Dynamic working mode switching method of offshore wind power positioning terminal based on multi-sensor fusion

Abstract

The application discloses a dynamic working mode switching method for an offshore wind power positioning terminal based on multi-sensor fusion, and relates to the technical field of positioning terminals.The application solves the problem of obvious deficiency in intelligent switching of the working mode of a traditional offshore wind power positioning terminal.The application converts multi-sensor data features into an intuitive and calculable geometric figure matching relationship by adopting a quantitative comparison mode of associated polygons and standard polygons, and automatically determines and switches the working mode by area ratio.Compared with a traditional threshold judgment mode, the application has stronger anti-interference ability and lower misjudgment rate, can adapt to complex and changeable working conditions at sea, realizes non-inductive, real-time and accurate switching of the working mode of the positioning terminal, determines the optimal working mode, adjusts the GNSS antenna gain in a small range on the basis of the optimal working mode, and automatically locks the optimal gain state by taking the polygon matching area ratio as an evaluation index, so that the multi-sensor fusion effect is further strengthened.

Description

Technical Field

[0001] This invention relates to the field of positioning terminal technology, specifically to a method for switching dynamic working modes of offshore wind power positioning terminals using multi-sensor fusion. Background Technology

[0002] Offshore wind power equipment operates in a marine environment characterized by high temperature, high humidity, high salt spray, strong vibration, and complex and variable weather for extended periods. Its positioning terminal relies on a multi-sensor system consisting of GNSS, INS, gyroscopes, millimeter-wave radar, and various environmental sensors to achieve status monitoring and accurate positioning.

[0003] Traditional offshore wind power positioning terminals mostly adopt a fixed working mode, with sensor data acquisition, processing logic and signal gain parameters all preset, making it difficult to adapt to complex and ever-changing offshore conditions.

[0004] On the one hand, multi-source sensors have inherent differences in hardware response, data transmission and acquisition timing, which can easily lead to data spatiotemporal asynchrony, directly affecting the accuracy and reliability of fusion positioning. On the other hand, existing technologies mostly rely on a single threshold or simple logic to judge the working status, lacking a comprehensive quantitative evaluation mechanism for multi-dimensional data. When there are sudden changes in the environment such as salt spray, vibration, and electromagnetic interference, problems such as mode misjudgment, switching lag or positioning failure are likely to occur.

[0005] Meanwhile, GNSS antenna gain is usually set with fixed parameters, which cannot be dynamically optimized according to real-time operating conditions. This leads to frequent signal attenuation and loss of lock, further reducing the stability and continuous availability of the positioning system.

[0006] In summary, traditional offshore wind turbine positioning terminals have significant shortcomings in multi-source data synchronous calibration, intelligent switching of working modes, and adaptive adjustment of signal gain, making it difficult to meet the actual needs of long-term stable and high-precision positioning and safe operation and maintenance of offshore wind turbines. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for dynamic working mode switching of offshore wind power positioning terminals based on multi-sensor fusion, which solves the problem that traditional offshore wind power positioning terminals have significant deficiencies in intelligent switching of working modes.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal, comprising the following steps: Step 1: Determine the data sources associated with different types of multi-source data from the positioning terminal, perform synchronization pulse processing on different data sources, record the feedback delay associated with different data sources, and then perform delay compensation on the monitoring data associated with different data sources based on the feedback delay. The specific method is as follows: Send a pulse signal to a single data source, record the pulse signal transmission time t1, and then record the return time t2 of the monitored related data from the single data source. Based on the sending time t1 and the return time t2, the delay time corresponding to the associated data is confirmed, and the delay time is (t2-t1)÷2. The confirmed delay time is used as the compensation time associated with the data source, and delay compensation is performed on each set of data monitored by the data source based on the confirmed compensation time. After completing the delay compensation, the timestamps of the associated data generated by the pulse signal at the same time from multiple different data sources are confirmed, and the time range is confirmed based on the minimum and maximum timestamps. Step Two: Based on the confirmed time range, identify the associated data from different data sources. Then, based on the historical data characteristics associated with multiple sets of associated data, generate data standards associated with different working modes. Finally, based on the specific data range of the generated sets of associated data, confirm the accuracy of the currently associated working mode and switch it in real time. The specific method is as follows: Based on the historical associated data generated by different data sources, the minimum and maximum values ​​of the historical associated data corresponding to a single data source in different working modes are determined, and the range of historical associated data associated with a single data source in the corresponding working mode is determined. Based on the total number G of different data sources, generate associated polygons with the same number of sides, and record the internal midpoint of the associated polygon as the starting point. Confirm the connection line corresponding to the starting point and the corner point of the associated polygon, and randomly match the connection line with different data sources one by one. Record the matched connection line as the associated measurement line of the corresponding data source, and assign the measurement standard associated with the unit length of different associated measurement lines. Based on the historical data range associated with the corresponding data source under the corresponding working mode, the associated measurement segment is confirmed within the corresponding measurement line. The measurement segments associated with the same working mode are determined one by one, and the adjacent starting points associated with the corresponding measurement segments are connected one by one. Then, the adjacent ending points are connected one by one to generate the measurement standard polygon associated with the corresponding working mode. Based on the confirmed time range, identify the associated data from different data sources within the same time range, and based on the associated measurement lines corresponding to the associated data, identify the measurement points where the associated data is located. With the measurement points as the end points and the boundary points associated with the front of the measurement points as the starting points, the boundary points are the boundary points associated between different measurement standard polygons. Connect several measurement points to obtain the polygon to be verified corresponding to the current time range. The confirmed polygon to be verified is checked against the measurement standard polygons associated with different working modes. The intersection area between the polygon to be verified and the different measurement standard polygons is identified. The area ratio of the intersection area to the total area of ​​the polygon to be verified is recorded. The maximum value is selected from the confirmed different area ratios, and the working mode associated with the maximum value is recorded as the determined mode. It identifies whether the current positioning terminal is operating in a deterministic mode. If not, it adjusts the operating mode of the positioning terminal to deterministic mode. Step 3: Define a set of monitoring periods, adjust the GNSS antenna gain in real time within the monitoring periods, record the crossover characteristics associated with different antenna gain states, and select the optimal gain state from the confirmed crossover characteristics and execute it. The specific method is as follows: Identify the antenna gain associated with the determined mode state, determine a set of adjustment ranges, set the current antenna gain as F, determine a set of monitoring cycles, and within the monitoring cycle, adjust the antenna gain in real time, with the value adjusted gradually. Each time the antenna gain is adjusted, a set of confirmed time ranges is retained, and the polygons to be verified associated with the time range are determined. The area ratio between the polygons to be verified and the measurement standard polygons is confirmed simultaneously. The different area proportions associated with different antenna gains are confirmed in sequence, and the maximum value is selected from the confirmed group of area proportions. The antenna gain associated with the maximum value is recorded as the execution gain, so that the positioning terminal is in the optimal gain state.

[0009] Preferably, in step one, the data source includes: a GNSS module, an INS unit, a gyroscope, a millimeter-wave radar, and an environmental sensor group, which includes a salt spray sensor, a vibration sensor, and a light intensity sensor.

[0010] Preferably, the debugging range is [0.9F, 1.1F].

[0011] This invention provides a method for dynamic switching of operating modes for offshore wind power positioning terminals based on multi-sensor fusion. Compared with existing technologies, it has the following advantages: By using synchronous pulse triggering, real-time calculation of feedback delay and delay compensation processing, the time offset caused by hardware differences, transmission links and marine interference of GNSS, INS, gyroscope, millimeter-wave radar and environmental sensors such as salt spray, vibration and light intensity is effectively eliminated, ensuring that multi-source monitoring data are aligned under a unified time reference, providing a high-quality and highly consistent data foundation for subsequent fusion decision-making and pattern recognition. By adopting a quantitative comparison method of associated polygons and measurement standard polygons, the characteristics of multi-sensor data are transformed into intuitive and calculable geometric matching relationships. The working mode is automatically determined and switched by the area ratio. Compared with the traditional threshold judgment method, it has stronger anti-interference ability and lower misjudgment rate. It can adapt to the complex and ever-changing working conditions at sea and realize the seamless, real-time and accurate switching of the positioning terminal's working mode. Based on the determination of the optimal working mode, the GNSS antenna gain is adjusted with a small-range gradient, and the polygon matching area ratio is used as the evaluation index to automatically lock the optimal gain state, further enhancing the multi-sensor fusion effect, reducing the attenuation and impact of high salt spray, strong vibration and strong electromagnetic interference at sea on the positioning signal, and ensuring that the positioning terminal maintains continuous, stable and high-precision positioning output in harsh environments. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] First Embodiment Please see Figure 1 This application provides a method for switching dynamic working modes of an offshore wind power positioning terminal based on multi-sensor fusion, including the following steps: Step 1: Determine the data source associated with different types of multi-source data from the positioning terminal (i.e., the associated monitoring sensors, such as GNSS modules, INS units, gyroscopes, millimeter-wave radars, and environmental sensor groups, including salt spray sensors, vibration sensors, and light intensity sensors), and perform synchronization pulse processing on different data sources. Record the feedback delay associated with different data sources, and then perform delay compensation on the monitoring data associated with different data sources based on the feedback delay, so that the associated data monitored by different data sources can be synchronized in time and space. The specific methods for implementing delay compensation are as follows: Send a pulse signal to a single data source, record the pulse signal transmission time t1, and then record the return time t2 of the monitored related data from the single data source. Based on the sending time t1 and the return time t2, the delay time corresponding to the associated data is confirmed, and the delay time is (t2-t1)÷2. The confirmed delay time is used as the compensation time associated with the data source, and delay compensation is performed on each set of data monitored by the data source based on the confirmed compensation time. After completing the delay compensation, the timestamps of the associated data generated by the pulse signal at the same time from multiple different data sources are confirmed, and the time range is confirmed based on the minimum and maximum timestamps. Specifically, if the timestamp of the corresponding associated data is set as SC1, then after delay compensation, the corrected timestamp is (SC1 - compensation time). Step 2: Based on the confirmed time range, identify the associated data generated by different data sources, and based on the historical data characteristics associated with multiple sets of associated data, generate data standards associated with different working modes. Then, based on the specific data range of the generated multiple sets of associated data, confirm whether the current associated working mode is accurate and switch it in real time. The specific method for switching the current working mode in real time is as follows: Based on the historical associated data generated by different data sources, the minimum and maximum values ​​of the historical associated data corresponding to a single data source in different working modes are determined, and the range of historical associated data associated with a single data source in the corresponding working mode is determined. Based on the total number G of different data sources, generate associated polygons with the same number of sides, and record the internal midpoint of the associated polygon as the starting point. Confirm the connection line corresponding to the starting point and the corner point of the associated polygon, and randomly match the connection line with different data sources one by one. Record the matched connection line as the associated measurement line of the corresponding data source, and assign the measurement standard associated with the unit length of different associated measurement lines. Based on the historical data range associated with the corresponding data source under the corresponding working mode, the associated measurement segment is identified within the corresponding measurement line (the two endpoints of the measurement segment correspond to the two range values ​​of the data range, the starting point of the measurement segment is associated with the minimum range value, and the ending point is associated with the maximum range value). The measurement segments associated with the same working mode are determined one by one, and the adjacent starting points associated with the corresponding measurement segments are connected one by one, and the adjacent ending points are connected one by one to generate the measurement standard polygon associated with the corresponding working mode. Based on the confirmed time range, identify the associated data from different data sources within the same time range, and based on the associated measurement lines corresponding to the associated data, identify the measurement points where the associated data is located. With the measurement points as the end points and the boundary points associated with the front of the measurement points as the starting points, the boundary points are the boundary points associated between different measurement standard polygons. Connect several measurement points to obtain the polygon to be verified corresponding to the current time range. The confirmed polygon to be verified is checked against the measurement standard polygons associated with different working modes. The intersection area between the polygon to be verified and the different measurement standard polygons is identified. The area ratio of the intersection area to the total area of ​​the polygon to be verified is recorded. The maximum value is selected from the confirmed different area ratios, and the working mode associated with the maximum value is recorded as the determined mode. The system identifies whether the current positioning terminal is operating in a defined mode. If so, no mode switching is required. If not, the system adjusts the positioning terminal's operating mode to the defined mode, completing the operating mode switching process. Step 3: Define a set of monitoring periods, adjust the GNSS antenna gain in real time within the monitoring periods, record the crossover characteristics associated with different antenna gain states, and select the optimal gain state from the confirmed crossover characteristics and execute it. The specific method for selecting and executing the optimal gain state is as follows: Identify the antenna gain associated with the determined mode state and determine a set of adjustment ranges. Set the current antenna gain as F, and its adjustment range is [0.9F, 1.1F]. Determine a set of monitoring cycles, which are preset cycles. The specific values ​​are determined by the operator based on experience. Within the monitoring cycle, the antenna gain is adjusted in real time, and its value is gradually adjusted from 0.9F to 1.1F. Each time the antenna gain is adjusted, a set of confirmed time ranges is retained, and the polygons to be verified associated with the time range are determined. The area ratio between the polygons to be verified and the measurement standard polygons is confirmed simultaneously. The different area proportions associated with different antenna gains are confirmed in sequence, and the maximum value is selected from the confirmed group of area proportions. The antenna gain associated with the maximum value is recorded as the execution gain, so that the positioning terminal is in the optimal gain state.

[0015] Second Embodiment In this embodiment, compared to the above embodiments, the switching process of the working mode of the corresponding positioning terminal is mainly completed by combining the corresponding device and sensor, specifically including: Step 1: Multi-source data preprocessing and spatiotemporal synchronization Data acquisition: Each sensor outputs raw data at its initial frequency (e.g., GNSS outputs latitude, longitude, altitude, and signal-to-noise ratio; INS outputs angular velocity and acceleration; environmental sensors output salt spray concentration and vibration frequency).

[0016] Spatiotemporal alignment: Timestamp compensation method is used to eliminate synchronization errors. Hardware triggering: A synchronization pulse (period 10ms) is sent through the controller's GPIO pin, and all sensors receive the pulse and sample synchronously. Software calibration: Timestamp offset compensation is performed on delayed data (such as radar data transmission delay of 20ms) to ensure that the time deviation of multi-source data is ≤1ms.

[0017] Data cleaning: GNSS outliers are removed using the 3σ criterion (e.g., data with a signal-to-noise ratio < 25dB are marked as invalid data). Apply a moving average filter (window size 5) to the INS data to reduce high-frequency vibration noise.

[0018] Step 2: Environmental Status Assessment and Model Decision A hybrid decision-making model based on fuzzy logic and reinforcement learning outputs the optimal working mode.

[0019] 2.1 Input parameter quantization: Input parameters Quantitative Dimensions Range of values Sensor source Positioning accuracy requirements Task Priority 0~1 (0 = cruise, 1 = maintenance) Host computer commands (input via RS-485 interface) Environmental interference level GNSS signal-to-noise ratio 0~1 (<25dB=0,>35dB=1) GNSS module Vibration frequency 0~1 (>5Hz=0, <2Hz=1) Vibration sensor Remaining battery power Battery percentage 0~1 (<20%=0, >80%=1) Power management module.

[0020] 2.2 Fuzzy Logic Decision Model: Fuzzification: The input parameters are mapped to fuzzy subsets (such as "low battery", "medium interference", "high priority"), and the membership function adopts a triangular function.

[0021] Rule base: A total of 9 core rules are designed. Example: IF (Accuracy requirement = High) AND (Interference level = Low) AND (Battery level = High) → THEN High accuracy mode; IF (Accuracy requirement = Medium) AND (Interference level = Medium) AND (Battery level = Medium) → THEN Balanced mode; IF (accuracy requirement = low) AND (interference level = high) AND (power consumption = low) → THEN Low power mode.

[0022] Defuzzification: The centroid method is used to output the mode decision value (0~1), corresponding to 3 working modes: 2.3 Definition of Working Mode: High precision mode GNSS, INS, and radar all enabled GNSS 20Hz, INS 100Hz, Radar 10Hz Federal Kalman Filter (FKF) ≤±0.1m 1500mW.

[0023] Balanced mode GNSS (interval sampling) + INS GNSS 1Hz (sampled once every 10 seconds), INS 50Hz Adaptive Kalman Filter (AKF) ≤±0.5m 700mW.

[0024] Low power mode INS as the primary device, with GNSS timed wake-up. INS 20Hz, GNSS 0.0028Hz (once per hour) Zero Rate Adjustment (ZUPT) algorithm ≤±1.5m (short-term) 200mW.

[0025] Step 3: Dynamically switch execution The switching mode is executed by the Markov Decision Process (MDP) switching controller, and the core is to optimize the switching timing through the "state-action-reward" mechanism.

[0026] 3.1 MDP Model Definition: State space S: {positioning error e (RMSE), remaining battery power b, environmental interference level d}, quantized into discrete states (e.g., e∈[0,0.1m),[0.1m,0.5m),...).

[0027] Action Space A: {Maintain the current mode, switch to high precision / balanced / low power mode}, a total of 4 actions.

[0028] Reward function R: R=α×(1-e / emax)+β×(b / bmax)-γ×switch_cost R=\alpha\cdot(1-e / e_{\text{max}})+\beta\cdot(b / b_{\text{max}})-\gamma\cdot\text{switch\_cost} R = α × (1 - e / emax) + β × (b / bmax) - γ × switch_cost where: α = 0.6 (precision weight), β = 0.3 (power weight), γ = 0.1 (switching cost, to avoid frequent switching); e_max = 3m (maximum permissible error), b_max = 100% (full power).

[0029] 3.2 Switching execution logic: The controller calculates the reward value R every 100ms and selects the action that maximizes the cumulative reward (by iteratively optimizing the strategy using the Q-learning algorithm).

[0030] At the hardware level, sensor start-up and shutdown are implemented through GPIO interrupts: for example, when switching to low-power mode, the controller sends a command to shut down the radar power supply (controlled by a MOSFET) and configures the GNSS module to enter sleep mode (only retaining the RTC timer wake-up function).

[0031] Step 4: Feedback Optimization Positioning accuracy feedback: Calculate the positioning error e (RMSE) in the current mode. If e exceeds the mode's allowable threshold three times in a row (e.g., e > 0.5m in balanced mode), trigger an emergency switch (force jump to high-precision mode and wake up the radar).

[0032] Energy consumption feedback: Real-time monitoring of power consumption rate. If the predicted battery life in the current mode is less than 7 days, the sampling frequency of non-core sensors will be automatically reduced (e.g., radar will be reduced from 10Hz to 5Hz).

[0033] Algorithm parameter adaptation: Based on historical data (storing the most recent 1000 sets of decision-result data), the weight coefficients in the fuzzy logic rule base are optimized by gradient descent (e.g., automatically increasing the weight of the "vibration frequency" parameter during typhoon season).

[0034] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0035] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for switching dynamic working modes of an offshore wind power positioning terminal based on multi-sensor fusion, characterized in that, Includes the following steps: Step 1: Determine the data source associated with different types of multi-source data from the positioning terminal, perform synchronization pulse processing on different data sources, record the feedback delay associated with different data sources, and then perform delay compensation on the monitoring data associated with different data sources based on the feedback delay. Step 2: Based on the confirmed time range, identify the associated data generated by different data sources, and based on the historical data characteristics associated with multiple sets of associated data, generate data standards associated with different working modes. Then, based on the specific data range of the generated multiple sets of associated data, confirm whether the current associated working mode is accurate and switch it in real time. Step 3: Define a set of monitoring periods, adjust the GNSS antenna gain in real time within the monitoring periods, record the cross-features associated with different antenna gain states, and select the optimal gain state from the confirmed cross-features and execute it.

2. The method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal according to claim 1, characterized in that, In step one, the data sources include: a GNSS module, an INS unit, a gyroscope, a millimeter-wave radar, and an environmental sensor group, which includes a salt spray sensor, a vibration sensor, and a light intensity sensor.

3. The method for dynamic working mode switching of a multi-sensor fusion offshore wind power positioning terminal according to claim 1, characterized in that, In step one, the specific method for delay compensation for different data sources is as follows: Send a pulse signal to a single data source, record the pulse signal transmission time t1, and then record the return time t2 of the monitored related data from the single data source. Based on the sending time t1 and the return time t2, the delay time corresponding to the associated data is confirmed, and the delay time is (t2-t1)÷2. The confirmed delay time is used as the compensation time associated with the data source, and delay compensation is performed on each set of data monitored by the data source based on the confirmed compensation time. After completing the delay compensation, the timestamps of the associated data generated by the pulse signals at the same time from multiple different data sources are confirmed, and the time range is confirmed based on the minimum and maximum timestamps.

4. The method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal according to claim 1, characterized in that, In step two, the specific method for confirming the data standards associated with different working modes is as follows: Based on the historical associated data generated by different data sources, the minimum and maximum values ​​of the historical associated data corresponding to a single data source in different working modes are determined, and the range of historical associated data associated with a single data source in the corresponding working mode is determined. Based on the total number G of different data sources, generate associated polygons with the same number of sides, and record the internal midpoint of the associated polygon as the starting point. Confirm the connection line corresponding to the starting point and the corner point of the associated polygon, and randomly match the connection line with different data sources one by one. Record the matched connection line as the associated measurement line of the corresponding data source, and assign the measurement standard associated with the unit length of different associated measurement lines. Based on the historical data range associated with the corresponding data source under the corresponding working mode, the associated measurement segment is identified within the corresponding associated measurement line. The measurement segments associated with the same working mode are determined one by one, and the adjacent starting points associated with the corresponding measurement segments are connected one by one. Then, the adjacent ending points are connected one by one to generate the measurement standard polygon associated with the corresponding working mode.

5. The method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal according to claim 4, characterized in that, In step two, the specific method for verifying the accuracy of the working mode associated with the current positioning terminal and switching it in real time is as follows: Based on the confirmed time range, identify the associated data from different data sources within the same time range, and based on the associated measurement lines corresponding to the associated data, identify the measurement points where the associated data is located. With the measurement points as the end points and the boundary points associated with the front of the measurement points as the starting points, the boundary points are the boundary points associated between different measurement standard polygons. Connect several measurement points to obtain the polygon to be verified corresponding to the current time range. The confirmed polygon to be verified is checked against the measurement standard polygons associated with different working modes. The intersection area between the polygon to be verified and the different measurement standard polygons is identified. The area ratio of the intersection area to the total area of ​​the polygon to be verified is recorded. The maximum value is selected from the confirmed different area ratios, and the working mode associated with the maximum value is recorded as the determined mode. The system identifies whether the current positioning terminal is operating in a defined mode. If not, it adjusts the positioning terminal's operating mode to defined mode.

6. The method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal according to claim 5, characterized in that, If the current positioning terminal is operating in a defined mode, no mode switching is required.

7. The method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal according to claim 1, characterized in that, In step three, the specific method for selecting and executing the optimal gain state is as follows: Identify the antenna gain associated with the determined mode state, determine a set of adjustment ranges, set the current antenna gain as F, determine a set of monitoring cycles, and within the monitoring cycle, adjust the antenna gain in real time, with the value adjusted gradually. Each time the antenna gain is adjusted, a set of confirmed time ranges is retained, and the polygons to be verified associated with the time range are determined. The area ratio between the polygons to be verified and the measurement standard polygons is confirmed simultaneously. The different area proportions associated with different antenna gains are confirmed in sequence, and the maximum value is selected from the confirmed group of area proportions. The antenna gain associated with the maximum value is recorded as the execution gain, so that the positioning terminal is in the optimal gain state.

8. The method for switching dynamic working modes of a multi-sensor fusion offshore wind power positioning terminal according to claim 7, characterized in that, The debugging range is [0.9F, 1.1F].

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