Regionalized battery warehouse management method and system and storage medium thereof

Abstract

The invention discloses a regional battery warehouse management method and system and a storage medium thereof, and relates to the field of shared electric vehicle battery management, and the method comprises the steps: constructing a service area and a hierarchical parking point network through data driving, precisely defining a trunk area based on multi-source people flow data, customizing a minimum electric quantity threshold value, and intelligently analyzing the urgency of a battery replacement request. And implementing a cross-hub battery borrowing mechanism, and dynamically optimizing global battery resource scheduling. Through data-driven refined operation, intelligence and high efficiency of battery scheduling in the battery warehouse of the shared electric vehicle are realized, the half-way drop risk of a user is remarkably reduced, global battery resources are converted into a value chain of dynamic circulation, coexistence of resource idleness and shortage is avoided, and the service life of the battery warehouse is prolonged. The service range and the threshold value are automatically optimized by continuously analyzing mobile data, sustainable infrastructure support is provided for urban shared travel, and meanwhile operation of batteries in a whole warehouse is maximized.

Description

Technical Field

[0001] This invention relates to the field of shared electric vehicle battery management, specifically to a management method, system, and storage medium for a regionalized battery warehouse. Background Technology

[0002] The core challenge facing the current shared electric vehicle industry is the low efficiency of battery scheduling and management. Traditional battery warehouses adopt a centralized management model and lack a refined scheduling mechanism based on regional needs, resulting in an imbalance in the distribution of battery resources. Existing systems mostly rely on manual experience to set uniform power thresholds, which cannot adapt to the travel characteristics of different regions, causing the contradiction of "local battery shortages and regional resource idleness".

[0003] In some areas, insufficient power prevents users from using vehicles, while in other areas, batteries are too high to be deployed in time. At the same time, the lack of in-depth analysis of historical mobility data makes it impossible to accurately predict peak demand in different areas. Battery replenishment often lags behind actual demand, exacerbating the dual pressure on operating costs and user experience.

[0004] Existing technologies have also failed to establish a cross-regional battery value circulation mechanism. When there is a battery shortage in a certain region, it is impossible to dynamically identify and utilize the "excessive" battery resources in other places, resulting in low operation and maintenance efficiency and insufficient resource utilization, thus preventing the batteries in the battery warehouse from being effectively and fully utilized. Summary of the Invention

[0005] The purpose of this invention is to provide a management method, system and storage medium for a regionalized battery warehouse, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a management method for a regionalized battery warehouse, comprising the following steps:

[0007] Service areas are divided based on the location of battery warehouses, and a hierarchical parking network including hub parking points and their ancillary parking points is established within the service areas.

[0008] Monitor the movement data of shared electric vehicles from the hub parking point to each affiliated parking point within a preset time period, and generate image line data representing the dynamic relationship between parking points;

[0009] By aggregating image line data from peak travel periods in historical data, and using image overlay and development techniques, a group of auxiliary parking spots closely associated with hub parking spots is identified, so as to construct the main area corresponding to the hub parking spot;

[0010] Based on the spatial range of the main area, a minimum battery threshold is set for the corresponding hub parking point for shared electric vehicles, and the shared electric vehicles entering the hub parking point automatically match this threshold.

[0011] When the battery level of a shared electric vehicle falls below the minimum battery threshold of its designated parking spot, a battery swap request is generated. The battery warehouse terminal analyzes the number of battery swap requests and their corresponding historical image data, and then initiates a battery swap strategy based on the analysis results.

[0012] Preferably, the specific method for constructing the main area corresponding to the hub parking point includes the following steps:

[0013] Historical data is retrieved daily to identify and extract image line data for one or more peak time periods with the highest movement of shared electric vehicles within the hub parking area each day.

[0014] The extracted image line data from peak time periods are overlaid and merged to calculate the total historical movement from the hub parking point to each affiliated parking point, generating aggregated line data.

[0015] The aggregated connection data is filtered using a development threshold to retain the auxiliary parking points whose historical total movement exceeds a preset threshold, forming a core group of auxiliary parking points that has a stable and strong correlation with the corresponding hub parking points.

[0016] The network coverage area formed by the hub parking point and the core auxiliary parking point group is defined as the main area of ​​the hub parking point.

[0017] Preferably, the method for setting a minimum battery level threshold for shared electric vehicles at the hub parking area includes the following steps:

[0018] Based on the main area, calculate the distance from the hub parking point to one or more key points in the core auxiliary parking point group to determine a key distance representing the spatial range of the main area.

[0019] Based on the key distance and the average energy consumption standard of shared electric vehicles, the basic electricity required to complete the distance is calculated;

[0020] A safety margin is added above the base power level to generate a minimum power threshold specific to the hub parking area, which is then stored in the server database.

[0021] As a preferred method, the battery warehouse terminal analyzes the number of battery swapping requests and their corresponding historical image connection data, including the following steps:

[0022] The number of battery swapping requests from the same hub parking point within a preset time period was counted.

[0023] Retrieve historical image data of the parking area at the hub to identify its historical peak travel periods;

[0024] The current battery swapping request aggregation is correlated with historical peak travel periods. If the current time is close to or within a historical peak travel period and the number of battery swapping requests exceeds the first threshold, it is determined to be a high-urgency demand. If the current time is far from a historical peak travel period and the number of battery swapping requests is below the second threshold, it is determined to be a low-urgency demand.

[0025] Preferably, the battery swapping strategy based on the analysis results includes initiating a battery swapping strategy with corresponding priority based on the urgency of the demand. The battery swapping strategy includes emergency battery swapping work orders and planned maintenance work orders. The emergency battery swapping work order executes the immediate transport of batteries from the battery warehouse to the corresponding hub parking point for battery swapping. The planned maintenance work order executes the battery swapping while following the regular maintenance of shared electric vehicles. For high urgency demands, an emergency battery swapping work order is generated for immediate execution; for low urgency demands, the battery swapping task is included in the planned maintenance work order.

[0026] Preferably, the battery swapping strategy also includes an emergency battery borrowing strategy across hub parking points, which is triggered when the number of batteries in the battery warehouse is insufficient when an emergency battery swapping work order is executed. The emergency battery borrowing strategy across hub parking points includes the following steps:

[0027] Taking the hub parking point that needs to execute an emergency battery swap work order as the demand point, other hub parking points with a minimum power threshold lower than the demand point are selected from the service area as candidate battery supply points.

[0028] Among the candidate battery supply points, batteries of shared electric vehicles parked there with a charge level higher than the minimum charge threshold of the demand point are identified and marked as borrowable batteries. The portion of the battery quantity in the battery warehouse that is insufficient is supplemented by the borrowable batteries.

[0029] Carry the remaining batteries in the battery warehouse to the candidate battery supply point closest to the demand point to retrieve the available batteries, and then go to the demand point to replace the batteries on the shared electric vehicle that needs to be swapped.

[0030] Batteries removed from the demand points with a charge level below their own threshold but above the minimum charge level of the candidate battery supply point are transported and added to the shared electric vehicles at the candidate battery supply point to ensure that the battery charge level of these vehicles meets the minimum charge level of their respective hub parking points. The remaining batteries are taken back to the battery warehouse.

[0031] Preferably, an early warning analysis is performed on the number of battery swapping requests. The early warning analysis includes issuing an early warning report when the battery swapping requests at the hub parking point are continuously abnormal, re-analyzing the data of the hub parking point, and adjusting the main area and minimum power threshold of the point.

[0032] As a preferred option, the automatic matching step for the minimum battery threshold of shared electric vehicles includes: when the shared electric vehicle completes parking settlement at the hub parking point, the vehicle terminal automatically uploads its location information to the server, the server queries and issues the minimum battery threshold corresponding to the hub parking point to the vehicle terminal, and the vehicle terminal updates its low battery judgment standard accordingly.

[0033] To address the aforementioned technical problems, the present invention also provides a management system for a regionalized battery warehouse, comprising:

[0034] Memory, used to store computer programs;

[0035] A processor for executing the computer program, which, when executed by the processor, implements the steps of a regionalized battery warehouse management method as described in any of the preceding claims.

[0036] To address the aforementioned technical problems, the present invention also provides a readable storage medium having a computer program stored thereon.

[0037] When the computer program is executed by the processor, it implements the steps of a regionalized battery warehouse management method as described in any of the above.

[0038] In summary, the beneficial effects of this invention are:

[0039] This invention achieves intelligent and efficient battery scheduling in shared electric vehicle battery warehouses through data-driven refined operations. Its core innovation lies in dividing the urban network into an "efficient local circulation system" centered on hub parking points. Based on multi-source pedestrian flow data, it accurately defines the main areas and sets a customized minimum battery threshold for each hub point, significantly reducing the risk of users breaking down midway. The intelligent battery swapping strategy can dynamically identify the urgency of demand, distinguish between sporadic requests and clustered demands, and innovatively implement a cross-hub battery borrowing mechanism, transforming global battery resources into a dynamically circulating value chain, avoiding both resource idleness and shortage. By continuously analyzing mobile data, it automatically optimizes the service range and thresholds, making battery scheduling more accurate, operation and maintenance more efficient, and user experience smoother. Ultimately, it achieves a triple benefit of reduced operating costs, improved resource utilization, and enhanced user satisfaction, providing sustainable infrastructure support for urban shared mobility while maximizing the operation of batteries throughout the warehouse.

[0040] The present invention also provides a regionalized battery warehouse management system and its storage medium, which have the above-mentioned beneficial effects, and will not be elaborated further here. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the overall framework and process structure of a regionalized battery warehouse management method according to the present invention;

[0043] Figure 2 This is a schematic diagram of the process framework for analyzing battery swapping requests in a regionalized battery warehouse management method of the present invention.

[0044] Figure 3 This is a schematic diagram of the borrowing emergency strategy process framework in the regionalized battery warehouse management method of the present invention;

[0045] Figure 4 This is a schematic diagram of the warehouse system terminal interface in a regionalized battery warehouse management method of the present invention;

[0046] Figure 5 This is a schematic diagram of the newly added warehouse interface in the warehouse system terminal of the regionalized battery warehouse management method of the present invention. Detailed Implementation

[0047] The present invention will now be described in further detail with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. These drawings are simplified schematic diagrams, which are only used to illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0048] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate several embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the invention will be more thorough and complete.

[0049] All features disclosed in this specification, or steps in all methods or processes disclosed herein, may be combined in any way, except for mutually exclusive features and / or steps.

[0050] Any feature disclosed in this specification (including any appended claims, abstract, and drawings) may be replaced by other equivalent or similar features for a similar purpose, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features.

[0051] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of at least two elements or the interaction relationship of at least two elements, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0052] The following is combined Figures 1-5 The present invention will be described in detail below. One embodiment of the present invention provides a method for managing a regionalized battery warehouse, comprising the following steps:

[0053] Step 1: Building the data foundation and initial architecture

[0054] Service areas are divided according to the location of the battery warehouses. Information on all battery warehouses is stored in the central system terminal. (Refer to...) Figure 4 and Figure 5 The battery warehouse stores batteries for shared electric vehicles, centralizing battery scheduling and replenishment within the service area. By dividing the vast urban operation network into multiple "service areas" centered around the battery warehouse, the global and complex scheduling problem is decomposed into multiple local and more manageable "efficient local circulation systems." A hierarchical parking network containing hub parking points and their associated parking points is established within the service area, with the selection of hub parking points based on pedestrian flow data.

[0055] Specifically, densely populated areas indicate high travel demand and are hotspots for shared electric bikes. Setting these areas as central nodes aligns with the natural flow of users.

[0056] The sources of pedestrian flow data can be multi-dimensional:

[0057] Static data: City POI (Point of Interest) data, such as subway stations, bus terminals, large commercial districts, office buildings, schools, hospitals and other areas that naturally attract people.

[0058] Dynamic data:

[0059] Internal platform data: Historical order data generated by the shared electric vehicle app itself; the most concentrated pick-up and drop-off points are natural hubs.

[0060] Mobile signaling data: Anonymized data on human gatherings and movement obtained through telecommunications operators.

[0061] Other map data: Access to pedestrian heat maps and real-time traffic flow data provided by map service providers.

[0062] By analyzing the above data, several "hot spots" with significantly higher pedestrian density than the surrounding areas were identified on the service area map. The parking spots where these hot spots are located or nearby were established as hub parking spots. Once the hub point is determined, other parking spots within a certain distance (e.g., within a radius of 500-1000 meters) are naturally designated as its subordinate parking spots. This forms a "star-shaped" or "tree-shaped" network with the hub point as the core and multiple subordinate points radiating outwards.

[0063] Step 2: Data Collection

[0064] Monitor the movement data of shared electric vehicles from the hub parking point to each affiliated parking point within a preset time period, and generate image line data representing the dynamic relationship between parking points;

[0065] Specifically, the preset time period is not set arbitrarily, but rather has a clear analytical purpose, for example:

[0066] Morning rush hour: 07:00 - 10:00;

[0067] Evening rush hour: 17:00 - 20:00;

[0068] 24 / 7: 00:00 - 23:59 (for overall analysis);

[0069] The system will only filter out riding records whose "start time" falls within a preset time period. After obtaining all riding records within the preset time period, the system will perform a key filter: only retain records whose "starting stop point" is a certain "hub stop point".

[0070] The filtered data is still fragmented trip records. For example, during the morning rush hour, 100 independent trip records may be generated from hub point H, of which 15 may have gone to auxiliary point A, 10 may have gone to auxiliary point B, and so on.

[0071] The system groups and counts these 100 records.

[0072] Group the trips by "Destination Parking Point ID", then count the number of trips to each destination to obtain a summary table, such as the table below (preset time period: 7:00-9:00 on a certain day, hub point: H):

[0073] Hub parking Ancillary parking area Number of moving vehicles H A1 50 times H A2 85 times H B2 45 times H B3 60 times

[0074] Generate image connection data; the structure of the graph:

[0075] Nodes: Represent parking points. In this example, there is one core node (hub point H) and multiple associated nodes (subordinate points A, B, C, D...).

[0076] Edge (i.e. "connection"): Represents the movement relationship from the starting point to the ending point. Each edge starts from node H and points to nodes A, B, C, etc.

[0077] The attributes of the connection (quantification of "dynamic association"):

[0078] Each connection (such as from H to A) is assigned a weight, which is the number of vehicles that move from H to A within a preset time period (i.e., the "number of moving vehicles" in the table above).

[0079] The greater the weight, the closer the connection from H to A and the greater the flow during this period. The line will be drawn "thicker" or "darker" in color when visualized, and finally form the image connection data for the corresponding time period.

[0080] Step 3: Construct the main area

[0081] Its goal is to filter out noise from the large volume of daily, sometimes random, vehicle movement records, extracting stable, high-frequency movement patterns to accurately define the core service area of ​​each hub parking point. This involves the following steps:

[0082] Extract peak period data daily;

[0083] Objective: To capture the most representative travel patterns. Travel during peak hours (such as morning and evening rush hours) has the strongest purpose and the most stable data, which can effectively filter out noise from low-frequency travel such as leisure and random travel.

[0084] It will look back at a historical period (for example, the past 30 days).

[0085] For each day, analyze the time distribution curve of all vehicles moving out of the hub to identify "one or more peak time periods with the highest shared electric vehicle movement" (e.g., 8:00-10:00 am and 6:00-8:00 pm on weekdays).

[0086] Then, the image line data generated only during these specific peak periods is extracted, which yields a "data snapshot" representing typical travel patterns for each day.

[0087] Overlaying and merging generates aggregated connection data (data condensation);

[0088] Objective: To combine multiple days of "data snapshots" into a "comprehensive photo" that reflects long-term trends.

[0089] The image line data extracted in the first step, spanning multiple days (e.g., 30 days), is overlaid to obtain an aggregated line data chart. In this chart, the "weight" of each line becomes a historical total movement. The larger this value, the more stable and frequent the use of the path is during peak periods. For example, one type of aggregated line data chart is shown below (hub point H, 30-day data accumulation):

[0090] Terminal Parking Total historical movement (cumulative peak periods over 30 days) A 450 times B 300 times C 280 times D 25 times E 10 times

[0091] By applying a development threshold screening method, truly important patterns are "developed" while random and minor correlations are filtered out.

[0092] Apply a preset threshold to the aggregated data for filtering. This threshold is key to distinguishing between "core associations" and "casual associations".

[0093] The threshold can be set in the following ways:

[0094] Absolute numerical method: For example, only retain connections with a total historical movement of more than 100 times.

[0095] Relative proportion method (more scientific): For example, only retain the top 20% of the total movement volume of the affiliated points, or the points whose movement volume accounts for a certain percentage (such as 3%) or more of the total outflow of the hub points.

[0096] "Development" results: After applying the threshold, weak connections such as those between auxiliary points D and E (only 25 and 10 times) in the table above will be filtered out, while auxiliary points A, B, and C will stand out due to their high weight. These retained auxiliary parking points constitute the "core auxiliary parking point group," which represents the most mainstream and stable travel destinations departing from this hub.

[0097] Define the main areas (output results)

[0098] In a geographic information system, the coordinates of the hub parking point (H) and the identified core auxiliary parking point group (A, B, C...) are marked. These points and their coverage area (usually approximated by the smallest convex polygon of these points or the area delineated by the actual road network) are formally defined by the system as the "main area" of the hub point H.

[0099] Step 4: Setting the minimum battery threshold

[0100] The battery requirements are customized based on the unique main area of ​​each hub point, so that the battery is sufficient to meet the next most likely travel needs before the vehicle is used, which significantly reduces the risk of users breaking down due to insufficient battery halfway, and improves user experience and operational efficiency.

[0101] Based on the spatial range of the main area, a minimum battery threshold is set for the corresponding hub parking point for shared electric vehicles, and the shared electric vehicles entering the hub parking point automatically match this threshold.

[0102] Specifically, the first step is to determine the key distance:

[0103] The "main area" generated in the previous stage is a group of core auxiliary parking spots that have a stable and strong connection with the hub. The path distance from the hub parking spot (H) to each core auxiliary parking spot in its main area is calculated (usually based on the actual riding distance obtained from the map route planning API, rather than the straight-line distance).

[0104] Determine the "critical distance": This is the basis for setting thresholds and can be achieved using one or a combination of the following strategies:

[0105] The furthest distance strategy: Select the distance to the furthest core auxiliary parking point. This is the most conservative strategy, ensuring that vehicles can reach any point within the area;

[0106] High-frequency weighted distance strategy: This is a smarter strategy; it combines the weights (i.e. historical movement) in the "image development line data" to calculate a weighted average distance. For example, the weight of going to the frequently used point A (10 km away) is much higher than that of going to the occasionally used point B (15 km away), so the critical distance will be closer to 10 km rather than 15 km.

[0107] Quantile distance strategy: For example, ensuring that the battery has enough power to cover the distance reached by 90% of historical travel demand.

[0108] Minimum battery threshold calculation:

[0109] Calculate the base energy consumption: Based on the company's average energy consumption standard for vehicles (e.g., 0.1 kWh per kilometer), convert the critical distance determined in the previous step into electricity demand and add a safety margin: Considering variables such as actual road conditions (uphill and downhill, congestion), weather (headwind, low temperature), and user riding habits, a safety margin must be added to the base energy consumption. The margin can be a fixed value (e.g., the electricity required for an additional 5 kilometers) or a percentage.

[0110] Finally, the basic energy consumption and safety margin constitute the final minimum power threshold. This threshold will be stored in the attribute database of the hub parking point in the form of a relative value (such as 40% of the battery's full charge) or an absolute value (such as 2 kWh).

[0111] Each hub parking spot has a unique "minimum battery threshold". For example, the threshold for a widely covered transportation hub may be 50%, while the threshold for a downtown business district may be only 20%.

[0112] When a shared electric vehicle completes parking settlement at the hub parking point, the vehicle terminal automatically uploads its location information to the server. The server queries and sends the minimum battery threshold corresponding to the hub parking point to the vehicle terminal, and the vehicle terminal updates its low battery judgment standard accordingly.

[0113] Specifically, when a user finishes a ride and successfully locks the bike at a parking spot to settle the payment, the vehicle's central control system (via GPS / BeiDou positioning) will upload its precise location to the cloud server.

[0114] The cloud server compares the location with the electronic fence range of all predefined "hub parking points" in the system. Once a match is found, it is determined that the vehicle has "entered" hub point H. The vehicle or the settlement system sends a message to the server, which includes at least the following content: "Vehicle ID=V001, parked at hub point H".

[0115] After receiving the message, the server immediately queries the database to obtain the minimum battery threshold preset for the hub point H. The server then sends a configuration command to vehicle V001 via the wireless network, which reads: "Update your low battery alarm threshold and the rentable status trigger battery to the minimum battery threshold H." The central control system of vehicle V001 receives and saves the command.

[0116] From this moment on, the vehicle's battery management strategy is tied to hub point H;

[0117] If the vehicle's current battery level is 35%, and the threshold for hub point H is 40%, then:

[0118] The vehicle may be marked as "low battery, battery swap recommended" or become unavailable for rent on the user's app, and a battery swap request may be generated.

[0119] Step 5: Implement the battery swapping strategy

[0120] When the battery level of a shared electric vehicle falls below the minimum battery threshold at its designated parking hub, a battery swap request is generated. This request is not a simple alert but a structured data packet, typically containing at least the following:

[0121] Vehicle ID: A unique identifier.

[0122] Location information: The ID of the hub parking spot.

[0123] Current battery level: The specific value below the threshold.

[0124] Timestamp: The time the request was generated.

[0125] Trigger threshold: What is the minimum battery level threshold that is being matched?

[0126] The battery warehouse terminal analyzes the number of battery swapping requests and their corresponding historical image data:

[0127] Analysis Dimension 1: Real-time Aggregation of Battery Swap Requests

[0128] Real-time statistics on the number of battery swapping requests from the same hub parking point H, for example:

[0129] Scenario A: Sporadic requests. For example, at 10:00 AM, hub H may only have 1-2 battery swap requests. This could be because some vehicles have been parked there for a long time, causing their battery power to naturally decrease.

[0130] Scenario B: Cluster Requests. For example, after the evening rush hour at 7 PM, hub H received 15 battery swap requests in a short period of time. This indicates that a large number of users parked their low-battery vehicles there after peak usage.

[0131] Analysis Dimension Two: Correlation Analysis with "Historical Image Line Data"

[0132] Data retrieval: The system will retrieve the historical image line data of the hub point H (especially the constructed "main area" and "developed line data").

[0133] Pattern comparison: Does the current time period with a large number of battery swap requests (e.g., 7 pm) coincide with the historical peak movement periods of hub point H (e.g., 5-7 pm is the peak inflow period)?

[0134] Demand Forecasting: Based on historical data, predict future vehicle demand. For example, historical data shows that vehicles parked at point H each night are heavily driven towards the Science Park during the morning rush hour (7-9 am). Therefore, ensuring that the vehicles at that point are fully charged before the next morning is crucial.

[0135] The "developed connection data" indicates the main travel direction, which can also provide a reference for maintenance personnel to plan the power swapping lines (i.e., prioritize the power supply on the main lines).

[0136] Based on the above analysis, the warehouse terminal system will make different decision levels:

[0137] For scenario A: sporadic requests and the early stages of non-peak demand, the current time is far from historical peak travel periods, and the number of battery swap requests is below the second threshold, then it is judged as a low-urgency demand.

[0138] Decision: Emergency battery swapping will not be implemented for the time being. These vehicles will be marked as "planned battery swapping" and included in the regular maintenance work order in the early morning of the following day. Maintenance personnel will handle them uniformly during the low-priority period.

[0139] For scenario B: Cluster requests are about to enter a peak demand period, the current time is approaching or within a historical peak travel period, and the number of battery swapping requests exceeds the first threshold, which is judged as a high-urgency demand.

[0140] Decision: Immediately execute the battery swapping strategy, generate a high-priority emergency battery swapping work order, and the system will notify maintenance personnel to immediately go to the hub point H to perform batch battery replacement. At the same time, the system may intelligently recommend the number of batteries to be carried based on historical data.

[0141] It should be noted that in this embodiment, the number of battery swapping requests is analyzed for early warning. The early warning analysis includes issuing an early warning report when the battery swapping requests at the hub parking point are continuously abnormal. The system may determine that the travel pattern in the area has changed (such as the construction of a large office building), re-analyze the data of the hub parking point, and adjust the main area and minimum power threshold of the point.

[0142] It is worth mentioning that in this embodiment, if the number of batteries in the battery warehouse is insufficient when an emergency battery swapping work order is executed, an emergency strategy of borrowing batteries from other hub parking points is implemented. The core of this strategy is to dynamically reconstruct the battery value chain. The "qualification" of a battery is relative to the standard of its location. By establishing a temporary "battery value circulation channel" between hub points with different standards, global battery resources are activated to cope with local emergency shortages. Specifically, this includes the following steps:

[0143] Identifying demand points and screening candidate supply points

[0144] Demand point: This refers to the hub parking point (denoted as point A) where a battery shortage occurs and an emergency battery swap work order needs to be executed. Its characteristic is that the minimum battery threshold is relatively high (e.g., 50%).

[0145] The system uses point A as the center and quickly scans other hub points within its service area.

[0146] The core screening criterion is that the minimum power threshold of the candidate supply point must be lower than the threshold of point A.

[0147] For example, points B (business district, threshold 30%) and C (community, threshold 25%) are both selected as candidate supply points because a battery with 35% charge is unqualified for point A, but qualified for points B and C.

[0148] Marking available batteries

[0149] The system remotely queries the actual battery charge of all parked vehicles at each candidate supply point (point B, point C) and identifies the batteries whose charge is higher than the minimum charge threshold (50%) at demand point A.

[0150] For example, at point B, it was found that the battery charge of 5 vehicles was between 55% and 80%; at point C, it was found that the battery charge of 3 vehicles was between 60% and 90%. These batteries were marked as "available for loan".

[0151] These batteries are high-quality assets that "exceed the required standards" at the supply point. Transferring them to point A, which has the most urgent need for high-standard demand, can maximize the value of these resources.

[0152] Optimize the path and execute the borrowing and swapping of electricity.

[0153] Path planning: The system plans the optimal route for operations and maintenance personnel, which is usually: from the battery warehouse to the nearest candidate supply point (such as point B) to the demand point A.

[0154] Starting from the warehouse: The maintenance vehicle carries all the remaining qualified batteries in the warehouse to the supply point for pickup. Upon arrival at point B, the maintenance personnel, according to the system prompts, remove batteries with a charge level higher than 50% from the marked vehicles and load them onto the vehicles. At this time, these vehicles are temporarily out of power, but will be replenished later.

[0155] Heading to Demand Points for Battery Swapping: Upon arriving at demand point A, use batteries brought from the warehouse and batteries borrowed from point B to swap batteries for vehicles with low battery levels at point A. When swapping batteries at point A, the old batteries removed are not considered waste. Although their charge level is below the threshold (50%) at point A, it may be above the threshold (30%) at the supply point (point B).

[0156] For example: When replacing a batch of batteries with a charge level of 35%-45%, the maintenance personnel will transport these batteries, which were recovered from point A and have a charge level of 35%-45%, back to point B and install them on the vehicles at point B where the batteries were removed. For point B (threshold 30%), batteries with a charge level of 35% are fully qualified and the vehicles can be put into normal use.

[0157] If the number of batteries recovered from point A exceeds the demand at point B, the excess batteries are taken back to the battery warehouse for charging.

[0158] In summary, this regionalized battery warehouse management method, through data-driven refined operations, achieves intelligent and efficient battery scheduling in shared electric vehicle battery warehouses. Its core innovation lies in dividing the urban network into an "efficient local circulation system" centered on hub parking points. Based on multi-source pedestrian flow data, it accurately defines the main areas and sets customized minimum power thresholds for each hub point, significantly reducing the risk of users breaking down midway. The intelligent battery swapping strategy can dynamically identify the urgency of demand, distinguish between sporadic requests and clustered demands, and innovatively implement a cross-hub battery borrowing mechanism, transforming global battery resources into a dynamically circulating value chain, avoiding both resource idleness and shortage. By continuously analyzing mobile data, it automatically optimizes the service range and thresholds, making battery scheduling more accurate, operation and maintenance more efficient, and user experience smoother. Ultimately, it achieves a triple benefit of reduced operating costs, improved resource utilization, and enhanced user satisfaction, providing sustainable infrastructure support for urban shared mobility while maximizing the operation of batteries throughout the warehouse.

[0159] The above describes in detail an embodiment of a regional battery warehouse management method. Based on this, the present invention also discloses a regional battery warehouse management system and storage medium corresponding to the above method.

[0160] A management system for a regionalized battery warehouse includes:

[0161] Memory, used to store computer programs;

[0162] A processor is used to execute the computer program, which, when executed by the processor, is capable of implementing the relevant steps in a regionalized battery warehouse management method disclosed in any of the foregoing embodiments.

[0163] The processor may include one or more processing cores, such as a core processor or a core processor. The processor can be implemented using at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may also include a main processor and coprocessors. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0164] In some embodiments, the processor may integrate a Graphics Processing Unit (GPU) responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor may also include an Artificial Intelligence (AI) processor for handling computational operations related to machine learning.

[0165] The memory may include one or more readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory is used to store at least the following computer program, which, after being loaded and executed by a processor, is capable of implementing the relevant steps in the regionalized battery warehouse management method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory may also include an operating system and data, and the storage method may be temporary or permanent storage. The operating system may be Windows. The data may include, but is not limited to, the data involved in the above methods.

[0166] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules can be implemented in hardware or as software functional modules. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of the present invention.

[0167] To this end, embodiments of the present invention also provide a readable storage medium storing a computer program, which, when executed by a processor, implements steps such as those of a regionalized battery warehouse management method.

[0168] The readable storage medium may include: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program code.

[0169] The computer program contained in the readable storage medium provided in this embodiment can implement the steps of a regionalized battery warehouse management method as described above when executed by a processor, with the same effect.

[0170] The foregoing has provided a detailed description of a regionalized battery warehouse management method, system, and storage medium provided by the present invention. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus, devices, and readable storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0171] The above description is merely a specific embodiment of the invention, but the scope of protection of the invention is not limited thereto. Any variations or substitutions conceived without inventive effort should be included within the scope of protection of the invention. Therefore, the scope of protection of the invention should be determined by the scope defined in the claims.

Claims

1. A management method for a regionalized battery warehouse, characterized in that: Includes the following steps: Service areas are divided based on the location of battery warehouses, and a hierarchical parking network including hub parking points and their ancillary parking points is established within the service areas. Monitor the movement data of shared electric vehicles from the hub parking point to each of the affiliated parking points within a preset time period, and generate image line data representing the dynamic relationship between parking points; By aggregating the image line data from peak travel periods in historical data, and using image overlay and development techniques, a group of affiliated parking spots closely associated with the hub parking spot is identified, so as to construct the main area corresponding to the hub parking spot; Based on the spatial range of the main area, a minimum battery threshold is set for the corresponding hub parking point for shared electric vehicles, and the shared electric vehicles entering the hub parking point automatically match this threshold. When the battery level of a shared electric vehicle falls below the minimum battery threshold of its designated parking spot, a battery swap request is generated. The battery warehouse terminal analyzes the number of battery swap requests and their corresponding historical image data, and then initiates a battery swap strategy based on the analysis results.

2. The management method for a regionalized battery warehouse according to claim 1, characterized in that: The specific method for constructing the main area corresponding to the hub parking point includes the following steps: Historical data is retrieved daily to identify and extract image line data for one or more peak time periods with the highest movement of shared electric vehicles within the hub parking area each day. The extracted image line data from peak time periods are overlaid and merged to calculate the total historical movement from the hub parking point to each affiliated parking point, generating aggregated line data. The aggregated connection data is filtered using a development threshold to retain the auxiliary parking points whose historical total movement exceeds a preset threshold, forming a core group of auxiliary parking points that has a stable and strong correlation with the corresponding hub parking points. The network coverage area formed by the hub parking point and the core auxiliary parking point group is defined as the main area of ​​the hub parking point.

3. The management method for a regionalized battery warehouse according to claim 2, characterized in that: The method for setting the minimum battery level threshold for shared electric vehicles at the hub parking points includes the following steps: Based on the main area, calculate the distance from the hub parking point to one or more key points in the core auxiliary parking point group to determine a key distance representing the spatial range of the main area. Based on the key distance and the average energy consumption standard of shared electric vehicles, the basic electricity required to complete the distance is calculated; A safety margin is added above the base power to generate a minimum power threshold specific to the hub parking area, and this threshold is stored in the server database.

4. The management method for a regionalized battery warehouse according to claim 3, characterized in that: The method for analyzing battery warehouse terminals based on the number of battery swapping requests and their corresponding historical image data includes the following steps: The number of battery swapping requests from the same hub parking point within a preset time period was counted. Retrieve historical image data of the parking area at the hub to identify its historical peak travel periods; The current battery swapping request aggregation is correlated with historical peak travel periods. If the current time is close to or within a historical peak travel period and the number of battery swapping requests exceeds the first threshold, it is determined to be a high-urgency demand. If the current time is far from a historical peak travel period and the number of battery swapping requests is below the second threshold, it is determined to be a low-urgency demand.

5. The management method for a regionalized battery warehouse according to claim 4, characterized in that: The battery swapping strategy based on the analysis results includes initiating a battery swapping strategy with corresponding priority based on the urgency of the demand. The battery swapping strategy includes emergency battery swapping work orders and planned maintenance work orders. The emergency battery swapping work order executes the immediate transport of batteries from the battery warehouse to the corresponding hub parking point for battery swapping. The planned maintenance work order executes the battery swapping in conjunction with the regular maintenance of shared electric vehicles. For high urgency demands, an emergency battery swapping work order is generated for immediate execution; for low urgency demands, the battery swapping task is included in the planned maintenance work order.

6. The management method for a regionalized battery warehouse according to claim 5, characterized in that: The battery swapping strategy also includes an emergency battery borrowing strategy across hub parking points, which is triggered when the number of batteries in the battery warehouse is insufficient when an emergency battery swapping work order is executed. The emergency battery borrowing strategy across hub parking points includes the following steps: Taking the hub parking point that needs to execute an emergency battery swap work order as the demand point, other hub parking points with a minimum power threshold lower than the demand point are selected from the service area as candidate battery supply points. Among the candidate battery supply points, batteries of shared electric vehicles parked there with a charge level higher than the minimum charge threshold of the demand point are identified and marked as borrowable batteries. The portion of the battery quantity in the battery warehouse that is insufficient is supplemented by the borrowable batteries. Carry the remaining batteries in the battery warehouse to the candidate battery supply point closest to the demand point to retrieve the available batteries, and then go to the demand point to replace the batteries on the shared electric vehicle that needs to be swapped. Batteries removed from the demand points with a charge level below their own threshold but above the minimum charge level of the candidate battery supply point are transported and added to the shared electric vehicles at the candidate battery supply point to ensure that the battery charge level of these vehicles meets the minimum charge level of their respective hub parking points. The remaining batteries are taken back to the battery warehouse.

7. The management method for a regionalized battery warehouse according to claim 6, characterized in that: It also includes early warning analysis of the number of battery swapping requests. The early warning analysis includes issuing an early warning report when the battery swapping requests at the hub parking point are continuously abnormal, re-analyzing the data of the hub parking point, and adjusting the main area and minimum power threshold of the point.

8. The management method for a regionalized battery warehouse according to claim 7, characterized in that: The automatic matching steps for the minimum battery threshold of shared electric vehicles include: when a shared electric vehicle completes parking settlement at the hub parking point, the vehicle terminal automatically uploads its location information to the server. The server queries and issues the minimum battery threshold corresponding to the hub parking point to the vehicle terminal, and the vehicle terminal updates its low battery judgment standard accordingly.

9. A management system for a regionalized battery warehouse, characterized in that: include Memory, used to store computer programs; A processor for executing the computer program, which, when executed by the processor, implements the steps of a regionalized battery warehouse management method as described in any one of claims 1-8.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the management method for a regionalized battery warehouse as described in any one of claims 1-8.

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