Navigation method, electronic equipment and storage medium

Abstract

The invention discloses a navigation method, electronic equipment and a storage medium, and relates to the technical field of vehicle navigation. According to the scheme, the method comprises the following steps: identifying a traffic jam event and an affected abnormal lane based on a real-time traffic state of each lane in a target route; and in response to a traffic jam event, carrying out weighted fusion on the historical and real-time passing time data of each lane by adopting a dynamic weight fusion strategy to obtain the estimated passing time of each lane. Wherein the strategy can reduce the fusion weight of the historical transit time data of the abnormal lane or improve the fusion weight of the real-time transit time data of the abnormal lane, so that the reference value of the historical transit time data for the current emergency scene is weakened, the influence of the real-time transit time data on the estimation result is enhanced, and the estimation accuracy is improved. The dynamic adaptive capacity to traffic changes is effectively improved; and finally, a target lane is preferentially selected based on the estimated passing time of each lane, so that a navigation prompt for guiding the vehicle to run to the target lane is generated, and lane-level dynamic path optimization is realized.

Description

Technical Field

[0001] This application relates to the field of vehicle navigation technology, and in particular to a navigation method, electronic device and storage medium. Background Technology

[0002] Currently, mainstream navigation systems generally lack the ability to perceive and respond to real-time dynamic changes at the lane level within congested road sections. Their route planning and guidance strategies are still mainly at the macro-route level, making it difficult to effectively address specific lane congestion caused by local factors such as sudden accidents, temporary construction, or vehicle breakdowns. This technological limitation often makes it difficult for the system to adjust its guidance strategy in a timely manner when micro-traffic conditions change abruptly, potentially leading vehicles to abnormal lanes with significantly reduced or even interrupted traffic flow, thereby exacerbating traffic pressure.

[0003] Statistics show that during peak hours on urban roads, additional delays caused by improper lane-level guidance can account for 15% to 25% of the total. This problem not only directly increases the cost of travel for individual vehicles and energy consumption, but also reduces the overall operational efficiency of the road network at the system level. Furthermore, it causes driver anxiety and safety hazards due to frequent lane changes, fully exposing the significant shortcomings of existing navigation technologies in dynamic lane-level optimization guidance. Summary of the Invention

[0004] This application provides a navigation method, electronic device, and storage medium to solve the problem of inaccurate path planning caused by the lack of lane-level real-time perception and dynamic guidance capabilities in existing navigation technologies. The specific technical solution is as follows: A navigation method, comprising: Based on the real-time traffic status information of each lane in the target route, identify whether there is a traffic congestion event and at least one abnormal lane affected by the traffic congestion event. In response to the identification of the traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane; wherein, the dynamic weighted fusion strategy includes: reducing the fusion weight of the historical travel time data of the abnormal lane, and / or increasing the fusion weight of the real-time travel time data of the abnormal lane; From all lanes of the target route, identify the target lane whose estimated travel time meets the preset conditions; Generate navigation prompts to guide the vehicle to the target lane.

[0005] The navigation method of this application embodiment is based on the real-time traffic status information of each lane in the target route, accurately identifying whether there is a traffic congestion event and at least one abnormal lane affected by the event. Subsequently, in response to the identification of a traffic congestion event, a dynamic weight fusion strategy is adopted to perform weighted fusion of the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane. The dynamic weight fusion strategy is configured to automatically reduce the fusion weight of the historical travel time data of the abnormal lane when an abnormal lane is detected, and simultaneously increase the fusion weight of its real-time travel time data, thereby weakening the reference value of the historical travel time data for the current emergency scenario, enhancing the impact of the real-time travel time data on the prediction result, and effectively improving the dynamic adaptability to traffic changes. Finally, based on the estimated travel time of each lane, the target lane is selected to generate navigation prompts to guide vehicles to the target lane, thereby realizing lane-level dynamic path optimization.

[0006] Optionally, the dynamic weight fusion strategy specifically includes: reducing the fusion weight of historical travel time data for the abnormal lane, and / or increasing the fusion weight of real-time travel time data for the abnormal lane, so that the fusion weight of historical travel time data for the abnormal lane is lower than the fusion weight of real-time travel time data. This technical solution, by establishing a dynamic weight adjustment mechanism, automatically adjusts the data fusion strategy when an abnormal lane is detected, effectively solving the adaptability problem of traditional fixed-weight fusion methods in sudden traffic scenarios. Specifically, reducing the weight of historical data weakens the negative impact of historical patterns failing due to sudden events, while increasing the weight of real-time data enhances the system's sensitivity to the current traffic state. This asymmetric weight configuration ensures that real-time traffic information dominates decision-making under abnormal conditions, significantly improving the accuracy and timeliness of travel time prediction. Simultaneously, this strategy, through adaptive adjustment at the data level, achieves precise response to sudden congestion without changing the core algorithm structure, ensuring system stability and enhancing adaptability to dynamic traffic environments. Ultimately, it provides a more reliable data foundation for lane-level navigation decision-making, effectively improving vehicle traffic efficiency in congested sections.

[0007] Optionally, the step of weightedly fusing historical and real-time travel time data of each lane in the target route to obtain the estimated travel time for each lane includes: predicting a first estimated travel time for each lane based on the historical travel time data of each lane in the target route, and weighting the first estimated travel time with the fusion weight of the historical travel time data corresponding to the lane to obtain a first weighted calculation result for each lane; and predicting a second estimated travel time for each lane based on the real-time travel time data of each lane, and weighting the second estimated travel time with the fusion weight of the real-time travel time data corresponding to the lane to obtain a second weighted calculation result; and summing the first and second weighted calculation results for each lane to obtain the estimated travel time for each lane. This technical solution achieves refined processing and effective integration of multi-source heterogeneous traffic data by establishing a phased weighted fusion calculation framework. This structured fusion approach separates the processing flows of historical and real-time data, enabling the system to employ differentiated processing strategies based on the characteristics of different data types: historical data is emphasized for its statistical regularity, while real-time data is prioritized for its instantaneous accuracy. The fusion mechanism, which calculates weighted results separately and then linearly superimposes them, not only preserves the reference value of historical traffic patterns but also ensures the critical impact of real-time traffic conditions on the prediction results, effectively avoiding the limitations of traditional single-prediction models in data feature extraction.

[0008] Optionally, before determining the target lane whose estimated travel time meets the preset conditions from all lanes of the target route, the method further includes: correcting the estimated travel time of abnormal lanes in the target route based on a pre-configured travel time penalty factor, wherein the corrected estimated travel time is greater than the original estimated travel time. This technical solution, by introducing a penalty mechanism, establishes an active avoidance strategy for abnormal lanes at the decision-making level, effectively improving the safety and reliability of the navigation system. Specifically, this penalty correction mechanism has multiple technical effects: First, by significantly increasing the estimated travel time of abnormal lanes at the numerical level, it ensures that these lanes are naturally excluded from the optimal selection process in subsequent comparisons, thereby preventing the system from guiding vehicles to lanes with low traffic efficiency or safety hazards. Second, it transforms the qualitative impact of traffic congestion events into quantifiable travel time costs, enhancing the system's ability to characterize complex traffic scenarios. Furthermore, this correction method based on penalty factors has formed a good synergy with the aforementioned dynamic weight fusion strategy: the former increases the weight of real-time data at the data level to enhance prediction accuracy, while the latter imposes penalties at the decision-making level to ensure the rationality of guidance. Together, they construct a complete technical closed loop from perception to decision-making.

[0009] Optionally, the abnormal lanes include: a first abnormal lane and a second abnormal lane; the first abnormal lane refers to the lane where the traffic congestion event occurred; the second abnormal lane is the lane adjacent to the first abnormal lane; wherein, the correction magnitude of the travel time penalty factor corresponding to the first abnormal lane is greater than that of the second abnormal lane. This technical solution achieves accurate modeling and refined processing of the impact range of traffic congestion events by establishing a differentiated, tiered penalty mechanism: First, it accurately reflects the differences in the degree of impact of traffic events on different types of lanes. By applying a larger penalty to the first abnormal lane (the lane where the event occurred), it ensures that this lane is effectively excluded during the decision-making process, avoiding guiding vehicles to completely impassable areas; simultaneously, a relatively smaller penalty is applied to the second abnormal lane (the adjacent lane), considering both the potential decrease in its traffic capacity due to vehicle avoidance and onlookers, while preserving its availability under specific circumstances. This differentiated processing approach enables the system to more accurately simulate the propagation patterns of event impacts in real traffic scenarios.

[0010] Optionally, generating navigation prompts to guide the vehicle to the target lane includes: determining whether the vehicle meets the safe lane-changing conditions relative to the target lane; if so, outputting navigation prompts guiding the vehicle to change lanes to the target lane. This technical solution, by introducing a safety verification mechanism to assess the feasibility of lane changes before providing navigation guidance, ensures the practicality and safety of the system's output suggestions, effectively solving the technical deficiency of traditional navigation systems that only consider optimal paths while ignoring actual execution conditions. While ensuring traffic efficiency, it significantly reduces traffic safety risks caused by forced lane changes, improving system reliability and user trust.

[0011] Optionally, the safe lane-changing conditions include: no obstacles within a preset distance in front of the vehicle, wherein the preset distance is positively correlated with the vehicle's speed; no oncoming vehicles to the side and rear of the vehicle closest to the target lane; oncoming vehicles to the side and rear of the vehicle closest to the target lane, and the vehicle's speed is higher than the oncoming vehicle's speed; and no traffic markings prohibiting lane changes or physical barriers blocking lane changes between the vehicle and the target lane. This technical solution, by constructing a multi-dimensional safety assessment system, achieves comprehensive protection for the safety of lane-changing operations, significantly improving the applicability and reliability of the navigation system in real road environments. This composite safety condition design reflects a deep understanding and technical reproduction of real driving scenarios. The mechanism of dynamically linking the forward safe distance with vehicle speed can adaptively adjust the safety threshold according to the actual driving state of the vehicle, avoiding the risks caused by insufficient safe distance at high speeds and preventing overly conservative decisions due to excessively long safe distances at low speeds. Regarding the judgment of vehicles approaching from the side and rear, the system not only considers the presence of the vehicle but also incorporates comparative analysis of relative speeds. This allows the system to accurately identify scenarios where, even with oncoming vehicles, there is still a safe opportunity to change lanes, significantly improving the accuracy of guidance timing and the system's usability. Furthermore, the requirements for detecting traffic markings and physical media demonstrate the system's strict adherence to road rules and physical constraints, ensuring that guidance suggestions are both compliant with traffic regulations and practically feasible. This multi-factor collaborative judgment mechanism effectively overcomes the technical limitations of traditional navigation systems that only consider path optimization while neglecting execution safety, constructing a complete technical closed loop from path planning to safe execution.

[0012] Optionally, identifying the existence of traffic congestion events and at least one abnormal lane affected by the traffic congestion event based on real-time traffic status information of each lane in the target route includes: acquiring real-time traffic status information of the target route from multiple data sources; performing spatiotemporal alignment of the real-time traffic status information from the multiple data sources; if, after spatiotemporal alignment, real-time traffic status information from at least two data sources indicates that a traffic congestion event exists on the target route, then the traffic congestion event is determined to be valid, and the at least one abnormal lane affected by the traffic congestion event is identified. This technical solution significantly improves the accuracy and reliability of traffic congestion event detection by constructing a cross-validation mechanism based on multi-source data. Specifically, the spatiotemporal alignment process ensures the uniformity of location references and timestamps in data from different sources, laying a technical foundation for subsequent multi-source information collaborative judgment. In addition, the decision mechanism of "at least two data sources collaboratively confirming the establishment of a traffic congestion event" greatly reduces false alarms caused by false alarms from a single data source or noise interference.

[0013] An electronic device includes: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the navigation method described above.

[0014] A computer-readable storage medium storing a computer program that, when executed, implements the above-described navigation method.

[0015] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a first embodiment of the navigation method of this application.

[0018] Figure 2 This is a schematic diagram of a second flowchart of the navigation method according to an embodiment of this application.

[0019] Figure 3 This is a schematic diagram of the structure of a navigation device according to an embodiment of this application.

[0020] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0022] In current traffic management, mainstream navigation systems generally lack the ability to perceive and respond to real-time dynamic changes at the lane level within congested road sections. The fundamental reason is that when faced with localized congestion caused by accidents, construction, or other temporary factors, they cannot adapt to lane-level impact assessments and may still guide vehicles to lanes with lower traffic efficiency. Specifically, the data processing mechanisms of existing systems struggle to support the real-time analysis and comprehensive interpretation of multi-source, high-frequency information, resulting in an inability to accurately construct and update lane-level traffic status profiles in real time. Furthermore, their decision-making models are often based on fixed rules or macro-statistics, lacking the ability to dynamically assess the chain reactions caused by sudden traffic events, thus making it difficult to predict the evolution trend of localized congestion. In addition, there is an inherent delay in the response chain from information perception to the generation of guidance instructions, meaning that by the time users receive navigation suggestions, the actual road conditions may have changed, causing them to miss the optimal lane-changing window. These limitations not only affect individual traffic efficiency but also weaken the overall utilization efficiency of road resources at the system level.

[0023] To address the aforementioned problems, this application proposes a navigation method, an electronic device, and a storage medium. The technical solutions provided by the various embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0024] One embodiment of this application provides a navigation method that can be applied to smart devices with navigation functions, such as vehicles, mobile phones, and smartwatches. Figure 1 This is a flowchart illustrating the navigation method, which includes the following steps: S101, based on the real-time traffic status information of each lane in the target route, identify whether there is a traffic congestion event and at least one abnormal lane affected by the traffic congestion event.

[0025] This embodiment identifies potential traffic congestion events and their impact range in real time during the navigation service provided to vehicles. The target route can be the vehicle's current route or a route the vehicle is about to reach within a predetermined distance. The traffic congestion events described herein mainly refer to sudden traffic accidents, temporary road construction, vehicle breakdowns, or other emergencies that significantly reduce lane capacity. These events typically cause a sharp deterioration in local traffic flow and generate a chain reaction affecting the normal operation of surrounding lanes.

[0026] To achieve accurate identification of traffic congestion events, this embodiment relies on multi-source heterogeneous real-time traffic status information as the basis for analysis. This information can originate from various channels, such as floating car GPS data, traffic camera video streams, and accident alarm signals issued by traffic management platforms. A complete traffic situation awareness network is constructed by fusing real-time traffic status information from different sources. In the specific identification process, these multi-source real-time traffic status information are first spatiotemporally aligned to ensure that the real-time traffic status information of various modalities maintains a high degree of consistency in spatial location and temporal reference, laying the foundation for subsequent collaborative analysis to determine whether a traffic congestion event has occurred. Subsequently, a multi-source cross-validation mechanism is used to determine the traffic congestion event. The core of this mechanism is that real-time traffic status information from at least two independent data sources, after spatiotemporal alignment, both indicate the presence of a traffic congestion event at the same location before the traffic congestion event is finally confirmed. This collaborative validation method significantly improves the reliability of event detection and effectively avoids false alarms caused by signal loss, transmission errors, or perception deviations from a single data source. Once a traffic congestion event is confirmed, further analysis based on spatiotemporally aligned multi-source real-time traffic status information, including spatial correlation analysis and impact range assessment, can precisely identify the affected lanes, including directly affected core lanes and indirectly affected lanes. It should be understood that this cross-validation mechanism not only effectively overcomes the perceptual limitations and transmission errors that may exist with a single data source, but also significantly improves the confidence and accuracy of traffic congestion event detection.

[0027] In practical implementation, to reduce resource consumption and improve operational efficiency, this embodiment only initiates the subsequent complete traffic congestion event identification process when the target route meets specific traffic congestion conditions, such as an average vehicle speed below a certain speed threshold for a duration reaching a certain time threshold. This design fully considers the limited terminal resources in practical applications, avoiding resource waste caused by continuously executing computationally complex event identification algorithms when traffic flow is smooth.

[0028] S102, in response to the detection of a traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane; wherein, the dynamic weighted fusion strategy includes: reducing the fusion weight of the historical travel time data of the abnormal lane, and / or increasing the fusion weight of the real-time travel time data of the abnormal lane.

[0029] This embodiment integrates historical travel time data (historical patterns) and real-time travel time data (real-time status) for each lane to provide vehicles with lane-level travel time predictions that are both statistically reliable and consistent with current traffic conditions. The key innovation of the dynamic weighted fusion strategy lies in its ability to dynamically adjust the data fusion ratio based on traffic conditions. Specifically, when an abnormal lane is detected, the fusion weight of the lane's historical travel time data is automatically reduced, while the fusion weight of the real-time travel time data is correspondingly increased, forming an asymmetric weight configuration. This dynamic adjustment mechanism effectively overcomes the limitations of traditional fixed-weighted fusion methods in dealing with sudden traffic scenarios. By mitigating the negative impact of historical patterns becoming invalid due to sudden events, and enhancing the sensitivity to current traffic conditions, it ensures that real-time traffic information plays a leading role in decision-making under abnormal conditions, significantly improving the accuracy and timeliness of travel time prediction.

[0030] In practical implementation, this embodiment can employ a dual-channel weighted fusion calculation framework to process historical travel time data and real-time travel time data separately. Specifically: First, a first estimated travel time is predicted based on the historical travel time data for each lane, and this estimated travel time is weighted and calculated with the corresponding fusion weights of the historical travel time data to obtain a first weighted result; simultaneously, a second estimated travel time is predicted based on the real-time travel time data, and this second estimated travel time is weighted and calculated with the fusion weights of the real-time travel time data to obtain a second weighted result; finally, the two weighted results are added together to obtain the final estimated travel time.

[0031] It should be noted that the historical travel time data mentioned in this article refers to the statistical travel time characteristics of each lane under different time periods, weather conditions, and traffic conditions, extracted from a large-scale historical traffic database. Essentially, it reflects the long-term patterns and steady-state characteristics of lane traffic. Real-time travel time data, on the other hand, refers to the instantaneous traffic status information of each lane at the current moment, obtained from real-time data sources such as floating car GPS (sampling rate 1Hz), traffic camera video streams (1080P resolution), and roadside sensing devices. Its characteristics include high timeliness and dynamic variability. Furthermore, this embodiment does not limit the calculation methods for the first and second estimated travel times. As a feasible implementation, the historical travel time data of each lane can be input into a trained historical big data model (e.g., an ARIMA-LSTM hybrid model) to predict the first estimated travel time; similarly, the real-time travel time data of each lane can be input into a trained real-time big data model to predict the second estimated travel time.

[0032] It should be understood that the aforementioned weighted fusion computing framework has multiple advantages: Firstly, by decoupling the processing flow of historical data from that of real-time data, the system can implement differentiated strategies for different data characteristics: historical data emphasizes its statistical regularity and stability, while real-time data emphasizes its instantaneous accuracy and dynamic response capability. This parallel processing mechanism not only retains the reference value of historical traffic patterns but also ensures the dominant influence of real-time traffic conditions on the prediction results, effectively overcoming the limitations of traditional single prediction models in feature extraction. Secondly, the step-by-step weighted summation fusion method has significant advantages in computational efficiency. The two prediction channels can be processed in parallel, greatly reducing data processing latency and providing an algorithmic foundation for the system to achieve second-level response. In addition, this architecture also has good scalability and adjustability. When it is necessary to introduce new data sources or adjust the weight strategy, only local modifications need to be made to the corresponding computing channels without reconstructing the overall fusion framework, effectively ensuring the system's evolution capability and ease of maintenance.

[0033] As a specific implementation example, the formula for calculating the estimated travel time of a lane is:

[0034] in, This indicates the prediction based on historical data. Lane passage time; Indicates the prediction based on real-time data. Lane passage time; This represents dynamic weighting coefficients, for example: under normal circumstances, A value of 0.7 reflects the dominance of historical travel time data; however, when traffic congestion events are detected, The value is automatically adjusted to 0.3, significantly enhancing the impact of real-time traffic data. This parameter configuration allows the system to flexibly adapt to different traffic scenarios, ensuring the reasonableness of the prediction results.

[0035] Furthermore, after calculating the estimated travel time for each lane, this embodiment can introduce a travel time penalty factor to correct the estimated travel time of abnormal lanes. Specifically, abnormal lanes are divided into a first abnormal lane (the lane where the traffic congestion event directly occurs) and a second abnormal lane (the lane adjacent to the first abnormal lane). A larger travel time penalty factor is assigned to the first abnormal lane, while a relatively smaller travel time penalty factor is assigned to the second abnormal lane, forming a differentiated, tiered penalty mechanism. This design accurately reflects the different degrees to which different types of lanes are affected by events. By applying a larger correction to the first abnormal lane, it ensures that this lane is effectively excluded from the preferred lane selection range; simultaneously, a moderate penalty is applied to the second abnormal lane, taking into account its potential decrease in traffic capacity due to factors such as vehicle avoidance, while preserving its availability under specific conditions. This penalty mechanism, combined with the dynamic weight fusion strategy, forms a good technical synergy. The former improves prediction accuracy at the data level, while the latter ensures the rationality of guidance at the decision-making level, jointly constructing a complete technical closed loop from perception to decision-making, providing comprehensive and reliable technical support for the efficient passage of vehicles in congested road sections.

[0036] As a specific implementation example, the modified formula for estimating travel time is:

[0037] in, Indicates the travel time penalty factor; This represents the final estimated travel time after adjustment by the travel time penalty factor.

[0038] This embodiment configures different levels of travel time penalty factors based on the type of abnormal lane, as an example: when the... When a lane is the first abnormal lane (i.e., the lane where the traffic congestion event directly occurs), the following applies: The time penalty factor means that the estimated travel time for that lane will be corrected to twice the original value; when the... When a lane is the second abnormal lane (i.e., the lane adjacent to the first abnormal lane), the following applies: The time penalty factor adjusts the estimated travel time for the corresponding lane to 1.5 times the original value. This differentiated penalty strategy accurately reflects the varying degrees to which different types of lanes are affected by traffic congestion events; directly affected lanes receive heavier penalties, while indirectly affected lanes receive relatively lighter penalties. Furthermore, the time penalty factor can be adjusted based on the type of traffic congestion event (such as accidents, construction, temporary traffic control, etc.) to achieve differentiated handling strategies.

[0039] S103: From all lanes of the target route, determine the target lane whose estimated travel time meets the preset conditions.

[0040] The preset conditions in this embodiment can be a single decision criterion, or they can be flexibly configured as a composite decision criterion according to the actual scenario.

[0041] Taking a single decision criterion as an example, the lane with the shortest estimated travel time can be directly selected as the target lane. This simple and efficient selection strategy is based on optimization theory and can ensure that vehicles achieve the best traffic efficiency at the current moment. This strategy is particularly suitable for scenarios such as straight road sections and highways where subsequent path changes do not need to be considered, or when the system detects that there are no significant differences in the travel paths of each lane.

[0042] Taking a composite decision-making criterion as an example, a set of candidate lanes with estimated travel times below a set threshold can be selected. This initial screening ensures that the selected lanes meet basic requirements in terms of traffic efficiency. Subsequently, a lane matching strategy is introduced for secondary decision-making, comprehensively considering the vehicle's driving intentions and the road topology. For example, when a right turn is detected a certain distance ahead, the rightmost candidate lane is prioritized; correspondingly, if a left turn is required, the leftmost lane is prioritized. This hierarchical decision-making mechanism, by combining real-time traffic efficiency with driving path planning, provides a more forward-looking guidance strategy, ensuring current traffic efficiency while preparing for subsequent driving paths, effectively avoiding the problem of frequent lane changes caused by simply pursuing the shortest current travel time.

[0043] S104, Generate navigation prompts to guide the vehicle to the target lane.

[0044] The navigation prompts in this embodiment can be presented in various forms, including but not limited to voice broadcasts, graphic prompts on the central control screen, head-up display projection, and haptic feedback, to ensure that drivers can receive and understand guidance information in the most convenient and safest way.

[0045] In practical implementation, it can be first determined whether the vehicle meets the safe lane-changing conditions relative to the target lane, and the corresponding navigation prompt is only output when the conditions are confirmed to be met. The introduction of this safety verification mechanism reflects the fundamental difference between this embodiment and traditional navigation systems: it not only focuses on the rationality of route planning but also emphasizes the actual feasibility of guidance suggestions. As an example, safe lane-changing conditions include at least one of the following: 1) There are no obstacles within a preset distance in front of the vehicle, and the preset distance is positively correlated with the vehicle's current speed. This design, which dynamically links the forward safety distance with the vehicle speed, can adaptively adjust the safety threshold according to the actual driving state of the vehicle. This avoids the risks caused by insufficient safety distance when driving at high speeds, and also prevents overly conservative decisions caused by excessively long safety distances at low speeds.

[0046] 2) There are no oncoming vehicles to the side and rear of the vehicle on the side closest to the target lane, or although there are oncoming vehicles, the vehicle's speed is higher than the speed of the oncoming vehicles; that is, the judgment conditions for oncoming vehicles to the side and rear not only consider the existence of oncoming vehicles, but also introduce precise analysis of relative speed. This enables the system to intelligently identify those scenarios where there is still a safe opportunity to change lanes even if there are oncoming vehicles, which significantly improves the accuracy of guidance timing and the practicality of the system.

[0047] 3) There are no traffic markings prohibiting lane changes or physical barriers blocking lane changes between the vehicle and the target lane. This ensures that the guidance suggestions comply with traffic regulations and are practically feasible.

[0048] In summary, the method in this embodiment accurately identifies traffic congestion events and at least one abnormal lane affected by such events based on real-time traffic status information of each lane in the target route. Subsequently, in response to the identification of a traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse historical and real-time travel time data of each lane in the target route to obtain the estimated travel time for each lane. The dynamic weighted fusion strategy is configured to automatically reduce the fusion weight of the historical travel time data of the abnormal lane when an abnormal lane is detected, and simultaneously increase the fusion weight of its real-time travel time data. This weakens the reference value of historical travel time data for the current emergency scenario, enhances the impact of real-time travel time data on the estimation results, and effectively improves the dynamic adaptability to traffic changes. Finally, based on the estimated travel time of each lane, the target lane is selected to generate navigation prompts to guide vehicles to the target lane, thereby achieving lane-level dynamic path optimization.

[0049] The specific application of the navigation method in this embodiment will be described below.

[0050] The navigation method in this embodiment is applied to a vehicle's navigation system, and the corresponding execution flow is as follows: Figure 2 As shown, it includes: First, the navigation system detects in real time that the average speed of the vehicle on the current target route is less than 20 km / h for 10 seconds, and then determines that the target route is in a congested state and activates the lane-level navigation guidance mode.

[0051] Next, the navigation system enters the decision-making stage, which integrates real-time traffic status information of the target route from multiple data sources, such as floating car GPS data, traffic camera video streams, and accident alarm signals provided by the traffic management platform, to identify traffic congestion events on the target route. In this identification process, the navigation system first performs spatiotemporal alignment processing on these heterogeneous data, and then uses a cross-validation mechanism for analysis and judgment: if cross-validation confirms that there is no specific traffic congestion event, the navigation system determines it to be a regular congestion scenario. At this time, the default weight configuration (such as the fusion weight of historical travel time data is α=0.7, and the fusion weight of travel time data is 1-α=0.3) is used to weight and fuse the historical travel time data and real-time travel time data of each lane to determine the estimated travel time of each lane; conversely, if cross-validation confirms that there is a traffic congestion event, a dynamic weight fusion strategy is immediately activated to significantly reduce the fusion weight of the historical travel time data of the affected abnormal lanes (such as adjusting α to 0.3), and / or correspondingly increase the fusion weight of its real-time travel time data (adjusted to 0.7). The adjusted fusion weight is then used to weight and fuse the historical travel time data and real-time travel time data of each lane to obtain an estimated travel time that more accurately reflects the current actual traffic conditions.

[0052] After calculating the estimated travel time for each lane, the navigation system selects the target lane from all lanes. This selection process not only considers the basic principle of minimizing travel time, but also takes into account multiple factors such as vehicle driving intentions and lane functional attributes to ensure that the recommended target lane meets both immediate travel needs and long-term driving plans.

[0053] Subsequently, the navigation system enters the safety verification phase, a crucial step in ensuring the feasibility of the guidance recommendations. The navigation system uses onboard sensors and roadside equipment to acquire real-time multi-dimensional parameters such as the relative position and speed difference between the vehicle and the target lane, the status of vehicles approaching from the sides and rear, and road physical conditions. This data is used to construct a comprehensive safety assessment model, which, based on preset safety rules, determines whether the vehicle meets the safe lane-changing conditions relative to the target lane. These safety conditions include, but are not limited to: maintaining a safe distance in front of the vehicle that is positively correlated with its speed; no conflicting vehicles approaching from the sides or rear, or a speed advantage; and the absence of lane-changing prohibition signs and physical barriers in the target lane.

[0054] Ultimately, only when all safe lane-changing conditions are fully met will the navigation system provide the driver with guidance to change lanes to the target lane through diverse interactive methods such as voice prompts, graphical displays, and haptic feedback. Furthermore, if the navigation system determines that the current conditions for a safe lane change are not met, it will intelligently maintain the current navigation prompt while continuously monitoring environmental changes. Once the system detects that the conditions for a safe lane change are met, it will then promptly provide the driver with guidance to change lanes to the target lane.

[0055] In addition, corresponding to Figure 1 The method shown in this application, in another embodiment, also provides a navigation device. Wherein, Figure 3 This is a structural diagram of the navigation device 300, including: The congestion event identification module 310 is used to identify whether there is a traffic congestion event and at least one abnormal lane affected by the traffic congestion event based on the real-time traffic status information of each lane in the target route.

[0056] The travel time estimation module 320 is used to respond to the detection of the traffic congestion event by employing a dynamic weighted fusion strategy to weight and fuse the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane; wherein, the dynamic weighted fusion strategy includes: reducing the fusion weight of the historical travel time data of the abnormal lane, and / or increasing the fusion weight of the real-time travel time data of the abnormal lane; The lane selection module 330 is used to determine the target lane whose estimated travel time meets the preset conditions from all lanes of the target route; Lane-level navigation guidance module 340 is used to generate navigation prompts to guide the vehicle to the target lane.

[0057] Optionally, the dynamic weight fusion strategy specifically includes: reducing the fusion weight of historical travel time data for the abnormal lane, and / or increasing the fusion weight of real-time travel time data for the abnormal lane, so that the fusion weight of historical travel time data for the abnormal lane is lower than the fusion weight of real-time travel time data. This technical solution, by establishing a dynamic weight adjustment mechanism, automatically adjusts the data fusion strategy when an abnormal lane is detected, effectively solving the adaptability problem of traditional fixed-weight fusion methods in sudden traffic scenarios. Specifically, reducing the weight of historical data weakens the negative impact of historical patterns failing due to sudden events, while increasing the weight of real-time data enhances the system's sensitivity to the current traffic state. This asymmetric weight configuration ensures that real-time traffic information dominates decision-making under abnormal conditions, significantly improving the accuracy and timeliness of travel time prediction. Simultaneously, this strategy, through adaptive adjustment at the data level, achieves precise response to sudden congestion without changing the core algorithm structure, ensuring system stability and enhancing adaptability to dynamic traffic environments. Ultimately, it provides a more reliable data foundation for lane-level navigation decision-making, effectively improving vehicle traffic efficiency in congested sections.

[0058] Optionally, the travel time estimation module 320 performs weighted fusion of historical and real-time travel time data for each lane in the target route to obtain the estimated travel time for each lane. This includes: predicting a first estimated travel time for each lane based on the historical travel time data of each lane in the target route, and weighting the first estimated travel time with the fusion weight of the historical travel time data corresponding to the lane to obtain a first weighted calculation result for each lane; and predicting a second estimated travel time for each lane based on the real-time travel time data of each lane, and weighting the second estimated travel time with the fusion weight of the real-time travel time data corresponding to the lane to obtain a second weighted calculation result; and summing the first and second weighted calculation results for each lane to obtain the estimated travel time for each lane. This technical solution achieves refined processing and effective integration of multi-source heterogeneous traffic data by establishing a phased weighted fusion calculation framework. This structured fusion approach separates the processing flows of historical and real-time data, enabling the system to employ differentiated processing strategies based on the characteristics of different data types: historical data is emphasized for its statistical regularity, while real-time data is prioritized for its instantaneous accuracy. The fusion mechanism, which calculates weighted results separately and then linearly superimposes them, not only preserves the reference value of historical traffic patterns but also ensures the critical impact of real-time traffic conditions on the prediction results, effectively avoiding the limitations of traditional single-prediction models in data feature extraction.

[0059] Optionally, the travel time estimation module 320 is further configured to: before determining the target lane whose estimated travel time meets the preset conditions from all lanes of the target route, correct the estimated travel time of abnormal lanes in the target route based on a pre-configured travel time penalty factor, wherein the corrected estimated travel time is greater than the original estimated travel time. This technical solution, by introducing a penalty mechanism, establishes an active avoidance strategy for abnormal lanes at the decision-making level, effectively improving the safety and reliability of the navigation system. Specifically, this penalty correction mechanism has multiple technical effects: First, by significantly increasing the estimated travel time of abnormal lanes at the numerical level, it ensures that these lanes are naturally excluded from the optimal selection process in subsequent comparisons, thereby preventing the system from guiding vehicles to lanes with low traffic efficiency or safety hazards. Second, it transforms the qualitative impact of traffic congestion events into a quantified travel time cost, enhancing the system's ability to characterize complex traffic scenarios. Furthermore, this correction method based on penalty factors has formed a good synergy with the aforementioned dynamic weight fusion strategy: the former increases the weight of real-time data at the data level to enhance prediction accuracy, while the latter imposes penalties at the decision-making level to ensure the rationality of guidance. Together, they construct a complete technical closed loop from perception to decision-making.

[0060] Optionally, the abnormal lanes include: a first abnormal lane and a second abnormal lane; the first abnormal lane refers to the lane where the traffic congestion event occurred; the second abnormal lane is the lane adjacent to the first abnormal lane; wherein, the correction magnitude of the travel time penalty factor corresponding to the first abnormal lane is greater than that of the second abnormal lane. This technical solution achieves accurate modeling and refined processing of the impact range of traffic congestion events by establishing a differentiated, tiered penalty mechanism: First, it accurately reflects the differences in the degree of impact of traffic events on different types of lanes. By applying a larger penalty to the first abnormal lane (the lane where the event occurred), it ensures that this lane is effectively excluded during the decision-making process, avoiding guiding vehicles to completely impassable areas; simultaneously, a relatively smaller penalty is applied to the second abnormal lane (the adjacent lane), considering both the potential decrease in its traffic capacity due to vehicle avoidance and onlookers, while preserving its availability under specific circumstances. This differentiated processing approach enables the system to more accurately simulate the propagation patterns of event impacts in real traffic scenarios.

[0061] Optionally, the lane-level navigation guidance module generates navigation prompts to guide the vehicle to the target lane, including: determining whether the vehicle meets the safe lane-changing conditions relative to the target lane; if so, outputting navigation prompts to guide the vehicle to change lanes to the target lane. This technical solution introduces a dynamic safety verification mechanism to conduct a multi-dimensional feasibility assessment of lane-changing behavior before providing navigation guidance, thereby further ensuring the practicality and driving safety of the system's output suggestions based on the rationality of the route planning. This design effectively compensates for the technical shortcomings of traditional navigation systems that only use the shortest path or optimal time as the decision-making basis, ignoring actual road conditions and dynamic traffic environments. It not only ensures traffic efficiency in complex road conditions but also significantly reduces traffic safety risks caused by inappropriate lane-changing operations, thereby improving the reliability, adaptability, and user confidence and trust in the entire navigation system.

[0062] Optionally, the safe lane-changing conditions include: no obstacles within a preset distance in front of the vehicle, wherein the preset distance is positively correlated with the vehicle's speed; no oncoming vehicles to the side and rear of the vehicle closest to the target lane; oncoming vehicles to the side and rear of the vehicle closest to the target lane, and the vehicle's speed is higher than the oncoming vehicle's speed; and no traffic markings prohibiting lane changes or physical barriers blocking lane changes between the vehicle and the target lane. This technical solution, by constructing a multi-dimensional safety assessment system, achieves comprehensive protection for the safety of lane-changing operations, significantly improving the applicability and reliability of the navigation system in real road environments. This composite safety condition design reflects a deep understanding and technical reproduction of real driving scenarios. The mechanism of dynamically linking the forward safe distance with vehicle speed can adaptively adjust the safety threshold according to the actual driving state of the vehicle, avoiding the risks caused by insufficient safe distance at high speeds and preventing overly conservative decisions due to excessively long safe distances at low speeds. Regarding the judgment of vehicles approaching from the side and rear, the system not only considers the presence of the vehicle but also incorporates comparative analysis of relative speeds. This allows the system to accurately identify scenarios where, even with oncoming vehicles, there is still a safe opportunity to change lanes, significantly improving the accuracy of guidance timing and the system's usability. Furthermore, the requirements for detecting traffic markings and physical media demonstrate the system's strict adherence to road rules and physical constraints, ensuring that guidance suggestions are both compliant with traffic regulations and practically feasible. This multi-factor collaborative judgment mechanism effectively overcomes the technical limitations of traditional navigation systems that only consider path optimization while neglecting execution safety, constructing a complete technical closed loop from path planning to safe execution.

[0063] Optionally, the congestion event identification module 310 identifies whether a traffic congestion event exists and at least one abnormal lane affected by the traffic congestion event based on the real-time traffic status information of each lane in the target route. This includes: acquiring real-time traffic status information of the target route from multiple data sources; performing spatiotemporal alignment on the real-time traffic status information from the multiple data sources; if, after spatiotemporal alignment, the real-time traffic status information from at least two data sources indicates that a traffic congestion event exists on the target route, then the traffic congestion event is determined to be valid, and the at least one abnormal lane affected by the traffic congestion event is identified. This technical solution significantly improves the accuracy and reliability of traffic congestion event detection by constructing a multi-source data cross-validation mechanism. Specifically, the spatiotemporal alignment process ensures the uniformity of location references and timestamps for data from different sources, laying a technical foundation for subsequent multi-source information collaborative judgment. In addition, the decision mechanism of "at least two data sources collaboratively confirming the establishment of a traffic congestion event" greatly reduces false alarms caused by false alarms from a single data source or noise interference.

[0064] In summary, the device in this embodiment accurately identifies traffic congestion events and at least one abnormal lane affected by such events based on real-time traffic status information of each lane in the target route. Subsequently, in response to the identification of a traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse historical and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane. The dynamic weighted fusion strategy is configured to automatically reduce the fusion weight of the historical travel time data of the abnormal lane when an abnormal lane is detected, and simultaneously increase the fusion weight of its real-time travel time data. This weakens the reference value of historical travel time data for the current emergency scenario, enhances the impact of real-time travel time data on the estimation results, and effectively improves the dynamic adaptability to traffic changes. Finally, based on the estimated travel time of each lane, the target lane is selected to generate navigation prompts to guide vehicles to the target lane, thereby achieving lane-level dynamic path optimization.

[0065] It should be noted that the specific methods by which each module performs its operation in the navigation device described in the above embodiments have been described in detail in the embodiments of the method, and will not be elaborated here.

[0066] In addition, another embodiment of this application provides an electronic device. Figure 4 This is a schematic diagram of the electronic device, including a memory 401 and a processor 402. The memory 401 stores executable program code 4011, and the processor 402 is used to call and execute the executable program code 4011 to perform the electronic device control method provided in the above embodiment. The corresponding steps include: Based on real-time traffic status information of each lane in the target route, identify whether there is a traffic congestion event and at least one abnormal lane affected by the traffic congestion event.

[0067] In response to the identification of the traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane; wherein, the dynamic weighted fusion strategy includes: reducing the fusion weight of the historical travel time data of the abnormal lane, and / or increasing the fusion weight of the real-time travel time data of the abnormal lane.

[0068] From all lanes of the target route, identify the target lane whose estimated travel time meets the preset conditions.

[0069] Generate navigation prompts to guide the vehicle to the target lane.

[0070] The beneficial effects of the above embodiments can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0071] This embodiment can divide the electronic device into functional modules according to the above method example. For example, each module can correspond to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0072] When each functional module is divided according to its corresponding function, the electronic device may include: a congestion event recognition module, a travel time estimation module, a lane selection module, and a lane-level navigation guidance module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0073] When using integrated units, the electronic device may include a processing module and a storage module. The processing module is used to control and manage the operation of the electronic device. The storage module is used to support the execution of program code and data by the electronic device.

[0074] The processing module may be a processor or a controller, which can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module may be a memory.

[0075] Furthermore, another embodiment of this application provides a computer-readable storage medium storing computer program code. When the computer program code is executed on a computer, the computer performs the aforementioned method steps to implement the navigation method provided in the above embodiments, which includes the following steps: Based on real-time traffic status information of each lane in the target route, identify whether there is a traffic congestion event and at least one abnormal lane affected by the traffic congestion event.

[0076] In response to the identification of the traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane; wherein, the dynamic weighted fusion strategy includes: reducing the fusion weight of the historical travel time data of the abnormal lane, and / or increasing the fusion weight of the real-time travel time data of the abnormal lane.

[0077] From all lanes of the target route, identify the target lane whose estimated travel time meets the preset conditions.

[0078] Generate navigation prompts to guide the vehicle to the target lane.

[0079] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0080] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0082] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A navigation method characterized by, include: Based on the real-time traffic status information of each lane in the target route, identify whether there is a traffic congestion event and at least one abnormal lane affected by the traffic congestion event. In response to the identification of the traffic congestion event, a dynamic weighted fusion strategy is adopted to weight and fuse the historical travel time data and real-time travel time data of each lane in the target route to obtain the estimated travel time of each lane; wherein, the dynamic weighted fusion strategy includes: reducing the fusion weight of the historical travel time data of the abnormal lane, and / or increasing the fusion weight of the real-time travel time data of the abnormal lane; From all lanes of the target route, identify the target lane whose estimated travel time meets the preset conditions; Generate navigation prompts to guide the vehicle to the target lane.

2. The method according to claim 1, characterized in that, The dynamic weight fusion strategy specifically includes: reducing the fusion weight of the historical passage time data of the abnormal lane, and / or increasing the fusion weight of the real-time passage time data of the abnormal lane, so that the fusion weight of the historical passage time data of the abnormal lane is lower than the fusion weight of its real-time passage time data.

3. The method according to claim 1, characterized in that, The step of weightedly fusing historical and real-time travel time data for each lane in the target route to obtain the estimated travel time for each lane includes: Based on the historical travel time data of each lane in the target route, a first estimated travel time for each lane is predicted, and the first estimated travel time is weighted by the fusion weight of the historical travel time data corresponding to the lane, to obtain a first weighted calculation result for each lane; and based on the real-time travel time data of each lane, a second estimated travel time for each lane is predicted, and the second estimated travel time is weighted by the fusion weight of the real-time travel time data corresponding to the lane, to obtain a second weighted calculation result; the first weighted calculation result and the second weighted calculation result for each lane are summed to obtain the estimated travel time for each lane.

4. The method of claim 1, wherein, Before determining the target lane whose estimated travel time meets the preset conditions from all lanes of the target route, the method further includes: Based on a pre-configured travel time penalty factor, the estimated travel time of abnormal lanes in the target route is corrected, wherein the corrected estimated travel time is greater than the original estimated travel time.

5. The method according to claim 4, characterized in that, The abnormal lanes include: a first abnormal lane and a second abnormal lane; the first abnormal lane refers to the lane where the traffic congestion event occurred; the second abnormal lane is the lane adjacent to the first abnormal lane; wherein, the correction magnitude of the travel time penalty factor corresponding to the first abnormal lane is greater than the travel time penalty factor of the second abnormal lane.

6. The method according to claim 1, characterized in that, The generation of navigation prompts to guide the vehicle to the target lane includes: Determine whether the vehicle meets the safe lane-changing conditions relative to the target lane; If the conditions are met, a navigation prompt will be output to guide the vehicle to change lanes to the target lane.

7. The method according to claim 6, characterized in that, The safe lane change conditions include: There are no obstacles within a preset distance in front of the vehicle, wherein the preset distance is positively correlated with the vehicle speed; There are no oncoming vehicles to the side and rear of the vehicle on the side closest to the target lane; There is an oncoming vehicle to the side and rear of the vehicle on the side closest to the target lane, and the vehicle's speed is higher than that of the oncoming vehicle. There are no traffic markings prohibiting lane changes or physical barriers preventing lane changes between the vehicle and the target lane.

8. The method according to any one of claims 1 to 7, characterized in that, The method of identifying whether a traffic congestion event exists and at least one abnormal lane affected by the traffic congestion event, based on real-time traffic status information of each lane in the target route, includes: Obtain real-time traffic status information for the target route from multiple data sources; The real-time traffic status information provided by the multiple data sources is spatiotemporally aligned. If, after spatiotemporal alignment, real-time traffic status information from at least two data sources indicates that there is a traffic congestion event on the target route, then the traffic congestion event is determined to be valid, and at least one abnormal lane affected by the traffic congestion event is identified.

9. An electronic device comprising: processor; And a memory arranged to store computer-executable instructions, characterized in that, when executed, the executable instructions cause the processor to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 8.

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