Vehicle-machine GPS positioning navigation system based on vehicle-machine mobile phone interconnection

Abstract

This invention discloses a vehicle-mounted GPS positioning and navigation system based on vehicle-to-mobile phone interconnection, specifically relating to the field of positioning and navigation technology. The system includes acquiring navigation data built into an electric scooter and a mobile phone, using multi-sensor fusion technology to obtain a real navigation route, calculating the deviations between the electric scooter and mobile phone navigation routes and the real navigation route, using Wasserstein distance to calculate the difference in deviations between the electric scooter and mobile phone navigation routes, calculating the curvature fluctuation of the real navigation route, determining the driving position information based on the real navigation route within a road segment, aligning the timestamps of the navigation data from the electric scooter and mobile phone, using a dynamic time warping algorithm to calculate the time alignment difference, determining the driving time information based on the real navigation route within a road segment, and comprehensively analyzing the driving position information and driving time information of the real navigation route. This invention helps improve the accuracy and reliability of the navigation system.

Description

Technical Field

[0001] This invention relates to the field of positioning and navigation technology, and more specifically, to a vehicle GPS positioning and navigation system based on vehicle-to-mobile phone interconnection. Background Technology

[0002] With the increasing popularity of electric scooters, their application in urban transportation is gradually expanding. As a lightweight and environmentally friendly short-distance travel tool, electric scooters are widely used in urban roads and shared mobility scenarios. However, due to the complex and ever-changing urban road environment, the positioning accuracy and navigation route planning of electric scooter navigation systems still have certain problems on different road sections. Some high-end electric scooters have begun to use built-in sensors (such as IMU and speedometer) for assisted navigation and integrate data with the navigation system on a mobile phone to improve positioning accuracy. However, there is often a certain discrepancy between the positioning information provided by the built-in navigation system of the electric scooter and the built-in navigation system of the mobile phone. Furthermore, in complex road sections, the navigation system of the electric scooter is prone to errors or lag, leading to frequent adjustments to the route optimization algorithm, which affects navigation efficiency and accuracy.

[0003] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a vehicle GPS positioning and navigation system based on vehicle-to-mobile phone interconnection to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The vehicle-mounted GPS positioning and navigation system based on vehicle-to-phone interconnection includes a navigation data acquisition and fusion module, a navigation driving position analysis module, a navigation driving time analysis module, and an optimized frequency evaluation module, with signal connections between the modules;

[0007] The navigation data acquisition and fusion module is used to acquire navigation data built into the electric scooter and the mobile phone, and uses multi-sensor fusion technology to obtain the real navigation route.

[0008] The navigation driving position analysis module is used to calculate the deviation between the electric scooter, mobile phone navigation and the actual navigation route respectively. It uses Wasserstein distance to calculate the difference between the deviation of the electric scooter and the mobile phone navigation route, and calculates the curvature fluctuation of the actual navigation route to determine the driving position information based on the actual navigation route in the road segment.

[0009] The navigation travel time analysis module is used to align the timestamps of navigation data from electric scooters and mobile phones, and uses a dynamic time warping algorithm to calculate the time alignment difference to determine the travel time information based on the actual navigation route in the road segment.

[0010] The frequency optimization assessment module is used to comprehensively analyze the driving location and driving time information of the actual navigation route to determine whether the optimization frequency needs to be adjusted.

[0011] In a preferred embodiment, the actual navigation route's location information and travel time information include:

[0012] During the interconnection process between the electric scooter and the mobile phone, the navigation routes built into both the scooter and the mobile phone are extracted to obtain the system's actual navigation route. Based on the scooter's travel process on the road segment, the travel position and travel time information of the scooter based on the actual navigation route are obtained. The travel position information of the scooter based on the actual navigation route on the road segment is represented by a probability similarity distance coefficient and a route curvature fluctuation coefficient, while the travel time information of the scooter based on the actual navigation route on the road segment is represented by a time warping difference coefficient. The probability similarity distance coefficient, The coefficient for the curvature fluctuation of the route. This is the time-normalized difference coefficient.

[0013] In a preferred embodiment, the logic for obtaining the probabilistic similarity distance coefficient is as follows:

[0014] Based on the navigation routes built into the electric scooter, the navigation routes built into the mobile phone, and the actual navigation routes of the system, the navigation deviation of the electric scooter and the navigation deviation of the mobile phone are calculated respectively, and the navigation deviation of the electric scooter is marked as: The mobile navigation deviation is marked as: ,in, , , This indicates the location of the electric scooter at timestamp r. This refers to the location of the actual navigation route at timestamp r. This represents the location of the mobile phone at timestamp n. The location of the actual navigation route at timestamp n, where r = 1, 2, 3, ..., R, n = 1, 2, 3, ..., N, and R and N are positive integers;

[0015] The probability density functions of electric scooter navigation deviation and mobile phone navigation deviation are calculated using kernel density estimation. These probability density functions are then denoted as: and ,in, h is the bandwidth parameter, K is the kernel function, and x is the data point for the electric scooter navigation deviation density function. y represents the data points of the mobile navigation deviation density function;

[0016] The Wasserstein distance between the probability density functions of the electric scooter navigation deviation and the mobile phone navigation deviation is calculated to obtain the probability similarity distance coefficient. The formula for calculating the probability similarity distance coefficient is as follows: ;in, For all possible joint distributions The set, where inf represents the minimization operation.

[0017] In a preferred embodiment, the logic for obtaining the route curvature fluctuation coefficient is as follows:

[0018] Based on the system's actual navigation route, the location information of the electric scooter on the road segment is obtained, and the position coordinates of the electric scooter at each moment on the road segment are marked as follows: ,in, The horizontal axis represents the change of the electric scooter over time. The vertical axis represents the change of the electric scooter over time;

[0019] The curvature of the actual navigation route in a road segment is calculated using the curvature formula: ;in, The curvature of the actual navigation route within the road segment;

[0020] Calculate the mean and standard deviation of the curvature of the actual navigation route, and compare the standard deviation of the curvature of the actual navigation route with the mean of the curvature of the actual navigation route to obtain the route curvature fluctuation coefficient.

[0021] In a preferred embodiment, the logic for obtaining the time warping difference coefficient is as follows:

[0022] Obtain the GPS coordinates corresponding to the timestamps of the navigation routes built into the electric scooter and the GPS coordinates corresponding to the timestamps of the navigation routes built into the mobile phone, and align the timestamps of the GPS coordinates of the navigation routes built into the electric scooter and the GPS coordinates of the navigation routes built into the mobile phone to ensure that the GPS coordinates have the same time step.

[0023] For each pair of time points, calculate the Euclidean distance between the electric scooter and the mobile phone GPS coordinates. Mark the Euclidean distance between the electric scooter and the mobile phone GPS coordinates for each pair of time points as d(j,k), where j is the index of the electric scooter's built-in navigation route and k is the index of the time point in the mobile phone's built-in navigation route. Use dynamic programming to construct the DTW distance matrix and mark the DTW distance matrix as D.

[0024] The recursive formula for the DTW distance matrix is: ;

[0025] The time regularization variance coefficient is calculated using the following formula: Where g is the number of GPS coordinate timestamps in the built-in navigation route of the electric scooter, and m is the number of GPS coordinate timestamps in the built-in navigation route of the mobile phone.

[0026] In a preferred embodiment, the comprehensive analysis of the actual navigation route's location information and travel time information includes:

[0027] A comprehensive analysis of driving location and time information based on real navigation routes within a road segment is performed. A road segment driving navigation evaluation model is constructed through weighted calculations using probability similarity distance coefficients, route curvature fluctuation coefficients, and time warping difference coefficients. This generates road segment driving navigation evaluation coefficients, calculated using the following formula: ;in, This is the evaluation coefficient for navigation on the road segment. , , These are the proportional coefficients for the probability similarity distance coefficient, the route curvature fluctuation coefficient, and the time warping difference coefficient, respectively. , , All are greater than 0.

[0028] In a preferred embodiment, determining whether the optimization frequency needs to be adjusted includes:

[0029] Set a threshold for the road segment navigation evaluation coefficient. Compare the road segment navigation evaluation coefficient with the threshold. If the road segment navigation evaluation coefficient is less than the threshold, a warning signal is generated. If the road segment navigation evaluation coefficient is greater than the threshold, no warning signal is generated.

[0030] The technical effects and advantages of this invention are as follows:

[0031] This invention generates accurate and efficient real-world navigation routes through real-time data fusion. The navigation systems built into the electric scooter and the mobile phone generate an optimal navigation path through continuous feedback and algorithm optimization. By combining Wasserstein distance, dynamic time warping, and route curvature fluctuation coefficient, the navigation accuracy can be accurately evaluated, and the optimization frequency can be dynamically adjusted according to the actual situation. This invention helps to improve the accuracy and reliability of the navigation system. Attached Figure Description

[0032] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0033] Figure 1 This is a schematic diagram of the vehicle GPS positioning and navigation system based on vehicle-to-mobile phone interconnection according to the present invention. Detailed Implementation

[0034] 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.

[0035] Figure 1 This is a schematic diagram of the vehicle GPS positioning and navigation system based on vehicle-to-mobile phone interconnection of the present invention, which includes a navigation data acquisition and fusion module, a navigation driving position analysis module, a navigation driving time analysis module, and an optimization frequency evaluation module, with signal connections between the modules;

[0036] The navigation data acquisition and fusion module is used to acquire navigation data built into the electric scooter and the mobile phone, and uses multi-sensor fusion technology to obtain the real navigation route.

[0037] The navigation driving position analysis module is used to calculate the deviation between the electric scooter, mobile phone navigation and the actual navigation route respectively. It uses Wasserstein distance to calculate the difference between the deviation of the electric scooter and the mobile phone navigation route, and calculates the curvature fluctuation of the actual navigation route to determine the driving position information based on the actual navigation route in the road segment.

[0038] The navigation travel time analysis module is used to align the timestamps of navigation data from electric scooters and mobile phones, and uses a dynamic time warping algorithm to calculate the time alignment difference to determine the travel time information based on the actual navigation route in the road segment.

[0039] The frequency optimization assessment module is used to comprehensively analyze the driving location and driving time information of the actual navigation route to determine whether the optimization frequency needs to be adjusted.

[0040] During the interconnection process between the electric scooter and the mobile phone, the navigation routes built into both the electric scooter and the mobile phone are extracted to obtain the system's actual navigation route. The actual navigation route refers to the optimal route obtained through the collaborative work and algorithm optimization of the navigation systems built into the electric scooter and the mobile phone. Based on the electric scooter's travel process on the road segment, the travel position information and travel time information of the electric scooter based on the actual navigation route are obtained. The travel position information of the electric scooter based on the actual navigation route on the road segment is represented by the probability similarity distance coefficient and the route curvature fluctuation coefficient, and the travel time information of the electric scooter based on the actual navigation route on the road segment is represented by the time warping difference coefficient.

[0041] The built-in navigation system of the electric scooter is equipped with sensors such as GPS and inertial measurement unit (IMU), which can monitor the current position and motion status in real time and perform some basic navigation calculations, such as path planning, turning, and speed control, to ensure that basic navigation tasks can be completed independently without relying on external equipment.

[0042] The mobile phone's built-in navigation system combines multiple sensors such as GPS, Wi-Fi, Bluetooth, accelerometer, and gyroscope to improve positioning accuracy through sensor fusion.

[0043] To generate a real navigation route for the system, the electric scooter continuously acquires real-time data from GPS, IMU, speedometer, etc., providing information such as location, speed, and direction. At the same time, the mobile phone acquires its own location data, which may include GPS, Wi-Fi signal strength, Bluetooth device information, etc., to correct the electric scooter's positioning.

[0044] Since the GPS positioning accuracy of electric scooters may be affected by interference or errors, mobile phone navigation systems use multi-sensor fusion technology (such as Kalman filtering) to perform a weighted average of positioning data from electric scooters and mobile phones to obtain a more accurate location information.

[0045] The actual navigation route is a continuous feedback process between the navigation routes built into the electric scooter and the navigation routes built into the mobile phone. The electric scooter and the mobile phone will continuously exchange information, and through data fusion and collaborative computing, the navigation accuracy, route selection and system robustness can be enhanced. For example, the built-in sensors of the electric scooter (such as IMU, speedometer, etc.) can help the mobile phone system compensate for the weak GPS signal, especially when moving quickly in a short period of time. The data from the electric scooter system can supplement the mobile phone and further improve the positioning accuracy.

[0046] The advantages and functions of the probabilistic similarity distance coefficient include:

[0047] The probabilistic similarity distance coefficient uses Wasserstein distance to take into account the overall differences in distribution shape, offset, and scale. Therefore, even if the mean error of two navigation systems is similar, but the distribution differences are large, it can still be captured. Furthermore, if the navigation deviation data exhibits multimodal characteristics (such as unstable deviations caused by road segment changes), Wasserstein distance can still accurately measure the differences between the overall distributions, rather than being misled by local peaks.

[0048] By calculating distance coefficients according to road segments or time zones, it is possible to identify locations where the error distributions of the two systems differ significantly under certain road segments. This serves as a basis for early warning of potential weak GPS areas or interference areas. Furthermore, in the fusion algorithms of mobile phone and electric scooter navigation systems (such as Kalman filtering and multi-model fusion), the probability similarity distance coefficient is used as a priori consistency index before fusion, which is used to dynamically adjust the source or weight of the fused signal.

[0049] The logic for obtaining the probability similarity distance coefficient is as follows: For the navigation route built into the electric scooter, the navigation route built into the mobile phone, and the actual navigation route of the system, the navigation deviation of the electric scooter and the navigation deviation of the mobile phone are calculated respectively, and the navigation deviation of the electric scooter is marked as: The mobile navigation deviation is marked as: ,in, , , This indicates the location of the electric scooter at timestamp r. This refers to the location of the actual navigation route at timestamp r. This represents the location of the mobile phone at timestamp n. The location of the actual navigation route at timestamp n, where r = 1, 2, 3, ..., R, n = 1, 2, 3, ..., N, and R and N are positive integers;

[0050] It should be noted that r and n represent the location data of the electric scooter's built-in navigation system and the mobile phone's built-in navigation system at different timestamps. The location data collection frequency or time distribution of the two may be different, which will lead to different calculations of their navigation deviations.

[0051] The probability density functions of electric scooter navigation deviation and mobile phone navigation deviation are calculated using kernel density estimation. These probability density functions are then denoted as: and ,in, h is the bandwidth parameter, K is the kernel function, and x is the data point for the electric scooter navigation deviation density function. y represents the data points of the mobile navigation deviation density function;

[0052] The Wasserstein distance between the probability density functions of the electric scooter navigation deviation and the mobile phone navigation deviation is calculated to obtain the probability similarity distance coefficient. The formula for calculating the probability similarity distance coefficient is as follows: ;in, The probability similarity distance coefficient, For all possible joint distributions The set, where inf represents the minimization operation.

[0053] As can be seen from the formula, the larger the probability similarity distance coefficient, the greater the difference between the navigation deviation distribution of the electric scooter and the navigation deviation distribution of the mobile phone on this road segment, and the stronger the inconsistency between the navigation systems, i.e., the larger the Wasserstein distance. Therefore, on this road segment, it is necessary to increase the frequency of fusion optimization of the navigation routes built into the electric scooter and the navigation routes built into the mobile phone.

[0054] It should be noted that, assuming both the electric scooter and the mobile phone attempt to track the same real route, but their deviation statistical patterns (probability densities) are different, it means that the two systems may have significantly different positioning accuracy in certain road sections or time periods. Furthermore, if the deviation behaviors of the two are significantly different, it will be more difficult to achieve consistency or mutual compensation when merging the two into a unified navigation path.

[0055] Significant curvature differences typically indicate that the electric scooter has encountered sharp turns, complex road conditions, or other situations with drastic path changes. Navigation systems need to make rapid adjustments under these conditions; however, with large curvature changes, the system's optimization process can lead to the accumulation of errors, resulting in decreased navigation accuracy and potentially requiring frequent adjustments. These frequent adjustments can cause path instability, increase the system's computational burden, cause response delays, and ultimately affect navigation accuracy.

[0056] The logic for obtaining the route curvature fluctuation coefficient is as follows: Based on the system's actual navigation route, obtain the electric scooter's position information on the road segment, and mark the electric scooter's position coordinates on the road segment at each moment as follows: ,in, The horizontal axis represents the change of the electric scooter over time. The vertical axis represents the change of the electric scooter over time;

[0057] The curvature of the actual navigation route in a road segment is calculated using the curvature formula: ;in, The curvature of the actual navigation route within the road segment;

[0058] Calculate the mean and standard deviation of the curvature of the actual navigation route. Compare the standard deviation of the curvature of the actual navigation route with the mean of the curvature of the actual navigation route, and use this as the route curvature fluctuation coefficient. The route curvature fluctuation coefficient is denoted as: .

[0059] It should be noted that a larger route curvature fluctuation coefficient indicates a greater variation in the curvature of the path. This means that the curvature of the route changes rapidly at different locations, with frequent transitions from sharp turns to relatively straight sections, potentially resulting in significant changes in direction. Consequently, the navigation system needs to make more frequent adjustments to keep the vehicle on the optimal path, leading to a gradual accumulation of errors. Therefore, it is necessary to reduce the frequency of merging and optimizing the navigation routes built into the electric scooter and the mobile phone to ensure the accuracy of the actual navigation route.

[0060] The advantages and functions of the time warping difference coefficient are as follows:

[0061] The core function of the time warping difference coefficient is to measure the temporal consistency between the navigation paths of the electric scooter and the mobile phone. By comparing the navigation paths of the electric scooter and the mobile phone, calculating the time warping difference coefficient can effectively assess whether the two paths can be effectively aligned within the same time range. The better the path alignment, the higher the accuracy of the generated real navigation path;

[0062] The time warping difference coefficient helps determine the time discrepancy between the electric scooter's and the phone's paths. Based on this coefficient, the navigation system can be optimized to reduce time errors, thereby generating a more accurate and realistic navigation path. It is an important reference indicator in the time synchronization and path optimization process.

[0063] The logic for obtaining the time warping difference coefficient is as follows: obtain the GPS coordinates corresponding to the timestamp of the navigation route built into the electric scooter and the GPS coordinates corresponding to the timestamp of the navigation route built into the mobile phone, align the timestamp of the GPS coordinates of the navigation route built into the electric scooter with the timestamp of the GPS coordinates of the navigation route built into the mobile phone, and ensure that the GPS coordinates have the same time step.

[0064] For each pair of time points, calculate the Euclidean distance between the electric scooter and the mobile phone GPS coordinates. Mark the Euclidean distance between the electric scooter and the mobile phone GPS coordinates for each pair of time points as d(j,k), where j is the index of the electric scooter's built-in navigation route and k is the index of the time point in the mobile phone's built-in navigation route. Use dynamic programming to construct the DTW distance matrix and mark the DTW distance matrix as D.

[0065] The recursive formula for the DTW distance matrix is: ;

[0066] The time regularization variance coefficient is calculated using the following formula: ;in, denoted as the time warping difference coefficient, g represents the number of GPS coordinate timestamps in the built-in navigation route of the electric scooter, and m represents the number of GPS coordinate timestamps in the built-in navigation route of the mobile phone.

[0067] As shown by the formula, the larger the time warping difference coefficient, the higher the matching degree between the time points of the navigation routes built into the electric scooter and the navigation routes built into the mobile phone. Their alignment effect in the time series is better. Therefore, the actual navigation path generated by the optimized navigation routes built into the electric scooter and the navigation routes built into the mobile phone is better for this road segment. Conversely, it indicates poor path matching degree and poor time consistency, which may lead to inaccurate actual navigation path after optimization and fail to reflect the actual road segment well. Therefore, it is necessary to increase the frequency of fusion optimization of the navigation routes built into the electric scooter and the navigation routes built into the mobile phone.

[0068] It should be noted that the DTW algorithm is used to measure the temporal consistency between the built-in navigation route of the electric scooter and the built-in navigation route of the mobile phone. The worse the consistency, the more likely there is a deviation in the actual navigation performance. The DTW distance matrix is ​​used to store the minimum cost path between each pair of time points. The lower right element D(g,m) of the DTW distance matrix D represents the minimum matching cost between the last time point of the built-in navigation route of the electric scooter and the last time point of the built-in navigation route of the mobile phone.

[0069] A comprehensive analysis of driving location and time information based on real navigation routes within a road segment is performed. A road segment driving navigation evaluation model is constructed through weighted calculations using probability similarity distance coefficients, route curvature fluctuation coefficients, and time warping difference coefficients. This generates road segment driving navigation evaluation coefficients, calculated using the following formula: ;in, This is the evaluation coefficient for navigation on the road segment. , , These are the proportional coefficients for the probability similarity distance coefficient, the route curvature fluctuation coefficient, and the time warping difference coefficient, respectively. , , All are greater than 0.

[0070] As can be seen from the formula, the smaller the road segment navigation evaluation coefficient, the stronger the time and location consistency between the electric scooter and the mobile phone navigation system. The generated real navigation path is more accurate and can better reflect the actual situation of the road segment. This means that the navigation data of the electric scooter and the mobile phone can be effectively integrated, and the generated navigation path has high reliability and accuracy. Especially when integrating the consistency of path time and space, the optimization effect is more obvious. It also indicates that the electric scooter makes fewer sharp turns and changes direction during the road segment.

[0071] A threshold for the road segment navigation evaluation coefficient is set. The road segment navigation evaluation coefficient is compared with the threshold. If the road segment navigation evaluation coefficient is less than the threshold, a warning signal is generated, indicating that the actual navigation route based on the fusion of the navigation route built into the electric scooter and the navigation route built into the mobile phone may have low accuracy and the optimization frequency needs to be increased to reduce the deviation. If the road segment navigation evaluation coefficient is greater than the threshold, no warning signal is generated.

[0072] It should be noted that frequent optimization of navigation data for electric scooters and mobile phones helps to correct deviations in a timely manner, especially in complex road sections (such as curves and intersections) and areas with frequent traffic changes. Increasing the optimization frequency allows the navigation system to respond to these changes more quickly, ensuring that the vehicle always travels along the optimal path.

[0073] This invention generates accurate and efficient real-world navigation routes through real-time data fusion. The navigation systems built into the electric scooter and the mobile phone generate an optimal navigation path through continuous feedback and algorithm optimization. By combining Wasserstein distance, dynamic time warping, and route curvature fluctuation coefficient, the navigation accuracy can be accurately evaluated, and the optimization frequency can be dynamically adjusted according to the actual situation. This invention helps to improve the accuracy and reliability of the navigation system.

[0074] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0075] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0076] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0078] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 system, 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 apparatuses or units may be electrical, mechanical, or other forms.

[0080] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A GPS positioning and navigation system based on the interconnection between a car machine and a mobile phone, characterized in that, It includes a navigation data acquisition and fusion module, a navigation driving position analysis module, a navigation driving time analysis module, and an optimized frequency evaluation module, with signal connections between the modules; The navigation data acquisition and fusion module is used to acquire navigation data built into the electric scooter and the mobile phone, and uses multi-sensor fusion technology to obtain the real navigation route; The navigation driving position analysis module is used to calculate the deviation between the electric scooter, mobile phone navigation and the actual navigation route respectively. It uses Wasserstein distance to calculate the difference between the deviation of the electric scooter and the mobile phone navigation route, and calculates the curvature fluctuation of the actual navigation route to determine the driving position information based on the actual navigation route in the road segment. The navigation travel time analysis module is used to align the timestamps of navigation data from electric scooters and mobile phones, and uses a dynamic time warping algorithm to calculate the time alignment difference to determine the travel time information based on the actual navigation route in the road segment. The frequency optimization evaluation module is used to comprehensively analyze the driving location and driving time information of the actual navigation route to determine whether the optimization frequency needs to be adjusted. The actual navigation route's location and travel time information includes: In the process of interconnection between the electric skateboard and the mobile phone, the navigation route built in the electric skateboard and the navigation route built in the mobile phone are extracted respectively, and the real navigation route of the system is obtained. According to the driving process of the electric skateboard on the road section, the driving position information and the driving time information of the electric skateboard on the road section based on the real navigation route are obtained. The driving position information of the electric skateboard on the road section based on the real navigation route is expressed by a probability similar distance coefficient and a route curvature fluctuation coefficient. The driving time information of the electric skateboard on the road section based on the real navigation route is expressed by a time regularization difference coefficient, wherein, the probability similar distance coefficient is, the route curvature fluctuation coefficient is, the time regularization difference coefficient is. The logic for obtaining the probability similarity distance coefficient is as follows: Based on the navigation routes built into the electric scooter, the navigation routes built into the mobile phone, and the actual navigation routes of the system, the navigation deviation of the electric scooter and the navigation deviation of the mobile phone are calculated respectively, and the navigation deviation of the electric scooter is marked as: The mobile navigation deviation is marked as: ,in, , , This indicates the location of the electric scooter at timestamp r. This refers to the location of the actual navigation route at timestamp r. This represents the location of the mobile phone at timestamp n. The location of the actual navigation route at timestamp n, where r = 1, 2, 3, ..., R, n = 1, 2, 3, ..., N, and R and N are positive integers; The probability density functions of electric scooter navigation deviation and mobile phone navigation deviation are calculated using kernel density estimation. These probability density functions are then denoted as: and ,in, h is the bandwidth parameter, K is the kernel function, and x is the data point for the electric scooter navigation deviation density function. y represents the data points of the mobile navigation deviation density function; The Wasserstein distance between the probability density functions of the electric scooter navigation deviation and the mobile phone navigation deviation is calculated to obtain the probability similarity distance coefficient. The formula for calculating the probability similarity distance coefficient is as follows: ;in, For all possible joint distributions The set, where inf represents the minimization operation.

2. The vehicle-mounted GPS positioning and navigation system based on vehicle-to-mobile phone interconnection according to claim 1, characterized in that, The logic for obtaining the curvature fluctuation coefficient of the route is as follows: Based on the system's actual navigation route, the location information of the electric scooter on the road segment is obtained, and the position coordinates of the electric scooter at each moment on the road segment are marked as follows: ,in, The horizontal axis represents the change of the electric scooter over time. The vertical axis represents the change of the electric scooter over time; The curvature of the actual navigation route in a road segment is calculated using the curvature formula: ;in, The curvature of the actual navigation route within the road segment; Calculate the mean and standard deviation of the curvature of the actual navigation route, and compare the standard deviation of the curvature of the actual navigation route with the mean of the curvature of the actual navigation route to obtain the route curvature fluctuation coefficient.

3. The vehicle-mounted GPS positioning and navigation system based on vehicle-to-mobile phone interconnection according to claim 2, characterized in that, The logic for obtaining the time warping difference coefficient is as follows: Obtain the GPS coordinates corresponding to the timestamps of the navigation routes built into the electric scooter and the GPS coordinates corresponding to the timestamps of the navigation routes built into the mobile phone, and align the timestamps of the GPS coordinates of the navigation routes built into the electric scooter and the GPS coordinates of the navigation routes built into the mobile phone to ensure that the GPS coordinates have the same time step. For each pair of time points, calculate the Euclidean distance between the electric scooter and the mobile phone GPS coordinates. Mark the Euclidean distance between the electric scooter and the mobile phone GPS coordinates for each pair of time points as d(j,k), where j is the index of the electric scooter's built-in navigation route and k is the index of the time point in the mobile phone's built-in navigation route. Use dynamic programming to construct the DTW distance matrix and mark the DTW distance matrix as D. The recursive formula for the DTW distance matrix is: ; The time regularization variance coefficient is calculated using the following formula: Where g is the number of GPS coordinate timestamps in the built-in navigation route of the electric scooter, and m is the number of GPS coordinate timestamps in the built-in navigation route of the mobile phone.

4. The vehicle-mounted GPS positioning and navigation system based on vehicle-to-mobile phone interconnection according to claim 3, characterized in that, Comprehensive analysis of the actual navigation route's location and travel time information, including: A comprehensive analysis of driving location and time information based on real navigation routes within a road segment is performed. A road segment driving navigation evaluation model is constructed through weighted calculations using probability similarity distance coefficients, route curvature fluctuation coefficients, and time warping difference coefficients. This generates road segment driving navigation evaluation coefficients, calculated using the following formula: ;in, This is the evaluation coefficient for navigation on the road segment. , , These are the proportional coefficients for the probability similarity distance coefficient, the route curvature fluctuation coefficient, and the time warping difference coefficient, respectively. , , All are greater than 0.

5. The vehicle-mounted GPS positioning and navigation system based on vehicle-to-mobile phone interconnection according to claim 4, characterized in that, Determining whether the optimization frequency needs to be adjusted includes: Set a threshold for the road segment navigation evaluation coefficient. Compare the road segment navigation evaluation coefficient with the threshold. If the road segment navigation evaluation coefficient is less than the threshold, a warning signal is generated. If the road segment navigation evaluation coefficient is greater than the threshold, no warning signal is generated.

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