Ship dynamic liquid level high-precision measuring system and measuring method thereof

Abstract

According to the ship dynamic liquid level high-precision measuring system and the using method thereof, distributed data acquisition and transmission are achieved through the system based on a CAN bus and a ship local area network, and dynamic liquid level modeling and multi-algorithm cooperative processing (first-order low-pass filtering, classical Kalman filtering and optimized Kalman filtering) are carried out through the method. The liquid level measurement precision under the complex motion conditions of ship swinging, heaving and the like is obviously improved; the optimized Kalman filtering solves the lag problem when the liquid level changes rapidly by expanding state variables and adjusting dynamic noise. The ship dynamic liquid level measurement method can be conveniently applied to other dynamic liquid level measurement occasions, and has the advantages of high-precision measurement, real-time performance, adaptability, system integration, wide applicability and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of ship liquid level measurement, and in particular to a high-precision ship dynamic liquid level measurement system and a measurement method thereof. Background Art

[0002] During navigation, ships must monitor the levels of liquids such as fuel and fresh water in tanks in real time to ensure navigation safety and resource management. Currently, measuring the liquid levels in each tank primarily involves a combination of manual and automatic measurement. A certain ship is equipped with an engine room monitoring system with a liquid level monitoring function, which displays the liquid level in each compartment in real time, allowing personnel to calculate and record oil and water consumption. Furthermore, each compartment is equipped with a manual measuring pipe, which allows manual measurement of the liquid depth by releasing a measuring tape. Both automatic and manual measurement methods offer high accuracy under static conditions. However, under dynamic conditions, especially during navigation in strong winds and waves, the tank liquid level can fluctuate significantly due to factors such as the swaying, heaving, and vibration of the hull. This can lead to significant fluctuations in measurement results and a significant reduction in measurement accuracy, making it difficult to accurately determine oil and water consumption.

[0003] Existing technologies mainly rely on liquid level measurement methods in static or quasi-static environments, including manual measuring tapes and automatic sensor monitoring systems. Therefore, existing technologies have the following drawbacks:

[0004] (1) Manual measurement: It relies on manual operation, has poor real-time performance, large errors in dynamic environments, and poses safety risks.

[0005] (2) Direct measurement by automatic sensors: The sensor output signal is seriously disturbed by the ship's motion. The original data contains high-frequency noise and low-frequency disturbances (such as ship rolling periodic signals), which causes the measured value to fluctuate significantly and cannot reflect the actual liquid level.

[0006] (3) Traditional filtering data processing methods: Existing technologies often use low-pass filtering algorithms with fixed parameters. While these algorithms can suppress some high-frequency noise, they are inadequate for separating low-frequency ship-shaking disturbances and exhibit lag when the liquid level changes rapidly. For example, Chinese patent CN110260948B discloses a liquid level measurement method based on acoustic resonance frequency nonlinear filtering. However, this method does not consider the dynamic motion model of the ship, resulting in reduced accuracy in severe shaking scenarios.

[0007] Therefore, there is an urgent need for a high-precision measurement system and method that can effectively separate dynamic disturbances and adapt to the rate of change of liquid level. Summary of the Invention

[0008] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a high-precision measurement system for dynamic liquid level of ships and its use method, which significantly improves the liquid level measurement accuracy under complex motion conditions through dynamic modeling and multi-algorithm collaboration.

[0009] The object of the present invention is achieved like this:

[0010] A high-precision ship dynamic liquid level measurement system includes multiple sensors, multiple data acquisition terminals, a CAN bus, a CAN bridge, and a data processing terminal. Each sensor is connected to a data acquisition terminal, which is connected to the CAN bus. The CAN bus is connected via a CAN bus unidirectional bridge. The CAN bus unidirectional bridge is connected to a display terminal via the ship's existing local area network. Remote transmission and display of liquid level data are achieved via the local area network.

[0011] The sensor and data acquisition terminal are both installed in the ship's engine room. The sensor is connected to the data acquisition terminal. After the data acquisition terminal completes the conversion, conditioning and acquisition of the sensor signal, it displays the liquid level data on its own display screen and transmits the data to the CAN bus.

[0012] The CAN bus one-way bridge is set near the network interface, obtains the liquid level data of all sensors from the CAN bus, and forwards it to the ship's local area network and transmits it to the display terminal that needs to display the data.

[0013] Furthermore, the sensor is arranged on the bulkhead at the bottom of the liquid tank, and the sensor output signal is a 4~20mA standard current signal, and the signal is connected to the data acquisition terminal via a shielded cable.

[0014] Furthermore, the data acquisition terminal includes a signal conditioning circuit, a single-chip microcomputer circuit, a CAN bus interface circuit, an OLED display circuit and a power supply module. The signal conditioning circuit converts the 4~20mA current signal input by the liquid level sensor into a 0~3.3V voltage signal and sends it to the single-chip microcomputer circuit. The single-chip microcomputer completes the digital sampling of the liquid level signal through the A / D conversion module, calculates the conversion coefficient according to the sensor range and the density of the measured liquid, converts the sampled digital signal into liquid level data, and displays it in real time on the OLED circuit. At the same time, the liquid level data is encoded in a specified format and transmitted to the CAN bus through the CAN bus interface circuit.

[0015] Furthermore, the CAN bus unidirectional bridge includes a CAN bus interface circuit, a single-chip microcomputer circuit, a network interface circuit and a power module. The CAN bus interface circuit monitors the CAN bus signal in real time, and sends the received CAN data frame to the single-chip microcomputer circuit for reception and demodulation. The single-chip microcomputer extracts the CAN data ID, data length, and data payload from the CAN data frame, encodes them according to the specified format, and transmits them to the ship's local area network through the network interface circuit; the data acquisition terminal only sends but does not receive CAN data, the CAN bridge adopts a unidirectional transmission design, and the network data transmission adopts the UDP transmission protocol.

[0016] Furthermore, it also includes a data processing terminal, which is set in the ship monitoring center, runs data smoothing algorithm testing software, configures the remote connection CAN bridge IP address and data receiving port, and supports multi-algorithm parallel processing and comparative analysis.

[0017] Furthermore, 120Ω terminal resistors are connected to both ends of the CAN bus, and the data acquisition terminal sends the liquid level data in a standard frame format.

[0018] Furthermore, the CAN bus one-way bridge encapsulates the data into UDP messages and broadcasts them to the display terminal through the ship's local area network.

[0019] A measurement method for a high-precision measurement system of a ship's dynamic liquid level, comprising the following contents:

[0020] S1. Data collection and transmission:

[0021] The liquid level signal is collected in real time by the sensor and transmitted to the data processing terminal via the CAN bus and the local area network;

[0022] S2. Dynamic liquid level modeling:

[0023] Establish a dynamic model that includes ship rolling, heaving, and liquid level changes, and distinguish high-frequency noise, low-frequency disturbances, and true liquid level signals;

[0024] S3. Select filtering algorithm for data processing based on ship motion characteristics:

[0025] S31, first-order low-pass filtering algorithm, sets the cutoff frequency lower than the ship's rolling frequency, implements real-time filtering through differential equations, and suppresses high-frequency noise;

[0026] S32, the classic Kalman filter algorithm, is based on a fixed liquid level model and achieves optimal estimation results through state prediction and observation update. It is suitable for scenarios with stable liquid levels.

[0027] S33. Optimize the Kalman filter algorithm, introduce a liquid level uniform change model, expand the state variables to liquid level height and change rate, dynamically adjust the process noise covariance, and adapt to the scenario of rapid liquid level rise and fall.

[0028] Furthermore, in step S32,

[0029] The state prediction equation is:

[0030] ;

[0031] The state prediction mean square error calculation equation is:

[0032] ;

[0033] The Kalman filter gain calculation equation is:

[0034] ;

[0035] The state estimation equation is:

[0036] ;

[0037] According to the state prediction equation, the state estimation equation can be simplified as:

[0038] ;

[0039] The state estimation mean square error calculation equation is:

[0040] .

[0041] Furthermore, in the liquid level uniform change model of step S33,

[0042] The state prediction equation is:

[0043] ;

[0044] The state prediction mean square error calculation equation is:

[0045] ;

[0046] The Kalman filter gain calculation equation is:

[0047] ;

[0048] The state estimation equation is:

[0049] ;

[0050] The state estimation mean square error calculation equation is:

[0051] .

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention provides a high-precision measurement system for the dynamic liquid level of a ship and a method for using the same. The measurement system of the present invention is designed with a CAN bus data acquisition terminal and a CAN bus one-way bridge in mind, in order to transmit the liquid level measurement data to the ship's local area network, thereby realizing remote transmission of the measurement data. The ship's dynamic liquid level measurement method of the present invention can be conveniently applied to other dynamic liquid level measurement situations. In environments where the liquid level is relatively stable, such as the ship's draft depth and ballast water tanks, a Kalman filter algorithm with a fixed liquid level model can be used. In environments where the liquid level changes evenly, such as oil tanks, a Kalman filter algorithm with a linear motion model can be used. The Kalman filter algorithm has low computational requirements and can be easily applied in embedded systems, with the data processing algorithm built into a sensor with a processor. Therefore, the measurement system and method of the present invention have the following advantages:

[0054] (1) High-precision measurement: By optimizing the Kalman filter algorithm, the peak-to-peak value of liquid level fluctuation is reduced from 0.071m in the original data to 0.003m, and the accuracy is improved by more than 20 times.

[0055] (2) Real-time and adaptability: The Kalman filter optimized by the present invention can still maintain low hysteresis when the liquid level changes rapidly. For example, the response time in the refueling stage is shortened by 40%.

[0056] (3) System integration: The present invention is based on a distributed architecture of the CAN bus and the ship's local area network, which reduces wiring complexity and supports simultaneous monitoring of multiple compartments.

[0057] (4) Wide applicability: The present invention can select the filtering algorithm according to the liquid level change characteristics, such as using a fixed model for the ballast water tank and a linear motion model for the fuel tank, which has strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a structural schematic diagram of the high-precision measurement system for dynamic liquid level of a ship according to the present invention.

[0059] Figure 2 This is a schematic diagram of the main interface of the data smoothing algorithm testing software of the present invention.

[0060] Figure 3 Schematic diagram of a first-order low-pass filter circuit of the present invention.

[0061] Figure 4 This is a data curve diagram of the filtering algorithm of the present invention.

[0062] Figure 5 This is a data curve diagram of the optimized filtering algorithm of the present invention. DETAILED DESCRIPTION

[0063] To better understand the technical solution of the present invention, the following detailed description is provided with reference to the relevant illustrations. It should be understood that the following specific embodiments are not intended to limit the specific implementation of the technical solution of the present invention; they are merely examples of possible implementations of the technical solution of the present invention. It should be noted that references herein to the positional relationships of various components, such as component A being located above component B, are based on the relative positions of the components in the illustrations and are not intended to limit the actual positional relationships of the components.

[0064] Example 1:

[0065] See also Figure 1-Figure 5 , Figure 1 A schematic diagram of a high-precision dynamic liquid level measurement system for ships is provided. As shown in the figure, the system comprises multiple sensors and data acquisition terminals, a CAN bus, a CAN bus one-way bridge, and a power module. Each sensor is connected to a data acquisition terminal, which is connected to the CAN bus via a CAN bus one-way bridge. The CAN bus one-way bridge is connected to a display terminal via the ship's existing local area network. Liquid level data is transmitted and displayed via the ship's existing local area network.

[0066] The sensors are connected to the data acquisition terminal. After completing the conversion, conditioning, and acquisition of sensor signals, the terminal displays the liquid level data on its own display and transmits the data to the CAN bus. The CAN bridge, deployed near the network interface, is responsible for acquiring liquid level data from all sensors on the CAN bus and forwarding it to the ship's local area network for transmission to the display terminal where the data needs to be displayed.

[0067] The sensors and data acquisition terminals are both installed in the ship's engine room to minimize cable connections. 120Ω terminal resistors are connected to both ends of the CAN bus, and the data acquisition terminal sends liquid level data in a standard frame format (11-bit ID). The CAN bridge encapsulates the data into UDP messages and broadcasts them to the display terminal via the ship's local area network.

[0068] The sensor adopts an explosion-proof differential pressure liquid level sensor, which is installed on the bulkhead near the bottom of the liquid tank. The sensor output signal is a 4~20mA standard current signal, and the signal is connected to the data acquisition terminal via a shielded cable.

[0069] The data acquisition terminal includes a signal conditioning circuit, a single-chip microcomputer circuit, a CAN bus interface circuit, an OLED display circuit and a power supply module. The signal conditioning circuit converts the 4-20mA current signal input by the liquid level sensor into a 0-3.3V voltage signal and sends it to the single-chip microcomputer circuit. The single-chip microcomputer completes the digital sampling of the liquid level signal through the A / D conversion module, calculates the conversion coefficient according to the sensor range and the density of the measured liquid, converts the sampled digital signal into liquid level data, and displays it in real time on the OLED circuit. At the same time, the liquid level data is encoded in a specified format and transmitted to the CAN bus through the CAN bus interface circuit.

[0070] The CAN bus unidirectional bridge includes a CAN bus interface circuit, a single-chip microcomputer circuit, a network interface circuit and a power module. The CAN bus interface circuit monitors the CAN bus signal in real time, and sends the received CAN data frame to the single-chip microcomputer circuit for reception and demodulation. The single-chip microcomputer extracts the CAN data ID, data length, and data payload from the CAN data frame, encodes them according to the specified format, and transmits them to the ship's local area network through the network interface circuit; the data acquisition terminal only sends but does not receive CAN data. Therefore, the CAN bridge adopts a unidirectional transmission design, and the network data transmission adopts the UDP transmission protocol.

[0071] Different CAN bus messages are distinguished by message identification codes (IDs). The CAN bus supports both standard and extended frame formats. The ID length in a standard frame is 11 bits, while the ID length in an extended frame is 29 bits. In this embodiment, CAN bus communication uses the standard frame format, distinguishing different terminals by different IDs. The data length in a CAN bus data packet can range from 0 to 8 bytes. During network forwarding, in order to distinguish different CAN bus data frames, the network data packet of the forwarder needs to contain information such as the CAN data ID, CAN data length, and CAN data. The network frame format designed and used in this embodiment is shown in Table 1 below:

[0072] Serial number symbol Length (bytes) illustrate 1 ID 2 Standard frame ID, low byte first 2 LEN 1 Data length, the value is 2 3 DATA 2 Liquid level data, low byte first, divide by 1000 to get the actual value

[0073] Table 1 Network forwarding frame format

[0074] See also Figure 2 , Figure 2 This is a schematic diagram of the main interface of the data smoothing algorithm testing software of the present invention. The high-precision ship dynamic liquid level measurement system of the present invention also includes a data processing terminal, which is located in the ship monitoring center and runs the data smoothing algorithm testing software. The terminal is configured with a remotely connected CAN bridge IP address and data receiving port, and supports multi-algorithm parallel processing and comparative analysis.

[0075] The data smoothing algorithm testing software receives data from a CAN bridge over the network and can simultaneously process two channels of liquid level data. It extracts the ID and liquid level data based on the data frame format, distinguishes data from different data acquisition terminals by ID, and processes the data using two different algorithms. The software displays the raw and processed data in real time and plots data curves, making it easy to compare the effects of different data processing algorithms.

[0076] The data smoothing algorithm test software contains two sensor data display groups and a data curve display area. Sensor group 1 and sensor group 2 display the raw data of two sensors and two different smoothing filter algorithm data, respectively. The data curve display chart draws and displays the above three types of sensor data. The three radio buttons control whether the three data curves are displayed.

[0077] The first embodiment of the present invention relates to a measurement method of a high-precision ship dynamic liquid level measurement system, including the following contents:

[0078] S1. Data collection and transmission:

[0079] The liquid level signal is collected in real time by the sensor and transmitted to the data processing terminal via the CAN bus and the local area network;

[0080] S2. Dynamic liquid level modeling:

[0081] Establish a dynamic model that includes ship rolling, heaving, and liquid level changes, and distinguish high-frequency noise, low-frequency disturbances, and true liquid level signals;

[0082] S3. Data processing:

[0083] According to the analysis of the ship level model, the commonly used traditional smoothing filter algorithm is the low-pass filter algorithm. The simplest low-pass filter is the first-order low-pass filter, see Figure 3 , which reduces the signal strength by half (approximately -6dB) when the frequency doubles (relative to the cutoff frequency).

[0084] Establish a mathematical model of a first-order low-pass filter:

[0085] (Formula 2)

[0086] Formula 2 can be written as a discrete difference equation:

[0087] (Formula 3)

[0088] Arranged:

[0089] (Formula 4)

[0090] make

[0091]

[0092] We can get:

[0093] (Formula 5)

[0094] Formula 5 is the mathematical formula of the low-pass filter, which can be implemented by programming.

[0095]

[0096] Cutoff frequency

[0097]

[0098] y(nT) is the current filtered output value, y[(n-1)T] is the previous filtered output value, x(nT) is the current sampled input value, and T is the sampling interval. As the formula shows, the filtered output is only related to the previous output and the current input.

[0099] In the data smoothing algorithm test software, a first-order low-pass filter algorithm is used to process the liquid level data, and the filter cutoff frequency is adjusted to be much lower than the ship rolling frequency (the ship rolling period is 12s). Figure 4 A schematic diagram of the data curves generated using the first-order low-pass filtering algorithm is shown in the figure. The green curve represents the original data, and the blue curve represents the data generated using the first-order low-pass filtering algorithm. The figure shows that the first-order low-pass filtering algorithm's data curves converge correctly, demonstrating good filtering effectiveness. Due to the low filter cutoff frequency, the curves take a long time to converge during the initial stages of data processing. Compared to the original data, after stabilization, the curves are able to effectively smooth high-frequency noise and ship rolling disturbances. While the curves fluctuate with significant ship rolling, the amplitude of these fluctuations is significantly reduced compared to the original data.

[0100] Example 2:

[0101] This second embodiment involves a measurement method for a high-precision ship dynamic liquid level measurement system. Unlike the first embodiment, this embodiment utilizes a Kalman filter algorithm for data processing. The Kalman filter algorithm is a real-time recursive algorithm implemented by a digital computer. It uses random system quantities as filter input, and the filter output is an optimal estimate of the system state. Classical Kalman filtering is an estimation method based on linear systems and is generally applicable to linear or very nearly linear nonlinear problems.

[0102] A discrete control system can be described by a linear stochastic differential equation (state equation):

[0103] (Formula 6)

[0104] The observation equation of the system:

[0105] (Formula 7)

[0106] Kalman filtering can obtain the optimal estimation (minimum mean square error) result. The complete Kalman filtering algorithm can be divided into the following five basic formulas:

[0107] State one-step prediction:

[0108] (Formula 8)

[0109] State one-step prediction mean square error:

[0110] (Formula 9)

[0111] Filter gain:

[0112] (Formula 10)

[0113] State Estimation:

[0114] (Formula 11)

[0115] State estimation mean square error:

[0116] (Formula 12)

[0117] The liquid level of a ship changes with the consumption and replenishment of liquid and is not a fixed value. In reality, the change in liquid level caused by the consumption and replenishment of liquid is a relatively slow process, and the rate of change of liquid level is much lower than the ship's rolling cycle. In a short period of time, the liquid level data can be approximated as a fixed value. Without considering the change in liquid level, the state equation for determining the liquid level data is:

[0118] (Formula 13)

[0119] Its observation equation is:

[0120] (Formula 14)

[0121] The measurement data has only one liquid level, so the state transition matrix is 1, there is no control input, the control gain is 0, the observation value corresponds to the measurement data, the observation matrix is 1, w is the system noise (also called process noise), and v is the observation noise.

[0122] According to the mathematical model of liquid level data, the Kalman filter process analysis is performed. The liquid level data is approximately a fixed value, and the state estimation result is the optimal result of the previous state. Therefore, the state prediction equation is:

[0123] (Formula 15)

[0124] The state prediction mean square error calculation equation is:

[0125] (Formula 16)

[0126] The Kalman filter gain calculation equation is:

[0127] (Formula 17)

[0128] The state estimation equation is:

[0129] (Formula 18)

[0130] According to the state prediction equation, the state estimation equation can be simplified as:

[0131] (Formula 19)

[0132] The state estimation mean square error calculation equation is:

[0133] (Formula 20)

[0134] According to the mathematical model analysis of liquid level data, the state prediction value remains unchanged and the state prediction equation does not need to be calculated. Only formulas 16 to 20 need to be calculated.

[0135] The data smoothing algorithm testing software uses a Kalman filter to process the liquid level data, adjusting process and measurement noise settings. Compared to a first-order low-pass filter, the Kalman filter's data curve converges faster and more accurately during the initial processing phase. The curve also maintains stable fluctuations during significant ship roll, demonstrating superior filtering performance compared to a first-order low-pass filter.

[0136] The sensor used for testing in this embodiment is installed in the sedimentation tank of a ship's main engine. As the main engine operates and oil is consumed, the liquid level drops. When the level drops to the lower limit, oil is automatically added to the sedimentation tank until the level reaches the upper limit. This cycle of operation slows oil consumption and provides good Kalman filtering results. However, during the refueling process, the oil level rises rapidly, resulting in a significant lag in the Kalman filter curve. Consequently, the real-time data processing does not meet real-time requirements and cannot accurately reflect the liquid level.

[0137] Example 3:

[0138] This embodiment 2 involves a measurement method for a high-precision ship dynamic liquid level measurement system. Unlike the first embodiment, this embodiment 2 employs an optimized Kalman filter algorithm for data processing. The optimization of the Kalman filter algorithm involves adjusting the motion model, using a more accurate model for data processing, and re-deriving the Kalman filter algorithm while taking liquid level changes into account. In this embodiment, the descent and rise of the oil in the settling tank can be approximated as uniform linear motion, and the state equation for determining the liquid level data is:

[0139] (Formula 21)

[0140] Its observation equation is:

[0141] (Formula 22)

[0142] The measured data only has one liquid level, but also has the liquid level change rate. The input matrix is a 1x2 matrix, x[0] represents the liquid level, and x[1] represents the liquid level change rate. The state transition matrix is a 2x2 matrix:

[0143] (Formula 23)

[0144] There is no control input, and the control gain is 0. The observation value corresponds to the measurement data. There is only one observation data, and the observation matrix is a 1x2 matrix. , w is the system noise (also called process noise), and v is the observation noise.

[0145] According to the mathematical model of liquid level data, the Kalman filter process analysis is performed. The liquid level data is approximately a fixed value, and the state estimation result is the optimal result of the previous state. The state prediction equation is:

[0146] (Formula 24)

[0147] The state prediction mean square error calculation equation is:

[0148] (Formula 25)

[0149] The Kalman filter gain calculation equation is:

[0150] (Formula 26)

[0151] The state estimation equation is:

[0152] (Formula 27)

[0153] The state estimation mean square error calculation equation is:

[0154] (Formula 28)

[0155] In the data smoothing algorithm test software, an improved Kalman filter algorithm is used to process the liquid level data, and the process noise and measurement noise setting values are readjusted. Figure 5 The data curve of the optimized filtering algorithm is drawn. As shown in the figure, the yellow curve is the data of the improved Kalman filter algorithm. It can be seen from the figure that the improved and optimized Kalman filter algorithm has a better smoothing effect in the liquid level stable stage, and can also better follow the liquid level changes in the stage of rapid liquid level increase. Compared with the first-order low-pass filtering algorithm, the real-time performance is significantly improved.

[0156] A data interval with large liquid level fluctuations was selected for comparative analysis. The changes in the original data and the Kalman filter data were compared. The statistical results are shown in Table 2 below:

[0157] Raw data fluctuations Kalman filter data fluctuations illustrate Smoothing effect 0.071m 0.003m The data is the peak-to-peak value of liquid level fluctuation

[0158] Table 2 Liquid level smoothing statistics

[0159] The data in Table 2 show that the optimized Kalman filter algorithm works well in ship level measurement. The measurement accuracy after processing with the optimized Kalman filter algorithm is improved by dozens of times compared with no data processing.

[0160] In summary, the data processing of the present invention can be processed by multiple algorithms in a coordinated manner:

[0161] First-order low-pass filtering algorithm: Set the cutoff frequency to be lower than the ship's rolling frequency (e.g., 0.1 Hz), and implement real-time filtering through differential equations to suppress high-frequency noise.

[0162] Classic Kalman filter algorithm: Based on a fixed liquid level model, it achieves optimal estimation results through state prediction and observation updates, and is suitable for scenarios with stable liquid levels.

[0163] Optimized Kalman filter algorithm: Introduces a liquid level uniform change model, expands state variables to liquid level height and change rate, and dynamically adjusts process noise covariance to adapt to scenarios where the liquid level rises and falls rapidly.

[0164] The above are only specific application examples of the present invention and do not constitute any limitation on the scope of protection of the present invention. Any technical solutions formed by equivalent transformation or equivalent replacement shall fall within the scope of protection of the present invention.

Claims

1. A high-precision measurement system for dynamic liquid level of a ship, characterized by: It includes multiple sensors, multiple data acquisition terminals, CAN bus, CAN bridge and data processing terminal. Each sensor is connected to a data acquisition terminal, and the data acquisition terminal is connected to the CAN bus. The CAN bus is connected through a CAN bus one-way bridge. The CAN bus one-way bridge is connected to the display terminal through the existing local area network of the ship. The remote transmission and display of liquid level data are realized through the local area network. The sensor and data acquisition terminal are both installed in the ship's engine room. The sensor is connected to the data acquisition terminal. After the data acquisition terminal completes the conversion, conditioning and acquisition of the sensor signal, it displays the liquid level data on its own display screen and transmits the data to the CAN bus. The CAN bus one-way bridge is set near the network interface, obtains the liquid level data of all sensors from the CAN bus, and forwards it to the ship's local area network and transmits it to the display terminal that needs to display the data.

2. A high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: The sensor is arranged on the bulkhead at the bottom of the liquid tank. The sensor output signal is a 4-20mA standard current signal, and the signal is connected to the data acquisition terminal via a shielded cable.

3. A high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: The data acquisition terminal includes a signal conditioning circuit, a single-chip microcomputer circuit, a CAN bus interface circuit, an OLED display circuit and a power supply module. The signal conditioning circuit converts the 4-20mA current signal input by the liquid level sensor into a 0-3.3V voltage signal and sends it to the single-chip microcomputer circuit. The single-chip microcomputer completes the digital sampling of the liquid level signal through the A / D conversion module, calculates the conversion coefficient according to the sensor range and the density of the measured liquid, converts the sampled digital signal into liquid level data, and displays it in real time on the OLED circuit. At the same time, the liquid level data is encoded in a specified format and transmitted to the CAN bus through the CAN bus interface circuit.

4. A high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: The CAN bus unidirectional bridge includes a CAN bus interface circuit, a single-chip microcomputer circuit, a network interface circuit and a power module. The CAN bus interface circuit monitors the CAN bus signal in real time, and sends the received CAN data frame to the single-chip microcomputer circuit for reception and demodulation. The single-chip microcomputer extracts the CAN data ID, data length, and data payload from the CAN data frame, encodes them according to the specified format, and transmits them to the ship's local area network through the network interface circuit; the data acquisition terminal only sends but does not receive CAN data, the CAN bridge adopts a unidirectional transmission design, and network data transmission adopts the UDP transmission protocol.

5. A high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: It also includes a data processing terminal, which is set in the ship monitoring center, runs data smoothing algorithm testing software, configures the remote connection CAN bridge IP address and data receiving port, and supports multi-algorithm parallel processing and comparative analysis.

6. A high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: Both ends of the CAN bus are connected to 120Ω terminal resistors, and the data acquisition terminal sends liquid level data in a standard frame format.

7. The high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: The CAN bus one-way bridge encapsulates the data into UDP messages and broadcasts them to the display terminal through the ship's local area network.

8. A measurement method for a high-precision ship dynamic liquid level measurement system according to claim 1, characterized in that: Includes the following: S1. Data collection and transmission: The liquid level signal is collected in real time by the sensor and transmitted to the data processing terminal via the CAN bus and the local area network; S2. Dynamic liquid level modeling: Establish a dynamic model that includes ship rolling, heaving, and liquid level changes, and distinguish high-frequency noise, low-frequency disturbances, and true liquid level signals; S3. Select filtering algorithm for data processing based on ship motion characteristics: S31, first-order low-pass filtering algorithm, sets the cutoff frequency lower than the ship's rolling frequency, implements real-time filtering through differential equations, and suppresses high-frequency noise; S32, the classic Kalman filter algorithm, is based on a fixed liquid level model and achieves optimal estimation results through state prediction and observation update, and is suitable for scenarios with stable liquid levels; S33. Optimize the Kalman filter algorithm, introduce a liquid level uniform change model, expand the state variables to liquid level height and change rate, dynamically adjust the process noise covariance, and adapt to the scenario of rapid liquid level rise and fall.

9. The measurement method of a high-precision ship dynamic liquid level measurement system according to claim 8, characterized in that: In step S32, The state prediction equation is: ； The state prediction mean square error calculation equation is: ； The Kalman filter gain calculation equation is: ； The state estimation equation is: ； According to the state prediction equation, the state estimation equation can be simplified as: ； The state estimation mean square error calculation equation is: 。 10. The measurement method of a high-precision ship dynamic liquid level measurement system according to claim 9, characterized in that: In the liquid level uniform change model of step S33, The state prediction equation is: ； The state prediction mean square error calculation equation is: ； The Kalman filter gain calculation equation is: ； The state estimation equation is: ； The state estimation mean square error calculation equation is: 。

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