Satellite navigation route optimization system and method based on big data

Abstract

The invention discloses a satellite navigation route optimization system and method based on big data, and relates to the technical field of navigation route optimizing.When a satellite navigation system receives a navigation task, information of the navigation task is collected, a navigation route is generated according to the navigation task, and after the navigation route is generated, road sections passed by the navigation route are obtained; comprehensively analyzing the current situation of the road section and the historical road section condition, obtaining the similarity between the historical environment and the current environment, obtaining the historical driving data of the road section, analyzing the historical congestion condition of the road section, comprehensively considering the historical traffic condition of the road section and the similarity between the historical environment and the current environment, and obtaining an adaptive navigation route. After the adaptive navigation route is obtained, the current situation of route passing is considered, the optimal route is provided for the user, the optimal route is updated in real time, the user is prompted whether to update the optimal route or not, and the scientificity and reliability of route planning are improved by analyzing the congestion degree and constructing a dynamically updated optimization mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of navigation route optimization, in particular to a satellite navigation route optimization system and method based on big data. BACKGROUND

[0002] The current satellite navigation system adopts a general planning logic when planning a route, and fails to fully combine the attributes of the driving tool in the navigation task for targeted screening. After receiving a navigation task, the system often directly generates a route based on the starting point and the ending point, ignoring the traffic restriction requirements of different types of vehicles, such as guiding a small passenger vehicle to drive into a road exclusively for large trucks or allowing a non-motor vehicle to enter a motor vehicle fast lane. This planning method is prone to cause a mismatch between the driving tool and the road segment attributes, which not only may cause a violation of traffic rules, but also may force the user to detour midway due to the unsuitability of the road segment, reducing the practicability and reliability of the route planning and being difficult to meet the precise navigation needs of different types of vehicles. The existing satellite navigation system generally relies on a one-time route planning mode, and after generating an initial route, it is difficult to dynamically adjust the route according to real-time traffic changes. When the system plans a route, it only refers to the traffic information at the planning time, and in the subsequent driving process, even if there are sudden traffic jams, temporary road smoothness or other traffic changes, the route plan cannot be updated in a timely manner. The user needs to continue driving along the fixed route, which is prone to be in a low traffic efficiency dilemma, especially during periods of heavy traffic and complex road conditions. The lag of this static planning is more prominent, which cannot guarantee the traffic efficiency of the entire journey and is difficult to cope with complex and variable traffic environments. The current satellite navigation system has obvious defects in route recommendation and data processing: on the one hand, the navigation route pushed to the user often lacks reasonable sorting, and a large number of routes are presented in disorder, requiring the user to compare the traffic time, congestion and other information of each route, which consumes a lot of time and effort in the decision-making process. On the other hand, when analyzing traffic data, interference information is not effectively filtered, and abnormal driving data of a faulty vehicle may be included in the analysis range, causing deviations in the judgment of road traffic conditions and recommending inaccurate routes. This not only may increase the probability of detouring for the user, but also may increase the risk of violating the rules by guiding the vehicle into a prohibited road segment, resulting in a poor overall user experience. SUMMARY

[0003] The present application aims to provide a satellite navigation route optimization system and method based on big data to solve the problems raised in the background.

[0004] In order to solve the above technical problems, the present application provides the following technical scheme: a satellite navigation route optimization method based on big data, comprising the following steps: S1, when the satellite navigation system receives a navigation task, information of the navigation task is collected, and a navigation route is generated according to the navigation task; S2. After generating the navigation route, obtain the road segments traversed by the navigation route, and conduct a comprehensive analysis of the current status of the road segments and the historical status of the road segments to obtain the similarity between the historical environment and the current environment. S3. Obtain historical driving data for road segments and analyze the historical congestion status of road segments; S4. Taking into account the historical traffic conditions of the road segment and the similarity between the historical environment and the current environment, a suitable navigation route is obtained; S5. After obtaining the adapted navigation route, consider the current traffic conditions of the route and provide the best route to the user. S6. Update the best route in real time during route usage and prompt the user whether to update the best route.

[0005] Furthermore, in step S1, when the satellite navigation system receives a navigation task, the navigation task includes a navigation start point, a navigation end point, and a mode of transportation. The mode of transportation includes non-motorized vehicles, small motorized vehicles, large motorized vehicles, and motorcycles. Road segments are selected based on the mode of transportation in the navigation task. After removing road segments that the mode of transportation is not allowed to pass through in the navigation task, a navigation route is generated based on the navigation task. The navigation route is composed of connected road segments. When the navigation route is generated, the satellite that generated the navigation route is marked and stored in the navigation route database. By selecting road segments based on the type of transportation and removing roads that are prohibited from passing through, situations such as non-motorized vehicles mistakenly entering motorized vehicle expressways or large motorized vehicles entering restricted-class roads can be avoided. This reduces the risk of illegal passage from the source and prevents detours due to incompatible road segments, ensuring the practicality of the route planning.

[0006] Furthermore, in step S2, when the satellite navigation system receives a navigation task, it retrieves all navigation routes to obtain the road segments involved in all navigation routes, denoted as {A1, A2, ..., A...}. m ,…,A M}, where M represents the total number of road segments, A m Let A represent the m-th road segment. m The analysis divides a day into N time periods. The navigation task reception time belongs to the nth time period, n=1,2,…,N. Driving data from the nth time period of P days prior to receiving the navigation task is used as reference data for the navigation task, where P represents the predetermined reference number of days. Driving data from the mth road segment of the nth time period of P days is retrieved. The driving data from the nth time period of day p is analyzed, and environmental data related to road driving is retrieved. After preprocessing, Q environmental features are obtained, denoted as {B1, B2,…, B…}. q ,…,B Q}, where B qrepresents the qthenvironmental feature, the environmental similarity C of the nthtime period of the pthday and the time period corresponding to the navigation task receiving time point is calculated p : ; wherein b q represents the qthenvironmental feature of the time period corresponding to the task receiving time point, and p=1, 2, …, P is substituted one by one to obtain the environmental similarity {C1, C2, …, CP} of the nthtime period of the P days before receiving the navigation task and the time period corresponding to the navigation task receiving time point p … P}; By selecting the historical driving data of the same time period before receiving the navigation task as the reference, and combining the environmental features to screen the historical time period data with high environmental similarity, the interference of large environmental difference can be effectively excluded, so that the historical data used for analysis is more suitable for the road traffic rules under the current environment. This can improve the accuracy of the judgment of the road section traffic condition, provide reliable data support for the subsequent generation of navigation route with high adaptation degree, reduce the deviation of route planning caused by unreasonable data reference, make the navigation route more suitable for the current actual traffic environment, and enhance the scientificity and reliability of route planning.

[0007] Further, in step S3, the driving data of the nthtime period of the pthday is analyzed, and vehicle driving data related to road driving is called, the vehicle driving data including: vehicle fault information, actual driving speed of the vehicle and maximum driving speed of the vehicle limited by the road, and the faulty vehicle is excluded through the vehicle fault information. After excluding the faulty vehicle, the normal driving vehicle is analyzed, the number of normal driving vehicles in the nthtime period of the pthday is Y, and the vehicle driving hindering degree E of the nthtime period of the pthday is calculated p : ; wherein y represents the ythnormal driving vehicle, G y represents the maximum speed limit of the mthroad section for the driving tool type corresponding to the ythnormal driving vehicle, g y represents the average driving speed of the ythnormal driving vehicle in the mthroad section, and p=1, 2, …, P is substituted one by one to obtain the vehicle driving hindering degree {E1, E2, …, EP} of the nthtime period of the P days before p … PThis step first uses vehicle fault information to eliminate faulty vehicles, avoiding interference from their abnormal driving data in assessing road conditions. This ensures that the vehicle data analyzed better reflects road conditions under normal driving conditions. Next, by calculating the difference between the actual speed of normally driving vehicles and the corresponding road speed limit, the degree of obstruction is determined. This accurately measures the smoothness of traffic flow on the road segment, providing crucial information for selecting highly suitable navigation routes. This makes route planning more aligned with actual traffic efficiency, reducing the probability of users encountering congestion and other obstacles, and improving the practicality and accuracy of navigation.

[0008] Furthermore, in step S4, when the satellite navigation system receives a navigation task in the nth time period, it obtains X navigation routes, where the xth navigation route contains route segments {H1, H2, ..., H...}. i ,…,H I}, where x = 1, 2, ..., X, I represents the number of route segments contained in the x-th navigation route, and H i This represents the i-th route segment contained in the x-th navigation route, where the length of each route segment is {J1, J2, ..., J...} i ,…,J I}, and then obtain the priority recommendation coefficient f of the i-th road segment. i : ; Substitute each i = 1, 2, ..., I to obtain the priority recommendation coefficients {f1, f2, ..., f} for the I road segments. i ,…,f I}; The task adaptation coefficient Fx of the xth navigation route is obtained by comprehensively considering the priority recommendation coefficient of the road segment and the road segment coefficient corresponding to the priority recommendation coefficient, Fx is the sum of the product of the priority recommendation coefficient of the Ith road segment in the xth navigation route and the road segment coefficient corresponding to the priority recommendation coefficient multiplied by the route coefficient of the xth navigation route, wherein the road segment coefficient corresponding to the priority recommendation coefficient fi is the ratio of the length of the ith road segment to the sum of the lengths of the I road segments, and the route coefficient of the xth navigation route is the ratio of the sum of the lengths of the route segments of the xth navigation route to the sum of the lengths of the route segments of the X navigation routes, x=1, 2, …, X is substituted in turn to obtain the task adaptation coefficients of the X navigation routes, and then the X navigation routes are reordered from high to low according to the task adaptation coefficients, T pieces of adaptation navigation routes with the highest adaptation coefficient are obtained, T is the number of adaptation navigation routes set, and the priority recommendation coefficient of the road segment and the task adaptation coefficient of the navigation route are calculated by comprehensively considering the environmental characteristics, the vehicle driving hindering situation and the road segment, the route length and other multi-dimensional factors, which can comprehensively and accurately measure the adaptability of each route and avoid the planning deviation caused by a single factor. By sorting according to the adaptation coefficient, a number of routes with the highest adaptation degree are selected, which not only makes the route most suitable for the current task be presented first, but also provides high-quality alternatives, reduces the possibility of the user being trapped in poor traffic conditions due to a single route, and improves the scientificity and flexibility of the navigation route planning, facilitates the user selection, and optimizes the navigation experience.

[0009] Further, in step S5, after obtaining T adaptive navigation routes, the driving state of the T adaptive navigation routes is analyzed, the tth adaptive navigation route is analyzed, t = 1, 2, …, T, the current driving condition of the tth adaptive navigation route is collected through the satellite navigation system, the total number of vehicles in the tth adaptive navigation route is R, if the ratio of the driving speed of the rth vehicle to the maximum speed limit of the driving tool type corresponding to the rth normal driving vehicle in the route segment where the rth vehicle is located is lower than the preset slow vehicle driving threshold, the rth vehicle is judged to be a slow driving vehicle, r = 1, 2, …, R; otherwise, the rth vehicle is judged to be a normal driving vehicle, and then the driving congestion degree of the tth adaptive navigation route is obtained, the driving congestion degree of the tth adaptive navigation route represents the ratio of the number of slow driving vehicles to the total number of vehicles in the tth adaptive navigation route, t = 1, 2, …, T, and the driving congestion degrees of the T adaptive navigation routes are obtained, and then the T0 best routes with the lowest driving congestion degree are selected from the T adaptive navigation routes and are placed at the top to be provided to the user for selection, and the other adaptive navigation routes are provided to the user for selection in order from high to low according to the adaptive coefficient, this step can dynamically avoid real-time congestion road segments and avoid the user being trapped in a low traffic efficiency dilemma by collecting the current driving condition of the adaptive navigation route in real time through the satellite and accurately judging the congestion degree of each route. At the same time, the route with the lowest congestion degree is preferentially recommended to be placed at the top, and the remaining routes are provided in order according to the adaptive coefficient, which not only provides the user with the optimal choice of the most unobstructed route, but also retains the backup scheme with strong adaptability, avoids the limitation of single recommendation, makes the user's decision more convenient, and greatly improves the practicality and flexibility of navigation under complex real-time road conditions.

[0010] Further, in step S6, after a preset route update duration, the adaptive navigation route and the best route are reanalyzed with the current position as the starting point, if the best route includes the current route, the current navigation state is maintained; if the best route does not include the current route, the user is prompted whether to update the best route.

[0011] A satellite navigation route optimization system based on big data, the system comprising: a task collection and route generation module, a road segment condition and similarity analysis module, a historical data and congestion analysis module, a comprehensive adaptive route generation module, a best route top recommendation module, and a real-time route update prompt module; The task collection and route generation module is used for collecting the information of a navigation task when the satellite navigation system receives the navigation task, and generating a navigation route according to the navigation task; The road segment condition and similarity analysis module is used for obtaining the road segments passed by the navigation route after the navigation route is generated, and comprehensively analyzing the current road segment condition and the historical road segment condition to obtain the similarity between the historical environment and the current environment; The historical data and congestion analysis module is used to acquire historical driving data of road segments and analyze the historical congestion status of road segments. The comprehensive adaptive route generation module is used to comprehensively consider the historical traffic conditions of road segments and the similarity between historical and current environments to obtain an adapted navigation route. The optimal route recommendation module is used to obtain the adapted navigation route, take into account the current traffic conditions of the route, and provide the optimal route to the user. The real-time route update prompt module is used to update the best route in real time during route usage and prompt the user whether to update the best route.

[0012] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: On the one hand, when receiving navigation tasks, it first filters passable road segments based on the attributes of the driving vehicle, directly eliminating roads where such vehicles are prohibited, thus avoiding violations caused by mismatches between road segments and vehicle attributes (such as small cars entering truck lanes or non-motorized vehicles entering motor vehicle expressways). On the other hand, it refers to historical road segment traffic data from the same period, combined with current environmental characteristics, and filters historical data similar to the current environment (such as weather conditions and road background conditions) as the basis for analysis. Simultaneously, it eliminates interference from faulty vehicles, analyzes the matching between the vehicle's actual driving speed and the road speed limit, determines the degree of obstruction to road segment passage, and finally, combines these factors to lock in routes with high suitability. This planning method breaks the limitations of traditional universal navigation, making routes more closely aligned with specific vehicle types, historical traffic patterns, and the current environment, significantly reducing detours and road restrictions, and improving the scientific nature and reliability of route planning. On one hand, a real-time analysis and dynamic update optimization mechanism is constructed: after selecting routes with high suitability, the current traffic conditions of these routes are collected in real time via satellite. The degree of congestion is judged based on the ratio of slow-moving vehicles to the total number of vehicles on the road segment. The optimal route with the lowest congestion is prioritized from the suitable routes to ensure that the recommended route matches the real-time traffic conditions. On the other hand, a regular update cycle is set. After the cycle ends, the optimal route is re-analyzed from the user's current location. If the current driving route has deviated from the new optimal solution, the user is prompted to update. This mechanism can dynamically respond to changes in traffic conditions (such as sudden congestion or temporary road opening), preventing users from getting stuck in traffic difficulties due to fixed routes, and ensuring traffic efficiency throughout the journey. It is especially suitable for scenarios with peak traffic and complex road conditions. On the other hand, a hierarchical recommendation model is adopted. First, routes with high suitability are selected, and then the smoothest preferred route is selected from the top. The remaining routes are sorted according to their suitability, providing both the best current travel plan and retaining highly suitable alternatives, avoiding the limitation of users facing a single route choice. The entire planning process preprocesses the data (such as excluding data on malfunctioning vehicles and filtering data on similar environments) to ensure accurate analysis results and reduce route misjudgments caused by data interference. Users do not need to compare the advantages and disadvantages of multiple routes themselves, greatly reducing decision-making costs. At the same time, it avoids road sections where vehicles are prohibited in advance, reducing the risk of illegal driving and making navigation both practical and safe. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of a satellite navigation route optimization system based on big data according to the present invention; Figure 2 This is a flowchart of a satellite navigation route optimization method based on big data according to the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 and Figure 2 This invention provides a technical solution: a satellite navigation route optimization method based on big data, comprising the following steps: S1. When the satellite navigation system receives a navigation task, it collects the information of the navigation task and generates a navigation route based on the navigation task. S2. After generating the navigation route, obtain the road segments traversed by the navigation route, and conduct a comprehensive analysis of the current status of the road segments and the historical status of the road segments to obtain the similarity between the historical environment and the current environment. S3. Obtain historical driving data for road segments and analyze the historical congestion status of road segments; S4. Taking into account the historical traffic conditions of the road segment and the similarity between the historical environment and the current environment, a suitable navigation route is obtained; S5. After obtaining the adapted navigation route, consider the current traffic conditions of the route and provide the best route to the user. S6. Update the best route in real time during route usage and prompt the user whether to update the best route.

[0016] In step S1, when the satellite navigation system receives a navigation task, the navigation task includes a navigation start point, a navigation end point, and a mode of transportation. The mode of transportation includes non-motorized vehicles, small motorized vehicles, large motorized vehicles, and motorcycles. Road segments are selected based on the mode of transportation in the navigation task, removing road segments that the mode of transportation is not permitted to pass through. A navigation route is then generated based on the navigation task. The navigation route is composed of connected road segments. When the navigation route is generated, the satellite that generated the route is identified and stored in the navigation route database. Selecting road segments based on the mode of transportation and removing prohibited roads can prevent non-motorized vehicles from mistakenly entering motorized vehicle expressways and large motorized vehicles from entering restricted-class roads, reducing the risk of illegal passage from the source. It also prevents detours due to incompatible road segments, ensuring the practicality of the route planning.

[0017] In step S2, when the satellite navigation system receives a navigation task, it retrieves all navigation routes and obtains the road segments involved in all navigation routes, denoted as {A1, A2, ..., A...}. m ,…,A M}, where M represents the total number of road segments, A m Let A represent the m-th road segment. m The analysis divides a day into N time periods. The navigation task reception time belongs to the nth time period, n=1,2,…,N. Driving data from the nth time period of P days prior to receiving the navigation task is used as reference data for the navigation task, where P represents the predetermined reference number of days. Driving data from the mth road segment of the nth time period of P days is retrieved. The driving data from the nth time period of day p is analyzed, and environmental data related to road driving is retrieved. After preprocessing, Q environmental features are obtained, denoted as {B1, B2,…, B…}. q ,…,B Q}, where B q Let q be the environmental feature, and calculate the environmental similarity C between the nth time period on day p and the time period corresponding to the navigation task reception time. p : ; Where b q Let the q-th environmental feature of the time period corresponding to the task reception time be represented. Substitute each feature into p=1,2,…,P to obtain the environmental similarity {C1,C2,…,C} between the n-th time period P days before receiving the navigation task and the time period corresponding to the navigation task reception time. p ,…,C PBy selecting historical driving data from the period before receiving the navigation task and the same period as the current time as a reference, and combining this with environmental characteristics to filter historical time periods with high environmental similarity, interference from large environmental differences can be effectively eliminated. This allows the historical data used for analysis to better reflect road traffic patterns under the current environment. This improves the accuracy of judging road conditions, provides reliable data support for generating highly adaptable navigation routes, reduces route planning deviations caused by unreasonable data references, and makes navigation routes more consistent with the current actual traffic environment, enhancing the scientific rigor and reliability of route planning.

[0018] In step S3, the driving data for the nth time period on day p is analyzed. Vehicle driving data related to road driving is retrieved, including vehicle fault information, actual vehicle speed, and the maximum speed limit imposed by the road. Faulty vehicles are eliminated based on the fault information. After eliminating faulty vehicles, the normally driving vehicles are analyzed. The number of normally driving vehicles in the nth time period on day p is Y. The degree of vehicle driving obstruction E in the nth time period on day p is calculated. p : ; Where y represents the y-th normally driving vehicle, G y G represents the maximum speed limit for the type of vehicle corresponding to the y-th normally traveling vehicle on the m-th road segment. y Let p represent the average speed of the y-th normally traveling vehicle in the m-th road segment. Substituting each value into p = 1, 2, ..., P, we obtain the degree of vehicle obstruction {E1, E2, ..., E} for the n-th time period of the previous P days. p ,…,E P This step first uses vehicle fault information to eliminate faulty vehicles, avoiding interference from their abnormal driving data in assessing road conditions. This ensures that the vehicle data analyzed better reflects road conditions under normal driving conditions. Next, by calculating the difference between the actual speed of normally driving vehicles and the corresponding road speed limit, the degree of obstruction is determined. This accurately measures the smoothness of traffic flow on the road segment, providing crucial information for selecting highly suitable navigation routes. This makes route planning more aligned with actual traffic efficiency, reducing the probability of users encountering congestion and other obstacles, and improving the practicality and accuracy of navigation.

[0019] In step S4, when the satellite navigation system receives a navigation task in the nth time period, it obtains X navigation routes, where the xth navigation route contains route segments {H1, H2, ..., H...}. i ,…,H I}, where x = 1, 2, ..., X, I represents the number of route segments contained in the x-th navigation route, and H iThis represents the i-th route segment contained in the x-th navigation route, where the length of each route segment is {J1, J2, ..., J...} i ,…,J I}, and then obtain the priority recommendation coefficient f of the i-th road segment. i : ; Substitute each i = 1, 2, ..., I to obtain the priority recommendation coefficients {f1, f2, ..., f} for the I road segments. i ,…,f I}; Taking into account both the priority recommendation coefficient of the road segment and the corresponding road segment coefficient, the task adaptation coefficient Fx of the x-th navigation route is obtained. Fx is the sum of the products of the priority recommendation coefficient of road segment I and the corresponding road segment coefficient of the priority recommendation coefficient in the x-th navigation route, multiplied by the route coefficient of the x-th navigation route. The road segment coefficient corresponding to the priority recommendation coefficient fi is the ratio of the length of the i-th route segment to the sum of the lengths of the i-th route segments. The route coefficient of the x-th navigation route is the sum of the lengths of the route segments of the x-th navigation route and the sum of the lengths of the X navigation routes. The ratio of the length of each route segment to the length of the destination route is substituted into x=1,2,…,X to obtain the task adaptability coefficient of X navigation routes. These X routes are then reordered from highest to lowest task adaptability coefficient to obtain the T routes with the highest adaptability coefficients, where T is the predetermined number of adaptable navigation routes. By comprehensively considering environmental characteristics, vehicle traffic obstacles, road segments, route lengths, and other multi-dimensional factors, the priority recommendation coefficient of road segments and the task adaptability coefficient of the navigation routes are calculated. This allows for a comprehensive and accurate measurement of the adaptability of each route, avoiding planning deviations caused by a single factor. By sorting by adaptability coefficient, the routes with the highest adaptability are selected, prioritizing the routes most suitable for the current task while providing high-quality alternatives. This reduces the possibility of users encountering poor traffic conditions due to a single route, improving the scientific nature and flexibility of navigation route planning, facilitating user selection, and optimizing the navigation experience.

[0020] In step S5, after obtaining T adapted navigation routes, the driving status of the T adapted navigation routes is analyzed. The t-th adapted navigation route is analyzed, where t = 1, 2, ..., T. The current driving status of the t-th adapted navigation route is collected through the satellite navigation system. The total number of vehicles in the t-th adapted navigation route is R. If the ratio of the speed of the r-th vehicle to the maximum speed limit of the route segment where the r-th vehicle is located for the vehicle type corresponding to the r-th normally driving vehicle is lower than the pre-set slow vehicle driving threshold, then the r-th vehicle is determined to be a slow-moving vehicle, r = 1, 2, ..., R; otherwise, the r-th vehicle is determined to be a normally driving vehicle, thus obtaining the t-th adapted navigation route. The congestion level of the t-th adapted navigation route is represented by the ratio of the number of slow-moving vehicles to the total number of vehicles on the t-th adapted navigation route. Substituting each value into t=1,2,…,T, we obtain the congestion levels of the T adapted navigation routes. Then, we select the optimal route T0 with the lowest congestion level from these T routes and present it to the user. The other adapted navigation routes are then presented to the user in descending order of their compatibility coefficients. This step utilizes real-time satellite data to collect the current traffic conditions of the adapted navigation routes, accurately determining the congestion level of each route and dynamically avoiding congested sections, preventing users from experiencing low traffic efficiency. Simultaneously, prioritizing the route with the lowest congestion level and providing other routes in order of compatibility coefficients offers users both the currently smoothest optimal choice and highly adaptable alternatives, avoiding the limitations of a single recommendation, making user decision-making more convenient, and improving the practicality and flexibility of navigation in complex real-time traffic conditions.

[0021] In step S6, after a preset route update time has elapsed, the adaptive navigation route and the optimal route are re-analyzed starting from the current location. If the optimal route includes the current route, the current navigation state is maintained; if the optimal route does not include the current route, the user is prompted whether to update the optimal route.

[0022] A satellite navigation route optimization system based on big data, the system comprising: a task acquisition and route generation module, a road segment condition and similarity analysis module, a historical data and congestion analysis module, a comprehensive adaptation route generation module, a top-ranked optimal route recommendation module, and a real-time route update prompt module; The task acquisition and route generation module is used by the satellite navigation system to acquire navigation task information and generate navigation routes based on the navigation task when the satellite navigation system receives a navigation task. The road segment condition and similarity analysis module is used to obtain the road segments traversed by the navigation route after the navigation route is generated, and to comprehensively analyze the current condition of the road segments and the historical condition of the road segments to obtain the similarity between the historical environment and the current environment. The historical data and congestion analysis module is used to acquire historical driving data for road segments and analyze the historical congestion status of those road segments. The comprehensive adaptation route generation module is used to comprehensively consider the historical traffic conditions of road segments and the similarity between historical and current environments to obtain an adapted navigation route; The "Best Route Top Recommendation" module is used to provide the best route to users after obtaining the adapted navigation route and considering the current traffic conditions of the route. The real-time route update prompt module is used to update the best route in real time during route usage and prompt the user whether to update the best route.

[0023] Example 1: When a user uses the satellite navigation system, step S1 is triggered first: After receiving a navigation task containing the starting point, destination, and vehicle type (e.g., small motor vehicle), the system filters out permitted road segments based on the vehicle's traffic restrictions, and removes road segments prohibited to small motor vehicles (e.g., truck lanes, non-motorized vehicle expressways), thereby generating a navigation route formed by connecting the filtered road segments. Simultaneously, the system indicates the satellites on which the route is generated and stores the route in the navigation route database, reserving basic resources for subsequent planning.

[0024] Next, in step S2, the system retrieves all relevant navigation routes and identifies all road segments involved. Then, the day is divided into multiple time periods to determine the time period to which the navigation task was received. Driving data from several days prior to the task's reception, falling within the same time period as the current time, is retrieved for reference. Furthermore, the system accesses environmental data related to road travel (such as weather conditions and surrounding activities), processes this data to obtain multiple environmental features, calculates the environmental similarity between the same time period on those days and the current time period, and filters out historical time periods with similar environments. This ensures that subsequent analysis better reflects current road traffic patterns under the current conditions.

[0025] Then, step S3 is executed: the selected historical driving data is analyzed, and vehicle driving data (including vehicle fault information, actual driving speed and road speed limit) is retrieved. Faulty vehicles are excluded by using fault information, and only vehicles driving normally are analyzed. The difference between the actual driving speed of these vehicles and the corresponding road speed limit is calculated to obtain the degree of vehicle driving obstruction in each historical period, thereby accurately measuring the smoothness of traffic flow on the road segment.

[0026] In step S4, based on the aforementioned analysis results, the system obtains multiple navigation routes. Taking into account factors such as environmental similarity, vehicle obstruction, and road segment length, the system calculates the priority recommendation coefficient for each road segment within each route. Then, combining the proportion of road segment length and the proportion of total route length, the system obtains the "task suitability coefficient" for each route. The navigation routes with the highest suitability are selected by sorting them from highest to lowest suitability coefficient.

[0027] Step S5: The system collects real-time data on the current driving conditions of these adapted routes via satellite, counts the total number of vehicles on each route, and determines the number of slow-moving vehicles based on whether the ratio of vehicle speed to the speed limit of the road segment is lower than a set threshold, thus determining the congestion level of each adapted route. Afterwards, the routes with the lowest congestion levels are highlighted and recommended to the user, while the remaining routes are sorted according to the task compatibility coefficient for the user to choose from.

[0028] Finally, in step S6: After the set route update time, the system re-analyzes and adapts the navigation route and the optimal route, starting from the user's current location. If the current route is still the optimal route, the current navigation status is maintained; if a better route exists, the system will prompt the user whether to update to the optimal route, ensuring that the navigation can adapt to changes in road conditions in real time throughout the entire journey.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary sensing device embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A satellite navigation route optimization method based on big data, characterized in that: The method includes the following steps: S1. When the satellite navigation system receives a navigation task, it collects the information of the navigation task and generates a navigation route based on the navigation task. S2. After generating the navigation route, obtain the road segments traversed by the navigation route, and conduct a comprehensive analysis of the current status of the road segments and the historical status of the road segments to obtain the similarity between the historical environment and the current environment. S3. Obtain historical driving data for road segments and analyze the historical congestion status of road segments; S4. Taking into account the historical traffic conditions of the road segment and the similarity between the historical environment and the current environment, a suitable navigation route is obtained; S5. After obtaining the adapted navigation route, consider the current traffic conditions of the route and provide the best route to the user. S6. Update the best route in real time during route usage and prompt the user whether to update the best route.

2. The satellite navigation route optimization method based on big data according to claim 1, characterized in that: In step S1, when the satellite navigation system receives a navigation task, the navigation task includes a navigation start point, a navigation end point, and a vehicle. Based on the vehicle in the navigation task, road segments are filtered, and road segments that the vehicle is not allowed to pass through are removed. Then, a navigation route is generated based on the navigation task. The navigation route is composed of road segments connected together. When the navigation route is generated, the satellite that generated the navigation route is marked and stored in the navigation route database.

3. The satellite navigation route optimization method based on big data according to claim 2, characterized in that: In step S2, when the satellite navigation system receives a navigation task, it retrieves all navigation routes and obtains the road segments involved in all navigation routes, denoted as {A1, A2, ..., A...}. m ,…,A M }, where M represents the total number of road segments, A m Let A represent the m-th road segment. m The analysis divides a day into N time periods. The navigation task reception time belongs to the nth time period, n=1,2,…,N. Driving data from the nth time period of P days prior to receiving the navigation task is used as reference data for the navigation task, where P represents the predetermined reference number of days. Driving data from the mth road segment of the nth time period of P days is retrieved. The driving data from the nth time period of day p is analyzed, and environmental data related to road driving is retrieved. After preprocessing, Q environmental features are obtained, denoted as {B1, B2,…, B…}. q ,…,B Q }, where B q Let q be the environmental feature, and calculate the environmental similarity C between the nth time period on day p and the time period corresponding to the navigation task reception time. p : ; Where b q Let the q-th environmental feature of the time period corresponding to the task reception time be represented. Substitute each feature into p=1,2,…,P to obtain the environmental similarity {C1,C2,…,C} between the n-th time period P days before receiving the navigation task and the time period corresponding to the navigation task reception time. p ,…,C P } 4. The satellite navigation route optimization method based on big data according to claim 3, characterized in that: In step S3, the driving data for the nth time period on day p is analyzed. Vehicle driving data related to road driving is retrieved, including vehicle fault information, actual vehicle speed, and the maximum speed limit imposed by the road. Faulty vehicles are eliminated based on the fault information. After eliminating faulty vehicles, the normally driving vehicles are analyzed. The number of normally driving vehicles in the nth time period on day p is Y. The degree of vehicle driving obstruction E in the nth time period on day p is calculated. p : ; Where y represents the y-th normally driving vehicle, G y G represents the maximum speed limit for the type of vehicle corresponding to the y-th normally traveling vehicle on the m-th road segment. y Let p represent the average speed of the y-th normally traveling vehicle in the m-th road segment. Substituting each value into p = 1, 2, ..., P, we obtain the degree of vehicle obstruction {E1, E2, ..., E} for the n-th time period of the previous P days. p ,…,E P } 5. The satellite navigation route optimization method based on big data according to claim 4, characterized in that: In step S4, when the satellite navigation system receives a navigation task in the nth time period, it obtains X navigation routes, where the xth navigation route contains route segments {H1, H2, ..., H...}. i ,…,H I }, where x = 1, 2, ..., X, I represents the number of route segments contained in the x-th navigation route, and H i This represents the i-th route segment contained in the x-th navigation route, where the length of each route segment is {J1, J2, ..., J...} i ,…,J I }, and then obtain the priority recommendation coefficient f of the i-th road segment. i : ; Substitute each i = 1, 2, ..., I to obtain the priority recommendation coefficients {f1, f2, ..., f} for the I road segments. i ,…,f I } 6. The satellite navigation route optimization method based on big data according to claim 5, characterized in that: Taking into account both the priority recommendation coefficient of the road segment and the corresponding road segment coefficient, the task adaptation coefficient F of the x-th navigation route is obtained. x F x The route coefficient of navigation route x is the sum of the products of the priority recommendation coefficient of road segment I in navigation route x and the coefficient of the corresponding road segment in navigation route x, multiplied by the route coefficient of navigation route x, where the priority recommendation coefficient f i The corresponding road segment coefficient is the ratio of the length of the i-th route segment to the sum of the lengths of the I route segments. The route coefficient of the x-th navigation route is the ratio of the sum of the lengths of the x-th navigation route segments to the sum of the lengths of the X navigation routes. Substituting each of x=1,2,…,X, we obtain the task adaptation coefficients of the X navigation routes. Then, we reorder the X navigation routes from high to low according to the task adaptation coefficients to obtain the T adapted navigation routes with the highest adaptation coefficients, where T is the number of pre-set adapted navigation routes.

7. The satellite navigation route optimization method based on big data according to claim 6, characterized in that: In step S5, after obtaining T adapted navigation routes, the driving status of the T adapted navigation routes is analyzed. The analysis is performed on the t-th adapted navigation route, t=1,2,…,T. The current driving status of the t-th adapted navigation route is collected through the satellite navigation system. The total number of vehicles in the t-th adapted navigation route is R. If the ratio of the speed of the r-th vehicle to the maximum speed limit for the vehicle type corresponding to the r-th normally driving vehicle on the route segment where the r-th vehicle is located is lower than the pre-set slow-moving vehicle threshold, then the r-th vehicle is determined to be a slow-moving vehicle, r=1,2,… R; otherwise, determine that the r-th vehicle is a normally driving vehicle, and then obtain the traffic congestion level of the t-th adapted navigation route. The traffic congestion level of the t-th adapted navigation route represents the ratio of the number of slow-moving vehicles to the total number of vehicles in the t-th adapted navigation route. Substitute each of these values ​​into t=1,2,…,T to obtain the traffic congestion levels of T adapted navigation routes. Then, select the T0 optimal route with the lowest traffic congestion level from the T adapted navigation routes and place it at the top for the user to choose from. Provide the other adapted navigation routes to the user in descending order of their adaptation coefficients.

8. The satellite navigation route optimization method based on big data according to claim 6, characterized in that: In step S6, after the preset route update time has elapsed, the current location is used as the starting point to re-analyze the adapted navigation route and the optimal route. If the current route is included in the optimal route, the current navigation state is maintained. If the current route is not included in the optimal route, the user will be prompted whether to update the optimal route.

9. A satellite navigation route optimization system based on big data, wherein the system is applied to the satellite navigation route optimization method based on big data as described in any one of claims 1-8, characterized in that: The system includes: a task acquisition and route generation module, a road segment condition and similarity analysis module, a historical data and congestion analysis module, a comprehensive adaptive route generation module, a top-ranked best route recommendation module, and a real-time route update prompt module. The task acquisition and route generation module is used by the satellite navigation system to acquire navigation task information and generate navigation routes based on the navigation task when the satellite navigation system receives a navigation task. The road segment condition and similarity analysis module is used to obtain the road segments traversed by the navigation route after the navigation route is generated, and to comprehensively analyze the current status of the road segments and the historical road segment conditions to obtain the similarity between the historical environment and the current environment. The historical data and congestion analysis module is used to acquire historical driving data of road segments and analyze the historical congestion status of road segments. The comprehensive adaptive route generation module is used to comprehensively consider the historical traffic conditions of road segments and the similarity between historical and current environments to obtain an adapted navigation route. The optimal route recommendation module is used to obtain the adapted navigation route, take into account the current traffic conditions of the route, and provide the optimal route to the user. The real-time route update prompt module is used to update the best route in real time during route usage and prompt the user whether to update the best route.

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