Intelligent scheduling method and system for vehicle-mounted mobile charging system

Abstract

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an intelligent scheduling method and system for a vehicle-mounted mobile charging system. Background Art

[0002] At present, vehicle-mounted mobile charging systems can provide charging services for electric vehicles anytime and anywhere without relying on fixed charging facilities. They can adapt to a variety of environments and scenarios, providing new energy vehicle users with more convenient and efficient charging solutions.

[0003] In existing on-board mobile charging systems, one scheduling method is a priority-based scheduling strategy. This method prioritizes charging vehicles by comprehensively considering factors such as the urgency of the user's charging needs, the charge status of the charging vehicle, and traffic conditions at the target location, thereby achieving rational resource allocation. However, when a sudden surge in charging demand occurs in a certain area, this strategy cannot quickly determine whether to prioritize charging vehicles that are closer but have lower charges, or those that are more distant but have sufficient charges. While the former approach results in a faster response, it may result in vehicles running low on charge after completing a charging task, making them unable to continue with subsequent tasks. While the latter approach may result in a vehicle with sufficient charge, the response time may be longer, potentially failing to meet the user's urgent needs.

[0004] In summary, in the existing technology, unreasonable allocation of charging vehicles between different areas may occur, thereby affecting the efficiency of scheduling. Summary of the Invention

[0005] The present invention provides an intelligent scheduling method and system for a vehicle-mounted mobile charging system, so as to achieve reasonable scheduling of charging vehicles and improve overall efficiency.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent scheduling method for a vehicle-mounted mobile charging system, comprising: Obtain the urgency, geographic location, and charging demand level of all charging needs in the current area and establish a demand priority list; Obtain the real-time power status, location information, and power consumption rate of all charging vehicles and create a vehicle availability list; Calculate the estimated time for each charging vehicle to reach the target location and the remaining power based on the demand priority list and the vehicle availability list, taking into account the distance to the target location, road conditions, and traffic conditions; When the estimated time is less than the preset time threshold and the remaining power meets the requirement, the vehicle is marked as an available vehicle; When the available vehicles are insufficient, information about charging vehicles in neighboring areas is obtained, and based on the distance and road conditions of cross-regional dispatch routes, it is determined whether to prioritize dispatching vehicles in neighboring areas, thereby generating a global dispatch instruction. A scheduling algorithm is used to optimize the global scheduling instruction to obtain an optimized global scheduling instruction, and charging vehicles are dynamically scheduled according to the optimized global scheduling instruction.

[0007] In an optional embodiment, obtaining the urgency values, geographic locations, and charging demand level values ​​of all charging demands in the current area and establishing a demand priority list includes: The data acquisition interface is used to obtain the location points, charging amount, and time values ​​of all charging needs in the area, and the urgency value of each location point is calculated by combining the preset load value and capacity value; A clustering algorithm is used to group the locations according to their distance values, and a charging requirement level value is calculated for each group. The charging requirement level value is obtained by weighting the charging amount and time value. A demand priority list is constructed according to the urgency value and the charging demand level value, and each location point in the demand priority list is assigned a priority value, which is determined by a linear combination of the urgency value and the charging demand level value.

[0008] In an optional embodiment, obtaining the real-time power status, location information, and power consumption rate of all charging vehicles and establishing a vehicle availability list includes: Obtain the current power, location information, and power consumption rate of all charging vehicles in the area through the data acquisition interface; Calculate the availability index of each vehicle based on the preset power threshold and driving range; A sorting algorithm is used to sort the vehicles whose availability index is higher than a preset availability index threshold, and a vehicle availability list is generated.

[0009] In an optional embodiment, the calculating, based on the demand priority list and the vehicle availability list, and in combination with the distance to the target location, road conditions, and traffic conditions, the estimated time for each charging vehicle to reach the target location and the remaining power, includes: Planning an optimal path for the charging vehicle to reach the target location based on the demand priority list and the vehicle availability list, taking into account the distance to the target location, road conditions, and traffic conditions; Calculating the estimated travel distance and estimated time for the charging vehicle to reach the target location under the optimal path; The power consumption value under the optimal path is calculated based on the power consumption rate of the charging vehicle and the expected driving distance, thereby obtaining the remaining power of the charging vehicle when reaching the target location.

[0010] In an optional embodiment, when the estimated time is less than a preset time threshold and the remaining power meets the requirement, marking the vehicle as an available vehicle includes: Making a judgment based on the estimated time and the remaining power in combination with a preset time threshold and a preset remaining power threshold; When the estimated time is less than a preset time threshold and the remaining power is greater than a preset remaining power threshold, the charging vehicle is marked as an available vehicle.

[0011] In an optional embodiment, when the available vehicles are insufficient, information about charging vehicles in neighboring areas is obtained, and combined with the distance and road conditions of cross-regional dispatch routes, a determination is made as to whether to prioritize dispatching vehicles in neighboring areas, thereby generating a global dispatch instruction, including: Get the number of available vehicles in the current area and determine whether it is less than the preset threshold of available vehicles; When the number of available vehicles is less than a preset threshold number of available vehicles, charging vehicle information obtained from a neighboring area; Based on the charging vehicle information, combined with the cross-region dispatch route distance and road condition data, determine whether to prioritize dispatching nearby vehicles; Generate global scheduling instructions based on the judgment results.

[0012] In an optional embodiment, the determining whether to prioritize dispatching adjacent vehicles based on the charging vehicle information in combination with cross-region dispatch route distance and road condition data includes: Obtaining availability data of charging vehicles in a nearby area, the availability data including the charging status and range of the charging vehicles; Screening a set of candidate charging vehicles that meet cross-regional scheduling requirements based on the availability data; Extracting the travel time of the cross-regional routes corresponding to the candidate charging vehicle set, wherein the travel time is integrated with the real-time road congestion index; Calculate the matching degree between the travel time of the cross-region route and the cruising range by using a path planning algorithm; If the matching degree exceeds the preset matching degree threshold, the adjacent vehicle is dispatched first; if the matching degree does not exceed the preset matching degree threshold, the adjacent vehicle is not dispatched first.

[0013] In an optional embodiment, the method of optimizing the global scheduling scheme using a scheduling algorithm to obtain an optimized global scheduling scheme, and dynamically scheduling charging vehicles according to the optimized global scheduling instructions, includes: Use scheduling algorithms to optimize the global scheduling plan and generate optimized scheduling instructions; Dynamically dispatch charging vehicles in neighboring areas based on optimized dispatch instructions; Obtain real-time location information of dispatched vehicles and update the global dispatch plan; By dynamically adjusting scheduling instructions, the scheduling plan is ensured to be consistent with real-time data.

[0014] In a second aspect, the present invention provides an intelligent scheduling system for a vehicle-mounted mobile charging system, comprising: A data acquisition module is used to obtain the urgency value, geographical location and charging demand level value of all charging demands in the current area, and establish a demand priority list; obtain the real-time power status, location information and power consumption rate of all charging vehicles, and establish a vehicle availability list; an available vehicle determination module is used to calculate the estimated time and remaining power of each charging vehicle to reach the target location based on the demand priority list and the vehicle availability list, combined with the target location distance, road conditions and traffic conditions; when the estimated time is less than the preset time threshold and the remaining power meets the demand, the vehicle is marked as an available vehicle; a scheduling module is used to obtain the charging vehicle information of the neighboring area when the available vehicles are insufficient, and combine the cross-regional scheduling route distance and road conditions to determine whether to prioritize the scheduling of vehicles in the neighboring area, thereby generating a global scheduling instruction; a scheduling optimization module is used to optimize the global scheduling instruction using a scheduling algorithm to obtain an optimized global scheduling instruction, and dynamically schedule charging vehicles according to the optimized global scheduling instruction.

[0015] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent scheduling method for the vehicle-mounted mobile charging system as described in any one of claims 1 to 8 is implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) By establishing a demand priority list and a vehicle availability list, the present invention can quickly identify the most urgent charging needs and the most suitable vehicles, thereby improving response speed.

[0017] (2) The present invention combines the distance to the target location, road conditions, and traffic conditions to calculate the estimated time for each charging vehicle to reach the target location and the remaining power, which helps to optimize the resource allocation of charging vehicles and ensure the efficiency of charging services.

[0018] (3) When there are insufficient available vehicles in the area, the system can obtain information on charging vehicles in adjacent areas and, based on the distance and road conditions of cross-regional dispatch routes, determine whether to prioritize dispatching vehicles in adjacent areas, thereby improving overall dispatch efficiency.

[0019] (4) The present invention uses a scheduling algorithm to optimize the global scheduling instructions, and can obtain optimized global scheduling instructions, further dynamically schedule charging vehicles, and improve the flexibility and adaptability of scheduling.

[0020] In summary, this invention can effectively address the imbalance between charging demand and vehicle resource allocation, achieve rational scheduling of charging vehicles, and improve charging service efficiency. Through intelligent scheduling and cross-regional collaboration, this invention can flexibly respond to various complex scenarios, maximize charging resource utilization, and provide electric vehicle users with more convenient and efficient charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of an intelligent scheduling method for a vehicle-mounted mobile charging system of the present invention; Figure 2 Schematic diagram of an intelligent scheduling system for a vehicle-mounted mobile charging system of the present invention. DETAILED DESCRIPTION

[0022] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Reference Figure 1 This embodiment provides an intelligent scheduling method for a vehicle-mounted mobile charging system, comprising the following steps: S1, obtain the urgency value, geographic location and charging demand level value of all charging demands in the current area, and establish a demand priority list; S2, obtain the real-time power status, location information and power consumption rate of all charging vehicles, and establish a vehicle availability list; S3, based on the demand priority list and the vehicle availability list, combined with the target location distance, road conditions and traffic conditions, calculate the estimated time and remaining power of each charging vehicle to reach the target location; S4, when the estimated time is less than the preset time threshold and the remaining power meets the demand, the vehicle is marked as an available vehicle; S5, when the available vehicles are insufficient, obtain the charging vehicle information in the neighboring area, combine the cross-regional scheduling route distance and road conditions, and determine whether to prioritize the scheduling of vehicles in the neighboring area, thereby generating a global scheduling instruction; S6, use the scheduling algorithm to optimize the global scheduling instruction to obtain the optimized global scheduling instruction, and dynamically schedule charging vehicles according to the optimized global scheduling instruction.

[0024] In step S1, the urgency value, geographic location, and charging demand level value of all charging demands in the current area are obtained, and a demand priority list is established, including: The data acquisition interface is used to obtain the location points, charging amount, and time values ​​of all charging needs in the area, and the urgency value of each location point is calculated by combining the preset load value and capacity value; A clustering algorithm is used to group the locations according to their distance values. The charging demand level is calculated for each group. The charging demand level is weighted by the charging amount and time value. A priority list is constructed according to the urgency value and the demand level, and each location point in the priority list is assigned a priority value, which is determined by a linear combination of the urgency value and the charging demand level value.

[0025] Specifically, the data collection process for charging demand locations needs to consider the distribution of charging demand points across different regions. For example, within a city's commercial district, charging demand points might include shopping mall parking lots, office building parking spaces, and temporary roadside parking spots. Each demand point has different charging capacity and time values. For example, the demand for EV charging in a shopping mall parking lot might reach 50 kWh per hour on weekdays, while demand in an office building parking lot might drop to 10 kWh per hour after hours.

[0026] The urgency value needs to be comprehensively evaluated in combination with the preset load value and capacity value and is calculated using the following formula:

[0027] in, Indicates the urgency value of location point i, represents the charging capacity of the kth charging demand, represents the capacity value of location i, Indicates the maximum waiting time, represents the waiting time of the kth demand, Indicates the current load value at position i, Indicates the maximum load value that position point i can withstand.

[0028] When using a clustering algorithm to group location points, you can choose a distance-based clustering method. For example, within a certain area of ​​a city, charging demand points within 500 meters of each other can be grouped together to better manage charging resources in adjacent areas. Specifically, multiple office buildings in a commercial center may form one cluster group, while charging points in surrounding residential areas form another cluster group. The demand level value is calculated by normalizing the charging amount and time values ​​and then weighting them. Assuming that the charging demand in a commercial area during the morning rush hour is 100 kWh and the time urgency is 90%, while the demand in the same area during the lunch break is 50 kWh and the time urgency is 40%, the demand level during these two periods will be significantly different.

[0029] The construction of the priority list requires a reasonable linear combination of the urgency value and the demand level value. For example, assuming the weight of the urgency value is , the weight of the demand level value is ,in For each location point , whose urgency value is , the requirement level is , then the priority value of the location point It can be calculated by the following formula:

[0030] Here Indicates location point The priority value of Indicates location point The urgency value of Indicates location point The demand level value of and It can be adjusted according to actual conditions to reflect the importance of the urgency value and the demand level value in the decision-making process.

[0031] This approach balances the current load of charging vehicles with actual user demand, achieving efficient allocation of charging resources. This priority management mechanism effectively addresses the uneven distribution of charging resources. For example, during the morning rush hour, the system prioritizes high-urgency, high-demand charging points within the business district, while also considering the needs of other nearby charging points, thereby achieving balanced allocation of charging resources within the region. This approach not only improves the efficiency of charging vehicles but also better meets user charging needs. The core value of this mechanism lies in the intelligent allocation of charging resources. By comprehensively analyzing multi-dimensional data such as location information, time requirements, and device load, the system can dynamically adjust charging priorities, thereby improving overall service quality, reducing user wait times, and increasing the utilization efficiency of charging facilities.

[0032] In step S2, the real-time power status, location information, and power consumption rate of all charging vehicles are obtained, and a vehicle availability list is established, including: Obtain the current power, location information, and power consumption rate of all charging vehicles in the area through the data acquisition interface; Calculate the availability index of each vehicle based on the preset power threshold and driving range; A sorting algorithm is used to sort the vehicles whose availability index is higher than a preset availability index threshold, and a vehicle availability list is generated.

[0033] Specifically, the design of the data collection interface needs to consider the real-time and accuracy of charging vehicle data. For example, in a certain city's smart charging system, the data collection interface uploads real-time status information about charging vehicles via onboard terminals. This information includes the vehicle's current geographic coordinates, remaining battery percentage, and energy consumption per kilometer. For example, a charging vehicle's onboard terminal uploads a data packet to the system every minute. This data packet contains information such as the vehicle's remaining battery at 120 degrees 30 minutes east longitude and 31 degrees 15 minutes north latitude, a 40% remaining battery level, and an average power consumption of 0.8 kWh per kilometer.

[0034] The setting of power thresholds and driving ranges is related to the reliability of vehicle dispatch. For example, the available power of a charging vehicle must be higher than 40% to ensure that the charging vehicle has enough power to charge other vehicles; the remaining driving range of a charging vehicle must be able to cover a driving range of 100km to ensure that the charging vehicle can reach the target vehicle that needs to be charged, and the vehicle's power consumption rate is used to calculate the remaining driving range. The driving range needs to be considered in combination with the vehicle's current location, surrounding road conditions, and weather factors. When urban roads are unobstructed, a charging vehicle with a remaining power of 40% is expected to travel 80 kilometers. However, in congested areas or cold weather, the actual driving range of the same power may drop to 60 kilometers.

[0035] The availability index calculation method reflects the possibility of a vehicle being put into use. The index is mainly composed of two parts: the current battery sufficiency and the potential driving ability score. The calculation formula is as follows: ,

[0036] in, Indicates the current battery level. Indicates the current power level. Indicates the preset power threshold; represents the potential driving ability score, Indicates the estimated mileage. Indicates daily driving range.

[0037] For example, a charging vehicle's current charge is 70%, exceeding the preset threshold of 20%, resulting in a charge adequacy score of 0.625. Furthermore, the vehicle's estimated range is 120 kilometers, with a daily driving range of approximately 60 kilometers, resulting in a driving capability score of 2.0. The system then calculates the vehicle's availability index using weights (assuming the weights are 0.5 and 0.5, respectively) to be 1.31.

[0038] The selection of a sorting algorithm requires a balance between computational efficiency and sorting accuracy. In practical applications, a modified quick sorting algorithm can be used to sort vehicles from highest to lowest availability index. For example, in a certain area, the system selects ten charging vehicles with an availability index exceeding 0.6 and sorts them to generate a list of available vehicles. This list is dynamically updated, and the system automatically adjusts the sorting results when new vehicles with sufficient charge become available or when existing vehicles' charge levels drop below a threshold. This dynamic management mechanism ensures the continuity and efficiency of charging vehicle scheduling, maximizing the utilization of charging resources.

[0039] In step S3, the estimated time for each charging vehicle to reach the target location and the remaining power are calculated based on the demand priority list and the vehicle availability list, combined with the distance to the target location, road conditions, and traffic conditions, including: Planning an optimal path for the charging vehicle to reach the target location based on the demand priority list and the vehicle availability list, taking into account the distance to the target location, road conditions, and traffic conditions; Calculating the estimated travel distance and estimated time for the charging vehicle to reach the target location under the optimal path; The power consumption value under the optimal path is calculated based on the power consumption rate of the charging vehicle and the expected driving distance, thereby obtaining the remaining power of the charging vehicle when reaching the target location.

[0040] Specifically, the route planning system needs to comprehensively consider multiple factors to determine the optimal driving route. For example, in a certain urban area, when the system receives a charging request, it first matches the target location with the list of currently available vehicles. From the vehicles with the highest availability index, the system selects the one that is closest to the target location and has sufficient battery life. After determining the vehicle, the system analyzes the possible routes using electronic map data. For example, there are three main routes from the charging vehicle's location to the target location of the charging vehicle: one is via the city expressway, a distance of 15 kilometers, with a normal travel time of approximately 25 minutes; the second is via a main road, a distance of 18 kilometers, with a travel time of approximately 40 minutes; and the third is via a secondary road, a distance of 16 kilometers, with a travel time of approximately 35 minutes. The system then selects the optimal route based on real-time traffic information. If traffic is congested on the expressway, the system automatically adjusts to a secondary road route. When assessing road conditions, the system also considers temporary factors such as weather and road construction. For example, during rainy days, detours may be required on flooded roads, and the system will adjust the route accordingly. For example, if there is a temporary road maintenance, the original 20-minute journey may be extended to 40 minutes. At this time, the system will recalculate the travel time of each route and select a better alternative route.

[0041] Power consumption prediction is a key component of route planning. The system analyzes energy consumption levels under different road conditions based on historical data. On urban expressways, a charging vehicle consumes approximately 15 kWh per 100 kilometers. On stop-and-go urban roads, energy consumption may rise to 20 kWh for the same distance. Using these parameters, the system estimates power consumption for different routes. For example, a charging vehicle's current battery level is 80%, and the planned route to the destination is 16 kilometers. Considering congestion on main roads during the morning rush hour, the system selects a secondary route. This route is estimated to take 35 minutes. Based on current road and weather conditions, the estimated power consumption per 100 kilometers is 18 kWh. Therefore, the estimated power consumption for this journey is 2.88 kWh, approximately 7% of the battery capacity. Based on this, the charging vehicle can be calculated to have a remaining battery level of 73% upon arrival at the destination, fully meeting the required charge.

[0042] By establishing this dynamic path planning and energy consumption prediction mechanism, the system can plan the most appropriate route for each charging vehicle, ensuring matching efficiency while avoiding mission interruptions due to insufficient power. This intelligent scheduling method significantly improves the operational efficiency of charging vehicles and reduces energy consumption, providing strong support for the intelligent development of on-board mobile charging.

[0043] In step S4, when the estimated time is less than the preset time threshold and the remaining power meets the requirement, the vehicle is marked as an available vehicle, including: Making a judgment based on the estimated time and the remaining power in combination with a preset time threshold and a preset remaining power threshold; When the estimated time is less than a preset time threshold and the remaining power is greater than a preset remaining power threshold, the charging vehicle is marked as an available vehicle.

[0044] Specifically, the vehicle dispatch system uses a threshold comparison mechanism to determine the availability of charging vehicles. The preset time threshold and the preset remaining power threshold are key reference values ​​for system operation, directly impacting the quality and efficiency of charging services. The preset time threshold is typically set based on the user's expected waiting time, taking into account urban traffic characteristics and the scope of the service area. The preset remaining power threshold is determined based on the battery capacity of the charging vehicle and the energy consumption requirements of the charging task.

[0045] In urban core areas, the system sets a shorter time threshold, such as 30 minutes, to provide a faster response. In suburban or remote areas, the time threshold can be relaxed to 45 or 60 minutes. The preset remaining battery threshold ensures that the charging vehicle still has enough battery left to return to the charging station after completing its mission, and is typically set at around 50%. For example, let's consider the charging needs of a business district. The system receives a request for charging from an electric vehicle. Two charging vehicles are available for dispatch nearby. Vehicle 1 has an estimated arrival time of 20 minutes and a remaining battery of 65%, while Vehicle 2 has an estimated arrival time of 35 minutes and a remaining battery of 80%. The preset time threshold for this area is 30 minutes, and the preset remaining battery threshold is 60%. The system determines that Vehicle 1's estimated arrival time is less than the preset time threshold and its remaining battery is above the preset battery threshold, so it is marked as available.

[0046] In special weather conditions, such as rainy days, the system dynamically adjusts the preset time threshold. For example, in the event of heavy rain, the system will extend the time threshold by 20% to account for driving safety and changing road conditions. During peak power consumption periods, such as large-scale events or holidays, the system will increase the preset remaining power threshold to ensure that charging vehicles have sufficient power to cope with the intensive charging demand. In a commercial area, a charging vehicle received an emergency charging request with an estimated arrival time of 28 minutes and a remaining power of 55%. Although the arrival time met the preset threshold of 30 minutes, because the remaining power was below the threshold of 60%, the system did not mark it as an available vehicle and instead searched for a more suitable charging vehicle.

[0047] This dual-threshold judgment mechanism effectively avoids the risk of vehicles running out of range during missions. By rationally setting and dynamically adjusting threshold parameters, the system ensures both service quality and the continued availability of charging vehicles. This threshold-based screening mechanism provides a reliable pool of vehicle candidates for subsequent scheduling decisions, improving overall efficiency.

[0048] In step S5, when the available vehicles are insufficient, information on charging vehicles in the adjacent area is obtained, and combined with the distance and road conditions of the cross-regional dispatch route, it is determined whether to prioritize dispatching vehicles in the adjacent area, thereby generating a global dispatch instruction including: S51, obtaining the number of available vehicles in the current area and determining whether it is less than a preset threshold value of the number of available vehicles; S52, when the number of available vehicles is less than a preset threshold number of available vehicles, acquiring charging vehicle information from a neighboring area; S53, based on the charging vehicle information, combined with the cross-region dispatch route distance and road condition data, determining whether to prioritize dispatching of nearby vehicles; S54: Generate a global scheduling instruction based on the judgment result.

[0049] Specifically, in step S51, the system realizes cross-regional linkage scheduling through regional available vehicle number threshold judgment. The preset available vehicle number threshold is a key parameter determined based on the historical service data of each region and the demand characteristics during peak hours. In busy business districts, the preset threshold is usually set at five to eight vehicles to cope with intensive charging needs. When the number of available vehicles in a certain area is lower than the threshold, the system activates the cross-regional coordination mechanism. For example, during the morning rush hour on weekdays in a certain city's business district, there are only two available charging vehicles in the area, which is lower than the preset threshold of six. The system automatically scans the vehicle resources in the neighboring areas and finds that there are four idle charging vehicles in the residential area on the east side and three standby charging vehicles in the industrial park on the west side.

[0050] In step S52, the system obtains specific information about these vehicles, including real-time location, remaining battery power, task status, and other data. When scheduling across regions, the system needs to comprehensively consider the distance of the scheduling route and road conditions. The distance of the scheduling route directly affects service response time, while road conditions are related to the reliability of scheduling. The system uses real-time road condition data to evaluate travel time for different routes. For example, during the morning rush hour, although the straight-line distance to the industrial park is relatively close, due to congestion on the main road, the system prioritizes dispatching charging vehicles from residential areas via secondary roads. The system has set up a multi-dimensional scoring mechanism for priority judgment. A commercial district experiences a peak in charging demand in the afternoon. There is a charging vehicle in the southeast direction, four kilometers away from the target area, with an estimated travel time of 25 minutes. There are two charging vehicles in the northwest direction, five kilometers away, but due to clear roads, the estimated travel time is only 20 minutes. When generating the scheduling instruction, the system selects the vehicle in the northwest direction.

[0051] The generation of global dispatch instructions requires balancing the service capabilities of multiple regions. During holidays, there was only one available vehicle in a shopping mall area, which was lower than the preset threshold. Although there were surplus vehicles in two adjacent areas, considering that demand peaks may also occur in adjacent areas, the system only dispatched some vehicles from one area to provide support, retaining basic service capabilities. In special weather conditions, the system will adjust the cross-regional dispatch strategy. When encountering heavy rain, there are insufficient available vehicles in a certain area. When generating dispatch instructions, the system will increase the weight of road safety factors and give priority to routes with better main road conditions, even if these routes are relatively far away. By dynamically adjusting the dispatch strategy, it ensures that a reasonable service level can be maintained in all situations.

[0052] In step S53, based on the charging vehicle information, combined with the cross-region dispatch route distance and road condition data, it is determined whether to prioritize dispatching adjacent vehicles, including: Obtaining availability data of charging vehicles in a nearby area, the availability data including the charging status and range of the charging vehicles; Screening a set of candidate charging vehicles that meet cross-regional scheduling requirements based on the availability data; Extracting the travel time of the cross-regional routes corresponding to the candidate charging vehicle set, wherein the travel time is integrated with the real-time road congestion index; Calculate the matching degree between the travel time of the cross-region route and the cruising range by using a path planning algorithm; If the matching degree exceeds the preset matching degree threshold, the adjacent vehicle is dispatched first; if the matching degree does not exceed the preset matching degree threshold, the adjacent vehicle is not dispatched first.

[0053] Specifically, charging vehicle availability data reflects the comprehensive usage of vehicles in the adjacent area. Availability data includes two key indicators: charging status and range. Charging status can be categorized as fully charged, charging, or waiting to be charged. Range indicates the estimated distance a vehicle can travel with its current charge. For example, a charging vehicle in a certain adjacent area with a 95% charge and an estimated range of 300 kilometers would meet the basic requirements for cross-regional dispatch. The selection of candidate charging vehicles requires comprehensive consideration of multiple factors. For example, in a certain city, a minimum charge of 80% and a minimum range of 200 kilometers are set as screening criteria to ensure that vehicles have sufficient power to complete cross-regional missions.

[0054] For example, if there are ten charging vehicles in a neighboring area, after screening, only four may meet the criteria and be included in the candidate set. The calculation of cross-regional route travel time requires the integration of real-time road congestion index. For example, dispatching a vehicle from Area A to Area B takes one hour under normal road conditions, but during the morning rush hour, the road congestion index reaches 2.5, and the actual travel time may increase to two and a half hours. By introducing a real-time congestion index, vehicle dispatch time can be estimated more accurately. When calculating the matching degree, the path planning algorithm needs to balance the relationship between travel time and range. Suppose a cross-regional route takes two hours under current road conditions, the distance is 150 kilometers, and the candidate vehicle has a range of 250 kilometers. Considering factors such as possible changes in weather and road conditions along the way, it is reasonable to reserve 40% of the battery reserve. In this case, the vehicle has a high matching degree with the route.

[0055] In step S54, a global dispatch instruction is generated based on the judgment result. The setting of the matching threshold directly affects the dispatch decision. If the matching threshold is set to 80%, it means that the vehicle's cruising range is required to be at least 1.8 times the actual mileage. When the cruising range is 300 kilometers and the actual driving distance is 150 kilometers, the matching degree is 83%, which exceeds the threshold, and the system will give priority to dispatching the nearby vehicle. Conversely, if the cruising range is only 200 kilometers and the actual driving distance is 150 kilometers, the matching degree is about 75%, which is below the threshold, and the vehicle will not be given priority.

[0056] This precise matching calculation and decision-making mechanism effectively reduces risks during cross-regional dispatching, avoiding delays caused by low vehicle batteries or road congestion. It also improves overall dispatching efficiency, ensuring the optimal vehicle is selected for cross-regional dispatching.

[0057] In step S6, the global scheduling scheme is optimized using a scheduling algorithm to obtain an optimized global scheduling scheme, and charging vehicles are dynamically scheduled according to the optimized global scheduling instructions, including: Use scheduling algorithms to optimize the global scheduling plan and generate optimized scheduling instructions; Dynamically dispatch charging vehicles in neighboring areas based on optimized dispatch instructions; Obtain real-time location information of dispatched vehicles and update the global dispatch plan; By dynamically adjusting scheduling instructions, the scheduling plan is ensured to be consistent with real-time data.

[0058] Specifically, optimizing the dispatch algorithm is a key component of building an efficient dispatch solution. A multi-level scoring mechanism is employed to globally optimize vehicle dispatch. Scoring metrics include key factors such as vehicle distance, travel time, road conditions, and vehicle battery level. The system dynamically adjusts the weighting of each metric based on real-time road conditions and service demand. For example, during peak traffic hours, if the congestion index on a major road exceeds 70%, the weighting of travel time is automatically increased, and the system prioritizes secondary routes.

[0059] The system employs a hierarchical response mechanism to dynamically dispatch vehicles from neighboring areas. When a charging vehicle shortage occurs in a commercial district, the system first scans vehicles within a five-kilometer radius. For example, if only two vehicles are available in a particular commercial district, and the system detects three idle vehicles in the East District, with good road conditions and an estimated arrival time of fifteen minutes, it initiates the dispatch process. The system also reserves a threshold number of vehicles to prevent over-dispatching from impacting service capacity in other areas.

[0060] Real-time location information updates are an important means of ensuring the accuracy of scheduling plans. The system updates vehicle location information every thirty seconds using on-board terminal positioning data. When a vehicle is detected to have deviated from the preset route or encountered traffic control, the system immediately replans the route. If a charging vehicle encounters temporary road construction during the scheduling process, the system will automatically calculate a new optimal route based on real-time road conditions. The dynamic adjustment of the global scheduling plan needs to consider multiple factors. During holidays, demand in a certain commercial area suddenly increased. The system predicted the peak period by analyzing historical data and initiated a cross-regional scheduling plan half an hour in advance. When it is found that the charging demand in a certain area exceeds expectations for three consecutive periods, the system automatically increases the vehicle ownership requirements in that area. The system also adjusts the scheduling strategy according to weather changes. In the event of heavy rain, the estimated travel time will be automatically extended to ensure the reliability of the scheduling plan.

[0061] The real-time nature of dispatch instructions significantly impacts service quality. The system uses in-vehicle intelligent terminals to instantly issue and provide feedback on dispatch instructions. For example, if multiple charging requests arise in a particular business district, the system combines information such as vehicle location and battery status to calculate the optimal service sequence. In the event of an emergency, the system can replan the plan within ten seconds, ensuring the optimal dispatching strategy remains. This dynamic adjustment mechanism effectively improves the responsiveness of charging services and enhances user experience.

[0062] To facilitate understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0063] In this example, in a central business district of a large city, charging demand increases significantly during the morning rush hour on weekdays. To effectively manage and dispatch charging vehicles, a system is needed to dynamically allocate resources. The working process is as follows: Step 1: Collect the urgency value, geographic location, and charging demand level value of all charging needs in the current area; obtain the real-time power status, location information, and power consumption rate of all charging vehicles.

[0064] Step 2: Establish a demand priority list and sort the charging needs according to the urgency value and demand level value; establish a vehicle availability list and evaluate the charging vehicles based on the real-time power status and location information.

[0065] Step 3: Calculate the estimated time and remaining power of each charging vehicle to reach the target location based on the distance to the target location, road conditions, and traffic conditions.

[0066] Step 4: When the estimated time is less than the preset time threshold and the remaining power meets the demand, the vehicle is marked as an available vehicle.

[0067] Step 5: When there are insufficient available vehicles, obtain information about charging vehicles in neighboring areas, and combine the distance and road conditions of cross-regional dispatch routes to determine whether to prioritize dispatching vehicles in neighboring areas.

[0068] Step 6: Based on the above information, generate global scheduling instructions to optimize resource allocation.

[0069] Step 7: Use the scheduling algorithm to optimize the global scheduling instructions to obtain the optimized global scheduling instructions.

[0070] Step 8: Dynamically dispatch charging vehicles according to the optimized global dispatch instructions to meet the charging needs in the business district.

[0071] Compared with the prior art, the present invention has the following beneficial effects: (1) By establishing a demand priority list and a vehicle availability list, the present invention can quickly identify the most urgent charging needs and the most suitable vehicles, thereby improving response speed.

[0072] (2) The present invention combines the distance to the target location, road conditions, and traffic conditions to calculate the estimated time for each charging vehicle to reach the target location and the remaining power, which helps to optimize the resource allocation of charging vehicles and ensure the efficiency of charging services.

[0073] (3) When there are insufficient available vehicles in the area, the system can obtain information on charging vehicles in adjacent areas and, based on the distance and road conditions of cross-regional dispatch routes, determine whether to prioritize dispatching vehicles in adjacent areas, thereby improving overall dispatch efficiency.

[0074] (4) The present invention uses a scheduling algorithm to optimize the global scheduling instructions, and can obtain optimized global scheduling instructions, further dynamically schedule charging vehicles, and improve the flexibility and adaptability of scheduling.

[0075] In summary, this invention can effectively address the imbalance between charging demand and vehicle resource allocation, achieve rational scheduling of charging vehicles, and improve charging service efficiency. Through intelligent scheduling and cross-regional collaboration, this invention can flexibly respond to various complex scenarios, maximize charging resource utilization, and provide electric vehicle users with more convenient and efficient charging services.

[0076] Reference Figure 2 , an embodiment of the present invention provides an intelligent scheduling system for a vehicle-mounted mobile charging system, comprising: A data acquisition module is used to obtain the urgency value, geographical location and charging demand level value of all charging demands in the current area, and establish a demand priority list; obtain the real-time power status, location information and power consumption rate of all charging vehicles, and establish a vehicle availability list; an available vehicle determination module is used to calculate the estimated time and remaining power of each charging vehicle to reach the target location based on the demand priority list and the vehicle availability list, combined with the target location distance, road conditions and traffic conditions; when the estimated time is less than the preset time threshold and the remaining power meets the demand, the vehicle is marked as an available vehicle; a scheduling module is used to obtain the charging vehicle information of the neighboring area when the available vehicles are insufficient, and determine whether to prioritize the scheduling of vehicles in the neighboring area based on the cross-regional scheduling route distance and road conditions, thereby generating a global scheduling instruction; a scheduling optimization module is used to optimize the global scheduling instruction using a scheduling algorithm to obtain an optimized global scheduling instruction, and dynamically schedule charging vehicles according to the optimized global scheduling instruction.

[0077] It should be noted that the intelligent scheduling system of a vehicle-mounted mobile charging system provided in an embodiment of the present invention is used to execute all the process steps of the intelligent scheduling method of a vehicle-mounted mobile charging system in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0078] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent control program for an OLED display screen. When the processor executes the computer program, the steps in the above-mentioned embodiments of the intelligent control method for an OLED display screen are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the display adjustment module.

[0079] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0080] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0081] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0082] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0083] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0084] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0085] The above embodiment is only one of the preferred implementation methods of the present invention and should not be used to limit the scope of protection of the present invention. Any changes or modifications that have no substantive meaning made to the main design concept and spirit of the present invention, as long as the technical problems they solve are still consistent with the present invention, should be included in the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for a vehicle-mounted mobile charging system, characterized in that: The method comprises: Obtain the urgency, geographic location, and charging demand level of all charging needs in the current area and establish a demand priority list; Obtain the real-time power status, location information, and power consumption rate of all charging vehicles and create a vehicle availability list; Calculate the estimated time for each charging vehicle to reach the target location and the remaining power based on the demand priority list and the vehicle availability list, taking into account the distance to the target location, road conditions, and traffic conditions; When the estimated time is less than the preset time threshold and the remaining power meets the demand, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, the information of charging vehicles in the neighboring areas is obtained, and combined with the cross-regional scheduling route distance and road conditions, it is determined whether to prioritize scheduling vehicles in the neighboring areas, thereby generating a global scheduling instruction; the global scheduling instruction is optimized using a scheduling algorithm to obtain an optimized global scheduling instruction, and charging vehicles are dynamically scheduled according to the optimized global scheduling instruction.

2. The intelligent scheduling method for the vehicle-mounted mobile charging system according to claim 1, characterized in that: The step of obtaining the urgency value, geographic location, and charging demand level value of all charging demands in the current area and establishing a demand priority list includes: The data acquisition interface is used to obtain the location points, charging amount, and time values ​​of all charging needs in the area, and the urgency value of each location point is calculated by combining the preset load value and capacity value; A clustering algorithm is used to group the locations according to their distance values, and a charging requirement level value is calculated for each group. The charging requirement level value is obtained by weighting the charging amount and time value. A demand priority list is constructed according to the urgency value and the charging demand level value, and each location point in the demand priority list is assigned a priority value, which is determined by a linear combination of the urgency value and the charging demand level value.

3. The intelligent scheduling method for a vehicle-mounted mobile charging system according to claim 1, characterized in that: The process of obtaining the real-time power status, location information, and power consumption rate of all charging vehicles and establishing a vehicle availability list includes: Obtain the current power, location information, and power consumption rate of all charging vehicles in the area through the data acquisition interface; Calculate the availability index of each vehicle based on the preset power threshold and driving range; A sorting algorithm is used to sort the vehicles whose availability index is higher than a preset availability index threshold, and a vehicle availability list is generated.

4. The intelligent scheduling method for a vehicle-mounted mobile charging system according to claim 1, characterized in that: The method of calculating the estimated time for each charging vehicle to reach the target location and the remaining power based on the demand priority list and the vehicle availability list, combined with the distance to the target location, road conditions, and traffic conditions, includes: Planning an optimal path for the charging vehicle to reach the target location based on the demand priority list and the vehicle availability list, taking into account the distance to the target location, road conditions, and traffic conditions; Calculating the estimated travel distance and estimated time for the charging vehicle to reach the target location under the optimal path; The power consumption value under the optimal path is calculated based on the power consumption rate of the charging vehicle and the expected driving distance, thereby obtaining the remaining power of the charging vehicle when reaching the target location.

5. The intelligent scheduling method for a vehicle-mounted mobile charging system according to claim 1, characterized in that: When the estimated time is less than a preset time threshold and the remaining power meets the requirement, marking the vehicle as an available vehicle includes: Making a judgment based on the estimated time and the remaining power in combination with a preset time threshold and a preset remaining power threshold; When the estimated time is less than a preset time threshold and the remaining power is greater than a preset remaining power threshold, the charging vehicle is marked as an available vehicle.

6. The intelligent scheduling method for a vehicle-mounted mobile charging system according to claim 1, characterized in that: When the available vehicles are insufficient, information about charging vehicles in the adjacent area is obtained, and combined with the distance and road conditions of the cross-regional dispatch routes, it is determined whether to prioritize dispatching vehicles in the adjacent area, thereby generating a global dispatch instruction, including: Get the number of available vehicles in the current area and determine whether it is less than the preset threshold of available vehicles; When the number of available vehicles is less than a preset threshold number of available vehicles, charging vehicle information obtained from a neighboring area; Based on the charging vehicle information, combined with the cross-region dispatch route distance and road condition data, determine whether to prioritize dispatching nearby vehicles; Generate global scheduling instructions based on the judgment results.

7. The intelligent scheduling method for a vehicle-mounted mobile charging system according to claim 6, characterized in that: The determining whether to prioritize dispatching of adjacent vehicles based on the charging vehicle information and combined with cross-region dispatch route distance and road condition data includes: Obtaining availability data of charging vehicles in a nearby area, the availability data including the charging status and range of the charging vehicles; Screening a set of candidate charging vehicles that meet cross-regional scheduling requirements based on the availability data; Extracting the travel time of the cross-regional routes corresponding to the candidate charging vehicle set, wherein the travel time is integrated with the real-time road congestion index; Calculate the matching degree between the travel time of the cross-region route and the cruising range by using a path planning algorithm; If the matching degree exceeds the preset matching degree threshold, the adjacent vehicle is dispatched first; if the matching degree does not exceed the preset matching degree threshold, the adjacent vehicle is not dispatched first.

8. The intelligent scheduling method for a vehicle-mounted mobile charging system according to claim 1, characterized in that: The method of optimizing the global scheduling scheme by using a scheduling algorithm to obtain an optimized global scheduling scheme and dynamically scheduling charging vehicles according to the optimized global scheduling instructions includes: Use scheduling algorithms to optimize the global scheduling plan and generate optimized scheduling instructions; Dynamically dispatch charging vehicles in neighboring areas based on optimized dispatch instructions; Obtain real-time location information of dispatched vehicles and update the global dispatch plan; By dynamically adjusting scheduling instructions, the scheduling plan is ensured to be consistent with real-time data.

9. An intelligent dispatching system for a vehicle-mounted mobile charging system, characterized in that: include: The data acquisition module is used to obtain the urgency value, geographic location and charging demand level value of all charging demands in the current area and establish a demand priority list; Obtain the real-time power status, location information, and power consumption rate of all charging vehicles and create a vehicle availability list; An available vehicle determination module is configured to calculate the estimated time for each charging vehicle to reach the target location and the remaining power of the vehicle based on the demand priority list and the vehicle availability list, in combination with the distance to the target location, road conditions, and traffic conditions; if the estimated time is less than a preset time threshold and the remaining power meets the demand, the vehicle is marked as an available vehicle; The scheduling module is used to obtain the information of charging vehicles in the adjacent area when the available vehicles are insufficient, and determine whether to prioritize the scheduling of vehicles in the adjacent area based on the distance and road conditions of the cross-regional scheduling routes, thereby generating a global scheduling instruction; the scheduling optimization module is used to optimize the global scheduling instruction using a scheduling algorithm to obtain an optimized global scheduling instruction, and dynamically schedule charging vehicles according to the optimized global scheduling instruction.

10. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent scheduling method for the vehicle-mounted mobile charging system according to any one of claims 1 to 8 is implemented.

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