Energy complementing work order processing method and system for electric vehicle

Abstract

The invention provides an energy complementing work order processing method and system for an electric vehicle. The method comprises the steps that a platform end receives an energy complementing request submitted by a user and generates an initial work order; the platform end executes a multi-modal work order intelligent matching and resource optimization algorithm according to the type and the multi-dimensional information of the initial work order in combination with real-time state data, prediction data and a preset optimization target of the energy complementing network so as to determine an optimal energy complementing scheme; according to a dynamic distribution strategy, distributing to corresponding energy complementing resources or reminding a client to execute; and the platform end monitors the execution progress of the executable work order in real time, and starts dynamic adjustment and redistribution of the work order according to real-time feedback or abnormal conditions generated in the execution process. According to the invention, efficient matching, dynamic distribution and full-life-cycle intelligent management of the energy complementing work order of the electric vehicle are realized, and the efficiency, the cost benefit and the user experience of the energy complementing service are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to a method and system for processing electric vehicle energy replenishment work orders. Background Technology

[0002] With the global energy transition and increasing awareness of environmental protection, electric vehicles, as an important carrier of green transportation, are experiencing rapid development. The widespread adoption of electric vehicles relies heavily on a well-developed charging infrastructure. Currently, electric vehicle charging methods mainly include fixed charging stations, mobile charging vehicles for on-site charging, and battery swapping.

[0003] While traditional fixed charging stations provide a stable charging service, their construction is limited by site constraints and coverage, making it difficult to meet the charging needs of electric vehicles during fragmented time periods, in remote areas, or in emergencies. To compensate for the shortcomings of fixed charging, mobile charging services have emerged. Existing mobile charging solutions typically involve dispatching logistics vehicles or charging robots equipped with mobile charging piles to the user's designated location for charging.

[0004] However, existing mobile charging services and work order processing methods still have many shortcomings. First, at the work order processing level, the scheduling logic of most solutions is relatively simple, mainly based on distance and resource availability for dispatching orders, lacking comprehensive consideration and intelligent optimization of multi-dimensional information such as user needs (e.g., time sensitivity, service type preference), energy costs (e.g., real-time electricity prices, photovoltaic power generation benefits), resource status (e.g., charging station queuing status, mobile charging pile model compatibility), and traffic conditions. This results in low dispatching efficiency, poor resource utilization, and difficulty in minimizing operating costs while ensuring user experience. Second, at the resource coordination level, the existing mobile charging services lack deep integration and unified scheduling capabilities with various energy replenishment modes such as fixed charging networks and battery swapping services. Especially in the process of mobile charging pile recycling, dispatching, and turnover, there is a lack of targeted and intelligent management, failing to fully utilize the advantages of new energy replenishment sites that integrate photovoltaic, energy storage, charging, and battery swapping functions, resulting in room for improvement in the turnover efficiency of mobile charging piles and the overall resource utilization rate of the energy replenishment network. Therefore, how to build an electric vehicle energy replenishment system that can intelligently process multiple types of energy replenishment work orders, collaboratively schedule multimodal energy replenishment resources, and realize end-to-end optimization decision-making is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems in the existing technology, this invention proposes a method and system for processing energy replenishment work orders for electric vehicles, thereby resolving these technical issues.

[0006] According to a first aspect of the present invention, a method for processing energy replenishment work orders for electric vehicles is proposed, comprising:

[0007] S1: The platform receives the user's power replenishment request and generates an initial work order. The power replenishment request includes the vehicle's location, current vehicle battery level, desired power replenishment, desired completion time, and desired power replenishment service type. The desired power replenishment service type includes station power replenishment and mobile power replenishment.

[0008] S2: Based on the type and multi-dimensional information of the initial work order, the platform executes a multi-modal work order intelligent matching and resource optimization algorithm, combined with the real-time status data, prediction data and preset optimization goals of the power replenishment network, to determine the best power replenishment solution;

[0009] S3: The platform encapsulates the best power replenishment solution into an executable work order and dispatches it to the corresponding power replenishment resources or reminds the client to execute it according to the dynamic distribution strategy;

[0010] S4: The platform monitors the execution progress of executable work orders in real time, and initiates dynamic adjustment and redistribution of work orders based on real-time feedback or abnormal situations generated during the execution process.

[0011] In some specific embodiments, the energy replenishment network includes integrated photovoltaic-storage-charging-transmission charging stations, fixed battery swapping stations for mobile charging piles, and mobile charging pile replenishment stations. Real-time status data includes the number of available fixed / mobile charging piles, the number of mobile charging robots that can be used to transport mobile charging piles within the station, real-time load, queuing status, local energy storage capacity, photovoltaic power generation forecast, and the current location, remaining power, current task status, estimated idle time, affiliated scheduling system, and historical service efficiency of each charging / battery swapping device.

[0012] In some specific embodiments, the multimodal work order intelligent matching and resource optimization algorithm specifically includes:

[0013] S21: Based on the order address, assess whether there is a mobile charging robot that can meet the demand, according to the current location, current task status, estimated idle time, and available power of the mobile charging station it carries.

[0014] S22: In response to the absence of a mobile charging robot that meets the conditions, a logistics power delivery path is constructed. The goal of constructing the path is to minimize the service response time and overall delivery cost. Specifically, this includes: searching for and prioritizing power delivery personnel without work orders within the specified radius of the work order location; matching the nearest mobile charging pile with available and demand-compliant charging stations or integrated photovoltaic-storage-charging-delivery charging stations as replenishment points based on the location of the power delivery personnel. The selection process comprehensively considers the real-time electricity price, load, number of available outlets, and pile type matching degree of the replenishment point.

[0015] In some specific implementations, if there are multiple orders to be dispatched in the work order pool, the algorithm will cluster the multiple orders and plan the route when constructing the path, so that a single power delivery worker can complete multiple adjacent orders at the same time, or complete the collection task on the return trip.

[0016] In some specific embodiments, the selection of supply points in S22 is optimized using the following objective function: C supply =min(w d ·D deliver +w e ·P cost +w t ·T wait +w m ·M match ), where C supply To cover supply costs, D deliver P is the distance from the power delivery operator's current location to the resupply point. cost The unit cost of electricity used to provide power to a resupply point, T wait M is the estimated waiting time for the supply point. match For pile type matching degree, w d ,w e ,w t ,w m These are dynamically adjustable weighting coefficients.

[0017] In some specific embodiments, after an executable work order is completed, the platform generates a recycling work order. The processing method for the recycling work order is also determined based on a multimodal work order intelligent matching and resource optimization algorithm. Specifically, this includes: assigning a higher priority to the recycling work order to ensure rapid return and turnover of mobile charging piles; the platform intelligently matching and selecting the optimal return point based on the company and type of the mobile charging pile to be recycled, combined with the number of available outlets in the charging / swapping station, local energy storage capacity, current load, future demand forecasts, and distance; the selection objective is to minimize recycling costs, maximize the resource utilization rate of the return point, and optimize the future allocation efficiency of the mobile charging piles.

[0018] In some specific embodiments, the optimization function for the return point of the mobile charging station is: C recovery =min(w d ·D return +w r ·R match +w s ·S capacity +w f ·F demand ), where C recovery To recover costs, D return R is the distance from the power delivery operator's location to the return point. match To determine the matching degree between pile ownership and return point operators, Scapacity To optimize the utilization rate of idle outlets and energy storage capacity at return points, F demand Forecasting future charging demand in the area near the return point, w d ,w r ,w s ,w f These are dynamically adjustable weighting coefficients.

[0019] In some specific embodiments, when the voltage of a mobile charging pile drops below a preset standard value, the platform generates a scheduling work order, matches a new charging pile of the same type that is available and has high power in the surrounding area, and plans for the delivery personnel to first pick up the new charging pile, then deliver it to the designated site to retrieve the old charging pile, and finally send the old charging pile back to the designated charging station for charging. The path optimization objective is to minimize the total time and round-trip cost.

[0020] In some specific embodiments, the multimodal work order intelligent matching and resource optimization algorithm, when determining the best energy replenishment scheme, also includes predicting and scheduling the next relay mobile charging pile for seamless energy replenishment for work orders that cannot meet the expected power in a single energy replenishment. The scheduling selection of the next relay mobile charging pile takes into account its expected arrival time and available power to ensure that the next mobile charging pile can take over in time before the current mobile charging pile runs out of power.

[0021] According to a second aspect of the present invention, a power replenishment work order processing system for electric vehicles is provided, comprising:

[0022] Initial work order generation module: This module is configured to receive user-submitted power replenishment requests and generate initial work orders on the platform. The power replenishment request includes the vehicle location, current vehicle battery level, desired power replenishment amount, desired completion time, and desired power replenishment service type. The desired power replenishment service type includes station power replenishment and mobile power replenishment.

[0023] The power replenishment scheme determination module is configured to use the platform to perform multimodal work order intelligent matching and resource optimization algorithms based on the type of the initial work order and multi-dimensional information, combined with the real-time status data, prediction data and preset optimization targets of the power replenishment network, in order to determine the best power replenishment scheme.

[0024] Dynamic work order distribution and execution module: This module is configured to encapsulate the best power replenishment solution into an executable work order on the platform side, and dispatch it to the corresponding power replenishment resources or remind the client to execute it according to the dynamic distribution strategy.

[0025] Redundancy strategy and exception handling module: Configured for real-time monitoring of the execution progress of executable work orders on the platform side, and to initiate dynamic adjustment and redistribution of work orders based on real-time feedback or exceptions generated during the execution process.

[0026] The electric vehicle refueling work order processing method and system proposed in this invention achieves highly intelligent and refined work order processing by leveraging a multi-dimensional intelligent matching and dynamic distribution algorithm. The system can process refueling requests from multiple dimensions, including vehicle location, battery level, and expected time, accurately understanding user needs. Scheduling decisions integrate real-time status with predicted data such as photovoltaic power generation and grid load, making them more forward-looking. The core optimization algorithm comprehensively quantifies and balances benefits, costs, and energy efficiency, achieving multiple optimizations in economic benefits, social benefits, and user experience.

[0027] In terms of the coordinated and efficient utilization of multimodal energy replenishment resources, the system seamlessly integrates new energy replenishment resources such as integrated photovoltaic-storage-charging-transmission charging stations, achieving unified management and intelligent scheduling, making energy replenishment services more flexible and diverse. For different work order types such as charging orders, recycling, and scheduling, differentiated order dispatch and resource selection logic is designed. For example, optimizing the matching of delivery personnel, the selection of replenishment points, and multi-task joint optimization to improve delivery efficiency, and intelligently selecting recycling points based on multiple factors to maximize resource turnover efficiency, ensuring the stable operation of core resources.

[0028] The system also enhances robustness and user experience. In the face of order dispatch failures or execution anomalies, it can immediately initiate intelligent assessment and re-dispatch, and even switch charging modes to ensure service continuity. By optimizing the entire mobile charging pile process, it reduces resource idle time and user waiting time. Integrating the energy management capabilities of photovoltaic, energy storage, charging, and transmission integrated sites, it utilizes peak-valley electricity price differences to reduce operating costs, providing users with more economical charging options. Attached Figure Description

[0029] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and, together with the description, serve to explain the principles of the invention. Other embodiments and many anticipated advantages of the embodiments will be readily recognized as they become better understood through reference to the following detailed description. Other features, objects, and advantages of the invention will become more apparent from reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0030] Figure 1 This is a flowchart of a method for processing a refueling work order for an electric vehicle according to an embodiment of the invention;

[0031] Figure 2 This is a flowchart illustrating a method for processing energy replenishment work orders for electric vehicles according to a specific embodiment of the invention.

[0032] Figure 3 This is a framework diagram of an electric vehicle energy replenishment work order processing system according to an embodiment of the invention. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0034] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] Figure 1 A flowchart illustrating a method for processing energy replenishment work orders for an electric vehicle according to an embodiment of the invention is shown, as follows: Figure 1 As shown, the specific steps include the following:

[0036] S1: The platform receives the user's power replenishment request and generates an initial work order. The power replenishment request includes the vehicle's location, current vehicle battery level, desired power replenishment amount, desired completion time, and desired power replenishment service type. The desired power replenishment service type includes station power replenishment and mobile power replenishment.

[0037] In a specific embodiment, the client (e.g., a mobile app) receives the user's input of a charging request. When submitting a request, in addition to the usual vehicle location, current battery level, desired charging capacity, and desired completion time, the user can also select the desired charging service type, such as: Regular fixed-station order: The user wants to go to a fixed station for charging, typically referring to an integrated photovoltaic-storage-charging-delivery charging station. Charging order: The user wants mobile charging equipment (robot or logistics vehicle) to provide on-site service. After receiving this information, the platform standardizes it and generates an initial work order containing multi-dimensional information, such as work order ID, timestamp, user ID, vehicle VIN, geographical coordinates, desired charging capacity, and service priority (determined based on user level or urgency).

[0038] S2: Based on the type of the initial work order and multi-dimensional information, the platform combines the real-time status data, prediction data and preset optimization goals of the power replenishment network to execute a multi-modal work order intelligent matching and resource optimization algorithm to determine the best power replenishment solution.

[0039] In a specific embodiment, the platform executes a multimodal work order intelligent matching and resource optimization algorithm to determine the best power replenishment scheme. This is the core step of the present invention. The platform collects real-time data and prediction data of the entire power replenishment network and inputs them into the multimodal work order intelligent matching and resource optimization engine.

[0040] In a specific embodiment, the acquisition and analysis of real-time status data and prediction data includes the analysis of site data, mobile charging device data, and external environment data. The site data includes integrated photovoltaic-storage-charging-transmission charging stations, fixed mobile charging pile battery swapping stations, and mobile charging pile replenishment stations (charging, replenishment, and battery swapping stations). Integrated photovoltaic-storage-charging-transmission charging stations monitor the occupancy status and charging power of each fixed charging pile and mobile energy storage battery swapping terminal in real time. The key is to obtain local photovoltaic power generation forecast data (e.g., 24-hour solar intensity forecast, combined with photovoltaic panel efficiency to estimate power generation), local energy storage capacity and charging / discharging power, and real-time marginal power supply costs calculated through its intelligent energy management system (e.g., current grid off-peak electricity price, photovoltaic power generation self-consumption revenue conversion cost, energy storage charging / discharging losses, etc.). Fixed mobile charging pile battery swapping stations obtain real-time information on the availability status of each battery swapping station, the number of fully charged mobile charging piles in stock, pile type information, and the estimated time required to complete one battery swap. Replenishment stations (charging, replenishment, and battery swapping stations) obtain real-time information on the number, model, capacity, local energy storage capacity, number of available replenishment outlets, and current load of internal mobile charging piles. Mobile charging equipment data includes: Mobile charging robots: real-time GPS location, current battery level, battery level of the mobile charging piles they carry, current task status (idle, charging, en route), and estimated idle time. Their affiliated dispatching system (self-operated) and historical service efficiency data are also used for evaluation. Logistics charging vehicles: real-time GPS location, current vehicle status (idle, loaded, performing a task), driver status, number, battery level, and model of the mobile charging piles they carry. Similarly, their affiliated dispatching system (self-operated or third-party) and historical service efficiency (such as average delivery time and failure rate) are also considered. External environmental data includes real-time traffic conditions (congestion), weather forecasts, regional traffic restriction policies, real-time grid electricity prices, and regional load forecasts obtained through interfaces.

[0041] In a specific implementation, the platform will construct and evaluate a set of candidate scheduling paths for multimodal power replenishment work orders for different types of work orders:

[0042] For "regular fixed station orders", the algorithm will evaluate the path of the client vehicle to the integrated photovoltaic-storage-charging-transportation station or the mobile charging pile fixed battery swapping station. The evaluation factors include: driving distance and time, estimated queuing time, real-time load of the target station, current charging pile power, and the overall energy cost of the station.

[0043] For "mobile charging orders," the system prioritizes robot matching and evaluation. First, using the user's order address as the center, the system searches for mobile charging robots within a nearby radius based on real-time map data. It assesses whether the robot is idle, whether its mobile charging pile has sufficient power to meet the demand, and the estimated arrival time of its autonomous driving path. If a suitable robot exists and has the highest overall evaluation score (e.g., shortest response time, lowest service cost), it is prioritized for dispatch. If the robot cannot meet the demand or has a low priority, a logistics delivery route is constructed. The platform searches for logistics delivery personnel who are idle and have no work orders within a 3km radius (or 5km if none are found, and so on) centered on the user's order address. The delivery personnel's qualification level (e.g., service qualification for specific vehicle models, service qualification for high-risk areas) can also be used as a matching condition. The algorithm dynamically calculates the path and time to the nearest mobile charging station (charging / swapping station) or integrated photovoltaic-storage-charging-delivery station based on the selected delivery personnel's current location. When selecting a replenishment point, not only distance is considered, but also an optimization objective function C is introduced. supply =min(w d ·D deliver +w e ·P cost +w t ·T wait +w m ·M match ), where C supply To cover supply costs, D deliver P is the distance from the power delivery operator's current location to the resupply point. cost The unit cost of electricity used to provide power to a resupply point, T wait M is the estimated waiting time for the supply point. match For pile type matching degree, w d ,w e ,w t ,w m The weighting coefficients are dynamically adjustable. When there are multiple charging orders or simultaneous recycling orders, the system will attempt to cluster the orders. For example, if two charging orders are on the same delivery person's reasonable delivery route, or if a recycling unit is on the delivery person's return delivery route, the system will generate a joint work order, enabling a single delivery person to efficiently complete multiple tasks, reducing empty mileage and improving overall efficiency. Example: Delivery person A receives a delivery work order, and customer B is located in region X. When dispatching the order, the system finds that customer C (recycling order) is also located in region X, and the recycling pile type meets delivery person A's return refueling point requirements. The system will generate an optimized path: Delivery person A -> Refueling point for pile pickup -> Customer B for power delivery -> Customer C for old pile recycling -> Refueling point for pile return.

[0044] For "recycling orders," the platform automatically generates a recycling work order when a user finishes charging or the vehicle is fully charged. Recycling orders are given high priority to ensure mobile charging stations are returned to the depot as quickly as possible, freeing up resources. Matching recycling delivery personnel is similar to placing a charging order, searching for nearby available delivery personnel. Optimal return point selection for mobile charging stations: This is the core optimization point for recycling orders. The system intelligently matches and selects based on the company to which the mobile charging station to be recycled belongs (e.g., self-operated stations or partner operator stations), the type of station, and the real-time status of each mobile charging station's charging / replenishment / swapping station. Optimization function C recovery =min(w d ·D return +w r ·R match +w s ·S capacity +w f ·F demand ), where C recovery To recover costs, D return R is the distance from the power delivery operator's location to the return point. match To determine the matching degree between pile ownership and return point operators, S capacity To optimize the utilization rate of idle outlets and energy storage capacity at return points, F demand Forecasting future charging demand in the area near the return point, w d ,w r ,w s ,w f These are dynamically adjustable weighting coefficients.

[0045] For "dispatch orders," when the battery voltage of a mobile energy storage battery swapping station or mobile robot placed at a fixed site drops below a preset standard value, the system automatically generates a dispatch order and assigns it high priority. The dispatcher is matched with the same dispatcher matching logic as for ordinary door-to-door power delivery orders. The system intelligently matches a replenishment point with a new, fully charged, available mobile charging pile of the same type (e.g., the nearest mobile charging pile replenishment station or mobile charging pile fixed battery swapping station). The platform plans a complete closed-loop path: the dispatcher goes to the replenishment point to pick up a new, fully charged mobile charging pile; the dispatcher delivers the new pile to the location specified in the dispatch order (e.g., an empty parking space at a fixed battery swapping station, or the parking point of a mobile robot); the dispatcher retrieves the old, low-charge mobile charging pile; the system matches the nearest mobile charging pile replenishment station with an available outlet as the final return point for the old pile; the dispatcher returns the old pile to the designated replenishment station for self-charging and ends the work order. The optimization goal of this path is to minimize total time and round-trip costs, ensuring rapid turnover and availability of core energy replenishment resources.

[0046] For "relay orders," which are work orders where a single charging session cannot meet the expected power level, the system predicts and schedules the next mobile charging station for seamless charging. The scheduling of the next mobile charging station takes into account its estimated arrival time and available power to ensure that the next mobile charging station can take over in time before the current mobile charging station runs out of power.

[0047] S3: The platform encapsulates the best power replenishment solution into an executable work order and dispatches it to the corresponding power replenishment resources or reminds the client to execute it according to the dynamic distribution strategy.

[0048] In specific embodiments, for the aforementioned different work orders, the system can dispatch them to both the self-operated dispatch system and the third-party dispatch system based on a dynamic distribution strategy. Priority is given to dispatching to mobile charging robots or logistics delivery personnel under the self-operated dispatch system, ensuring the controllability and quality assurance of core services. If self-operated resources are saturated, unavailable, or inefficient, the platform will broadcast an emergency dispatch order to cooperating third-party dispatch systems (such as logistics companies or crowdsourcing platforms) via an intelligent interface (API). After receiving the work order, the third-party platform will dispatch its resources to execute it and send the progress back to the platform in real time.

[0049] S4: The platform monitors the execution progress of executable work orders in real time, and initiates dynamic adjustment and redistribution of work orders based on real-time feedback or abnormal situations generated during the execution process.

[0050] In a specific implementation, if the preferred delivery person is unable to reach the destination, the system will immediately select a secondary route from the candidate route set and reassign the order. If the mobile power delivery service is delayed due to traffic congestion or insufficient resources, the system may automatically assess and recommend that the user switch to the nearest integrated photovoltaic-storage-charging-delivery station for on-site power replenishment, and push navigation information, while informing the user of possible changes in costs and time. For pre-booked or non-urgent orders, if current resources are scarce, the system may choose to temporarily delay order dispatch, waiting for the status of the power replenishment point to improve or for the delivery person to become available.

[0051] Figure 2 A flowchart illustrating a method for processing energy replenishment work orders for electric vehicles according to a specific embodiment of the invention is shown, such as... Figure 2As shown, this logic flowchart fully presents the entire process of an electric vehicle charging work order from initiation to completion, covering core aspects such as multi-scenario branches, resource scheduling, and dynamic optimization. The process starts with "Start," first entering the charging request receiving stage. The platform receives the request submitted by the user, which includes the vehicle's location, current battery level, desired charging capacity, desired completion time, and service type (station charging or mobile charging), and generates an initial work order.

[0052] Based on the service type selected by the user, the process is divided into two main branches. For "site-based power replenishment," the platform directly matches users with fixed charging stations, taking into account the real-time status of the charging stations, including the number of available charging piles, real-time load, electricity price, and charging pile type compatibility, to ensure that user expectations are met. For "mobile power replenishment," a multimodal intelligent work order matching process is initiated. First, the availability of mobile charging robots is assessed based on the user's location. According to the robot's current location, task status, estimated idle time, and available power of the charging pile, it is determined whether there is a robot that can meet the needs. If a robot is available, it is directly assigned to perform the task, and the work order includes the power replenishment location, expected power, and time requirements. If no robot is available, a logistics power delivery route is constructed, prioritizing the search for idle power delivery personnel within a specified radius and matching them with the nearest replenishment point (such as an integrated photovoltaic-storage-charging-delivery station). The matching process determines the optimal replenishment point by optimizing the objective function (combining distance, electricity price, waiting time, and charging pile type compatibility). If there are multiple pending orders, order clustering and route planning are also performed to improve delivery efficiency. Meanwhile, if a single charging session cannot meet the demand, the platform will dispatch a relay charging station to ensure seamless continuity.

[0053] The processing results of all the above branches eventually enter the executable work order packaging and dispatch stage. The platform packages the best solution into a work order and dispatches it to the corresponding executor (mobile charging robot, power delivery operator, or user client). During work order execution, the platform monitors the progress in real time, tracking the movement trajectory of resources, changes in power, and abnormal signals (such as equipment failure, low voltage, etc.). If there are no abnormalities, monitoring continues until the work order is completed, and the process loop ends. If an abnormality occurs, a dynamic adjustment mechanism is immediately activated to re-match resources, plan routes, and dispatch adjusted work orders, which are then included in the monitoring again to ensure that the power replenishment task is ultimately completed. The entire process, through intelligent optimization and dynamic adjustment, takes into account both user needs and platform efficiency, forming a complete, flexible, and reliable power replenishment work order processing system.

[0054] Figure 3 A framework diagram of a refueling work order processing system for an electric vehicle according to an embodiment of the invention is shown, such as... Figure 3As shown, the system includes an initial work order generation module 301, a power replenishment plan determination module 302, a dynamic work order distribution and execution module 303, and a redundancy strategy and exception handling module 304. The initial work order generation module 301 is configured to receive power replenishment requests submitted by users and generate initial work orders. The power replenishment request includes vehicle location, current vehicle battery level, desired power replenishment amount, desired completion time, and desired power replenishment service type, which includes station power replenishment and mobile power replenishment. The power replenishment plan determination module 302 is configured to determine the power replenishment plan based on the type and multiple dimensions of the initial work order. The system combines real-time status data, predicted data, and preset optimization targets of the power replenishment network to execute a multimodal work order intelligent matching and resource optimization algorithm to determine the optimal power replenishment solution. The dynamic work order distribution and execution module 303 is configured to encapsulate the optimal power replenishment solution into an executable work order on the platform side and dispatch it to the corresponding power replenishment resources or remind the client to execute it according to the dynamic distribution strategy. The redundancy strategy and exception handling module 304 is configured to monitor the execution progress of executable work orders in real time on the platform side and initiate dynamic adjustment and redistribution of work orders based on real-time feedback or exceptions generated during execution. Each module of the system can perform the aforementioned operations. Figure 1 The specific steps of the method in the embodiments are as follows.

[0055] The electric vehicle energy replenishment work order processing method and system of the present invention, through the detailed description of the above embodiments, can realize intelligent and efficient processing of multiple types of energy replenishment work orders, significantly improve the response speed, resource utilization and user satisfaction of energy replenishment services, and optimize operating costs and energy efficiency.

[0056] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the specific combination of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for processing energy replenishment work orders for electric vehicles, characterized in that, include: S1: The platform receives the user's power replenishment request and generates an initial work order. The power replenishment request includes the vehicle location, the vehicle's current battery level, the expected power replenishment amount, the expected completion time, and the expected power replenishment service type. The expected power replenishment service type includes station power replenishment and mobile power replenishment. S2: The platform executes a multimodal work order intelligent matching and resource optimization algorithm based on the type and multidimensional information of the initial work order, combined with the real-time status data, prediction data and preset optimization targets of the power replenishment network, in order to determine the best power replenishment solution; S3: The platform encapsulates the optimal power replenishment solution into an executable work order and dispatches it to the corresponding power replenishment resources or reminds the client to execute it according to the dynamic distribution strategy; S4: The platform monitors the execution progress of the executable work order in real time, and initiates dynamic adjustment and redistribution of the work order based on real-time feedback or abnormal situations generated during the execution process.

2. The method for processing energy replenishment work orders for electric vehicles according to claim 1, characterized in that, The energy replenishment network includes integrated photovoltaic-storage-charging-transmission charging stations, fixed battery swapping stations for mobile charging piles, and mobile charging pile replenishment stations. The real-time status data includes the number of available fixed / mobile charging piles, the number of mobile charging robots that can be used to transport mobile charging piles within the station, real-time load, queuing status, local energy storage capacity, photovoltaic power generation forecast, and the current location, remaining power, current task status, estimated idle time, affiliated scheduling system, and historical service efficiency of each charging / battery swapping device.

3. The method for processing energy replenishment work orders for electric vehicles according to claim 2, characterized in that, The multimodal work order intelligent matching and resource optimization algorithm specifically includes: S21: Taking the order address as the center, based on the current location, current task status, estimated idle time, and available power of the mobile charging robot and the mobile charging pile it carries, assess whether there is a mobile charging robot that can meet the requirements; S22: In response to the absence of a mobile charging robot that meets the conditions, a logistics power delivery path is constructed. The goal of constructing the path is to minimize the service response time and overall delivery cost. Specifically, this includes: searching for and prioritizing power delivery personnel without work orders within a specified radius of the work order location; matching the nearest available and demand-compliant mobile charging pile charging / swapping station or photovoltaic-storage-charging-delivery integrated charging station as a replenishment point based on the location of the power delivery personnel. The selection process comprehensively considers the real-time electricity price, load, number of available outlets, and pile type matching degree of the replenishment point.

4. The method for processing energy replenishment work orders for electric vehicles according to claim 3, characterized in that, If there are multiple orders to be dispatched in the work order pool, the algorithm will cluster the multiple orders and plan the route when constructing the path, so that a single power delivery worker can complete multiple adjacent orders at the same time, or complete the collection task on the way back.

5. The method for processing energy replenishment work orders for electric vehicles according to claim 3, characterized in that, The selection of the supply point in S22 is optimized using the following objective function: C supply =min(w d ·D deliver +w e ·P cost +w t ·T wait +w m ·M match ), where C supply To cover supply costs, D deliver P represents the distance from the power delivery operator's current location to the resupply point. cost The unit cost of electricity used to provide power to a resupply point, T wait M is the estimated waiting time for the supply point. match For pile type matching degree, w d ,w e ,w t ,w m These are dynamically adjustable weighting coefficients.

6. The method for processing energy replenishment work orders for electric vehicles according to claim 3, characterized in that, The platform also generates a recycling work order after the executable work order is completed. The processing method for the recycling work order is determined based on the multimodal work order intelligent matching and resource optimization algorithm. Specifically, this includes: assigning a higher priority to the recycling work order to ensure the rapid return and turnover of the mobile charging piles; the platform intelligently matching and selecting the optimal return point based on the company and type of the mobile charging pile to be recycled, combined with the number of available charging / swapping stations, local energy storage capacity, current load, future demand forecasts, and distance; the selection objective is to minimize recycling costs, maximize the resource utilization rate of the return point, and optimize the future allocation efficiency of the mobile charging piles.

7. The method for processing energy replenishment work orders for electric vehicles according to claim 6, characterized in that, The optimization function for the return point of the mobile charging pile is: C recovery =min(w d ·D return +w r ·R match +w s ·S capacity +w f ·F demand ), where C recovery To recover costs, D return R is the distance from the power delivery operator's location to the return point. match To determine the matching degree between pile ownership and return point operators, S capacity To optimize the utilization rate of idle outlets and energy storage capacity at return points, F demand Forecasting future charging demand in the area near the return point, w d ,w r ,w s ,w f These are dynamically adjustable weighting coefficients.

8. The method for processing energy replenishment work orders for electric vehicles according to claim 3, characterized in that, When the voltage of the mobile charging pile drops below a preset standard value, the platform generates a scheduling work order, matches a new charging pile of the same type that is available and has high power in the surrounding area, and plans for the power delivery personnel to first pick up the new charging pile, then deliver it to the designated site to retrieve the old charging pile, and finally send the old charging pile back to the designated charging station for charging. The path optimization objective is to minimize the total time and round-trip cost.

9. The method for processing energy replenishment work orders for electric vehicles according to claim 3, characterized in that, When determining the optimal energy replenishment scheme, the multimodal work order intelligent matching and resource optimization algorithm also includes predicting and scheduling the next relay mobile charging pile for seamless energy replenishment for work orders that cannot meet the expected power in a single energy replenishment. The scheduling selection of the next relay mobile charging pile takes into account its expected arrival time and available power to ensure that the next mobile charging pile can take over in time before the current mobile charging pile runs out of power.

10. A refueling work order processing system for electric vehicles, characterized in that, include: Initial work order generation module: configured to receive user-submitted power replenishment requests and generate initial work orders on the platform. The power replenishment request includes vehicle location, current vehicle battery level, desired power replenishment amount, desired completion time, and desired power replenishment service type. The desired power replenishment service type includes station power replenishment and mobile power replenishment. The power replenishment scheme determination module is configured to use the platform to perform a multimodal work order intelligent matching and resource optimization algorithm based on the type and multi-dimensional information of the initial work order, combined with the real-time status data, prediction data and preset optimization targets of the power replenishment network, in order to determine the best power replenishment scheme. Dynamic work order distribution and execution module: configured to encapsulate the optimal power replenishment solution into an executable work order on the platform side, and dispatch it to the corresponding power replenishment resources or remind the client to execute it according to the dynamic distribution strategy; Redundancy strategy and exception handling module: Configured for real-time monitoring of the execution progress of the executable work order on the platform, and to initiate dynamic adjustment and redistribution of the work order based on real-time feedback or exceptions generated during the execution process.

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