A method and system for managing a regional battery warehouse and a storage medium thereof

Abstract

The application discloses a kind of regional battery warehouse management method, system and its storage medium, it is related to shared electric vehicle battery management field, including by data-driven construction service area and hierarchical parking point network, based on multi-source flow data accurately defining main area and customizing minimum power threshold, intelligent analysis battery replacement request urgency, implement cross-hub battery loan mechanism, dynamically optimize global battery resource scheduling.The application realizes the intelligentization and high efficiency of battery scheduling in the battery warehouse of shared electric vehicle by the fine operation of data-driven, significantly reduces the risk of user halfway stranded, converts global battery resources into dynamic circulation value chain, avoids the coexistence of resource idling and shortage, automatically optimizes service range and threshold by continuous analysis of mobile data, provides sustainable infrastructure support for urban shared travel, and maximizes the operation of the entire warehouse battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of shared electric vehicle battery management, in particular to a regional battery warehouse management method and system and a storage medium thereof. BACKGROUND

[0002] The current shared electric vehicle industry is facing the core challenge of low efficiency of battery scheduling management. Traditional battery warehouses adopt centralized management mode, lack of fine scheduling mechanism based on regional demand, leading to unbalanced distribution of battery resources. Existing systems rely on manual experience to set unified power thresholds, which cannot adapt to the characteristics of different regions, causing the contradiction of "local battery shortage and regional resource idling coexist".

[0003] Some places cause users to be unable to use vehicles due to insufficient power, while other places cannot be timely deployed due to high power. At the same time, lack of deep mining of historical mobile data, unable to accurately predict regional demand peak, battery supply often lags behind actual demand, increasing the dual pressure of operating cost and user experience.

[0004] The prior art has also failed to establish a cross-regional battery value circulation mechanism. When a certain region is short of batteries, it is unable to dynamically identify and utilize the "excessively qualified" battery resources in other places, resulting in low operation efficiency and insufficient resource utilization, so that the batteries in the battery warehouse cannot be effectively and fully utilized. SUMMARY

[0005] The purpose of the present application is to provide a regional battery warehouse management method, system and storage medium to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a regional battery warehouse management method, comprising the following steps:

[0007] Divide the service area based on the location of the battery warehouse, and establish a hierarchical parking point network including the hub parking point and its affiliated parking points in the service area;

[0008] Monitor the shared electric vehicle movement data from the hub parking point to each affiliated parking point in a preset time period, and generate image connection data representing the dynamic association relationship between parking points;

[0009] Aggregate the image connection data of the moving peak period in the historical data, and identify the affiliated parking point group closely associated with the hub parking point through image overlapping and developing technology, to construct the main area corresponding to the hub parking point;

[0010] According to the spatial range of the main area, set the minimum power threshold of the shared electric vehicle for the corresponding hub parking point, and the shared electric vehicle entering the hub parking point automatically matches this threshold.

[0011] When the shared electric vehicle's power is lower than the minimum power threshold of the hub parking point where it is located, a battery replacement request is generated. The battery warehouse terminal analyzes the number of battery replacement requests and their corresponding historical image connection data, and initiates a battery replacement strategy based on the analysis results.

[0012] As a preferred embodiment, the specific method for constructing the main area corresponding to the hub parking point comprises the following steps:

[0013] The historical data is retrieved by day, and the image connection data of one or more peak time periods in which the shared electric vehicle moves the most in the hub parking point is identified and extracted;

[0014] The extracted image connection data of the peak time period is superimposed and merged, the historical total movement from the hub parking point to each subsidiary parking point is calculated, and the aggregated connection data is generated;

[0015] The aggregated connection data is applied to the development threshold screening, and the subsidiary parking points with historical total movement exceeding the preset threshold are retained to form a core subsidiary parking point group with stable strong association with the corresponding hub parking point;

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

[0017] As a preferred embodiment, the method for setting the minimum power threshold of the shared electric vehicle in the hub parking point comprises the following steps:

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

[0019] According to the key distance and the average energy consumption standard of the shared electric vehicle, the basic power required to complete the distance is calculated;

[0020] A safety margin is added to the basic power to generate a minimum power threshold dedicated to the hub parking point, and it is stored in the server database.

[0021] As a preferred embodiment, the method for analyzing the number of battery replacement requests and their corresponding historical image connection data by the battery warehouse terminal comprises the following steps:

[0022] The number of battery replacement requests from the same hub parking point within a preset time period is counted;

[0023] The historical image connection data of the hub parking point is retrieved, and the historical movement peak period is identified;

[0024] The current battery replacement request aggregation condition is analyzed in association with historical mobile peak periods. If the current time point is close to or in a historical mobile peak period, and the number of battery replacement requests exceeds a first threshold, it is determined that there is a high urgency demand. If the current time point is far from a historical mobile peak period, and the number of battery replacement requests is lower than a second threshold, it is determined that there is a low urgency demand.

[0025] As a preferred embodiment, the battery replacement strategy is started according to the urgency demand determination result. The battery replacement strategy includes emergency battery replacement work orders and planned operation work orders. The emergency battery replacement work orders are executed to immediately go to the corresponding hub parking point from the battery warehouse to replace the battery. The planned operation work orders are executed to replace the battery when following the regular shared electric vehicle operation processing. For the high urgency demand, an emergency battery replacement work order is generated for immediate execution. For the low urgency demand, the battery replacement task is included in the planned operation work order.

[0026] As a preferred embodiment, the battery replacement strategy further includes a cross-hub parking point battery loan emergency strategy, which is triggered when the number of batteries in the battery warehouse is insufficient when executing the emergency battery replacement work order. The cross-hub parking point battery loan emergency strategy includes the following steps:

[0027] The hub parking point that needs to execute the emergency battery replacement work order is taken as a demand point. Other hub parking points with battery levels below the demand point are selected from the service area as candidate battery supply points.

[0028] In the candidate battery supply points, the batteries parked in the shared electric vehicles are identified, which have battery levels higher than the minimum battery level threshold of the demand point, and are marked as loanable batteries. The insufficient number of batteries in the battery warehouse is supplemented by the loanable batteries.

[0029] The remaining batteries in the battery warehouse are carried to the candidate battery supply point closest to the demand point to extract the loanable batteries, and then the batteries on the shared electric vehicles that need to be replaced are replaced at the demand point.

[0030] The batteries with battery levels below the threshold of the demand point but higher than the minimum battery level threshold of the candidate battery supply point are transported and supplemented into the shared electric vehicles in the candidate battery supply point to ensure that the battery levels of these vehicles meet the minimum battery level threshold of the hub parking point. The remaining batteries are taken back to the battery warehouse.

[0031] As a preferred embodiment, the number of battery replacement requests is analyzed for early warning. The early warning analysis includes issuing a warning report when the battery replacement requests of the hub parking point are continuously abnormal. The data of the hub parking point is reanalyzed, and the main area and the minimum battery level threshold of the hub parking point are adjusted.

[0032] Preferably, the minimum power threshold of the shared electric vehicle is automatically matched by the following steps: 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 delivers the minimum power threshold corresponding to the hub parking point to the vehicle terminal, and the vehicle terminal updates its low power judgment standard accordingly.

[0033] To solve the above technical problems, the application further provides a regional battery warehouse management system, comprising:

[0034] a memory for storing a computer program;

[0035] a processor for executing the computer program, which, when executed by the processor, implements the steps of the regional battery warehouse management method according to any one of the above.

[0036] To solve the above technical problems, the application further provides a readable storage medium having a computer program stored thereon,

[0037] which, when executed by a processor, implements the steps of the regional battery warehouse management method according to any one of the above.

[0038] In summary, the application has the following advantages:

[0039] The application realizes the intelligentization and high efficiency of battery scheduling in the battery warehouse of the shared electric vehicle through data-driven fine operation. The core innovation is to divide the city network into an "efficient local circulation system" centered on the hub parking point, accurately define the main area based on multi-source flow data, customize the minimum power threshold for each hub point, significantly reduce the risk of user half-way anchor, dynamically identify the urgency of demand, distinguish between sporadic requests and cluster demand, and innovatively implement cross-hub battery loan mechanism, convert global battery resources into dynamic flow value chain, avoid coexistence of resource idling and shortage, automatically optimize service range and threshold through continuous analysis of mobile data, make battery scheduling more accurate, operation and maintenance more efficient, and user experience more smooth, ultimately achieve the three benefits of reducing operation cost, improving resource utilization and enhancing user satisfaction, provide sustainable infrastructure support for urban shared travel, and maximize the operation of batteries in the entire warehouse.

[0040] The application further provides a regional battery warehouse management system and its storage medium, which have the above advantages and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0042] Figure 1 A schematic diagram of the overall framework process structure of the management method of the regional battery warehouse of the present application is shown in the figure.

[0043] Figure 2 A schematic diagram of the framework structure of the battery replacement request analysis in the management method of the regional battery warehouse of the present application is shown in the figure.

[0044] Figure 3 A schematic diagram of the framework structure of the management method of the regional battery warehouse of the present application is shown in the figure.

[0045] Figure 4 A schematic diagram of the terminal interface of the warehouse system in the management method of the regional battery warehouse of the present application is shown in the figure.

[0046] Figure 5 A schematic diagram of the terminal interface of the warehouse system in the management method of the regional battery warehouse of the present application is shown in the figure. DETAILED DESCRIPTION

[0047] The present application will now be further described in detail with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application. The drawings are simplified schematic diagrams and only show the basic structure of the present application.

[0048] In order to facilitate understanding of the present application, the present application will be described more fully with reference to the related drawings, which show several embodiments of the present application. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

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

[0050] Any feature in the foregoing specification that can be added to the application, unless explicitly recited otherwise, can be excluded in some embodiments of the application. Thus, unless otherwise stated, the foregoing specification is intended to be illustrative for the scope of the application rather than to limit the scope of the application. Also, the following claims are in no way intended to represent the breadth of the present application to be limited to a preferred embodiment or phrased in a specific manner.

[0051] In the present application, unless specifically stated and limited otherwise, the terms "mounting", "connection", "connecting", "fixed", and the like should be interpreted broadly, for example, can be fixed connection, can also be detachable connection, or integrated; can be mechanical connection, can be directly connected, or indirectly connected through an intermediate medium, can be internal communication of at least two elements or interaction relationship between at least two elements, unless otherwise specifically limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0052] The following will be described in detail Figures 1-5 The present application is described in detail, and an embodiment provided by the present application is: a management method of regionalized battery warehouse, comprising the following steps:

[0053] First step: building data cornerstone and initial architecture

[0054] According to the position of the battery warehouse, the corresponding service area is divided, and the information of all battery warehouses is equipped in the total system terminal, for reference Figure 4 With Figure 5 The battery warehouse is placed with batteries for sharing electric vehicles, so that the scheduling and replenishment of the battery are concentrated in the service area. By dividing the huge urban operation network into a plurality of "service areas" centered on the battery warehouse, the global and complex scheduling problem is decomposed into a plurality of local and more manageable "efficient local circulation system", and a hierarchical parking point network including hub parking points and their affiliated parking points is established in the service area, wherein the hub parking point is selected according to the flow data;

[0055] Specifically, the place with dense flow means that the travel demand is strong, which is a hot area for sharing electric vehicles, and is set as a center node, which conforms to the law of natural flow of users.

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

[0057] Static data: city POI (point of interest) data, such as subway station, bus terminal, large commercial district, office building, school, hospital and other areas that naturally attract flow.

[0058] Dynamic data:

[0059] Platform internal data: Share the historical order data generated by the electric vehicle APP itself. The most concentrated point of picking up and returning the car is the natural hub.

[0060] Mobile phone signaling data: Obtain anonymous human flow aggregation and movement data from telecom operators.

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

[0062] By analyzing the above data, several "hot spots" with significantly higher human flow density than the surrounding areas are identified on the service area map. These hot spots are located in or adjacent to the hub parking points. Once the hub point is determined, the other parking points within a certain distance (e.g. 500-1000 meters radius) around it are naturally designated as its affiliated parking points. In this way, a "star-shaped structure" or "tree-shaped structure" network is formed with the hub point as the core and multiple affiliated points radiating to the surrounding areas.

[0063] Step 2: Data collection

[0064] Monitor the shared electric vehicle movement data from the hub parking point to each affiliated parking point within the preset time period to generate image connection data representing the dynamic association relationship between parking points;

[0065] Specifically, the setting of the preset time period is not arbitrary, but has a clear analysis purpose, for example:

[0066] Early morning peak period: 07:00 - 10:00;

[0067] Late peak period: 17:00 - 20:00;

[0068] All day: 00:00 - 23:59 (for overall analysis);

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

[0070] The filtered data is still scattered trip records, for example, within the 2-hour early morning peak period, 100 independent trip records may have been generated from hub point H, of which 15 may have gone to affiliated point A, 10 to affiliated point B, etc.

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

[0072] Group by "End Point ID", and count the number of trips to each end point, to get a summary table, for example, the following table (preset time period: 7:00-9:00 on a certain day, hub point: H):

[0073] Hub parking point Dependent parking point 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, and the graph is composed of:

[0075] Node: represents a parking point, in this case, there is a 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 end point, each edge starts from node H and points to nodes A, B, C, etc.

[0077] Properties of the connection ("quantification of dynamic association relationship"):

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

[0079] The larger the weight, the closer the association from H to A and the greater the traffic in this time period, so the thicker or darker the connection will be when visualized, finally forming the image connection data for the corresponding time period.

[0080] Step 3: Build the main area

[0081] The goal is to filter out noise from a large number of vehicle movement records every day, sometimes randomly, and extract those stable and high-frequency movement patterns, so as to accurately define the core service range of each hub parking point, which includes the following steps:

[0082] Extract peak period data by day;

[0083] Purpose: Capture the most representative travel patterns, as the peak period (such as the morning and evening rush hours) has the strongest travel purpose, and the data is most stable, effectively filtering out noise from leisure, random, and low-frequency travel.

[0084] It will go back a certain period of history (for example, the past 30 days).

[0085] For each day, analyze the time distribution curve of all vehicle movements from this hub point, and identify "one or more peak time periods with the largest number of shared electric vehicle movements" (for example, 8:00-10:00 in the morning and 18:00-20:00 in the evening on weekdays).

[0086] Then, extract the image-link data generated only in these specific peak hours, which results in a "data snapshot" representing the typical travel pattern of each day.

[0087] Superimpose and merge to generate aggregated link data (data condensation);

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

[0089] Superimpose all the image-link data of multiple days (e.g., 30 days) extracted in the first step to obtain an aggregated link data graph, where the "weight" of each link becomes a historical total movement amount. The larger this value, the more stable and frequent the path is used during peak hours. For example, the data chart of one of the aggregated link data graphs is as follows (hub point H, 30-day data accumulation):

[0090] End dependent parking point Total historical movement (30-day peak period accumulation) A 450 times B 300 times C 280 times D 25 times E 10 times

[0091] Apply a development threshold to filter out the truly important patterns and filter out accidental and secondary associations.

[0092] Apply a preset threshold to the aggregated link data for screening, which is the key to distinguishing "core associations" from "accidental associations".

[0093] Threshold setting methods can be:

[0094] Absolute numerical method: For example, only keep links with a historical total movement amount exceeding 100 times.

[0095] Relative proportion method (more scientific): For example, only keep the top 20% of subsidiary points in terms of total movement amount, or points with a movement amount exceeding a certain proportion (e.g., 3%) of the total outflow amount of the hub point.

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

[0097] Define the main area (output)

[0098] In the geographic information system, identify the coordinates of the hub parking point (H) and the identified core subsidiary parking point group (A, B, C...). These points and the radiation range they cover (which can be approximated by the minimum convex polygon of these points or the range outlined according to the actual road network) are formally defined as the "main area" of the hub point H by the system.

[0099] Step 4: Setting the minimum power threshold

[0100] Customize the power requirement according to the unique trunk area of each hub point, so as to ensure that the power is sufficient to meet the next most likely travel demand before the vehicle is used, significantly reducing the risk of users stranded halfway due to insufficient power, improving user experience and operational efficiency;

[0101] According to the spatial range of the trunk area, set the minimum power threshold for shared electric vehicles entering the corresponding hub parking point, and automatically match the threshold for shared electric vehicles entering the hub parking point;

[0102] Specifically, first is the key distance determination:

[0103] The "trunk area" generated in the previous stage, i.e. the core group of affiliated parking points with stable and high intensity association with the hub point, calculates the path distance from the hub parking point (H) to each core affiliated parking point in its trunk area (usually based on map path planning API to get the actual riding distance, not the straight-line distance).

[0104] Determine the "key distance": this is the basis for setting the threshold, and one or a combination of the following strategies can be used:

[0105] Farthest distance strategy: select the distance to the farthest core affiliated parking point. This is the most conservative strategy, which can guarantee that the vehicle can reach any point in the area;

[0106] High-frequency weighted distance strategy: this is a more intelligent strategy; combined with the weight in the "image development connection data" (i.e. historical movement), a weighted average distance is calculated, for example, the weight of going to frequently used point A (10 kilometers away) is much higher than that of occasionally used point B (15 kilometers away), so the key distance will be closer to 10 kilometers than 15 kilometers.

[0107] Quantile distance strategy: for example, ensure that the power is sufficient to cover the distance reached by 90% of historical travel demand.

[0108] Minimum power threshold calculation:

[0109] Calculate the basic energy consumption: according to the average energy consumption standard of company vehicles (for example, 0.1 kilowatt-hour per kilometer), convert the key distance determined in the previous step into power requirement, and add a safety margin: considering actual road conditions (uphill, downhill, congestion), weather (headwind, low temperature), user riding habits and other variables, a safety margin must be added to the basic energy consumption, which can be a fixed value (such as the power required for an additional 5 kilometers) or a proportion.

[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 point where the vehicle is located.

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

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

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

[0126] Battery warehouse terminal analyzes based on the number of battery swap requests and their corresponding historical image connection data:

[0127] Analysis dimension one: Real-time aggregation of battery swap requests

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

[0129] Scenario A: Sporadic requests. For example, at 10 am, hub point H has only 1-2 battery swap requests. This may be due to individual vehicles parked for a long time, resulting in natural battery degradation.

[0130] Scenario B: Clustered requests. For example, after the evening peak at 7 pm, hub point H receives 15 consecutive battery swap requests in a short period of time. This indicates that a large number of users have parked their low-battery vehicles in this area after the peak.

[0131] Analysis dimension two: Association analysis with "historical image connection data"

[0132] Data call: The system will call the historical image connection data of this hub point H (especially the "main area" and "developed connection data" that have been constructed).

[0133] Pattern comparison: Does the time period when a large number of battery swap requests are generated (such as 7 pm) coincide with the historical peak of movement at this hub point H (such as 5-7 pm for the inflow peak)?

[0134] Demand prediction: Based on historical data, predict future vehicle demand. For example, historical data shows that vehicles parked at H point every night will be ridden in large numbers towards the science and technology park direction during the morning peak (7-9 am). Therefore, it is crucial to ensure that the vehicle's battery level at this point is replenished before tomorrow morning.

[0135] "Developed connection data" indicates the main travel direction, which can also provide reference for operation personnel to plan battery swap routes (i.e. prioritize battery supply on main routes).

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

[0137] For scenario A: sporadic requests and pre-demand peak, the current time point is far from the historical mobile peak period, and the number of battery replacement requests is less than the second threshold, it is determined as low urgency demand

[0138] Decision: Do not execute emergency battery replacement, mark such vehicles as "planned battery replacement", and include in the next day's regular operation work order. The operation personnel will process it in a low priority period.

[0139] For scenario B: cluster request and about to enter demand peak, the current time point is close to or in the historical mobile peak period, and the number of battery replacement requests exceeds the first threshold, it is determined as high urgency demand

[0140] Decision: Immediately execute the battery replacement strategy, generate a high-priority emergency battery replacement work order, and the system will notify the operation 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 the present embodiment, the number of battery replacement requests is analyzed for early warning, which includes issuing a warning report when the battery replacement requests of the hub parking point are continuously abnormal. The system may determine that the travel mode of the area has changed (such as the construction of a large office building), reanalyze the data of the hub parking point, and adjust the main area and the minimum battery threshold of the point.

[0142] It is worth mentioning that in the present embodiment, if the number of batteries in the battery warehouse is not enough when executing the emergency battery replacement work order, the cross-hub parking point battery loan emergency strategy is executed, the core of which is to dynamically reconstruct the value chain of the battery. Whether the battery is "qualified" or not is relative to the standard of its location. By establishing a temporary "battery value circulation channel" between different standard hub points, the global battery resources are activated to cope with local emergency shortages. The specific steps include the following:

[0143] Identify demand points and select candidate supply points

[0144] Demand point: the hub parking point (denoted as point A) where battery shortage occurs and emergency battery replacement work order needs to be executed, which is characterized by a high minimum battery threshold (e.g. 50%).

[0145] The system quickly scans other hub points within the service area to which point A belongs.

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

[0147] For example: point B (commercial area, threshold 30%) and point C (community, threshold 25%) are both filtered as candidate supply points, because a battery with 35% power is not qualified for point A, but qualified for point B and point C.

[0148] Marked on-call batteries

[0149] The system remotely queries the real battery power of all parked vehicles in each candidate supply point (point B, point C), and finds the batteries with power higher than the minimum power threshold (50%) of the demand point A.

[0150] For example: 5 vehicles in point B are found to have battery power between 55%-80%; 3 vehicles in point C are found to have battery power between 60%-90%, these batteries are marked as "on-call batteries".

[0151] These batteries are "over-qualified" assets in supply points, and they will be transferred to the most urgently needed high-standard demand point A, which can maximize the value of resources.

[0152] Optimize the path and execute the on-call battery swap

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

[0154] From the warehouse: the operation and maintenance vehicle carries all the remaining qualified batteries in the warehouse, goes to the supply point to pick up, arrives at point B, and the operation and maintenance personnel take down the batteries with power higher than 50% from the marked vehicles according to the system prompt, and load the car, at this time, these vehicles are temporarily short of power, but will be supplemented later.

[0155] Go to the demand point to swap: arrive at the demand point A, use the batteries brought from the warehouse and the on-call batteries from point B to jointly swap the low-power vehicles in point A, when swapping in point A, the old batteries taken down are not waste, although they are lower than the threshold (50%) of point A, but they may be higher than the threshold of the supply point (point B threshold 30%).

[0156] For example: swap a batch of batteries with power between 35%-45%, the operation and maintenance personnel will take back the batteries with power between 35%-45% from point A, and install them on the vehicles in point B that have been taken away. For point B (threshold 30%), a battery with 35% power is completely qualified, and the vehicle can be normally put into use.

[0157] If the number of batteries recovered from point A is more than the demand of point B, the excess part will be taken back to the battery warehouse for charging.

[0158] In summary, the regionalized battery warehouse management method realizes the intelligentization and high efficiency of battery scheduling in the battery warehouse of shared electric vehicles through data-driven fine operation. The core innovation lies in dividing the city network into an "efficient local circulation system" centered on hub parking points, accurately defining the main area based on multi-source flow data, customizing the minimum power threshold for each hub point, significantly reducing the risk of user half-way breakdown, intelligently changing the power strategy to dynamically identify demand urgency, distinguishing between sporadic requests and cluster demand, and innovatively implementing cross-hub battery loan mechanism to convert global battery resources into a dynamic flow value chain, avoiding the coexistence of resource idling and shortage. By continuously analyzing mobile data to automatically optimize service range and threshold, the battery scheduling is more accurate, the operation and maintenance efficiency is higher, and the user experience is smoother, ultimately achieving the triple benefits of reducing operation cost, improving resource utilization, and enhancing user satisfaction, providing sustainable infrastructure support for urban shared travel, and maximizing the operation of batteries in the entire warehouse.

[0159] The above describes an embodiment of a management method of a regionalized battery warehouse. The present application also discloses a management system of a regionalized battery warehouse and a storage medium corresponding to the above method.

[0160] A management system of a regionalized battery warehouse comprises:

[0161] A memory for storing a computer program;

[0162] A processor for executing the computer program, which can implement the related steps of the management method of the regionalized battery warehouse disclosed in any of the preceding embodiments when the computer program is executed by the processor.

[0163] The processor can include one or more processing cores, such as core processors, core processors, etc. The processor can be implemented in at least one of the following hardware forms: digital signal processing DSP (Digital Signal Processing), field programmable gate array FPGA (Field-Programmable Gate Array), programmable logic array PLA (Programmable Logic Array). The processor can also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake-up state, also known as the central processing unit CPU (Central Processing Unit). The coprocessor is a low-power processor for processing data in the standby state.

[0164] In some embodiments, the processor can be integrated with a graphics processing unit (GPU) for rendering and drawing contents required to be displayed by the display screen. In some embodiments, the processor can further include an artificial intelligence (AI) processor for processing computing operations related to machine learning.

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

[0166] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of 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 solutions of the present application, essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application.

[0167] Therefore, the embodiment of the present application further provides a readable storage medium, which stores a computer program. When the computer program is executed by the processor, the steps of the management method of the regional battery warehouse are realized.

[0168] The readable storage medium can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0169] The computer program contained in the readable storage medium provided in the embodiment can realize the steps of the management method of the regionalized battery warehouse when executed by the processor, and the effects are the same as above.

[0170] The management method of the regionalized battery warehouse, the system and the storage medium thereof provided by the application are described in detail above. Each embodiment in the description is described in a progressive manner, and each embodiment mainly describes the difference from other embodiments. The same or similar parts between each embodiment can be understood by referring to each other. For the device, the equipment and the readable storage medium disclosed by the embodiments, since they correspond to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be understood by referring to the method part. It should be pointed out that, for those skilled in the art, without departing from the principle of the application, the application can be improved and modified in several ways, and these improvements and modifications also fall within the protection scope of the claims of the application.

[0171] The above is only a specific implementation of the application, but the protection scope of the application is not limited to this. Any changes or replacements without creative labor should be covered within the protection scope of the application. Therefore, the protection scope of the application should be limited by the protection 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; Identify and extract image line data for one or more peak time periods with the highest daily shared electric vehicle movement within the hub parking point. Overlay and merge the extracted peak time period image line data, calculate the historical total movement from the hub parking point to each auxiliary parking point, generate aggregated line data, apply a development threshold to filter, retain auxiliary parking points with historical total movement exceeding a preset threshold, form a core auxiliary parking point group with a stable and strong correlation with the corresponding hub parking point, and define the network coverage area formed by the hub parking point and the core auxiliary parking point group as the backbone area of ​​the hub parking point. Based on the spatial range of the main area, the distance from the hub parking point to one or more key points in the core auxiliary parking point group is calculated 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 power required to complete the distance is calculated. A safety margin is added to the basic power, and a minimum power threshold for the hub parking point is generated and stored in the server database. Shared electric vehicles entering the hub parking point are automatically matched to 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 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.

3. The management method for a regionalized battery warehouse according to claim 2, 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.

4. The management method for a regionalized battery warehouse according to claim 3, characterized in that: The battery swapping strategy also includes a cross-hub parking point battery borrowing emergency strategy, which is triggered when the number of batteries in the battery warehouse is insufficient when an emergency battery swapping work order is executed. The cross-hub parking point battery borrowing emergency strategy 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.

5. The management method for a regionalized battery warehouse according to claim 4, 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.

6. The management method for a regionalized battery warehouse according to claim 5, 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.

7. 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 1-6.

8. 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 1-6.

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