A method and equipment for coordinated scheduling of urban parking and charging stations

Abstract

A method and device for coordinated scheduling of urban charging and parking is disclosed, relating to the field of intelligent transportation scheduling. By employing a simulation-based approach to statistically analyze the number of delayed tasks and a candidate combination scheme selection driven by optimization objectives, the method constructs hypothetical execution sequences by randomly inserting residual charging tasks into different predetermined task sequences for simulation. The optimal combination scheme is selected from candidate mobile charging devices with the goal of minimizing the number of delayed tasks. Therefore, it effectively solves the technical problem in existing technologies where the impact on existing tasks cannot be accurately assessed during emergency scheduling, easily leading to a chain reaction of service quality degradation. This achieves the technical effect of ensuring emergency response efficiency while reducing negative impacts on other user services and improving the overall reliability of charging services.

Description

Technical Field

[0001] This application relates to the field of intelligent traffic scheduling, and in particular to a method and equipment for coordinated urban parking and charging scheduling. Background Technology

[0002] With the acceleration of urbanization and the rapid growth of electric vehicle ownership, urban charging infrastructure is facing unprecedented dual challenges. On the one hand, the concentrated charging of a large number of electric vehicles can cause huge instantaneous impacts on the regional power grid, threatening the stability and security of the grid. On the other hand, the uneven distribution of charging pile resources and the randomness of users' parking behavior often lead to "charging anxiety" for users, reducing the convenience of using electric vehicles.

[0003] In related technologies, to mitigate the impact of centralized charging on local power grids and improve service capabilities under limited power distribution capacity, users can initiate a scheduled charging request via a mobile application (App) after parking their vehicles in any parking space within the facility. The request includes information such as the vehicle's location, current battery level, and desired charging amount. Upon receiving the request, a cloud-based dispatch center assesses the current status of all mobile charging devices within the facility, including their geographical location, remaining battery level, and availability. Subsequently, based on preset rules, such as assigning the nearest available charging device with sufficient battery power to the target parking space to provide charging service, the system returns to the standby area or executes the next assigned task after completing the task.

[0004] However, when a user's travel information changes, and the deadline for a charging task is suddenly brought forward, relevant technologies, if using the remaining work completion time as the evaluation criterion directly applied to this emergency relay scenario, treat all candidate robots as homogeneous resources that can be interrupted and reassigned at any time. This will cause the system to implicitly create another impending service delay or failure while resolving a visible service failure, increasing the uncertainty and unreliability of the entire service network and affecting the reliability of the charging service. Summary of the Invention

[0005] This application provides a method and equipment for coordinated scheduling of urban charging outages and parking, which can improve the reliability of charging services for mobile charging devices.

[0006] Firstly, this application provides a city-wide coordinated charging and stopping scheduling method, applied to a scheduling device. The method includes: responding to a failure signal in executing a target vehicle charging task, obtaining the remaining charging task of the target vehicle, the remaining charging task including the remaining charging demand of the target vehicle; broadcasting a connection request containing the remaining charging demand to a charging device cluster, and receiving capability declaration data from multiple candidate mobile charging devices, the capability declaration data including the current available charging capacity, estimated arrival time, and predetermined task sequence of the candidate mobile charging device; simulating multiple hypothetical execution sequences constructed by randomly inserting different predetermined task sequences into the remaining charging task, and counting the number of delayed tasks that cannot be completed on time in the predetermined task sequence; selecting one or more devices from the candidate mobile charging devices to form a candidate combination scheme, the candidate combination scheme including at least one candidate mobile charging device, with the remaining charging demand not exceeding the currently available charging capacity as a constraint and the minimum number of delayed tasks as an optimization objective; determining the candidate combination scheme with the minimum number of delayed tasks as the target execution scheme; and issuing connection execution instructions containing the respective responsible charging capacity and execution timing to all candidate mobile charging devices in the target execution scheme based on the target execution scheme.

[0007] By adopting the above technical solution, the scheduling equipment can immediately obtain residual charging demand after a charging task fails and quickly collect candidate resources by broadcasting a succession request to the charging equipment cluster. The capability declaration data includes key information such as the currently available charging capacity, estimated arrival time, and predetermined task sequence, providing a comprehensive decision-making basis for subsequent precise scheduling. By randomly inserting residual charging tasks into different predetermined task sequences and constructing hypothetical execution sequences for simulation, the system can predict the impact of each scheduling scheme on existing tasks. The feasibility of the scheme is ensured by using the constraint that the residual charging demand should not exceed the currently available charging capacity; the optimization objective of minimizing the number of delayed tasks guarantees the optimization of overall service quality. Finally, by issuing succession execution instructions containing the charging capacity and execution sequence of each device in the target execution scheme, the system achieves both efficient emergency response and reduced negative impacts on other user services, improving the overall reliability of the charging service and user satisfaction.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of simulating multiple hypothetical execution sequences constructed by randomly inserting the residual charging task into different predetermined task sequences, and counting the number of delayed tasks that cannot be completed on time in the predetermined task sequence, specifically includes: extracting the user-specific service level protocol constraint for each predetermined task from the predetermined task sequence of the candidate mobile charging device, wherein the user-specific service level protocol constraint includes at least the user-preset charging completion deadline and the current state of charge reported by the vehicle battery management system; recalculating the expected completion time of the predetermined task in the hypothetical execution sequence after randomly inserting the residual charging task into the predetermined task sequence to obtain the hypothetical execution sequence based on the charging time requirement of the residual charging task and the movement time of the candidate mobile charging device; comparing the expected completion time with the user-specific service level protocol constraint of the corresponding predetermined task, and counting the number of delayed tasks that cannot be completed on time in the predetermined task sequence.

[0009] By adopting the above technical solution, we can deeply explore the user-specific service level agreement (SLA) constraints of each given task, including the user-preset charging completion deadline and the current state of charge reported by the vehicle's battery management system. This refined constraint information provides a reliable basis for accurately assessing task conflicts. By recalculating the estimated completion time based on the charging time requirements of the remaining charging tasks and the movement time of candidate mobile charging devices, we can accurately simulate the specific impact of inserting a new task on the original task sequence. The mechanism of comparing the estimated completion time with the corresponding user-specific SLA constraints of the given task makes the statistics on the number of delayed tasks more accurate and objective. This simulation evaluation method based on real constraints avoids the problem of neglecting some aspects in traditional scheduling, ensuring that while addressing emergency charging needs, it can quantify the risk of default on existing user service commitments, thereby achieving more intelligent and responsible resource scheduling.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of obtaining the residual charging task of the target vehicle in response to a failure signal of executing the target vehicle charging task specifically includes: in response to a failure signal of executing the target vehicle charging task, parsing the failure type from the failure signal; when the failure type is determined to be a task timeout failure, querying the real-time arrival time of the corresponding flight or / and train from an external real-time traffic data source based on the return trip information of the booked user associated with the target vehicle; determining the real-time arrival time as the charging deadline of the residual charging task, the charging deadline being earlier than the preset original charging deadline of the target vehicle charging task; and combining the charging deadline with the target vehicle charging task to form the residual charging task.

[0011] By employing the aforementioned technical solution, the failure type can be accurately analyzed from failure signals, particularly identifying the critical scenario of task timeout failure. Based on the return trip information of the booked users associated with the target vehicle, the system queries real-time arrival times of flights or trains from external real-time traffic data sources, enabling it to obtain the latest and most accurate time constraint information. The mechanism of determining the real-time arrival time as the charging deadline for the remaining charging task ensures that the new time constraint is more urgent and closer to actual needs than the original charging deadline. By combining the updated charging deadline with the target vehicle's charging task to form the remaining charging task, an intelligent conversion from static booking time to dynamic real-time constraints is achieved. This dynamic time constraint update mechanism based on real-time traffic information effectively solves the problem of mismatch between user travel changes and charging service time windows, improving the success rate of charging tasks and the accuracy of services in time-sensitive scenarios.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of obtaining the residual charging task of the target vehicle in response to a failure signal in executing the target vehicle charging task, the residual charging task including the residual charging demand of the target vehicle, the method further includes: verifying the user return information associated with the scheduled charging task for all scheduled charging tasks being executed or to be executed in the charging equipment cluster according to a preset period; retrieving the historical early arrival ratio of the flight or train number corresponding to the user return information from the historical traffic database; when the historical early arrival ratio exceeds a preset ratio, pre-matching a standby mobile charging device for the scheduled charging task, and adding the standby scheduled charging task to the task sequence for the standby mobile charging device, the standby scheduled charging task being obtained by modifying the scheduled charging task based on the historical average early arrival time of the flight number.

[0013] By adopting the above technical solution, and verifying user return information at preset intervals and retrieving historical early arrival rates from the historical traffic database based on the bus number, high-time-risk pre-arrival charging tasks can be identified. When the historical early arrival rate exceeds a preset rate, a strategy of pre-matching standby mobile charging equipment is implemented, achieving a shift from passive emergency response to proactive risk prevention. Adding standby pre-arrival charging tasks to standby mobile charging equipment, adjusted based on historical average early arrival times, ensures the timeliness and relevance of the contingency plan. This historical data-driven prediction and resource reservation mechanism allows the system to skip time-consuming global scheduling processes and directly activate contingency resources when facing high-probability risk events such as users returning early. This significantly shortens emergency response time and improves service assurance capabilities in time-sensitive scenarios.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of broadcasting a connection request containing the remaining charging demand to the charging device cluster specifically includes: determining whether the standby mobile charging device exists; if it exists, the standby mobile charging device performs the standby scheduled charging task; if it does not exist, broadcasting a connection request containing the remaining charging demand to the charging device cluster.

[0015] By adopting the above technical solution, the system can differentiate between different types of failure scenarios and employ corresponding handling strategies by determining whether standby mobile charging devices exist. When standby devices exist, a fast track for standby scheduled charging tasks is directly executed, avoiding unnecessary resource searching and bidding processes. When no standby devices exist, the regular process of broadcasting a connection request to the charging device cluster is initiated, ensuring comprehensive coverage of emergency response. This dual-track emergency handling mechanism allows the system to select the optimal response path based on specific circumstances: for anticipated high-risk scenarios, contingency plans are used for rapid response; for sudden and unforeseen failure scenarios, global broadcasting ensures full mobilization of resources. This differentiated emergency strategy not only improves overall response efficiency but, more importantly, enables the efficient allocation and utilization of emergency resources.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the candidate combination scheme with the fewest delayed tasks as the target execution scheme, the method further includes: multiplying the number of target delayed tasks of the target candidate mobile charging devices in the target execution scheme by the weight coefficient corresponding to the membership level of the associated user, and accumulating the results to obtain a weighted delay score; if the weighted delay score exceeds the delay threshold, then re-executing the step of simulating multiple hypothetical execution sequences constructed by randomly inserting different predetermined task sequences into the remaining charging tasks, and counting the number of delayed tasks that cannot be completed on time in the predetermined task sequence, until a specified number of times is reached; if it is determined that the specified number of times has been reached, the target vehicle charging task is sent to the emergency personnel client; if the weighted delay score does not exceed the delay threshold, then the target execution scheme is determined to be executed.

[0017] By adopting the above technical solution, a weighted delay score is obtained by multiplying the number of delayed tasks by the corresponding membership level weight coefficient of the associated user and summing the results. This allows the system to comprehensively consider both the quantity and quality of the delay impact. When the weighted delay score exceeds the delay threshold, the iterative optimization mechanism of simulation statistics is re-executed, ensuring continuous improvement and quality assurance of the scheduling scheme. A manual takeover mechanism, which sends tasks to emergency personnel clients after a specified number of attempts, provides a reliable safety net for extremely complex scenarios. This multi-layered quality control system enables the system not only to achieve optimal scheduling through algorithms in most cases but also to ensure that service quality does not fall below preset standards within the limits of the algorithm's capabilities. By incorporating user level differentiation into decision-making, a fairer and more personalized service allocation is achieved, improving overall operational efficiency while ensuring a better service experience for high-value users.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the step of obtaining the residual charging task of the target vehicle in response to a failure signal of executing the target vehicle charging task, the residual charging task including the residual charging demand of the target vehicle, specifically includes: in response to a failure signal of executing the target vehicle charging task, parsing the failure type from the failure signal; if the failure type is a mobile failure fault, generating a temporary dynamic risk area centered on the geographical coordinates of the occurrence of the mobile failure fault in the failure signal on the digital map of the scheduling system, the temporary dynamic risk area having a preset spatial range and a preset effective duration, and in the temporary dynamic risk area, the mobile charging device preferentially selects a detour path or activates a high-precision sensing mode; combining the dynamic risk area, the residual charging demand of the target vehicle, and the target vehicle charging task into the residual charging task.

[0019] By adopting the above technical solution, and by analyzing the mobile failure type from the failure signal and generating a temporary dynamic risk zone based on the geographical coordinates of the failure, the impact range of local safety events can be accurately defined and dynamically managed. The temporary dynamic risk zone is designed with a preset spatial range and a preset effective duration, ensuring the pertinence and timeliness of safety protection measures and avoiding overly conservative long-term restrictions. Within this area, mobile charging devices prioritize detour paths or activate high-precision sensing modes, effectively reducing the probability of subsequent devices experiencing similar failures in the same area. The mechanism of combining dynamic risk zone information with residual charging demand and target vehicle charging tasks into residual charging tasks ensures that safety constraints are considered by subsequent scheduling algorithms. This dynamic safety management system based on real-time fault information not only improves the operational safety of mobile charging devices but also achieves an intelligent balance between safety protection and service efficiency.

[0020] In a second aspect, this application provides a scheduling device comprising: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code including computer instructions, wherein the one or more processors invoke the computer instructions to cause the scheduling device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on a scheduling device, cause the scheduling device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a scheduling device, cause the scheduling device to perform the method described in the first aspect and any possible implementation thereof. One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By employing simulation-based statistical analysis of delayed tasks and optimization-driven candidate combination scheme selection techniques, and by constructing hypothetical execution sequences after randomly inserting residual charging tasks into different predetermined task sequences for simulation, the optimal combination scheme is selected from candidate mobile charging devices with the goal of minimizing the number of delayed tasks. Therefore, it effectively solves the technical problem in the prior art that it is impossible to accurately assess the impact on existing tasks during emergency dispatch, which can easily lead to a chain reaction of service quality degradation. This achieves the technical effect of reducing the negative impact on other user services and improving the overall reliability of charging services while ensuring emergency response efficiency.

[0023] 2. By adopting the above technical solution, it is possible to deeply analyze the user-specific service level agreement (SLA) constraints of each given task, including the user-preset charging completion deadline and the current state of charge reported by the vehicle's battery management system. This refined constraint information provides a reliable basis for accurately assessing task conflicts. The estimated completion time is recalculated based on the charging time requirements of the remaining charging tasks and the movement time of candidate mobile charging devices. The mechanism of comparing the estimated completion time with the corresponding user-specific SLA constraints of the given task makes the statistics on the number of delayed tasks more accurate and objective. This simulation evaluation method based on real constraints ensures that while addressing emergency charging needs, it can quantify and assess the risk of default on existing user service commitments, thereby achieving more intelligent and responsible resource scheduling.

[0024] 3. By adopting a dynamic time constraint update technology based on real-time traffic data, the system queries the real-time arrival time of flights or trains from external real-time traffic data sources and determines it as the charging deadline for the remaining charging tasks. This effectively solves the technical problem in the existing technology where the static reservation time does not match the dynamic changes in the user's actual travel, resulting in an inaccurate charging service time window. This achieves the technical effect of intelligent conversion from static reservation time to dynamic real-time constraints, improving the success rate of charging tasks and the accuracy of services in time-sensitive scenarios. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a city-wide coordinated scheduling method for parking and charging, as described in an embodiment of this application. Figure 2 This is another flowchart illustrating a city-wide coordinated scheduling method for parking and charging, as described in this application. Figure 3 This is a schematic diagram of an exemplary hardware structure of the scheduling device in an embodiment of this application. Detailed Implementation

[0026] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0027] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0028] Please see Figure 1 This is a flowchart illustrating a city-wide coordinated scheduling method for parking and charging, as described in this application.

[0029] S101. In response to the failure signal of the target vehicle charging task, obtain the remaining charging task of the target vehicle.

[0030] Among them, the failure signal is a data packet generated by the mobile charging device or the background monitoring system that performs the charging task, which is used to report that the current charging task cannot continue to be executed as scheduled. The signal usually includes the failure type, timestamp, faulty device identifier and target vehicle information.

[0031] When a previously assigned and executing charging task is interrupted, the scheduling device receives a failure signal reported by a device or determines that a device is out of contact through a heartbeat timeout mechanism. The scheduling device first parses the received failure signal, extracting key fields, including but not limited to the faulty device ID, target vehicle ID, failure reason code (e.g., mechanical failure, communication interruption, task timeout, insufficient battery power), and on-site data such as the geographical coordinates at the time of failure and the amount of charge already received. Subsequently, based on the target vehicle ID, the scheduling device retrieves all information of the original "target vehicle charging task" from the task database, including the total charging demand, original charging deadline, and Service Level Agreement (SLA). By subtracting the completed charging amount carried in the failure signal or queried from the vehicle's Battery Management System (BMS) from the original total charging demand, the "residual charging demand" of the target vehicle is accurately calculated. Meanwhile, the scheduling equipment dynamically adjusts task constraints based on the failure type. For example, if the failure is due to a user returning early, causing a "task timeout failure," the scheduling equipment will call an external real-time traffic information interface to query the actual arrival time of the user's associated flight or train, and set a more urgent new charging deadline accordingly. If the failure is due to a "mobility failure," it may be necessary to generate a temporary risk area by adding the coordinates of the failure point to the task information. Finally, the scheduling equipment integrates the calculated residual charging demand, the updated charging deadline, and other possible environmental constraints (such as risk areas) into a structured "residual charging task" data object.

[0032] Specifically, if the failure type is "mobility failure," the scheduling device will further extract the geographical coordinates of the failure from the failure signal. Based on these coordinates, the scheduling device will generate a temporary dynamic risk zone with a preset spatial range (e.g., a radius of 50 meters) and a preset effective duration (e.g., 30 minutes) centered on this coordinate point on its internally maintained digital map system. This zone information will be broadcast to all devices and used as a constraint for path planning. The path weight within this zone will be reduced, causing subsequent path planning algorithms to prioritize detour routes. For devices that must traverse this zone, their high-precision perception mode can be activated (e.g., enabling LiDAR for more refined obstacle detection) to ensure safety. Finally, the scheduling device will recombine and package this newly generated dynamic risk zone information, along with the calculated residual charging demand of the target vehicle and the original target vehicle charging task information, to form a "residual charging task," providing on-site context information for subsequent rescheduling.

[0033] S102, broadcast a connection request containing residual charging demand to the charging equipment cluster, and receive capability declaration data from multiple candidate mobile charging devices.

[0034] Among them, the charging equipment cluster refers to the collection of all mobile charging equipment and fixed charging piles distributed in a specific service area, which are uniformly managed and coordinated by the scheduling equipment. They share the same communication network and scheduling platform. Capability declaration data refers to the structured data that candidate mobile charging equipment actively reports to the scheduling equipment when responding to a connection request, describing its current service capabilities. This data includes at least the device ID, current precise location, current total charging capacity, battery state of charge (SoC), estimated time required to reach the target vehicle location, and the established sequence of tasks that have been accepted but not yet completed.

[0035] After successfully constructing the residual charging task, including the residual charging demand (e.g., needing to replenish 30kWh of electricity), the precise geographical coordinates or parking space number of the target vehicle, and the updated charging deadline, the scheduling device broadcasts this request to the entire charging device cluster under its jurisdiction via its communication module. This "broadcast" is a logical "one-to-many" communication mode, designed to ensure that all online mobile charging devices receive the request. Upon receiving the broadcast request, each mobile charging device in the cluster immediately performs a self-assessment: first, it checks whether its current available charging capacity is sufficient to meet the requested residual charging demand; second, based on its current location and the location of the target vehicle provided in the request, it calls its built-in route planning module or queries an external map service to calculate the estimated travel time to the site. Only when the device determines that its battery is sufficient and its status is normal will it construct a capability declaration, including its own ID, the calculated estimated arrival time, the current available charging capacity, and the complete set of tasks in its internal task queue, and send this information back to the scheduling device via unicast. The scheduling device will continuously listen to and collect capability declaration data sent back by all responding devices within a preset time window (e.g., 5-10 seconds) after the broadcast is sent, forming a pool of candidate mobile charging devices.

[0036] In some embodiments, broadcasting to and receiving responses from the charging device cluster can be implemented in several ways: Optionally, the scheduling device can utilize a message middleware (such as MQTT) based on a publish / subscribe (Pub / Sub) model. The scheduling device, acting as a publisher, publishes the connection request to a specific topic, such as "emergency_tasks". All online mobile charging devices subscribe to this topic as subscribers. When a device receives a message, it performs a self-evaluation. If it meets the criteria, it publishes its capability declaration data to another dedicated response topic, such as "task_bids / [request_ID]". The scheduling device collects information on all candidates by subscribing to this response topic. Optionally, the scheduling device can also employ a dynamic query method based on geolocation and status. The scheduling device maintains a real-time in-memory database (such as Redis) containing the location, battery level, and task status of all devices. When a connection request is generated, the scheduling device first performs a geospatial query in this database to filter out all devices within a certain range (e.g., 2 kilometers) of the target vehicle. Then, it further filters out devices with low battery levels or those in a faulty state. Finally, the scheduling device sends point-to-point capability query requests directly to this pre-filtered, significantly reduced list of devices and awaits their direct responses. It is understood that other methods can also be used to achieve this step; this is not limited here.

[0037] S103. Simulate multiple hypothetical execution sequences constructed by randomly inserting different predetermined task sequences into the residual charging task, and count the number of delayed tasks that cannot be completed on time in the predetermined task sequence.

[0038] The predetermined task sequence refers to a series of charging tasks that already exist in the internal task queue of a candidate mobile charging device when it responds to a connection request, and which need to be executed sequentially according to the plan.

[0039] The scheduling device iterates through each candidate mobile charging device collected in S102. For each candidate device, the scheduling device obtains its reported "predetermined task sequence". Then, the scheduling device attempts to randomly insert the current "residual charging task" into the possible positions of the predetermined task sequence. For example, if a candidate device's original task sequence is [Task A, Task B], then after inserting the residual charging task (denoted as Task R), there are three possible hypothetical execution sequences: [Task R, Task A, Task B], [Task A, Task R, Task B], and [Task A, Task B, Task R]. For each constructed hypothetical execution sequence, the scheduling device initiates a detailed simulation calculation. The simulation starts from the first task in the sequence and calculates its completion time, which will be used as the start time of the next task. When specifically calculating the completion time of a task, it is necessary to consider the travel time from the end point of the previous task (or the current location of the device) to the location of the target vehicle for this task, as well as the service time (i.e., charging time) required to complete the task. The scheduling device calculates the estimated completion time for each given task based on the current state of charge reported by the vehicle's BMS and user-defined service level agreement (SLA) constraints, such as the user-preset charging completion deadline. This newly calculated estimated completion time is then compared to the original deadline agreed upon in the SLA. If the estimated completion time is later than the agreed deadline, the given task is marked as "delayed." After simulating and comparing all given tasks in a hypothetical execution sequence, the scheduling device calculates the total number of delayed tasks caused by that sequence. By simulating multiple hypothetical execution sequences generated from all possible insertion positions, the scheduling device can find the insertion position that minimizes the number of delayed tasks for a candidate device and the corresponding minimum delay number. This process is repeated for all candidate mobile charging devices, ultimately calculating the minimum number of delayed tasks under an optimal insertion strategy for each candidate.

[0040] In some embodiments, the simulation and statistical steps can be implemented in several ways: Optionally, the scheduling device can maintain a separate simulation thread or process for each candidate mobile charging device. In this thread, the program first loads the given task sequence and residual charging tasks into a data structure (such as a linked list or array). Then, through a loop, the residual charging tasks are sequentially inserted into the 0th position, the 1st position, and so on until the end of the sequence, generating a new hypothetical execution sequence copy with each insertion. For each copy, the program starts an inner loop, traversing from the beginning of the sequence and accumulating the estimated completion time of each task. During the calculation, the time is compared in real time with the SLA deadline of the tasks, and a counter records the number of delayed tasks. After the simulation of all insertion positions is completed, the thread returns the minimum delay number for the candidate device. Optionally, a more efficient incremental calculation method can be used. The scheduling device first calculates the completion time of each task in the original given task sequence without inserting any tasks. When attempting to insert a residual charging task into a position, it is not necessary to recalculate the entire sequence from the beginning. We only need to calculate the total time (travel time + charging time) spent performing the residual charging task, and then apply this "delay increment" to the original estimated completion time of all predetermined tasks after the insertion point to obtain a new estimated completion time, which is then compared and statistically analyzed. Understandably, this step can also be implemented in other ways, such as using a discrete event simulation framework to build a more refined model that considers more dynamic factors such as charging power curves and road congestion; this is not limited here.

[0041] In some embodiments, when the given task sequence for a candidate mobile charging device is very long (e.g., more than 10 pending tasks), the computational cost of trying every insertion point and performing a complete simulation becomes very high, potentially leading to decision delays if there are many candidate devices. Therefore, before performing insertion simulations, the scheduling device calculates an "urgency" or "slackness" index for each task in the given task sequence. This index can be defined as (task deadline - current time - estimated task execution time). Tasks with lower slackness have tighter time windows. When selecting an insertion point, the scheduling device prioritizes insertion before tasks with higher slackness and, based on a pre-set threshold, skips simulation attempts to insert tasks with slackness below the threshold. For example, for the sequence [task A (slackness 5 minutes), task B (slackness 60 minutes), task C (slackness 10 minutes)], the scheduling device might prioritize simulating the insertion of the remaining task before task B, potentially ignoring insertion before task A altogether, as this could very likely delay task A. In this way, the number of hypothetical execution sequences that need to be simulated can be significantly reduced without sacrificing too much decision quality, thereby significantly speeding up the computation.

[0042] In other embodiments, the scheduling device extracts a predetermined task sequence from the capability claim data reported by candidate mobile charging devices. For each predetermined task in the sequence, the scheduling device queries and extracts its associated "user-specific service level agreement constraint" from the task database. This constraint includes at least the latest "charging completion deadline" set by the user during reservation, and the "current state of charge" reported by the vehicle's battery management system (BMS) for accurately calculating charging duration. The scheduling device then begins constructing and evaluating hypothetical execution sequences. It tentatively inserts "residual charging tasks" into various possible positions within the predetermined task sequence. For each formed hypothetical execution sequence, the scheduling device recalculates the "estimated completion time" of all predetermined tasks after the insertion point in the sequence based on the "charging time requirement of the residual charging task" and the "movement time of the candidate mobile charging device" (from the current location to the residual task target point, and the transfer time between tasks). Finally, the scheduling device compares the newly calculated "estimated completion time" for each predetermined task with the "charging completion deadline" in the task's "user-specific service level agreement constraint". If the estimated completion time is later than the deadline, the task is counted as a delayed task. By simulating all possible insertion points for a candidate device, the scheduling device ultimately calculates the value that minimizes the number of delayed tasks caused by that device.

[0043] S104. With the constraint that the residual charging demand is no greater than the current available charging capacity, and with the optimization objective of minimizing the number of delayed tasks, select one or more devices from the candidate mobile charging devices to form a candidate combination scheme.

[0044] The scheduling device first performs a screening process, dividing all candidate mobile charging devices into two categories: the first category is "independent completers," which are devices whose "current available charging capacity" is greater than or equal to their "residual charging demand"; the second category is "cooperative participants," which are devices with insufficient power to complete the task independently. Then, the scheduling device begins constructing candidate combination schemes. First, it examines all "independent completers," treating each as an independent candidate combination scheme, with the number of delayed tasks in that scheme being the minimum number of delayed tasks calculated in S103 for that device. Next, to explore the possibility of multi-device collaboration, especially when there are no "independent completers" or the delays caused by "independent completers" are too large, the scheduling device attempts to combine devices from all candidate devices (including both categories). It may employ a combination optimization algorithm, for example, first sorting all candidate devices from lowest to highest according to their minimum number of delayed tasks, and then starting with the optimal device, attempting to construct combinations one by one until the sum of the total "current available charging capacity" of all devices within the combination meets the "residual charging demand." Each successful combination creates a new candidate combination scheme, whose total number of delayed tasks is the sum of the minimum number of delayed tasks for each device within the combination. Through this process, the scheduling device generates one or more valid candidate combination schemes, each satisfying power constraints and associated with a defined total number of delayed tasks.

[0045] In some embodiments, the selection and construction of candidate combination schemes can be implemented in several ways: Optionally, a fast construction strategy based on a greedy algorithm can be adopted. First, all candidate mobile charging devices are sorted in ascending order according to the minimum number of delayed tasks calculated in S103. Then, the system creates an empty combination scheme and adds devices to the combination sequentially, starting from the top of the sorted list (least delayed). For each device added, its available charging capacity is accumulated. This process continues until the total available charging capacity of the devices in the combination is greater than or equal to the residual charging demand for the first time. At this point, the set of devices selected by the greedy algorithm becomes a candidate combination scheme, and its total delay is the sum of the delays of each device. This method aims to quickly find a feasible solution with low delay cost. Optionally, an integer linear programming method can be used to find the optimal combination. The scheduling device sets whether each candidate mobile charging device i is selected as a binary decision variable x_i (0 or 1). The optimization objective is to minimize the total delay, i.e., Minimize Σ(delay_count_i*x_i). The constraint is that the total power supply must meet the demand, i.e., Σ(available_charge_i*x_i)>=residual_demand. Inputting this model into a standard ILP solver will directly output a set of optimal x_i values. All devices corresponding to x_i=1 collectively constitute the candidate combination scheme with the absolute minimum delay. It is understandable that other methods can be used to implement this step, such as dynamic programming or metaheuristic algorithms (e.g., genetic algorithms, simulated annealing) to search for better combinations in a larger solution space; this is not limited here. Additionally, when the selected candidate combination scheme includes multiple mobile charging devices, the scheduling device also needs to determine the specific charging amount each device is responsible for. A simple allocation strategy is proportional allocation, that is, allocating the remaining charging demand based on the proportion of each device's available charging capacity to the total available charging capacity of the combination. For example, if device A can provide 40kWh, device B can provide 20kWh, and the total demand is 30kWh, then A is responsible for 30*(40 / (40+20))=20kWh, and B is responsible for 10kWh.

[0046] S105. The candidate combination scheme with the fewest delayed tasks is determined as the target execution scheme.

[0047] The scheduling device holds a list of all feasible candidate combinations, each specifying the participating mobile charging devices, its optimal task insertion sequence, and the total number of delayed tasks it will cause. Specifically, the scheduling device iterates through this list, comparing the "number of delayed tasks" metric for each candidate combination. It finds and identifies the combination with the smallest value. In the simplest case, if only one combination has the smallest number of delayed tasks, that combination is directly selected as the target execution plan. However, in real-world scenarios, multiple different candidate combinations may have the same minimum number of delayed tasks. In this case, to ensure the uniqueness and optimality of the decision, the scheduling device initiates a pre-defined, multi-layered "Tie-Breaking" mechanism. This mechanism sequentially filters options using secondary optimization objectives. For example, it first compares the total travel time of these options and selects the one with the shortest travel time to save energy and time. If a tie still exists, it may further compare the number of mobile charging devices involved in each option and select the option with fewer devices to reduce communication and coordination complexity. If a distinction still cannot be made, it may select the option with the highest remaining total battery power among all devices in the combination after completing the task, thus preserving a stronger ability to cope with future uncertainties. Through this hierarchical decision-making rule, the scheduling equipment can ensure that it can always determine a unique and optimal target execution plan from multiple equally optimal options.

[0048] In some embodiments, the determination of the target execution plan can be implemented in several ways: Optionally, the scheduling device can store all candidate combination plans in a list or array data structure, where each element contains attributes such as plan ID, device list, number of delayed tasks, total movement time, and number of devices. Then, a sorting algorithm that supports multiple sorting conditions is called to sort the list. The primary key for sorting is "number of delayed tasks" (ascending order), the secondary key is "total movement time" (ascending order), and the third secondary key is "number of devices" (ascending order). After sorting, the candidate combination plan corresponding to the first element in the list is the final determined target execution plan. This method is simple to implement and can efficiently and deterministically complete the selection. Optionally, the scheduling device can adopt a decision model based on weighted scoring. The system pre-sets a weight coefficient for each evaluation dimension (number of delayed tasks, movement time, number of devices, etc.), where the weight of "number of delayed tasks" is much greater than that of other dimensions. For each candidate combination plan, the scheduling device calculates a comprehensive score. After calculating the scores of all plans, the plan with the lowest score is selected as the target execution plan. This approach offers greater flexibility, allowing operators to fine-tune scheduling strategies by adjusting weights. Understandably, this step can also be implemented in other ways, such as by building a decision tree or using simple loop comparison logic for step-by-step filtering; this is not a limitation here.

[0049] In some embodiments, the scheduling device first identifies all predetermined tasks that will be delayed based on the target execution plan determined in S105. Then, for each delayed task, the scheduling device queries the membership level of its associated user and obtains the "weight coefficient" corresponding to that level (e.g., 1 for ordinary users, 3 for silver cards, and 5 for gold cards). Next, the weight coefficients corresponding to all delayed tasks in the plan are summed to obtain the "weighted delay score" of the target execution plan. Subsequently, the scheduling device compares this score with a preset "delay threshold". If the weighted delay score does not exceed the delay threshold, it means that the service quality impact caused by the plan is within an acceptable range, and the scheduling device finally determines to execute the target execution plan, and the process continues to S106. Conversely, if the weighted delay score exceeds the delay threshold, it means that the cost of the plan is too high. At this time, the scheduling device will trigger a retry mechanism to re-execute the simulation step in S103, but in this simulation, the candidate mobile charging device (or group of candidate devices) that cause the high score may be temporarily excluded in order to find a suboptimal plan with a lower total score. This re-execution process has a specified limit on the number of times (e.g., 3 times). If, after reaching the specified number of times, a solution with a weighted delay score below the threshold is still not found, the system determines that the remaining charging task cannot be resolved automatically without causing significant service loss. At this point, the scheduling device will package and send the detailed information of the target vehicle charging task, along with the reason for failure and the scheduling attempt, to the emergency personnel's client for manual intervention.

[0050] It should be noted that the "number of delayed tasks" metric used in this step can be replaced with "weighted delay score" to inherently take into account the differences in user levels and delay durations when making decisions.

[0051] S106. Based on the target execution plan, issue a succession execution instruction containing the charging amount and execution sequence of each candidate mobile charging device in the target execution plan.

[0052] The scheduling device parses the target execution plan and identifies all selected mobile charging devices included within it. For each selected device, the scheduling device generates a follow-up execution instruction. The core of this instruction is the updated complete task sequence for that device, constructed based on the optimal insertion point found in the S103 simulation, clearly defining the position of the remaining charging tasks (follow-up tasks). If the target execution plan involves multiple devices co-charging, the scheduling device first calculates the specific charging amount each device needs to be responsible for according to a preset allocation strategy (such as proportional allocation) and includes this value in the instruction. The instruction also includes detailed execution timing information; for example, it marks the updated estimated start time, estimated arrival time, and estimated completion time for each task in the sequence (including existing tasks and newly inserted follow-up tasks). After the instructions are generated, the scheduling device uses its communication module to precisely send these highly specific follow-up execution instructions to each corresponding mobile charging device in the target execution plan in a point-to-point (unicast) manner. After receiving the instruction, the device will clear or update its local task queue and start executing tasks according to the sequence and timing defined in the new instruction, thereby completing the entire closed loop of coordinated scheduling for stopping and charging.

[0053] In this embodiment, the scheduling device simulates inserting residual charging tasks into different predetermined task sequences, quantifying the cascading impact of each candidate device or combination on its original service commitment after taking on a new task—that is, the number of delayed tasks. Using this as an optimization objective, and under the premise of satisfying basic charging volume constraints, a global optimization is performed to select the candidate combination scheme with the least disturbance to the entire service network as the final decision. This effectively solves the cascading service failure problem in existing technologies where simple assignment strategies lead to "solving one failure and causing multiple delays," avoiding a decline in the overall reliability of the service network. Furthermore, it achieves not only rapid response to sudden charging demands but also ensures the stability and reliability of the entire charging service system, improving the overall service experience for users and realizing the technical effect of utilizing charging equipment resources globally.

[0054] However, in practical applications, there are scenarios with extremely high requirements for response timeliness, such as when a user's original charging time window is drastically compressed due to earlier flight or train departures. In such scenarios, although the aforementioned emergency response mechanisms remain effective, they require a certain reaction time. This application improves the reliability of emergency response by integrating "risk prediction and resource pre-matching based on user return information" technology to pre-lock standby resources.

[0055] Please see Figure 2 This is another flowchart illustrating a city parking and charging coordinated scheduling method in this application embodiment.

[0056] S201. Verify the user return information associated with all scheduled charging tasks that are being executed or pending in the charging equipment cluster according to the preset cycle.

[0057] The preset period refers to the time interval set by the system administrator or through an adaptive algorithm to trigger periodic check tasks, such as every 15 minutes or 30 minutes.

[0058] This step involves continuous background operation of the system. An internal timer or scheduling program runs within the dispatching equipment, triggering a global task verification process at a preset interval (e.g., every 30 minutes). Each time a task is triggered, the dispatching equipment queries its internal task database, filtering out all scheduled charging tasks with a status of "in execution" or "pending execution" and associated with "user return trip information." For each selected task, the dispatching equipment extracts its associated flight number or train number. Subsequently, the dispatching equipment calls the application programming interface (API) of an external real-time traffic data source, using these flight numbers as query parameters to request the latest information on these flights or trains, such as estimated arrival time, actual arrival time, or current status (e.g., "departed," "delayed," "arrived," etc.). The purpose of this verification process is to compare the planned return time based on the user's reservation with the current actual predicted return time, thereby identifying potential time discrepancies and providing data input for subsequent prediction and pre-scheduling (e.g., S202, S203).

[0059] S202. Based on the flight or train number corresponding to the user's return trip information, retrieve the historical early arrival rate of the flight number from the historical transportation database.

[0060] Among them, the flight number refers to the code used to uniquely identify a flight or train on a specific date and route, such as flight number "CA1234" or train number "G1"; the historical traffic database refers to a dedicated database maintained or accessed by the dispatching system itself, which stores and accumulates various historical operation data of various flights over a long period of time, and records the planned take-off / arrival / departure times and actual take-off / arrival / departure times of each flight over a period of time.

[0061] For each scheduled charging task linked to valid user return information selected in S201, the dispatching device has obtained its corresponding schedule number. In this step, the dispatching device uses this schedule number as the query key to access its internal or external "historical traffic database." This database stores every run record of this schedule over a considerable period of time (e.g., the past year). For each record, the database saves its "planned arrival time" and "actual arrival time." The dispatching device performs a query and aggregation operation, first filtering out all historical records matching the schedule number, then iterating through these records to count the total number of records where the "actual arrival time" is earlier than the "planned arrival time" (referred to as the number of early arrivals), and the total number of valid records (referred to as the total number of early arrivals). Finally, by calculating (number of early arrivals / total number of early arrivals) * 100%, the "historical early arrival ratio" for this schedule is obtained. This ratio value will be appended to the corresponding scheduled charging task as a direct input for the next decision (S203).

[0062] S203. When the proportion of historical early arrivals exceeds the preset proportion, pre-match standby mobile charging devices for the scheduled charging task, and add the standby scheduled charging task to the task sequence for the standby mobile charging device.

[0063] After S202 completes the risk assessment of a scheduled charging task, the scheduling device compares the calculated "historical early arrival rate" with a system-preset threshold. If the historical rate does not exceed the threshold, the risk of the task failing due to the user's early return is low, and the scheduling device will not perform any special processing; the task will proceed as planned. Conversely, if the historical rate exceeds the preset rate, the scheduling device determines the task as high-risk and immediately initiates a pre-matching process. First, the scheduling device filters out mobile charging devices currently in an "idle" or "standby" state with sufficient power and suitable location from the charging device cluster as candidates. Then, it may select the optimal device based on factors such as distance and remaining power, designating it as the "standby mobile charging device" for the high-risk scheduled charging task. Next, the scheduling device creates a "standby scheduled charging task." This task is essentially an "early warning version" of the original scheduled charging task. The key difference lies in the adjustment of the execution time: the dispatching equipment queries the historical traffic database for the average early arrival time of this route (e.g., 15 minutes early on average), and then advances the charging deadline of the original scheduled charging task by the corresponding amount, forming a new deadline for the standby task. Finally, the dispatching equipment adds this standby scheduled charging task to the task sequence within the selected standby mobile charging device in a special "pending activation" state. In this way, the device is associated with a high-risk task; although it may still be performing other tasks or waiting in the standby area, its subsequent dispatching planning has already taken this potential emergency task into account.

[0064] S204. In response to the failure signal of executing the target vehicle charging task, obtain the remaining charging task of the target vehicle.

[0065] When a previously assigned and executing charging task is interrupted for any reason (including but not limited to equipment failure, communication interruption, or "task timeout failure" that will be handled in subsequent steps of this process), the scheduling device needs to immediately activate the emergency response mechanism. Specifically, the scheduling device will continuously monitor the status reports or heartbeat signals from all mobile charging devices under its jurisdiction. When it receives a failure signal reported by a device, or determines that a device is out of contact through the heartbeat timeout mechanism, it triggers this step. The scheduling device first parses the received failure signal, extracting key fields, including but not limited to the faulty device ID, target vehicle ID, failure reason code, and on-site data at the time of failure (such as the amount of charge already received). Subsequently, based on the target vehicle ID, the scheduling device will retrieve all information of the corresponding original "target vehicle charging task" from the task database, including the total charging demand, the original charging deadline, the Service Level Agreement (SLA), and possibly related user return trip information. By subtracting the amount of charge already completed from the failure signal or retrieved from the vehicle's Battery Management System (BMS) from the original total charging demand, the "residual charging demand" of the target vehicle is accurately calculated.

[0066] S205. Determine whether a standby mobile charging device exists.

[0067] The scheduling device uses the task ID or target vehicle ID of the currently failed task as an index to query its internal task management database or in-memory state table. It searches for a record indicating that the task was marked as "high-risk" and associated with an ID of a "standby mobile charging device." If the query returns a valid device ID, and the device's current status is indeed "standby" or "available for activation," the result is "exists." Conversely, if the query finds no associated standby device record, or if the record exists but the associated device is currently in a "faulty," "offline," or performing other uninterruptible high-priority tasks, the result is "does not exist."

[0068] If it exists, proceed to step S206; If it does not exist, proceed to step S207.

[0069] S206. Standby mobile charging devices perform standby scheduled charging tasks.

[0070] The dispatching equipment sends an explicit "activate task" instruction to the standby mobile charging device. The core of this instruction is to inform the device that the standby scheduled charging task in its task sequence, which was previously in a "standby" state, now needs to be immediately activated and elevated to the highest priority. The instruction includes the latest "residual charging demand" calculated in S204 to cover the estimated charging amount in the original standby task. Simultaneously, if the failure type is "task timeout failure," the dispatching equipment will also update the activation instruction with the latest charging deadline determined in S209 based on real-time flight / train information, ensuring that the standby device executes the most accurate and urgent task version. Upon receiving the activation instruction, the standby mobile charging device immediately adjusts its current execution plan. If it is idle, it will immediately begin moving towards the target vehicle's location according to the path planned in the standby task. If it is executing a preemptible low-priority task, it will interrupt the current task (and report the interruption status to the dispatch center for subsequent processing) and execute the activated, higher-priority standby task.

[0071] In some embodiments, a "task timeout failure" triggers S206, and the standby device has already departed. However, a few minutes later, the user's flight issues a new delay notification, causing the previously updated "real-time arrival time" to become inaccurate again, and the new charging deadline becomes more lenient. If the standby device continues to proceed at full speed according to the previous urgent instructions, it may unnecessarily consume more energy and potentially affect the scheduling of its subsequent tasks. Specifically, during the execution of an activated task, the standby mobile charging device maintains a high-frequency heartbeat and status synchronization with the dispatch center's communication link. The dispatch center continuously monitors external information sources associated with the task (such as flight dynamics). Once the periodic verification process in S201 detects a new and significant change in the flight's real-time arrival time, the dispatch device immediately recalculates the charging deadline and generates a "task update" instruction, rather than a completely new task instruction. This update instruction only contains the changed fields (e.g., the new deadline) and is immediately sent to the standby device en route. After receiving the update command, the device does not need to interrupt the entire task. It can simply dynamically adjust its internal execution parameters. For example, if time becomes more abundant, it can appropriately reduce the driving speed to save power, or choose a more economical but not necessarily the fastest route in path planning.

[0072] S207. Broadcast a connection request containing residual charging demand to the charging equipment cluster.

[0073] The dispatching device constructs a standard connection request message. The core of this message is the "residual charging demand" calculated in S204, along with the target vehicle's precise geographical location, vehicle model (to determine the charging protocol and power), and a preliminary charging cutoff time (which may be further refined in subsequent steps such as S209). Once constructed, the dispatching device broadcasts or multicasts this connection request to all "online" mobile charging devices within its jurisdiction via its communication network. Devices that are "faulty," "offline," "severely low on power," or performing uninterrupted special tasks may be excluded from the broadcast list. Upon receiving this broadcast request, each mobile charging device in the cluster independently determines its ability to respond based on its current status (including location, remaining power, and the busyness of existing task queues). The purpose of this broadcast step is to inform all potential rescuers of the need for assistance in the shortest possible time and trigger them to conduct self-assessments.

[0074] S208. When the failure type is determined to be task timeout failure, based on the return trip information of the booked users associated with the target vehicle, query the real-time arrival time of the corresponding flight or / and train from the external real-time traffic data source.

[0075] This step can also be executed in S204, and proceed directly to step S209 after step S207; or it can be performed according to the current step number sequence. This step is triggered when the scheduling device parses the failure signal and determines that its failure type is "task timeout failure". The scheduling device will initiate an information verification process. First, it will query the task database to see if the task is associated with "reserved user return information" based on the target vehicle ID associated with the failed task. If not associated, this step cannot be executed, and the system will use the default or original deadline. If valid return information is found (e.g., flight number "CA1234"), the scheduling device will use its integrated external API client to initiate a query request to one or more "external real-time traffic data sources". This request takes the flight number as a parameter to obtain the latest "real-time arrival time" of the flight. For example, the scheduling device may call a flight tracking API, input "CA1234", and the API will return the estimated time of arrival (ETA) of the flight, which is dynamically calculated based on the actual takeoff time of the aircraft, the current cruise status, etc. This query action aims to switch the time base for scheduling decisions from the static, potentially outdated planned time at the time of user reservation to the real-time time that is dynamically synchronized with the user's actual itinerary.

[0076] It should be noted that the real-time arrival time obtained is usually the time when the flight or train arrives at the airport / station, while the user still needs some time (such as collecting luggage or exiting the station) to reach the parking lot to pick up the car. Therefore, after obtaining the real-time arrival time, the system should automatically add a preset or user-defined "buffer time" (e.g., 30 minutes).

[0077] S209. The real-time arrival time is determined as the charging deadline for the remaining charging task.

[0078] The scheduling device uses the real-time arrival time obtained in S208 as a baseline. Considering that users need a certain amount of time from landing at the airport or arriving at the train station to finally picking up their car at the parking lot (e.g., retrieving luggage, walking), the scheduling device adds a preset or user-defined "connection buffer time" (e.g., 30 minutes) to this baseline. This final time point is used by the scheduling device to update or overwrite the "charging deadline" attribute within the residual charging task object generated in S204. Through this step, the residual charging task no longer uses the potentially inaccurate static deadline from the original reservation, but instead has a more pressing deadline strongly correlated with the user's actual itinerary. This updated charging deadline will serve as a time dimension constraint for subsequent broadcasts to the charging device cluster (S211) and simulation screening (S212, S213).

[0079] Meanwhile, the system can proactively notify users via app push notifications: "We have detected that your flight has arrived ahead of schedule. To ensure charging is completed, we will serve you as quickly as possible. The estimated completion time is XX:XX. Please be aware of this." In this way, the system avoids subsequent invalid scheduling attempts and provides timely and transparent expectation management for users.

[0080] S210. Combine the charging deadline with the target vehicle charging task to form a residual charging task.

[0081] The scheduling equipment performs a data assembly operation. It creates a new task data structure and then populates its attributes from various sources, including: the "residual charging demand" calculated in S204; the "charging cutoff time" determined in S209, which already includes the buffer time; and copies essential information that remains valid and necessary even after task failure from the original "target vehicle charging task" record, such as the target vehicle's ID, geographical location, vehicle model, and user SLA level. Through this assembly process, fragmented emergency information, originally scattered across different processing steps, is integrated into a logically unified and data-complete "residual charging task" object.

[0082] S211. Broadcast a connection request containing residual charging demand to the charging equipment cluster, and receive capability declaration data from multiple candidate mobile charging devices.

[0083] S212. Simulate multiple hypothetical execution sequences constructed by randomly inserting different predetermined task sequences into the residual charging task, and count the number of delayed tasks that cannot be completed on time in the predetermined task sequence.

[0084] S213. With the constraint that the residual charging demand is no greater than the current available charging capacity, and with the optimization objective of minimizing the number of delayed tasks, select one or more devices from the candidate mobile charging devices to form a candidate combination scheme.

[0085] S214. The candidate combination scheme with the fewest delayed tasks is determined as the target execution scheme.

[0086] S215. Based on the target execution plan, issue a succession execution instruction containing the charging amount and execution sequence of each candidate mobile charging device in the target execution plan.

[0087] Steps S211~S215 and Figure 1 Steps S102 to S106 in the illustrated embodiment are similar and will not be repeated here.

[0088] In this embodiment, because a dynamic deadline update mechanism based on real-time traffic data is adopted, the system can dynamically adjust task constraints according to the user's actual return journey progress, effectively solving the technical problem of mismatch between static task constraints and dynamic scenarios in the prior art, thereby achieving the technical effects of improving the accuracy of scheduling decisions and reducing ineffective scheduling and resource waste. The exemplary scheduling device 300 provided in the embodiments of this application is described below. Figure 3 This is an exemplary hardware structure diagram of the scheduling device 300 provided in this application embodiment.

[0089] In some embodiments, the scheduling device 300 is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0090] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0091] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0092] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0093] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for coordinated scheduling of parking and charging in urban areas, characterized in that, Applied to scheduling equipment, the method includes: According to the preset cycle, verify the user return information associated with all scheduled charging tasks that are being executed or pending in the charging equipment cluster. Based on the flight or train number corresponding to the user's return information, retrieve the historical early arrival rate of the flight number from the historical transportation database; When the historical early arrival rate exceeds the preset rate, a standby mobile charging device is pre-matched for the scheduled charging task, and the standby scheduled charging task is added to the task sequence for the standby mobile charging device. The standby scheduled charging task is obtained by changing the scheduled charging task based on the historical average early arrival time of the shift number. In response to a failure signal indicating the execution of a target vehicle charging task, the failure type is parsed from the failure signal; When the failure type is determined to be a task timeout failure, the real-time arrival time of the corresponding flight or / and train is queried from an external real-time traffic data source based on the return trip information of the booked user associated with the target vehicle. The real-time arrival time is determined as the charging cutoff time for the remaining charging task, and the charging cutoff time is earlier than the original charging cutoff time preset for the target vehicle charging task. The charging deadline is combined with the target vehicle charging task to form the residual charging task, which includes the residual charging needs of the target vehicle. Broadcast a connection request containing the remaining charging demand to the charging device cluster, and receive capability declaration data from multiple candidate mobile charging devices, the capability declaration data including the current charging capacity, estimated arrival time and predetermined task sequence of the candidate mobile charging devices; By simulating multiple hypothetical execution sequences constructed by randomly inserting the residual charging tasks into different predetermined task sequences, the number of delayed tasks that cannot be completed on time in the predetermined task sequences is counted. With the constraint that the remaining charging demand is no greater than the currently available charging capacity, and with the optimization objective of minimizing the number of delayed tasks, one or more devices are selected from the candidate mobile charging devices to form a candidate combination scheme, wherein the candidate combination scheme includes at least one candidate mobile charging device. The candidate combination scheme with the fewest delayed tasks is determined as the target execution scheme. Based on the number of target delayed tasks for candidate mobile charging devices in the target execution plan, the number of target delayed tasks is multiplied by the weighting coefficient corresponding to the membership level of the associated user, and then summed to obtain a weighted delay score; If the weighted delay score exceeds the delay threshold, the step of re-executing multiple hypothetical execution sequences constructed by randomly inserting different predetermined task sequences into the residual charging task, and counting the number of delayed tasks that cannot be completed on time in the predetermined task sequence, is repeated until a specified number of times is reached; Upon confirming that the specified number of charging attempts has been reached, the target vehicle charging task will be sent to the emergency personnel's client. If the weighted delay score does not exceed the delay threshold, then the target execution plan is determined to be executed; Based on the target execution scheme, a succession execution instruction containing the charging amount and execution sequence of each candidate mobile charging device is issued to all candidate mobile charging devices in the target execution scheme.

2. The method according to claim 1, characterized in that, The step of simulating multiple hypothetical execution sequences constructed by randomly inserting the residual charging tasks into different predetermined task sequences, and counting the number of delayed tasks that cannot be completed on time in the predetermined task sequences, specifically includes: Extract the user-specific service level protocol constraints for each predetermined task from the predetermined task sequence of the candidate mobile charging devices. The user-specific service level protocol constraints include at least the user-preset charging completion deadline and the current state of charge reported by the vehicle battery management system. Based on the charging time requirements of the remaining charging tasks and the movement time of the candidate mobile charging devices, the estimated completion time of the predetermined tasks in the hypothetical execution sequence is recalculated after randomly inserting the remaining charging tasks into the predetermined task sequence to obtain the hypothetical execution sequence. The estimated completion time is compared with the user-specific service level agreement constraints of the corresponding predetermined task, and the number of delayed tasks that cannot be completed on time in the predetermined task sequence is counted.

3. The method according to claim 1, characterized in that, The step of broadcasting a connection request containing the remaining charging demand to the charging equipment cluster specifically includes: Determine whether the standby mobile charging device exists; If present, the standby mobile charging device executes the standby scheduled charging task; If not, a connection request containing the remaining charging demand is broadcast to the charging equipment cluster.

4. The method according to claim 1, characterized in that, The step of obtaining the residual charging task of the target vehicle in response to a failure signal in executing the target vehicle charging task, wherein the residual charging task includes the residual charging demand of the target vehicle, specifically includes: In response to a failure signal indicating the execution of a target vehicle charging task, the failure type is parsed from the failure signal; If the failure type is a mobile failure fault, then based on the geographical coordinates of the mobile failure fault in the failure signal, a temporary dynamic risk area is generated in the digital map of the scheduling system with the geographical coordinates as the center. The temporary dynamic risk area has a preset spatial range and a preset effective duration, and in the temporary dynamic risk area, the mobile charging device will preferentially select a detour path or activate a high-precision sensing mode. The dynamic risk area, the residual charging demand of the target vehicle, and the charging task of the target vehicle are combined into the residual charging task.

5. A scheduling device, characterized in that, The scheduling device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors invoke the computer instructions to cause the scheduling device to perform the method as described in any one of claims 1-4.

6. A computer program product containing instructions, characterized in that, When the computer program product is run on the scheduling device, the scheduling device performs the method as described in any one of claims 1-4.

7. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the scheduling device, the scheduling device performs the method as described in any one of claims 1-4.

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