Method and system for determining competition channel and storage medium

Abstract

The invention provides a method and system for determining a competition channel and a storage medium, and the method comprises the steps: obtaining the real-time position data of a plurality of buoys in the channel, the plurality of buoys comprise at least one reference buoy and a plurality of common buoys, the reference buoy provides first positioning data of a first precision level, and the common buoys provide first positioning data of a second precision level; the common buoy provides second positioning data of a second precision level lower than the first precision level; correcting the second positioning data based on the at least one first positioning data to obtain a buoy position set; processing the buoy position set through a constraint fitting algorithm to generate a dynamic channel datum line, and obtaining environment data and competition stage information in real time based on the dynamic channel datum line; and processing the environment data, the competition stage information and the dynamic channel datum line by using a lightweight trajectory prediction model to generate the risk level and dynamic safety guidance information of each moving target. According to the invention, the deployment and operation cost can be reduced on the premise of ensuring the precision, and the dynamic channel reference line and the safety guidance can be generated in real time.

Description

Technical Field

[0001] This application relates to the field of water traffic management and sports competition support technology, and in particular to a method, system and storage medium for determining competition routes. Background Technology

[0002] In water sports competitions such as rowing and canoeing, the navigation channel is a critical infrastructure for ensuring fairness and safety. Traditional navigation channel systems typically use physical buoys anchored to the seabed by chains to mark the channel boundaries. However, due to the continuous effects of environmental factors such as currents and waves, the buoys drift around their anchor points, causing their actual positions to deviate from the predetermined theoretical channel line. This drift is random and inconsistent, making the entire channel an irregular curve rather than an ideal straight line or smooth arc, seriously affecting athletes' judgment of their navigation paths and the fairness of the competition.

[0003] To address the aforementioned issues, existing technologies have attempted to introduce smart buoys based on Global Navigation Satellite Systems (GNSS). These buoys acquire their positions in real time using high-precision positioning technology and generate virtual navigation lines accordingly. However, such systems typically require all buoys to be equipped with high-precision GNSS receivers, such as RTK-GNSS, resulting in high hardware costs and hindering large-scale deployment in small-to-medium-sized events or long-distance waterways. Furthermore, processing large amounts of high-precision positioning data incurs high computational complexity and communication load, leading to delays in baseline generation and safety guidance, failing to meet the real-time requirements of high-speed competitive scenarios. Therefore, existing technologies struggle to balance high-precision positioning with low cost and high efficiency, limiting the widespread adoption and effectiveness of smart navigation systems. Summary of the Invention

[0004] In order to significantly reduce system deployment and operation costs while ensuring high positioning accuracy across the entire waterway, and to improve the real-time performance of dynamic waterway baseline generation and safety guidance, this application provides a method, system, and storage medium for extracting and integrating contract content.

[0005] Firstly, this embodiment provides a method for determining a racecourse, the method comprising: The real-time position data of multiple buoys in the waterway are acquired, wherein the multiple buoys include at least one reference buoy and multiple ordinary buoys, the reference buoy provides first positioning data of a first accuracy level, and the ordinary buoys provide second positioning data of a second accuracy level, wherein the first accuracy level is higher than the second accuracy level; The set of buoy positions for the entire waterway is obtained by correcting the second positioning data based on at least one first positioning data. A dynamic navigation baseline is generated by processing the buoy position set through a constraint fitting algorithm. Environmental data and event stage information are acquired in real time based on the dynamic navigation baseline. A lightweight trajectory prediction model is used to process the environmental data, event stage information, and the dynamic navigation baseline to generate the risk level of each moving target in the navigation channel and the corresponding dynamic safety guidance information.

[0006] In some embodiments, acquiring the real-time position data of multiple buoys in the channel includes: Reference buoys and ordinary buoys are deployed on both sides of the waterway in an alternating manner, wherein the interval between the reference buoys is set to a first distance, and the interval between the ordinary buoys is set to a second distance that is less than the first distance; Each reference buoy is equipped with a first GNSS module and a dual-mode communication unit with real-time dynamic differential positioning function, which are used to report first positioning data at a first sampling frequency. Each ordinary buoy is equipped with a second GNSS module with precise single-point positioning function and a low-power long-range communication unit, which is used to report second positioning data at a second sampling frequency lower than the first sampling frequency.

[0007] In some embodiments, the method further includes: A motion state detection sensor is installed in the ordinary buoy to monitor the buoy attitude change value and determine whether the buoy attitude change value exceeds a preset threshold. If it does, the second positioning data is reported using a first sampling frequency.

[0008] In some embodiments, the step of correcting the second positioning data based on at least one first positioning data to obtain the set of buoy positions for the entire waterway includes: Constructing a dynamic positioning correction network divides the waterway into multiple correction units defined by adjacent reference buoys, wherein each correction unit includes at least two reference buoys and at least one ordinary buoy; A local relative coordinate system is established in each correction unit using the first positioning data as the reference anchor point; In the local relative coordinate system, an adaptive weighted positioning algorithm is used to calculate the precise relative position of the ordinary buoy with respect to the reference anchor point. The weight coefficients in the adaptive weighted positioning algorithm are dynamically adjusted according to the distance vector between the ordinary buoy and each reference anchor point. The precise relative position is fused with the second positioning data using a Kalman filter to generate corrected position data. The buoy position set is obtained by spatiotemporally aligning and integrating all the first positioning data and the corrected position data of the entire waterway.

[0009] In some embodiments, the buoy position set includes multiple reference positioning data and general positioning data, and the step of processing the buoy position set using a constraint fitting algorithm to generate a dynamic waterway baseline includes: Obtain the reference positioning data of all reference buoys in the buoy position set, and use the reference positioning data as fixed constraint anchor points in the fitting. Based on the fixed constraint anchor points, divide the waterway into multiple local fitting intervals defined by adjacent reference buoys. Extract the normal positioning data of ordinary buoys located within each local fitting interval, and use them as dynamic fitting nodes; Using the fixed constraint anchor point as a strong constraint condition, a piecewise cubic spline curve fitting algorithm is used to locally fit the dynamic fitting node to obtain multiple local baseline segments; The local baseline segments are smoothly spliced ​​together at the fixed constraint anchor points to obtain a globally continuous and smooth dynamic track baseline.

[0010] In some embodiments, the process of using a lightweight trajectory prediction model to process the environmental data, event phase information, and the dynamic waterway baseline to generate risk levels and corresponding dynamic safety guidance information for each moving target within the waterway includes: Acquire real-time motion status data of each moving target within the waterway, and convert the real-time motion status data into horizontal offset and longitudinal progress relative to the dynamic waterway baseline based on the dynamic waterway baseline. The lateral offset, the longitudinal progress, and the environmental data are input into the lightweight trajectory prediction model to obtain the probability distribution corridor of each target in the future time period; The collision probability between any two targets is calculated based on the probability distribution corridor. A dynamic risk threshold is determined based on the event stage information. The risk level of each moving target is obtained by classifying the collision probability based on the dynamic risk threshold. Based on the risk level, corresponding dynamic safety guidance information is generated.

[0011] In some embodiments, the method further includes: In real time, it is determined whether the dynamic waterway baseline exceeds the preset baseline range. If so, the position of exceeding the standard is obtained, and a local adjustment signal is generated to update the baseline at the position of exceeding the standard.

[0012] In some embodiments, after generating the risk level and corresponding dynamic safety guidance information for each moving target within the waterway, the method further includes visually displaying the risk level and corresponding dynamic safety guidance information.

[0013] Secondly, this embodiment provides a system for determining racecourses, the system comprising: an acquisition module, a correction module, and a processing module; wherein, The acquisition module is used to acquire real-time position data of multiple buoys in the waterway. The multiple buoys include at least one reference buoy and multiple ordinary buoys. The reference buoy provides first positioning data at a first accuracy level, and the ordinary buoys provide second positioning data at a second accuracy level. The first accuracy level is higher than the second accuracy level. The correction module is used to correct the second positioning data based on at least one first positioning data to obtain a set of buoy positions for the entire waterway; The processing module is used to process the buoy position set through a constraint fitting algorithm to generate a dynamic navigation baseline. Based on the dynamic navigation baseline, environmental data and event stage information are acquired in real time. A lightweight trajectory prediction model is used to process the environmental data, event stage information, and the dynamic navigation baseline to generate the risk level of each moving target in the navigation channel and the corresponding dynamic safety guidance information.

[0014] Thirdly, this embodiment provides a computer-readable storage medium having a computer program stored thereon that can run on a processor, wherein when the computer program is executed by the processor, it implements a method for determining a race course as described in the first aspect.

[0015] By employing the above method, this application first acquires real-time position data of multiple buoys in the waterway. These multiple buoys include at least one reference buoy and multiple ordinary buoys. The reference buoy provides first positioning data at a first accuracy level, and the ordinary buoys provide second positioning data at a second accuracy level, with the first accuracy level being higher than the second accuracy level. Then, based on at least one first positioning data point, the second positioning data is corrected to obtain a set of buoy positions for the entire waterway. Finally, a constraint fitting algorithm is used to process the buoy position set to generate a dynamic waterway baseline. Based on the dynamic waterway baseline, environmental data and event phase information are acquired in real time. A lightweight trajectory prediction model is used to process the environmental data, event phase information, and the dynamic waterway baseline to generate the risk level of each moving target within the waterway and the corresponding dynamic safety guidance information. This significantly reduces system deployment and operation costs while ensuring high positioning accuracy throughout the waterway, and improves the real-time performance of dynamic waterway baseline generation and safety guidance. Attached Figure Description

[0016] Figure 1 This is a block diagram of a method for determining a racecourse provided in this application.

[0017] Figure 2 This is a block diagram of a method for obtaining real-time position data of multiple buoys in a waterway, as provided in this application.

[0018] Figure 3 This is a block diagram of a method provided in this application for obtaining a set of buoy positions for the entire waterway by correcting second positioning data based on at least one first positioning data.

[0019] Figure 4 This is a block diagram of the method provided in this application for generating dynamic waterway baselines by processing a set of buoy positions using a constraint fitting algorithm.

[0020] Figure 5 This is a schematic diagram of a system connection for determining a racecourse, as provided in this application. Detailed Implementation

[0021] To better understand the purpose, technical solutions, and advantages of this application, it has been described and illustrated below with reference to the accompanying drawings and embodiments. However, those skilled in the art should understand that this application can be implemented without these details. It will be apparent to those skilled in the art that various modifications can be made to the embodiments disclosed in this application, and the general principles defined in this application can be applied to other embodiments and application scenarios without departing from the principles and scope of this application. Therefore, this application is not limited to the illustrated embodiments, but is consistent with the broadest scope claimed in this application.

[0022] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0023] Figure 1 This is a block diagram of a method for determining a racecourse provided in this application. Figure 1 As shown, a method for determining a racecourse includes the following steps: Step S100: Obtain real-time position data of multiple buoys in the channel. The multiple buoys include at least one reference buoy and multiple ordinary buoys. The reference buoy provides first positioning data at a first accuracy level, and the ordinary buoys provide second positioning data at a second accuracy level. The first accuracy level is higher than the second accuracy level.

[0024] This application is described from the perspective of the processing end. Figure 2 This is a block diagram illustrating the method for acquiring real-time position data of multiple buoys in a waterway, as provided in this application. Figure 2 As shown, obtaining real-time position data of multiple buoys in the waterway includes the following steps: Step S101: The reference buoy and ordinary buoys are deployed on both sides of the channel in an alternating manner, wherein the interval between the reference buoys is set to a first distance, and the interval between the ordinary buoys is set to a second distance that is less than the first distance.

[0025] Step S102: Configure each reference buoy with a first GNSS module and a dual-mode communication unit with real-time dynamic differential positioning function, for reporting first positioning data at a first sampling frequency.

[0026] Step S103: Configure each ordinary buoy with a second GNSS module and a low-power long-range communication unit with precise single-point positioning function, for reporting second positioning data at a second sampling frequency lower than the first sampling frequency.

[0027] In acquiring real-time position data of multiple buoys in the waterway, an optimized buoy deployment and data acquisition mechanism significantly reduces hardware investment and communication load while maintaining overall positioning accuracy. The reference buoy is equipped with a high-precision positioning and redundant communication module, achieving centimeter-level positioning accuracy. The ordinary buoy is equipped with a simplified positioning and single-mode communication module, achieving decimeter-level positioning accuracy. The values ​​corresponding to the first and second distances can be determined based on actual conditions, but the first distance is greater than the second distance. By deploying reference buoys and ordinary buoys alternately, forming a reference-ordinary-reference deployment pattern, and through sparse deployment of high-cost reference buoys and dense deployment of low-cost ordinary buoys, the overall hardware cost is controlled while ensuring both stable high-precision positioning anchors and sufficient local position data support in the waterway.

[0028] The first GNSS module refers to a satellite navigation receiver module that supports real-time dynamic differential positioning technology, capable of providing centimeter-level accuracy in absolute position information. The dual-mode communication unit refers to a wireless communication module that simultaneously supports two different communication protocols, used to achieve communication redundancy for data backhaul and improve reliability. The first sampling frequency refers to the time derating of the reference buoy reporting its positioning data, such as once per second, to meet the real-time requirements of high-precision positioning. Thus, by equipping the reference buoy with high-precision positioning and redundant communication capabilities, the accuracy and robustness of the positioning reference are established.

[0029] The second GNSS module refers to a satellite navigation receiver module that supports precise point positioning technology. Its positioning accuracy is lower than that of the first GNSS module, typically at the decimeter level, but its hardware cost is significantly reduced. The low-power long-range communication unit refers to a wireless communication module optimized for low data rates and long-distance transmission, such as LoRa. Its power consumption is lower than that of a dual-mode communication unit, helping to extend the buoy's endurance. The second sampling frequency refers to the time interval at which a regular buoy reports its positioning data, such as every ten seconds. The second sampling frequency is lower than the first sampling frequency, helping to reduce the overall communication load and the energy consumption of the regular buoy. Thus, by adopting a lower-cost and lower-power hardware configuration, a large number of regular buoys can be deployed economically.

[0030] This application constructs a hierarchical positioning data acquisition system. This system provides a high-precision, high-reliability positioning benchmark through reference buoys, and supplements local position information between the reference buoys using densely deployed ordinary buoys. Combined with subsequent correction algorithms, it ultimately achieves centimeter-level equivalent positioning accuracy across the entire waterway. Compared to existing solutions that require all buoys to be equipped with high-precision GNSS modules, this application significantly reduces hardware costs and communication load without sacrificing overall positioning accuracy, effectively resolving the contradiction between high-precision positioning and low-cost deployment.

[0031] After completing the design of the tiered deployment and data acquisition mechanism for the buoys, to further improve the data effectiveness and response timeliness of ordinary buoys in dynamic water environments, this application also adds a motion state detection sensor to the ordinary buoys and realizes adaptive adjustment of the sampling frequency based on its monitoring data. Preferably, this application also sets a motion state detection sensor in the ordinary buoys to monitor the buoy attitude change value, determine whether the buoy attitude change value exceeds a preset threshold, and if it does, report the second positioning data using the first sampling frequency.

[0032] First, a motion state detection sensor is installed. This sensor, integrated within a standard buoy, is a sensing device used to detect the buoy's own motion attitude in real time. Preferably, it is an inertial measurement unit combining a three-axis accelerometer and a gyroscope. This motion state detection sensor continuously collects the buoy's acceleration and angular velocity data in three-dimensional space and calculates the buoy's attitude change value through a built-in processor. The buoy attitude change value refers to a comprehensive index of the buoy's tilt angle change or vibration amplitude relative to its stationary state.

[0033] Next, it is determined whether the buoy attitude change value exceeds a preset threshold. The processing unit sets a preset threshold for the buoy attitude change value; this threshold is a critical value used to distinguish between calm and turbulent water conditions, calibrated based on historical data or experiments. When the motion detection sensor detects that the current buoy attitude change value exceeds this preset threshold, it indicates that the buoy is in a turbulent environment such as strong winds, waves, or strong currents, and its position drift may intensify. In this case, the data reporting frequency needs to be increased to capture more continuous position changes.

[0034] Finally, the second positioning data is reported using the first sampling frequency. That is, when the judgment condition is met, the ordinary buoy switches its data reporting frequency from the default, lower second sampling frequency to the same, higher first sampling frequency as the reference buoy. This switch is automatically executed by the buoy's internal controller, ensuring that during periods of environmental turbulence, the ordinary buoy can provide a position data sequence with the same density as the reference buoy, thereby providing the central processing module with more timely and richer information on local channel deformation.

[0035] If the judgment condition is not met, the ordinary buoy will still use the second required frequency for data reporting. By introducing motion state perception and adaptive sampling mechanisms, it can intelligently identify changes in the aquatic environment and automatically increase the data reporting frequency of the ordinary buoy when necessary. This effectively avoids the problem of data loss or lag in key positions due to insufficient sampling frequency during periods of high winds and waves, enhances the ability to capture dynamic changes in the waterway and improves the real-time response, further ensuring the accuracy of dynamic waterway baseline generation and the timeliness of safety guidance. At the same time, since the frequency is increased only when necessary, it balances low power consumption and communication efficiency.

[0036] Step S200: Correct the second positioning data based on at least one first positioning data to obtain the buoy position set for the entire waterway.

[0037] After completing the design of the tiered deployment and data acquisition mechanism for buoys and realizing the adaptive adjustment of the sampling frequency of ordinary buoys, in order to further improve the accuracy of the positioning data of ordinary buoys and make it reach a centimeter-level accuracy comparable to that of the reference buoys throughout the entire waterway, this application corrects the second positioning data of ordinary buoys based on the high-precision first positioning data provided by the reference buoys. Figure 3 This is a block diagram of a method provided in this application for obtaining a set of buoy positions for the entire waterway by correcting second positioning data based on at least one first positioning data. Figure 3 As shown, obtaining the set of buoy positions for the entire waterway by correcting the second positioning data based on at least one first positioning data includes the following steps: Step S201: Construct a dynamic positioning correction network to divide the waterway into multiple correction units defined by adjacent reference buoys, wherein each correction unit includes at least two reference buoys and at least one ordinary buoy.

[0038] Step S202: Establish a local relative coordinate system in each correction unit using the first positioning data as the reference anchor point.

[0039] Step S203: In the local relative coordinate system, the precise relative position of the ordinary buoy with respect to the reference anchor point is calculated using an adaptive weighted positioning algorithm. The weight coefficients in the adaptive weighted positioning algorithm are dynamically adjusted according to the distance vector between the ordinary buoy and each reference anchor point.

[0040] Step S204: The precise relative position and the second positioning data are fused using Kalman filtering to generate corrected position data.

[0041] Step S205: All the first positioning data and correction position data of the entire waterway are spatiotemporally aligned and integrated to obtain the buoy position set.

[0042] The dynamic positioning correction network refers to the logical construction of the entire waterway into a set of multiple continuous, independent, and dynamically adjustable local correction regions based on the actual deployment positions of the reference buoys. Each region is bounded by adjacent reference buoys, forming a structured correction framework of reference-ordinary-reference. The correction unit is the basic processing unit of the dynamic positioning correction network. Each correction unit consists of two adjacent reference buoys and all ordinary buoys located between these two reference buoys, ensuring that each ordinary buoy is within the correction coverage of at least one reference buoy. The local relative coordinate system is a two-dimensional Cartesian coordinate system established based on the first positioning data of the two reference buoys within a single correction unit. It is used to eliminate potential cumulative errors in the global coordinate system and focuses on calculating the relative positional relationships within the correction unit. The reference anchor point refers to the precise position point of the two reference buoys within the correction unit. Its coordinates are directly determined by the first positioning data and serve as a fixed reference point for the local relative coordinate system, not participating in subsequent fitting or adjustment.

[0043] Specifically, the first step is to construct a dynamic positioning correction network and divide it into correction units. The first step involves reading the real-time positioning data of all reference buoys and extracting their coordinate distribution information along the waterway. The second step is to divide the waterway into continuous correction units according to the principle of pairing adjacent reference buoys. For example, if reference buoys are deployed at 200-meter intervals and there are three ordinary buoys between adjacent reference buoys, then the waterway between the first and second reference buoys, along with the three ordinary buoys, is divided into the first correction unit, with the second and third reference buoys as boundaries. This process is repeated to achieve full coverage of correction units throughout the entire waterway. The third step involves assigning a unique identifier to each correction unit, associating it with its corresponding reference buoy ID and ordinary buoy ID, establishing a mapping relationship between correction units and buoys, and providing a logical basis for subsequent local corrections.

[0044] Then, a local relative coordinate system is established within each calibration unit. Specifically, for each calibration unit, one reference buoy is selected as the origin, and the longitude and latitude from the first positioning data of that reference buoy are mapped to the X and Y axes of the local relative coordinate system, respectively. The first positioning data of the other reference buoy is converted into coordinate values ​​under this local relative coordinate system, with the direction of the line connecting the two points serving as the reference direction of the coordinate system. Simultaneously, based on the centimeter-level positioning accuracy of the reference buoys, the scale accuracy of the coordinate system is set to 1 centimeter to ensure the accuracy of relative position calculations. Finally, the second positioning data of all ordinary buoys within the calibration unit are synchronously converted to this local relative coordinate system, completing coordinate unification and laying the foundation for subsequent relative position calculations.

[0045] Next, an adaptive weighted positioning algorithm is used to calculate the precise relative position of the ordinary buoys. The first step involves calculating the distance vectors d1 and d2 between each ordinary buoy in each correction unit and two reference anchor points in the local relative coordinate system. Here, d1 refers to the distance to the origin reference anchor point, and d2 refers to the distance to the other reference anchor point. The second step calculates weighted systems w1 and w2 based on the distance vectors, using the formulas: w1 = d2 / (d1 + d2) and w2 = d1 / (d1 + d2), ensuring that closer reference anchor points receive higher weights. The third step uses the coordinates of the two reference anchor points as a reference and combines them with weighting coefficients to perform a weighted calculation on the original second positioning data of the ordinary buoys, obtaining a preliminary relative position. The fourth step introduces channel direction constraints, meaning the relative position of the ordinary buoys should be roughly distributed along the line connecting the reference anchor points. This fine-tunes the preliminary relative position, eliminating abnormal offsets caused by buoy drift, ultimately obtaining the precise relative position of the ordinary buoys.

[0046] Next, corrected position data is generated through Kalman filtering fusion. The first step involves establishing a Kalman filter state equation and observation equation for each ordinary buoy. The state equation describes the buoy's position change over time, assuming a slow drift, and the state transition matrix is ​​set as an identity matrix. The observation equation uses the original second positioning data as observations and correlates it with the precise relative position. The second step sets initial filtering parameters, including initial state estimates, a state covariance matrix, and an observation noise covariance matrix. The initial state estimate uses the first calculated precise relative position as the initial value, the state covariance matrix is ​​set based on the statistical values ​​of the original positioning error of the ordinary buoy, and the observation noise covariance matrix is ​​set based on the nominal error of the second GNSS module. The third step follows the Kalman filter's prediction-update process. Each time second positioning data from an ordinary buoy is received, it is combined with its corresponding precise relative position to complete state prediction, observation update, and covariance correction operations. The fourth step outputs the filtered position data, i.e., the corrected position data of the ordinary buoy, thus improving the accuracy of the original second positioning data.

[0047] Finally, spatiotemporal alignment and integration are performed to obtain the buoy position set. The first step is time alignment: the system extracts the timestamps of all initial positioning data and correction position data. Using the timestamp of the first positioning data of the reference buoy as a benchmark, linear interpolation is used to synchronize the time of the correction position data of ordinary buoys, ensuring that each correction position data matches the corresponding reference positioning data time node and eliminating position deviations caused by time differences. The second step is spatial integration: all the initial positioning data of the reference buoys and the time-aligned correction position data of ordinary buoys are uniformly transformed into a global coordinate system, ensuring that the spatial benchmark of all data is consistent. The third step is to verify the validity of the integrated data, removing abnormal data that exceeds a preset reasonable range, such as a single buoy's position deviating from the position of an adjacent buoy by more than 1 meter. The remaining valid data is organized according to dimensions such as buoy ID, channel segment, and timestamp to form a structured buoy position set, providing complete and accurate data support for the subsequent generation of dynamic channel baselines.

[0048] This approach utilizes sparsely deployed high-precision reference buoys as stable anchor points, constructing a dynamic positioning correction network and a local relative coordinate system to improve the original decimeter-level positioning data of low-cost ordinary buoys to centimeter-level equivalent accuracy. Furthermore, it employs an adaptive weighted positioning algorithm combined with Kalman filtering technology to effectively overcome the positioning errors inherent in ordinary buoys and environmental interference, ensuring high accuracy and consistency of buoy position data across the entire waterway. In addition, this correction method has a moderate computational load, making it easy to execute in real-time at edge nodes or the central processing module, laying a reliable data foundation for the subsequent generation of high-precision dynamic waterway baselines, while significantly reducing overall hardware costs.

[0049] Step S300: The buoy position set is processed by a constraint fitting algorithm to generate a dynamic navigation baseline. Environmental data and event stage information are acquired in real time based on the dynamic navigation baseline. A lightweight trajectory prediction model is used to process the environmental data, event stage information and dynamic navigation baseline to generate the risk level of each moving target in the navigation channel and the corresponding dynamic safety guidance information.

[0050] After constructing the entire buoy position set, to generate a dynamic channel baseline that balances global consistency and local adaptability, and to avoid baseline distortion or response delay issues caused by a single fitting algorithm, this application employs a constrained fitting algorithm to perform hierarchical processing on the buoy position set. The buoy position set includes multiple reference positioning data and general positioning data. Figure 4 This is a block diagram of the method provided in this application for generating dynamic waterway baselines by processing a set of buoy positions using a constraint fitting algorithm. Figure 4 As shown, the process of generating a dynamic navigation baseline by processing the buoy position set using a constraint fitting algorithm includes the following steps: Step S301: Obtain the reference positioning data of all reference buoys in the buoy position set, and use the reference positioning data as fixed constraint anchor points in the fitting. Based on the fixed constraint anchor points, divide the waterway into multiple local fitting intervals defined by adjacent reference buoys.

[0051] Step S302: Extract the normal positioning data of the ordinary buoy located in each local fitting interval and use it as a dynamic fitting node.

[0052] Step S303: Using fixed constraint anchor points as strong constraints, a piecewise cubic spline curve fitting algorithm is used to locally fit the dynamically fitted nodes to obtain multiple local baseline segments.

[0053] Step S304: Smoothly splice the local baseline segments at fixed constraint anchor points to obtain a globally continuous and smooth dynamic track baseline.

[0054] Specifically, the first step is to select the initial positioning data of all reference buoys from the buoy location set. This data has been calibrated to centimeter-level accuracy using an RTK-GNSS module and has undergone spatiotemporal alignment processing, possessing stable and reliable positioning reference attributes. The reference positioning data of each reference buoy is marked as a fixed constraint anchor point, whose core characteristic is that its coordinates remain unchanged during the fitting process, serving as a rigid support point for the channel baseline. Following the channel direction, adjacent fixed constraint anchor points are paired and bound together. The line connecting two adjacent fixed constraint anchor points is used as the boundary to divide this segment of the channel into independent local fitting intervals. For example, if the reference buoys are deployed at 200-meter intervals, the 200-meter channel range between two adjacent reference buoys constitutes a local fitting interval, containing the corrected position data of several ordinary buoys. Each local fitting interval is assigned a unique identifier, associated with the corresponding fixed constraint anchor point ID and the corrected position data of the ordinary buoys within the interval, ensuring independent control of the fitting process and avoiding interference between data from different intervals.

[0055] Then, for each local fitting interval, based on the fixed constraint anchor point coordinate range of the interval boundary, the corrected position data of all ordinary buoys falling within that coordinate range are extracted from the buoy position set; these are the ordinary positioning data. Since the ordinary positioning data has been fused with an adaptive weighted positioning algorithm and Kalman filtering, it possesses centimeter-level equivalent accuracy and maintains spatiotemporal consistency with the baseline positioning data; therefore, it is defined as a dynamic fitting node. The core feature of the dynamic fitting node is that it can participate in the fitting calculation of the local baseline according to the actual position distribution, flexibly adapting to slight buoy drift, while being constrained by the coordinates of the fixed constraint anchor points, avoiding excessive deviation of the local fitting from the overall channel direction. During the extraction process, the ordinary positioning data undergoes a secondary validity check, eliminating isolated abnormal data caused by extreme environments, ensuring that the dynamic fitting nodes within each local fitting interval can reflect the actual position distribution trend of that segment of the channel.

[0056] Next, for each local fitting interval, the coordinates of the fixed constraint anchor points at both ends of the interval are used as strong constraints. That is, the fitted curve must strictly pass through the two fixed constraint anchor points to ensure that the local baseline segment is connected to the baseline of the adjacent interval without deviation. Subsequently, a piecewise cubic spline interval fitting algorithm is used to fit the dynamic fitting nodes within the interval. That is, let the number of dynamic fitting nodes in the local fitting interval be n, with coordinates (X1,Y1), (X2,Y2), ..., (Xn,Yn), and the coordinates of the fixed constraint anchor points at both ends be (X0,Y0) and (Xn+1,Yn+1). A piecewise cubic spline function S(X) is constructed such that S(X) is cubic polymorphic in each subinterval [Xi,Xi+1] (i=0,1,2,…,n), satisfying the strong constraints S(X0)=Y0 and S(Xn+1)=Yn+1. Simultaneously, the first and second derivatives of S(X) at nodes Xi (i=1,…,n) are continuous, ensuring a smooth fitted curve without inflection points. By solving the coefficient equations of the spline function, the piecewise cubic spline curve within this local fitting interval is obtained, which is the local baseline segment for that interval. This fitting process only calculates the dynamic fitting nodes within a single local fitting interval, avoiding the computational delay caused by synchronous fitting of all channel data and improving the efficiency of baseline generation.

[0057] Finally, since each local baseline segment starts and ends at a fixed constraint anchor point and strictly passes through that anchor point, during splicing, the fixed constraint anchor point is used as the connection node to directly connect the local baseline segments of two adjacent local fitting intervals. For example, the fixed constraint anchor point at the end of the k-th local fitting interval and the fixed constraint anchor point at the beginning of the (k+1)-th local fitting interval are the reference positioning data of the same reference buoy, and the midpoint of the k-th local baseline segment coincides with the starting point of the (k+1)-th local baseline segment. Simultaneously, thanks to the continuous first and second derivatives of the curve guaranteed by the piecewise cubic spline curve fitting algorithm, the tangent slopes and curvatures of adjacent local baseline segments at the fixed constraint anchor points are consistent. The spliced ​​global dynamic waterway baseline exhibits no abrupt changes or sharp angles, displaying a continuous and smooth characteristic. Subsequently, the spliced ​​global baseline is verified as a whole, and the overall straightness error of the baseline and the curvature deviation at the junction of adjacent segments are calculated to ensure that the baseline not only conforms to the theoretical direction of the waterway design, but also accurately adapts to the actual position distribution of the buoys, so as to provide a reliable route benchmark for subsequent safety guidance.

[0058] This approach, on the one hand, decomposes the global fitting problem into multiple local fitting intervals and employs a piecewise cubic spline curve fitting algorithm, significantly reducing computational complexity and the computational burden on the central processing module, thereby increasing the baseline generation speed and meeting the real-time requirements of high-speed racing scenarios. On the other hand, the use of cubic spline curve fitting and natural splicing at fixed constraint anchor points ensures the continuity and smoothness of the dynamic navigation baseline across the entire navigation channel, avoiding the broken lines or cusp phenomena that may occur with traditional linear fitting or simple interpolation, providing athletes with a more natural and easier-to-follow visual or AR guidance path. Furthermore, when individual ordinary buoy data is abnormal, due to the strong constraint effect of the fixed constraint anchor points and the independence of local fitting, the abnormal data only affects its local interval and does not cause distortion of the global baseline, significantly enhancing fault tolerance.

[0059] After generating the dynamic waterway baseline, to ensure it consistently conforms to the theoretical direction and safety regulations of the waterway design during actual use, and to prevent excessive deviation of the baseline from the preset range due to extreme environments or sudden interference, this application further implements a real-time monitoring and adaptive adjustment mechanism for the dynamic waterway baseline. Preferably, this application also determines in real time whether the dynamic waterway baseline exceeds the preset baseline range; if so, it obtains the position exceeding the limit and generates a local adjustment signal to update the baseline at the position exceeding the limit.

[0060] Specifically, firstly, a preset reference range is established. This preset reference range refers to the maximum allowable deviation range of the dynamic waterway baseline, set according to waterway design specifications and event safety requirements. It is typically a strip-shaped area with a fixed width on both sides, centered on the theoretical waterway line. The processing unit acquires the full coordinate data of the current dynamic waterway baseline in real time and compares it point by point with the preset reference range.

[0061] Then, it is determined whether the dynamic waterway baseline exceeds the preset baseline range. That is, the lateral distance between the actual position of each sampling point on the dynamic waterway baseline and the corresponding point on the theoretical waterway is calculated. If the lateral distance of any sampling point exceeds the boundary value of the preset baseline range, it is determined to be out of standard, and the spatial coordinates and the amount of out of standard of the out-of-standard position are recorded.

[0062] Next, the out-of-range locations are obtained, that is, the coordinate information of all out-of-range locations is extracted, and cluster analysis is performed based on the distribution characteristics of the out-of-range locations to identify consecutive or several out-of-range segments. For each out-of-range segment, the cause of its movement is further analyzed, such as whether it is caused by multiple ordinary buoys in the segment simultaneously drifting in the same direction, or whether it is caused by the accumulation of local fitting errors of edge computing nodes.

[0063] Finally, a local adjustment signal is generated to update the baseline at the out-of-range location. Specifically, for the identified out-of-range sections, a local adjustment signal is generated containing adjustment parameters such as baseline translation, rotation angle, or curvature correction coefficients. This local adjustment signal is sent to the central control module or the corresponding edge computing node, triggering the baseline refitting process. During refitting, adjacent non-out-of-range fixed constraint anchor points are used as strong constraint points. A piecewise cubic spline curve fitting algorithm is employed to locally refit the dynamic fitting nodes within the out-of-range section, ensuring that the newly generated local baseline segments smoothly connect at the fixed constraint anchor points and remain within the preset baseline range. If a single adjustment cannot complete the correction due to drastic environmental changes, an iterative adjustment mechanism can be initiated until the dynamic waterway baseline fully meets the preset baseline range requirements.

[0064] This approach ensures, on the one hand, that the dynamic course baseline remains within a preset safety range through real-time monitoring and automatic adjustment, preventing athletes from being misled or causing course crossing risks due to excessive baseline deviation, thus guaranteeing the fairness and safety of the competition. On the other hand, the strategy of local adjustment rather than global reconstruction significantly reduces the consumption of computing resources and improves real-time performance in long courses and multi-buoy scenarios, ensuring that the baseline generation and adjustment process does not affect the real-time operation of other modules.

[0065] Building upon the generation of a dynamic navigation baseline, this application introduces a real-time acquisition mechanism for environmental data and event-stage information based on the dynamic navigation baseline to further enhance the perception and response capabilities to complex event environments and dynamic risks. This mechanism uses the dynamic navigation baseline as a spatial reference benchmark to achieve structured collection and fusion of key information such as meteorological conditions, water flow status, and event progress, providing accurate and context-sensitive input data for subsequent risk prediction and safety guidance.

[0066] The real-time acquisition of environmental data and event-stage information based on the dynamic navigation baseline includes environmental data acquisition, event-stage information identification, and data fusion and structuring. For environmental data acquisition, multiple environmental monitoring points are pre-set along the dynamic navigation baseline. Each monitoring point is equipped with a multi-source sensor array to collect real-time data on wind speed, wind direction, visibility, and water flow velocity. Environmental monitoring points are preferentially deployed at the locations of reference buoys, and the reference coordinate system of the monitoring data is dynamically calibrated based on the spatial coordinates of the dynamic navigation baseline to ensure strict alignment between the environmental data and the spatial position of the navigation channel.

[0067] For the identification of information during the race stages, radio frequency identification (RFID) readers or image recognition devices deployed at the start, finish, and key locations along the course are used to acquire real-time data on the passing time and location of each boat. Combined with the race schedule and timing data, the current race stage is automatically identified, including the start stage, the intermediate stage, and the sprint stage. This race stage information is linked to the dynamic course baseline to form a race status map with spatiotemporal consistency.

[0068] For data fusion and structuring, environmental data and event phase information are fused according to timestamps and spatial locations to generate an environment-event state matrix with a dynamic waterway baseline as a reference, which serves as one of the inputs to the lightweight trajectory prediction model.

[0069] This mechanism achieves deep coupling between environmental data and event phase information and the dynamic navigation baseline. Firstly, environmental data is no longer collected in isolation but calibrated and fused using the dynamic navigation baseline as a spatial reference, eliminating environmental reference system errors caused by buoy drift in traditional methods and improving the consistency between data and the actual course of the waterway. Secondly, by automatically identifying event phases and associating them with the baseline, it is possible to distinguish the risk characteristics and guidance strategies for different stages, enabling precise safety decisions at different stages and in different areas.

[0070] Building upon the generation of a dynamic navigation baseline, this application further introduces a risk assessment and guidance generation mechanism based on a lightweight trajectory prediction model to achieve accurate risk warnings and dynamic safety guidance for various moving targets within the navigation channel, such as rowing boats. This mechanism uses the dynamic navigation baseline as a spatial reference, combines real-time environmental data and event-stage information, and predicts target trajectories and assesses collision risks through efficient algorithms. Ultimately, it generates graded and visualized safety guidance information, thus ensuring event safety while maintaining real-time performance and computational efficiency. The steps involved in using a lightweight trajectory prediction model to process environmental data, event-stage information, and the dynamic navigation baseline to generate risk levels and corresponding dynamic safety guidance information for various moving targets within the navigation channel include: Step S305: Obtain real-time motion status data of each moving target within the waterway, and convert the real-time motion status data into horizontal offset and longitudinal progress relative to the dynamic waterway baseline based on the dynamic waterway baseline.

[0071] Step S306: Input the lateral offset, longitudinal progress and environmental data into the lightweight trajectory prediction model to obtain the probability distribution corridor of each target in the future time period.

[0072] Step S307: Calculate the collision probability between any two targets based on the probability distribution corridor, determine the dynamic risk threshold based on the event stage information, and classify the risk level of each moving target based on the collision probability according to the dynamic risk threshold.

[0073] Step S308: Generate corresponding dynamic safety guidance information based on the risk level.

[0074] Real-time motion state data of moving targets includes their position coordinates, velocity vector, and heading angle, acquired in real time by the GNSS module and inertial measurement unit onboard the target. Upon receiving this data, the processing unit establishes a local coordinate system with the dynamic waterway baseline as the vertical axis and the direction perpendicular to the baseline as the horizontal axis. Within this local coordinate system, the vertical distance between the current position of each moving target and the dynamic waterway baseline is calculated as the lateral offset. Simultaneously, the distance between the target's projected position along the baseline and the starting point is calculated as the longitudinal progress. This transformation unifies the target's global absolute position to a local relative position relative to the dynamic waterway baseline, eliminating the influence of the overall baseline offset on target positioning and providing a consistent and stable input benchmark for subsequent trajectory prediction.

[0075] The lightweight trajectory prediction model is constructed using a simplified particle filter algorithm. Its core optimization lies in reducing the number of particles in traditional particle filtering from thousands to hundreds, and simplifying the state equation based on the inertial characteristics of the target's motion, significantly reducing computational load while maintaining prediction accuracy. Model inputs include the target's lateral offset, longitudinal progress, velocity, heading angle, and environmental data such as wind speed and water flow velocity. Through particle propagation and weight updates, the model simulates the target's possible trajectories over a preset future time period, ultimately outputting a probability distribution corridor—the probability density distribution area of ​​the target's positions at various future time points—rather than a single trajectory line. This probability distribution corridor reflects the uncertainty of the target's motion, and its width gradually increases with the prediction time, better reflecting the dynamic characteristics of targets moving in actual water.

[0076] The collision probability is calculated by integrating the overlapping area of ​​the probability distribution corridors of two targets in space and time. A dynamic risk threshold mechanism is introduced to automatically adjust the risk assessment criteria based on information from different stages of the race. Specifically, during the start and finish sprint phases, due to the high density of targets and significant speed variations, the collision risk threshold is set lower to improve warning sensitivity. During the intermediate phases, the relative coefficient of target distribution is higher, and the threshold is set higher to avoid frequent false alarms. Based on the comparison between the collision probability and the dynamic risk threshold, the risk level is divided into three levels: low risk, medium risk, and high risk. This grading mechanism achieves refined risk assessment and reduces the problems of insufficient or excessive warnings caused by a single threshold.

[0077] Differentiated safety guidance information is generated for different risk levels and output in real time through multimodal channels such as light strips, displays, and audible and visual alarms. In low-risk situations, the light strip remains constantly green, the display shows the baseline and target position, and there are no additional alarms. In medium-risk situations, the light strip switches to flashing yellow, the display overlays a yellow warning box and deviation indication, and the audible and visual alarm emits a low-frequency warning sound. In high-risk situations, the light strip turns red and flashes at a high frequency, the display shows a red danger sign and a voice prompt, and the audible and visual alarm activates a high-frequency alarm and a directional voice alert. This tiered guidance strategy ensures that athletes receive appropriate information intensity under different risk conditions, avoiding information overload that could interfere with the competition, while providing clear and strong avoidance guidance in critical situations.

[0078] Based on the dynamic navigation baseline, a complete, efficient, and accurate closed-loop system for risk prediction and safety guidance is constructed. On the one hand, a lightweight trajectory prediction model outputs a probability distribution corridor, more realistically reflecting the uncertainty of target motion and improving the scientific rigor of collision risk assessment. Simultaneously, the model's computational load is significantly reduced, alleviating prediction latency and meeting the real-time requirements of high-speed racing scenarios. On the other hand, variable risk thresholds based on race stages are introduced to achieve precise risk assessments across stages and scenarios, avoiding insufficient warnings or excessive interference caused by single thresholds, thus improving the system's applicability and user experience in actual races.

[0079] Preferably, after generating the risk level and corresponding dynamic safety guidance information for each moving target within the waterway, the method further includes visually displaying the risk level and corresponding dynamic safety guidance information to facilitate an intuitive understanding of the results.

[0080] Figure 5 This is a schematic diagram of a system connection for determining a racecourse, as provided in this application. Figure 5 As shown, a system for determining a race course includes: an acquisition module, a correction module, and a processing module.

[0081] The system comprises several modules: an acquisition module, which acquires real-time position data of multiple buoys in the waterway, including at least one reference buoy and multiple ordinary buoys. The reference buoy provides first positioning data at a first accuracy level, and the ordinary buoys provide second positioning data at a second accuracy level, with the first accuracy level being higher than the second accuracy level. A correction module corrects the second positioning data based on at least one first positioning data point to obtain a set of buoy positions for the entire waterway. A processing module processes the buoy position set using a constraint fitting algorithm to generate a dynamic waterway baseline. Based on the dynamic waterway baseline, it acquires environmental data and event-stage information in real time. A lightweight trajectory prediction model is then used to process the environmental data, event-stage information, and the dynamic waterway baseline to generate risk levels for each moving target within the waterway and corresponding dynamic safety guidance information.

[0082] The other functions performed by the acquisition module, correction module, and processing module, as well as the technical details of each function, are the same as or similar to the corresponding features in the method for determining the race course described above, so they will not be repeated here.

[0083] This application also provides a computer storage medium storing a computer program that, when run on a computer, enables the computer to perform the steps in the previously described method for determining a racecourse.

[0084] It should be understood that although the steps in the flowcharts in the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order requirement for the execution of these steps, and they can be performed in other orders.

[0085] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining a racecourse, characterized in that, The method includes: The real-time position data of multiple buoys in the waterway are acquired, wherein the multiple buoys include at least one reference buoy and multiple ordinary buoys, the reference buoy provides first positioning data of a first accuracy level, and the ordinary buoys provide second positioning data of a second accuracy level, wherein the first accuracy level is higher than the second accuracy level; The set of buoy positions for the entire waterway is obtained by correcting the second positioning data based on at least one first positioning data. A dynamic navigation baseline is generated by processing the buoy position set through a constraint fitting algorithm. Environmental data and event stage information are acquired in real time based on the dynamic navigation baseline. A lightweight trajectory prediction model is used to process the environmental data, event stage information, and the dynamic navigation baseline to generate the risk level of each moving target in the navigation channel and the corresponding dynamic safety guidance information.

2. The method according to claim 1, characterized in that, The acquisition of real-time position data of multiple buoys in the waterway includes: Reference buoys and ordinary buoys are deployed on both sides of the waterway in an alternating manner, wherein the interval between the reference buoys is set to a first distance, and the interval between the ordinary buoys is set to a second distance that is less than the first distance; Each reference buoy is equipped with a first GNSS module and a dual-mode communication unit with real-time dynamic differential positioning function, which are used to report first positioning data at a first sampling frequency. Each ordinary buoy is equipped with a second GNSS module with precise single-point positioning function and a low-power long-range communication unit, which is used to report second positioning data at a second sampling frequency lower than the first sampling frequency.

3. The method according to claim 2, characterized in that, The method further includes: A motion state detection sensor is installed in the ordinary buoy to monitor the buoy attitude change value and determine whether the buoy attitude change value exceeds a preset threshold. If it does, the second positioning data is reported using a first sampling frequency.

4. The method according to claim 3, characterized in that, The process of correcting the second positioning data based on at least one first positioning data to obtain the set of buoy positions for the entire waterway includes: Constructing a dynamic positioning correction network divides the waterway into multiple correction units defined by adjacent reference buoys, wherein each correction unit includes at least two reference buoys and at least one ordinary buoy; A local relative coordinate system is established in each correction unit using the first positioning data as the reference anchor point; In the local relative coordinate system, an adaptive weighted positioning algorithm is used to calculate the precise relative position of the ordinary buoy with respect to the reference anchor point. The weight coefficients in the adaptive weighted positioning algorithm are dynamically adjusted according to the distance vector between the ordinary buoy and each reference anchor point. The precise relative position is fused with the second positioning data using a Kalman filter to generate corrected position data. The buoy position set is obtained by spatiotemporally aligning and integrating all the first positioning data and the corrected position data of the entire waterway.

5. The method according to claim 4, characterized in that, The buoy position set includes multiple reference positioning data and ordinary positioning data. The step of processing the buoy position set using a constraint fitting algorithm to generate a dynamic waterway baseline includes: Obtain the reference positioning data of all reference buoys in the buoy position set, and use the reference positioning data as fixed constraint anchor points in the fitting. Based on the fixed constraint anchor points, divide the waterway into multiple local fitting intervals defined by adjacent reference buoys. Extract the normal positioning data of ordinary buoys located within each local fitting interval, and use them as dynamic fitting nodes; Using the fixed constraint anchor point as a strong constraint condition, a piecewise cubic spline curve fitting algorithm is used to locally fit the dynamic fitting node to obtain multiple local baseline segments; The local baseline segments are smoothly spliced ​​together at the fixed constraint anchor points to obtain a globally continuous and smooth dynamic track baseline.

6. The method according to claim 1, characterized in that, The process of using a lightweight trajectory prediction model to process the environmental data, event phase information, and dynamic channel baseline to generate risk levels and corresponding dynamic safety guidance information for each moving target within the channel includes: Acquire real-time motion status data of each moving target within the waterway, and convert the real-time motion status data into horizontal offset and longitudinal progress relative to the dynamic waterway baseline based on the dynamic waterway baseline. The lateral offset, the longitudinal progress, and the environmental data are input into the lightweight trajectory prediction model to obtain the probability distribution corridor of each target in the future time period; The collision probability between any two targets is calculated based on the probability distribution corridor. A dynamic risk threshold is determined based on the event stage information. The risk level of each moving target is obtained by classifying the collision probability based on the dynamic risk threshold. Based on the risk level, corresponding dynamic safety guidance information is generated.

7. The method according to claim 5, characterized in that, The method further includes: In real time, it is determined whether the dynamic waterway baseline exceeds the preset baseline range. If so, the position of exceeding the standard is obtained, and a local adjustment signal is generated to update the baseline at the position of exceeding the standard.

8. The method according to claim 1, characterized in that, After generating the risk level and corresponding dynamic safety guidance information for each moving target within the waterway, the method also includes visually displaying the risk level and corresponding dynamic safety guidance information.

9. A system for determining racecourses, characterized in that, The system includes: an acquisition module, a correction module, and a processing module; wherein... The acquisition module is used to acquire real-time position data of multiple buoys in the waterway. The multiple buoys include at least one reference buoy and multiple ordinary buoys. The reference buoy provides first positioning data at a first accuracy level, and the ordinary buoys provide second positioning data at a second accuracy level. The first accuracy level is higher than the second accuracy level. The correction module is used to correct the second positioning data based on at least one first positioning data to obtain a set of buoy positions for the entire waterway; The processing module is used to process the buoy position set through a constraint fitting algorithm to generate a dynamic navigation baseline. Based on the dynamic navigation baseline, environmental data and event stage information are acquired in real time. A lightweight trajectory prediction model is used to process the environmental data, event stage information, and the dynamic navigation baseline to generate the risk level of each moving target in the navigation channel and the corresponding dynamic safety guidance information.

10. A computer-readable storage medium having a computer program stored thereon that can run on a processor, characterized in that, When the computer program is executed by the processor, it implements a method for determining a racecourse as described in any one of claims 1 to 8.

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