Method and apparatus for scheduling charging robots, and computer device

Abstract

The application relates to the technical field of robot scheduling, and discloses a charging robot scheduling method and device and computer equipment. The application obtains the multi-dimensional state parameters of each vehicle when multiple vehicles enter a charging platform, evaluates the charging urgency of each vehicle according to the multi-dimensional state parameters, determines the charging priority of the multiple vehicles according to the charging urgency, and then distributes the charging robots in sequence according to the charging priority to charge when the number of charging robots is less than the number of vehicles, so that the technical scheme solves the technical problem that, in the prior art, charging is scheduled only according to the residual power or arrival order, the influence of multi-dimensional factors on the charging urgency cannot be comprehensively considered, and the charging resource allocation is disengaged from the actual urgent demand of the vehicles, the charging robots are preferentially allocated to the high-urgency vehicles under the condition of insufficient resources, and the service safety, resource scheduling rationality and user satisfaction of the charging platform are improved.

Description

Technical Field

[0001] This invention relates to the field of robot scheduling technology, specifically to a scheduling method, apparatus, and computer equipment for charging robots. Background Technology

[0002] With the increasing popularity of electric vehicles, centralized charging platforms face the challenge of allocating charging resources when multiple vehicles arrive simultaneously. Most existing charging scheduling solutions determine the charging order based on the remaining battery power of the vehicles or their arrival order, such as charging from low to high battery power or queuing according to the first-come, first-served principle.

[0003] However, in real-world applications, the actual urgency of charging for different vehicles does not solely depend on remaining battery power or arrival time. For example, some vehicles may pose safety hazards such as children left inside or excessive levels of harmful gases, requiring priority attention; some vehicles may need rapid charging because the user is about to use the vehicle; and some vehicles may require charging while undergoing maintenance due to malfunctions. In other words, the technology may mismatch between the allocation of charging resources and the actual urgent needs of vehicles, thus affecting the service safety and user satisfaction of the charging platform. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus and computer equipment for scheduling charging robots, in order to solve the problem in the prior art that scheduling charging robots based solely on remaining power or arrival order leads to a disconnect between charging resource allocation and the actual emergency needs of vehicles.

[0005] In a first aspect, embodiments of the present invention provide a scheduling method for a charging robot, the method comprising: When multiple vehicles enter the charging platform, multi-dimensional status parameters of each vehicle are acquired, and the charging urgency of the vehicle is evaluated based on the multi-dimensional status parameters. The charging priority of the plurality of vehicles is determined based on the degree of urgency of charging. When the number of charging robots is less than the number of vehicles, the charging robots are assigned to each vehicle for charging in sequence according to the charging priority.

[0006] This invention addresses the technical problem in existing technologies that rely solely on remaining battery power or arrival order for charging scheduling, failing to comprehensively consider the impact of multiple factors on charging urgency. This leads to a disconnect between charging resource allocation and the actual urgent needs of vehicles. The invention achieves priority allocation of charging robots to vehicles with high urgency when resources are insufficient, improving the service security, resource allocation rationality, and user satisfaction of the charging platform.

[0007] In conjunction with the first aspect, in one implementation, the charging urgency level is determined based on a weighted or sorted average of one or more of the multidimensional state parameters; the multidimensional state parameters include at least one of the following: the presence status of living beings inside the vehicle, the safety status of the vehicle environment, the urgency of the user's vehicle use, the vehicle's energy status, the vehicle's health status, and the storage status of items inside the vehicle.

[0008] This invention addresses the technical problem in existing technologies where the assessment of charging urgency is based on a single dimension, failing to differentiate between multiple levels of emergency needs and leading to a mismatch between charging priority and actual danger. This solution enables a refined, multi-dimensional quantitative assessment of charging urgency, allowing charging robots to prioritize vehicles involving life and environmental safety, significantly improving the safety assurance capabilities and intelligent scheduling level of the charging platform. The multi-dimensional state parameters include at least one of the following: the presence of living beings inside the vehicle, the safety status of the in-vehicle environment, the urgency of user use, the vehicle's energy status, the vehicle's health status, and the storage status of items inside the vehicle.

[0009] In conjunction with the first aspect or its corresponding implementation, in one implementation, the urgency of vehicle use is determined in the following manner: In response to receiving a vehicle use command actively triggered by the driver and / or passengers through a terminal device, the vehicle's urgency level is marked as high urgency; or, The system acquires real-time geographic location information between the vehicle and the terminal device carried by the driver and / or passenger; calculates the real-time distance between the driver and / or passenger and the vehicle, and the rate of change of the real-time distance over time, based on the geographic location information; and determines the urgency of vehicle use based on the real-time distance and the rate of change of distance. Alternatively, The planned navigation route information and vehicle usage scenario settings of the driver and / or passenger are synchronized from the driver's and / or passenger's terminal device or cloud server. The vehicle usage scenario settings include at least the planned departure time. The urgency of vehicle use is determined by calculating the time difference between the planned departure time and the current system time.

[0010] This invention addresses the technical problem in existing technologies where the urgency of vehicle use is determined by responding to vehicle usage commands initiated by the driver and / or passengers, acquiring real-time geographical location information between the driver's and / or passenger's terminal device and the vehicle to calculate real-time distance and distance change rate, or synchronizing planned navigation routes and planned departure times from the terminal device or cloud server to calculate the time difference with the current system time. This solution utilizes at least one of these three methods to determine the urgency of vehicle use. It solves the problem that existing technologies cannot dynamically perceive the urgency of user vehicle usage needs, relying solely on a single triggering method, leading to inaccurate urgency determination and delayed response, thus affecting the rationality of charging priority. The invention achieves multi-source, real-time, and accurate determination of user vehicle usage urgency, enabling charging robot scheduling to respond promptly to users' temporary vehicle usage or plan change requests, thereby improving the scheduling response efficiency of charging services.

[0011] In conjunction with the first aspect, in one implementation, prior to the step of multiple vehicles entering the charging platform, it is determined whether the vehicles are permitted to enter the charging platform by the following method: When the vehicle enters the electronic fence area of ​​the charging platform, it is determined whether there is an empty parking space or a vehicle leaving the parking space based on the current parking space occupancy status of the charging platform. If so, an entry command is sent to the vehicle. In response to the access command, when the vehicle arrives at the entrance of the charging platform, the vehicle identification information and license plate information of the vehicle are read. When the vehicle identification information matches the license plate information, the vehicle is allowed to enter the charging platform.

[0012] This invention addresses the technical problems of existing technologies, such as the inability to predict parking space status leading to invalid waiting and the single identification method causing identification errors. It achieves both parking space prediction before vehicle entry and dual identity verification upon entry, effectively avoiding invalid queuing and identification conflicts, and improving the operating efficiency of the charging platform and the reliability of vehicle access.

[0013] In conjunction with the first aspect, in one implementation, the step of assigning the charging robot to each vehicle for charging according to the charging priority includes: According to the order in which each vehicle enters the charging platform, the vehicles are divided into several consecutive queue batches, and each queue batch contains a fixed number of vehicles, the fixed number being a pre-set batch capacity value. Based on the queue batch, the charging robot is assigned to each vehicle for charging in sequence according to the charging priority.

[0014] This invention addresses the technical problems in existing technologies where, when there are many vehicles waiting to be charged and limited charging robot resources, global sorting leads to excessive computational load, low-priority vehicles cannot obtain charging resources for a long time, and the charging order is unstable, thus affecting the user experience. This solution achieves effective execution of charging priority while controlling the computational complexity of a single batch, and avoids indefinite waiting for low-priority vehicles, thereby improving the operational stability and fairness of the charging robot scheduling system. It divides vehicles into several consecutive queues according to the order in which they enter the charging platform, with each queue containing a fixed number of vehicles (a pre-set batch capacity). The solution then assigns a charging robot to each vehicle based on its charging priority according to the queue batch.

[0015] In conjunction with the first aspect or its corresponding implementation, in one implementation, the step of assigning the charging robot to each vehicle for charging according to the charging priority based on the queuing batch includes: In the queue, the vehicles are sorted according to their charging priority to obtain a charging order, and charging robots are assigned to the vehicles in turn based on the charging order. Among them, the charging priority of vehicles in different queues is not compared across batches, and vehicles in a later batch do not participate in the allocation of charging robots until the vehicles in the previous batch have finished charging. After any vehicle in the current queue completes charging and releases the charging robot, the vehicle that has completed charging is removed from the charging sequence to obtain the remaining charging sequence. The charging robot is assigned to the next vehicle based on the remaining order of vehicles to be charged.

[0016] This invention solves the technical problems of frequent reordering across batches leading to wasted computing resources and frequent changes in charging order in the prior art. It achieves a stable scheduling mechanism that strictly charges according to priority within a batch and executes sequentially between batches, improving the predictability of the scheduling system and user satisfaction. The invention also addresses the issue of cross-batch priority comparison between different batches, and vehicles in later batches are not allocated until vehicles in previous batches have finished charging. Once a vehicle finishes charging and releases its charging robot, the vehicle is removed from the charging order and a charging robot is allocated to the next vehicle based on the remaining order.

[0017] In conjunction with the first aspect, in one embodiment, the method further includes: Real-time acquisition of voice and behavioral data of drivers and / or passengers; analysis of the voice and behavioral data to obtain semantic recognition results and behavioral recognition results, respectively. When the semantic recognition result contains emergency vehicle use command semantics, and / or the behavior recognition result contains abnormal behavior characteristics, the vehicle is marked as an emergency vehicle use vehicle. The charging priority of the emergency vehicle is raised to the highest level, and the charging of the currently charging vehicle is interrupted in order to allocate the charging robot to the emergency vehicle.

[0018] This invention provides a solution that addresses the technical problems of existing technologies in sensing emergency vehicle needs and responding slowly. It achieves proactive sensing and immediate response to users' emergency vehicle needs, significantly improving the emergency response capability and user experience of the charging dispatch system in emergency scenarios. This is achieved by collecting real-time voice and behavioral data from drivers and / or passengers, analyzing the results, and identifying semantic and behavioral recognition results. When the semantic recognition results contain emergency vehicle use instructions and / or the behavioral recognition results contain abnormal behavioral characteristics, the vehicle is marked as urgently needed, and its charging priority is raised to the highest level. The solution also interrupts the charging of currently charging vehicles and allocates charging robots to the emergency vehicle.

[0019] In conjunction with the first aspect or its corresponding implementation, in one implementation, the step of sequentially assigning the charging robot to each vehicle for charging according to the charging priority includes: The charging robot is controlled to charge the vehicle to the target charging level. The target charging capacity is determined in the following way: Obtain the vehicle's subsequent usage plan and multi-dimensional energy consumption impact parameters, and calculate the vehicle's first actual driving mileage under the subsequent usage plan based on the multi-dimensional energy consumption impact parameters; When the first actual driving mileage is less than the first expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is increased so that the second actual driving mileage of the vehicle after charging is not less than the sum of the first expected driving mileage and the preset safety margin. When the first actual driving mileage is greater than or equal to the first expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is maintained at the charging completion capacity corresponding to the current remaining energy of the vehicle.

[0020] This invention addresses the technical problem of existing charging scheduling methods that rely solely on a simple comparison between the instrument-displayed range and the travel distance to determine the charging amount. This approach fails to consider the impact of multiple factors such as battery health, driving habits, road conditions, and load information on the actual range, leading to insufficient charging, breakdowns, or overcharging and energy waste. It achieves precise matching between the target charging amount and the user's actual electricity needs, ensuring safe travel while avoiding unnecessary energy consumption and waiting time.

[0021] In conjunction with the first aspect or its corresponding implementation, in one implementation, the method further includes: During the charging process of the vehicle within the charging platform, it is detected whether the driver and / or passengers have changed their subsequent vehicle usage plans. When a change in the subsequent vehicle use plan is detected, the multi-dimensional energy consumption impact parameters of the vehicle are obtained, and the third actual driving mileage of the vehicle after the change in the subsequent vehicle use plan is determined based on the multi-dimensional energy consumption impact parameters. If the third actual driving mileage is less than the second expected driving mileage after the subsequent vehicle use plan is changed, the current target charging power of the vehicle is adjusted so that the fourth actual driving mileage of the vehicle after charging is not less than the sum of the second expected driving mileage and the preset safety margin. When the third actual driving mileage is greater than or equal to the second expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is maintained at the charging completion capacity corresponding to the current remaining energy of the vehicle.

[0022] This invention addresses the technical problem in existing technologies where the charging process and travel plans are disconnected, and the inability to dynamically respond to plan changes. It achieves real-time perception and dynamic adaptation to changes in user travel plans during the charging process, ensuring the electricity demand of the changed trip and improving the intelligence and personalization of charging services. This is achieved by detecting whether the driver and / or passengers change their subsequent travel plans during vehicle charging. When a change is detected, multi-dimensional energy consumption impact parameters are obtained, and the actual mileage after the change is determined based on these parameters. If the actual mileage is less than the expected mileage after the change, the current target charging capacity of the vehicle is adjusted.

[0023] Secondly, embodiments of the present invention provide a scheduling device for a charging robot, the device comprising: The parameter acquisition module is used to acquire multi-dimensional status parameters of each vehicle when multiple vehicles enter the charging platform, and to evaluate the charging urgency of the vehicle based on the multi-dimensional status parameters. A priority determination module is used to determine the charging priority of the plurality of vehicles based on the degree of urgency of charging. The charging scheduling module is used to allocate a charging robot to each vehicle for charging in sequence according to the charging priority when the number of charging robots is less than the number of vehicles.

[0024] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the scheduling method of the charging robot described in the first aspect or any corresponding embodiment. Attached Figure Description

[0025] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a scheduling method for a charging robot according to some embodiments of the present invention; Figure 2 This is a structural block diagram of a scheduling device for a charging robot according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] According to an embodiment of the present invention, a scheduling method for a charging robot is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a scheduling method for a charging robot. Figure 1 This is a flowchart of a scheduling method for a charging robot according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: When multiple vehicles enter the charging platform, acquire the multi-dimensional state parameters of each vehicle, and assess the charging urgency of the vehicle based on the multi-dimensional state parameters.

[0030] Step S102: Determine the charging priority of multiple vehicles based on the urgency of charging.

[0031] Step S103: When the number of charging robots is less than the number of vehicles, a charging robot is assigned to each vehicle for charging according to the charging priority.

[0032] The charging robot scheduling method provided in this embodiment obtains multi-dimensional state parameters of each vehicle when multiple vehicles enter the charging platform, evaluates the charging urgency of each vehicle based on the multi-dimensional state parameters, determines the charging priority of multiple vehicles based on the charging urgency, and then allocates charging robots to charge sequentially according to the charging priority when the number of charging robots is less than the number of vehicles. This technical solution solves the technical problem in the prior art that charging scheduling is based only on the remaining power or arrival order, and cannot comprehensively consider the impact of multi-dimensional factors on the charging urgency, thus leading to a disconnect between charging resource allocation and the actual urgent needs of vehicles. It realizes the priority allocation of charging robots to vehicles with high urgency when resources are insufficient, improving the service security, resource allocation rationality and user satisfaction of the charging platform.

[0033] The steps described above are explained in detail below.

[0034] In one implementation, before the step of multiple vehicles entering the charging platform, it is determined whether vehicles are allowed to enter the charging platform by the following method: When a vehicle enters the electronic fence area of ​​the charging platform, it is determined whether there is an empty parking space or a vehicle leaving the parking space based on the current parking space occupancy status of the charging platform. If so, an entry command is sent to the vehicle. In response to the entry command, when the vehicle arrives at the entrance of the charging platform, the vehicle identification information and license plate information are read. When the vehicle identification information matches the license plate information, the vehicle is allowed to enter the charging platform.

[0035] In this embodiment, before the vehicle enters the charging platform, a communication connection is established between the vehicle and the charging management device in the charging platform via wireless communication technology (V2X, Vehicle to Everything). Wireless communication technology refers to the communication method for information exchange between the vehicle and external devices. When the vehicle enters a pre-set electronic fence area, the charging management device determines the occupancy status of parking spaces within the charging platform. The electronic fence area refers to a certain geographical range pre-defined with the charging platform as the center, used to trigger communication interaction between the vehicle and the charging management device. Parking space occupancy status includes vacant parking spaces and occupied parking spaces. Vacant parking spaces are those that are not currently occupied by any vehicle and are available for newly entering vehicles to park and charge.

[0036] If there are vacant parking spaces on the current charging platform, or if there are vehicles leaving the parking spaces, the charging management device sends an entry command to the vehicles. Vehicles leaving the parking spaces refer to vehicles that have finished charging and are preparing to leave the charging platform and are driving out of the parking spaces.

[0037] Upon receiving the entry permission command, the vehicle continues its journey towards the charging platform entrance. When the vehicle arrives at the entrance, the charging management equipment uses radio frequency identification (RFID) technology to read the vehicle's identification information. RFID technology uses radio waves to identify electronic tags carried on vehicles to obtain their unique identifiers. Simultaneously, it collects the vehicle's license plate information, for example, by reading the license plate number from the license plate using image acquisition and image processing algorithms.

[0038] The system compares the read vehicle identification information with the license plate information. If the vehicle identification information matches the license plate information, the vehicle is allowed to enter the charging platform. If the vehicle identification information does not match the license plate information, or if one or more of the radio frequency identification (RFID) or license plate recognition technologies experience communication failures or recognition malfunctions, the system can switch to user-initiated QR code scanning. In this case, the user scans the identification code set at the entrance of the charging platform using a handheld terminal to upload vehicle information to the charging management equipment. The handheld terminal refers to a mobile electronic device with communication and QR code scanning functions carried by the user. The identification code can be a graphic code or QR code set at the entrance of the charging platform for the handheld terminal to scan to obtain vehicle identification information. The vehicle information uploaded by the user is used as the final confirmation basis to allow the vehicle to enter the charging platform.

[0039] This embodiment solves the technical problems in the prior art of inaccurate waiting due to the inability to predict parking space status and identification errors due to a single identification method. It achieves parking space prediction before vehicle entry and dual identity verification upon entry, effectively avoiding invalid queuing and identification conflicts, and improving the operating efficiency of the charging platform and the reliability of vehicle access.

[0040] In step S101, when multiple vehicles enter the charging platform, multi-dimensional state parameters of each vehicle are acquired, and the charging urgency of the vehicle is evaluated based on the multi-dimensional state parameters.

[0041] In one implementation, the charging urgency level is determined based on a weighted or sorted combination of one or more of the multidimensional state parameters; the multidimensional state parameters include at least one of the following: the presence status of living beings in the vehicle, the safety status of the in-vehicle environment, the urgency of vehicle use, the vehicle's energy status, the vehicle's health status, and the storage status of items in the vehicle.

[0042] In this embodiment, the charging urgency level is determined by weighted calculation based on one or more of the multi-dimensional state parameters, or by priority ranking based on one or more of the multi-dimensional state parameters. The charging urgency level is used to characterize the vehicle's urgent need for charging resources at the current moment. Weighted calculation refers to the operation of assigning different weight values ​​to different multi-dimensional state parameters and then summing or multiplying them to obtain a comprehensive score; priority ranking refers to arranging different multi-dimensional state parameters according to a preset order of importance, with the highest priority parameter as the primary criterion.

[0043] The multidimensional state parameters in this embodiment include at least one of the following: the presence status of living beings inside the vehicle, the safety status of the vehicle environment, the urgency of vehicle use, the energy status of the vehicle, the health status of the vehicle, and the storage status of items inside the vehicle.

[0044] The presence of living beings inside a vehicle refers to the presence and type of living beings within the vehicle. These types include children, adults, and pets. In this embodiment, the presence of living beings inside the vehicle can be obtained and identified through cameras, millimeter-wave radar, seat pressure sensors, and infrared scanning equipment deployed inside the vehicle. Specifically, cameras collect in-vehicle image data, millimeter-wave radar detects the breathing and heartbeat characteristics of occupants by emitting electromagnetic waves, seat pressure sensors detect the pressure value on the seat, and infrared scanning equipment confirms the presence of living beings by detecting infrared radiation emitted by the human body.

[0045] The safety status of the in-vehicle environment refers to the content of harmful gases and the environmental quality of the air inside the vehicle. This can be obtained through air quality detection equipment deployed inside the vehicle. For example, air quality detection equipment analyzes the composition of the air by drawing in the air inside the vehicle and detects the concentration of harmful gases such as carbon monoxide, formaldehyde, and volatile organic compounds.

[0046] Vehicle urgency refers to the user's level of eagerness to use the vehicle, and can be determined through at least one of the following methods: user-initiated triggering, real-time changes in the distance between the user and the vehicle, or the user's planned navigation route and departure time. Specifically, vehicle urgency is determined as follows: In response to receiving a vehicle use command actively triggered by the driver and / or passengers through a terminal device, the vehicle's urgency level is marked as high urgency; or, Real-time acquisition of the geographical location information between the driver and / or passenger's carried terminal device and the vehicle; calculation of the real-time distance between the driver and / or passenger and the vehicle, and the rate of change of the real-time distance over time, based on the geographical location information; assessment of the urgency of vehicle use based on the real-time distance and the rate of change of distance; or, Synchronize the driver's and / or passenger's planned navigation route information and vehicle usage scenario settings from the driver's and / or passenger's terminal devices or cloud server. The vehicle usage scenario settings include at least the planned departure time. Calculate the time difference between the planned departure time and the current system time to determine the urgency of vehicle usage.

[0047] Specifically, this embodiment can continuously monitor signals from the terminal device held by the driver or passenger. When a vehicle use command is actively triggered by the driver or passenger through the terminal device, the urgency of vehicle use is marked as high urgency. The terminal device refers to a mobile electronic device with communication functions carried by the driver or passenger, including smartphones, smartwatches, or vehicle remote keys. A vehicle use command refers to a signal actively issued by the driver or passenger indicating that they are about to use the vehicle, by clicking a button on the terminal device screen, pressing a physical button, or issuing a voice command.

[0048] For example, when a driver clicks the "Ready to Go" button on a mobile application, the phone converts the click event into a vehicle use command and sends it to the charging management device on the charging platform. After receiving the command, the charging management device sets the vehicle's usage urgency level to high urgency.

[0049] In another embodiment, the geographical location information between the terminal device carried by the driver or passenger and the vehicle can be acquired in real time. This geographical location information refers to the latitude and longitude coordinates of the terminal device and the vehicle in a geographic coordinate system. Based on the acquired geographical location information, the real-time distance between the driver or passenger and the vehicle is calculated. This real-time distance is the straight-line distance between the location of the terminal device and the location of the vehicle. Simultaneously, the rate of change of this real-time distance over time is calculated. The rate of change of distance refers to the amount of change in real-time distance per unit time, used to characterize the speed at which the driver or passenger approaches or moves away from the vehicle.

[0050] When the real-time distance is detected to be continuously shortening and the rate of change of distance exceeds the preset threshold for the rate of change of distance, it is determined that the driver or passenger is approaching the vehicle at a relatively fast speed. At this time, the urgency of using the vehicle is marked as high urgency. The threshold for the rate of change of distance can be configured according to the actual application scenario, for example, it can be set to two meters per second.

[0051] When a continuous increase in real-time distance is detected, regardless of whether the rate of change exceeds a preset threshold, it indicates that the vehicle is leaving the charging platform, and there is no need to focus on the vehicle's urgency at this time. When a continuous decrease in real-time distance is detected, and the rate of change does not exceed a preset threshold, the vehicle's urgency is marked as low.

[0052] For example, after a driver parks their vehicle on a charging platform and leaves, the real-time distance between their mobile phone and the vehicle is 200 meters. When the driver prepares to return to retrieve the vehicle, the real-time distance between the phone and the vehicle gradually decreases, from 200 meters to 150 meters, 100 meters, and 50 meters, with the rate of change stabilizing at 3 meters per second. The charging management equipment detects that the real-time distance is continuously decreasing and the rate of change exceeds the threshold of 2 meters per second, thus determining that the vehicle's urgency is high.

[0053] In another embodiment, the planned navigation route information and vehicle usage scenario settings of the driver or passenger are synchronized from the driver's or passenger's terminal device or a cloud server. The navigation route information refers to the driving path data from the departure point to the destination, including waypoints, estimated driving distance, and estimated driving time. The vehicle usage scenario settings include at least the planned departure time, which is the time set by the user to depart using the vehicle. Then, the time difference between the planned departure time and the current system time is calculated; the time difference is the time interval obtained by subtracting the current system time from the planned departure time.

[0054] When the calculated time difference is less than the preset time threshold, it is determined that the user will use the vehicle in a short period of time. At this time, the urgency of vehicle use is marked as high urgency. The time threshold can be configured according to the actual application scenario, for example, it can be set to thirty minutes.

[0055] For example, a driver plans a navigation route from the current charging platform to the airport using a mobile application, and sets the planned departure time to 10:00 AM. The charging management device in the charging platform synchronizes this navigation route information and planned departure time from the cloud server. The current system time is 9:40 AM, and the time difference is 20 minutes. Since this time difference is less than the preset 30-minute threshold, the charging management device marks the vehicle's urgency as high.

[0056] The three determination methods mentioned above are executed independently. The charging management device can use at least one of these methods to determine the urgency of vehicle use. When the conditions of any determination method are met, the charging management device will mark the vehicle's urgency as high urgency. This solves the technical problem in the existing technology that it is impossible to dynamically perceive the urgency of users' vehicle use needs and can only rely on a single triggering method, resulting in inaccurate determination of vehicle use urgency and delayed response, which affects the rationality of charging priority. It realizes multi-source, real-time and accurate determination of users' vehicle use urgency, enabling the charging robot scheduling to respond to users' temporary vehicle use or plan change needs in a timely manner, and improving the scheduling response efficiency of charging services.

[0057] Vehicle energy status refers to the vehicle's remaining energy value and current battery health value. Remaining energy refers to the amount of electricity currently stored in the vehicle's power battery that can be used for driving, while battery health refers to the percentage of the current battery's actual capacity to its nominal capacity.

[0058] Vehicle health status refers to the working and aging status of the vehicle's mechanical and electronic control components. It can be obtained by real-time synchronization of the working status data of the vehicle's electronic control components to the cloud, or by estimation based on the usage time of mechanical components, or by identification based on the execution feedback of electronic control signals to mechanical components, or by visual detection through cameras deployed inside the vehicle.

[0059] The status of items stored inside the vehicle refers to the types of items left behind, which can be obtained through image recognition using cameras deployed inside the vehicle. Item types include perishable goods and non-perishable goods. Perishable goods refer to food items that require preservation within a certain temperature and time range, while non-perishable goods refer to other items left behind besides perishable goods.

[0060] After obtaining the above multi-dimensional state parameters, the charging urgency of each vehicle is assessed by weighted calculation or priority ranking based on one or more of the following: the presence of living beings in the vehicle, the safety status of the in-vehicle environment, the urgency of vehicle use, the energy status of the vehicle, the health status of the vehicle, and the storage status of items in the vehicle. The higher the charging urgency, the more urgently the vehicle needs to obtain charging resources.

[0061] This embodiment determines the charging urgency level by weighting or sorting one or more of the multi-dimensional state parameters. The multi-dimensional state parameters include at least one of the following: the presence status of living beings inside the vehicle, the safety status of the in-vehicle environment, the urgency of user vehicle use, the vehicle's energy status, the vehicle's health status, and the storage status of items inside the vehicle. This solution solves the technical problem in the prior art where the assessment dimension of charging urgency is single, unable to distinguish multi-level emergency needs, resulting in a mismatch between charging priority and the actual degree of danger. It achieves a refined and multi-dimensional quantitative assessment of charging urgency, enabling the allocation of charging robots to prioritize vehicles involving life safety and environmental safety, and significantly improving the safety assurance capability and scheduling intelligence level of the charging platform.

[0062] In step S102, the charging priority of multiple vehicles is determined based on the urgency of charging.

[0063] In this embodiment, the charging management device determines the charging priority of multiple vehicles based on the charging urgency of each vehicle. The charging priority refers to the order in which multiple vehicles receive charging robot services, with vehicles having a higher charging urgency receiving a higher charging priority.

[0064] Specifically, the charging management equipment ranks multiple vehicles according to a preset order of importance and comprehensively evaluates the urgency of charging to determine the charging priority of each vehicle. For example, the order of importance from highest to lowest can be set as follows: a child left in the vehicle, harmful gases present in the vehicle, the user marks the vehicle as needing immediate use, the vehicle's remaining battery power is below a preset threshold, the vehicle has a malfunction and needs repair, fresh produce left in the vehicle, and other non-fresh produce and items left in the vehicle that are neither children nor elderly people.

[0065] For example, the charging management device first identifies a child under the age of a preset age inside the vehicle using an in-vehicle camera, millimeter-wave radar, seat pressure sensor, or infrared scanning device. For vehicles with children left behind, the charging priority is given first, regardless of other judgment conditions.

[0066] If no vehicles with children left behind are found, or if vehicles with children left behind have already been prioritized, the charging management equipment continues to check if any of the remaining vehicles have harmful gases present in their interior environment. For example, it uses in-vehicle air quality detection equipment to check if the concentrations of carbon monoxide, formaldehyde, or volatile organic compounds in the air exceed preset safety thresholds. For vehicles with harmful gases present in their interior environment, the charging management equipment prioritizes them after vehicles with children left behind but before other vehicles.

[0067] If no vehicles with harmful gases are present in their interiors, or if vehicles with harmful gases have already been prioritized, the charging management device continues to check the remaining vehicles to see if any have been marked by the user as needing immediate use. For example, the vehicle's current usage status can be determined by at least one of the following methods: the user actively triggers a usage command; the real-time distance between the user and the vehicle continuously decreases and the rate of change exceeds a preset threshold; or the time difference between the user's planned departure time and the current system time is less than a preset time threshold. For vehicles marked as needing immediate use, their charging priority is prioritized after vehicles with harmful gases and before other vehicles.

[0068] If no vehicles are marked as immediately needed, or if vehicles marked as immediately needed have already been prioritized, the charging management device continues to check if any of the remaining vehicles have a remaining battery level below a preset threshold. A remaining battery level below the preset threshold means that the vehicle's currently stored usable battery capacity is lower than a pre-set warning value, such as less than 20% of its total capacity. Vehicles with remaining battery levels below the preset threshold are prioritized after vehicles marked as immediately needed but before other vehicles.

[0069] If no vehicles have remaining battery levels below the preset threshold, or if low-battery vehicles have already been prioritized, the charging management equipment continues to check the remaining vehicles for any that require repair due to a malfunction. A vehicle requiring repair is identified through at least one of the following methods: real-time operational status data synchronized to the cloud by the vehicle's electronic control components, estimated usage time of mechanical components, execution feedback information from electronic control signals reaching mechanical components, or visual inspection images captured by in-vehicle cameras. Vehicles requiring repair are prioritized for charging after low-battery vehicles but before other vehicles.

[0070] If there are no vehicles that need to be repaired, or if the vehicles that need to be repaired have been prioritized, the charging management device continues to use the in-vehicle camera to identify whether there are any food items that need to be kept fresh within a certain temperature and time range left inside the vehicle. For vehicles with fresh food items left inside, the charging management device will prioritize them after the vehicles that need to be repaired and before other vehicles.

[0071] If no vehicles have left fresh produce inside, or if vehicles with fresh produce have already been prioritized, then all vehicles with other non-fresh produce items or items left behind that are neither children's nor elderly people's will be ranked last. Other non-fresh produce items and items left behind that are neither children's nor elderly people's refer to items other than fresh produce, as well as items left behind by passengers other than children and the elderly, such as clothing, documents, and ordinary packages.

[0072] By judging the vehicles in order of importance as described above, a complete charging priority ranking is determined for all vehicles awaiting charging. Vehicles ranked higher in the charging priority ranking receive charging service earlier.

[0073] For example, suppose there are five vehicles waiting to be charged at the same time on the charging platform. The first vehicle contains a child, the second vehicle has excessive levels of harmful gases detected, the driver of the third vehicle has marked it as needing to use immediately, the fourth vehicle has less than 20% battery remaining, and the fifth vehicle contains fresh produce. The charging management equipment prioritizes the vehicles according to their importance, assigning the first vehicle the highest priority, the second the second, the third the third, the fourth the fourth, and the fifth the fifth.

[0074] For another example, suppose there are three vehicles waiting to be charged at the charging platform. The first vehicle has a child left inside and its remaining battery is below 20%. The second vehicle has fresh produce left inside. The third vehicle contains harmful gases and also has fresh produce left inside. The charging management equipment prioritizes the vehicles according to their importance, first assigning the first vehicle (with the child) the highest priority. Then, it checks if any of the remaining vehicles contain harmful gases; the third vehicle does, so it's assigned the second highest priority. Finally, the second vehicle (with only fresh produce left inside) is assigned the third highest priority. Although the first vehicle meets both the criteria of a child left behind and low battery, the priority of the child left behind is higher than that of low battery, so the first vehicle remains the highest priority.

[0075] This embodiment uses a technical solution to determine the charging priority of each vehicle by ranking them according to the importance of factors such as the presence of a child left in the vehicle, the presence of harmful gases in the vehicle environment, the user marking the vehicle as needing immediate use, the vehicle's remaining battery power being below a preset threshold, the vehicle having a malfunction requiring repair, fresh food left in the vehicle, and other non-fresh food and items left in the vehicle that are neither children nor elderly people. This solution solves the technical problem in existing technologies where the charging priority determination standard is singular and cannot distinguish between multiple levels of emergency needs such as life-threatening situations, health threats, time constraints, energy shortages, equipment malfunctions, and the preservation of items, leading to a serious mismatch between charging resource allocation and the actual level of danger. It achieves a progressively refined ranking of charging priorities, ensuring that vehicles involving life-threatening situations and health threats receive the highest priority charging service, significantly improving the safety assurance capabilities and resource allocation rationality of the charging dispatch system.

[0076] In step S103, when the number of charging robots is less than the number of vehicles, charging robots are assigned to each vehicle for charging according to charging priority.

[0077] In one implementation, a charging robot is assigned to each vehicle sequentially according to charging priority, including: According to the order in which each vehicle enters the charging platform, the vehicles are divided into several consecutive queue batches. Each queue batch contains a fixed number of vehicles, which is the pre-set batch capacity value. Based on the queuing batches, charging robots are assigned to each vehicle in sequence according to charging priority.

[0078] In this embodiment, when the charging management device detects that the number of available charging robots is less than the number of vehicles waiting to be charged, the charging management device divides all vehicles into several consecutive queues according to the order in which each vehicle enters the charging platform. The time a vehicle enters the charging platform refers to the moment when the vehicle officially enters the internal area of ​​the charging platform after passing identity verification at the entrance. The charging management device records the entry time for each vehicle and sorts the vehicles according to the order of their entry times.

[0079] Each queue batch contains a fixed number of vehicles, which is a pre-set batch capacity value. The batch capacity value refers to the maximum number of vehicles that can be accommodated in each queue batch. This value can be configured based on the actual scale of the charging platform, the total number of charging robots, and the total number of parking spaces in the charging platform. For example, the batch capacity value can be set to 5, meaning that each queue batch can contain a maximum of 5 vehicles.

[0080] For example, the first 5 vehicles to enter the queue are assigned to the first queue batch, where N equals the batch capacity. The next 5 vehicles to enter are assigned to the second queue batch, and so on, until all vehicles waiting to be charged are assigned to their respective queue batches. The last queue batch may contain fewer vehicles than the batch capacity, meaning the last batch can accommodate fewer vehicles than the batch capacity.

[0081] After the queuing batches are divided, charging robots are assigned to each vehicle sequentially based on their charging priority. Specifically, the charging management equipment first processes the first queuing batch, assigning charging robots to each vehicle in descending order of charging priority. Once all vehicles in the first queuing batch have finished charging, the second queuing batch is processed, and charging robots are assigned sequentially based on the descending charging priority of each vehicle in the second batch. Subsequent queuing batches are processed in the same manner.

[0082] For example, suppose there are twelve vehicles waiting to be charged on the charging platform, and the pre-set batch capacity is five. The charging management device divides the vehicles into three batches based on their arrival time: the first five vehicles arrive in the first batch, the next five in the second batch, and the last two in the third batch. Assume there are currently three available charging robots, fewer than the total number of twelve vehicles. The charging management device first processes the first batch, sorting the five vehicles by their charging priority from highest to lowest, and assigning charging robots to the first three vehicles. After the first vehicle finishes charging and releases its charging robot, the charging management device assigns that robot to the fourth vehicle in the batch, and so on, until all five vehicles in the first batch have finished charging. Then, the charging management device processes the second batch, assigning charging robots to the vehicles in the second batch in the same manner.

[0083] For another example, suppose there are three vehicles waiting to be charged on the charging platform, the pre-set batch capacity is five, and there are currently two available charging robots. Since the total number of vehicles is less than the batch capacity, the charging management device divides all three vehicles into the same queue batch. The charging management device sorts the three vehicles in the batch from highest to lowest charging priority, assigning the first two vehicles to charging robots. After one vehicle finishes charging and releases its charging robot, that robot is then assigned to the third vehicle in the queue.

[0084] This embodiment divides vehicles into several consecutive queues based on the order in which they enter the charging platform. Each queue contains a fixed number of vehicles, which is a pre-set batch capacity value. Based on the queue, charging robots are assigned to each vehicle according to their charging priority. This solves the technical problems in the prior art where, when there are many vehicles waiting to be charged and charging robot resources are limited, global sorting leads to excessive computational load, low-priority vehicles cannot obtain charging resources for a long time, and the charging order is unstable, thus affecting the user experience. This embodiment ensures the effective execution of charging priority while controlling the computational complexity of a single batch, and avoids indefinite waiting for low-priority vehicles, thereby improving the operational stability and fairness of the charging robot scheduling system.

[0085] In one implementation, based on queuing batches, a charging robot is assigned to each vehicle sequentially according to charging priority, including: In the queuing batches, the vehicles are sorted according to their charging priority to obtain the order of waiting to be charged, and charging robots are assigned to the vehicles in turn based on the order of waiting to be charged. Among them, the charging priority of vehicles in different queuing batches is not compared across batches, and vehicles in a later batch do not participate in the allocation of charging robots until the vehicles in the previous batch have finished charging. After any vehicle in the current queue completes charging and releases the charging robot, the vehicle that has completed charging is removed from the charging sequence, resulting in the remaining charging sequence. The charging robot is assigned to the next vehicle based on the remaining order of vehicles waiting to be charged.

[0086] In this embodiment, within each queuing batch, the charging management device sorts the vehicles in that batch according to their charging priority from high to low, resulting in a sequential order of vehicles waiting to be charged. The order of vehicles waiting to be charged refers to the sequence of vehicles arranged in descending order of charging priority within the same queuing batch, with vehicles ranked earlier in the sequence receiving charging service sooner.

[0087] The charging management equipment assigns charging robots to vehicles sequentially based on their charging order. Specifically, it assigns an available charging robot to the vehicle ranked first in the charging order to begin charging. When there are sufficient charging robots, it assigns charging robots to the second, third, and subsequent vehicles in the charging order, until all charging robots are occupied or all vehicles in the batch have received a charging robot.

[0088] For vehicles in different queuing batches, no cross-batch comparison of charging priority is performed. Cross-batch comparison refers to the process of reordering vehicles from the previous batch with those from the next batch. In this embodiment, all vehicles in the previous batch always have a higher charging priority than all vehicles in the next batch, regardless of the charging priority of the vehicles themselves in the next batch.

[0089] Vehicles in the later batch will not participate in the charging robot's allocation process until vehicles in the previous batch have finished charging. That is, even if there are vehicles in the later batch with extremely high charging urgency, such as children left in the car or harmful gases in the car environment, these vehicles must wait until all vehicles in the previous batch have finished charging before they can enter the charging robot's allocation process.

[0090] In the current queuing batch, once any vehicle completes charging and releases the charging robot, the vehicle that has completed charging is removed from the corresponding charging sequence of that batch, resulting in the remaining charging sequence. A vehicle that has completed charging refers to a vehicle whose battery level has reached the target charging level and whose charging robot has disconnected and released it. The remaining charging sequence refers to the vehicle sequence in which the remaining vehicles retain their original order after the removal of the vehicle that has completed charging.

[0091] Next, the charging management device checks if there are any vehicles in the current queue that have not yet received a charging robot. If there are still vehicles waiting to be charged in the remaining queue, the charging management device takes the vehicle ranked first in the remaining queue, assigns the released charging robot to that vehicle, and begins charging the vehicle.

[0092] If there are no more vehicles waiting to be charged in the remaining charging sequence, meaning all vehicles in the current queue have received a charging robot, but some have not yet finished charging, the charging management device will allocate the released charging robot to the vehicle with the highest charging priority in the next queue. This is because there are no vehicles in the current queue that have not yet started charging; all vehicles are either charging or have finished. The released charging robot does not need to wait for other vehicles in the current queue that have not yet finished charging and can directly serve subsequent batches.

[0093] Repeating the above process, whenever a charging robot is released, the charging management device first determines whether there are any vehicles in the current queue that have not yet started charging. If so, the charging robot is assigned to the vehicle with the highest remaining charging priority in the current queue; if not, the charging robot is assigned to the vehicle with the highest charging priority in the next queue, and the next queue begins to be processed.

[0094] For example, suppose the first queue contains five vehicles, and the order of vehicles waiting to be charged, sorted from highest to lowest charging priority, is: Vehicle A, Vehicle B, Vehicle C, Vehicle D, and Vehicle E. Assume there are three charging robots currently available. The charging management device first assigns the three charging robots to Vehicle A, Vehicle B, and Vehicle C respectively, and these three vehicles begin charging simultaneously. At this point, Vehicle D and Vehicle E are the vehicles in the first queue that have not yet started charging.

[0095] Assuming vehicle B finishes charging first and releases the charging robot it occupied, the charging management device checks if there are any vehicles in the first queue that have not yet started charging. It finds that vehicles D and E have not yet started charging. Therefore, it takes the vehicle with the highest priority that has not yet started charging from the remaining queue, namely vehicle D, and assigns the released charging robot to vehicle D. Vehicle D starts charging. At this point, only vehicle E remains in the first queue that has not yet started charging.

[0096] Subsequently, vehicle A completes charging and releases its charging robot. The charging management equipment then checks the first queue batch again to see if there are any vehicles that have not yet started charging, and finds that vehicle E has not yet started charging. Therefore, the released charging robot is assigned to vehicle E, and vehicle E begins charging. At this point, all five vehicles in the first queue batch have started charging, with vehicles A and B having completed charging, and vehicles C, D, and E still charging.

[0097] Suppose vehicle C finishes charging and releases the charging robot. The system checks if there are any vehicles in the first queue that haven't started charging yet. It finds that all five vehicles in the first queue have started charging, and there are no vehicles yet to start. At this point, the charging management device assigns the released charging robot to the vehicle with the highest charging priority in the second queue, such as vehicle F. Vehicle F then begins charging. Meanwhile, vehicles D and E in the first queue are still charging, but vehicles in the second queue have already started receiving charging services.

[0098] Once vehicle D completes charging and releases its charging robot, the charging management device checks again the first queue batch for any vehicles that haven't started charging yet, and finds none. At this point, the charging management device assigns the released charging robot to the highest-priority vehicle in the second queue batch, and so on, until all batches of vehicles have completed charging.

[0099] For another example, suppose the first queue contains three vehicles, with the charging order as: Vehicle A, Vehicle B, and Vehicle C, and two charging robots are currently available. The charging management device first assigns the two charging robots to Vehicle A and Vehicle B. At this point, Vehicle C is the only vehicle in the first queue that has not yet started charging. After Vehicle A finishes charging and releases its robot, the charging management device assigns the released robot to Vehicle C. Once Vehicle C starts charging, all three vehicles in the first queue have begun charging. When Vehicle B finishes charging and releases its robot, there are no more vehicles in the first queue that have not yet started charging. The charging management device then assigns that charging robot to the vehicle with the highest charging priority in the second queue.

[0100] This embodiment solves the technical problems of frequent reordering across batches leading to wasted computing resources and frequent changes in charging order in the existing technology. It achieves a stable scheduling mechanism that strictly charges according to priority within a batch and executes sequentially between batches, improving the predictability of the scheduling system and user satisfaction. The technical solution is to obtain the order of vehicles to be charged based on charging priority in the queue batches and allocate charging robots in sequence. There is no cross-batch priority comparison between different batches. Vehicles in later batches do not participate in the allocation until the vehicles in the previous batch have finished charging. When any vehicle finishes charging and releases the charging robot, the vehicle that has finished charging is removed from the order of vehicles to be charged and the charging robot is allocated to the next vehicle based on the remaining order.

[0101] In real-world applications, users may encounter unexpected situations requiring immediate vehicle use while charging, such as a sudden family emergency requiring immediate return, an urgent work task, or a passenger experiencing sudden illness requiring immediate medical attention. In these cases, the user's scheduled charging plan needs to be immediately interrupted, and the charging robot should prioritize serving the user's urgent vehicle needs. However, existing charging scheduling systems can only execute charging according to a preset charging priority order and cannot detect a user's sudden urgent need for a vehicle. This results in the user having to wait for all vehicles in the original charging sequence to finish charging before receiving charging service, failing to meet the immediate vehicle needs in emergency situations. To solve this technical problem, this embodiment also includes the following steps: Real-time collection of voice and behavioral data of drivers and / or passengers; analysis of voice and behavioral data to obtain semantic recognition results and behavioral recognition results respectively; When the semantic recognition result contains the semantics of an emergency vehicle use instruction, and / or the behavior recognition result contains abnormal behavior characteristics, the vehicle will be marked as an emergency vehicle use. Prioritize charging vehicles for emergencies to the highest level and interrupt the charging of currently charging vehicles to allocate charging robots to emergency vehicles.

[0102] Voice data refers to the sound signals emitted by the driver or passengers, collected by an in-vehicle intelligent terminal deployed inside the vehicle. The in-vehicle intelligent terminal includes a built-in microphone array and voice acquisition module. The charging management device performs semantic recognition analysis on the collected voice data. Semantic recognition is a natural language processing process that converts voice signals into text information and extracts the intended meaning from the text. The semantic recognition results include the core intent expressed by the user, the type of instruction, and the urgency level.

[0103] Behavioral data refers to information on the body movements and operational behaviors of drivers or passengers collected through in-vehicle cameras, millimeter-wave radar, and seat pressure sensors deployed inside the vehicle. In-vehicle cameras capture facial expressions and limb movement trajectories of occupants; millimeter-wave radar detects breathing rate, heart rate, and subtle body movements by emitting electromagnetic waves; and seat pressure sensors collect the pressure distribution and changes experienced by the seats. The charging management equipment performs behavioral recognition analysis on the collected data. Specifically, this involves extracting and classifying motion features from images, radar signals, and pressure signals. The resulting behavioral recognition results include the driver's facial expression categories, limb movement types, frequency of vehicle control element operations, and the accuracy of route judgment during driving.

[0104] Next, the charging management device comprehensively judges the semantic recognition results and behavioral recognition results. When the semantic recognition results contain the semantics of an emergency vehicle use instruction, the charging management device determines that there is a sudden emergency vehicle use need. The semantics of an emergency vehicle use instruction refer to keywords or phrases in the voice text that express the need to leave immediately, use a vehicle immediately, leave urgently, or similar emergency intentions, such as "I have to leave immediately," "I have an urgent matter and need to drive," "Charge my car quickly, I have to leave," "My child is sick, go to the hospital immediately," etc.

[0105] When the behavior recognition results contain abnormal behavioral characteristics, the charging management device also determines that there is a sudden emergency need for vehicle use. Abnormal behavioral characteristics refer to behavioral manifestations that are significantly different from the behavior patterns in normal driving or waiting states, including at least one of the following: the driver's face shows a panicked expression, such as furrowed brows, wandering eyes, and tense facial muscles; the driver's number of vehicle operation errors exceeds a preset operation error threshold, such as multiple failed attempts to start the vehicle or multiple accidental touches of operation buttons; the driver performs repeated steering operations, that is, the steering wheel is turned back and forth more times in a short period of time than a preset steering operation threshold; the driver makes route judgment errors, such as making incorrect steering decisions at familiar intersections.

[0106] The charging management equipment uses a logical OR rule for determination. This means that a vehicle is marked as urgently needed if either the semantic recognition result contains the semantics of an emergency vehicle use command, or the behavioral recognition result contains abnormal behavioral characteristics. The reason for using this logical OR rule is that some users may be unable to clearly express their voice commands due to emotional distress; in this case, behavioral data can supplement the recognition. Conversely, some users may express themselves clearly but behave normally; in this case, the voice data is sufficient to trigger an emergency determination. The two complement each other, ensuring that no urgent vehicle use needs are overlooked.

[0107] Furthermore, the charging priority of vehicles marked as emergency vehicles is raised to the highest level, meaning that the charging priority of these vehicles is higher than that of all vehicles not marked as emergency vehicles, including vehicles that are at the forefront of the current queue according to their original charging priority, and vehicles that are higher than the batch order constraints between different queues.

[0108] The charging management equipment interrupts the charging of currently charging vehicles to allocate charging robots to emergency vehicles. Specifically, it suspends the charging operation currently being performed on the vehicle, controls the charging robot to disconnect from the charging interface of the current vehicle, releases the charging robot, and allocates the released charging robot to the location of the emergency vehicle to prioritize charging that vehicle.

[0109] For example, suppose there are three vehicles currently charging on the charging platform: Vehicle A, Vehicle B, and Vehicle C. According to the original charging priority, Vehicle D is fourth in line waiting to be charged. At this moment, the charging management device captures voice data from the driver of Vehicle D saying, "There's an emergency at home, I have to leave immediately." The semantic recognition result contains the urgent driving instruction "leave immediately." The charging management device marks Vehicle D as having an emergency and elevates its charging priority to the highest level. The charging management device selects one of the three currently charging vehicles, interrupts its charging operation, and deploys a charging robot to Vehicle D's location to begin charging it. The interrupted vehicle enters a waiting queue after the charging robot is released, and its charging operation resumes only after Vehicle D's emergency charging need is met.

[0110] For another example, suppose the driver of vehicle E does not issue any voice commands, but the onboard camera captures a panicked expression on the driver's face, while the seat pressure sensor detects that the driver has repeatedly adjusted their posture and leaned forward, and the millimeter-wave radar detects a significant increase in the driver's heart rate. The charging management equipment's behavior recognition results determine that there are abnormal behavioral characteristics, marking vehicle E as an emergency vehicle, increasing its charging priority, and assigning a charging robot to charge it.

[0111] For another example, suppose the vehicle's driver issues a voice command saying, "I have an urgent meeting, please charge me quickly," while simultaneously exhibiting unusual behaviors such as repeatedly checking their phone and frequently starting the vehicle. If both the semantic and behavioral recognition results of the charging management device meet the judgment criteria, the vehicle is marked as requiring emergency use, and a charging robot is prioritized for its deployment.

[0112] Through the above method, this embodiment collects voice and behavioral data of drivers and / or passengers in real time, analyzes the results to obtain semantic recognition and behavioral recognition. When the semantic recognition result contains the semantics of an emergency vehicle use command and / or the behavioral recognition result contains abnormal behavioral characteristics, the vehicle is marked as an emergency vehicle use vehicle, and its charging priority is raised to the highest level. The charging of the currently charging vehicle is interrupted to dispatch a charging robot to the emergency vehicle use vehicle. This technical solution solves the technical problems of existing technologies that cannot perceive the demand for emergency vehicles and have a delayed response. It realizes the proactive perception and immediate response to the user's emergency vehicle use intention, and significantly improves the emergency handling capability and user experience of the charging dispatch system in emergency scenarios.

[0113] In another practical application scenario, electric vehicle users often encounter situations where the remaining driving range displayed on the instrument panel is significantly different from the actual driving range. For example, a vehicle's instrument panel displays a remaining range of 160 kilometers, while the user's planned destination is 70 kilometers away, leading the user to believe that the remaining range is sufficient to complete the journey. However, when the vehicle is carrying three people and driving on a highway in rainy weather, the battery may suddenly run out after traveling only 50 kilometers, causing the vehicle to stop in the middle of the highway, creating a serious safety hazard. In summary, the remaining driving range displayed on the instrument panel is calculated based on ideal energy consumption under standard operating conditions, without considering actual influencing factors such as battery health degradation, differences in driving habits, road conditions, weather conditions, and the weight carried. That is, if the charging management equipment cannot accurately calculate the vehicle's actual power demand during charging, it may lead to insufficient charging causing the vehicle to break down en route, or overcharging causing energy waste and extended waiting time for the user. To solve this technical problem, the method of assigning the charging robot to each vehicle according to the charging priority also includes the following steps: The charging robot is controlled to charge the vehicle to the target charging level. The target charging capacity is determined in the following way: Obtain the vehicle's subsequent usage plan and multi-dimensional energy consumption impact parameters, and calculate the vehicle's first actual driving mileage under the subsequent usage plan based on the multi-dimensional energy consumption impact parameters; When the first actual driving mileage is less than the first expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is increased so that the second actual driving mileage of the vehicle after charging is not less than the sum of the first expected driving mileage and the preset safety margin. When the first actual driving mileage is greater than or equal to the first expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is maintained at the charging completion capacity corresponding to the current remaining energy of the vehicle.

[0114] In this embodiment, the charging management device can obtain the vehicle's subsequent usage plan through user-defined settings or cloud synchronization. User-defined settings refer to the user manually entering or selecting the destination, route, departure time, and estimated return time on the terminal application; cloud synchronization refers to the automatic synchronization of navigation routes, calendar schedules, or travel plans planned by the user in other applications to the charging management device's cloud server.

[0115] Multidimensional energy consumption influencing parameters include at least one of the following: vehicle's remaining energy, battery health, owner's driving habits data, historical energy consumption data of the vehicle's brand and model, and road surface type, road conditions, and load information corresponding to the planned route. Remaining energy refers to the amount of electricity currently stored in the vehicle's battery that can be used for driving; battery health refers to the percentage of the battery's actual capacity to its factory-statistical capacity; owner's driving habits data includes acceleration frequency, braking frequency, and average driving speed per unit time, used to characterize the owner's driving style as economical, standard, or aggressive; historical energy consumption data of the vehicle's brand and model includes battery degradation curve data accumulated under real-world driving scenarios or official testing conditions, passability data under different terrain conditions, and range achievement rate data under different load conditions; road surface type includes asphalt, cement, gravel, and muddy roads; road conditions include the number of curves, the distribution of braking sections, the distribution of acceleration sections, and the gradient changes of the route; load information includes the expected number of passengers and the expected weight of cargo.

[0116] The charging management equipment calculates the vehicle's first actual driving mileage under the subsequent usage plan based on the acquired multi-dimensional energy consumption impact parameters. The first actual driving mileage refers to the distance the vehicle can actually travel with its current remaining energy, predicted based on the current vehicle status and multi-dimensional energy consumption impact parameters. Specifically, the range achievement rate from historical energy consumption data can be used as a baseline value, and then progressively corrected based on battery health, driving habit data, road type and road conditions, and load information to obtain the final mileage achievement rate prediction value. The current remaining energy is then multiplied by this final mileage achievement rate prediction value to obtain the first actual driving mileage.

[0117] Next, the first actual mileage is compared with the first expected mileage corresponding to the subsequent driving plan. The first expected mileage refers to the driving distance planned by the user from the current charging platform location to the destination, including the sum of the distances of all road segments in the planned route.

[0118] When the initial actual driving mileage is less than the initial expected driving mileage, it is determined that the vehicle's current remaining energy is insufficient to support the user in completing the planned trip, requiring additional charging. The charging management device increases the target charging capacity so that the vehicle's second actual driving mileage after charging is not less than the sum of the initial expected driving mileage and the preset safety margin. The second actual driving mileage refers to the actual driving distance recalculated based on the vehicle's remaining charge level after this charging is completed. The preset safety margin refers to the driving distance corresponding to the extra energy reserved to cope with unexpected situations that may occur during the trip. For example, it can be set to 20 kilometers or 30 kilometers, and can also be configured differently according to the trip type, such as urban commuting, long-distance travel, or crossing uninhabited areas.

[0119] Specifically, when increasing the target charging capacity, the minimum charge level required to achieve the first expected driving mileage is first determined. This minimum charge level is equal to the first expected driving mileage divided by the predicted final mileage achievement rate. Then, the minimum charge level is added to the charge level corresponding to a safety margin to obtain the target charging capacity. The charging management equipment controls the charging robot to charge the vehicle until the vehicle battery reaches the target charging capacity.

[0120] When the first actual driving mileage is greater than or equal to the first expected driving mileage, the charging management device determines that the vehicle's current remaining energy is sufficient to support the user in completing the planned trip, and no additional charging is required. The charging management device maintains the target charging capacity at the level corresponding to the vehicle's current remaining energy, that is, it does not charge the vehicle, or only performs maintenance charging to compensate for self-discharge losses during the charging process.

[0121] For example, suppose a vehicle's current remaining energy corresponds to a range of 400 kilometers as indicated on the instrument panel, and the user's planned mileage, or first desired mileage, is 300 kilometers. The charging management device obtains the vehicle's battery health as 85%, the owner's driving habits as aggressive, the planned route as winding mountain roads, and the expected passenger capacity as four. The charging management device retrieves the baseline mileage achievement rate for this brand and model under standard operating conditions from historical energy consumption data, which is 80%. This is adjusted to 76% based on the 85% battery health, 66% based on aggressive driving habits, 56% based on winding mountain road conditions, and 50% based on carrying four people, resulting in a final predicted mileage achievement rate of 50%. The charging management device calculates the first actual mileage as 400 kilometers multiplied by 50%, which equals 200 kilometers. If the distance is less than 300 kilometers, the charging management equipment determines that the target charging level needs to be increased. The minimum charging level required to complete the 300-kilometer journey is 300 kilometers divided by 50%, which equals the charging level corresponding to 600 kilometers. Adding the safety margin of 30 kilometers, the target charging level is set to the charging level corresponding to 630 kilometers. The charging robot is then controlled to charge the vehicle to the target charging level.

[0122] For another example, suppose another vehicle's current remaining energy corresponds to a range of 350 kilometers as indicated on its instrument panel, and the user's initial desired driving range is 200 kilometers. The charging management device calculates a final mileage achievement rate prediction of 90%, and the initial actual driving range is 315 kilometers, which is greater than 200 kilometers. The charging management device maintains the target charging level at the level corresponding to the vehicle's current remaining energy and does not perform additional charging for that vehicle.

[0123] In this embodiment, during the process of the charging robot allocating charging resources to vehicles according to charging priority, the target charging capacity is determined in a personalized manner based on the subsequent usage plan of each vehicle and multi-dimensional energy consumption impact parameters. This ensures that the actual driving mileage after charging can meet the user's planned trip and reserve a safety margin, avoiding vehicle breakdowns due to insufficient charging, and also avoiding energy waste and extended waiting time for users caused by overcharging.

[0124] Furthermore, if the maximum actual driving range of a vehicle, even when fully charged, is still less than the first expected driving range corresponding to the user's original destination, it indicates that the vehicle cannot reach the original destination in one trip using its own battery capacity and needs to be recharged by other charging platforms along the way.

[0125] The charging management device calculates the vehicle's maximum actual driving range when fully charged and compares it with the first expected driving range. A fully charged state refers to the vehicle's battery reaching 100% of its rated capacity, or reaching the maximum charging cutoff level allowed by the battery management system.

[0126] When the maximum actual driving range is still less than the first expected driving range, it is determined that the vehicle cannot reach the original destination in one go using its own battery power, and it is necessary to replan the driving route through other charging platforms for the user.

[0127] At this point, data on the distribution of charging platforms around the original destination, as well as the location information of charging platforms along the route from the current charging platform to the original destination, are acquired. Specifically, with the original destination as the final goal and the vehicle's maximum actual driving range as the single-trip range constraint, a driving route passing through at least one other charging platform is planned. The planning principle is as follows: after the vehicle departs from the current charging platform, it reaches the first charging platform along the way to recharge before the battery is depleted; after recharging, it continues driving to reach the second charging platform along the way before the battery is depleted, and so on, until it finally reaches the original destination.

[0128] When planning the route, the system also considers the number of available charging robots at each charging platform along the way, the estimated waiting time, and the charging power, selecting the optimized route that minimizes the total travel time or the number of charging stops. The replanned route is then pushed to the user's device, displayed as a map on the device, marking the location of each charging platform along the way, the estimated arrival time, the estimated charging waiting time, and the estimated total travel time. The user can choose to accept the planned route, adjust the original destination, or cancel the trip.

[0129] For example, suppose the user's planned destination is 500 kilometers from the current charging platform, while the calculated maximum actual driving range of the vehicle on a full charge is 300 kilometers, less than 500 kilometers. In this case, data on the distribution of charging platforms along the route from the current charging platform to the original destination is obtained. It is found that there is a charging platform A at 200 kilometers and a charging platform B at 380 kilometers. The planned route is as follows: the vehicle starts from the current charging platform with a full charge, travels 200 kilometers to charging platform A, recharges to a full charge, continues driving, travels another 180 kilometers to charging platform B, recharges to a full charge, continues driving, and finally travels 120 kilometers to reach the original destination. This planned route is then pushed to the user's mobile application.

[0130] For another example, suppose there are multiple possible combinations of charging platforms along the route, and the optimal combination can be selected. For instance, there is charging platform A at 200 kilometers, charging platform C at 250 kilometers, and charging platform D at 300 kilometers. Comparing the total travel time under different combinations: Choosing option A requires two charging stops, but the waiting time at charging platforms A and B is shorter; choosing options C and D requires two charging stops, but the waiting time at charging platforms C and D is longer; choosing options A and D requires two charging stops but the travel distance is more evenly distributed. Based on preset optimization objectives, such as minimizing total travel time or minimizing the number of charging stops, the optimal route is selected and pushed to the user.

[0131] In this way, when the vehicle's own range is insufficient to reach the original destination, this embodiment proactively plans a charging route through other charging platforms for the user, avoiding the user's predicament of being unable to recharge along the way due to insufficient range, and further improving the travel guarantee capability of the charging dispatch system.

[0132] In real-world applications, users may temporarily change their travel plans while the vehicle is charging. For example, a user might originally plan to travel alone but decide to travel with three others during charging; or a user might originally plan to go straight home but decide to go shopping first; or a user might originally plan to leave in two hours but decide to leave an hour later after finishing some tasks. These changes will affect the actual amount of electricity the vehicle needs. If these changes are not detected and the charging amount is not dynamically adjusted, the vehicle may be undercharged and unable to complete the changed trip, or overcharged, causing unnecessary delays for the user. Therefore, this embodiment also includes the following steps: During the charging process of the vehicle within the charging platform, it is detected whether the driver and / or passengers have changed their subsequent vehicle usage plans. When a change in the subsequent vehicle use plan is detected, the multi-dimensional energy consumption impact parameters of the vehicle are obtained, and the third actual driving mileage of the vehicle after the change in the subsequent vehicle use plan is determined based on the multi-dimensional energy consumption impact parameters. If the third actual driving mileage is less than the second expected driving mileage after the subsequent vehicle use plan is changed, the current target charging power of the vehicle is adjusted so that the fourth actual driving mileage of the vehicle after charging is not less than the sum of the second expected driving mileage and the preset safety margin. When the third actual driving mileage is greater than or equal to the second expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is maintained at the charging completion capacity corresponding to the current remaining energy of the vehicle.

[0133] Specifically, during the vehicle's charging process at the charging platform, the system continuously monitors whether the driver or passengers have changed their subsequent usage plans. Changes to these plans can be detected by the charging management device in various ways. For example, users can actively modify their destination, planned route, or departure time via a terminal application, which then synchronizes the modified plan to the charging management device's cloud server. Alternatively, changes in trip-related data can be synchronously retrieved from multiple applications, such as navigation, calendar, and social media applications, based on the user's unified identity identifier. Or, even without the user actively uploading data or other application data, the system can predict the trip based on the current time and vehicle location, calling the user's historical trip data; if the predicted result differs from the currently recorded plan, a plan change is determined.

[0134] When a change in subsequent vehicle usage plans is detected, multi-dimensional energy consumption impact parameters of the vehicle are obtained. These parameters include at least one of the following: the vehicle's remaining energy, battery health, driver's driving habits data, historical energy consumption data of the vehicle's brand and model, and road surface type, road conditions, and load information corresponding to the changed planned route. Remaining energy refers to the amount of electricity currently stored in the vehicle's power battery that can be used for driving; battery health refers to the percentage of the current battery's actual capacity to its factory-statistical capacity; driver's driving habits data includes acceleration frequency, braking frequency, and average driving speed per unit time; historical energy consumption data of the vehicle's brand and model includes battery degradation curve data and range achievement rate data accumulated under real-world driving scenarios or official testing conditions; road surface type includes asphalt, cement, and gravel roads; road conditions include the number of turning sections, braking section distribution, acceleration section distribution, and gradient changes on the planned route; and load information includes the changed expected number of passengers and expected weight of cargo.

[0135] Based on the acquired multi-dimensional energy consumption impact parameters, the third actual driving mileage after the change of subsequent driving plan is determined. The third actual driving mileage refers to the actual driving distance that the vehicle can actually complete with the current remaining energy, predicted based on the current vehicle status and the changed multi-dimensional energy consumption impact parameters. Specifically, using the range achievement rate in historical energy consumption data as the baseline value, it is successively corrected according to battery health, driving habit data, road surface type and road conditions of the changed route, and changed load information to obtain the predicted value of the final mileage achievement rate after the change. Then, the current remaining energy is multiplied by the predicted value of the final mileage achievement rate after the change to obtain the third actual driving mileage.

[0136] Next, the third actual driving mileage is compared with the second expected driving mileage after the subsequent change of the vehicle use plan. The second expected driving mileage refers to the driving distance from the current charging platform location to the changed destination corresponding to the user's changed planned route, including the sum of the distances of all road segments in the changed planned route.

[0137] When the third actual driving mileage is less than the second expected driving mileage, it is determined that the vehicle's current remaining energy is insufficient to support the user in completing the modified trip, and the charging amount needs to be adjusted. At this point, the vehicle's current target charging amount needs to be adjusted so that the fourth actual driving mileage after charging is not less than the sum of the second expected driving mileage and the preset safety margin. The fourth actual driving mileage refers to the actual drivable distance recalculated after the vehicle completes charging, based on the initial charge level and the modified multi-dimensional energy consumption parameters. The preset safety margin refers to the driving distance corresponding to the extra energy reserved to cope with unexpected situations that may occur during the trip.

[0138] When the third actual mileage is greater than or equal to the second expected mileage, it is determined that the vehicle's current remaining energy is sufficient to support the user in completing the changed trip, and no adjustment to the charging amount is needed. In this case, the target charging amount is maintained at the charging amount corresponding to the vehicle's current remaining energy, i.e., the charging plan is not adjusted. If the vehicle is currently charging and the amount already charged exceeds the amount corresponding to the current remaining energy, the charging robot can be controlled to terminate charging early to avoid unnecessary energy waste and extended user waiting time.

[0139] For example, suppose a vehicle is charging at a charging platform, and the instrument panel shows a remaining range of 150 kilometers. The user originally planned to travel alone for 100 kilometers. The charging management device calculates a predicted mileage achievement rate of 80% under the given driving habits and road conditions, with an actual mileage of 120 kilometers, which is greater than 100 kilometers, meaning no charging or only a small amount of charging is needed. During charging, the user changes the plan via a mobile application to travel with three other people and adds a destination passing through a shopping mall, changing the expected mileage to 160 kilometers. After detecting the plan change, the charging management device re-acquires multi-dimensional energy consumption impact parameters and calculates that the predicted mileage achievement rate has decreased to 60%, and the actual mileage is 150 kilometers multiplied by 60%, which equals 90 kilometers, less than 160 kilometers. At this point, the target charging capacity is adjusted. The minimum energy required to complete 160 kilometers is calculated as 160 divided by 60%, approximately 267 kilometers, plus a safety margin of 20 kilometers. The adjusted target charging capacity is set at 287 kilometers. The charging management equipment controls the charging robot to continue charging the vehicle until the battery reaches the target charging level.

[0140] For another example, suppose another vehicle is charging, and the user originally planned to drive 200 kilometers. The charging management device has set the target charging capacity to correspond to 250 kilometers. During charging, the user changes their plan to drive 100 kilometers. After detecting the plan change, the system calculates the actual mileage corresponding to the current remaining energy. Assuming the current remaining energy corresponds to 180 kilometers, which is greater than the changed 100-kilometer range, it determines that further charging is unnecessary and controls the charging robot to terminate charging early to avoid overcharging.

[0141] For another example, suppose a user moves their planned departure time forward during the charging process. For instance, the user originally planned to leave in two hours, and the charging management device was charging at the standard charging power. However, the user suddenly moves their departure time forward to half an hour later. Upon detecting this change in planned departure time, the system determines that charging needs to be completed in a shorter time. In addition to adjusting the target charging capacity, it can also increase the charging power, using a higher current to quickly replenish the vehicle's energy, ensuring that charging is completed before the user's departure.

[0142] This embodiment solves the technical problem in the prior art where the charging process and travel plan are disconnected and cannot dynamically respond to plan changes. It achieves real-time perception and dynamic adaptation of the charging process to changes in the user's travel plan, ensuring the power demand of the changed trip and improving the intelligence and personalization of the charging service. This is achieved by detecting whether the driver and / or passengers change their subsequent travel plans during the vehicle charging process. When a change is detected, multi-dimensional energy consumption impact parameters are obtained, and the actual driving range after the change is determined based on these parameters. If the actual driving range is less than the expected driving range after the change, the current target charging capacity of the vehicle is adjusted.

[0143] This embodiment also provides a scheduling device for a charging robot, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0144] This embodiment provides a scheduling device for a charging robot, such as... Figure 2 As shown, it includes: The parameter acquisition module 201 is used to acquire multi-dimensional status parameters of each vehicle when multiple vehicles enter the charging platform, and to evaluate the charging urgency of the vehicle based on the multi-dimensional status parameters.

[0145] Priority determination module 202 is used to determine the charging priority of multiple vehicles based on the urgency of charging.

[0146] The charging scheduling module 203 is used to allocate a charging robot to each vehicle in order of charging priority when the number of charging robots is less than the number of vehicles.

[0147] In one implementation, the charging urgency level is determined based on a weighted or sorted combination of one or more of the multidimensional state parameters; the multidimensional state parameters include at least one of the following: the presence status of living beings in the vehicle, the safety status of the in-vehicle environment, the urgency of the user's vehicle use, the vehicle's energy status, the vehicle's health status, and the storage status of items in the vehicle.

[0148] In one implementation, the urgency of vehicle use is determined by the following module: The first urgency determination module is used to mark the vehicle's urgency as high urgency when it receives a vehicle use command actively triggered by the driver and / or passengers through a terminal device; or, The second urgency assessment module is used to acquire real-time geographic location information between the terminal device carried by the driver and / or passenger and the vehicle; calculate the real-time distance between the driver and / or passenger and the vehicle and the rate of change of that distance over time based on the geographic location information; and determine the urgency of vehicle use based on the real-time distance and the rate of change of that distance; or... The third urgency judgment module is used to synchronize the driver's and / or passenger's planned navigation route information and vehicle usage scenario setting information from the driver's and / or passenger's terminal device or cloud server. The vehicle usage scenario setting information includes at least the planned departure time. Based on the time difference between the planned departure time and the current system time, the urgency of vehicle usage is judged.

[0149] In one implementation, before the parameter acquisition module 201, the following module determines whether the vehicle is allowed to enter the charging platform: The first threshold judgment module is used to determine whether there is an empty parking space or a vehicle leaving the parking space when the vehicle enters the electronic fence area of ​​the charging platform. If so, it sends an entry command to the vehicle.

[0150] The second threshold judgment module is used to respond to the entry command. When the vehicle arrives at the entrance of the charging platform, it reads the vehicle identification information and license plate information of the vehicle. When the vehicle identification information and license plate information match, the vehicle is allowed to enter the charging platform.

[0151] In one embodiment, the charging scheduling module 203 includes: The batch division unit is used to divide vehicles into several consecutive queue batches according to the order in which each vehicle enters the charging platform. Each queue batch contains a fixed number of vehicles, and the fixed number is a pre-set batch capacity value.

[0152] The charging scheduling unit is used to allocate charging robots to each vehicle based on queuing batches and charging priority.

[0153] In one embodiment, the charging scheduling unit includes: The charging sorting subunit is used to sort the vehicles in the queuing batch according to their charging priority to obtain the order of vehicles to be charged, and to allocate charging robots to the vehicles in the order of vehicles to be charged. Among them, the charging priority of vehicles in different queuing batches is not compared across batches, and vehicles in the later batch do not participate in the allocation of charging robots until the vehicles in the previous batch have finished charging.

[0154] The sorting update subunit is used to remove vehicles that have completed charging from the charging sequence after any vehicle in the current queue has finished charging and released the charging robot, thus obtaining the remaining charging sequence.

[0155] The charging scheduling subunit is used to allocate a charging robot to the next vehicle based on the remaining order of vehicles to be charged.

[0156] In one embodiment, the apparatus further includes: The data acquisition module is used to collect voice and behavioral data of drivers and / or passengers in real time, analyze the voice and behavioral data, and obtain semantic recognition results and behavioral recognition results, respectively.

[0157] The emergency vehicle marking module is used to mark a vehicle as an emergency vehicle when the semantic recognition result contains the semantics of an emergency vehicle command and / or the behavior recognition result contains abnormal behavior features.

[0158] The priority adjustment unit is used to elevate the charging priority of vehicles in emergency situations to the highest level and interrupt vehicles currently charging to allocate charging robots to vehicles in emergency situations.

[0159] In one embodiment, the charging scheduling module 203 further includes: The charging unit is used to control the charging robot to charge the vehicle to the target charging level.

[0160] The target charging capacity is determined by the following unit: The first actual mileage determination unit is used to obtain the vehicle's subsequent usage plan and multi-dimensional energy consumption impact parameters, and calculate the vehicle's first actual driving mileage under the subsequent usage plan based on the multi-dimensional energy consumption impact parameters.

[0161] The first power adjustment unit is used to increase the target charging power when the first actual driving mileage is less than the first expected driving mileage corresponding to the subsequent vehicle use plan, so that the second actual driving mileage of the vehicle after charging is not less than the sum of the first expected driving mileage and the preset safety margin.

[0162] The second power maintenance unit is used to maintain the target charging power as the charging completion power corresponding to the current remaining energy of the vehicle when the first actual driving mileage is greater than or equal to the first expected driving mileage corresponding to the subsequent vehicle use plan.

[0163] In one embodiment, the charging scheduling module 203 further includes: The planned change monitoring unit is used to detect whether the driver and / or passengers have changed the vehicle's subsequent usage plan while the vehicle is charging at the charging platform.

[0164] The second actual mileage determination unit is used to obtain the multi-dimensional energy consumption impact parameters of the vehicle when a change in the subsequent vehicle use plan is detected, and to determine the third actual driving mileage of the vehicle after the change in the subsequent vehicle use plan based on the multi-dimensional energy consumption impact parameters.

[0165] The third battery level adjustment unit is used to adjust the current target charging level of the vehicle if the third actual driving mileage is less than the second expected driving mileage after the subsequent driving plan is changed, so that the fourth actual driving mileage of the vehicle after charging is not less than the sum of the second expected driving mileage and the preset safety margin.

[0166] The fourth power adjustment unit is used to maintain the target charging power as the charging completion power corresponding to the current remaining energy of the vehicle when the third actual driving mileage is greater than or equal to the second expected driving mileage corresponding to the subsequent vehicle use plan.

[0167] In this embodiment, the scheduling device for the charging robot is presented in the form of a functional unit. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0168] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0169] This invention also provides a computer device having the above-described features. Figure 2 The diagram shows the scheduling device for the charging robot.

[0170] The following is a detailed reference. Figure 3The diagram illustrates a structural schematic suitable for implementing a computer device according to embodiments of the present invention. The computer device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on a program stored in a read-only memory (i.e., ROM 302) or a program loaded from memory 308 into random access memory (i.e., RAM 303). The RAM 303 also stores various programs and data required for the operation of the computer device. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. Input / output (i.e., I / O interface 305) is also connected to the bus 304.

[0171] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows the computer device to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Computer equipment with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0172] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the scheduling method of the charging robot according to embodiments of the present invention.

[0173] Figure 3 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0174] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the scheduling method for the charging robot shown in the above embodiments is implemented.

[0175] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0176] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A scheduling method for a charging robot, characterized in that, The method includes: When multiple vehicles enter the charging platform, multi-dimensional status parameters of each vehicle are acquired, and the charging urgency of the vehicle is evaluated based on the multi-dimensional status parameters. The charging priority of the plurality of vehicles is determined based on the degree of urgency of charging. When the number of charging robots is less than the number of vehicles, the charging robots are assigned to each vehicle for charging in sequence according to the charging priority.

2. The method according to claim 1, characterized in that, The charging urgency level is determined based on a weighted or sorted average of one or more of the multidimensional state parameters; the multidimensional state parameters include at least one of the following: the presence status of living beings inside the vehicle, the safety status of the vehicle environment, the urgency of vehicle use, the vehicle's energy status, the vehicle's health status, and the storage status of items inside the vehicle.

3. The method according to claim 2, characterized in that, The urgency of vehicle use is determined in the following way: In response to receiving a vehicle use command actively triggered by the driver and / or passengers through a terminal device, the vehicle's urgency level is marked as high urgency; or, The system acquires real-time geographic location information between the terminal device carried by the driver and / or passenger and the vehicle, and calculates the real-time distance between the driver and / or passenger and the vehicle and the rate of change of the real-time distance over time based on the geographic location information. The urgency of vehicle use is determined based on the real-time distance and the rate of change of distance; or, The planned navigation route information and vehicle usage scenario settings of the driver and / or passenger are synchronized from the driver's and / or passenger's terminal device or cloud server. The vehicle usage scenario settings include at least the planned departure time. The urgency of vehicle use is determined by calculating the time difference between the planned departure time and the current system time.

4. The method according to claim 1, characterized in that, Before the step of multiple vehicles entering the charging platform, it is determined whether the vehicles are allowed to enter the charging platform by the following method: When the vehicle enters the electronic fence area of ​​the charging platform, it is determined whether there is an empty parking space or a vehicle leaving the parking space based on the current parking space occupancy status of the charging platform. If so, an entry command is sent to the vehicle. In response to the access command, when the vehicle arrives at the entrance of the charging platform, the vehicle identification information and license plate information of the vehicle are read. When the vehicle identification information matches the license plate information, the vehicle is allowed to enter the charging platform.

5. The method according to claim 1, characterized in that, The step of assigning the charging robot to each vehicle sequentially according to the charging priority includes: According to the order in which each vehicle enters the charging platform, the vehicles are divided into several consecutive queue batches, and each queue batch contains a fixed number of vehicles, the fixed number being a pre-set batch capacity value. Based on the queue batch, the charging robot is assigned to each vehicle for charging in sequence according to the charging priority.

6. The method according to claim 5, characterized in that, The step of assigning the charging robot to each vehicle for charging according to the charging priority based on the queue batch includes: In the queue, the vehicles are sorted according to their charging priority to obtain a charging order, and charging robots are assigned to the vehicles in turn based on the charging order. Among them, the charging priority of vehicles in different queues is not compared across batches, and vehicles in a later batch do not participate in the allocation of charging robots until the vehicles in the previous batch have finished charging. After any vehicle in the current queue completes charging and releases the charging robot, the vehicle that has completed charging is removed from the charging sequence to obtain the remaining charging sequence. The charging robot is assigned to the next vehicle based on the remaining order of vehicles to be charged.

7. The method according to claim 1, characterized in that, The method further includes: Real-time acquisition of voice and behavioral data of drivers and / or passengers; analysis of the voice and behavioral data to obtain semantic recognition results and behavioral recognition results, respectively. When the semantic recognition result contains emergency vehicle use command semantics, and / or the behavior recognition result contains abnormal behavior characteristics, the vehicle is marked as an emergency vehicle use vehicle. The charging priority of the emergency vehicle is raised to the highest level, and the charging of the currently charging vehicle is interrupted in order to allocate the charging robot to the emergency vehicle.

8. The method according to claim 1, characterized in that, The step of assigning the charging robot to each vehicle sequentially according to the charging priority includes: The charging robot is controlled to charge the vehicle to the target charging level. The target charging capacity is determined in the following way: Obtain the vehicle's subsequent usage plan and multi-dimensional energy consumption impact parameters, and calculate the vehicle's first actual driving mileage under the subsequent usage plan based on the multi-dimensional energy consumption impact parameters; When the first actual driving mileage is less than the first expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is increased so that the second actual driving mileage of the vehicle after charging is not less than the sum of the first expected driving mileage and the preset safety margin. When the first actual driving mileage is greater than or equal to the first expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is maintained at the charging completion capacity corresponding to the current remaining energy of the vehicle.

9. The method according to claim 8, characterized in that, The method further includes: During the charging process of the vehicle within the charging platform, it is detected whether the driver and / or passengers have changed their subsequent vehicle usage plans. When a change in the subsequent vehicle use plan is detected, the multi-dimensional energy consumption impact parameters of the vehicle are obtained, and the third actual driving mileage of the vehicle after the change in the subsequent vehicle use plan is determined based on the multi-dimensional energy consumption impact parameters. If the third actual driving mileage is less than the second expected driving mileage after the subsequent vehicle use plan is changed, the current target charging power of the vehicle is adjusted so that the fourth actual driving mileage of the vehicle after charging is not less than the sum of the second expected driving mileage and the preset safety margin. When the third actual driving mileage is greater than or equal to the second expected driving mileage corresponding to the subsequent vehicle use plan, the target charging capacity is maintained at the charging completion capacity corresponding to the current remaining energy of the vehicle.

10. A scheduling device for a charging robot, characterized in that, The device includes: The parameter acquisition module is used to acquire multi-dimensional status parameters of each vehicle when multiple vehicles enter the charging platform, and to evaluate the charging urgency of the vehicle based on the multi-dimensional status parameters. A priority determination module is used to determine the charging priority of the plurality of vehicles based on the degree of urgency of charging. The charging scheduling module is used to allocate a charging robot to each vehicle for charging in sequence according to the charging priority when the number of charging robots is less than the number of vehicles.

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