Energy efficiency calculation method of dredger based on energy efficiency analysis and evaluation model

Abstract

The present invention discloses a dredger energy efficiency calculation method based on an energy efficiency analysis and evaluation model, and belongs to the technical field of dredger energy efficiency detection. The present invention solves the problem that the existing dredger energy efficiency calculation method has errors, resulting in inaccurate energy efficiency evaluation of the dredger, by automatically collecting and detecting the main energy-consuming equipment such as the main engine, auxiliary engine, boiler, and dredging shaft power equipment, CO2 emissions, and ship water environment parameters of the dredger, and establishing an energy efficiency analysis database; introducing a correlation analysis algorithm to analyze the correlation of various factors, and obtaining the degree of correlation between the parameters; constructing a trailing suction dredger energy efficiency evaluation model and a cutter suction dredger efficiency evaluation model according to the type of dredger, and taking the oil consumption per 10,000 cubic meters of soil under various working conditions as the optimization target of the energy efficiency evaluation model for a single ship, and establishing a ship energy consumption evaluation level based on this, thereby providing auxiliary decision-making for dredging operation energy efficiency, thereby completing an intelligent energy efficiency management system.

Description

Technical Field

[0001] The present invention relates to the technical field of dredging vessel energy efficiency detection, and in particular to a dredging vessel energy efficiency calculation method based on an energy efficiency analysis and evaluation model. Background Art

[0002] With the rapid development of domestic economy and shipping, the construction and maintenance projects of ports and waterways have increased rapidly. Dredgers are the most widely used type of construction ships. Cutter suction dredgers have the characteristics of large workload, high power and high energy consumption. The annual energy consumption of a cutter suction dredger is equivalent to the energy consumption of a medium-sized port. Its energy consumption cost is relatively high, accounting for about 40% or more of the total cost. Therefore, the energy efficiency labeling system is an important measure to promote energy conservation, emission reduction and quality control in China. Practice has proved that the establishment and implementation of the energy efficiency labeling system in my country is an inevitable requirement for deepening energy conservation management, a fundamental solution to improve energy conservation requirements from the source and promote energy-saving technologies, an important measure for the government to transform its functions, strengthen the supervision and management of energy-using products, and standardize the market of energy-using products, and an effective way to promote energy conservation in a market-oriented manner and continuously improve the energy efficiency level of energy-using products.

[0003] However, the existing dredger energy efficiency calculation method usually detects and calculates various data, analyzes the difference between the current calculated value and the standard value, and thus obtains the evaluation result of the dredger energy efficiency; due to the complexity of the data, there is a certain error in the calculation, so it is difficult to ensure the accuracy of the dredger energy efficiency evaluation.

[0004] Therefore, it does not meet the existing needs. For this reason, we propose a dredger energy efficiency calculation method based on the energy efficiency analysis and evaluation model. Summary of the invention

[0005] The purpose of the present invention is to provide a dredger energy efficiency calculation method based on an energy efficiency analysis and evaluation model, by analyzing the main energy-consuming equipment such as the main engine, auxiliary engine, boiler, and dredging shaft power equipment of the dredger, The emission volume and ship water environment parameters are automatically collected and detected, and an energy efficiency analysis database is established; the correlation analysis algorithm is introduced to analyze the correlation of various factors to obtain the degree of correlation between the parameters; according to the type of dredger, a trailing suction dredger energy efficiency evaluation model and a cutter suction dredger efficiency evaluation model are respectively constructed, and the fuel consumption per 10,000 cubic meters of soil under various working conditions for a single ship is used as the optimization target of the energy efficiency evaluation model, and based on this, a ship energy consumption evaluation grade is established, thereby providing auxiliary decision-making for dredging operation energy efficiency, thereby completing the intelligent energy efficiency management system and solving the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The energy efficiency calculation method of a dredger based on an energy efficiency analysis and evaluation model includes the following steps:

[0008] S1. Combustion consumption of dredgers, dredging shaft power equipment, Automatically collect and detect the emission and data parameters of the ship’s water environment to obtain various energy efficiency data of the dredger;

[0009] S2. Pre-process the collected dredger energy efficiency data, remove abnormal data, ensure the validity of the dredger energy efficiency data, and then establish an energy efficiency analysis database;

[0010] S3. Introduce correlation analysis algorithm to analyze the correlation of various factors of dredger energy efficiency data, obtain the correlation degree between each parameter and the fuel consumption per 10,000 cubic meters of soil, and then measure the correlation degree between each parameter;

[0011] S4. According to the different working conditions of the dredger, energy efficiency evaluation models of the trailing suction dredger and the cutter suction dredger are established respectively, specifically including: constructing energy efficiency evaluation models of a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle for the trailing suction dredger, inputting the corresponding characteristic parameters for training and testing, and outputting the energy efficiency calculation results and automatically generating ship cycle, daily cycle, monthly cycle, and annual cycle reports; constructing energy efficiency evaluation models of a single vehicle travel cycle and anchor displacement ship cycle for the cutter suction dredger, inputting the corresponding characteristic parameters for training and testing, and outputting the energy efficiency calculation results and automatically generating ship cycle, daily cycle, monthly cycle, and annual cycle reports;

[0012] S5. For a single ship, the fuel consumption per 10,000 cubic meters of soil under various working conditions is used as the optimization target of the energy efficiency evaluation model. Based on this, the ship energy consumption evaluation level is established, and the energy efficiency calculation result of the current dredger output by the energy efficiency evaluation model is compared with the ship energy consumption evaluation level to obtain the energy efficiency evaluation result of the current dredger.

[0013] Furthermore, the combustion consumption of the dredger in S1 includes: real-time fuel consumption, operating fuel consumption, fuel consumption per unit time and fuel consumption per unit nautical mile; the real-time fuel consumption specifically includes: real-time fuel consumption of the main engine, auxiliary engine and boiler, in tons t; the real-time fuel consumption specifically includes: the operating fuel consumption of the trailing suction hopper includes: navigation fuel consumption, dredging fuel consumption, mud throwing fuel consumption and blowing and filling fuel consumption; the operating fuel consumption of the cutter suction hopper includes: dredging fuel consumption, in tons t.

[0014] Furthermore, in S1 Emissions, that is, carbon emissions from fossil fuel combustion, are calculated as follows:

[0015] ;

[0016] Where: i is the fuel type; is the consumption of fuel i, in J; Carbon dioxide for fuel The emission factor is: ; For carbon dioxide Emissions, unit: kg.

[0017] Furthermore, in calculating After the emission is calculated, the fuel consumption and supply are compared. The emission is detected and calculated, and the calculation formula is as follows:

[0018] (1)

[0019] Where: for The emission factor is the detected amount, FC is the energy consumption; is the greenhouse gas emission factor.

[0020] Furthermore, in calculating After the emission is calculated, the fuel consumption and supply are compared. The emission is detected and calculated, and the calculation formula is as follows:

[0021] (2)

[0022] Where: for The emission factor is the detected amount, DL is the dynamic activity level; is the greenhouse gas emission factor.

[0023] Furthermore, the collected dredger energy efficiency data is preprocessed to remove abnormal data, including:

[0024] Preprocessing the collected dredging vessel energy efficiency data to remove abnormal data from the dredging vessel energy efficiency data;

[0025] Retrieving the removed abnormal data and obtaining the data volume corresponding to the abnormal data;

[0026] Extract the data volume corresponding to the dredger energy efficiency data after removing abnormal data;

[0027] Obtaining a ratio of the amount of data corresponding to the abnormal data to the amount of data corresponding to the energy efficiency data of the dredger after the abnormal data is removed;

[0028] When the ratio of the data volume corresponding to the abnormal data to the data volume corresponding to the dredger energy efficiency data after the abnormal data is removed is greater than a preset ratio threshold, the specific data value corresponding to each abnormal data is retrieved;

[0029] According to the quantitative relationship between the abnormal data and the normal data, the abnormal data index coefficient is obtained; wherein the abnormal data index coefficient is obtained by the following formula:

[0030]

[0031] Among them, G 01 represents the index coefficient of abnormal data; Z represents the total number of abnormal data; m represents the number of data in the abnormal data that are judged as abnormal data due to garbled codes; n represents the number of data in the abnormal data that are judged as abnormal data due to data values ​​not meeting the normal numerical requirements; T r represents the data collection time corresponding to the rth garbled data; T r-1 represents the data collection time corresponding to the r-1th garbled data; T c Indicates the minimum interval allowed for the generation of the preset garbled data; X r Indicates the data value corresponding to the rth abnormal data that does not conform to the normal value; X cr represents the boundary value of the normal data range corresponding to the rth abnormal data that does not conform to the normal value, which has the smallest data difference with the abnormal data; T xr represents the data collection time corresponding to the rth abnormal data that does not meet the normal value; T nr Indicates the time when the garbled data is collected that is closest to the time when the data corresponding to the rth abnormal data that does not conform to the normal value is collected;

[0032] Obtaining an abnormal data evaluation parameter according to a data weight value corresponding to the abnormal data and an abnormal data index coefficient;

[0033] When the abnormal data evaluation parameter exceeds a preset evaluation parameter threshold, an abnormal data quality alarm of the dredger energy efficiency data is issued.

[0034] Furthermore, an abnormal data evaluation parameter is obtained according to the data weight value corresponding to the abnormal data combined with the abnormal data index coefficient;

[0035] Extracting a data weight value corresponding to the abnormal data;

[0036] The abnormal data weight index coefficient is obtained by using the data weight value corresponding to the abnormal data; wherein the abnormal data weight index coefficient is obtained by the following formula:

[0037]

[0038] Among them, G 02represents the weight index coefficient of abnormal data; m represents the number of data in the abnormal data that are judged as abnormal data due to garbled codes; n represents the number of data in the abnormal data that are judged as abnormal data due to data values ​​not meeting the normal numerical requirements; g mr Indicates the weight value corresponding to the rth garbled data; g nmax Indicates the maximum weight value that appears in abnormal data that does not conform to normal values; g nr Indicates the weight value corresponding to the rth abnormal data that does not conform to the normal value; g m01r and g m02r g represents the weight value corresponding to the garbled data before and after the collection time of the rth abnormal data that does not conform to the normal value; mp Indicates the weighted average value corresponding to the garbled data; g np Indicates the weighted average value corresponding to abnormal data that does not conform to normal values; g z It represents the sum of weight values ​​corresponding to all data types of dredger energy efficiency data;

[0039] Obtaining an abnormal data evaluation parameter according to the abnormal data weight index coefficient corresponding to the abnormal data and the abnormal data index coefficient;

[0040] The abnormal data evaluation parameter is obtained by the following formula:

[0041]

[0042] Among them, G represents the abnormal data evaluation parameter; G 01 Represents the abnormal data index coefficient; G 02 Represents the abnormal data weight indicator coefficient.

[0043] Furthermore, the correlation analysis algorithm is introduced in S3 to analyze the correlation of various factors of the dredger energy efficiency data, specifically:

[0044] set up and There are two types of parameters that need to be evaluated for correlation, and X and Y correspond to two groups of random variables respectively;

[0045] Where: p is the total number of vectors in group X, q is the total number of vectors in group Y;

[0046] Two sets of vectors to be evaluated: , (p≤q), merge the two sets of parameters into a set of vectors , the covariance matrix of the combined parameter variables is:

[0047] ;

[0048] in: , , ;

[0049] Finding parameters for two sets of random variables , The linear combination between (p≤q) is: , , and make the linear combination of X and Y changes , Correlation coefficient reach the maximum;

[0050] The coefficients of each linear combination expression are: , .

[0051] Furthermore, in S4, an energy efficiency evaluation model of a single ship dredging cycle, a dredging and dumping cycle, a dredging and blowing cycle, and a navigation cycle is constructed for the trailing suction hopper. Taking a single ship as a cycle, the time and fuel consumption of dredging, dumping, blowing and sailing are respectively counted. The cycle fuel consumption calculation formula is:

[0052] ;

[0053] Where: M is the fuel consumption of the trailing suction hopper ship, T is the fuel consumption time of the trailing suction hopper ship; i is the dredging volume of the trailing suction hopper ship during dredging, the mud dumping volume of the trailing suction hopper ship during dumping, the filling volume of the trailing suction hopper ship during filling and the sailing mileage of the trailing suction hopper ship during navigation.

[0054] Furthermore, in S4, an energy efficiency evaluation model of a single vehicle travel cycle and an anchor displacement ship cycle is constructed for a cutter suction dredger. Based on the working conditions, the time and fuel consumption of a single vehicle travel, an anchor displacement ship, and a pile replacement process are respectively counted; the cycle fuel consumption calculation formula is:

[0055] ;

[0056] Where: M is the fuel consumption of the cutter suction dredger, T is the fuel consumption time of the cutter suction dredger; i is the single trolley travel of the cutter suction dredger and the mileage of the anchor displacement ship of the cutter suction dredger.

[0057] Furthermore, the method for establishing the ship energy consumption assessment level in S5 is: by calculating the historical discharge of the dredger per unit earthwork volume The calculation formula is as follows:

[0058] ;

[0059] Where: for Emission factors; is the total fuel consumption of the ship, unit: t; is the earthwork volume, unit is t; j is the type of fuel.

[0060] Furthermore, the history obtained in S5 After the index range of the quantity is determined, a total of 5 levels are established for the ship energy consumption assessment level based on the historical number range, namely:

[0061] Low energy consumption: Level 1-2, with a green logo; Medium: Level 3-4, with a yellow-brown logo; High energy consumption: Level 5, with a red logo.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention can realize the dredging of the main engine, auxiliary engine, boiler and other main energy-consuming equipment and dredging shaft power equipment, The emission volume and ship water environment parameters are automatically collected and detected, and an energy efficiency analysis database is established. The correlation analysis algorithm is introduced to analyze the correlation of various factors and the degree of correlation between the parameters is obtained. According to the types of dredgers, the energy efficiency evaluation model of the trailing suction dredger and the efficiency evaluation model of the cutter suction dredger are respectively constructed. For a single ship, the fuel consumption per 10,000 cubic meters of soil under various working conditions is used as the optimization target of the energy efficiency evaluation model, and based on this, the ship energy consumption evaluation level is established, thereby providing auxiliary decision-making on the energy efficiency of dredging operations, thereby completing the intelligent energy efficiency management system. DETAILED DESCRIPTION

[0064] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] In order to solve the technical problem that the current dredger energy efficiency calculation method is usually to detect and calculate various data, analyze the difference between the current calculated value and the standard value, and thus obtain the evaluation result of the dredger energy efficiency; due to the complex data, there is a certain error in the calculation, so it is difficult to ensure the accuracy of the dredger energy efficiency evaluation, this embodiment provides the following technical solutions:

[0066] The energy efficiency calculation method of a dredger based on an energy efficiency analysis and evaluation model includes the following steps:

[0067] S1. Automatically collect and detect the data parameters of the dredger's combustion consumption, dredging shaft power equipment, CO2 emissions and ship water environment to obtain various energy efficiency data of the dredger; among them, the dredger's combustion consumption includes: real-time fuel consumption, operating fuel consumption, unit time fuel consumption and unit nautical mile fuel consumption; real-time fuel consumption specifically includes: real-time fuel consumption of the main engine, auxiliary engine and boiler, in tons; real-time fuel consumption specifically includes: operating fuel consumption of the trailing suction hopper includes: navigation fuel consumption, dredging fuel consumption , mud throwing fuel consumption and blowing filling fuel consumption; the working condition fuel consumption of the cutter suction dredger includes: dredging fuel consumption, unit: ton t; specifically, the unit time fuel consumption is divided into: unit time working condition fuel consumption and total unit time fuel consumption, the unit time working condition fuel consumption is: cycle fuel consumption, the unit time working condition fuel consumption is calculated by: by counting the operating time and fuel consumption of each operating stage, the calculation steps have been described in detail in step S4; the total unit time fuel consumption is calculated by: by counting the total fuel consumption and the total operating time, the calculation formula is: ; Unit nautical mile fuel consumption refers to the fuel consumption of the ship per nautical mile during the navigation phase. The calculation formula is: .

[0068] The above calculation method is used to obtain various parameters of the combustion consumption of the dredger, thereby providing data support for the energy efficiency analysis and evaluation model of the dredger, ensuring the authenticity of the output results of the energy efficiency analysis and evaluation model, and thus improving the accuracy of the dredger energy efficiency evaluation.

[0069] The CO2 emissions, i.e. the carbon emissions from the combustion of fossil fuels, are calculated as follows:

[0070] ;

[0071] Where: i is the fuel type; F i is the consumption of fuel i, in units of J; EF i is the emission factor of carbon dioxide (CO2) of fuel i, in kg / J; E m It is the emission of carbon dioxide CO2, the unit is: kg.

[0072] In this embodiment: the CO2 emission factor in the above formula is used to convert the carbon content of the fuel consumed by the ship into the release of CO2, and is a dimensionless conversion factor. In view of the low operability and poor accuracy in actual measurement of CO2, the IPCC proposed the concept of CO2 emission factor in the IPCC Greenhouse Gas Guidelines and Good Practice Guidelines, and provided two methods for detecting and calculating CO2 emissions.

[0073] Method 1: It is a simple algorithm based on fuel consumption and supply. The calculation formula is as follows:

[0074] ; (1)

[0075] Where: FC is energy consumption; C GHG is the greenhouse gas emission factor;

[0076] Method 2: Calculate greenhouse gas emissions by analyzing fuel supply and consumption, and the composition of technical forms. The calculation formula is as follows:

[0077] ; (2)

[0078] Where: DL is the dynamic activity level; C GHG is the greenhouse gas emission factor.

[0079] Specifically, the IPCC recommends that greenhouse gas emissions from maritime transport be calculated using Method 1. When calculating CO2 emissions, the greenhouse gas emission coefficient can also be called the carbon emission factor. Based on the molecular mass, the carbon emission factor of the fuel is as follows:

[0080] ;

[0081]

[0082] Table 1: Carbon content of various fuels and carbon emission factors

[0083] Among them, hourly discharge = m C / hr, hourly discharge = m C / day, hourly emissions = m C / n.

[0084] Secondly, CO2 emissions also include: EEOI, which is the amount of CO2 emitted per unit of transport work of a ship. According to the "Guidelines for the Voluntary Application of EEOI for Ships", the theoretical calculation formula for EEOI is as follows:

[0085] ;

[0086] If the average ship energy efficiency operation index of multiple sections needs to be calculated, the calculation formula is as follows:

[0087] ;

[0088] Where: C Fj is the CO2 emission factor; D is the ship's mileage, in nm; FC j is the total fuel consumption of the ship, in units of: t; m cargo is the cargo capacity, in t; j is the fuel type; i is the number of flight segments;

[0089] The sailing distance in the formula refers to the total nautical miles actually sailed by the ship in a certain voyage or time period, and is usually the sailing distance recorded in the logbook. At the same time, the calculation of EEOI also requires the following preparations: First, the calculation period of EEOI must be clarified, and the total fuel consumption, cargo capacity, sailing distance and other related ship operation data of the ship during this period must be counted, and finally the calculation is performed.

[0090] In this embodiment: the dredger energy efficiency data also includes: propulsion power, which mainly refers to the total power of the propulsion system, that is, the left propulsion motor power + the right propulsion power; fuel efficiency, which refers to how many grams of oil a diesel generator set consumes per kilowatt-hour, and then the unit is converted into tons to obtain the fuel cost, the unit is: t / KWH; slip rate, slip rate = (theoretical ship speed - actual ship speed) ÷ theoretical ship speed; theoretical ship speed = total speed × propeller pitch ÷ time; per 10,000 cubic meters of soil fuel consumption, for different dredgers, the calculation of per 10,000 cubic meters of soil fuel consumption is also different. In this project, the calculation of per 10,000 cubic meters of soil fuel consumption is distinguished by the construction conditions of the dredger. The bucket suction dredger is divided into the real-time per 10,000 cubic meters of soil fuel consumption during dredging and the per 10,000 cubic meters of soil fuel consumption during the digging and throwing cycle; the cutter suction dredger is the real-time per 10,000 cubic meters of soil fuel consumption during dredging; the per 10,000 cubic meters of soil calculation formula:

[0091] .

[0092] S2. Preprocess the collected energy efficiency data of the dredger, remove abnormal data, ensure the validity of the energy efficiency data of the dredger, and then establish an energy efficiency analysis database; specifically, after collecting and detecting the above-mentioned relevant data, the data can be cleaned, and the erroneous data, duplicate data and missing data can be deleted to ensure the validity of the data; the cleaned data are then stored, and the data can be divided according to the factors and categories to which the data belongs during storage, and the energy efficiency analysis database is formed after the data are classified and stored; the energy efficiency analysis database includes the current valid data of the dredger and the historical valid data of the dredger, and the energy efficiency analysis and evaluation model constructed subsequently can be trained and tested through the historical valid data, thereby ensuring the accuracy of the calculation of the energy efficiency analysis and evaluation model; the energy efficiency analysis and evaluation model can also be continuously trained and tested through the current valid data, thereby updating and iterating the feature set of the energy efficiency analysis and evaluation model, ensuring that the energy efficiency analysis and evaluation model can continuously update the feature set of the model as the current data changes, thereby ensuring the real-time and accuracy of the analysis of the energy efficiency analysis and evaluation model.

[0093] Specifically, the collected dredger energy efficiency data is preprocessed to remove abnormal data, including:

[0094] Preprocessing the collected dredging vessel energy efficiency data to remove abnormal data from the dredging vessel energy efficiency data;

[0095] Retrieving the removed abnormal data and obtaining the data volume corresponding to the abnormal data;

[0096] Extract the data volume corresponding to the dredger energy efficiency data after removing abnormal data;

[0097] Obtaining a ratio of the amount of data corresponding to the abnormal data to the amount of data corresponding to the energy efficiency data of the dredger after the abnormal data is removed;

[0098] When the ratio of the data volume corresponding to the abnormal data to the data volume corresponding to the dredger energy efficiency data after the abnormal data is removed is greater than a preset ratio threshold, the specific data value corresponding to each abnormal data is retrieved;

[0099] According to the quantitative relationship between the abnormal data and the normal data, the abnormal data index coefficient is obtained; wherein the abnormal data index coefficient is obtained by the following formula:

[0100]

[0101] Among them, G 01 represents the index coefficient of abnormal data; Z represents the total number of abnormal data; m represents the number of data in the abnormal data that are judged as abnormal data due to garbled codes; n represents the number of data in the abnormal data that are judged as abnormal data due to data values ​​not meeting the normal numerical requirements; T i represents the data collection time corresponding to the i-th garbled data; T i-1 represents the data collection time corresponding to the i-1th garbled data; T c Indicates the minimum interval allowed for the generation of the preset garbled data; X i Indicates the data value corresponding to the i-th abnormal data that does not conform to the normal value; X ci represents the boundary value of the normal data range corresponding to the i-th abnormal data that does not conform to the normal value, which has the smallest data difference with the abnormal data; T xi represents the data collection time corresponding to the i-th abnormal data that does not meet the normal value; T ni Indicates the time when the garbled data is collected that is closest to the time when the data corresponding to the i-th abnormal data that does not conform to the normal value is collected;

[0102] Obtaining an abnormal data evaluation parameter according to a data weight value corresponding to the abnormal data and an abnormal data index coefficient;

[0103] When the abnormal data evaluation parameter exceeds a preset evaluation parameter threshold, an abnormal data quality alarm of the dredger energy efficiency data is issued.

[0104] The technical effect of the above technical solution is: by pre-processing the dredger energy efficiency data, abnormal data is effectively eliminated. These data may be caused by equipment failure, sensor error, human error or data transmission problem, and their existence will seriously affect the accuracy and reliability of data analysis. After eliminating these abnormal data, the remaining energy efficiency data is purer, providing a more solid foundation for subsequent data analysis and decision-making.

[0105] By calculating the ratio of the amount of abnormal data to the total amount of data, the severity of the abnormal data can be quantitatively assessed. When the ratio exceeds the preset threshold, it means that the proportion of abnormal data is high and further investigation or corrective measures may be required. This step helps to detect and deal with potential data quality issues in a timely manner.

[0106] By retrieving the specific value of each abnormal data and combining the quantitative relationship between the abnormal data and the normal data, the abnormal data index coefficient (G01) is calculated. This coefficient not only takes into account the number of abnormal data (including garbled characters and abnormal data values), but also takes into account factors such as the time interval between their occurrence and the degree to which the data value deviates from the normal range, so as to more comprehensively reveal the cause of the abnormal data.

[0107] The abnormal data evaluation parameter is calculated based on the abnormal data weight value and abnormal data index coefficient. This parameter can more accurately evaluate the impact of abnormal data on the overall data quality. When the evaluation parameter exceeds the preset threshold, the system can automatically trigger the data quality abnormality alarm and promptly notify relevant personnel to take measures to prevent abnormal data from adversely affecting subsequent analysis and decision-making.

[0108] The entire technical solution builds an automated data quality monitoring and response mechanism. By real-time monitoring of dredger energy efficiency data, abnormal data can be discovered and processed in a timely manner to ensure the continuous stability of data quality. At the same time, the efficiency and response speed of data monitoring are improved through preset thresholds and automated alarm processes.

[0109] In summary, this technical solution has significantly improved the quality monitoring and management level of dredger energy efficiency data through systematic data preprocessing, quantitative analysis, in-depth cause exploration and precise impact assessment, providing more reliable and powerful support for data-based decision-making.

[0110] Specifically, the abnormal data evaluation parameter is obtained according to the data weight value corresponding to the abnormal data combined with the abnormal data index coefficient;

[0111] Extracting a data weight value corresponding to the abnormal data;

[0112] The abnormal data weight index coefficient is obtained by using the data weight value corresponding to the abnormal data; wherein the abnormal data weight index coefficient is obtained by the following formula:

[0113]

[0114] Among them, G 02 represents the weight index coefficient of abnormal data; m represents the number of data in the abnormal data that are judged as abnormal data due to garbled codes; n represents the number of data in the abnormal data that are judged as abnormal data due to data values ​​not meeting the normal numerical requirements; g mi Indicates the weight value corresponding to the i-th garbled data; g nmax Indicates the maximum weight value that appears in abnormal data that does not conform to normal values; g ni Indicates the weight value corresponding to the i-th abnormal data that does not conform to the normal value; g m01i and g m02i represents the weight value corresponding to the garbled data before and after the collection time of the i-th abnormal data that does not conform to the normal value; g mp Indicates the weighted average value corresponding to the garbled data; g np Indicates the weighted average value corresponding to abnormal data that does not conform to normal values; g z It represents the sum of weight values ​​corresponding to all data types of dredger energy efficiency data;

[0115] Obtaining an abnormal data evaluation parameter according to the abnormal data weight index coefficient corresponding to the abnormal data and the abnormal data index coefficient;

[0116] The abnormal data evaluation parameter is obtained by the following formula:

[0117]

[0118] Among them, G represents the abnormal data evaluation parameter; G 01 Represents the abnormal data index coefficient; G 02 Represents the abnormal data weight indicator coefficient.

[0119] The technical effect of the above technical solution is that by introducing the abnormal data weight value, the technical solution can more finely evaluate the impact of abnormal data on the overall data quality. Different types of abnormal data (such as garbled data, data that does not meet normal numerical requirements) and their respective importance in the overall data set (reflected by the weight value) are taken into consideration, thereby obtaining a more comprehensive and accurate abnormal data evaluation parameter.

[0120] By using the abnormal data weight index coefficient (G02), the solution can distinguish the severity of different types of abnormal data and their different impacts on the overall data quality. For example, abnormal data with higher weights may have a greater impact on the overall data quality, so more attention and priority should be given to it. This differentiated processing method improves the accuracy and efficiency of data quality monitoring.

[0121] By defining the weight value of abnormal data, the solution allows the weight of different types of data to be adjusted according to actual needs, thereby adapting to different application scenarios and data characteristics. This flexibility makes the technical solution more versatile and practical, and can meet the needs of different users and data sets.

[0122] The abnormal data index coefficient (G01) and the abnormal data weight index coefficient (G02) are combined to calculate the abnormal data evaluation parameter (G) through the formula, realizing a comprehensive evaluation of abnormal data. This evaluation parameter not only takes into account the number and type of abnormal data, but also their importance and the degree of impact on the overall data quality, providing strong support for subsequent data processing and decision-making.

[0123] When the abnormal data evaluation parameters exceed the preset threshold, the system can automatically trigger the data quality abnormality alarm. This automated alarm and response mechanism can promptly detect and handle data quality issues to prevent them from having adverse effects on subsequent data analysis and decision-making. At the same time, it also reduces the burden of manual monitoring and improves the efficiency and accuracy of data processing.

[0124] In summary, this technical solution significantly improves the accuracy and efficiency of data quality monitoring by introducing abnormal data weight values ​​and a method for comprehensive evaluation of abnormal data, providing more reliable and powerful support for data-based decision-making.

[0125] S3. Introduce correlation analysis algorithm to analyze the correlation of various factors of dredger energy efficiency data, obtain the correlation degree between each parameter and the fuel consumption per 10,000 cubic meters of soil, and then measure the correlation degree between each parameter;

[0126] Specifically, after obtaining each working state in the dredger through step S1, in order to avoid parameter redundancy of the input energy efficiency analysis and evaluation model, thereby increasing the complexity of the calculation of the energy efficiency analysis and evaluation model and reducing the accuracy of the energy efficiency analysis and evaluation model, it is necessary to analyze the correlation of various factors affecting the oil consumption of the dredger respectively;

[0127] The correlation analysis algorithm is introduced to analyze the correlation of various factors of dredger energy efficiency data, specifically:

[0128] Let X1, X2, …, X p and Y1,Y2,…,Y qThere are two types of parameters that need to be evaluated for correlation, and X and Y correspond to two groups of random variables respectively;

[0129] Two sets of vectors to be evaluated: X=(X1,X2,…,X p ) T , Y=(Y1,Y2,…,Y q ) T (p≤q), merge the two sets of parameters into a set of vectors (X T , Y T )=(X1,X2,…,X p， Y1,Y2,…,Y q ) T , the covariance matrix of the combined parameter variables is:

[0130] ;

[0131] in: ;

[0132] Find two sets of random variables with parameters X=(X1,X2,…,X p ) T , Y=(Y1,Y2,…,Y q ) T The linear combination between (p≤q) is: U1=a1 T X=a 11 X1+a 12 X2+…+a 1p X p , V1=b1 T Y=b 11 Y1+b 12 Y2+…+b 1q Y q And make the correlation coefficient ρ(U1,V1) of the linear combination U1 and V1 of X and Y changes reach the maximum;

[0133] The coefficients of each linear combination expression are: a1 T =(a 11 ,a 12, …,a 1p ), b1 T =(b 11 ,b 12 ,…,b 1q );

[0134] In summary, we need to have two sets of variables (X1, X2, …, X p ) and (Y1,Y2,…,Y q ) find a corresponding linear combination, considering (X1, X2, ..., Xp ), a linear combination U and (Y1,Y2,…,Y q ) into a set of vectors, hoping to find the maximum possible correlation coefficient with V, so as to fully reflect the correlation between the two sets of variables. In this way, the problem of studying the correlation between two sets of random variables of influencing factors is transformed into studying the correlation between two random variables.

[0135] In this embodiment: taking a drag suction dredge as an example, the correlation between the mud pump speed, high-pressure flushing pump speed, drag lip angle to the ground, navigation speed, overflow tube height, diesel engine speed and 10,000 cubic meter soil fuel consumption is studied respectively, and the correlation coefficient is calculated; according to the correlation analysis theory, it can be known that the positive and negative values ​​of the correlation coefficient only represent the correlation between the two related parameters, and the size of the correlation coefficient indicates the strength of the correlation between the parameters; therefore, in order to emphasize the strength of the correlation between the parameters, this algorithm uses the absolute value of the correlation coefficient of the two parameters to represent the correlation between the energy efficiency parameters; when the correlation coefficient is greater than 0.8, it means that the factor is highly correlated with the 10,000 cubic meter soil fuel consumption; if the correlation coefficient is between 0.8-0.5, it means that there is a large correlation; if the correlation coefficient is between 0.5-0.3, it means that there is a correlation; if the correlation coefficient is less than 0.3, it is slightly correlated; therefore, through the correlation coefficient analysis, the factors that have a high impact on the 10,000 cubic meter soil fuel consumption can be excavated, thereby realizing the analysis and evaluation of energy consumption.

[0136] S4. According to the different working conditions of the dredger, energy efficiency evaluation models for the trailing suction hopper dredger and the cutter suction hopper dredger are established respectively, specifically including: constructing energy efficiency evaluation models for a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle for the trailing suction hopper dredger, inputting the corresponding characteristic parameters for training and testing, and outputting the energy efficiency calculation results and automatically generating ship cycle, daily cycle, monthly cycle, and annual cycle reports; constructing energy efficiency evaluation models for a single vehicle travel cycle and anchor displacement ship cycle for the cutter suction hopper dredger, inputting the corresponding characteristic parameters for training and testing, and outputting the energy efficiency calculation results and automatically generating ship cycle, daily cycle, monthly cycle, and annual cycle reports; specifically, after creating the energy efficiency evaluation model for the trailing suction hopper dredger and the cutter suction hopper dredger respectively, it is also necessary to select the historical energy efficiency data of the trailing suction hopper dredger and the cutter suction dredger from the energy efficiency analysis database, and divide the two types of data respectively, dividing each type of data into The data sets are divided into training set and test set respectively, and then the training set and test set belonging to the trailing suction hopper ship are successively imported into the trailing suction hopper ship energy efficiency evaluation model for training and testing, until the test results are consistent with the training results, thereby establishing the feature set of the trailing suction hopper ship energy efficiency evaluation model, and ensuring the accuracy of the analysis of the trailing suction hopper ship energy efficiency evaluation model; the training set and test set belonging to the cutter suction dredger are successively imported into the cutter suction dredger energy efficiency evaluation model for training and testing, until the test results are consistent with the training results, thereby establishing the feature set of the cutter suction dredger energy efficiency evaluation model, and ensuring the accuracy of the analysis of the cutter suction dredger energy efficiency evaluation model; after the feature sets of the trailing suction hopper ship energy efficiency evaluation model and the cutter suction dredger energy efficiency evaluation model are established, after the energy efficiency of the current dredger is effectively correlated, the main component data is selected and imported into the energy efficiency analysis and evaluation model for analysis and calculation, and the energy efficiency calculation result of the current dredger is output.

[0137] An energy efficiency evaluation model for the single-ship dredging cycle, dredging and dumping cycle, dredging and blowing cycle, and navigation cycle is constructed for the trailing suction hopper. Taking a single ship as the cycle, the time and fuel consumption of dredging, dumping, blowing and sailing are calculated respectively. The cycle fuel consumption calculation formula is:

[0138] ;

[0139] Where: M is the fuel consumption of the hopper dredger, T is the fuel consumption time of the hopper dredger; i is the dredging volume of the hopper dredger, the mud dumping volume of the hopper dredger, the filling volume of the hopper dredger and the mileage of the hopper dredger, the unit is (t / hr);

[0140] The energy efficiency evaluation model of the single vehicle travel cycle and anchor displacement ship cycle is constructed for the cutter suction dredger. Based on the working conditions, the time and fuel consumption of the single vehicle travel, anchor displacement ship and pile replacement process are calculated respectively; the cycle fuel consumption calculation formula is:

[0141] ;

[0142] Where: M is the fuel consumption of the cutter suction dredger, T is the fuel consumption time of the cutter suction dredger; i is the single trolley travel of the cutter suction dredger and the mileage of the anchor displacement ship of the cutter suction dredger, respectively, and the unit is: (t / hr).

[0143] S5. For a single ship, the fuel consumption per 10,000 cubic meters of earthwork under various working conditions is used as the optimization target of the energy efficiency evaluation model. Based on this, the ship energy consumption evaluation level is established, and the energy efficiency calculation result of the current dredger output by the energy efficiency evaluation model is compared with the ship energy consumption evaluation level to obtain the current dredger energy efficiency evaluation result; the method for establishing the ship energy consumption evaluation level is: by calculating the amount of CO2 emitted per unit earthwork volume of the dredger in history, the calculation formula is as follows:

[0144] ;

[0145] Where: C Fj is the CO2 emission factor; FC j is the total fuel consumption of the ship, in t; M D is the earthwork volume, unit: t; j is the type of fuel.

[0146] After obtaining the historical CO2 index range, a total of 5 levels were established for ship energy consumption assessment based on the historical index range, namely:

[0147] Low energy consumption: Level 1-2, with a green logo; Medium: Level 3-4, with a yellow-brown logo; High energy consumption: Level 5, with a red logo.

[0148] Specifically, the dredging energy efficiency index of the current dredger is calculated by the above formula, and the energy consumption level of the ship is graded based on the index and the current energy efficiency label. After the classification, the energy efficiency parameters of the current dredger are evaluated and analyzed according to the energy efficiency analysis and evaluation model. After analysis by the energy efficiency analysis and evaluation model, if the output result index is at level 1-2, the current dredger is regarded as low energy consumption and is displayed as a green label; if the output result index is at level 3-4, the current dredger is regarded as medium energy consumption and is displayed as a yellow-brown label; if the output result index is at level 5, the current dredger is regarded as high energy consumption and is displayed as a red label. The energy efficiency status of the current dredger is quickly judged based on the label, thereby providing the dredger with auxiliary decision-making for dredging operation energy efficiency and completing the intelligent energy efficiency management system of the dredger.

[0149] Working principle: Through automatic collection and detection of main energy-consuming equipment such as the main engine, auxiliary engine, boiler, dredging shaft power equipment, CO2 emissions, and ship water environment parameters of the dredger, an energy efficiency analysis database is established; the correlation analysis algorithm is introduced to analyze the correlation of various factors to obtain the degree of correlation between various parameters; according to the type of dredger, a trailing suction dredger energy efficiency evaluation model and a cutter suction dredger efficiency evaluation model are respectively constructed, and the fuel consumption per 10,000 cubic meters of soil under various working conditions for a single ship is used as the optimization target of the energy efficiency evaluation model, and based on this, the ship energy consumption evaluation level is established, thereby providing auxiliary decision-making for dredging operation energy efficiency, thereby completing the intelligent energy efficiency management system.

[0150] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0151] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. The energy efficiency calculation method of a dredger based on the energy efficiency analysis and evaluation model includes the following steps: S1. Combustion consumption of dredgers, dredging shaft power equipment, Automatically collect and detect the emission and data parameters of the ship’s water environment to obtain various energy efficiency data of the dredger; S2. Pre-process the collected dredger energy efficiency data, remove abnormal data, ensure the validity of the dredger energy efficiency data, and then establish an energy efficiency analysis database; S3. Introduce correlation analysis algorithm to analyze the correlation of various factors of dredger energy efficiency data, obtain the correlation degree between each parameter and the fuel consumption per 10,000 cubic meters of soil, and then measure the correlation degree between each parameter; S4. According to different working conditions of dredgers, energy efficiency evaluation models of trailing suction dredgers and cutter suction dredgers are established respectively; the energy efficiency evaluation models specifically include: Construct an energy efficiency evaluation model for a single dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle for a trailing suction hopper vessel Input the relevant characteristic parameters for training and testing, and output the energy efficiency calculation results and automatically generate ship cycle, daily cycle, monthly cycle, and annual cycle reports; Construct an energy efficiency evaluation model for the single vehicle travel cycle and anchor displacement ship cycle for the cutter suction dredger, input the corresponding characteristic parameters for training and testing, output the energy efficiency calculation results and automatically generate ship cycle, daily cycle, monthly cycle, and annual cycle reports; S5. For a single ship, the fuel consumption per 10,000 cubic meters of soil under various working conditions is used as the optimization target of the energy efficiency evaluation model. Based on this, the ship energy consumption evaluation level is established, and the energy efficiency calculation result of the current dredger output by the energy efficiency evaluation model is compared with the ship energy consumption evaluation level to obtain the energy efficiency evaluation result of the current dredger.

2. The dredging vessel energy efficiency calculation method based on the energy efficiency analysis and evaluation model according to claim 1 is characterized in that: The S1 The emission is calculated as follows: ； Where: i is the fuel type; is the consumption of fuel i, Carbon dioxide for fuel The emission factor is For carbon dioxide emissions.

3. The dredging vessel energy efficiency calculation method based on the energy efficiency analysis and evaluation model according to claim 2 is characterized in that: In calculating After the emission is calculated, the fuel consumption and supply are compared. The emission is detected and calculated, and the calculation formula is as follows: （1） Where: for The emission factor is the detected amount, FC is the energy consumption; is the greenhouse gas emission factor.

4. The dredging vessel energy efficiency calculation method based on the energy efficiency analysis and evaluation model according to claim 2 is characterized in that: In calculating After the emission is calculated, the fuel consumption and supply are compared. The emission is detected and calculated, and the calculation formula is as follows: （2） Where: for The emission factor is the detected amount, DL is the dynamic activity level; is the greenhouse gas emission factor.

5. The dredging vessel energy efficiency calculation method based on the energy efficiency analysis and evaluation model according to claim 1 is characterized in that: Pre-process the collected dredger energy efficiency data and remove abnormal data, including: Preprocessing the collected dredging vessel energy efficiency data to remove abnormal data from the dredging vessel energy efficiency data; Retrieving the removed abnormal data and obtaining the data volume corresponding to the abnormal data; Extract the data volume corresponding to the dredger energy efficiency data after removing abnormal data; Obtaining a ratio of the amount of data corresponding to the abnormal data to the amount of data corresponding to the energy efficiency data of the dredger after the abnormal data is removed; When the ratio of the data volume corresponding to the abnormal data to the data volume corresponding to the dredger energy efficiency data after the abnormal data is removed is greater than a preset ratio threshold, the specific data value corresponding to each abnormal data is retrieved; According to the quantitative relationship between the abnormal data and the normal data, the abnormal data index coefficient is obtained; wherein the abnormal data index coefficient is obtained by the following formula: ； Among them, G 01 represents the index coefficient of abnormal data; Z represents the total number of abnormal data; m represents the number of data in the abnormal data that are judged as abnormal data due to garbled codes; n represents the number of data in the abnormal data that are judged as abnormal data due to data values ​​not meeting the normal numerical requirements; T r represents the data collection time corresponding to the rth garbled data; T r-1 represents the data collection time corresponding to the r-1th garbled data; T c Indicates the minimum interval allowed for the generation of the preset garbled data; X r Indicates the data value corresponding to the rth abnormal data that does not conform to the normal value; X cr represents the boundary value of the normal data range corresponding to the rth abnormal data that does not conform to the normal value, which has the smallest data difference with the abnormal data; T xr represents the data collection time corresponding to the rth abnormal data that does not meet the normal value; T nr Indicates the time when the garbled data is collected that is closest to the time when the data corresponding to the rth abnormal data that does not conform to the normal value is collected; Obtaining an abnormal data evaluation parameter according to a data weight value corresponding to the abnormal data and an abnormal data index coefficient; When the abnormal data evaluation parameter exceeds a preset evaluation parameter threshold, an abnormal data quality alarm of the dredger energy efficiency data is issued.

6. The method for calculating energy efficiency of a dredger based on an energy efficiency analysis and evaluation model according to claim 5 is characterized in that: Obtaining an abnormal data evaluation parameter according to a data weight value corresponding to the abnormal data and an abnormal data index coefficient; Extracting a data weight value corresponding to the abnormal data; The abnormal data weight index coefficient is obtained by using the data weight value corresponding to the abnormal data; wherein the abnormal data weight index coefficient is obtained by the following formula: ； Among them, G 02 represents the weight index coefficient of abnormal data; m represents the number of data in the abnormal data that are judged as abnormal data due to garbled codes; n represents the number of data in the abnormal data that are judged as abnormal data due to data values ​​not meeting the normal numerical requirements; g mr Indicates the weight value corresponding to the rth garbled data; g nmax Indicates the maximum weight value that appears in abnormal data that does not conform to normal values; g nr Indicates the weight value corresponding to the rth abnormal data that does not conform to the normal value; g m01r and g m02r g represents the weight value corresponding to the garbled data before and after the collection time of the rth abnormal data that does not conform to the normal value; mp Indicates the weighted average value corresponding to the garbled data; g np Indicates the weighted average value corresponding to abnormal data that does not conform to normal values; g z It represents the sum of weight values ​​corresponding to all data types of dredger energy efficiency data; Obtaining an abnormal data evaluation parameter according to the abnormal data weight index coefficient corresponding to the abnormal data and the abnormal data index coefficient; The abnormal data evaluation parameter is obtained by the following formula: ； Among them, G represents the abnormal data evaluation parameter; G 01 Represents the abnormal data index coefficient; G 02 Represents the abnormal data weight indicator coefficient.

7. The dredging vessel energy efficiency calculation method based on the energy efficiency analysis and evaluation model according to claim 1 is characterized in that: The correlation analysis algorithm is introduced in S3 to analyze the correlation of various factors of dredger energy efficiency data, specifically: set up and There are two types of parameters that need to be evaluated for correlation, and XY corresponds to two groups of random variables; Where: p is the total number of vectors in group X, q is the total number of vectors in group Y; Two sets of vectors to be evaluated: , (p≤q), merge the two sets of parameters into a set of vectors , the covariance matrix of the combined parameter variables is: ； in: , , ; Finding parameters for two sets of random variables , The linear combination between (p≤q) is: , And make the linear combination of X and Y changes , Correlation coefficient reach the maximum; The coefficients of each linear combination expression are: , .

8. The method for calculating energy efficiency of a dredger based on an energy efficiency analysis and evaluation model according to claim 1, characterized in that: In S4, an energy efficiency evaluation model for a single ship dredging cycle, a dredging and dumping cycle, a dredging and blowing cycle, and a navigation cycle is constructed for a trailing suction hopper vessel. Taking a single ship as a cycle, the time and fuel consumption of dredging, dumping, blowing and sailing are respectively counted. The cycle fuel consumption calculation formula is: ； Where: M is the fuel consumption of the trailing suction hopper ship, T is the fuel consumption time of the trailing suction hopper ship; i is the dredging volume of the trailing suction hopper ship during dredging, the mud dumping volume of the trailing suction hopper ship during dumping, the filling volume of the trailing suction hopper ship during filling and the sailing mileage of the trailing suction hopper ship during navigation.

9. The method for calculating energy efficiency of a dredger based on an energy efficiency analysis and evaluation model according to claim 1, characterized in that: In S4, an energy efficiency evaluation model for a single vehicle travel cycle and an anchor displacement ship cycle is constructed for a cutter suction dredger. Based on the working conditions, the time and fuel consumption of a single vehicle travel, anchor displacement ship, and pile replacement process are respectively counted. The cycle fuel consumption calculation formula is: ； Where: M is the fuel consumption of the cutter suction dredger, T is the fuel consumption time of the cutter suction dredger; i is the single trolley travel of the cutter suction dredger and the mileage of the anchor displacement ship of the cutter suction dredger.

10. The dredging vessel energy efficiency calculation method based on the energy efficiency analysis and evaluation model according to claim 1 is characterized in that: The method for establishing the ship energy consumption assessment level in S5 is: by calculating the historical discharge of the dredger per unit earthwork volume The calculation formula is as follows: ； Where: for Emission factors; is the total fuel consumption of the ship, unit: t; is the earthwork volume, unit is t; j is the type of fuel; The history obtained in S5 After the index range of the quantity is determined, a total of 5 levels are established for the ship energy consumption assessment level based on the historical number range, namely: Low energy consumption: Level 1-2, with a green logo; Medium: Level 3-4, with a yellow-brown logo; High energy consumption: Level 5, with a red logo.

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