline transportation of solid-liquid two-phase flow in a thin bed state. When the flow rate is high and there is no bed, there may be a large deviation. For this reason, the present invention considers to improve Fei Xiangjun's formula from the angle of flow form, promptly take whether there is bed as critical condition, when lower than this critical condition, adopt existing Fei Xiangjun's formula; , the carrier friction remains unchanged, and the bed friction tends to disappear. Based on the above considerations, the revised Fei Xiangjun formula is established as follows:

In the formula, _Vc is the critical velocity of the slurry (m/s), and the standard formula (JTS 181-5-2012) is used here:

V _c ＝(90C _vd ) ^1/3 g ^1/4 D ^1/2 ω ^1/2 d ₅₀ ^-1/4 (4)

Collect the data groups obtained by S7

Into the formula (3), the coefficient Km is fitted, and the calculation result of the formula after the fitting correction is drawn in Figure 5. In the figure, the friction loss value calculated by the revised formula and the measured value are in most working conditions The agreement is good, the overall deviation is within ±15%, and there is a large deviation from the measured value only in the small value area. Compared with the calculation results of empirical formulas (Durand formula, Jufin formula, original Fei Xiangjun formula, etc.) in Figure 4, it can be found that the revised formula (3) has higher calculation accuracy. Although the revised formula is an improvement based on the original Fei Xiangjun's empirical formula, the principle has changed, the structure of the formula has changed significantly, and the calculation results match well, so it can be regarded as a new formula. Then, in actual construction, when similar working conditions are encountered, the formula can be used to calculate the friction value of the transmission pipeline and guide the on-site operation, which has high application value.

Claims

A data processing method for long-distance pipeline transportation on-site dredging and transportation system, the data includes flow velocity data, pressure data, and concentration data; the flow velocity data and pressure data are obtained through existing methods, and it is characterized in that, first, the entire transportation pipeline is selected The initial point of the monitoring point is used as the monitoring point, and the flow velocity value of the sediment fluid at the monitoring point is regarded as the common flow velocity value of the entire downstream pipeline (including the target pipe section) at the same time, and the concentration value at the monitoring point is obtained. The time-varying concentration value deduces the concentration distribution on the entire pipeline at any time, matches the pressure difference of the target pipe section, and constructs a data group; then, cleans the data group; then, uses the data group as two-dimensional spatial data, and separates them according to their size Sorting, grouping, averaging, weakening the concentration of data.
The long-distance pipeline transportation site dredging transportation system data processing method as claimed in claim 1, characterized in that, specifically comprising the following steps:

S1: Long-distance pipeline transportation construction data collection, provided to S2; including three types of dynamic data: concentration data at the starting point of transportation, flow rate data, and pipeline pressure data at the test target section;

S2: dynamic data pre-processing for long-distance pipeline transmission, and clock matching; provided to S3;

S3: Deduction calculation of concentration distribution:

Control the flow rate of the slurry in the range higher than the critical flow rate. During the conveying process of the slurry, the particle volume concentration of the particle volume concentration of the slurry particle at the suction port of the pipeline at a certain moment is C vd , and the flow rate of the slurry is V changes with time and is a function of time t. The slurry microelement with concentration value C vd corresponds to different moving distances at different times. Therefore, after Δt time, the slurry is located at a distance from the starting point x, expressed as:

x＝∫t t +Δt V dt (1)

For the slurry element, the distance from the starting point at any time is calculated by integration. Similarly, for any slurry element, the moving distance is calculated by this integral method. In this way, the distance of the slurry at any time on the entire pipeline The concentration distribution is used to provide to step S4; the initial zero point of the pipeline is taken as the concentration and flow velocity measurement position, and the concentration distribution of the entire pipeline at different times is sequentially calculated based on the concentration and flow velocity measurement values measured at this point;

S4: Calculate the average concentration of the target pipe segment

The concentration distribution on the entire pipeline at different times is calculated in S3, and the average concentration of the target pipe section is calculated by numerical integration to obtain the corresponding average concentration of the target pipe section at all times

At any time in the target pipe section, the average concentration on the pipe section
Together with the three physical quantities of mud flow rate V and pressure difference P between the head and the tail of the pipeline, a data set is formed
The data in the data group are related to each other;

S5: Obtain the data set α corresponding to the flow rate-average concentration-pressure difference, and provide S6;

The average concentration at all times calculated on the target pipe section in S4
The data groups composed of the flow velocity V and the pressure difference P corresponding to each moment are collected together to form a data group set α. If α has n data groups, it is expressed as

S6: Data screening and elimination, provided to S7:

The data set obtained in S5 is not a completely ideal data set. There are many "problem data" that will affect the follow-up research. The obtained data group is screened and screened. If there is a problem with any data in the data group, or there is a contradiction between the data in the data group, the entire data group needs to be removed from the data group set. The removed data group set is β, Suppose there are m data groups, then

Eliminate the data groups with low concentration and large pressure difference, and remove the data groups with high concentration and normal pressure difference; remove the data whose pressure difference data is set to NaN in the preprocessing stage;

S7: Data post-processing:

Sort and classify the flow rate and concentration, and average the data within the same category to weaken data errors and data centralization.
The long-distance pipeline transportation site dredging transportation system data processing method as claimed in claim 2, characterized in that, in said S7, the specific processing method is as follows:

S7-1: Sort the m data groups in the data group set β according to the speed in the data group, and keep the corresponding relationship between the other two physical quantities and speeds. After sorting, each i data is a large category , classified in turn, provided to step S7-2;

S7-2: In each category, sort the data groups according to the concentration in the data groups, and take each j data group as a sub-category, classify them sequentially, and provide them to step S7-3;

S7-3: divide the data group in two-dimensional space with the speed as the abscissa and the concentration as the ordinate;

The data groups in the set β are sorted by speed, from small to large, and the data groups are divided into large categories; similarly, for the data groups in the same large category, sorted by concentration, from small to large, and then further divided into subcategories , then there are multiple sub-categories in the same category; then, average the data groups in the same category, that is, average the three physical quantities of flow velocity, average pipeline concentration, and pressure difference between the beginning and the end of the pipeline, and obtain a processed new data set for

S7-4: Merge these data groups into a set γ, as the final data group set, if γ has k data groups, it is expressed as