A method for evaluating the energy-saving effect of economic speed optimization of a liquid cargo transport ship

Abstract

The application provides a liquid cargo transport ship economic speed optimization energy-saving effect evaluation method, and the method comprises the following steps: S1, constructing a sister ship speed-oil consumption relationship baseline under different load conditions and different weather conditions; S2, extracting a ship navigation experience operation behavior benchmark; S3, based on the comparison between the total oil consumption of the experience operation behavior and the actual optimization simulation total oil consumption, calculating the percentage difference between the total oil consumption of the experience operation behavior and the actual simulation oil consumption, that is, the optimization simulation energy-saving effect data, the application can intuitively compare and obtain the economic speed optimization energy-saving effect, and each differential segment has data to depend on. The influence of different weather conditions on the speed and energy-saving effect is particularly considered, the speed in bad weather and good weather is calculated respectively, the speed and main engine oil consumption relationship curves under two weather conditions are established, and the simulation oil consumption optimization effect is further compared.

Description

Technical Field

[0001] This invention belongs to the field of marine energy conservation technology, and specifically relates to a method for evaluating the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels. Background Technology

[0002] Maritime liquid cargo transportation is one of the most internationally competitive and market-driven transportation industries. Unlike the highly alliance-based scheduled routes of container shipping, and unlike the stable and robust market demand for bulk cargo transportation, the profitability of maritime liquid cargo transportation is influenced by the widest range of factors and exhibits the most unpredictable fluctuations. Therefore, it requires greater operational flexibility and is more difficult to manage. Optimizing operations using economic speed is a common energy-saving and emission-reduction method used by maritime liquid cargo transportation companies. However, the environmental conditions encountered by each vessel during its voyage are unique, and the actual contractual requirements for each voyage are different. Unless an identical vessel accompanies the vessel simultaneously on the same route, the effectiveness of implementing economic speed optimization is unknown.

[0003] Current domestic and international research mainly focuses on the statistics of a ship's final navigation performance. For example, maritime weather and navigation companies often use the difference between the estimated total fuel consumption of a voyage under average speed in good weather and the fuel consumption range permitted by the voyage contract as an evaluation method for the voyage's energy-saving effect. This involves defining a weather condition, such as winds below Beaufort scale 5 and waves below Dow scale 4, and statistically analyzing the speed, fuel consumption, and duration of the voyage under good weather conditions, assuming the entire voyage is navigated in good weather, and estimating the total fuel consumption. This is then compared to the total fuel consumption range assuming the entire voyage is navigated at the contract speed and with a fixed daily fuel consumption, to obtain the energy-saving effect ratio. However, this method does not actually yield real energy-saving results; it only compares the average efficiency of a voyage under certain conditions with a pre-set efficiency. Even if the weather conditions in the sea areas traversed by the voyage are very severe, and the economic speed optimization is implemented very effectively for energy saving, using this conventional method may lead to the opposite conclusion. Summary of the Invention

[0004] To address the problem that existing technologies can only confirm the economic speed optimization effect through the final navigation performance of the ship and it is difficult to consider the impact of environmental factors on the economic speed optimization effect, especially in harsh environments, this invention proposes a method for evaluating the energy-saving effect of economic speed optimization for liquid cargo transport ships. This method does not require the final completion of the voyage; the energy-saving effect of economic speed optimization can be obtained by direct comparison during navigation based on different full load conditions and different weather conditions.

[0005] A method for evaluating the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels, the specific steps of which are as follows:

[0006] S1: Establish a baseline for the relationship between speed and fuel consumption of sister ships under different loading conditions and weather conditions;

[0007] S2: Extract benchmarks for ship navigation experience and operational behavior: Calculate the average difference in speed between sister ships under different weather conditions. Confirm the benchmarks for ship navigation experience and operational behavior;

[0008] S3: Comparison of total fuel consumption based on experience-based operational behavior and actual optimized simulation: using the average difference in speed. The average speed in good weather and bad weather is calculated based on the current average speed. The total fuel consumption of the empirical operation behavior is calculated by substituting it into the speed-fuel consumption relationship baseline. The percentage difference between the total fuel consumption of the empirical operation behavior and the actual simulated fuel consumption is then calculated to evaluate the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels. The actual simulated fuel consumption is calculated based on the actual ship type, the recommended ship speed calculated using the ship speed optimization algorithm, weather conditions, and loading status, by substituting it into the corresponding speed-fuel consumption relationship baseline.

[0009] The method for establishing a baseline for the relationship between speed and fuel consumption of sister ships under different loading conditions and weather conditions includes the following steps:

[0010] S1: Identify similar ship types, including bulk carriers, tankers, and container ships;

[0011] S2: To identify sister ships, if the tonnage difference between ships of the same type does not exceed 5%, they are identified as sister ships.

[0012] S3: Extract manually entered data from the sister ship, including the start time Ts, end time Te, total sailing time h, average speed Vg, total main engine fuel consumption F, bow draft Df, stern draft Da, and total sailing distance S.

[0013] S4: Based on the ship's actual bow draft Df, stern draft Da, and design load condition draft, the ship's loading status is distinguished into three types: ballast, full load, and intermediate status.

[0014] S5: Extract AIS data from the sister ship. Based on the start time Ts and end time Te determined in S3, find the corresponding ship's start latitude and longitude (London) in the AIS data. s ,Lat s ), Ending latitude and longitude (Lon) e ,Lat e The data includes latitude and longitude information at the top of the hour during the process; based on the latitude and longitude data and time, wind level data for the corresponding time and location is extracted, and the average wind level for the day is calculated based on the wind level data;

[0015] S6: Based on S4-S5, differentiate the manually entered data of sister ships to obtain the ship status and wind level corresponding to each manually entered data, and differentiate them according to status;

[0016] S7: Each weather condition is classified as good weather or bad weather according to wind level. The distinction is that wind level greater than 4 is considered bad weather, and wind level less than or equal to 4 is considered good weather. The main engine fuel consumption analysis results of the sister ships are obtained. The power function method is used to fit the relationship curve between speed and main engine fuel consumption under the two weather conditions, and the main engine fuel consumption relationship curve at that speed is used as the benchmark.

[0017] The wind speed data were calculated according to the Beaufort scale.

[0018] The power function method is as follows:

[0019] S1: Assume that the main engine speed (X) and main engine fuel consumption (Y) satisfy a power function relationship:

[0020]

[0021] Where a can be any constant;

[0022] S2: Assume there are n pairs of main engine speeds (x1, x2, ..., x...). n ) and main engine fuel consumption (y1, y2, ..., y n Relationship between )

[0023]

[0024]

[0025] S3: Calculate the value of 'a' in S1:

[0026] a = (a1 + a2 + ... + an) / n

[0027] Manually entered data for a total sailing time of h = 24 hours will be retained.

[0028] The method for distinguishing between ballast, full load, and intermediate states is as follows:

[0029] Ballast:

[0030] Fully loaded:

[0031] Intermediate state: All others are intermediate states;

[0032] The vessel is designed with a full-load bow draft of Mf and a stern draft of Ma; a ballast bow draft of Bf and a stern draft of Ba; and an actual bow draft of Df and a stern draft of Da.

[0033] The method for extracting benchmarks for ship navigation experience and operational behavior is as follows:

[0034] S1: Extract the manually entered data for each ship over a continuous period of time;

[0035] S2: Based on the latitude and longitude information of the AIS data, obtain the meteorological wind level data corresponding to the latitude and longitude of the hour within a certain time range, and at the same time obtain the air speed data provided by the AIS data. Store the meteorological wind level data and the air speed data in the form of a data group.

[0036] S3: Divide the data set obtained in S2 into two weather conditions: average ship speed under severe weather and average ship speed under good weather. Average speed in bad weather Calculate the difference in flight speed under two weather conditions. ;

[0037] S4: Extract all n data ranges N1, N2, N3, ..., Nn that satisfy S1 for a single ship, and calculate the speed difference between the n ranges according to S2 and S3. Repeat this step to extract the speed difference of other sister ships, and finally obtain the average of all speed differences. .

[0038] The difference in flight speed under the two weather conditions The calculation method is as follows:

[0039]

[0040] Average speed in good weather and Average speed during severe weather.

[0041] The method for determining severe and good weather is as follows: wind speeds greater than level 4 are considered severe weather, and wind speeds less than or equal to level 4 are considered good weather.

[0042] The method for comparing total fuel consumption based on empirical operating behavior with actual optimized simulation total fuel consumption is as follows:

[0043] S1: Calculate the overall average speed based on the estimated departure time, estimated arrival time, and route data. ;

[0044] S2: According to the above Calculate the latitude, longitude, and time of the ship's expected arrival every 6 hours, and extract the weather forecast wind level data based on the latitude, longitude, and time to determine whether the weather is good or bad at that time and place, and finally obtain the distribution of good and bad weather along the overall route;

[0045] S3: Calculate the ship's average speed V under good weather conditions based on the duration of good and bad weather, their average speed, and route distance. 好 and the average speed V in bad weather 恶 ;

[0046] S4: Calculate the total fuel consumption for empirical operational behavior under this route;

[0047] 1) Determine the ship's loading status based on the draft at the bow and stern;

[0048] 2) Calculate the total fuel consumption under empirical operation, based on the average cruising speed V in good weather. 好 and the average speed V in bad weather 恶， Based on the loading status, the speed is input into the corresponding speed and main engine fuel consumption relationship curve to obtain the hourly fuel consumption under the corresponding weather conditions, and the total fuel consumption of the route is calculated.

[0049] S5: Compare the total fuel consumption of the experienced operating behavior with the actual simulated fuel consumption, and obtain the percentage difference, which is the data on the optimized simulation energy-saving effect.

[0050] Calculate the average speed V under good weather conditions. 好 and the average speed V in bad weather 恶 The method is as follows:

[0051]

[0052]

[0053] t 好 For the duration of good weather, t 恶 S represents the duration of severe weather, S represents the total route mileage, and V represents the duration of severe weather. d This represents the difference in flight speed under two different weather conditions.

[0054] This invention proposes a method for evaluating the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels. The method involves: S1: establishing a baseline relationship between speed and fuel consumption of sister ships under different load conditions and weather conditions; S2: extracting benchmarks of ship navigation experience and operational behavior; S3: calculating the percentage difference between the total fuel consumption of the experience operation behavior and the actual optimized simulation fuel consumption based on a comparison of the total fuel consumption of the experience operation behavior and the actual simulated total fuel consumption. This percentage represents the optimized simulation energy-saving effect data, allowing for a direct comparison of the energy-saving effect of economic speed optimization and ensuring data availability for each differential segment. The method specifically considers the impact of different weather conditions on speed and energy-saving effects, calculating speeds under severe and favorable weather conditions and establishing speed and main engine fuel consumption relationship curves under both weather conditions, further comparing the optimized simulation fuel consumption effect. This invention also considers the impact of different load conditions on ship energy consumption, evaluating the energy-saving effect of optimizing the economic speed of transport vessels under different load conditions.

[0055] This invention conducts evaluations based on the fusion of ship AIS data, unified meteorological data, and message data, requiring no additional equipment investment or other non-recognized data sources. This invention breaks down the voyage process into micro-segments, simulating historical operating modes without energy-saving operations for each micro-segment based on the actual performance of the voyage and a historical performance database. The operation is simple. Attached Figure Description

[0056] Figure 1 Flowchart of a method for evaluating the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels.

[0057] Figure 2 : Curve showing the relationship between cruising speed and engine fuel consumption under good weather conditions.

[0058] Figure 3 : Curve showing the relationship between speed and fuel consumption of the main engine under severe weather conditions. Detailed Implementation

[0059] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0060] Example 1:

[0061] like Figure 1 As shown, a method for evaluating the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels is described, with the following specific steps:

[0062] S1: Establish a baseline for the relationship between speed and fuel consumption of sister ships under different loading conditions and weather conditions;

[0063] S2: Extract benchmarks for ship navigation experience and operational behavior;

[0064] S3: Based on the comparison between the total fuel consumption of the empirical operation behavior and the actual optimized simulation total fuel consumption, calculate the percentage difference between the total fuel consumption of the empirical operation behavior and the actual simulated fuel consumption, which is the data of the energy-saving effect of the optimized simulation.

[0065] Example 2: Establishing a baseline for the relationship between speed and fuel consumption of sister ships under different loading conditions and weather conditions.

[0066] ① Identify ships of the same type and classify them into three categories: bulk carriers, tankers, and container ships;

[0067] ② To identify sister ships, ships of the same type are considered sister ships if their tonnage differs by no more than 5%. For example, four ships A, B, C, and D: ship A has a tonnage of 10,000 tons, ship B has a tonnage of 12,000 tons, ship C has a tonnage of 10,300 tons, and ship D has a tonnage of 9,700 tons. Therefore, ships A, C, and D are sister ships.

[0068] ③ Extract manually entered data from sister ships. This manually entered data consists of daily message data, including the start time Ts, end time Te, total daily sailing time h, average speed Vg, total main engine fuel consumption F, bow draft Df, stern draft Da, and total sailing distance S. Only manually entered data for a total sailing time h = 24 hours is retained.

[0069] ④ Based on the ship's actual bow draft Df, stern draft Da, and design load condition draft, the ship's loading status is distinguished into three types: ballast, full load, and intermediate status. Assume the design full load bow draft Mf, stern draft Ma; and ballast bow draft Bf, stern draft Ba. The distinction method is as follows:

[0070] 1)Ballast: (Bf+Ba)*0.8 / 2<(Df+Da) / 2<(Bf+Ba)*1.20 / 2

[0071] 2) Fully loaded: (Mf+Ma)*0.8 / 2<(Df+Da) / 2<(Mf+Ma)*1.20 / 2

[0072] 3) Intermediate state: All others are intermediate states;

[0073] ⑤ Extract the AIS data of the sister ship. Based on the start time Ts and end time Te determined in step ③, find the corresponding ship's start latitude and longitude (London) in the AIS data. s ,Lat s ), Ending latitude and longitude (Lon) e ,Lat e The data includes latitude and longitude information at each hour in the process. Based on the latitude and longitude data and time, wind level data for the corresponding time and location is extracted, and the wind level is calculated according to the Beaufort scale. The average wind level for that day is then calculated. For example, for a ship A, step ③, the start time Ts is 2022-9-28 00:00 and the end time Te is 2022-9-29 00:00. There are 23 hours of latitude and longitude data in between, corresponding to 25 hours of wind level distribution. The average wind level for this period is calculated by averaging (rounding to the nearest integer).

[0074] ⑥ Based on steps ⑤ and ④, the manually entered data from sister ships can be distinguished to obtain the ship status and wind level corresponding to each manually entered data. The data can be categorized according to status, as shown in Table 1-3.

[0075] Table 1: Ballast Status Statistics of Sister Ships

[0076] A Ballast 24 18 35 2 A Ballast 24 19 38 3 B Ballast 24 18 35 3 B Ballast 24 17 37 3 C Ballast 24 18 35 3 A Ballast 24 20 35 3 C Ballast 24 18 38 4 A Ballast 24 21 40 5 C Ballast 24 18 35 3

[0077] Table 2: Statistics on the Full Load Status of Sister Ships

[0078] A Fully loaded 24 18 45 2 A Fully loaded 24 19 48 3 B Fully loaded 24 18 45 3 B Fully loaded 24 17 47 3 C Fully loaded 24 18 45 3 A Fully loaded 24 20 45 3 C Fully loaded 24 18 48 4 A Fully loaded 24 21 49 5 C Fully loaded 24 18 45 3

[0079] Table 3: Intermediate Status Statistics of Sister Ships

[0080] A intermediate state 24 18 45 2 A intermediate state 24 19 48 3 B intermediate state 24 18 45 3 B intermediate state 24 17 47 3 C intermediate state 24 18 45 3 A intermediate state 24 20 45 3 C intermediate state 24 18 48 4 A intermediate state 24 21 49 5 C intermediate state 24 18 45 3

[0081] ⑦ Each weather condition is classified as good weather or bad weather according to wind level. The distinction is that wind level greater than 4 is bad weather and wind level less than or equal to 4 is good weather. The main engine fuel consumption analysis results of the sister ships are obtained. The power function method is used to derive the relationship curve between speed and main engine fuel consumption under the two weather conditions, and the relationship curve between speed and main engine fuel consumption is used as the benchmark.

[0082] Example 3: Extracting benchmarks for ship navigation experience and operational behavior.

[0083] ① Extract the continuous time data obtained from Examples 2-③ for each ship. For example, for ship A, the data entered from January 1, 2022 to January 5, 2022 is continuous, so it is retained.

[0084] ② For the data time range obtained in step ①, according to the method for extracting AIS data in Examples 2-⑤, obtain the meteorological wind level data corresponding to the latitude and longitude of each hour within the time range, and at the same time obtain the hourly speed data provided by the AIS data. For example, for ship A, from January 1, 2022 to January 5, 2022, 120 hourly data points over 5 days are obtained, and the speed and wind level at each point are obtained;

[0085] ③ Divide the data set obtained in step ② into two weather conditions: severe weather and good weather, to determine the average ship speed under these two conditions: V 好 and V 恶 Calculate the difference in flight speed V under two weather conditions. d (Wind force greater than level 4 is considered severe weather, and level 4 or less is considered good weather.) See the formula:

[0086]

[0087] ④ Extract all n data ranges N1, N2, N3, ..., Nn that meet the criteria in step ① for a single ship, and calculate the speed difference among these n ranges according to steps ② and ③. Repeat this process to extract the speed differences among other sister ships, and finally obtain the average speed difference V. da .

[0088] Comparison of total fuel consumption based on empirical operating behavior and actual optimized simulation:

[0089] ① Calculate the overall average speed V based on the estimated departure time, estimated arrival time, and route data. avg ;

[0090] ② According to the overall Vavg The system calculates the estimated latitude, longitude, and time of arrival every 6 hours, extracts weather forecast wind speed data, and determines whether the weather at that time and location is good or bad, ultimately obtaining the overall distribution of good and bad weather along the route. For example: Ship A, with an estimated departure time of 00:00 on September 25, 2022, and an estimated arrival time of 00:00 on September 30, 2022, has a route length of 1440 nautical miles and sails along the planned route at a speed of 12 knots. It obtains 20 sets of latitude, longitude, and longitude data for arrival at 6 hours, along with corresponding weather conditions. Of these, 15 are good weather and 5 are bad weather.

[0091] ③ After obtaining the overall distribution of meteorological conditions, the duration of good weather, t 好 (Unit: h), Duration of severe weather: t 恶 (Unit: h) Calculate the average speed V under good weather conditions. 好 and the average speed V in bad weather 恶 Calculate according to the formula:

[0092]

[0093]

[0094] In the formula, S represents the total mileage of the route.

[0095] ④ Calculate the total fuel consumption for the empirical operation behavior under this route.

[0096] 1) Determine the ship's loading status based on its bow and stern draft;

[0097] 2) Calculate the total fuel consumption under empirical operation, based on the average cruising speed V in good weather. 好 and the average speed V in bad weather 恶， Substituting the load conditions and speed into Examples 2-7, we obtain the hourly fuel consumption under the corresponding weather conditions, and finally calculate the total fuel consumption of the route. For example, ship A, fully loaded, sails at a speed of 12 knots in good weather for 60 hours, and at a speed of 11 knots in bad weather for 20 hours. Based on the fuel consumption baseline obtained in Examples 2-7, the fuel consumption is 2 tons / hour at a speed of 12 knots in good weather and 1.8 tons / hour at a speed of 11 knots in bad weather. The total fuel consumption is 2*60 + 1.8*20 = 156 tons.

[0098] ⑤ Compare the total fuel consumption of the experienced operating behavior with the actual simulated fuel consumption to obtain the percentage difference. For example, if the total fuel consumption of the experienced operating behavior is 156 tons, and the actual optimized simulated fuel consumption is 150 tons, the optimized simulation improves the energy-saving effect by 3.846%.

[0099] The method for calculating the recommended speed using the ship speed optimization algorithm has been disclosed in patent CN111090911A "An auxiliary method for ship speed energy saving optimization". The method for obtaining the corresponding actual simulated fuel consumption is as follows: given the estimated speed and start and end times of each segment under the simulation calculation, based on the ship's loading status, the start and end latitude and longitude of each segment under the total route, and the start and end times of the segment, the corresponding weather conditions are determined. Then, based on the baseline of the speed-fuel consumption relationship, the total fuel consumption of each segment can be obtained, and finally the total fuel consumption of the route is obtained, which is the corresponding actual simulated fuel consumption. For example: Ship A is fully loaded, and the total route is divided into 10 segments. Each segment has a corresponding start and end point. Given the recommended speeds V1, V2, V3, ..., V10 for the 10 segments, the weather conditions for each segment are obtained. Based on the intermediate state statistics table of sister ships, the hourly fuel consumption of the main engine under the corresponding weather, loading status, and speed is obtained. Thus, the fuel consumption of the 10 segments (F1, F2, ..., F10) is calculated. The total fuel consumption F = F1 + F2 + ... + F10 is obtained, which corresponds to the actual simulated fuel consumption.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is impossible to exhaustively list all possible implementations here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of the present invention.

Claims

1. A method for evaluating the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels, characterized in that, The specific steps are as follows: S1: Establish a baseline for the relationship between speed and fuel consumption of sister ships under different load conditions and weather conditions; if the tonnage difference between the two ships is no more than 5%, they are identified as sister ships. The loading conditions include: ballast, full load, and intermediate conditions; the meteorological conditions include: good weather and bad weather. S2: Extract benchmarks for ship navigation experience and operational behavior: Calculate the average difference in speed between sister ships under different weather conditions. Confirm the benchmarks for ship navigation experience and operational behavior; S3: Comparison of total fuel consumption based on empirical operating behavior and actual optimized simulation: using the average difference in speed. The average speed in good weather and bad weather is calculated based on the current average speed. The total fuel consumption of the empirical operation behavior is calculated by substituting it into the speed-fuel consumption relationship baseline. The percentage difference between the total fuel consumption of the empirical operation behavior and the actual simulated fuel consumption is then calculated to evaluate the energy-saving effect of optimizing the economic speed of liquid cargo transport vessels. The actual simulated fuel consumption is calculated based on the actual ship type, the recommended ship speed calculated using the ship speed optimization algorithm, weather conditions, and loading status, by substituting it into the corresponding speed-fuel consumption relationship baseline. The method for extracting benchmarks for ship navigation experience and operational behavior is as follows: S1: Extract manually entered data for each ship over a continuous period of time; S2: Based on the latitude and longitude information of the AIS data, obtain the meteorological wind level data corresponding to the latitude and longitude of the hour within a certain time range, and at the same time obtain the air speed data provided by the AIS data. Store the meteorological wind level data and the air speed data in the form of a data group. S3: Divide the data set obtained in S2 into two weather conditions: average ship speed under severe weather and average ship speed under good weather. Average speed in bad weather Calculate the difference in flight speed under two weather conditions. ; S4: Extract all n data ranges N1, N2, N3, ..., Nn that satisfy S1 for a single ship, and calculate the speed difference between the n ranges according to S2 and S3. Repeat this step to extract the speed difference of other sister ships, and finally obtain the average of all speed differences. ; The difference in flight speed under the two weather conditions The calculation method is as follows: Average speed in good weather and Average speed during severe weather.

2. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 1, characterized in that, The method for establishing a baseline for the relationship between speed and fuel consumption of sister ships under different loading conditions and weather conditions includes the following steps: S1: Identify similar ship types, including bulk carriers, tankers, and container ships; S2: Confirm sister ships; S3: Extract manually entered data from the sister ship, including the start time Ts, end time Te, total sailing time h, average speed Vg, total main engine fuel consumption F, bow draft Df, stern draft Da, and total sailing distance S. S4: Based on the ship's actual bow draft Df, stern draft Da, and design load condition draft, the ship's loading status is distinguished into three types: ballast, full load, and intermediate status. S5: Extract AIS data from the sister ship. Based on the start time Ts and end time Te determined in S3, find the corresponding ship's start latitude and longitude (London) in the AIS data. s ,Lat s ), Ending latitude and longitude (Lon) e ,Lat e The data includes latitude and longitude information at the top of the hour during the process; based on the latitude and longitude data and time, wind level data for the corresponding time and location is extracted, and the average wind level for the day is calculated based on the wind level data; S6: Based on S4-S5, differentiate the manually entered data of sister ships to obtain the ship status and wind level corresponding to each manually entered data, and differentiate them according to status; S7: Each weather condition is classified as good weather or bad weather according to wind level. The distinction is that wind level greater than 4 is bad weather and wind level less than or equal to 4 is good weather. The main engine fuel consumption analysis results of the sister ships are obtained. The power function method is used to fit the relationship curve between speed and main engine fuel consumption under the two weather conditions, and the main engine fuel consumption relationship curve at that speed is used as the benchmark.

3. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 2, characterized in that, The wind level data was calculated according to the Beaufort scale.

4. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 2, characterized in that, The power function method is as follows: S1: Assume that the main engine speed (X) and main engine fuel consumption (Y) satisfy a power function relationship: Where a can be any constant; S2: Assume there are n pairs of main engine speeds (x1, x2, ..., x...). n ) and main engine fuel consumption (y1, y2, ..., y n Relationship between ) S3: Calculate the value of 'a' in S1: a = (a1 + a2 + ... + an) / n That is, we get a.

5. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 2, characterized in that, Manually entered data for a total sailing time of h = 24 hours will be retained.

6. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 2, characterized in that, The method for distinguishing between ballast, full load, and intermediate states is as follows: Ballast: Fully loaded: Intermediate state: All others are intermediate states; The vessel is designed with a full-load bow draft of Mf and a stern draft of Ma; a ballast bow draft of Bf and a stern draft of Ba; and an actual bow draft of Df and a stern draft of Da.

7. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 1, characterized in that, The method for determining severe and good weather is as follows: wind speeds greater than level 4 are considered severe weather, and wind speeds less than or equal to level 4 are considered good weather.

8. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 1, characterized in that, The method for comparing total fuel consumption based on empirical operating behavior with actual optimized simulation total fuel consumption is as follows: S1: Calculate the overall average speed based on the estimated departure time, estimated arrival time, and route data. ; S2: According to the above Calculate the expected latitude, longitude, and time of arrival of the ship every 6 full hours, and extract the weather forecast wind level data based on the latitude, longitude, and time to determine whether the weather is good or bad at that time and place, and finally obtain the distribution of good and bad weather along the overall route; S3: Calculate the ship's average speed V under good weather conditions based on the duration of good and bad weather, their average speed, and route distance. 好 and the average speed V in bad weather 恶 ; S4: Calculate the total fuel consumption for empirical operational behavior under this route; 1) Determine the ship's loading status based on the draft at the bow and stern; 2) Calculate the total fuel consumption under empirical operation, based on the average cruising speed V in good weather. 好 and the average speed V in bad weather 恶， Based on the loading status, the speed is input into the corresponding speed and main engine fuel consumption relationship curve to obtain the hourly fuel consumption under the corresponding weather conditions, and the total fuel consumption of the route is calculated. S5: Compare the total fuel consumption of the experienced operating behavior with the actual simulated fuel consumption to obtain the percentage difference, which is the data on the optimized simulation energy-saving effect.

9. The method for evaluating the energy-saving effect of optimizing the economic speed of a liquid cargo transport vessel as described in claim 8, characterized in that, Calculate the average speed V under good weather conditions. 好 and the average speed V in bad weather 恶 The method is as follows: t 好 For the duration of good weather, t 恶 S represents the duration of severe weather, S represents the total route mileage, and V represents the duration of severe weather. da This represents the average difference in flight speed under different weather conditions.

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