Ship energy consumption calculation method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of ships, and discloses a ship energy consumption calculation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining ship operation data and multi-source sensor data collected by a target sensor of a target ship; dynamically screening the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data; performing dynamic filtering on the first target multi-source sensor data based on the ship operation data to obtain second target multi-source sensor data; performing dynamic compensation on the second target multi-source sensor data based on the ship operation data to obtain corrected multi-source sensor data; calculating sub-ship energy consumption based on the corrected multi-source sensor data and the sensor type of the corresponding target sensor; and fusing the sub-ship energy consumption based on the ship operation data to obtain ship energy consumption data.

Description

Technical Field

[0001] The present invention relates to the field of ship technology, and in particular to a method, device, computer equipment and storage medium for calculating ship energy consumption. Background Art

[0002] In the shipping industry, the energy efficiency and environmental performance of ships are receiving increasing attention. Regulations such as the International Maritime Organization's Energy Efficiency Design Index, Ship Energy Efficiency Management Plan, and Carbon Intensity Index require ships to quantify their energy consumption and emissions. Based on this, accurate calculation of energy consumption data is the basis for compliance.

[0003] However, the ship energy consumption calculation method in the related art usually collects liquid level or flow parameters through sensors such as flow meters and liquid level gauges, and then calculates energy consumption based on a single parameter. However, this method has significant defects: First, the sensors installed on ships operating on rivers and oceans are seriously disturbed by the water environment. The ship's pitch or roll will cause distortion in the liquid level measurement, and in extreme cases, the measurement error can reach 15%; second, due to the zero drift error of the ship flow meter (for example, the typical value is ±0.5%) and the nonlinear error of the liquid level sensor (the error in the bilge part is up to 3%), the error between the sensor collected data and the actual data is too large; in addition, fuel refueling usually relies on manual recording, and its input error rate is about 2% to 5%; affected by the above factors, the traditional single-source calculation method generally has an error of more than 5% in actual ship applications, which makes it difficult to meet the calculation accuracy requirements of ship energy consumption calculation data.

[0004] In order to solve the error problem of single-source sensors, another energy consumption calculation method obtains multiple energy consumption-related parameters of the ship as multi-source data by collecting multiple sensor data of the ship, and further fuses the multi-source data to calculate the energy consumption; however, this method faces the core challenge of multi-source data fusion; first, multi-source data has data heterogeneity due to the influence of sampling frequency, sensor type, etc., and it is difficult to fuse the energy consumption data calculated based on the multi-source data obtained by fusion due to the influence of sampling frequency, sensor type, etc.; secondly, each sensor in the multi-source data may have collection errors and calculation errors like those in the single energy consumption calculation method. When the multi-source data is finally fused to obtain the energy consumption, it is still difficult to meet the calculation accuracy requirements of the ship energy consumption calculation data due to the influence of the errors of each sensor.

[0005] Therefore, the ship energy consumption calculation method in the related art has the technical problem of inaccurate energy consumption calculation and failure to meet the calculation accuracy requirements of ship energy consumption calculation data. Summary of the Invention

[0006] In view of this, the present invention provides a ship energy consumption calculation method, device, computer equipment and storage medium to solve the problem of inaccurate energy consumption calculation in the ship energy consumption calculation method in the related art.

[0007] In a first aspect, the present application provides a ship energy consumption calculation method, comprising: obtaining ship operation data and multi-source sensor data collected by target sensors of a target ship; wherein the target sensors include flow meters, liquid level meters, and AIS systems; dynamically screening the multi-source sensor data based on variation characteristics of the ship operation data and the multi-source sensor data, to obtain first target multi-source sensor data; wherein the variation characteristics of the ship operation data and the multi-source sensor data correspond to the time sequence of the multi-source sensor data; dynamically filtering the first target multi-source sensor data based on the ship operation data, to obtain second target multi-source sensor data; wherein the ship operation data corresponds to the time sequence of the multi-source sensor data; dynamically compensating the second target multi-source sensor data based on the ship operation data, to obtain corrected multi-source sensor data; wherein the ship operation data corresponds to the time sequence of the second target multi-source sensor data; calculating sub-ship energy consumption based on the corrected multi-source sensor data and corresponding sensor types; and fusing each of the sub-ship energy consumption based on the ship operation data, to obtain ship energy consumption data.

[0008] As an exemplary embodiment, the dynamic screening of the multi-source sensor data based on the variation characteristics of the ship operation data and the multi-source sensor data to obtain the first target multi-source sensor data comprises: dynamically screening the multi-source sensor data based on the ship operation data to obtain first multi-source sensor data within a preset numerical range; wherein the ship operation data corresponds to the time sequence of the multi-source sensor data, and the preset numerical range is determined based on the ship operation data and the numerical characteristics of the ship operation data corresponding to the time sequence; and dynamically screening the first multi-source sensor data based on the ship operation data and the variation characteristics to obtain the first target multi-source sensor data.

[0009] As an exemplary embodiment, the dynamic screening of the first multi-source sensor data based on the ship operation data and the variation characteristics to obtain the first target multi-source sensor data comprises:

[0010] calculating a first variation rate of a preset number of the multi-source sensor data that are continuous in time sequence; determining a first variation rate threshold based on the sensor type of the target sensor corresponding to the first variation rate; screening the multi-source sensor data based on the first variation rate threshold and the first variation rate, to obtain a plurality of second multi-source sensor data that exceed the first variation rate threshold; obtaining a first variation trend characteristic of third multi-source sensor data that are later in time sequence than the second multi-source sensor data; and excluding the second multi-source sensor data if the first variation trend characteristic meets a preset variation characteristic.

[0011] As an exemplary embodiment, before the second multi-source sensor data is eliminated, the ship energy consumption calculation method further comprises: obtaining a second change trend feature of the ship operation data based on the sensor type; if the second change trend feature meets a preset change feature, eliminating the second multi-source sensor data.

[0012] As an exemplary embodiment, the dynamic change feature screening of the first multi-source sensor data based on the ship operation data and the change feature to obtain first target multi-source sensor data comprises: calculating a change rate of a preset number of time-sequentially continuous multi-source sensor data; determining a change rate threshold based on the sensor type corresponding to the change rate; screening the multi-source sensor data based on the change rate threshold and the change rate to obtain a plurality of second multi-source sensor data exceeding the change rate threshold; obtaining a change trend feature of second target multi-source sensor data that is time-sequentially later than the second multi-source sensor data; if the change trend feature meets a preset change feature, eliminating the second multi-source sensor data.

[0013] As an exemplary embodiment, the dynamic filtering of the first target multi-source sensor data based on the ship operation data to obtain second target multi-source sensor data comprises: determining an operation state corresponding to each multi-source sensor data based on the ship operation data; determining a first filtering parameter and a second filtering parameter based on the operation state; filtering the first target multi-source sensor data based on the first filtering parameter and the second filtering parameter to obtain the second target multi-source sensor data.

[0014] As an exemplary embodiment, the dynamic compensation of the second target multi-source sensor data based on the ship operation data to obtain corrected multi-source sensor data comprises:

[0015] Extracting trim angle data, roll angle data, forward draft data, aft draft data, ship length data, and ship width data corresponding to the second target multi-source sensor data in time sequence from the ship operation data;

[0016] Compensating the second target multi-source sensor data based on the trim angle data, the roll angle data, the forward draft data, the aft draft data, the ship length data, and the ship width data using the following formula to obtain the corrected multi-source sensor data:

[0017]

[0018] θ = arctan(draft aft -draft fore) / LBP

[0019]

[0020] wherein, is the corrected multi-source sensor data, X is the second target multi-source sensor data, θ is the pitch angle data, draft fore is the forward draft data, draft aft is the aft draft data, LBP is the ship length data, Beam is the ship width data, is the roll angle data.

[0021] As an exemplary embodiment, the ship energy consumption data is obtained by fusing each of the sub-ship energy consumptions based on the ship operation data, including: in the ship operation data, at least one of the speed change rate data, the roll angle data and the wind speed data is obtained; the energy consumption fusion weight corresponding to each of the sensors is determined based on the speed change rate data, the roll angle data or the wind speed data; and the ship energy consumption data is obtained by fusing each of the sub-ship energy consumptions based on the energy consumption fusion weight.

[0022] In a second aspect, the present application provides a computer device, including: a memory and a processor, which are in communication connection with each other, and the memory stores computer instructions; the processor executes the computer instructions to perform the ship energy consumption calculation method of the first aspect or any of the corresponding embodiments thereof.

[0023] In a third aspect, the present application provides a computer readable storage medium, which stores computer instructions for making a computer execute the ship energy consumption calculation method of the first aspect or any of the corresponding embodiments thereof.

[0024] The application provides a ship energy consumption calculation method, device, computer equipment and storage medium, the ship energy consumption calculation method comprises the following steps: obtaining ship operation data and multi-source sensor data collected by a target sensor of a target ship; wherein the target sensor comprises a flowmeter, a liquid level meter and an AIS system; performing dynamic screening on the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data, to obtain first target multi-source sensor data; wherein the change characteristics of the ship operation data and the multi-source sensor data correspond to the time sequence of the multi-source sensor data; performing dynamic filtering on the first target multi-source sensor data based on the ship operation data, to obtain second target multi-source sensor data; wherein the ship operation data correspond to the time sequence of the multi-source sensor data; performing dynamic compensation on the second target multi-source sensor data based on the ship operation data, to obtain corrected multi-source sensor data; wherein the ship operation data correspond to the time sequence of the second target multi-source sensor data; calculating sub-ship energy consumption based on the corrected multi-source sensor data and the corresponding sensor type; and fusing each sub-ship energy consumption based on the ship operation data, to obtain ship energy consumption data; the above method can consider the respective collection errors of each sensor of the multi-source sensor data, the errors caused by the interference of the water environment on the ship, and the navigation state of the target ship, to perform dynamic screening, dynamic filtering and dynamic compensation on the multi-source sensor data, so that more accurate corrected multi-source sensor data is obtained; further, the corrected multi-source sensor data is used to calculate sub-ship energy consumption, and the ship operation data is used to fuse each sub-ship energy consumption, so that more accurate ship energy consumption data is obtained. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the specific embodiments or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative effort.

[0026] Figure 1 It is a flowchart of a ship energy consumption calculation method according to an embodiment of the present application;

[0027] Figure 2 It is a structural block diagram of a ship energy consumption calculation device according to an embodiment of the present application;

[0028] Figure 3 It is a hardware structure schematic diagram of a computer equipment according to an embodiment of the present application. EMBODIMENT

[0029] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of the present application.

[0030] According to the embodiments of the present application, a ship energy consumption calculation method is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0031] In the present embodiment, a ship energy consumption calculation method is provided, Figure 1 is a flowchart of the ship energy consumption calculation method according to the embodiments of the present application, as Figure 1 shown, the flow includes the following steps:

[0032] Step S101, acquiring ship operation data and multi-source sensor data collected by target sensors of a target ship; wherein the target sensors include flow meters, liquid level meters, AIS systems.

[0033] Exemplarily, the multi-source sensor data includes flow meter data, liquid level data and AIS data, which can be collected by a processor communicating with the target sensors, wherein the target sensors include at least one of flow meters, liquid level meters, and ship automatic identification systems (Automatic Identification System, AIS).

[0034] Exemplarily, the ship operation data includes ship main engine parameter data, trim value, roll, main engine speed, etc., wherein the ship operation data can be collected by a processor communicating with the main engine.

[0035] Step S102, dynamically filtering the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data; wherein the change characteristics of the ship operation data and the multi-source sensor data correspond to the time sequence of the multi-source sensor data.

[0036] To solve the error problem of multi-source sensors, in the present embodiment, the multi-source sensor data is dynamically filtered based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data.

[0037] As a possible implementation, after obtaining the multi-source sensor data, the data distribution range of the multi-source sensor data is extracted as a numerical feature, and each data is further screened or verified according to the data feature, and data conforming to the preset data distribution range is retained, and data not conforming to the preset distribution range is removed to obtain the first target multi-source sensor data, so as to eliminate the collection errors caused by various error factors of the sensor.

[0038] Exemplarily, when extracting the numerical features of the multi-source sensor data, in order to consider the data heterogeneity caused by the collection frequency of the sensor, different window sizes and different sliding steps of the sliding window can be provided for the sensor types of the multi-source sensor data to divide the sensor data of each type, so as to obtain sub-flow meter data, sub-liquid level data and sub-AIS speed data with sliding window sizes and sliding step lengths divided according to the sensor types, extract the data distribution range of the sub-flow meter data, the sub-liquid level data and the sub-AIS speed data as the numerical features, and further screen or verify each data according to the numerical features, retain the data conforming to the preset data distribution range, and remove the data not conforming to the preset distribution range, and finally combine the sub-flow meter data and the sub-liquid level data after removing the abnormal values in time sequence to obtain the first target multi-source sensor data, so as to eliminate the collection errors caused by various error factors of the sensor.

[0039] In an embodiment, in order to consider the ship running state to correct the sensor data, to solve the error problem of the multi-source sensor caused by the inaccurate collection of the sensor due to the ship running state, the multi-source sensor data can be dynamically screened based on the ship running data to obtain the first target multi-source sensor data. As a possible implementation, after obtaining the multi-source sensor data, the data distribution range of the multi-source sensor data is extracted as a numerical feature, and the running state of the ship is determined according to the ship running data corresponding to the time sequence of each multi-source sensor data, and the data distribution range is further corrected according to the running state of the ship. Finally, each data is screened or verified according to the corrected data distribution range, data conforming to the preset data distribution range is retained, and data not conforming to the preset distribution range is removed to obtain the first target multi-source sensor data, so as to eliminate the collection errors caused by various error factors of the sensor.

[0040] In an embodiment, the multi-source sensor data can be dynamically filtered based on the change feature of the multi-source sensor data alone to obtain first target multi-source sensor data; as a possible implementation, after obtaining the multi-source sensor data, the change feature of the multi-source sensor data is extracted, and each data is further filtered or verified according to the change feature, and the data meeting the preset change feature is retained, and the data not meeting the preset change feature is removed to obtain the first target multi-source sensor data, so as to eliminate the collection errors caused by various error factors of the sensor.

[0041] Exemplarily, after obtaining the multi-source sensor data, the change rate of the last N continuous sampling points is calculated as the instantaneous change rate, the instantaneous change rate is taken as the change feature of the multi-source sensor data, and each data is further filtered or verified, the data meeting the preset change feature is retained, and the data not meeting the preset change feature is removed to obtain the first target multi-source sensor data, so as to eliminate the collection errors caused by various error factors of the sensor; wherein N is a positive integer greater than or equal to 2.

[0042] Exemplarily, after obtaining the multi-source sensor data, the change rate of the last N continuous sampling points is calculated as the instantaneous change rate, the instantaneous change rate is taken as the change feature of the multi-source sensor data, and each data is further filtered or verified, the data meeting the preset change feature is retained, and the data not meeting the preset change feature is removed to obtain the first target multi-source sensor data, so as to eliminate the collection errors caused by various error factors of the sensor; wherein N is a positive integer greater than or equal to 2.

[0043] Exemplarily, N is 3.

[0044] Exemplarily, in order to consider the data heterogeneity caused by the sensor type of the target sensor to modify the sensor data, when the instantaneous change rate and / or the long-term change rate are taken as the change feature of the multi-source sensor data to filter or verify each data, different judgment thresholds can be given to the change feature according to the sensor type of the target sensor.

[0045] Specifically, for the instantaneous change rate of the flow corresponding to the flow meter, a judgment threshold of [-3% / s, +5% / s] can be given to it, and the data with the instantaneous change rate in the interval of [-3% / s, +5% / s] is retained, and the data with the instantaneous change rate not in the interval of [-3% / s, +5% / s] is removed.

[0046] Specifically, for the liquid level height instantaneous change rate corresponding to the liquid level meter, a determination threshold of [-2 cm / s, +1.5 cm / s] can be assigned; data in which the liquid level height instantaneous change rate is in the interval [-2 cm / s, +1.5 cm / s] is retained, and data in which the instantaneous change rate is not in the interval [-2 cm / s, +1.5 cm / s] is removed.

[0047] Specifically, for the AIS data corresponding to the AIS speed instantaneous change rate, a determination threshold of [-0.3 m / s 2 , +0.25 m / s 2 ] can be assigned; data in which the AIS speed instantaneous change rate is in the interval [-0.3 m / s 2 , +0.25 m / s 2 ] is retained, and data in which the instantaneous change rate is not in the interval [-0.3 m / s 2 , +0.25 m / s 2 ] is removed.

[0048] In an embodiment, in order to consider the ship running state and at the same time consider the data heterogeneity caused by the sensor type of the target sensor to correct the sensor data to solve the error problem of the multi-source sensor caused by the inaccuracy of the sensor acquisition due to the ship running state, the multi-source sensor data can be filtered based on the ship running data and the change characteristics to obtain first target multi-source sensor data. Specifically, after obtaining the multi-source sensor data, the change rate of the last N continuous sampling points is calculated as the instantaneous change rate, and the change rate of the continuous sampling points in the preset time length is calculated as the long-term change rate; further, the ship running data corresponding to each change rate time sequence is obtained, and whether the preset condition is triggered is determined based on the condition corresponding to the ship running data and the preset condition, and when the preset condition is triggered, the preset change characteristic is corrected.

[0049] Specifically, for the flow meter instantaneous change rate, the corresponding ship running data can be the main engine power; when the main engine power > 90%, it is confirmed that the preset condition is triggered, a determination threshold of +8% / s can be assigned to the preset change characteristic to correct the preset change characteristic, and then data in which the instantaneous change rate is in the interval [-3% / s, +8% / s] is retained, and data in which the instantaneous change rate is not in the interval [-3% / s, +8% / s] is removed.

[0050] Specifically, for the liquid level height instantaneous change rate, the corresponding ship operation data can be the refueling state of the ship; when the refueling state is in refueling, it is confirmed that the preset condition is triggered, the preset change feature is given a judgment threshold of +5 cm / s to correct the preset change feature, so as to retain the data whose instantaneous change rate is in the interval [-2 cm / s, +5 cm / s], and eliminate the data whose instantaneous change rate is not in the interval [-2 cm / s, +5 cm / s].

[0051] Specifically, for the AIS speed instantaneous change rate, the corresponding ship operation data can be the berthing state of the ship; when the berthing state is in berthing, it is confirmed that the preset condition is triggered, the preset change feature is given a judgment threshold of ±0.05 m / s 2 to correct the preset change feature, so as to retain the data whose instantaneous change rate is in ±0.05 m / s 2 , and eliminate the data whose instantaneous change rate is not in ±0.05 m / s 2 .

[0052] In an embodiment, in order to consider the ship operation state and at the same time consider the data heterogeneity caused by the sensor type of the target sensor to correct the sensor data, to solve the error problem of the multi-source sensor caused by the inaccuracy of the sensor acquisition due to the ship operation state, the multi-source sensor data can be filtered based on the ship operation data and the change feature to obtain first target multi-source sensor data; specifically, when each data is filtered according to the change feature of the multi-source sensor data, for the data not meeting the preset change feature, the change feature of the ship operation data corresponding to the time sequence is obtained, when the change feature of the ship operation data and the change feature do not match, the corresponding multi-source sensor data is eliminated to obtain the first target multi-source sensor data.

[0053] Illustratively, since the flow meter is generally positively correlated with the main engine power and the main engine speed, for the data not meeting the preset change feature, the sensor type of the target sensor corresponding to the change feature is obtained; when the sensor type of the target sensor is a flow meter, the main engine power and / or main engine speed data corresponding to the time sequence of each change feature in the ship operation data is obtained, the main engine power and / or main engine speed data is calculated as the operation data change rate, when the operation data change rate and the change feature do not match, the corresponding multi-source sensor data is eliminated to obtain the first target multi-source sensor data.

[0054] Illustratively, the similarity value of the operation data change rate and the change feature can be calculated, when the similarity value is less than or equal to a preset similarity judgment threshold, it is confirmed that the operation data change rate and the change feature do not match.

[0055] Exemplarily, since the liquid level meter is generally related to the ship roll and trim values, for data not meeting the preset change characteristics, the sensor type of the target sensor corresponding to the change characteristics is obtained; when the sensor type of the target sensor is a liquid level meter, the ship roll and / or trim values corresponding to the time sequence of each change characteristic in the ship operation data are obtained, the main ship roll and / or trim values are calculated as the operation data change rate, when the operation data change rate does not match the change characteristic, the corresponding multi-source sensor data is removed, and the first target multi-source sensor data is obtained.

[0056] In an embodiment, the multi-source sensor data is screened based on the ship operation data, the numerical characteristics and the change characteristics of the multi-source sensor data to obtain the first target multi-source sensor data; specifically, for the obtained multi-source sensor data, the multi-source sensor data is first screened based on the numerical characteristics of the multi-source sensor data and the ship operation data, and then the multi-source sensor data is screened based on the change characteristics of the multi-source sensor data and the ship operation data to obtain the first target multi-source sensor data; or, for the obtained multi-source sensor data, the multi-source sensor data is first screened based on the change characteristics of the multi-source sensor data and the ship operation data, and then the multi-source sensor data is screened based on the numerical characteristics of the multi-source sensor data and the ship operation data to obtain the first target multi-source sensor data.

[0057] In an embodiment, the multi-source sensor data is screened based on the ship operation data, the numerical characteristics and the change characteristics of the multi-source sensor data to obtain the first target multi-source sensor data; specifically, for the obtained multi-source sensor data, the multi-source sensor data is first screened based on the numerical characteristics of the multi-source sensor data and the ship operation data, and then the multi-source sensor data is screened based on the change characteristics of the multi-source sensor data and the ship operation data to obtain the first target multi-source sensor data; or, for the obtained multi-source sensor data, the multi-source sensor data is first screened based on the change characteristics of the multi-source sensor data and the ship operation data, and then the multi-source sensor data is screened based on the numerical characteristics of the multi-source sensor data and the ship operation data to obtain the first target multi-source sensor data.

[0058] For the filtering processing of the multi-source sensor data, on the one hand, the multi-source sensor data is collected by sensors with different sampling frequencies, and the liquid level data noise variance changes by 10 times due to the sea wave, finally, the fuel flow rate changes far more than the normal value when the main engine is accelerated, the error of the Kalman filter with fixed parameters increases by 4-8 times when the roll is greater than 5°, and the single model set without considering the ship operation state cannot adapt to the filtering of the collected data in the smooth sailing and maneuvering states.

[0059] Therefore, in the embodiment, the first target multi-source sensor data is dynamically filtered based on the ship operation data to obtain the second target multi-source sensor data.

[0060] Exemplarily, when dynamically filtering the first target multi-source sensor data, the sailing state of the ship can be determined based on the ship operation data, and different filtering strength filtering parameters are given based on different sailing states; for the filtering parameters of the filtering strength, the first filtering parameter is given for the relatively severe sailing state, and the second filtering parameter is given for the relatively stable sailing state; wherein the first filtering strength when filtered by the first filtering parameter is stronger than the second filtering strength of the second filtering parameter.

[0061] Wherein, as a possible implementation, Kalman filtering can be used to filter the first target multi-source sensor data to obtain the second target multi-source sensor data.

[0062] Specifically, Kalman filtering is an algorithm for optimal estimation of system state by using linear system state equation and observing data of system input and output; since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process; in the present application, the process noise parameter and the observation noise parameter when Kalman filtering is performed can be determined in advance, and the first target multi-source sensor data is further filtered by using the process noise parameter and the observation noise parameter to obtain the second target multi-source sensor data.

[0063] Step S104, dynamically compensating the second target multi-source sensor data based on the ship operation data to obtain the corrected multi-source sensor data; wherein the ship operation data and the second target multi-source sensor data are time-series corresponding.

[0064] The sensors arranged on the ships running in rivers and oceans are seriously disturbed by the water environment, and the ship trim or roll will cause distortion of liquid level measurement, and in extreme cases the measurement error can reach 15%; in the present embodiment, the second target multi-source sensor data is dynamically compensated based on the ship operation data to obtain the corrected multi-source sensor data.

[0065] Exemplarily, the trim data and roll data corresponding to the liquid level data in the second target multi-source sensor data in time series can be obtained in the ship operation data, and further the difference between the measured liquid level and the actual liquid level when the trim and roll cause liquid level distortion is calculated according to the trim data and the roll data, and finally the difference is compensated to the measured liquid level to compensate the liquid level of the second target multi-source sensor data to obtain the corrected multi-source sensor data.

[0066] Step S105, calculating the energy consumption of the sub-ship based on the corrected multi-source sensor data and the sensor type of the corresponding target sensor.

[0067] In the embodiment, first, the liquid level data is converted by using the pre-calibrated piecewise linear tank capacity conversion model to perform the metering unification processing; specifically, the corresponding relationship between the liquid level height and the corresponding volume under different tank capacities of the ship can be pre-tested, and further, the piecewise linear tank capacity conversion model is established, and finally, the tank liquid level data is converted into volume data according to the tank capacity conversion model.

[0068] Specifically, Table 1 is a schematic tank capacity original data table according to an embodiment of the present application.

[0069] Table 1, tank capacity original data table

[0070]

[0071] In Table 1, h represents the liquid level height, the unit is cm, and V represents the corresponding volume, the unit is m 3 .

[0072] Further, the piecewise linear tank capacity conversion model established by using formula (1) can be used:

[0073] V(h)=V k-1 +(V k -V k-1 ) / (h k -h k-1 )*(h-h k-1 ),h k-1 ≤h<h k (1)

[0074] In formula (1), V(h) represents the volume when the liquid level height is h, V k-1 represents the volume when the liquid level height is h k-1 , V k represents the volume when the liquid level height is h k , h k is the minimum liquid level height in the tank capacity original data table which is greater than h, and h k-1 is the maximum liquid level height in the tank capacity original data table which is less than h.

[0075] Further, for the flowmeter data, the flowmeter type is determined according to the data unit output by the flowmeter and the pre-established flowmeter type identification matrix, so as to obtain the volume; wherein, the flowmeter can include a volume flowmeter and a mass flowmeter, the data unit of the volume flowmeter can include m / h, m 3 , and the unit of the mass flowmeter can include kg / h, kg.

[0076] Further, based on the corrected multi-source sensor data and the sensor type of the corresponding target sensor, the energy consumption of each sub-ship corresponding to each sensor is calculated.

[0077] Specifically, for flow meter consumption data statistics, if the sensor type of the target sensor is a volumetric flow meter, the density value needs to be combined and converted into a mass unit, wherein the density value can be obtained by flow meter real-time density, manually reported density or standard density table; after conversion into a mass unit, further energy consumption is calculated.

[0078] Specifically, for instantaneous flow meters, the energy consumption is calculated using formula (2):

[0079] consume t0～t1 =∫v(t)dt,t∈(t0,t1) (2)

[0080] In formula (2), consume t0～t1 represents the energy consumption data in the period from t0 to t1 calculated by the instantaneous flow meter.

[0081] Specifically, for cumulative flow meters, the energy consumption is calculated using formula (3):

[0082] consume t0～t1 =x t1 -x t0 (3)

[0083] In formula (3), consume t0～t1 represents the energy consumption data in the period from t0 to t1 calculated by the cumulative flow meter, x t1 represents the value of the cumulative flow meter at t1, and x t0 represents the value of the cumulative flow meter at t0.

[0084] Specifically, for liquid level meter consumption data statistics, the liquid level data is converted into tank inventory data by a liquid level segmented linear tank capacity conversion model using formula (4), and the consumption is calculated in combination with the manually reported added amount data.

[0085] consumer t0～t1 =(M t1 -M t0 )ρ+add (4)

[0086] In formula (4), M t0 is the inventory at t0, M t1 is the inventory at t1, ρ is the density, and add is the manually reported added amount data.

[0087] Step S106, fusing each of the sub-ship energy consumptions based on the ship operation data to obtain ship energy consumption data.

[0088] As described above, since the liquid level meter is greatly affected by the trim and roll of the ship, the running state of the ship can be determined according to the ship running data, and different fusion weights are assigned to the energy consumptions of the sub-ships according to the running state of the ship.

[0089] In an embodiment, the energy consumptions of the sub-ships are fused based on the ship running data in a manner of dynamic weight matrix; specifically, after obtaining the ship running data, at least one of the rate of change of the speed, the roll angle and the wind speed is extracted from the ship running data.

[0090] For example, when the rate of change of the speed is less than 0.2 kn / min, the fusion weight of the energy consumption of the sub-ship corresponding to the flowmeter is 0.4, and the fusion weight of the energy consumption of the sub-ship corresponding to the liquid level meter is 0.6.

[0091] For example, when the roll angle is greater than 5°, the fusion weight of the energy consumption of the sub-ship corresponding to the flowmeter is 0.7, and the fusion weight of the energy consumption of the sub-ship corresponding to the liquid level meter is 0.3.

[0092] For example, when the wind speed is greater than 7, the fusion weight of the energy consumption of the sub-ship corresponding to the flowmeter is 0.65, and the fusion weight of the energy consumption of the sub-ship corresponding to the liquid level meter is 0.35.

[0093] Further, the energy consumptions of the sub-ships are fused by using formula (5) to obtain the ship energy consumption data:

[0094] consume=consume flow *w flow +consume liquid *w liquid (5)

[0095] In formula (5), consume flow represents the energy consumption of the sub-ship calculated by the flowmeter, w flow represents the fusion weight of the energy consumption of the sub-ship corresponding to the flowmeter, consume liquid represents the energy consumption of the sub-ship calculated by the liquid level meter, w liquid represents the fusion weight of the energy consumption of the sub-ship corresponding to the liquid level meter.

[0096] The embodiment provides a ship energy consumption calculation method, which comprises: acquiring ship running data and multi-source sensor data collected by a target sensor of a target ship ；The target sensor includes a flow meter, a liquid level meter, and an AIS system. The multi-source sensor data is dynamically filtered based on the change characteristics of the ship operation data and the multi-source sensor data, to obtain first target multi-source sensor data. The change characteristics of the ship operation data and the multi-source sensor data correspond to the time sequence of the multi-source sensor data. The first target multi-source sensor data is dynamically filtered based on the ship operation data, to obtain second target multi-source sensor data. The ship operation data corresponds to the time sequence of the multi-source sensor data. The second target multi-source sensor data is dynamically compensated based on the ship operation data, to obtain corrected multi-source sensor data. The ship operation data corresponds to the time sequence of the second target multi-source sensor data. The energy consumption of a sub-ship is calculated based on the corrected multi-source sensor data and the corresponding sensor type. The energy consumption data of the ship is obtained by fusing the energy consumptions of the sub-ships based on the ship operation data. The above method can consider the collection errors of each sensor of the multi-source sensor data, the errors caused by the interference of the water environment on the ship, and the navigation state of the target ship, to dynamically filter, dynamically filter, and dynamically compensate the multi-source sensor data, so as to obtain more accurate corrected multi-source sensor data. Further, the energy consumption of the sub-ship is calculated based on the corrected multi-source sensor data, and the ship operation data is used to fuse the energy consumptions of the sub-ships, to finally obtain more accurate energy consumption data of the ship.

[0097] As an exemplary embodiment, the method of dynamically filtering the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data comprises: dynamically filtering the multi-source sensor data based on the ship operation data to obtain first multi-source sensor data within a preset numerical range; wherein the ship operation data corresponds to the time sequence of the multi-source sensor data, and the preset numerical range is determined based on the ship operation data and the numerical characteristics of the ship operation data corresponding to the time sequence; and dynamically filtering the first multi-source sensor data based on the ship operation data and the change characteristics to obtain the first target multi-source sensor data.

[0098] In this embodiment, the multi-source sensor data is dynamically filtered based on the ship operation data to obtain first multi-source sensor data within a preset numerical range, so as to consider the running state of the ship when filtering the multi-source sensor data, and to give the ship a dynamic filtering condition. The ship operation data corresponds to the time sequence of the multi-source sensor data, and the preset numerical range is determined based on the ship operation data and the numerical characteristics of the ship operation data corresponding to the time sequence.

[0099] In the embodiment, the first multi-source sensor data is dynamically filtered based on the ship operation data and the change feature, so as to consider the running state of the ship when filtering the multi-source sensor data, and to give the ship a dynamic filtering condition; and the first target multi-source sensor data is obtained.

[0100] Specifically, as an exemplary embodiment, the dynamic numerical range filtering of the multi-source sensor data based on the ship operation data to obtain the first multi-source sensor data in a preset numerical range includes: determining a target sliding window length based on the acquisition frequency of the target sensor; sliding window intercepting the sensor data corresponding to each target sensor in the multi-source sensor data based on the target sliding window length to obtain a plurality of sliding window intervals; determining the quartile point of each sliding window interval based on the numerical feature of each sliding window interval; obtaining the roll angle data corresponding to the time sequence of each sliding window interval in the ship operation data; determining the correction coefficient of each sliding window interval based on the roll angle data; wherein the roll angle and the correction coefficient are positively correlated; and filtering each sensor data based on the quartile point and the correction coefficient to obtain the first multi-source sensor data.

[0101] In the embodiment, the dynamic IQR algorithm is used to realize the filtering considering the numerical feature of the multi-source sensor data; the IQR algorithm is an outlier detection method based on statistical quartiles, which determines the core range of data distribution by calculating the difference between the 25% (Q1) and 75% (Q3) quartiles of the data set; specifically, the target sliding window length is determined based on the acquisition frequency of the target sensor; after the sliding window intercepting each sensor data based on the target sliding window length to obtain a plurality of sliding window intervals, the quartile point of each sliding window interval is determined based on the numerical feature of each sliding window interval; further, [Q1-k*IQR, Q3+k*IQR] is used as the numerical feature, and each data is further filtered or verified according to the numerical feature; the data conforming to the preset data distribution range within the [Q1-k*IQR, Q3+k*IQR] interval is retained, and the data not conforming to the preset distribution range outside the [Q1-k*IQR, Q3+k*IQR] interval is removed, so as to finally obtain the first target multi-source sensor data; in the embodiment, K is 1.5.

[0102] Further, in order to adapt to the screening of the navigation state in consideration of the significant data distribution time-varyingness of the statistical characteristics of multi-source sensor data under different navigation states, in the present application, dynamic numerical range screening is further considered in combination with ship operation data to dynamically correct the quartile points; specifically, in the ship operation data, roll angle data corresponding to the time sequence of each sliding window interval is obtained; a correction coefficient of each sliding window interval is determined based on the roll angle data; wherein the roll angle is positively correlated with the correction coefficient; the sensor data is screened based on the quartile points and the correction coefficient to obtain the first target multi-source sensor data.

[0103] For example, after obtaining the roll angle data, for a roll angle less than 3°, it is confirmed that the ship is in a calm state at this time, and a detection strategy of strictly detecting weak abnormalities is adopted, and at this time the correction coefficient k is 1.5.

[0104] For example, after obtaining the roll angle data, for a roll angle greater than or equal to 3° and less than or equal to 8°, it is confirmed that the ship is in a normal navigation state at this time, and a detection strategy of moderately relaxing the threshold is adopted, and at this time the correction coefficient k is 2.

[0105] For example, after obtaining the roll angle data, for a roll angle greater than 8°, it is confirmed that the ship is in a severe sea state at this time, and a detection strategy of avoiding misjudgment motion-induced fluctuations is adopted, and at this time the correction coefficient k is 2.5.

[0106] As an exemplary embodiment, the dynamic change characteristic screening of the first multi-source sensor data based on the ship operation data and the change characteristic to obtain the first target multi-source sensor data comprises: calculating a first change rate of a preset number of time-sequentially continuous multi-source sensor data; determining a first change rate threshold based on the sensor type of the target sensor corresponding to the first change rate; screening the multi-source sensor data based on the first change rate threshold and the first change rate to obtain a plurality of second multi-source sensor data exceeding the first change rate threshold; obtaining a first change trend characteristic of third multi-source sensor data time-sequentially later than the second multi-source sensor data; if the first change trend characteristic meets a preset change characteristic, obtaining a second change trend characteristic of the ship operation data corresponding to the time sequence of the second multi-source sensor data based on the sensor type of the second multi-source sensor data;

[0107] If the second change trend characteristic meets the preset change characteristic, the second multi-source sensor data is excluded to obtain the first target multi-source sensor data.

[0108] In the present embodiment, the multi-source sensor data is screened based on the reasonableness verification of the change rate of adjacent data points to obtain the first target multi-source sensor data.

[0109] Specifically, a first change rate of a preset number of first multi-source sensor data in time sequence is calculated; a first change rate threshold is determined based on a sensor type of the target sensor corresponding to the first change rate; the first multi-source sensor data is filtered based on the first change rate threshold and the first change rate, to obtain a plurality of second multi-source sensor data exceeding the first change rate threshold, so as to filter a plurality of second multi-source sensor data with abnormal changes as suspicious points; further, a first change trend feature of third multi-source sensor data later in time sequence than the second multi-source sensor data is obtained; if the first change trend feature meets a preset change feature, a second change trend feature of the ship operation data corresponding to the second multi-source sensor data in time sequence is obtained based on the sensor type of the second multi-source sensor data; if the second change trend feature meets a preset change feature, the second multi-source sensor data is excluded, to obtain the first target multi-source sensor data, so as to dynamically filter the first multi-source sensor data by combining the first change trend feature of the second multi-source sensor data later in time sequence and the second change trend feature of the ship operation data corresponding in time sequence, to obtain the first target multi-source sensor data.

[0110] For the filtering processing of multi-source sensor data, on the one hand, the multi-source sensor data is collected by sensors with different sampling frequencies, and the noise variance of the liquid level data caused by sea waves changes by 10 times. Finally, the fuel flow rate changes far more than the normal value when the main engine accelerates rapidly. The error of the Kalman filter with fixed parameters increases by 4-8 times when the roll is greater than 5°. Moreover, the single model set without considering the ship operation state cannot simultaneously adapt to the filtering of collected data in the smooth sailing and maneuvering states.

[0111] To solve this problem, as an exemplary embodiment, the dynamic filtering of the first target multi-source sensor data based on the ship operation data to obtain the second target multi-source sensor data comprises: determining the operation state corresponding to each of the multi-source sensor data based on the ship operation data; determining a first filtering parameter and a second filtering parameter based on the operation state; filtering the first target multi-source sensor data based on the first filtering parameter and the second filtering parameter to obtain the second target multi-source sensor data; wherein the first filtering parameter can be a Q matrix corresponding to the process noise in the Kalman filter, and the second filtering parameter can be an R matrix corresponding to the observation noise.

[0112] Specifically, in this embodiment, the operation state corresponding to each of the multi-source sensor data is determined based on the ship operation data.

[0113] Exemplarily, Table 2 is a ship operation data and operation state comparison table according to an embodiment of the present application, as shown in Table 2:

[0114] Table 2, a table of correspondence between ship operation data and operation state

[0115]

[0116] Further, after determining the ship operation state, the process noise and observation noise parameters of Kalman filtering are dynamically adjusted according to the ship operation state.

[0117] Exemplarily, when the ship operation state is the smooth mode, the process noise Q of Kalman filtering for the data corresponding to the flowmeter is diag(0.01, 0.001), the observation noise R is 0.05, the process noise Q of Kalman filtering for the data corresponding to the liquid level meter is diag(0.02, 0.005), the observation noise R is 0.1, and the process noise Q of Kalman filtering for the data corresponding to the AIS is diag(0.1, 0.03), the observation noise R is 0.3.

[0118] Exemplarily, when the ship operation state is the maneuvering mode, the process noise Q of Kalman filtering for the data corresponding to the flowmeter is diag(0.05, 0.01), the observation noise R is 0.1, the process noise Q of Kalman filtering for the data corresponding to the liquid level meter is diag(0.1, 0.02), the observation noise R is 0.3, and the process noise Q of Kalman filtering for the data corresponding to the AIS is diag(0.3, 0.1), the observation noise R is 0.5.

[0119] Exemplarily, when the ship operation state is the rough sea condition mode, the process noise Q of Kalman filtering for the data corresponding to the flowmeter is diag(0.2, 0.05), the observation noise R is 0.3, the process noise Q of Kalman filtering for the data corresponding to the liquid level meter is diag(0.5, 0.1), the observation noise R is 1.2, and the process noise Q of Kalman filtering for the data corresponding to the AIS is diag(1.0, 0.3), the observation noise R is 2.0.

[0120] Exemplarily, when the ship operation state is the transition mode, the process noise of Kalman filtering for the data corresponding to the flowmeter is linearly interpolated, and the observation noise R is 1.5 times the maximum value of R; the process noise Q of Kalman filtering for the data corresponding to the liquid level meter is linearly interpolated, and the observation noise R is 2 times the maximum value of R.

[0121] As an exemplary embodiment, the dynamic compensation of the second target multi-source sensor data based on the ship operation data to obtain the corrected multi-source sensor data comprises: extracting the trim angle data, the roll angle data, the forward draft data, the aft draft data, the ship length data and the ship width data corresponding to the time sequence of the second target multi-source sensor data in the ship operation data; and performing liquid level compensation on the second target multi-source sensor data based on the trim angle data, the roll angle data, the forward draft data, the aft draft data, the ship length data and the ship width data to obtain the corrected multi-source sensor data according to formula (6):

[0122]

[0123] θ = arctan(draft aft -draft fore ) / LBP

[0124]

[0125] In formula (6), is the corrected multi-source sensor data, X is the second target multi-source sensor data, θ is the trim angle data, draft fore is the forward draft data, draft aft is the aft draft data, LBP is the ship length data, and Beam is the ship width data, is the roll angle data.

[0126] The embodiment provides a ship energy consumption calculation device, as shown in the figure, comprising: Figure 2

[0127] The acquisition module 501 is configured to acquire ship operation data and multi-source sensor data collected by a target sensor of a target ship; wherein the target sensor comprises a flowmeter, a liquid level meter and an AIS system.

[0128] The screening module 502 is configured to perform dynamic screening on the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data; wherein the change characteristics of the ship operation data and the multi-source sensor data correspond to the time sequence of the multi-source sensor data.

[0129] The filtering module 503 is configured to perform dynamic filtering on the first target multi-source sensor data based on the ship operation data to obtain second target multi-source sensor data; wherein the ship operation data corresponds to the time sequence of the multi-source sensor data.

[0130] ​The compensation module 504 is configured to perform dynamic compensation on the second target multi-source sensor data based on the ship operation data, to obtain corrected multi-source sensor data; wherein the ship operation data is time-corresponding to the second target multi-source sensor data.

[0131] The calculation module 505 is configured to calculate sub-ship energy consumption based on the corrected multi-source sensor data and corresponding sensor types.

[0132] The fusion module 506 is configured to fuse each of the sub-ship energy consumptions based on the ship operation data, to obtain ship energy consumption data.

[0133] It should be noted that the above modules and the examples and application scenarios realized by the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments.

[0134] It should be noted that the above modules as part of the device can be implemented by software or by hardware, wherein the hardware environment includes a network environment.

[0135] The embodiment of the application further provides a computer device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus, the memory is used for storing a computer program, and the processor is used for executing the method in any one of the above embodiments by running the computer program stored on the memory.

[0136] Figure 3 is a structural block diagram of an optional computer device according to the embodiment of the application, as shown in Figure 3 The computer device includes a processor 10, a communication interface 20, a memory 30 and a communication bus 40, wherein the processor 10, the communication interface 20 and the memory 30 complete mutual communication through the communication bus 40, wherein,

[0137] The memory 30 is used for storing a computer program.

[0138] The processor 10 is used for executing the computer program stored on the memory 30, to realize the ship energy consumption calculation method in any one of the above embodiments.

[0139] Optionally, in the embodiment, the above communication bus can be a PCI (Peripheral Component Interconnect, peripheral component interconnect) bus, or an EISA (Extended Industry Standard Architecture, extended industry standard architecture) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 3Only one bus or only one type of bus can be used. Alternatively, a bus can include one or more buses of either type.

[0140] The communication interface is configured to communicate between the computer device and other devices.

[0141] The memory can include a RAM and can also include a non-volatile memory, for example, at least one disk memory. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0142] The processor can be a general-purpose processor, which can include, but is not limited to, a CPU (Central Processing Unit), an NP (Network Processor), and the like. The processor can also be a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0143] Optionally, the specific examples in the embodiments can refer to the examples described in the above embodiments, and the embodiments will not be described here.

[0144] Those skilled in the art can understand that, Figure 3 The structure shown is only schematic, and the device implementing any one of the above embodiments can be a terminal device, which can be a smart phone (such as an Android phone, an IOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Figure 3 This does not limit the structure of the electronic device. For example, the terminal device can further include more or less components (such as a network interface, a display device, etc.) than Figure 3 shown, or have a different configuration than Figure 3 shown.

[0145] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device by a program, and the program can be stored in a computer readable storage medium, which can include a flash disk, a ROM, a RAM, a magnetic disk or an optical disk, and the like.

[0146] As an exemplary embodiment, the present application also provides a computer readable storage medium, which stores a computer program, wherein the computer program is configured to execute the method steps of any one of the embodiments when running.

[0147] Optionally, in the embodiment, the storage medium can be used for program codes to execute the method steps of the embodiments of the present application.

[0148] Optionally, in the embodiment, the storage medium can be located on at least one of the network devices in the network shown in the above embodiments.

[0149] Optionally, in the embodiment, the storage medium is configured to store the method for executing the above embodiments.

[0150] Optionally, the specific examples in the embodiment can refer to the examples described in the above embodiments, which will not be repeated here.

[0151] Optionally, in the embodiment, the storage medium can include but is not limited to: a U disk, a ROM, a RAM, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0152] The serial numbers of the embodiments of the present application are only for description, not representing the advantages or disadvantages of the embodiments.

[0153] The integrated units in the above embodiments, if realized in the form of software function units and sold or used as independent products, can be stored in the above computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or the whole or part of the technical solutions can be embodied in the form of software products, which are stored in the storage medium and include a number of instructions to make one or more computer devices (which can be personal computers, servers or network devices, etc.) execute all or part of the steps of the methods in the above embodiments.

[0154] In several embodiments provided by the present application, it should be understood that the disclosed client can be implemented by other means. Among them, the above-mentioned device embodiments are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual ones can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0155] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place or distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the scheme provided in the embodiment.

[0156] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0157] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can refer to the relevant description of other embodiments.

[0158] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method for calculating ship energy consumption, characterized in that: The ship energy consumption calculation method includes: Acquire ship operation data and multi-source sensor data collected by target sensors of the target ship; wherein the target sensors include flow meters, liquid level meters, and AIS systems; Dynamically screening the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data; wherein the change characteristics of the ship operation data and the multi-source sensor data correspond to the time series of the multi-source sensor data; Dynamically filtering the first target multi-source sensor data based on the ship operation data to obtain second target multi-source sensor data; wherein the ship operation data corresponds to the multi-source sensor data in time sequence; Dynamically compensating the second target multi-source sensor data based on the ship operation data to obtain corrected multi-source sensor data; wherein the ship operation data and the second target multi-source sensor data have a time sequence correspondence; Calculating the energy consumption of the sub-ship based on the corrected multi-source sensor data and the corresponding sensor type; The energy consumption of each sub-ship is integrated based on the ship operation data to obtain ship energy consumption data.

2. The ship energy consumption calculation method according to claim 1, characterized in that: The dynamically screening the multi-source sensor data based on the change characteristics of the ship operation data and the multi-source sensor data to obtain first target multi-source sensor data includes: performing dynamic numerical range screening on the multi-source sensor data based on the ship operation data to obtain first multi-source sensor data within a preset numerical range; wherein the ship operation data corresponds to a time series of the multi-source sensor data, and the preset numerical range is determined based on the ship operation data corresponding to the time series and a numerical feature of the ship operation data; Dynamic change feature screening is performed on the first multi-source sensor data based on the ship operation data and the change feature to obtain the first target multi-source sensor data.

3. The method for calculating ship energy consumption according to claim 2, wherein: The dynamically filtering the multi-source sensor data based on the ship operation data to obtain first multi-source sensor data within a preset value range includes: Determining a target sliding window length based on an acquisition frequency of the target sensor; Based on the target sliding window length, the sensor data corresponding to each target sensor in the multi-source sensor data is respectively intercepted by a sliding window to obtain multiple sliding window intervals; Determining the quartile of each sliding window interval based on the numerical characteristics of each sliding window interval; Acquire, from the ship operation data, roll angle data corresponding to each of the sliding window interval time series; Determining a correction coefficient for each sliding window interval based on the roll angle data; wherein the roll angle is positively correlated with the correction coefficient; The sensor data are filtered based on the quartiles and the correction coefficients to obtain the first multi-source sensor data.

4. The ship energy consumption calculation method according to claim 2, characterized in that: The dynamically changing characteristic screening of the first multi-source sensor data based on the ship operation data and the changing characteristic to obtain first target multi-source sensor data includes: Calculating a first change rate of a preset number of multi-source sensor data consecutive in time series; determining a first change rate threshold based on a sensor type of the target sensor corresponding to the first change rate; filtering the multi-source sensor data based on the first change rate threshold and the first change rate to obtain a plurality of second multi-source sensor data exceeding the first change rate threshold; Acquire a first change trend feature of third multi-source sensor data that is later in time than the second multi-source sensor data; If the first change trend feature satisfies a preset change feature, obtaining a second change trend feature of the ship operation data corresponding to the time series of the second multi-source sensor data based on the sensor type of the second multi-source sensor data; If the second change trend feature meets the preset change feature, the second multi-source sensor data is eliminated to obtain the first target multi-source sensor data.

5. The ship energy consumption calculation method according to claim 1, characterized in that: The dynamically filtering the first target multi-source sensor data based on the ship operation data to obtain second target multi-source sensor data includes: Determining an operating state corresponding to each of the multi-source sensor data based on the ship operating data; determining a first filtering parameter and a second filtering parameter based on the operating state; The first target multi-source sensor data is filtered based on the first filtering parameter and the second filtering parameter to obtain the second target multi-source sensor data.

6. The ship energy consumption calculation method according to claim 1, characterized in that: The dynamically compensating the second target multi-source sensor data based on the ship operation data to obtain corrected multi-source sensor data includes: Extracting, from the ship operation data, pitch angle data, roll angle data, front draft data, rear draft data, ship length data, and ship width data corresponding to the second target multi-source sensor data time series; Based on the pitch angle data, the roll angle data, the front draft data, the aft draft data, the ship length data, and the ship width data, the second target multi-source sensor data is level compensated using the following formula to obtain the corrected multi-source sensor data: θ=arctan(draft aft -draft fore ) / LBP Where, is the corrected multi-source sensor data, X is the second target multi-source sensor data, θ is the pitch angle data, and draft fore For the previous draft data, draft aft is the rear draft data, LBP is the length data, Beam is the ship width data, is the roll angle data.

7. The ship energy consumption calculation method according to claim 1, characterized in that: The fusing of the energy consumption of each sub-ship based on the ship operation data to obtain ship energy consumption data includes: From the ship operation data, at least one of speed change rate data, roll angle data and wind speed data is obtained; Determining the energy consumption fusion weight corresponding to each of the sensors based on the speed change rate data, the roll angle data or the wind speed data; The energy consumption of each of the sub-ships is fused based on the energy consumption fusion weight to obtain the ship energy consumption data.

8. A ship energy consumption calculation device, characterized in that: The ship energy consumption calculation device includes: An acquisition module is used to acquire ship operation data and multi-source sensor data collected by target sensors of the target ship; wherein the target sensors include flow meters, liquid level meters, and AIS systems; A screening module is used to screen the multi-source sensor data based on the ship operation data and the numerical characteristics and change characteristics of the multi-source sensor data to obtain the first target multi-source sensor data. a filtering module, configured to filter the first target multi-source sensor data based on the ship operation data to obtain second target multi-source sensor data; a liquid level compensation module, configured to perform liquid level compensation on the second target multi-source sensor data based on the ship operation data to obtain corrected multi-source sensor data; a calculation module, configured to calculate the energy consumption of the sub-ship based on the corrected multi-source sensor data and the corresponding sensor type; A fusion module is used to fuse the energy consumption of each sub-ship based on the ship operation data to obtain ship energy consumption data.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the ship energy consumption calculation method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the ship energy consumption calculation method according to any one of claims 1 to 7.

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