Self-service rental vehicle intelligent scheduling and dynamic battery distribution method, system and device and storage medium

Abstract

The invention discloses a self-service rental vehicle intelligent scheduling and dynamic battery distribution method, system and device and a storage medium. The self-service rental vehicle intelligent scheduling and dynamic battery distribution method comprises the following steps: S1, predicting a battery demand of a rental network in a future set time period through a linear regression model, and feeding back a prediction result to a cloud scheduling platform; s2, a leasing user requests to lease at a leasing network through a user side application program; s3, the cloud scheduling platform extracts the inventory number information of the lease network in the S2; s4, the cloud scheduling platform continues to extract the historical lease information of the lease user in the S2; s5, the cloud scheduling platform performs judgment through a linear regression model in combination with the detailed information of the batteries and the vehicles in the renting network in the S3 and the historical renting information of the renting users in the S4; and S6, the cloud scheduling platform notifies the battery replacement cabinet system to open a corresponding battery compartment door. The method has the advantages of intelligent scheduling and distribution, improved distribution efficiency, prolonged battery life and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of self-service rental vehicles, and specifically relates to a method, system, device and storage medium for intelligent scheduling and dynamic battery allocation of self-service rental vehicles. Background Art

[0002] With the development of society, unmanned self-service car rental has become more and more popular. The commonly used model is to scan the code to rent batteries and vehicles, take the batteries out of the battery cabinet and put them into the vehicle, and then ride them normally after assembly. However, how to efficiently allocate batteries to users, optimize the rental experience, and increase the battery life has become an urgent problem to be solved. Most of the existing battery allocation systems use a simple power priority algorithm, that is, simply allocate the fully charged batteries in the battery cabinet, lacking comprehensive consideration of multi-dimensional data such as user behavior, vehicle conditions, weather conditions, and battery health, resulting in low allocation efficiency and poor user experience, while accelerating excessive battery loss. Summary of the invention

[0003] In order to solve the deficiencies in the prior art, the present invention provides a method, system, device and storage medium for intelligent scheduling and dynamic battery allocation of self-service rental vehicles, which can intelligently schedule and allocate, improve allocation efficiency and extend battery life.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] The method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles is implemented based on a linear regression model and includes the following steps:

[0006] S1. Predict the battery demand of the rental outlets within a set time period in the future through a linear regression model and feed back the prediction results to the cloud scheduling platform. The cloud scheduling platform performs pre-scheduling based on the prediction results of the linear regression model.

[0007] S2, the rental user requests to rent at the rental outlet through the user-side application;

[0008] S3, the cloud dispatching platform extracts the inventory quantity information of the rental outlets in S2, and determines whether the inventory of vehicles and batteries in the rental outlets in S2 is sufficient. If so, the cloud dispatching platform continues to extract detailed information on batteries and vehicles in the rental outlets in S2;

[0009] S4, the cloud scheduling platform continues to extract the historical rental information of the rental users in S2;

[0010] S5. The cloud-based dispatching platform uses a linear regression model to combine the detailed information of the rental outlet battery and vehicle in S3 and the historical rental information of the rental user in S4 to determine whether the rental user is a long-distance demand user or a new user. If so, the cloud-based dispatching platform allocates a battery with sufficient power and long-distance endurance. If not, it continues to determine whether the rental user is a high-frequency demand user. If so, the cloud-based dispatching platform determines to allocate a battery with relatively low power but high health. If not, the cloud-based dispatching platform determines to allocate a battery with slightly poorer health.

[0011] S6. The cloud-based dispatching platform notifies the battery swap cabinet system to open the corresponding battery compartment door.

[0012] Preferably, the algorithm formula of the linear regression model is:

[0013] Y(t)=w1·U(t)+w2·B(t)+w3·V(t)+w4·W(t)+b,

[0014] Among them, Y(t) is the predicted value of battery demand of the rental outlet within the set time period t; U(t) is the user behavior characteristic; B(t) is the battery health condition characteristic; V(t) is the vehicle status characteristic; W(t) is the weather condition; and b is a constant.

[0015] Preferably, when there are multiple batteries that meet the allocation requirements in S5, batteries with a low number of charge and discharge cycles are allocated first.

[0016] Preferably, in S3, if the rental outlet in S2 does not have sufficient inventory of vehicles and batteries, the user is reminded that there is no equipment available for rent, and the rental user is guided to go to other rental outlets around the rental outlet and the distance and route planning are given;

[0017] When the inventory of vehicles and batteries at the rental outlet in S2 is insufficient, the cloud-based dispatching platform issues a work order to notify the operation and maintenance personnel to dispatch batteries and vehicles from surrounding outlets to the rental outlet.

[0018] Preferably, the detailed information of the rental outlet battery in S3 includes real-time power information, voltage information, temperature information, charge and discharge times information, SOH parameter information, alarm record information, and work order feedback related information;

[0019] The detailed information of the rental outlet vehicle in S3 includes real-time location information.

[0020] Preferably, after the cloud-based dispatching platform in S6 notifies the battery swap cabinet system to open the corresponding battery compartment door, the rental user extracts the battery and closes the battery compartment door, and then places the battery into the rental vehicle, activates the vehicle, and the vehicle's central control system reads the battery management system information, and then uploads the vehicle information and battery management system information to the remote dispatching platform.

[0021] The present invention also discloses a self-service rental vehicle intelligent scheduling and dynamic battery allocation system, including a cloud scheduling platform, a battery management system, a vehicle central control system, a battery swap cabinet system, a user-side application, a battery and a vehicle, wherein the battery management system is installed in the battery, the vehicle central control system is installed in the vehicle, the battery management system is used to record the battery's real-time power information, voltage information, temperature information, charge and discharge times information, SOH parameter information, alarm record information and work order feedback related information, the user-side application is connected to the cloud scheduling platform and the user-side application is used to receive user rental information and feed it back to the cloud scheduling platform, the battery management system is connected to the vehicle central control system and the battery swap cabinet system The battery management system is used to transmit information to the vehicle central control system and the battery swap cabinet system. The battery swap cabinet system and the vehicle central control system are both connected to the cloud scheduling platform. The vehicle central control system is used to transmit vehicle information and information fed back by the battery management system to the remote scheduling platform. The battery swap cabinet system is used to transmit information to the cloud scheduling platform. The battery swap cabinet system is also used to receive information from the cloud scheduling platform to open the battery compartment door of the corresponding battery swap cabinet system. The cloud scheduling platform predicts the battery demand of the rental outlets within a set time period in the future through a linear regression model and performs pre-scheduling. The cloud scheduling platform is also used to combine user rental information and battery swap cabinet system information for dynamic battery scheduling and allocation.

[0022] The present invention also discloses a self-service rental vehicle intelligent scheduling and dynamic battery allocation device, including a processor and a memory. The processor is used to execute the above-mentioned self-service rental vehicle intelligent scheduling and dynamic battery allocation method by calling the program or instruction stored in the memory.

[0023] The present invention also discloses a storage medium, wherein the storage medium stores a program or an instruction, and the program or the instruction executes the above-mentioned self-service rental vehicle intelligent scheduling and dynamic battery allocation method.

[0024] By adopting the above technical solution, the present invention has the following beneficial effects:

[0025] (1) The cloud scheduling platform of the present invention, combined with the linear regression model, can reasonably allocate batteries according to battery health status and user needs, avoid excessive use or idleness of batteries, and extend the service life of batteries;

[0026] (2) The linear regression model in the present invention can deeply analyze user behavior and provide personalized battery allocation solutions for different users, ensuring that users can obtain batteries that suit their travel needs, improving rental satisfaction, and can intelligently dispatch and allocate, thereby improving allocation efficiency;

[0027] (3) The present invention combines multiple data such as batteries, vehicles, and users to perform intelligent scheduling, which can significantly reduce battery failures and unnecessary maintenance, thereby reducing operation and maintenance costs and improving overall operational efficiency;

[0028] (4) The present invention can collect information about batteries and vehicles, and the cloud dispatching platform can judge and handle battery failures or user violations based on the information, thereby preventing asset losses.

[0029] In summary, the present invention has the advantages of intelligent scheduling and allocation, improved allocation efficiency, and extended battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a partial flow chart of the method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles of the present invention;

[0031] Figure 2 It is a structural schematic diagram of the self-service rental vehicle intelligent scheduling and dynamic battery allocation system of the present invention;

[0032] Figure 3 It is a schematic diagram of the interaction of various components of the self-service rental vehicle intelligent scheduling and dynamic battery allocation system of the present invention;

[0033] Figure 4 It is a schematic diagram of the overall process from start to finish of the self-service rental vehicle intelligent scheduling and dynamic battery allocation method of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0035] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention.

[0036] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0038] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] Example 1

[0040] In this embodiment, this embodiment proposes a method, system, device and storage medium for intelligent scheduling and dynamic battery allocation of self-service rental vehicles, which can solve the problems of single battery allocation logic, imperfect battery loss management and poor user experience in the prior art. The battery allocation logic in the prior art is mostly based on the battery power priority strategy, and does not consider multi-dimensional factors such as user rental behavior, vehicle usage scenarios, and battery health status, resulting in rigid allocation strategy and inability to fully utilize battery resources. The battery loss management in the prior art is imperfect, and there is a lack of intelligent monitoring of battery health. Excessive loss or uneven distribution often occurs during battery use, which reduces the overall service life of the battery. In addition, due to the low efficiency of battery scheduling in the prior art, some users may repeatedly obtain undercharged or aging batteries, affecting their travel experience and resulting in a decrease in satisfaction with the rental service. The present invention can solve these technical problems.

[0041] like Figure 1 and Figure 4 As shown, in one embodiment of the present invention, the self-service rental vehicle intelligent scheduling and dynamic battery allocation method of the present invention is implemented based on a linear regression model and includes the following steps:

[0042] S1. The battery demand of the rental outlets in the future set time period is predicted by a linear regression model and the prediction results are fed back to the cloud scheduling platform. The cloud scheduling platform performs pre-scheduling based on the prediction results of the linear regression model. Specifically, the algorithm formula of the linear regression model is:

[0043] Y(t)=w1·U(t)+w2·B(t)+w3·V(t)+w4·W(t)+b,

[0044] Among them, Y(t) is the predicted value of battery demand of the rental outlet within the set time period t; U(t) is the user behavior feature; B(t) is the battery health feature; V(t) is the vehicle status feature; W(t) is the weather condition; b is a constant, w1, w2, w3, and w4 are all regression coefficients;

[0045] It can be understood that the linear regression model predicts the battery demand in the future set time period based on historical data and real-time factors. The cloud scheduling platform performs pre-scheduling based on this prediction result. In the linear regression model, user behavior characteristics include rental frequency and rental time period. Battery health characteristics include battery remaining power and battery charge and discharge cycle times. Vehicle status characteristics include vehicle fault information and vehicle usage frequency. Weather conditions include temperature and rainfall. Constants represent the basic demand when all independent variables are 0. The specific training process of the linear regression model is based on the commonly used machine learning library Scikit-learn (open source Python machine learning library). Specifically, it can be The collected rental history data is divided into a training set and a test set, with the training set accounting for 80% and the test set accounting for 20%. Then the linear regression algorithm is used to train the model. After the training is completed, the test set is used to evaluate the accuracy of the modified linear regression model to see whether the linear regression model can accurately predict battery demand, and remove irrelevant or weakly related features based on the importance of the features, retain the core prediction variables and incorporate weather factors for dynamic update and adjustment, predict the battery demand of different rental outlets based on user behavior, deploy batteries in advance, dynamically adjust the distribution of vehicles and batteries, prevent battery shortages or surpluses at specific outlets, optimize the scheduling strategy for battery replacement, reduce battery failure rates, and improve user experience. Based on battery health and vehicle usage, predict when equipment needs to be maintained or replaced;

[0046] S2. The rental user requests to rent at the rental outlet through the user-side application. The rental here includes but is not limited to a single rental battery, a single rental vehicle, a battery and a vehicle at the same time, and a battery replacement. Battery replacement means that the rental user requests to replace the battery when the battery is exhausted or the battery fails after the rental. The user-side application includes the dedicated APP of the self-service rental vehicle or other built-in programs, such as WeChat applet, etc. The user-side application and the cloud scheduling platform can be connected and transmit information through the public network;

[0047] S3. The cloud dispatching platform extracts the inventory quantity information of the rental outlets in S2, and determines whether the inventory vehicles and batteries of the rental outlets in S2 are sufficient. If so, the cloud dispatching platform continues to extract the detailed information of the batteries and vehicles of the rental outlets in S2, wherein the detailed information of the batteries of the rental outlets includes real-time power information, voltage information, temperature information, charge and discharge times information, SOH parameter information, alarm record information and work order feedback related information, and the detailed information of the vehicles of the rental outlets includes real-time location information;

[0048] In S3, if the rental outlet in S2 does not have sufficient inventory of vehicles and batteries, the user is reminded that there is no equipment for rent (including prompts through the user-side application and information prompts such as voice of the battery swap cabinet system), and the rental user is guided to other rental outlets around the rental outlet and the distance and route planning are given. Specifically, the rental user can be guided to other rental outlets through the user-side application;

[0049] When the inventory of vehicles and batteries at the rental outlet in S2 is insufficient, the cloud dispatching platform issues a work order to notify the operation and maintenance personnel to dispatch batteries and vehicles from surrounding outlets to the rental outlet;

[0050] S4, the cloud scheduling platform continues to extract the historical rental information of the rental users in S2;

[0051] S5. The cloud scheduling platform uses a linear regression model to combine the detailed information of the rental outlet battery and the vehicle in S3 and the historical rental information of the rental user in S4 to determine whether the rental user is a long-distance demand user or a new user. If so, the cloud scheduling platform allocates a battery with sufficient power and long-distance endurance. If not, it continues to determine whether the rental user is a high-frequency demand user. If it is a high-frequency demand user, the cloud scheduling platform determines to allocate a battery with relatively low power but high health. If it is not a high-frequency demand user, the cloud scheduling platform determines to allocate a battery with slightly worse health. When there are multiple batteries that meet the allocation requirements in S5, batteries with low charge and discharge cycles are allocated first. When there are multiple batteries that meet the allocation requirements in S5, under the same conditions, batteries with high power are given priority.

[0052] S6. The cloud-based dispatching platform notifies the battery swap cabinet system to open the corresponding battery compartment door. After the cloud-based dispatching platform in S6 notifies the battery swap cabinet system to open the corresponding battery compartment door, the rental user extracts the battery and closes the battery compartment door, then places the battery into the rental vehicle and activates the vehicle. The vehicle's central control system reads the battery management system information, and then uploads the vehicle information and battery management system information to the remote dispatching platform.

[0053] Specific reference Figure 2 and Figure 3The present invention also proposes a self-service rental vehicle intelligent scheduling and dynamic battery allocation system, including a cloud scheduling platform, a battery management system, a vehicle central control system, a battery swap cabinet system, a user-side application, a battery and a vehicle, wherein the battery management system is installed in the battery, the vehicle central control system is installed in the vehicle, the battery management system is used to record the battery's real-time power information, voltage information, temperature information, charge and discharge times information, SOH parameter information, alarm record information and work order feedback related information, the user-side application is connected to the cloud scheduling platform and the user-side application is used to receive user rental information and feed it back to the cloud scheduling platform, the battery management system is connected to the vehicle central control system and the battery swap cabinet The system is connected in communication and the battery management system is used to transmit information with the vehicle central control system and the battery swap cabinet system. Both the battery swap cabinet system and the vehicle central control system are connected to the cloud dispatching platform. The vehicle central control system is used to transmit vehicle information and information fed back by the battery management system to the remote dispatching platform. The battery swap cabinet system is used to transmit information to the cloud dispatching platform. The battery swap cabinet system is also used to receive information from the cloud dispatching platform to open the battery compartment door of the corresponding battery swap cabinet system. The cloud dispatching platform predicts the battery demand of the rental outlets within a set time period in the future through a linear regression model and performs pre-dispatching. The cloud dispatching platform is also used to combine user rental information and battery swap cabinet system information for dynamic battery dispatch and allocation.

[0054] In the present invention, a self-service rental vehicle intelligent scheduling and dynamic battery allocation system is proposed, which aims to intelligently allocate battery resources, improve user experience, optimize battery life, and thus improve overall operational efficiency through real-time analysis of information such as user rental habits, vehicle usage scenarios, battery health data, and outlet inventory status.

[0055] The cloud scheduling platform of the present invention is the core brain of the present invention, which is responsible for processing user behavior data, battery health status, network inventory information and vehicle status, and then combines the linear regression model to dynamically schedule and allocate batteries. The user-side application is used for user rental requests, behavior data collection, usage feedback, etc. The battery management system (BMS) is used to monitor the battery power, temperature, charge and discharge cycle times, etc., and provide real-time health status data. The vehicle central control system is used to obtain the real-time status information of the vehicle and interact with the cloud scheduling platform to ensure the normal operation of the equipment during the rental process. The battery swap cabinet system is responsible for the storage and management of the battery, providing the hardware information of the battery swap cabinet and the battery status. Status, user behavior data specifically includes rental frequency, rental duration, commonly used outlets, rental time period, battery health status includes remaining power, charge and discharge times, battery life, temperature, outlet inventory information (that is, rental outlet inventory information): number of batteries at each outlet, battery availability, vehicle status includes vehicle mileage, usage frequency, fault information, weather information includes temperature, rainfall (affecting user willingness to rent a car), outlet inventory data includes the current number of available batteries at the outlet, the average health status of the outlet battery, the distribution density of the outlet battery, battery charging queue status, empty warehouse rate, full warehouse rate, the cloud-based scheduling platform of the present invention can be combined with the above data information for intelligent scheduling.

[0056] The specific implementation path of the present invention mainly relies on the following implementation in terms of user data collection: the vehicle is implemented by installing a vehicle central control system, the vehicle central control system includes GPS, Beidou positioning module, and the battery contains BMS (battery management system). After starting to rent a car through APP / applet, the vehicle's GPS module uploads the real-time location and driving information to the cloud scheduling platform. The user-side program collects data such as the location of the rental site and the rental method used by the user. During the operation of the battery swap cabinet, the battery selected by the user in the battery swap cabinet, the operation time, and the replacement frequency of the battery are recorded. The battery's BMS monitors and uploads battery power, charge and discharge times, health status and other information in real time. When the rental ends, the cloud scheduling platform generates a driving trajectory data record based on the historical positioning points during the vehicle rental period, and simultaneously records the total mileage, usage time, battery power consumption, user preferred outlets (rental and return locations) and other information;

[0057] The battery of the present invention communicates with the battery swap cabinet control board in real time through the RS485 bus in the battery swap cabinet to obtain battery health data, which is then uploaded to the cloud dispatching platform through the mobile network of the battery swap cabinet. The battery communicates with the vehicle central control system in real time through the RS485 bus in the vehicle to obtain battery health data, which is then uploaded to the cloud dispatching platform through the mobile network of the vehicle central control system.

[0058] The present invention is based on a linear regression model of historical data, and dynamically calculates through multi-dimensional data such as user behavior, vehicle status, and battery health status, adjusts the battery allocation plan in real time, and conducts user behavior analysis. The present invention analyzes the user's riding habits, travel frequency, and usage time based on historical rental data to generate a personalized battery allocation strategy. For users with longer riding distances or usage time, the present invention will give priority to allocating newer batteries to ensure the driving range; while for short-distance users, batteries with more battery cycles but still usable normally are allocated. The present invention scores the battery health status through the real-time battery power, voltage, temperature, charge and discharge times, SOH parameters, alarm records, and work order feedback associated with the battery uploaded by the BMS, and dynamically adjusts the battery allocation strategy based on the score to avoid excessive use of aging batteries. The present invention obtains the vehicle status and inventory information of each outlet in real time, and gives priority to whether the status of the vehicle required by the user is good, as well as the battery health status and whether the remaining capacity is suitable when allocating batteries. Suitable for current vehicles, the present invention adopts a multi-dimensional scheduling algorithm to dynamically allocate batteries based on user rental habits, battery health, vehicle status and network inventory information, with priority given to riding distance: for long-distance and new users, batteries with long driving range and high power are allocated; for users within a distance of 3-5km, batteries with relatively poor health are allocated, and for users with high frequency of use, batteries with relatively good health are allocated to ensure the user experience of repeat users. When allocating and screening batteries, the number of uses and the number of cycles are considered at the same time to achieve reasonable allocation to avoid excessive use of individual batteries. On the premise of meeting user needs, when there are multiple batteries, batteries with high power are allocated first.

[0059] The present invention also discloses a self-service rental vehicle intelligent scheduling and dynamic battery allocation device, including a processor and a memory. The processor is used to execute the above-mentioned self-service rental vehicle intelligent scheduling and dynamic battery allocation method by calling the program or instruction stored in the memory.

[0060] The present invention also discloses a storage medium, wherein the storage medium stores a program or instruction, and the program or instruction executes the above-mentioned self-service rental vehicle intelligent scheduling and dynamic battery allocation method.

[0061] This embodiment does not impose any formal limitation on the shape, material, structure, etc. of the present invention. Any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. Intelligent scheduling and dynamic battery allocation method for self-service rental vehicles, which is based on a linear regression model and is characterized by: The following steps are involved: S1. Predict the battery demand of the rental outlets within a set time period in the future through a linear regression model and feed back the prediction results to the cloud scheduling platform. The cloud scheduling platform performs pre-scheduling based on the prediction results of the linear regression model. S2, the rental user requests to rent at the rental outlet through the user-side application; S3, the cloud dispatching platform extracts the inventory quantity information of the rental outlets in S2, and determines whether the inventory of vehicles and batteries in the rental outlets in S2 is sufficient. If so, the cloud dispatching platform continues to extract detailed information on batteries and vehicles in the rental outlets in S2; S4, the cloud scheduling platform continues to extract the historical rental information of the rental users in S2; S5. The cloud-based dispatching platform uses a linear regression model to combine the detailed information of the rental outlet battery and vehicle in S3 and the historical rental information of the rental user in S4 to determine whether the rental user is a long-distance demand user or a new user. If so, the cloud-based dispatching platform allocates a battery with sufficient power and long-distance endurance. If not, it continues to determine whether the rental user is a high-frequency demand user. If so, the cloud-based dispatching platform determines to allocate a battery with relatively low power but high health. If not, the cloud-based dispatching platform determines to allocate a battery with slightly poorer health. S6. The cloud-based dispatching platform notifies the battery swap cabinet system to open the corresponding battery compartment door.

2. The method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles according to claim 1 is characterized by: The algorithm formula of the linear regression model is: Y(t)=w1·U(t)+w2·B(t)+w3·V(t)+w4·W(t)+b, Among them, Y(t) is the predicted value of battery demand of the rental outlet within the set time period t; U(t) is the user behavior characteristic; B(t) is the battery health condition characteristic; V(t) is the vehicle status characteristic; W(t) is the weather condition; and b is a constant.

3. The method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles according to claim 1 is characterized by: When there are multiple batteries that meet the allocation requirements in S5, batteries with a low number of charge and discharge cycles are allocated first.

4. The method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles according to claim 1 is characterized by: In S3, if the rental outlet in S2 does not have sufficient inventory of vehicles and batteries, the user is reminded that there is no equipment available for rent, and the rental user is guided to go to other rental outlets around the rental outlet and the distance and route planning are given; When the inventory of vehicles and batteries at the rental outlet in S2 is insufficient, the cloud-based dispatching platform issues a work order to notify the operation and maintenance personnel to dispatch batteries and vehicles from surrounding outlets to the rental outlet.

5. The method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles according to claim 1 is characterized by: The detailed information of the rental outlet battery in S3 includes real-time power information, voltage information, temperature information, charge and discharge times information, SOH parameter information, alarm record information and work order feedback related information; The detailed information of the rental outlet vehicle in S3 includes real-time location information.

6. The method for intelligent scheduling and dynamic battery allocation of self-service rental vehicles according to claim 1 is characterized by: After the cloud-based dispatching platform in S6 notifies the battery swap cabinet system to open the corresponding battery compartment door, the rental user extracts the battery and closes the battery compartment door, then places the battery into the rental vehicle, activates the vehicle, and the vehicle's central control system reads the battery management system information, and then uploads the vehicle information and battery management system information to the remote dispatching platform.

7. Self-service rental vehicle intelligent dispatching and dynamic battery allocation system, characterized by: It includes a cloud scheduling platform, a battery management system, a vehicle central control system, a battery swap cabinet system, a user-side application, a battery and a vehicle. The battery management system is installed in the battery, and the vehicle central control system is installed in the vehicle. The battery management system is used to record the real-time battery power information, voltage information, temperature information, charge and discharge times information, SOH parameter information, alarm record information and work order feedback related information. The user-side application is connected to the cloud scheduling platform and the user-side application is used to receive user rental information and feedback to the cloud scheduling platform. The battery management system is communicatively connected to the vehicle central control system and the battery swap cabinet system and the battery management system is used to transmit information to the vehicle central control system and the battery swap cabinet system. The battery swap cabinet system and the vehicle central control system are both connected to the cloud scheduling platform. The vehicle central control system is used to transmit vehicle information and information fed back by the battery management system to the remote scheduling platform. The battery swap cabinet system is used to transmit information to the cloud scheduling platform. The battery swap cabinet system is also used to receive information from the cloud scheduling platform to open the battery compartment door of the corresponding battery swap cabinet system. The cloud scheduling platform predicts the battery demand of the rental outlets within a set time period in the future through a linear regression model and performs pre-scheduling. The cloud scheduling platform is also used to dynamically schedule and allocate batteries based on user rental information and battery swap cabinet system information.

8. The self-service rental vehicle intelligent dispatching and dynamic battery allocation device is characterized by: It comprises a processor and a memory, wherein the processor is used to execute the self-service rental vehicle intelligent scheduling and dynamic battery allocation method as described in any one of claims 1 to 6 by calling the program or instruction stored in the memory.

9. A storage medium, characterized in that: The storage medium stores programs or instructions, and the programs or instructions execute the self-service rental vehicle intelligent scheduling and dynamic battery allocation method as described in any one of claims 1 to 6.

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