Intelligent scheduling method and system for sharing electric scooters

Abstract

The application discloses a kind of shared electric scooter intelligent scheduling method and system, it is related to shared travel field, the method comprises: obtaining the vehicle request of user;Real-time monitoring the state of each shared electric scooter;According to the vehicle request and the state of each shared electric scooter, generate scheduling instruction, to control corresponding shared electric scooter to complete the vehicle request;Wherein, the shared electric scooter is under the control of the scheduling instruction, based on auxiliary driving technology and AI intelligent navigation technology completes the vehicle request.The application improves the scheduling response speed of shared electric scooter, operation intelligent level and resource utilization, promotes shared travel industry to the direction of automation, intelligent development.

Description

Technical Field

[0001] This application relates to the field of shared mobility, and in particular to an intelligent scheduling method and system for shared electric scooters. Background Technology

[0002] With the rapid development of the ride-sharing industry, electric scooters have become an important mode of transportation for short-distance urban travel due to their convenience and flexibility. However, existing shared electric scooters have many technical shortcomings: Insufficient ease of use: Most existing shared electric scooters adopt a "fixed-point pick-up" model, requiring users to go to fixed parking spots to find the scooter, making door-to-door pickup and drop-off impossible, and vehicle dispatching relies on manual intervention, resulting in low response efficiency.

[0003] Insufficient driving safety and navigation accuracy: Most existing products lack mature driver assistance functions and rely solely on manual operation by the user, which can easily lead to safety accidents such as collisions and wrong-way driving; the navigation function only provides basic route guidance and cannot dynamically adjust the route based on real-time traffic conditions and vehicle range, and the positioning accuracy is insufficient, making it difficult to accurately reach the user's designated pick-up point.

[0004] Poor system coordination: Modules such as user interaction, vehicle control, back-end scheduling, and charging management are independent of each other and lack an integrated coordination mechanism, resulting in imperfect vehicle autonomous scheduling and recharging logic, and low operational efficiency.

[0005] To address the aforementioned issues, while some existing technologies have attempted to integrate driver assistance systems or solid-state batteries into electric scooters, they cannot meet users' needs for convenient, safe, and efficient travel. Summary of the Invention

[0006] The purpose of this application is to provide an intelligent scheduling method and system for shared electric scooters, which can improve the scheduling response speed, operational intelligence level and resource utilization of shared electric scooters.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides an intelligent scheduling method for shared electric scooters, comprising: Obtain the user's car usage request; Real-time monitoring of the status of each shared electric scooter; Based on the vehicle usage request and the status of each shared electric scooter, a dispatch instruction is generated to control the corresponding shared electric scooter to complete the vehicle usage request; wherein, under the control of the dispatch instruction, the shared electric scooter completes the vehicle usage request based on assisted driving technology and AI intelligent navigation technology.

[0008] In one embodiment, the vehicle request includes the user's current location and destination location. The process by which the shared electric scooter, under the control of the dispatch command, completes the vehicle request based on assisted driving technology and AI intelligent navigation technology includes: real-time acquisition of the shared electric scooter's location information; path planning using AI intelligent navigation technology based on the user's current location, the destination location, and the shared electric scooter's location information to obtain a path planning result; real-time perception of the shared electric scooter's surrounding environment to obtain environmental perception data; and, based on the environmental perception data and the path planning result, controlling the shared electric scooter to travel to the user's current location to trigger a task, and then traveling to the destination location to complete the vehicle request.

[0009] In one embodiment, after the shared electric scooter completes the usage request, the intelligent scheduling method for the shared electric scooter further includes: controlling the shared electric scooter to travel to a charging base station for charging based on assisted driving technology and AI intelligent navigation technology.

[0010] Secondly, this application provides an intelligent dispatching system for shared electric scooters, including: a user terminal, a back-end management system, and multiple shared electric scooters; The user terminal is used to generate a vehicle usage request and send it to the backend management system; The background management system is used to monitor the status of each shared electric scooter in real time, and generate dispatch instructions based on the usage request and the status of each shared electric scooter. The shared electric scooter is used to fulfill the vehicle request under the control of the dispatch command, based on assisted driving technology and AI intelligent navigation technology.

[0011] In one embodiment, the background management system includes: a communication unit for receiving a vehicle request sent by the user terminal; and a scheduling unit for monitoring the status of each shared electric scooter in real time, and sending scheduling instructions to the corresponding shared electric scooter based on the vehicle request and the status of each shared electric scooter according to the principles of distance priority and battery priority, while simultaneously feeding back the location and estimated arrival time of the corresponding shared electric scooter to the user terminal.

[0012] In one embodiment, the scheduling unit is further configured to dynamically schedule idle shared electric scooters to areas with high user demand based on the number of vehicle requests in different areas and the distribution of shared electric scooters.

[0013] In one embodiment, the vehicle request includes the user's current location and destination location; the shared electric scooter includes a frame, a solid-state battery pack, a charging interface, a communication module, a positioning module, a drive module, an intelligent navigation module, and an assisted driving module; the communication module is used to receive dispatch instructions sent by the background management system; the positioning module is used to collect the location information of the shared electric scooter in real time; the intelligent navigation module is used to perform path planning using AI intelligent navigation technology based on the user's current location, the destination location, and the location information of the shared electric scooter, and obtain the path planning result; the assisted driving module is used to perceive the surrounding environment of the shared electric scooter in real time, obtain environmental perception data, and based on the environmental perception data and the path planning result, drive the shared electric scooter to the user's current location to trigger a task, and then drive to the destination location to complete the vehicle request.

[0014] In one embodiment, the driver assistance module is a Level 3 driver assistance system.

[0015] In one embodiment, the solid-state battery pack uses a silicon-based negative electrode solid-state lithium battery and integrates an intelligent battery management system to monitor the battery power, temperature, and voltage in real time. When the battery power is lower than a preset threshold, a recharge request is automatically triggered and sent to the background management system. The background management system is also used to determine the target charging base station based on the location information of the shared electric scooter after receiving the recharge request, and send the optimal recharge path to the shared electric scooter so that the shared electric scooter can travel to the target charging base station for charging.

[0016] In one embodiment, the user terminal is a mobile application or a WeChat mini-program.

[0017] According to the specific embodiments provided in this application, this application achieves the following technical effects: By acquiring users' vehicle requests in real time and simultaneously monitoring the status of each shared electric scooter, supply and demand information can be quickly matched, shortening user waiting time and improving user experience and platform service efficiency. Furthermore, by generating dispatch instructions based on vehicle requests and the status of shared electric scooters, dynamic and intelligent allocation of shared electric scooters is achieved, avoiding redundancy or shortage of shared electric scooters in local areas and reducing manual dispatch costs and management difficulty. Most importantly, the dispatch instructions, combined with assisted driving technology and AI intelligent navigation technology, enable shared electric scooters to autonomously respond to dispatch and automatically drive to the target location, breaking through the traditional model of relying on manual handling or users finding scooters themselves, achieving unmanned and automated dispatch, thereby improving the dispatch response speed, operational intelligence level, and resource utilization rate of shared electric scooters, and promoting the development of the shared mobility industry towards automation and intelligence. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an intelligent scheduling method for shared electric scooters provided in one embodiment of this application.

[0020] Figure 2 This is a structural block diagram of an intelligent scheduling system for shared electric scooters provided in one embodiment of this application. Detailed Implementation

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

[0022] This application aims to overcome the technical deficiencies of existing electric scooters and solve the following core problems: how to realize the door-to-door pick-up and drop-off function of electric scooters to improve user convenience; how to improve range and charging efficiency and reduce operating costs through battery technology upgrades and intelligent management, combined with an automatic recharging mechanism; how to improve vehicle driving safety and route planning accuracy through the collaborative control of L3-level assisted driving and AI intelligent navigation; and how to build an integrated collaborative architecture of "user terminal - shared electric scooter - back-end management system - charging base station" to achieve intelligent operation throughout the entire process.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] In one exemplary embodiment, such as Figure 1 As shown, a smart scheduling method for shared electric scooters is provided, including the following steps 101 to 104.

[0025] Step 101: Obtain the user's car rental request. The car rental request includes the user's current location and destination location.

[0026] Step 102: Monitor the status of each shared electric scooter in real time.

[0027] Step 103: Based on the vehicle usage request and the status of each shared electric scooter, generate a dispatch command to control the corresponding shared electric scooter to complete the vehicle usage request. The shared electric scooter, under the control of the dispatch command, completes the vehicle usage request based on assisted driving technology and AI intelligent navigation technology.

[0028] Specifically, the process by which the shared electric scooter completes the vehicle request under the control of the dispatch instruction, based on assisted driving technology and AI intelligent navigation technology, includes the following steps (1) to (4).

[0029] (1) Real-time collection of the location information of the shared electric scooter.

[0030] (2) Based on the user's current location, the destination location and the location information of the shared electric scooter, AI intelligent navigation technology is used to perform path planning to obtain the path planning result.

[0031] (3) Real-time perception of the surrounding environment of the shared electric scooter to obtain environmental perception data.

[0032] (4) Based on the environmental perception data and the path planning results, control the shared electric scooter to drive to the user's current location to trigger the task, and then drive to the destination location to complete the vehicle use request.

[0033] Step 104: Based on assisted driving technology and AI intelligent navigation technology, control the shared electric scooter to travel to the charging base station for charging.

[0034] Among them, the charging base stations are intelligent charging facilities distributed throughout the city. Each charging base station is equipped with multiple automatic charging piles, and has a positioning identifier and communication module. It can communicate with the back-end management system and shared electric scooters in real time, provide feedback on the status of the charging piles, and automatically connect to the charging station for charging in conjunction with the shared electric scooters.

[0035] In another exemplary embodiment, this application also provides an intelligent scheduling system for shared electric scooters that performs the above-described method, such as... Figure 2 As shown, the intelligent dispatch system for shared electric scooters includes: a user terminal 201, a back-end management system 202, and multiple shared electric scooters 203.

[0036] The user terminal 201 is used to generate a car rental request and send it to the backend management system 202. The car rental request includes the user's current location and destination location.

[0037] In a specific application example, the user terminal 201 is a mobile application or a WeChat mini-program, and has the following functions: Order Initiation: Users enter or select their current location and destination location, select the type of shared electric scooter 203 (different range versions are available), and submit a ride request.

[0038] Real-time query: View the real-time location, estimated arrival time, battery level, and route planning results of the assigned shared electric scooter 203.

[0039] Interactive Control: After the shared electric scooter 203 arrives at the user's current location, it can be unlocked via Bluetooth. During the trip, the user can adjust the route and check the remaining trip time via voice or by clicking on the interface; upon reaching the destination, the user can end the trip via voice or by clicking on the interface.

[0040] Trip Management: Generates trip reports (mileage, cost, driving time, energy consumption), and supports order payment, invoice application, and complaint feedback.

[0041] The background management system 202 is used to monitor the status of each shared electric scooter 203 in real time, and generate dispatch instructions based on the usage request and the status of each shared electric scooter 203.

[0042] In a specific application example, the backend management system 202 includes: a communication unit, a scheduling unit, a base station management unit, a data statistics unit, and a security monitoring unit. The communication unit, scheduling unit, base station management unit, data statistics unit, and security monitoring unit are deployed in a distributed manner through a cloud server.

[0043] The communication unit is used to receive the vehicle request sent by the user terminal 201.

[0044] The scheduling unit is used to monitor the status of each shared electric scooter 203 in real time, and according to the usage request and the status of each shared electric scooter 203, it sends scheduling instructions to the corresponding shared electric scooter 203 based on the principles of distance priority and battery priority. At the same time, it feeds back the location and estimated arrival time of the corresponding shared electric scooter 203 to the user terminal 201.

[0045] Specifically, the dispatching unit queries the available and fully charged shared electric scooters 203 in the area based on the usage request, allocates the usage request based on the principles of distance priority and power priority, sends the usage request to the target shared electric scooter, and feeds back the location and estimated arrival time of the shared electric scooter 203 to the user terminal 201.

[0046] The scheduling unit is also used to dynamically schedule idle shared electric scooters 203 to areas with high user demand based on the number of vehicle requests in different areas and the distribution of shared electric scooters 203.

[0047] The base station management unit is used to monitor the charging pile occupancy status, charging power, and power supply stability of each charging base station in real time, and record the charging data (charging time and charging amount) of the shared electric scooter 203. When the charging pile fails, it will automatically alarm and mark it as unavailable.

[0048] The data statistics unit is used to collect statistics on user travel habits (travel time, common routes, average mileage), operating data of shared electric scooters 203 (mileage, battery consumption, frequency of failures), and battery life data, providing data support for the optimization of shared electric scooters 203 and the adjustment of scheduling strategies.

[0049] The safety monitoring unit is used to monitor the driving status (speed, route, whether it crosses the boundary) of the shared electric scooter 203 in real time, receive obstacle avoidance alarm information from the assisted driving system, and send a warning to the administrator when an abnormality occurs (such as speeding, crossing the boundary, or collision).

[0050] The shared electric scooter 203 is used to complete the vehicle usage request under the control of the dispatch command, based on assisted driving technology and AI intelligent navigation technology.

[0051] In a specific application example, the shared electric scooter 203 includes a frame, a solid-state battery pack, a charging interface, a communication module, a positioning module, a drive module, an intelligent navigation module, and a driver assistance module. The driver assistance module is a Level 3 driver assistance system.

[0052] The communication module is used to receive scheduling instructions sent by the backend management system 202. Specifically, the communication module adopts a 5G + Bluetooth 5.2 dual-mode communication architecture. The 5G network is used for long-distance data transmission between the shared electric scooter 203 and the backend management system 202 and the map server (transmission rate ≥1Gbps, latency ≤10ms), ensuring real-time synchronization of data such as route planning, road condition updates, and battery status; Bluetooth 5.2 is used for short-range communication between the user terminal 201 and the shared electric scooter 203, enabling vehicle unlocking, identity verification, and offline operation (such as task completion confirmation when there is no 5G signal).

[0053] The positioning module is used to collect the location information of the shared electric scooter 203 in real time. Specifically, the positioning module adopts GPS + Beidou dual-mode positioning, combined with base station assisted positioning technology, with a positioning accuracy of ≤1m (open environment) and ≤3m (obstructed environment). It feeds back the location of the shared electric scooter 203 to the back-end management system 202 and the user terminal 201 in real time to ensure accurate arrival at the pick-up point.

[0054] The charging interface is equipped with an automatic plug-in charging connector, along with a visual recognition sensor and a position calibration module, enabling automatic docking and charging with the charging base station. The charging interface has rainproof and short-circuit protection functions.

[0055] The intelligent navigation module is used to perform path planning using AI intelligent navigation technology based on the user's current location, the destination location, and the location information of the shared electric scooter 203, and obtain the path planning result.

[0056] Specifically, the intelligent navigation module includes a path planning unit, a real-time navigation unit, and an electronic fence unit.

[0057] The route planning unit calls the map application programming interface (API), combining the user's current location, destination location, real-time traffic conditions (congestion index, traffic accidents), the restricted areas of the shared electric scooter 203, and the remaining battery life of the solid-state battery, and adopts an improved A... The algorithm plans the optimal path, prioritizing non-motorized vehicle lanes and sections of road without slopes to ensure the feasibility and economy of the path.

[0058] The real-time navigation unit is used to obtain real-time traffic updates via the 5G network during driving, dynamically adjust the driving route, and synchronize navigation information with the user through the in-vehicle voice module and LCD display, including turn prompts, remaining mileage, and estimated arrival time.

[0059] The electronic fence unit is used to pre-store data on restricted areas and no-parking areas for urban scooters. When the shared electric scooter 203 approaches the fence boundary, it will issue a voice warning and automatically slow down. If it continues to cross the boundary, it will trigger a power-off protection.

[0060] The assisted driving module is used to perceive the surrounding environment of the shared electric scooter 203 in real time, obtain environmental perception data, and, based on the environmental perception data and the path planning results, drive the shared electric scooter 203 to the user's current location to trigger the task, and then drive to the destination location to complete the vehicle use request.

[0061] Specifically, the driver assistance module includes an environmental perception unit, a decision control unit, and a vehicle control unit.

[0062] The environmental perception unit integrates an 8-megapixel high-definition camera, a 16-line lidar, and a millimeter-wave radar to achieve 360° environmental detection without blind spots. It can accurately identify road markings, traffic lights, pedestrians, non-motorized vehicles, obstacles, and speed limit signs, with a detection distance of ≥50m and an accuracy rate of ≥98%.

[0063] The decision control unit is based on deep learning algorithms and combines environmental perception data and path planning results to achieve lane keeping, following (following distance can be dynamically adjusted according to vehicle speed), emergency obstacle avoidance and intersection passage decisions. When a sudden obstacle is detected, the response time is ≤0.3s.

[0064] The vehicle control unit works in conjunction with the drive module, braking system, and steering mechanism to precisely control the acceleration of the shared electric scooter 203 (maximum acceleration ≤ 1.2 m / s²). 2 ( ), deceleration (braking distance ≤ 3m / 20km / h), steering and stopping, to ensure driving stability.

[0065] The solid-state battery pack uses silicon-based negative electrode solid-state lithium batteries and integrates an intelligent battery management system to monitor battery power, temperature, and voltage in real time. When the battery power is lower than a preset threshold, it automatically triggers a recharge request and sends it to the background management system 202. The background management system 202, upon receiving the recharge request, determines the target charging base station based on the location information of the shared electric scooter 203 and sends the optimal recharge path to the shared electric scooter 203, enabling the shared electric scooter 203 to travel to the target charging base station for charging.

[0066] Specifically, the solid-state battery pack uses high-energy-density silicon-based anode solid-state lithium batteries with an energy density ≥400Wh / kg and a range ≥80km. It integrates an intelligent battery management system to monitor battery power, temperature, voltage and cycle life in real time. It communicates bidirectionally with the back-end management system 202 through the communication module. When the battery power is below 20%, it automatically triggers a recharge request. When the temperature exceeds 45℃, it activates a heat dissipation mechanism and limits the output power to avoid safety hazards.

[0067] The full-process operation logic of the intelligent dispatch system for the shared electric scooter 203 provided in this application is as follows: Ride-hailing service: The user initiates an order through the user terminal 201 → The back-end management system 202 allocates a shared electric scooter 203 → The shared electric scooter 203 navigates to the user's current location through the positioning module and intelligent navigation module → Bluetooth unlocking → The user gets on the scooter.

[0068] Intelligent driving: The intelligent navigation module provides real-time navigation, while the driver assistance module performs lane keeping, obstacle avoidance, and other operations. The battery management system monitors the battery status in real time, and the background system synchronizes road conditions and dynamically adjusts the route.

[0069] Trip End: Arrive at destination → User triggers task end → Settle fees and send feedback to user terminal 201.

[0070] Automatic recharging: The shared electric scooter 203 judges the battery level. If it is lower than the threshold, it sends a recharging request to the backend management system 202. The backend management system 202 allocates the optimal charging base station and path. The shared electric scooter 203 autonomously navigates to the charging base station. Automatic docking and charging are achieved through visual recognition and position calibration. After charging is completed, it enters an idle state to wait for orders.

[0071] This application, through the deep collaboration of L3-level assisted driving and AI intelligent navigation, combined with solid-state batteries and automatic recharging mechanisms, constructs a full-process intelligent operation system of "door-to-door pick-up and drop-off - intelligent driving - automatic recharging", which solves the technical defects of existing electric scooters in terms of convenience, safety and range, and can be widely used in the field of urban shared mobility.

[0072] The present application will be further described in detail below with reference to ride-hailing embodiments.

[0073] (a) System deployment.

[0074] Shared electric scooter 203 configuration: Features a lightweight carbon fiber frame, a 400Wh / kg silicon-based solid-state lithium battery with a range of 80km. Equipped with an 8MP camera, 16-line LiDAR and millimeter-wave radar, and an ARM Cortex-A76 octa-core control unit. Includes a 5G + Bluetooth 5.2 dual-mode communication module and a GPS + BeiDou dual-mode positioning module. Features an automatic plug-in charging port supporting 50W fast charging.

[0075] Backend Management System 202 Deployment: Utilizes Alibaba Cloud distributed servers to deploy functional modules such as order management, vehicle dispatching, and base station management. It also connects to the Gaode Map API to obtain real-time traffic data and builds a big data analysis platform for user behavior and vehicle status statistics.

[0076] Deployment of charging base stations: Deploy charging base stations in core urban business districts, residential areas, transportation hubs and other areas. Each base station is equipped with 8 automatic charging piles. The distance between base stations is ≤2km to ensure 203 charging coverage for shared electric scooters.

[0077] User Terminal Development 201: Develop mobile apps (supporting iOS / Android systems) and WeChat mini-programs to enable functions such as ride-hailing, location tracking, unlocking, payment, and voice interaction.

[0078] (II) Specific operation process.

[0079] Order Initiation and Vehicle Dispatch: The user opens the mobile APP, enters the user's current location and destination location, and submits a ride request; after receiving the request, the back-end management system 202 queries the available shared electric scooters 203 within a 1km radius of the user's current location, filters the shared electric scooters 203 with a battery level of ≥30%, selects the nearest shared electric scooter 203 to assign the order, sends the user's current location, destination location, and initial route planning information to the shared electric scooter 203, and simultaneously sends the user's current location identifier, real-time location, and estimated arrival time to the user terminal 201.

[0080] Vehicle pick-up: The shared electric scooter 203 obtains its own location through the GPS + Beidou dual-mode positioning module, and combined with the path planning results of the intelligent navigation module, it navigates to the user's current location. During the journey, the L3-level assisted driving system is activated. The environmental perception module identifies road markings, pedestrians and obstacles, and the decision control module controls the shared electric scooter 203 to keep traveling in the non-motorized vehicle lane and avoid pedestrians and obstacles. After the shared electric scooter 203 arrives at the user's current location, it establishes a connection with the user terminal 201 through Bluetooth 5.2, automatically unlocks after verifying the user's identity, and announces with a voice prompt "Please get on, we are about to go to Exit C of XX subway station".

[0081] Intelligent Driving: After the user gets in the vehicle, the intelligent navigation module starts real-time navigation, obtaining real-time traffic conditions through the 5G network. If the original route is congested, it dynamically adjusts to the optimal route and provides a voice prompt: "The road ahead is congested; your route has been adjusted." The L3-level assisted driving system continuously monitors the environment. When it detects a pedestrian crossing the road ahead, the decision control module immediately triggers an obstacle avoidance command. The shared electric scooter 203 decelerates and moves to the right to avoid the pedestrian. After obstacle avoidance is completed, it resumes its original driving state. The battery management system monitors the battery status in real time and synchronizes the battery level data with the backend management system 202.

[0082] Trip End: After the shared electric scooter 203 arrives at its destination, a voice prompt will say, "Destination reached, please confirm end of trip." Users can click the "End Trip" button on the app or say "Task ended" via voice. Upon receiving the instruction, the shared electric scooter 203 will stop moving, automatically lock, and the back-end management system 202 will automatically calculate the fee and send a trip report to the user.

[0083] Automatic Recharge Return: After the trip, the battery management system checks the battery level. If the current level is 18% (below the 20% threshold), it sends a recharge request to the backend management system 202. The backend management system 202 queries nearby charging stations and sends the location of the charging station and the recharge route to the shared electric scooter 203. The shared electric scooter 203 navigates to the charging station using the intelligent navigation module. Upon arrival, it identifies the charging station interface using a visual recognition sensor, and the position calibration module adjusts the vehicle's posture to control the automatic plug-in charging interface to connect with the charging station and start charging. The backend management system 202 monitors the charging status in real time. After charging is complete, the shared electric scooter 203 enters an idle state, waiting for the next order.

[0084] (III) Fault handling mechanism.

[0085] If the control unit detects a sensor malfunction (such as an interruption of the lidar signal) while the shared electric scooter 203 is in motion, it will immediately prompt the user with a voice message, "The vehicle has malfunctioned, please get off safely," and at the same time send a fault alarm message to the back-end management system 202. After receiving the alarm, the back-end management system 202 will dispatch nearby maintenance personnel to handle the situation and reassign a backup shared electric scooter 203 to the user to ensure that the user's travel is not affected.

[0086] Furthermore, the intelligent dispatch system of the shared electric scooter 203 provided in this application can also be applied to the fields of intelligent delivery or intelligent food delivery.

[0087] In summary, compared with the prior art, this application has the following significant advantages: (1) Improved convenience: It can realize door-to-door pick-up and drop-off and return the car at any point. Users do not need to go to a fixed parking point. They can complete the entire process through the user terminal 201. The voice interaction function reduces the operation threshold and meets the needs of different users.

[0088] (2) The battery life and charging efficiency of the shared electric scooter 203 have been optimized: high energy density solid-state batteries are used, which increases the battery life by more than 50%; with intelligent battery management system and automatic recharging mechanism, the battery can be replenished without manual intervention, reducing operating costs.

[0089] (3) Improved driving safety: The L3 level assisted driving and the AI ​​navigation module work together to perceive the environment through multi-sensor fusion, realize functions such as active obstacle avoidance and lane keeping, and reduce the accident rate.

[0090] (4) Strong system synergy: Construct an integrated architecture of "terminal-vehicle-back-base station" to realize intelligent control of the entire process of order, scheduling, charging and safety, with positioning accuracy ≤1m, ensuring the accuracy and efficiency of picking up passengers and returning to charging.

[0091] (5) Strong scalability: The back-end management system 202 supports the access and data processing of a large number of shared electric scooters 203. It can flexibly expand the number of base stations according to the city size, adapt to the travel needs of different cities, and has a wide range of application prospects.

[0092] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A smart scheduling method for shared electric scooters, characterized in that, The intelligent scheduling method for the shared electric scooters includes: Obtain the user's car usage request; Real-time monitoring of the status of each shared electric scooter; Based on the vehicle usage request and the status of each shared electric scooter, a dispatch instruction is generated to control the corresponding shared electric scooter to complete the vehicle usage request; wherein, under the control of the dispatch instruction, the shared electric scooter completes the vehicle usage request based on assisted driving technology and AI intelligent navigation technology.

2. The intelligent scheduling method for shared electric scooters according to claim 1, characterized in that, The car rental request includes the user's current location and destination location; The process by which the shared electric scooter completes the ride request under the control of the dispatch command, based on assisted driving technology and AI intelligent navigation technology, includes: Real-time collection of the location information of the shared electric scooters; Based on the user's current location, the destination location, and the location information of the shared electric scooter, AI intelligent navigation technology is used to perform route planning, and the route planning result is obtained. The surrounding environment of the shared electric scooter is perceived in real time to obtain environmental perception data; Based on the environmental perception data and the path planning results, the shared electric scooter is controlled to travel to the user's current location to trigger the task, and then travels to the destination location to complete the vehicle use request.

3. The intelligent scheduling method for shared electric scooters according to claim 1, characterized in that, After the shared electric scooter completes the usage request, the intelligent scheduling method for the shared electric scooter further includes: Based on driver assistance technology and AI intelligent navigation technology, the shared electric scooter is controlled to travel to the charging station for charging.

4. An intelligent scheduling system for shared electric scooters, executing the intelligent scheduling method for shared electric scooters as described in any one of claims 1-3, characterized in that, The intelligent dispatch system for the shared electric scooters includes: a user terminal, a back-end management system, and multiple shared electric scooters; The user terminal is used to generate a vehicle usage request and send it to the backend management system; The background management system is used to monitor the status of each shared electric scooter in real time, and generate dispatch instructions based on the usage request and the status of each shared electric scooter. The shared electric scooter is used to fulfill the vehicle request under the control of the dispatch command, based on assisted driving technology and AI intelligent navigation technology.

5. The intelligent dispatching system for shared electric scooters according to claim 4, characterized in that, The back-end management system includes: A communication unit is used to receive a vehicle request sent by the user terminal; The dispatching unit is used to monitor the status of each shared electric scooter in real time, and based on the usage request and the status of each shared electric scooter, send dispatching instructions to the corresponding shared electric scooter according to the principles of distance priority and battery priority, and at the same time feed back the location and estimated arrival time of the corresponding shared electric scooter to the user terminal.

6. The intelligent dispatching system for shared electric scooters according to claim 5, characterized in that, The scheduling unit is also used to dynamically schedule idle shared electric scooters to areas with high user demand based on the number of vehicle requests in different areas and the distribution of shared electric scooters.

7. The intelligent dispatching system for shared electric scooters according to claim 4, characterized in that, The car rental request includes the user's current location and destination location; The shared electric scooter includes a frame, a solid-state battery pack, a charging interface, a communication module, a positioning module, a drive module, an intelligent navigation module, and an assisted driving module. The communication module is used to receive scheduling instructions sent by the background management system; The positioning module is used to collect the location information of the shared electric scooter in real time. The intelligent navigation module is used to perform path planning using AI intelligent navigation technology based on the user's current location, the destination location, and the location information of the shared electric scooter, and obtain the path planning result. The assisted driving module is used to perceive the surrounding environment of the shared electric scooter in real time, obtain environmental perception data, and, based on the environmental perception data and the path planning results, drive the shared electric scooter to the user's current location to trigger the task, and then drive to the destination location to complete the vehicle use request.

8. The intelligent dispatching system for shared electric scooters according to claim 7, characterized in that, The driver assistance module is a Level 3 driver assistance system.

9. The intelligent dispatching system for shared electric scooters according to claim 7, characterized in that, The solid-state battery pack uses silicon-based negative electrode solid-state lithium batteries and integrates an intelligent battery management system to monitor battery power, temperature and voltage in real time. When the battery power is lower than a preset threshold, it automatically triggers a recharge request and sends it to the background management system. The background management system is also used to determine the target charging base station based on the location information of the shared electric scooter after receiving the return charging request, and send the optimal return charging route to the shared electric scooter so that the shared electric scooter can travel to the target charging base station for charging.

10. The intelligent dispatching system for shared electric scooters according to claim 4, characterized in that, The user terminal is a mobile application or a WeChat mini-program.

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