Intelligent navigation method, system and device integrating parking and charging

Abstract

The invention relates to the related technical field of parking intelligent navigation, in particular to an intelligent navigation method, system and device integrating parking and charging, and the method comprises the steps: determining a sensing signal according to a target vehicle and a charging station; and configuring an initialized parking navigation network layer and an initialized charging navigation network layer, judging whether a navigation condition is met by combining the idle state of the station and the residual endurance, and if not, pushing an alternative scheme for reminding. The technical problems that a parking navigation function and a charging navigation function are separated, a cooperation mechanism is lacked, joint decision making cannot be carried out according to the real-time state of a vehicle and the dynamic state of a station resource, and efficient and reliable integrated navigation service is difficult to provide in a complex traffic and energy supply scene are solved, and a parking and charging navigation network layer is constructed; the technical effects of performing core data real-time synchronization in combination with the charging station and the parking space detection equipment, performing accurate matching of the parking resources and the charging resources, performing path optimization planning, and effectively adapting to dynamically changing traffic and resource states are achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent parking navigation technology, specifically to intelligent navigation methods, systems, and devices that integrate parking and charging. Background Technology

[0002] With the rapid increase in the number of new energy vehicles, parking difficulties and charging difficulties have become the core pain points restricting the popularization and promotion of new energy vehicles. Especially in urban core areas and transportation hubs, the time cost of finding parking spaces and charging piles has increased significantly, which not only affects the user's travel experience, but also easily exacerbates road congestion. As a key carrier connecting user travel and transportation resources, the navigation system has extended its function from traditional route planning to value-added services such as parking and charging.

[0003] Current mainstream navigation technologies suffer from a severe disconnect between parking and charging functions. Existing navigation systems often separate parking and charging navigation into independent modules, requiring users to query parking and charging information separately. Core data such as charging station occupancy status and parking lot availability are often outdated. Furthermore, they fail to incorporate dynamic factors like road congestion indices and ambient temperature for comprehensive analysis, easily leading to situations where no parking spaces or available charging stations are found upon arrival at the destination. In addition, they do not adequately consider vehicle-specific information such as size, remaining battery power, and energy consumption parameters, as well as personalized needs such as user preferences for parking fees and charging power requirements. The resulting navigation solutions are generally applicable but lack specificity, failing to effectively adapt to dynamically changing traffic and resource conditions, thus limiting the user's travel experience.

[0004] In summary, existing technologies suffer from several technical problems: parking navigation and charging navigation functions are separated and lack a coordination mechanism; the logic for judging navigation conditions and generating alternative solutions is incomplete; and they cannot make joint decisions based on the real-time status of vehicles and the dynamics of station resources. As a result, they are unable to provide efficient and reliable integrated navigation services in complex traffic and energy replenishment scenarios. Summary of the Invention

[0005] This application provides an intelligent navigation method, system, and device that integrates parking and charging, aiming to solve the technical problems in the prior art where parking navigation and charging navigation functions are separated and lack a coordination mechanism, the logic for judging navigation conditions and generating alternative solutions is imperfect, and the system is unable to make joint decisions based on the real-time status of the vehicle and the dynamics of the station resources, making it difficult to provide efficient and reliable integrated navigation services in complex traffic and energy supply scenarios.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows:

[0007] In a first aspect, this application provides an intelligent navigation method integrating parking and charging, wherein the method includes: determining sensing signals based on the target vehicle and charging station, wherein the sensing signals include ambient temperature, remaining vehicle battery power, driving range, charging pile occupancy status, parking lot vacancy information, road congestion index, and charging power parameters, wherein the charging pile occupancy status is obtained in real time by the charging station management system, and the parking lot vacancy information is obtained by collaborative statistics from parking space detection equipment and the management platform; configuring and initializing a parking navigation network layer, and iteratively updating it based on the sensing signals and parking demand; configuring and initializing a charging navigation network layer, and iteratively updating it based on the sensing signals and charging demand information; connecting the iteratively updated parking navigation network layer and charging navigation network layer, and determining whether navigation conditions are met by combining the station vacancy status and remaining driving range; if met, generating an optimal navigation path integrating parking and charging; if not met, pushing alternative solutions as reminders.

[0008] In possible implementations, the initial parking navigation network layer is obtained by using the parking space vacancy rate and arrival time of each parking lot as training data; the initial charging navigation network layer is obtained by using the charging power and idle time distribution of charging piles in standard state as training data.

[0009] In one possible implementation, the optimal parking matching information for the target vehicle in its current state is estimated based on vehicle information and the sensing signal; wherein the vehicle information includes vehicle size and energy consumption parameters, the optimal parking matching information is positively correlated with the parking lot vacancy rate, and the optimal parking matching information is negatively correlated with arrival time.

[0010] In possible implementations, a navigation priority factor for each parking lot is determined based on the road congestion index, parking distance, parking space type suitability, and parking fee standard; the navigation priority factor for each parking lot is used to dynamically correct the weight allocation coefficients of the initial parking navigation network layer.

[0011] In a possible implementation, based on vehicle information and the sensing signal, the service characteristics of each charging pile under standard conditions are recorded within a preset time period; wherein, the service characteristics of each charging pile under standard conditions include the correlation between charging efficiency fluctuation range and ambient temperature.

[0012] In possible implementations, the optimal charging threshold under the standard state is determined based on the charging power change of the charging pile, the idle state update frequency, and the vehicle's remaining power demand. The optimal charging threshold under the standard state is used to determine whether the current operating state of the charging pile meets the vehicle's charging demand and serves as the core constraint for the iterative update of the charging navigation network layer.

[0013] In a second aspect, this application provides an intelligent navigation system integrating parking and charging, comprising: a sensing signal determination module, used to determine sensing signals based on a target vehicle and a charging station, wherein the sensing signals include ambient temperature, remaining vehicle battery power, driving range, charging pile occupancy status, parking lot vacancy information, road congestion index, and charging power parameters; the charging pile occupancy status is obtained in real time by a charging station management system, and the parking lot vacancy information is obtained through collaborative statistics from parking space detection equipment and a management platform; an initialization parking navigation network layer configuration module, used to configure and initialize a parking navigation network layer, and iteratively update it based on the sensing signals and parking demand; an initialization charging navigation network layer configuration module, used to configure and initialize a charging navigation network layer, and iteratively update it based on the sensing signals and charging demand information; and an alternative solution reminder push module, used to connect the iteratively updated parking navigation network layer and charging navigation network layer, and determine whether navigation conditions are met by combining the station vacancy status and remaining driving range; if met, an optimal navigation path integrating parking and charging is generated; if not met, an alternative solution reminder is pushed.

[0014] In a third aspect, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described intelligent navigation method integrating parking and charging.

[0015] In summary, one or more technical solutions provided in this application achieve the technical effect of constructing and connecting parking and charging navigation network layers, combining charging stations and parking space detection equipment for real-time synchronization of core data, realizing accurate matching of parking resources and charging resources, optimized route planning, and effectively adapting to dynamically changing traffic and resource conditions. Attached Figure Description

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

[0017] Figure 1 This application provides a flowchart illustrating an intelligent navigation method that integrates parking and charging.

[0018] Figure 2 This application provides a structural schematic diagram of an intelligent navigation system that integrates parking and charging.

[0019] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached drawings: Sensing signal determination module 11, initialization parking navigation network layer configuration module 12, initialization charging navigation network layer configuration module 13, alternative scheme reminder push module 14, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. Detailed Implementation

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides an intelligent navigation method that integrates parking and charging, the method comprising:

[0023] Based on the target vehicle and charging station, sensing signals are determined. These sensing signals include ambient temperature, remaining vehicle battery power, driving range, charging pile occupancy status, parking lot vacancy information, road congestion index, and charging power parameters. The charging pile occupancy status is obtained in real time by the charging station management system, and the parking lot vacancy information is obtained through collaborative statistics from parking space detection equipment and the management platform.

[0024] In one embodiment, the sensing signals refer to various real-time data related to the target vehicle and charging station, used to provide decision-making basis for the navigation system. The sensing signals include ambient temperature, remaining vehicle battery power, driving range, charging pile occupancy status, parking space availability, road congestion index, and charging power parameters. Specifically, ambient temperature reflects the impact of weather on driving and charging; remaining vehicle battery power determines the vehicle's driving distance; driving range refers to the driving distance calculated based on remaining battery power and energy consumption parameters; charging pile occupancy status indicates whether the charging pile is available; parking space availability information refers to the number of available parking spaces in the parking lot; road congestion index reflects road traffic conditions; and charging power parameters refer to the output power of the charging pile, including its rated power. Real-time synchronization means that the charging pile occupancy status data is dynamically updated through the charging station management system to ensure that the charging pile information obtained by the navigation system is up-to-date. Collaborative statistics refer to the parking space availability information being statistically analyzed jointly by the parking space detection equipment and the management platform to ensure data accuracy and real-time performance.

[0025] Optionally, by acquiring multi-dimensional perception signals, the system can comprehensively understand the vehicle's current status, surrounding environment, and site resources. For example, the vehicle's remaining battery power and driving range data can help the system determine whether the vehicle needs charging and the urgency of charging. Real-time acquisition of charging pile occupancy status and parking lot availability information avoids situations where users arrive at their destination only to find that there are no available charging piles or parking spaces. Combining road congestion index with optimized route planning reduces travel time and provides accurate data support for the initialization and iterative updates of the parking navigation network layer and the charging navigation network layer, thereby improving navigation reliability and user experience.

[0026] Configure and initialize the parking navigation network layer, and iteratively update it based on the sensed signals and parking demand. Configure and initialize the charging navigation network layer, and iteratively update it based on the sensed signals and charging demand information. Connect the iteratively updated parking navigation network layer and charging navigation network layer, and determine whether the navigation conditions are met by combining the station's idle status and remaining range. If the conditions are met, generate the optimal navigation path that integrates parking and charging. If the conditions are not met, push the alternative solution reminder.

[0027] In one embodiment, initializing the parking navigation network layer and initializing the charging navigation network layer refer to the initial algorithm models used for parking and charging navigation, respectively. Initializing the parking navigation network layer and initializing the charging navigation network layer involves preliminary configuration based on preset rules and data. Iterative updates refer to dynamically adjusting and optimizing these two network layers according to real-time sensing signals and user needs to adapt to constantly changing traffic and resource conditions. Connection refers to the collaborative integration of the parking navigation network layer and the charging navigation network layer, enabling them to cooperate and jointly generate the optimal navigation path. Navigation conditions refer to the comprehensive conditions for determining whether a vehicle can successfully reach the target parking lot and complete charging, including key factors such as the parking lot's availability and the vehicle's remaining range. The optimal navigation path refers to the best path obtained after comprehensively considering factors such as time, distance, and cost, while meeting the navigation conditions. Alternative solution reminders refer to providing users with alternative options when navigation conditions are not met, ensuring the flexibility and reliability of travel.

[0028] Optionally, the initialization of the parking navigation network layer and the initialization of the charging navigation network layer can be constructed and optimized, and the parking navigation network layer and the charging navigation network layer can be integrated collaboratively to achieve accurate matching and path optimization of parking and charging resources. Specifically, the initialization configuration of the parking navigation network layer and the charging navigation network layer provides the basic architecture for the navigation system. For example, the parking navigation network layer can make preliminary plans based on initial data such as the location of the parking lot and the vacancy rate of parking spaces, while the charging navigation network layer can make preliminary configurations based on information such as the distribution of charging piles and charging power.

[0029] Based on the sensing signals and user needs, the parking navigation network layer and the charging navigation network layer are dynamically iterated and updated. Furthermore, the sensing signals include the road congestion index and the occupancy status of charging piles, while user needs include parking fee preferences and charging power requirements. For example, if the sensing signals show that a certain road segment is congested, the parking navigation network layer will adjust the route planning to avoid the congested area; if the occupancy status of charging piles changes, the charging navigation network layer will re-evaluate the priority of charging stations.

[0030] The updated parking navigation network layer and charging navigation network layer are connected. By comprehensively analyzing navigation conditions such as the availability of charging stations and the remaining range of the vehicle, it is determined whether the navigation conditions are met. Specifically, if the conditions are met, an optimal navigation route that integrates parking and charging is generated. For example, for a vehicle with low remaining battery power, a route that is closer and has available charging stations is prioritized, while also considering the availability of parking spaces to ensure that the vehicle can park and charge smoothly. If the navigation conditions are not met, alternative solutions are pushed to remind users to go to other charging stations or adjust their travel plans.

[0031] In the above steps, the navigation system can dynamically adapt to complex traffic and resource conditions, providing efficient and reliable integrated navigation services. For example, in urban core areas where vehicle density is high and charging piles and parking spaces are scarce, the system can provide users with the optimal parking and charging solutions through dynamic iterative updates and collaborative integration, reducing the time spent searching for parking spaces and charging piles, improving travel efficiency, and avoiding travel interruptions due to insufficient resources, thereby significantly improving the user experience.

[0032] Furthermore, this application provides a method for configuring and initializing a parking navigation network layer, the method further comprising:

[0033] The initial parking navigation network layer is obtained by using the parking space vacancy rate and arrival time of each parking lot as training data; the initial charging navigation network layer is obtained by using the charging power and idle time distribution of charging piles in standard state as training data.

[0034] In one embodiment, training data refers to the input dataset used to construct and optimize the navigation network layer. For the parking navigation network layer, the training data includes the parking space vacancy rate and arrival time of each parking lot. Further, the parking space vacancy rate of each parking lot refers to the ratio of the number of vacant parking spaces to the total number of parking spaces, and the arrival time refers to the time required to travel from the current location to the parking lot. For the charging navigation network layer, the training data includes the charging power of the charging pile in its standard state and the distribution of idle time periods. Further, the standard state of the charging pile refers to the charging pile being in normal working condition and without faults, the charging power refers to the output power of the charging pile, and the distribution of idle time periods refers to the probability of the charging pile being idle in different time periods. Using the training data, the parking navigation network layer and the charging navigation network layer are initialized and configured to possess basic navigation decision-making capabilities. Initializing the parking navigation network layer and the charging navigation network layer refers to constructing an initial algorithm model based on the training data. By learning the patterns in the training data, the initialization of the parking navigation network layer and the initialization of the charging navigation network layer provides a foundation for dynamic updates and collaborative optimization.

[0035] Optionally, for the parking navigation network layer, using parking space vacancy rate and arrival time as training data, the availability and accessibility of different parking lots at different times can be learned. For example, a city may have 10 parking lots, and the parking space vacancy rate of each parking lot may differ between weekdays and weekends. By analyzing this data, the ease of reaching a certain parking lot at a specific time can be predicted. Specifically, if a parking lot has a high parking space vacancy rate and a short arrival time, that parking lot will have a higher priority in parking navigation. For example, if the parking space vacancy rate exceeds 60% and the arrival time is less than 15 minutes, the user satisfaction of the parking lot will be significantly improved, so the system will prioritize recommending this type of parking lot.

[0036] For the charging navigation network layer, the charging power and idle time distribution of charging piles are used as training data to evaluate the charging efficiency and availability of charging piles. For example, the idle rate of charging piles at a certain charging station is low during the peak hours of 17:00-19:00 on weekdays, but the idle rate is high during off-peak hours such as early morning. The charging navigation recommendation strategy is dynamically adjusted based on this data. By analyzing the charging power of charging piles, the most suitable charging piles are recommended for vehicles with different remaining battery levels. For example, for vehicles with less than 20% remaining battery level and in urgent need of charging, charging piles with higher charging power and available time are recommended first to reduce charging time.

[0037] By using this training data to initialize the navigation network layer, basic decision-making capabilities can be achieved at startup, providing a reliable starting point for dynamic updates and collaborative optimization. For example, in urban core areas with high vehicle density and scarce resources, the initialized navigation network layer can quickly provide users with preliminary parking and charging suggestions. These suggestions can be further optimized through iterative updates of real-time data, thereby improving the overall efficiency and reliability of the navigation system.

[0038] Furthermore, the method described in this application also includes:

[0039] Based on vehicle information and the sensing signals, the optimal parking matching information for the target vehicle in its current state is estimated; wherein, the vehicle information includes vehicle size and energy consumption parameters, the optimal parking matching information is positively correlated with the parking lot vacancy rate, and the optimal parking matching information is negatively correlated with arrival time.

[0040] In one embodiment, vehicle information refers to static parameters related to the target vehicle, including vehicle size and energy consumption parameters, used to assess the vehicle's parking and charging needs in a specific scenario. Further, vehicle size refers to the vehicle's length, width, and height, and energy consumption parameters refer to energy consumption per unit mileage. Optimal parking matching information refers to the parking location information most suitable for the target vehicle calculated by the system based on vehicle information and sensing signals. It reflects the degree of compatibility between the vehicle and the parking lot, taking into account factors such as whether the vehicle size is suitable for the parking space and whether the energy consumption parameters match the parking lot's charging facilities. Further, sensing signals include parking space vacancy rate and arrival time.

[0041] Optionally, by comprehensively considering vehicle information and perception signals, the system can predict the optimal parking matching information for users, thereby improving the accuracy of parking navigation and user experience. Specifically, it obtains vehicle information of the target vehicle, including vehicle size and energy consumption parameters. Vehicle size information is used to determine whether the vehicle can fit into the parking space size of a specific parking lot. For example, a large SUV may not be able to park in a small parking space, so parking lots with larger parking spaces will be recommended first. Energy consumption parameters are used to evaluate the vehicle's range and charging needs. Combined with the charging power parameters of the charging pile, the system can recommend the most suitable charging station for the vehicle.

[0042] By combining parking space vacancy rate and arrival time from the sensed signals, a comprehensive evaluation is conducted. Parking space vacancy rate is a key indicator of parking lot availability; for example, when the vacancy rate exceeds 60%, the probability of a vehicle quickly finding a parking space increases significantly. Therefore, parking lots with higher vacancy rates are prioritized. Arrival time is also a crucial factor affecting user experience. For instance, a parking lot with a 70% vacancy rate and a 10-minute arrival time is prioritized over one with a 30% vacancy rate and a 30-minute arrival time. These steps provide users with optimal parking matching information. This comprehensive evaluation based on vehicle information and sensed signals improves the accuracy of parking navigation, effectively reduces the time users spend searching for parking spaces, and enhances overall travel efficiency.

[0043] Furthermore, this application provides an iterative update method based on the perceived signals and parking demand, the method further including:

[0044] Based on road congestion index, parking distance, parking space type suitability, and parking fee standards, the navigation priority factor for each parking lot is determined; the navigation priority factor for each parking lot is used to dynamically correct the weight allocation coefficient of the initial parking navigation network layer.

[0045] In one embodiment, the road congestion index is a quantitative indicator measuring road traffic conditions, typically calculated from data such as traffic flow and vehicle speed, reflecting the current level of road congestion; the parking distance refers to the straight-line or actual driving distance from the target vehicle's current location to the parking lot; the parking space type suitability refers to the degree of matching between the parking space type provided by the parking lot and the size of the target vehicle; further, parking space types include small parking spaces, large parking spaces, and accessible parking spaces; the parking fee standard refers to the parking lot's charging standard, including different billing methods such as hourly charging and monthly charging; the navigation priority factor is a value calculated based on the above factors to evaluate the priority of each parking lot, reflecting the comprehensive advantages of the parking lot under current conditions; the weight allocation coefficient refers to the degree of influence of different factors on the final decision in the parking navigation network layer. By dynamically adjusting these weights through the navigation priority factor, the decision logic of the parking navigation network layer is optimized to better meet real-time traffic and user needs.

[0046] Optionally, by comprehensively considering factors such as road congestion index, parking distance, parking space type suitability, and parking fee standards, the navigation priority factor for each parking lot can be dynamically adjusted. This optimizes the weight allocation coefficient of the parking navigation network layer, improving the accuracy and adaptability of parking navigation. Specifically, the road congestion index, parking distance, parking space type suitability, and parking fee standards from the target vehicle's current location to each parking lot are obtained. Among these factors, the road congestion index directly affects the time cost for the vehicle to reach the parking lot; the parking distance determines the vehicle's mileage and time; the parking space type suitability ensures that the vehicle can be parked smoothly; and the parking fee standard reflects the user's economic cost. For example, if a parking lot is nearby but has a high road congestion index, its navigation priority factor will be calculated based on these factors.

[0047] Based on these factors, the navigation priority factor for each parking lot is calculated. For example, among three parking lots A, B, and C, parking lot A is closer but has a high congestion index, parking lot B is moderately closer and has a low congestion index, and parking lot C is farther away but has a high parking space type suitability and a low parking fee. The navigation priority factor for parking lot A decreases due to the high congestion index, the navigation priority factor for parking lot B is higher due to the moderate distance and low congestion index, and the navigation priority factor for parking lot C is increased due to the high parking space suitability and low parking fee.

[0048] By dynamically adjusting the weight allocation coefficients of the initial parking navigation network layer, more accurate parking suggestions can be provided to users. For example, if users are more concerned about parking fees, the system will appropriately increase the weight of the parking fee standard, thereby adjusting the navigation priority factor and prioritizing parking lots with lower fees. This dynamic optimization mechanism can effectively cope with complex traffic and resource conditions, ensure the real-time nature and relevance of parking navigation solutions, and improve user experience.

[0049] Furthermore, the method described in this application includes:

[0050] Based on vehicle information and the sensing signals, the service characteristics of each charging pile under standard conditions are recorded within a preset time period; wherein, the service characteristics of each charging pile under standard conditions include the charging efficiency fluctuation range and the correlation characteristics with ambient temperature.

[0051] In one embodiment, vehicle information refers to static parameters related to the target vehicle, used to assess the compatibility between the vehicle and the charging pile, including the vehicle's battery capacity and charging interface type; sensing signals refer to dynamic data related to the charging pile during actual operation, including ambient temperature and the charging pile's real-time power; preset time period refers to a fixed time period set by the system for collecting and analyzing the charging pile's operating data to ensure the timeliness and representativeness of the data, such as 24 hours or one week.

[0052] Standard state refers to the state in which a charging pile can provide stable charging services under normal operation and without faults; service characteristics refer to specific performance indicators exhibited by the charging pile during operation, including the charging efficiency fluctuation range and the correlation characteristics with ambient temperature. Among them, the charging efficiency fluctuation range refers to the change in charging power of the charging pile in different time periods, reflecting the stability and reliability of the charging pile; the correlation characteristics with ambient temperature refer to the correlation between the charging efficiency of the charging pile and the ambient temperature. For example, in a low-temperature environment, the charging efficiency may decrease.

[0053] Optionally, by recording the service characteristics of charging piles under standard conditions, the performance and applicability of charging piles can be evaluated more accurately, thereby providing users with more reliable charging recommendations. Specifically, vehicle information of the target vehicle, including battery capacity and charging interface type, is obtained. Combined with sensing signals, the service characteristics of each charging pile are recorded within a preset time period. Furthermore, the sensing signals include ambient temperature and real-time power of the charging pile. The charging power changes of the charging pile in different time periods are recorded to determine its charging efficiency fluctuation range. At the same time, the relationship between the charging efficiency of the charging pile and the ambient temperature is analyzed. For example, in the low-temperature environment of winter, the charging efficiency of the charging pile will decrease by 10%-20%, while in the high-temperature environment of summer, the charging efficiency may remain stable.

[0054] The service characteristics of each charging pile under standard conditions include the correlation between charging efficiency fluctuation range and ambient temperature. These service characteristics will be used for iterative updates of the charging navigation network layer. In the above steps, by analyzing the service characteristics of charging piles, more accurate charging suggestions can be provided to users. For example, if a vehicle needs to charge in a low-temperature environment, charging piles that perform stably in low-temperature environments will be prioritized. Furthermore, by recording the charging efficiency fluctuation range, more accurate charging time estimates can be provided to users, helping them to rationally plan their trips. This data-driven optimization mechanism can effectively cope with complex and changing environmental conditions, ensuring the real-time nature and relevance of the charging navigation solution, and improving the accuracy and reliability of charging navigation.

[0055] Furthermore, this application provides an iterative update method based on the sensed signal and charging demand information, the method further including:

[0056] Based on the charging power change of the charging pile, the idle state update frequency, and the vehicle's remaining power demand, the optimal charging threshold under the standard state is determined. The optimal charging threshold under the standard state is used to determine whether the current operating state of the charging pile meets the vehicle's charging demand and serves as the core constraint for the iterative update of the charging navigation network layer.

[0057] In one embodiment, the charging power variation of a charging pile refers to the fluctuation of its charging power over different time periods, reflecting its stability. For example, frequent fluctuations in the charging power of a charging pile within a short period indicate poor stability. The idle state update frequency refers to the frequency at which the charging pile management system updates the idle state of the charging pile, i.e., how often the system refreshes information on whether the charging pile is available. The vehicle's remaining battery demand refers to the amount of battery power that the target vehicle needs to replenish in its current state, usually expressed as a percentage or a specific battery value, used to assess the urgency of charging the vehicle. The optimal charging threshold is the value that achieves the best balance between the charging power, idle state, and vehicle's remaining battery demand under standard conditions. It is used to determine whether the charging pile can meet the vehicle's charging needs and serves as a core constraint for the iterative update of the charging navigation network layer. In short, a charging pile will only be recommended to the user when its operating state meets the optimal charging threshold.

[0058] Optionally, by comprehensively considering the charging power variation of the charging pile, the idle state update frequency, and the vehicle's remaining power demand, the optimal charging threshold can be dynamically determined. This ensures that the charging pile's operating status can meet the vehicle's charging needs and serves as the core constraint for the iterative update of the charging navigation network layer. Specifically, the charging power variation and idle state update frequency data of the charging pile are obtained, along with the remaining power demand of the target vehicle. The optimal charging threshold is then calculated. If a charging pile satisfies the following conditions: charging power variation ≤ upper limit of power fluctuation corresponding to the optimal charging threshold, idle state update frequency ≥ lower limit of refresh frequency corresponding to the optimal charging threshold, and its power supply capacity matches the remaining power demand of the target vehicle, then the charging pile is considered to meet the vehicle's charging needs.

[0059] The optimal charging threshold is used as the core constraint for the iterative update of the charging navigation network layer. Furthermore, during the dynamic update process, only charging stations that meet the optimal charging threshold are recommended to the user. For example, if the charging power variation of a charging station is ±20%, exceeding the optimal charging threshold, even if it is currently idle, the charging station will not be recommended to the user because its stability is insufficient to meet the vehicle's charging needs. In the above steps, by dynamically determining the optimal charging threshold, the most suitable charging station for the current vehicle's needs is selected, avoiding the recommendation of unstable or unreliable charging stations. Specifically, when the vehicle's remaining battery power is low and charging is urgently needed, charging stations with stable charging power and high idle state update frequency are prioritized, ensuring that the vehicle can complete charging quickly and stably. This data-driven dynamic optimization mechanism can effectively cope with complex charging scenarios, ensuring the real-time nature and relevance of the charging navigation solution, and improving the reliability of charging navigation and user experience.

[0060] In summary, the beneficial effects of the embodiments of this application are:

[0061] This application utilizes a method, system, and device for intelligent navigation that integrates parking and charging. It establishes and connects parking and charging navigation network layers, combining station availability and parking space detection equipment for real-time data synchronization. This achieves precise matching of parking and charging resources, optimized route planning, and effective adaptation to dynamically changing traffic and resource conditions. The system utilizes ambient temperature, remaining vehicle battery power, driving range, charging pile occupancy status, parking space availability, road congestion index, and charging power parameters. Charging pile occupancy status is obtained in real-time from the charging station management system, while parking space availability is obtained collaboratively from parking space detection equipment and the management platform. The system initializes the parking navigation network layer and iteratively updates it based on the perceived signals and parking demand. It connects the updated parking and charging navigation network layers, and uses station availability and remaining driving range to determine if navigation conditions are met. If met, an optimal navigation path integrating parking and charging is generated; otherwise, alternative routes are suggested.

[0062] Example 2, based on the same inventive concept as the intelligent navigation method integrating parking and charging in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent navigation system that integrates parking and charging, the system comprising:

[0063] The sensing signal determination module 11 is used to determine sensing signals based on the target vehicle and the charging station. The sensing signals include ambient temperature, vehicle remaining battery power, driving range, charging pile occupancy status, parking lot vacancy information, road congestion index, and charging power parameters. The charging pile occupancy status is obtained in real time by the charging station management system, and the parking lot vacancy information is obtained by the collaborative statistics of the parking space detection equipment and the management platform.

[0064] The initialization parking navigation network layer configuration module 12 is used to configure and initialize the parking navigation network layer, and to iteratively update it according to the sensing signals and parking demand.

[0065] The initialization charging navigation network layer configuration module 13 is used to configure and initialize the charging navigation network layer, and to iteratively update it according to the sensing signals and charging demand information.

[0066] The alternative solution reminder push module 14 is used to connect the iteratively updated parking navigation network layer and charging navigation network layer. It combines the station's idle status and remaining range to determine whether the navigation conditions are met. If they are met, it generates the optimal navigation route that integrates parking and charging. If they are not met, it pushes an alternative solution reminder.

[0067] Furthermore, the initialization parking navigation network layer configuration module 12 is also used to perform the following method:

[0068] The initial parking navigation network layer is obtained by using the parking space vacancy rate and arrival time of each parking lot as training data; the initial charging navigation network layer is obtained by using the charging power and idle time distribution of charging piles in standard state as training data.

[0069] Furthermore, the initialization parking navigation network layer configuration module 12 is also used to perform the following method:

[0070] Based on vehicle information and the sensing signals, the optimal parking matching information for the target vehicle in its current state is estimated; wherein, the vehicle information includes vehicle size and energy consumption parameters, the optimal parking matching information is positively correlated with the parking lot vacancy rate, and the optimal parking matching information is negatively correlated with arrival time.

[0071] Furthermore, the initialization parking navigation network layer configuration module 12 is also used to perform the following method:

[0072] Based on road congestion index, parking distance, parking space type suitability, and parking fee standards, the navigation priority factor for each parking lot is determined; the navigation priority factor for each parking lot is used to dynamically correct the weight allocation coefficient of the initial parking navigation network layer.

[0073] Furthermore, the initialization parking navigation network layer configuration module 12 is also used to perform the following method:

[0074] Based on vehicle information and the sensing signals, the service characteristics of each charging pile under standard conditions are recorded within a preset time period; wherein, the service characteristics of each charging pile under standard conditions include the charging efficiency fluctuation range and the correlation characteristics with ambient temperature.

[0075] Furthermore, the initialization charging navigation network layer configuration module 13 is also used to perform the following method:

[0076] Based on the charging power change of the charging pile, the idle state update frequency, and the vehicle's remaining power demand, the optimal charging threshold under the standard state is determined. The optimal charging threshold under the standard state is used to determine whether the current operating state of the charging pile meets the vehicle's charging demand and serves as the core constraint for the iterative update of the charging navigation network layer.

[0077] Example 3: Based on the same inventive concept as the intelligent navigation method integrating parking and charging in Example 1, the present invention also provides an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method described in Example 1.

[0078] like Figure 3 As shown, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, connecting various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent navigation method integrating parking and charging, characterized in that, The method includes: Based on the target vehicle and charging station, the sensing signals are determined. These sensing signals include ambient temperature, vehicle remaining battery power, driving range, charging pile occupancy status, parking lot vacancy information, road congestion index, and charging power parameters. The charging pile occupancy status is obtained in real time by the charging station management system, and the parking lot vacancy information is obtained by the collaborative statistics of the parking space detection equipment and the management platform. Configure and initialize the parking navigation network layer, and iteratively update it based on the sensed signals and parking demand. Configure and initialize the charging navigation network layer, and iteratively update it based on the sensed signals and charging demand information; The system connects the iteratively updated parking navigation network layer and charging navigation network layer, and combines the station's idle status and remaining range to determine whether the navigation conditions are met. If they are met, it generates the optimal navigation route that integrates parking and charging; otherwise, it pushes alternative solutions as a reminder.

2. The method as described in claim 1, characterized in that, The method for configuring and initializing the parking navigation network layer further includes: The initial parking navigation network layer is obtained by using the parking space vacancy rate and arrival time of each parking lot as training data; The initial charging navigation network layer is obtained by using the charging power and idle time distribution of the charging pile under standard conditions as training data.

3. The method as described in claim 2, characterized in that, The method includes: Based on the vehicle information and the sensing signals, the optimal parking matching information for the target vehicle in the current state is estimated. The vehicle information includes vehicle size and energy consumption parameters. The optimal parking matching information is positively correlated with the parking lot vacancy rate and negatively correlated with arrival time.

4. The method as described in claim 3, characterized in that, The method further includes iterative updates based on the perceived signals and parking demand, and also includes: Based on road congestion index, parking distance, parking space type suitability, and parking fee standards, the navigation priority factor for each parking lot is determined. The navigation priority factor for each parking lot is used to dynamically adjust the weight allocation coefficients of the initial parking navigation network layer.

5. The method as described in claim 2, characterized in that, The method includes: Based on vehicle information and the sensing signals, record the service characteristics of each charging pile under standard conditions within a preset time period; The service characteristics of each charging pile under standard conditions include the range of charging efficiency fluctuations and the correlation characteristics with ambient temperature.

6. The method as described in claim 5, characterized in that, Based on the sensed signal and charging demand information, the method performs iterative updates, and further includes: Based on the charging power change of the charging pile, the idle state update frequency, and the vehicle's remaining power demand, the optimal charging threshold under the standard state is determined. The optimal charging threshold under the standard state is used to determine whether the current operating state of the charging pile meets the vehicle charging requirements, and serves as the core constraint for the iterative update of the charging navigation network layer.

7. An intelligent navigation system integrating parking and charging, characterized in that, The system is used to implement the intelligent navigation method integrating parking and charging as described in any one of claims 1-6, the system comprising: The sensing signal determination module is used to determine sensing signals based on the target vehicle and the charging station. The sensing signals include ambient temperature, vehicle remaining battery power, driving range, charging pile occupancy status, parking lot vacancy information, road congestion index, and charging power parameters. The charging pile occupancy status is obtained in real time by the charging station management system, and the parking lot vacancy information is obtained by the collaborative statistics of the parking space detection equipment and the management platform. The initialization parking navigation network layer configuration module is used to configure and initialize the parking navigation network layer, and to iteratively update it based on the perceived signals and parking demand. The initialization charging navigation network layer configuration module is used to configure and initialize the charging navigation network layer, and to iteratively update it based on the sensed signals and charging demand information. The alternative solution reminder and push module is used to connect the iteratively updated parking navigation network layer and charging navigation network layer. It combines the station's idle status and remaining range to determine whether the navigation conditions are met. If they are met, it generates the optimal navigation route that integrates parking and charging. If they are not met, it pushes an alternative solution reminder.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent navigation method integrating parking and charging as described in any one of claims 1-6.

Images