Intelligent battery changing cabinet method and system based on integration of data driving and urban governance

Abstract

The invention relates to an intelligent power conversion cabinet method and system based on fusion of data driving and urban governance, in particular to a power conversion cabinet technology. The intelligent power conversion cabinet method based on data driving and urban governance fusion comprises the following steps that population density, traffic tide rules, POI distribution and rider trajectory data are integrated, and a power conversion demand hot spot area is identified through a clustering algorithm; with user experience maximization, operation cost minimization and city fusion degree optimization as targets, a dynamic planning model is constructed to solve an optimal network layout; monitoring bin positions and battery states in real time through a sensor, and dispatching battery inventory based on a prediction model; and setting an equipment state threshold, accessing data to a city management platform, and establishing a multi-department collaborative response mechanism. The transformation from post-treatment to pre-prevention is realized, and the municipal management pressure is reduced. Industry sustainable development is achieved, and a compliant and efficient operation normal form is provided for a battery changing cabinet enterprise.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to battery swap cabinet technology, in particular to an intelligent battery swap cabinet method and system based on data driving and urban governance integration. BACKGROUND

[0002] With the continuous growth of the number of electric bicycles, especially the increasing demand for efficient endurance in industries such as express delivery and take-out, intelligent battery swap cabinets, as an "exchange instead of charging" energy supply mode, have rapidly popularized. According to industry data, the service life of power batteries is generally 5-8 years. Since 2016, China's new energy vehicles and related electric bicycles have entered a large-scale retirement period. According to data provided by Wang Pan, director of the Power Battery Room of China Automotive Power Battery Industry Innovation Alliance, at the 2023 China Automotive Power Battery Industry Innovation Alliance Conference, by the end of 2022, a total of 510,000 new energy vehicles were scrapped nationwide, corresponding to a power battery retirement volume of 24.4 GWh (about 241,000 tons). Among them, lithium iron phosphate and ternary batteries accounted for 56.6% and 39.8%, respectively. At present, the proportion of recycled metal consumption in the total metal consumption is between 3.2% and 7.6%, and power battery recycling has not yet become a major source of metals, with key metals such as cobalt, nickel, and manganese mainly relying on imports. It is estimated that by 2025, the power battery retirement volume will exceed 820,000 tons, which will give birth to a billion-level recycling market. The policy level has also continued to increase, with the State Council passing the "Action Plan for Perfecting the New Energy Vehicle Power Battery Recycling System" to promote the construction of a "manufacturing-application-recycling-regeneration" full-chain closed-loop system, providing a foundation for the standardized development of the battery swap cabinet industry.

[0003] Despite the huge market potential, the current battery swap cabinet industry faces multiple challenges: extensive and disordered layout: Most of the battery swap cabinet layout relies on subjective experience, lacks data support, resulting in overcapacity and low utilization in some areas, while demand-intensive areas lack adequate service coverage.

[0004] For example, some areas have densely laid battery swap cabinets in areas with high human traffic, but they cannot accurately match the actual distribution path of the riders and the demand during peak hours, resulting in resource mismatch. Safety hazards are prominent: some enterprises ignore safety standards in pursuit of expansion speed, leading to problems such as placing battery swap cabinets near gas pipelines, in fire exits, or using substandard batteries, retired battery cells, and other issues. For example, in recent years, the number of battery swap cabinet fire accidents has been increasing year by year, with battery swap cabinets accounting for a large proportion, putting a huge pressure on urban fire safety. The line laying is not standardized, and there is a lack of automatic fire extinguishing devices. Once the battery overheats, it is easy to cause a fire accident. Urban governance conflicts: as a new facility, battery swap cabinets have not been effectively integrated into the urban planning system, often causing urban conflicts due to the arbitrary occupation of sidewalks and public spaces. Existing solutions focus more on improving hardware technology, but lack systematic integration with urban infrastructure, transportation networks, and safety standards.

[0005] To optimize the layout, some enterprises try to use simple POI (Point of Interest) data clustering analysis or linear regression methods to predict demand. For example, by integrating POI density data such as restaurants and residential areas to divide service areas, or using historical battery swap order time series characteristics (such as ARIMA model) for short-term demand prediction. However, this method has obvious limitations: the data dimension is single, relying only on static POI or historical order data, without integrating real-time traffic flow, rider path planning, urban policy constraints, and other dynamic information, resulting in a large deviation between predicted results and actual demand. One-sided optimization goal: models are mostly oriented towards single indicators such as cost or coverage, without considering multiple goals such as operator benefits, user convenience, and urban safety. Implementation disconnection: the site selection and operation and maintenance are disconnected, lacking an integrated solution from layout to dynamic adjustment, safety monitoring, resulting in slow system response and inability to adapt to the implementation requirements of urban governance. SUMMARY

[0006] The present disclosure provides an intelligent battery swap cabinet method and system based on data-driven and urban governance integration, which can solve the technical problems in the prior art.

[0007] According to a first aspect of the present disclosure, an intelligent battery swap cabinet method based on data-driven and urban governance integration is provided, comprising the following steps:

[0008] Integrate population density, traffic tide rules, POI distribution, and rider trajectory data to identify hotspots of battery swap demand through clustering algorithms;

[0009] Maximize user experience, minimize operating costs, and optimize urban integration to build a dynamic programming model to solve the optimal site layout;

[0010] Real-time monitoring of bin and battery status through sensors, and scheduling of battery inventory based on prediction models;

[0011] Setting device state threshold, data access city management platform, establishing multi-department collaborative response mechanism.

[0012] Optionally, the clustering algorithm is a K-means algorithm, and the clustering rationality is evaluated by a silhouette coefficient, and a calculation formula comprehensively considers geographical distance and functional correlation.

[0013] Optionally, the state transition equation of the dynamic programming model is:

[0014] f(i,j)=max(f(i-1,j),f(i-1,j-w i )+r(i,j)),

[0015] Wherein, r(i,j)=u(i,j)-c(i,j), u(i,j) is a user experience score function, and c(i,j) is a battery swap cabinet cost function.

[0016] Optionally, the dynamic programming model is based on historical order data and real-time traffic flow to predict peak demand, and the scheduling instruction is issued to the operation and maintenance terminal through the cloud platform.

[0017] Optionally, the safety warning threshold includes battery temperature > 50℃, voltage abnormal fluctuation ± 15%, and alarm information is pushed to the fire and city management department in real time.

[0018] According to the second aspect of the present disclosure, an intelligent battery swap cabinet system based on data driving and city governance integration is provided, which realizes any method of the first aspect, comprising:

[0019] Internet of Things sensing device deployed at the site;

[0020] Cloud data management and analysis platform;

[0021] User mobile terminal APP;

[0022] Operation and maintenance terminal.

[0023] Optionally, the cloud platform is interconnected with the city intelligent management platform, supports data access and instruction issuing of the fire and city management department.

[0024] The intelligent battery swap cabinet method based on data driving and city governance integration in the present disclosure includes the following steps: integrating population density, traffic tide law, POI distribution, and rider trajectory data, and identifying hot swap demand areas through a clustering algorithm; taking maximizing user experience, minimizing operating cost, and optimizing city integration as the goal, a dynamic programming model is constructed to solve the optimal network point layout; the battery inventory is dispatched based on a prediction model through real-time monitoring of the bin and battery status by sensors; a device state threshold is set, data is accessed to a city management platform, and a multi-department collaborative response mechanism is established.

[0025] Through multi-source data clustering analysis and multi-objective optimization, the matching degree of the battery swap cabinet network and demand is improved by more than 30%, and the average user search station time is shortened to within 5 minutes. For example, combined with a dynamic path planning engine and an intelligent dispatching center, a dynamic grid layout model based on rider path trajectory can significantly improve delivery efficiency. For example, through AI optimization, the number of daily per capita delivery orders is increased by 20%, and the overtime rate is reduced by 15%, thereby increasing the utilization rate of the equipment to 1.8 times the industry benchmark. The Internet of Things technology significantly improves the discovery rate of hidden dangers such as battery thermal runaway and equipment failure to more than 95% through real-time monitoring and threshold early warning mechanisms, and effectively reduces the accident rate to 50% through the cooperation of automatic fire extinguishing devices. The dynamic warehouse model reduces the idle rate of batteries by more than 20% by predicting peak demand, and reduces operating costs by 15%.

[0026] Overall, the present application realizes the deep coupling of the battery swap cabinet system and city governance: efficient integration of urban space: through the "one point one strategy" design, the battery swap cabinet changes from "road occupation facilities" to standardized infrastructure integrated into public transportation hubs and community corner spaces, beautifying the city and improving land resource utilization. Governance efficiency is improved: cross-department data sharing and linkage mechanism, integrating battery swap cabinet management into the smart city platform, realizing the change from "after-disposal" to "prevention", and reducing the pressure of municipal management. Sustainable development of the industry: providing a compliant and efficient operation paradigm for battery swap cabinet enterprises, helping the trillion-level market to transform from "wild growth" to "refined operation".

[0027] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0028] The accompanying drawings are used to better understand the present application and do not limit the present disclosure. Among them:

[0029] Figure 1 A flowchart of a method for an intelligent battery swap cabinet based on data driving and city governance integration provided by the embodiments of the present disclosure. DETAILED DESCRIPTION

[0030] Exemplary embodiments of the present disclosure are described herein with reference to the accompanying drawings, which are presented for the purpose of illustration and description. It is to be understood that the embodiments described herein are merely exemplary and that various changes and modifications can be made thereto without departing from the scope and spirit of the present disclosure. Also, the description below of the drawings serves merely to further illustrate the exemplary embodiments. Thus, it will be apparent to one of ordinary skill in the art that various changes and modifications can be made thereto without departing from the scope and spirit of the present disclosure. For the purpose of clarity and a concise description, descriptions of well-known functions and constructions are omitted from the following description.

[0031] It should be understood that, in the embodiments of the present disclosure, the character "or" generally indicates that the antecedent and the consequent are in a relationship of "or". The terms "first", "second", and the like are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated.

[0032] Figure 1 A flowchart of a method for an intelligent battery swap cabinet based on data driving and urban governance integration provided by an embodiment of the present disclosure is shown. As shown in Figure 1 The present application provides a method for an intelligent battery swap cabinet based on data driving and urban governance integration, comprising the following steps:

[0033] S101, integrate population density, traffic tidal regularity, POI distribution, and rider trajectory data, and identify the hot area of battery swap demand through a clustering algorithm.

[0034] The present scheme first integrates population density, traffic tidal regularity, POI distribution, and rider trajectory data, and then identifies the hot area of battery swap demand.

[0035] The hot area of battery swap demand is generally the area with the highest population density, and / or the area with POI distribution regularity, and the area where rider trajectories converge. This step is the basis of the entire system, and the purpose is to accurately locate where the battery swap cabinet is most needed.

[0036] To further illustrate, the following terms are explained.

[0037] Population density: reflects the basic flow of the region, and is a macro indicator of potential users.

[0038] Traffic tidal regularity: refers to the phenomenon that people flow regularly between residential areas, commercial areas, and office areas during rush hours. Mastering this regularity can predict demand fluctuations at different locations at different times.

[0039] POI distribution: POI (Point of Interest) covers types such as restaurants, office buildings, and residential areas. The activity trajectory of a delivery rider is highly related to these points, and directly reflects the demand density.

[0040] Rider trajectory data: This is the most direct behavior data, which can reveal high-frequency and fixed battery swap paths and areas.

[0041] Technical implementation: Through clustering algorithms such as K-means, spatial analysis is performed on these multi-source data. This algorithm can classify areas with similar data characteristics (i.e., similar battery swap demand patterns) on the map into the same category, thereby automatically identifying demand-intensive "hotspot areas". This realizes the transition from experience-driven to data-driven, making layout decisions more scientifically based.

[0042] S102, with the goal of maximizing user experience, minimizing operating costs, and optimizing urban integration, a dynamic programming model is constructed to solve the optimal site layout.

[0043] After determining the hotspot area, the problem of how many battery swap cabinets to set up and how much capacity to set up in specific locations needs to be solved, which is a complex trade-off process.

[0044] Maximize user experience: This means shortening the time users spend looking for cabinets to charge batteries, improving coverage and convenience.

[0045] Minimize operating costs: This involves equipment investment, maintenance, battery scheduling, and other costs, and requires efficient use of resources.

[0046] Optimize urban integration: The setting of battery swap cabinets should not affect the city's appearance, should not occupy fire passages, and should be organically combined with public transportation hubs and other urban functions.

[0047] Technical implementation: When faced with complex site selection problems, such as in e-commerce, retail, and manufacturing industries, dynamic programming models have been proven to be an effective solution. It breaks down the problem into a series of sub-problems and iteratively calculates the best site layout solution by considering factors such as cost and benefit using state transition equations. For example, the dynamic programming site selection method can help businesses find the best balance between transportation costs, customer service levels, and other factors, thereby maximizing the comprehensive benefits of cost reduction and customer satisfaction.

[0048] Optionally, the state transition equation of the dynamic programming model is:

[0049] f(i,j)=max(f(i-1,j),f(i-1,j-w i )+r(i,j)),

[0050] Where r(i,j)=u(i,j)-c(i,j), u(i,j) is the user experience score function, and c(i,j) is the battery swap cabinet cost function.

[0051] S103, Utilize Internet of Things technology to monitor battery status, location, and environmental parameters in real time through sensors and RFID technology, enabling automated data collection and transmission. Establish a comprehensive perception network covering all aspects of battery storage, enabling comprehensive control of battery information. Use Internet of Things gateways to aggregate data and transmit and manage remotely through a cloud platform, improving data real-time and reliability. Based on predictive models, optimize inventory levels to prevent shortages or surpluses, and improve warehouse utilization.

[0052] This is the core step after the system goes into operation, which can ensure the efficient operation of each battery swap cabinet and meet the dynamically changing needs.

[0053] Technical implementation: By deploying sensors inside the battery swap cabinet, two key states can be monitored in real time: storage space status (empty, full or depleted batteries) and battery status (such as voltage, temperature, and other health indicators). After these real-time data are aggregated on the cloud platform, the predictive model will predict the supply-demand gap that may occur at each site in the future (such as the next hour) based on historical data, real-time orders, and even weather conditions. Then, the system will automatically generate battery dispatch instructions to guide the operation and maintenance personnel to allocate batteries from surplus sites to tight sites, thereby minimizing the vacancy rate and ensuring that users can always have batteries to swap.

[0054] S104, Set equipment state thresholds, connect data to city management platform, and build multi-department collaborative response mechanism.

[0055] This step promotes the battery swap cabinet network to be part of the city's public safety governance, focusing on safety and resilience.

[0056] Technical implementation sets safety thresholds for key parameters such as battery temperature and voltage. For example, if the battery temperature exceeds 50°C, the system will trigger an alarm and disconnect the charging.

[0057] Once the sensor detects abnormal data, the system will immediately send an alarm through the city management platform (such as smart city management, fire department) that has been connected. This breaks down the information barrier between enterprise operation and city management, and builds a cross-department collaborative response mechanism. For example, the fire department can receive real-time alarm information and intervene in potential fire hazards in advance; the traffic management department can understand whether the battery swap cabinet has violated the rules of occupying the road. This greatly improves public safety and city management efficiency.

[0058] This step shifts from "experience-driven" to "data-driven", improving operational efficiency

[0059] This step fundamentally revolutionizes the traditional mode of relying on human experience for battery swap cabinet layout, promoting precision and efficiency.

[0060] Accurate demand identification: By integrating population density, a macro demand basis, time volatility reflected in traffic tide rules, POI (point of interest) such as commercial and residential area distribution, and rider trajectory data, the most direct behavior data, and using clustering algorithms such as K-means for in-depth analysis, real demand hotspots for battery replacement in different time periods and different regions can be accurately identified. This ensures that resources can be placed in the most needed places.

[0061] Scientifically optimized layout: Based on the identification of hotspots, a dynamic planning model can be built to balance user experience (such as coverage range, waiting time), operating costs (equipment investment, maintenance costs), and urban integration (such as combination with public transport hubs, compliance with city appearance requirements), and other multiple goals. This avoids one-sided pursuit of coverage rate while ignoring costs, or only considering economic benefits while affecting city appearance, and achieves maximum comprehensive benefits.

[0062] The system sets safety thresholds for key parameters such as battery temperature. Once the monitoring data exceeds the threshold, the system can automatically trigger an alarm and execute pre-set safety measures, such as automatic power-off, starting fire-fighting aerosol fire retardant, etc., to contain risks at the first time. Full-process traceability: All operation data and alarm records will be uploaded to the cloud platform, providing a complete data chain for post-analysis, responsibility definition, and continuous optimization.

[0063] By connecting the key safety data of the battery replacement cabinet to the city management platform (such as smart city management, fire safety system), the information barrier between enterprise operation and public management is broken. Once an anomaly occurs, the alarm information can be pushed to the enterprise and relevant government departments at the same time, establishing a cross-department rapid response process, greatly improving the efficiency of handling public safety incidents.

[0064] All these technical means will be translated into tangible benefits.

[0065] Improve user experience and operator benefits: Users can find available fully charged batteries more quickly, and operators can significantly improve single cabinet utilization and overall operational efficiency through optimized layout and intelligent scheduling.

[0066] Promote safety and sustainable development: The scheme helps reduce high-risk behaviors such as electric bicycle users charging at home from the source, thanks to improved safety standards, improved charging facilities, and crackdown on illegal modifications. At the same time, unified battery management also facilitates standardized recycling and tiered utilization of retired batteries, meeting green environmental protection requirements.

[0067] Optionally, the clustering algorithm is the K-means algorithm, and the clustering rationality is evaluated by the silhouette coefficient, which considers both geographical distance and functional correlation.

[0068] K-means is a classic unsupervised learning algorithm whose core goal is to automatically divide a set of data points into K clusters such that the data points within each cluster are as similar as possible, while the data points between different clusters are as dissimilar as possible.

[0069] The workflow of this algorithm generally follows the following steps:

[0070] Randomly initialize the center points: The algorithm first randomly selects K data points as the initial "cluster centers".

[0071] Assign data points to the nearest center: Calculate the distance (usually using Euclidean distance) from each data point to all cluster centers, and assign it to the nearest cluster center.

[0072] Recalculate the cluster center: After all data points are assigned, recalculate the center point position of each cluster (usually the average of all dimensions of the data points in the cluster).

[0073] Iterative optimization: Repeat steps 2 and 3 until the position of the cluster center no longer changes significantly, i.e., the algorithm converges.

[0074] In the scenario of battery swap cabinet site selection, the data points may be different cells or areas on the map, and the task of the algorithm is to classify areas with similar demand characteristics into one category.

[0075] The calculation idea of K-means algorithm is very ingenious, which considers the intra-cluster cohesion and inter-cluster separation of each data point:

[0076] Intra-cluster cohesion: Calculate the average distance between a data point and all other data points in the same cluster. The smaller this distance, the more similar the point is to the points in the same cluster, and the better the division.

[0077] Inter-cluster separation: Calculate the average distance between this data point and all data points in the next nearest cluster. The larger this distance, the farther the point is from other clusters, and the higher the inter-cluster separation.

[0078] The formula for calculating the silhouette coefficient is: (inter-cluster separation - intra-cluster cohesion) / max(inter-cluster separation, intra-cluster cohesion).

[0079] The value of the coefficient can be understood as follows:

[0080] Close to 1, indicating good clustering effect, compact data within the cluster, and significant separation between clusters.

[0081] Close to 0, indicating that the clustering effect is not significant, and the data points may be located on the boundary of two clusters.

[0082] Close to -1, indicating a poor clustering effect, and the data points may be incorrectly assigned to other clusters.

[0083] This step is used to automatically group multi-dimensional data using the K-means algorithm, and to verify the rationality of the grouping using the scientific indicator of silhouette coefficient, especially the comprehensive evaluation method combined with geographical and functional information. This ensures that the layout of the battery swap cabinet is not only data-driven, but also quality-tested, laying a solid foundation for precise site selection and improving operational efficiency.

[0084] Optionally, the dynamic warehouse adjustment model predicts peak demand based on historical order data and real-time traffic flow, and dispatch instructions are issued to the operation and maintenance terminal through the cloud platform.

[0085] This step analyzes the battery swap frequency and battery consumption patterns in different regions and time periods (such as weekday morning rush hour and weekend evening) over a period of time (such as weeks or months) using historical order data, in order to optimize the operational efficiency and user experience of the battery swap station. This helps to discover stable and predictable periodic patterns. For example, it may be found that the central business district has particularly high battery swap demand from 8am to 10am on Mondays.

[0086] The perception of real-time traffic flow is due to advanced data collection and analysis techniques. For example, real-time image data is obtained through road intersection cameras, and AI technology is used to analyze vehicle queuing time and calculate real-time cumulative queuing time on the lane, thereby obtaining the real-time traffic congestion degree value of the area. In addition, the real-time lane congestion index analysis system can locate vehicles to specific lanes through high-precision positioning systems, further analyzing real-time road vehicle flow and lane congestion index. For example, a large event or a traffic accident may cause a large number of riders to gather in a certain area in a short period of time, resulting in a sudden battery swap demand.

[0087] Peak demand prediction model: Based on the fusion of the above two types of data, the system will use a prediction model (such as time series analysis, machine learning algorithm) to predict the battery supply and demand situation of each net point in the next few hours. The model will integrate historical patterns and real-time status to calculate the risk of battery shortage or surplus that may occur at each battery swap cabinet.

[0088] Intelligent instruction issuance and execution: The prediction results will generate specific battery allocation instructions (such as "allocate 5 fully charged batteries from cabinet A to cabinet B"), which will be automatically issued to the operation and maintenance personnel's mobile terminal (such as a mobile phone APP or a vehicle-mounted tablet) through the cloud platform. This makes the action of the operation and maintenance team highly targeted, enabling them to solve potential supply and demand gaps before users feel the inconvenience, thereby significantly improving the battery swap success rate and service satisfaction, while optimizing the operation and maintenance team's driving path and reducing empty driving costs.

[0089] Optionally, the safety warning threshold includes battery temperature > 50℃, voltage abnormal fluctuation ±15%, and alarm information is pushed to the fire and urban management departments in real time.

[0090] The core goal of this step is to prevent problems from occurring. By setting clear danger thresholds and cross-departmental response mechanisms, safety hazards can be contained at the embryonic stage.

[0091] Scientific setting of early warning thresholds: Thresholds are not arbitrarily set, but are scientifically developed based on the chemical properties of batteries and safety standards. They are usually divided into different levels to achieve graded responses.

[0092] Battery temperature exceeds 50℃: The temperature of a lithium-ion battery during normal operation is generally maintained below 40℃. However, when the battery temperature continues to rise and breaks through the threshold of 50℃, it may indicate that an abnormal exothermic reaction has occurred inside the battery, posing a significant risk of thermal runaway, which is a potential precursor to fire. Therefore, setting 50℃ as a safety threshold helps to achieve early warning of potential dangers in lithium-ion batteries.

[0093] Voltage abnormal fluctuation ±15%: During the charging and discharging process of a lithium-ion battery, its voltage should remain relatively stable. If there is a sharp fluctuation of more than 15% of the normal value within a short period of time, it may indicate that there are serious problems such as short circuits or local overheating inside the battery. By monitoring voltage characteristics such as open-circuit voltage and internal resistance, the health status and potential abnormal conditions of the battery can be effectively identified.

[0094] Real-time monitoring and automatic push: The sensors in the battery swap cabinet will monitor these parameters 7x24 hours. Once any data exceeds the threshold, the system will immediately trigger an alarm. Alarm information will not only be sent to the operation and maintenance center of the battery swap cabinet operator, but also be pushed in real time to the monitoring platform of the fire department and urban management department through the data interface.

[0095] Multi-departmental collaborative response: This cross-departmental response is the key to qualitative change. It means:

[0096] Fire department: Can receive accurate fire warning and location information in the first time, facilitating rapid response and achieving "early and small" fire fighting.

[0097] Urban management department: Can master the safety status of municipal facilities in the jurisdiction and assist in on-site evacuation or order maintenance when necessary to prevent safety accidents from affecting public safety.

[0098] According to the present disclosure, an intelligent battery swap cabinet system based on data-driven and urban governance integration is provided, which realizes the method of any one of the first aspects, comprising:

[0099] IoT sensor equipment deployed at the site;

[0100] Cloud data management and analysis platform;

[0101] User mobile terminal APP;

[0102] Operation and maintenance terminal.

[0103] Optionally, the cloud platform is interconnected with a city wisdom management platform, and supports data retrieval and instruction issuing of fire departments and city management departments.

[0104] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0105] In an example embodiment, the electronic device includes at least one processor, and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the above embodiments.

[0106] In an example embodiment, the readable storage medium can be a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method according to the above embodiments.

[0107] In an example embodiment, the computer program product includes a computer program, which, when executed by a processor, implements the method according to the above embodiments.

[0108] The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit the implementations of the present disclosure described and / or claimed in this document.

[0109] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flow charts and / or block diagrams to be implemented. The program code can be entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine or entirely on a remote machine or server.

[0110] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT or LCD monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0112] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0113] The computer system typically includes a client and a server. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0114] It should be noted that the above process can be reordered, added or deleted steps according to actual needs. For example, the steps described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which is not limited herein.

[0115] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent replacement and improvement within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for intelligent battery swapping cabinets based on the integration of data-driven approaches and urban governance, characterized in that: Includes the following steps: By integrating population density, traffic flow patterns, POI distribution, and rider trajectory data, clustering algorithms are used to identify areas with high demand for battery swapping. With the goals of maximizing user experience, minimizing operating costs, and optimizing urban integration, a dynamic programming model is constructed to solve for the optimal network layout. Real-time monitoring of warehouse and battery status via sensors, and scheduling of battery inventory based on predictive models; Set equipment status thresholds, connect data to the city management platform, and establish a multi-departmental collaborative response mechanism.

2. The intelligent battery swapping cabinet method based on data-driven and urban governance integration according to claim 1, characterized in that, The clustering algorithm is the K-means algorithm, and its clustering rationality is evaluated by the silhouette coefficient. The calculation formula comprehensively considers geographical distance and functional correlation.

3. The intelligent battery swapping cabinet method based on data-driven and urban governance integration according to claim 2, characterized in that, The state transition equation of the dynamic programming model is: f(i,j)=max(f(i-1,j),f(i-1,j-w i )+r(i,j)), Where r(i,j)=u(i,j)-c(i,j), u(i,j) is the user experience score function, and c(i,j) is the battery swapping cabinet cost function.

4. The intelligent battery swapping cabinet method based on data-driven and urban governance integration according to claim 3, characterized in that, The dynamic warehouse adjustment model is based on historical order data and real-time traffic flow predictions of peak demand, and the scheduling instructions are sent to the operation and maintenance terminal through the cloud platform.

5. The intelligent battery swapping cabinet method based on data-driven and urban governance integration according to claim 4, characterized in that, The safety warning thresholds include battery temperature > 50℃ and abnormal voltage fluctuation ± 15%, and the alarm information is pushed to the fire department and urban management department in real time.

6. A smart battery swapping cabinet system based on the integration of data-driven approaches and urban governance, implementing the method as described in any one of claims 1-5, characterized in that, include: IoT sensing devices deployed at network points; Cloud-based data management and analysis platform; User mobile terminal APP; Operation and maintenance management terminal.

7. The intelligent battery swapping cabinet system based on data-driven and urban governance integration as described in claim 6, characterized in that, The cloud platform is interconnected with the city's smart management platform, supporting data retrieval and instruction issuance by fire and urban management departments.

Images