Vehicle battery ratio estimation analysis method and device and storage medium

Abstract

The invention discloses a vehicle battery ratio estimation analysis method and device and a storage medium, and the method comprises the steps: determining the number of battery replacement vehicles and the daily average battery replacement frequency through the battery replacement parameter processing based on a preset battery replacement frequency algorithm, and obtaining the daily total battery replacement frequency according to the number of the battery replacement vehicles and the daily average battery replacement frequency; according to the station end data table, obtaining daily total service capability through data preprocessing of preset station end data; and based on the daily total battery replacement frequency and the daily total service capability, determining a vehicle battery ratio condition. By means of the method, the technical problems that in an existing evaluation mode, the vehicle-electricity matching strategy is single, and the surplus and deficient conditions of the number of configured batteries in actual use are not considered are solved.

Description

[0001] This application claims priority based on the invention patent application filed with the China National Intellectual Property Administration on December 29, 2023, with the application number 202311867825.9 and the invention title "A Method, Device and Storage Medium for Estimating and Analyzing the Battery Ratio of Urban Vehicles". This application incorporates the full text of the above-mentioned Chinese patent application. Technical Field

[0002] This application relates to the technical field of information-based asset operation and management, and particularly to a method, device and storage medium for estimating and analyzing the battery ratio of vehicles. Background Art

[0003] To ensure the normal battery swapping of vehicles, it is usually necessary to equip a certain number of spare batteries at the battery swapping station. Currently, when determining the number of spare batteries required for newly added vehicles, the "vehicle-battery ratio" strategy is mainly adopted. There are some problems with this strategy in practical applications: First, the vehicle-battery ratio strategy is relatively single and cannot comprehensively consider various factors. When evaluating the rationality of the battery ratio, relying solely on the vehicle ratio cannot fully consider the actual usage of the batteries. In addition, the vehicle-battery ratio strategy does not take into account the surplus and shortage of the batteries already equipped in the actual usage process. In practice, if the batteries are equipped according to the fixed vehicle-battery ratio strategy, it may lead to a situation where the remaining amount of batteries for some vehicles is too much or too little in actual use. If the remaining amount of batteries is too much, it may cause waste of resources; if the remaining amount of batteries is too little, it may not meet the normal usage requirements of the vehicles. Summary of the Invention

[0004] The embodiments of this application provide a method, device and storage medium for estimating and analyzing the battery ratio of vehicles to solve the following technical problems: The vehicle-battery ratio strategy in the existing evaluation method is single, and the surplus and shortage of the batteries already equipped in the actual usage process are not considered.

[0005] In a first aspect, the embodiments of this application provide a method for estimating and analyzing the battery ratio of vehicles. The method includes: Based on a preset battery swapping times algorithm, through processing the battery swapping parameters, determining the number of battery swapping vehicles and the average daily battery swapping times, and obtaining the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times; According to the station-side data table, through preset preprocessing of the station-side data, obtaining the total daily service capacity; Based on the total daily battery swapping times and the total daily service capacity, determining the vehicle battery ratio situation.

[0006] In an implementation manner of the present application, based on a preset battery swapping times algorithm, through battery swapping parameter processing, the number of battery swapping vehicles and the average daily battery swapping times are determined, and the total daily battery swapping times are obtained according to the number of battery swapping vehicles and the average daily battery swapping times. Specifically, it includes: based on a preset battery swapping record form, through preprocessing of battery swapping data, the number of battery swapping orders in the daily dimension is determined; through statistics in the dimension of the number of newly added vehicles per day, the number of newly added first-time battery swapping vehicles in the daily dimension is obtained; self-association is performed on the number of newly added first-time battery swapping vehicles in the daily dimension to obtain the cumulative number of networked battery swapping vehicles in the daily dimension; according to the number of battery swapping orders in the daily dimension, through staggered interval processing, the number of silent vehicles in the daily dimension is determined; according to the cumulative number of networked vehicles in the daily dimension and the number of silent vehicles in the daily dimension, the number of effective inventory vehicles in the daily dimension is determined; according to the number of effective inventory vehicles in the daily dimension, the average daily battery swapping times are determined, and according to the distribution of the average daily battery swapping times, the average value in a predetermined range is taken as the average daily battery swapping times; according to the number of effective inventory vehicles in the daily dimension, the number of newly added vehicles, and the average daily battery swapping times, the total daily battery swapping times are obtained.

[0007] In the present application, the total daily battery swapping times are determined through data analysis, which is used to estimate the total daily battery swapping times of a city, so as to compare with the actual battery swapping capacity of the city and accurately and effectively estimate the total daily battery swapping times of the city.

[0008] In an implementation manner of the present application, according to the station-end data table, through preset preprocessing of station-end data, the total daily service capacity is obtained. Specifically, it includes: based on preset battery swapping station operation parameters, the daily operation duration of the station is determined, and according to the operation market of the station, through service capacity analysis, the service capacity during the station operation time is obtained; according to the service capacity during the station operation time, through analysis of the single-battery service capacity of the station, the single-battery daily service capacity is determined; based on the single-battery daily service capacity, the total daily service capacity is obtained.

[0009] In the present application, by determining the total daily service capacity, the total daily battery swapping service capacity of the city is reflected; because of the different battery models, the service capacity of a single battery of one model in one day is determined to compare with the total daily battery swapping times, so as to achieve accurate data analysis and complete statistical data.

[0010] In an implementation manner of the present application, based on the total daily battery swapping times and the total daily service capacity, the vehicle-battery ratio is determined. Specifically, it includes: based on the total daily battery swapping times and the total daily service capacity, it is judged whether a new station needs to be built and / or new batteries need to be purchased, and a first judgment result is obtained; under the condition that the first judgment result is that a new station does not need to be built and / or new batteries do not need to be purchased, through a preset battery number processing method, the theoretically required battery number and the actual configured battery number are determined; based on the theoretically required battery number and the actual configured battery number, it is judged whether new batteries need to be purchased, and a second judgment result is obtained.

[0011] In this application, the purchase situation of the battery for battery swapping is judged based on the total number of battery swaps per day and the total service capacity per day, realizing an accurate prediction of the relationship between the battery for battery swapping in the battery swapping station and the actual purchase demand, and avoiding the situation of redundancy or shortage of battery resources in the battery swapping station.

[0012] In an implementation manner of this application, based on the total number of battery swaps per day and the total service capacity per day, it is judged whether a new station needs to be built and / or new batteries need to be purchased, and a first judgment result is obtained. Specifically, it includes: when the total number of battery swaps per day is less than or equal to the total service capacity per day, the first judgment result is that a new station does not need to be built and / or new batteries do not need to be purchased; when the total number of battery swaps per day is greater than the total service capacity per day, the first judgment result is that a new station needs to be built and / or new batteries need to be purchased.

[0013] In this application, by judging the battery swapping demand after new vehicles are added and the existing actual station-side service capacity, it is determined whether new batteries need to be purchased, avoiding the situation of insufficient battery reserve and redundancy of the number of batteries.

[0014] In an implementation manner of this application, after the first judgment result is that a new station needs to be built and / or new batteries need to be purchased, the method further includes: based on the total number of battery swaps, the preset theoretical total service capacity, and the daily service capacity of a single battery, the number of new batteries to be equipped is determined.

[0015] In this application, by determining the required number of batteries when new batteries need to be purchased to supplement the battery reserve, the subsequent battery swapping service demand can be accurately met, realizing precise battery purchase.

[0016] In an implementation manner of this application, based on the theoretically required number of batteries and the actual configured number of batteries, it is judged whether new batteries need to be purchased, and a second judgment result is obtained. Specifically, it includes: when the theoretically required number of batteries is greater than or equal to the actual configured number of batteries, the second judgment result is that new batteries need to be purchased; when the theoretically required number of batteries is less than the actual configured number of batteries, the second judgment result is that new batteries do not need to be purchased.

[0017] In this application, by determining the required number of batteries when new batteries need to be purchased to supplement the battery reserve, the subsequent battery swapping service demand can be accurately met, realizing precise battery purchase.

[0018] In an implementation manner of this application, after the second judgment result is that new batteries need to be purchased, the method further includes: based on the second judgment result, calculating the number of new batteries to be purchased; wherein, the number of new batteries is the difference between the theoretically required number of batteries and the actual configured number of batteries.

[0019] In this application, by determining the number of batteries required in the case of needing to newly purchase batteries to supplement the battery reserve, the subsequent battery replacement service requirements can be precisely met, realizing precise battery purchase.

[0020] In one implementation manner of this application, after determining the theoretical number of batteries required and the actual configured number of batteries, the method further includes: based on the actual configured number of batteries, determining the theoretical maximum service times of the configured batteries; according to the theoretical maximum service times of the configured batteries and the daily average battery replacement times, determining the theoretical maximum number of service vehicles of the configured batteries.

[0021] In this application, by determining the theoretical maximum number of service vehicles of the configured batteries, the actual functional situation of the batteries in the battery replacement station is estimated and analyzed, improving the resource utilization rate of the battery replacement station.

[0022] In a second aspect, an embodiment of this application further provides a vehicle battery ratio estimation and analysis device, including: a battery replacement times module, configured to determine the number of battery replacement vehicles and the daily average battery replacement times based on a preset battery replacement times algorithm through battery replacement parameter processing, and obtain the total daily battery replacement times according to the number of battery replacement vehicles and the daily average battery replacement times; a service capacity module, configured to obtain the total daily service capacity according to the station-end data table through preset station-end data preprocessing; a ratio module, configured to determine the vehicle battery ratio situation based on the total daily battery replacement times and the total daily service capacity.

[0023] In a third aspect, an embodiment of this application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the vehicle battery ratio estimation and analysis method as described in any one of the above when executing the program.

[0024] In a fourth aspect, an embodiment of this application further provides a non-volatile computer storage medium, storing computer-executable instructions, where the computer-executable instructions implement the vehicle battery ratio estimation and analysis method as described in any one of the above when executed by a processor.

[0025] An embodiment of this application provides a vehicle battery ratio estimation and analysis method, device, and storage medium. Through integrating battery replacement data and corresponding data processing, the following technical problems are solved: the vehicle-battery ratio strategy is single in the existing evaluation method, and the surplus and deficiency situations of the batteries already equipped in the actual use process are not considered; it has the technical effects of improving data accuracy and operation efficiency and reducing resource waste caused by unreasonable battery ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of this application, and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application, and do not constitute an improper limitation to this application. In the drawings:

[0027] Figure 1 Flow chart of a method for estimating and analyzing vehicle battery ratio provided by an embodiment of the present application;

[0028] Figure 2 Specific flow chart of a method for estimating and analyzing vehicle battery ratio provided by an embodiment of the present application;

[0029] Figure 3 Schematic diagram of the internal structure of an electronic device provided by an embodiment of the present application. Specific implementation manners

[0030] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0031] An embodiment of the present application provides a method, device and storage medium for estimating and analyzing vehicle battery ratio. Through the integration of battery swapping data and corresponding data processing, the following technical problems are solved: the vehicle-battery ratio strategy is single in the existing evaluation method, and the surplus and deficiency of the number of batteries already equipped in the actual use process are not considered; it has the technical effects of improving data accuracy and operation efficiency and reducing resource waste caused by unreasonable battery ratio.

[0032] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the drawings.

[0033] Figure 1 Flow chart of a method for estimating and analyzing vehicle battery ratio provided by an embodiment of the present application. As Figure 1 shown, the flow chart of a method for estimating and analyzing vehicle battery ratio provided by an embodiment of the present application specifically includes the following steps: Step 101, based on a preset battery swapping times algorithm, through the processing of battery swapping parameters, determine the number of battery swapping vehicles and the average daily battery swapping times, and obtain the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times.

[0034] Specifically, it includes: based on a preset battery replacement record form, determining the number of daily battery replacement orders through preprocessing of battery replacement data; obtaining the number of newly added first-time battery replacement vehicles on a daily basis through statistics by the dimension of the number of newly added vehicles per day; performing self-association on the number of newly added first-time battery replacement vehicles on a daily basis to obtain the cumulative number of networked battery replacement vehicles on a daily basis; determining the number of silent vehicles on a daily basis according to the number of daily battery replacement orders through staggered interval processing; determining the number of effectively stocked vehicles on a daily basis based on the cumulative number of networked vehicles on a daily basis and the number of silent vehicles on a daily basis; determining the average daily battery replacement times according to the number of daily battery replacement times and the number of effectively stocked vehicles on a daily basis, and taking the average value within a predetermined range as the average daily battery replacement times according to the distribution of the average daily battery replacement times; obtaining the total number of daily battery replacement times based on the number of effectively stocked vehicles on a daily basis, the number of newly added vehicles, and the average daily battery replacement times. In this application, the total number of daily battery replacement times is determined through data analysis, which is used to estimate the total number of daily battery replacements in a city, so as to compare with the actual battery replacement capacity of the city and accurately and effectively estimate the total number of daily battery replacements in the city.

[0035] In the embodiment of this application, it is explained in detail through the following Example 1.

[0036] Example 1: Whether a city needs to newly purchase the demand for battery replacement is mainly determined by the total service capacity of the city for battery replacement. If the total service capacity of the city is greater than the estimated total number of daily battery replacements in the city after the addition of new vehicles, it is considered that there is no need to newly purchase batteries.

[0037] For the total service capacity of the city, the most intuitive one is the total number of daily battery replacements in the city (in all the following embodiments, " / " represents the division operation symbol in the case of expressions involving calculations, rather than "or"). Among them, the indicators to be calculated for determining the total number of daily battery replacements include:

[0038] (1) The number of daily battery replacements (it is necessary to exclude the situations of shift handover or invalid orders). Through the battery replacement record form, exclude the site order data determined as: shift handover, testing, batch addition, empty, in use, etc., and calculate the number of battery replacement orders on a daily basis. Then, calculate the number of newly added first-time battery replacement vehicles on a daily basis. Through the battery replacement record form, take the first-time battery replacement time in the vehicle dimension: full scale table, sort by vehicle and battery replacement time in descending order, and take the first record as the vehicle's first-time battery replacement time. Combine with the date dimension table to calculate the number of newly added vehicles corresponding to each date (according to the vehicle's first-time battery replacement date, taking this date as the current day and this vehicle as the newly added vehicle on the current day. If there are no newly added vehicles on the current day, it is counted as null). Statistically calculate the number of newly added first-time battery replacement vehicles on a daily basis.

[0039] (2) The cumulative number of networked vehicles on a daily basis. Through self-associating the number of newly added first-time battery replacement vehicles on a daily basis, calculate the cumulative number of networked battery replacement vehicles on a daily basis.

[0040] (3) The number of vehicles retiring from the network on a daily basis. According to the battery replacement information of the vehicles, determine the vehicle retirement date, and statistically calculate the number of vehicles retiring from the network on a daily basis.

[0041] (4) Number of silent vehicles on a certain day (the last battery swap was 60 days ago), by vehicle dimension, sort the battery swap order time in ascending order and then perform staggered subtraction; then repair the situation where the record of the vehicle's last battery swap cannot be matched during the staggered subtraction, and supplement it with the system current time - 1 (for example, if the last battery swap time of vehicle A is: 2022-06-01, then the supplemented time after staggering is: 2022-06-09); calculate the number of days between the two battery swaps before and after, and filter out the battery swap records where the number of days between the two battery swaps before and after ≥ 45 days; find all the dates corresponding to the order records between the two battery swap times of the vehicle that are more than 45 days apart, and find the date 45 days later, and this date corresponding to this vehicle can be considered a silent vehicle (for example, if the two battery swap times of vehicle A are: 2022-01-01 and 2022-03-01 respectively, and the number of days between the two battery swaps before and after is 90 days, then it is considered that vehicle A is a silent vehicle from 2022-02-14 to 2022-03-01); finally, calculate the number of silent vehicles by day dimension.

[0042] (5) Number of effective inventory vehicles on a certain day = Cumulative number of networked vehicles on a certain day - Number of off-network vehicles on a certain day - Number of silent vehicles on a certain day.

[0043] (6) Average daily battery swap times = Number of daily battery swaps / Number of effective inventory vehicles on a certain day.

[0044] (7) Average daily battery swap times (obtained through distribution statistics) = Analysis of the distribution of the number of daily battery swapping vehicles.

[0045] (8) Total number of daily battery swaps = (Number of effective inventory vehicles on a certain day + Newly added vehicles) * Average daily battery swap times.

[0046] The above detailed steps complete the confirmation of the total number of daily battery swaps.

[0047] Step 102: According to the station-side data table, obtain the daily total service capacity through pre-set preprocessing of the station-side data.

[0048] Specifically include: Based on the pre-set operation parameters of the battery swap station, determine the daily operation duration of the station, and according to the station operation market, through service capacity analysis, obtain the service capacity within the station operation time; according to the service capacity within the station operation time, through the analysis of the single-battery service capacity of the station, determine the daily service capacity of a single battery; based on the daily service capacity of a single battery, obtain the daily total service capacity.

[0049] In this application, by determining the daily total service capacity, it reflects the total battery swap service capacity of a city in a day; because of the different battery models, determine the service capacity of a single battery of one model in a day to achieve comparison with the total number of daily battery swaps, realize accurate data analysis, and complete statistical data.

[0050] In the embodiments of the present application, it is explained in detail by the following Example 2. Example 2: The station-end data table includes: the location of the battery swapping station, the operating hours, the station-end status, and the battery bin configuration. Determine the daily operating hours of the station. If the hour corresponding to close_time is 23, then the daily operating hours of the station = close_time - open_time + 1; otherwise, the average daily operating hours of the station = close_time - open_time, where close_time is the station's closing business hour and open_time is the station's opening business hour.

[0051] Then, determine the service capacity during the station's operating hours. The service capacity during the station's operating hours = (the number of batteries in the station bins / the theoretically configured number of batteries in the station) * (the daily operating hours of the station / 24), and summarize the service capacities of all stations as the urban service capacity.

[0052] Finally, confirm the daily service capacity of a single battery. The daily service capacity of a single battery = the service capacity of the station / the number of batteries in the station bins; since the service capacities of single batteries for different station types are different, take the average value of the service capacities of single batteries of all stations by city dimension.

[0053] Step 103: Based on the total daily battery swapping times and the total daily service capacity, determine the vehicle battery ratio.

[0054] Figure 2 This is a specific flowchart of a method for predicting and analyzing vehicle battery ratio provided by the embodiments of the present application. As Figure 2 shown, determining the vehicle battery ratio based on the total daily battery swapping times and the total daily service capacity specifically includes:

[0055] Step S203: Based on the total daily battery swapping times and the total daily service capacity, determine whether it is necessary to build a new station and / or purchase additional batteries, and obtain a first judgment result;

[0056] Step S204: Under the condition that the first judgment result is that it is not necessary to build a new station and / or not necessary to purchase additional batteries, according to the daily service capacity of a single battery, through a preset battery number processing method, determine the theoretically required number of batteries and the actually configured number of batteries;

[0057] Step S205: Based on the theoretically required number of batteries and the actually configured number of batteries, determine whether it is necessary to purchase new batteries, and obtain a second judgment result.

[0058] By judging to determine whether it is necessary to purchase additional batteries under this condition, it has the technical effect of avoiding insufficient battery reserve.

[0059] Based on the total daily battery swapping times and the total daily service capacity, determine whether a new station needs to be built and / or new batteries need to be purchased, and obtain a first judgment result, specifically including: when the total daily battery swapping times are less than or equal to the total daily service capacity, the first judgment result is that a new station does not need to be built and / or new batteries do not need to be purchased; when the total daily battery swapping times are greater than the total daily service capacity, the first judgment result is that a new station needs to be built and / or new batteries need to be purchased.

[0060] After the first judgment result is that a new station needs to be built and / or new batteries need to be purchased, the method further includes: based on the total battery swapping times, the preset theoretical total service capacity, and the daily service capacity of a single battery, determine the number of new batteries to be equipped.

[0061] Based on the theoretically required number of batteries and the actual configured number of batteries, determine whether new batteries need to be purchased, and obtain a second judgment result, specifically including: when the theoretically required number of batteries is greater than or equal to the actual configured number of batteries, the second judgment result is that new batteries need to be purchased; when the theoretically required number of batteries is less than the actual configured number of batteries, the second judgment result is that new batteries do not need to be purchased.

[0062] After the second judgment result is that new batteries need to be purchased, the method further includes: based on the second judgment result, calculate the number of additional batteries to be purchased; wherein, the number of additional batteries is the difference between the theoretically required number of batteries and the actual configured number of batteries.

[0063] In an implementation manner of the present application, after determining the theoretically required number of batteries and the actual configured number of batteries, the method further includes: based on the actual configured number of batteries and the daily service capacity of a single battery, determine the theoretical maximum service times of the configured batteries; according to the theoretical maximum service times of the configured batteries and the average daily battery swapping times, determine the theoretical maximum number of service vehicles of the configured batteries.

[0064] In the embodiments of the present application, it is explained in detail through the following Example 3.

[0065] Example 3: When it is determined that new batteries need to be purchased to supplement the battery reserve, determine the required number of batteries, which has the technical effect of avoiding excessive or insufficient battery surplus.

[0066] If the total daily battery swapping times ≥ the total daily service capacity, it means that the existing battery service capacity is not enough to support the battery swapping demand required by the city, and a new station needs to be built or new batteries need to be purchased. The number of new batteries to be equipped = (total battery swapping times - total service capacity) / the daily service capacity of a single battery in the city dimension.

[0067] The theoretically required number of batteries = the total daily battery swapping times / the daily service capacity of a single battery in the city dimension.

[0068] In this application, the prediction of the battery service capacity is realized by determining the theoretical maximum service times of the configured battery and then determining the theoretical maximum number of service vehicles of the configured battery.

[0069] When the total daily battery swapping times ≤ the total daily service capacity, the actual number of configured batteries of the battery swapping station is determined, and the theoretical maximum service times of the configured battery = the actual number of configured batteries * the daily service capacity of a single battery.

[0070] Then, determine the theoretical maximum number of service vehicles of the configured battery = the theoretical maximum service times of the configured battery / the daily battery swapping times.

[0071] If the theoretically required number of batteries ≥ the actual number of configured batteries, the batteries of the battery swapping station are insufficient to meet the expected usage, and batteries need to be purchased to meet the needs of more vehicles. Determine the number of newly added batteries = the theoretically required number of batteries - the actual number of configured batteries. Otherwise, no additional new batteries need to be purchased.

[0072] The above is the method embodiment proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a vehicle battery ratio estimation and analysis device, which includes: a battery swapping times module, configured to determine the number of battery swapping vehicles and the average daily battery swapping times based on a preset battery swapping times algorithm through battery swapping parameter processing, and obtain the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times; a service capacity module, configured to obtain the total daily service capacity according to the station-side data table through preset station-side data preprocessing; a ratio module, configured to determine the vehicle battery ratio situation based on the total daily battery swapping times and the total daily service capacity.

[0073] Figure 3 The following is a schematic internal structure diagram of an electronic device provided in an embodiment of this application. As Figure 3 shown, the device includes:

[0074] At least one processor 301;

[0075] And a memory 302 communicatively connected to at least one processor;

[0076] Wherein, the memory 302 stores instructions executable by at least one processor, and the instructions are executed by at least one processor 301 so that at least one processor 301 can implement the vehicle battery ratio estimation and analysis method in any of the above embodiments. In one embodiment, the method may specifically include:

[0077] Based on a preset battery swapping times algorithm, determine the number of battery swapping vehicles and the average daily battery swapping times through battery swapping parameter processing, and obtain the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times; obtain the total daily service capacity according to the station-side data table through preset station-side data preprocessing; determine the vehicle battery ratio situation based on the total daily battery swapping times and the total daily service capacity.

[0078] Some embodiments of the present application provide a non-volatile computer storage medium corresponding to Figure 1 a method for estimating and analyzing the vehicle battery ratio, storing computer-executable instructions, which can implement the method for estimating and analyzing the vehicle battery ratio in any of the above embodiments when executed by a computer. In one embodiment, the method may specifically include:

[0079] Based on a preset battery swapping times algorithm, through processing battery swapping parameters, determine the number of battery swapping vehicles and the average daily battery swapping times, and obtain the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times; according to the station terminal data table, obtain the total daily service capacity through preset preprocessing of the station terminal data; based on the total daily battery swapping times and the total daily service capacity, determine the vehicle battery ratio situation.

[0080] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0081] The systems and media provided by the embodiments of the present application correspond one-to-one with the methods. Therefore, the systems and media also have beneficial technical effects similar to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated here.

[0082] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0083] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementation in the processFigure 1 means for a process or processes and / or boxes Figure 1 specified in one box or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the functions specified in the process Figure 1 means for a process or processes and / or boxes Figure 1 specified in one box or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 means for a process or processes and / or boxes Figure 1 specified in one box or more boxes.

[0086] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0087] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory such as read only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

[0088] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer readable media such as modulated data signals and carrier waves.

[0089] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0090] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for estimating and analyzing the battery ratio of a vehicle, characterized in that, The method includes: Based on a preset battery swapping times algorithm, through battery swapping parameter processing, determine the number of battery swapping vehicles and the average daily battery swapping times, and obtain the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times; According to the station-end data table, through preset station-end data preprocessing, obtain the total daily service capacity; Based on the total daily battery swapping times and the total daily service capacity, determine the vehicle battery matching situation.

2. The vehicle battery ratio prediction and analysis method according to claim 1, wherein Based on a preset battery swapping times algorithm, through battery swapping parameter processing, determine the number of battery swapping vehicles and the average daily battery swapping times, and obtain the total daily battery swapping times according to the number of battery swapping vehicles and the average daily battery swapping times, which specifically includes: Based on a preset battery swapping record table, through battery swapping data preprocessing, determine the number of battery swapping orders in the daily dimension; Through statistics in the dimension of the number of newly added vehicles per day, obtain the number of newly added first-time battery swapping vehicles in the daily dimension; Perform self-association on the number of newly added first-time battery swapping vehicles in the daily dimension to obtain the cumulative number of networked battery swapping vehicles in the daily dimension; According to the number of battery swapping orders in the daily dimension, through staggered interval processing, determine the number of silent vehicles in the daily dimension; According to the cumulative number of networked vehicles in the daily dimension and the number of silent vehicles in the daily dimension, determine the number of effective inventory vehicles in the daily dimension; According to the number of effective inventory vehicles in the daily dimension, determine the average daily battery swapping times, and take the average value in a predetermined range as the average daily battery swapping times according to the distribution of the average daily battery swapping times; According to the number of effective inventory vehicles in the daily dimension, the number of newly added vehicles, and the average daily battery swapping times, obtain the total daily battery swapping times.

3. The vehicle battery ratio prediction and analysis method according to claim 1, wherein According to the station-end data table, through preset station-end data preprocessing, obtain the total daily service capacity, which specifically includes: Based on preset battery swapping station operation parameters, determine the daily operation duration of the station, and obtain the service capacity during the station operation period through service capacity analysis according to the station operation market; According to the service capacity during the station operation period, through single-battery service capacity analysis of the station, determine the daily service capacity of a single battery; Based on the daily service capacity of a single battery, obtain the total daily service capacity.

4. The vehicle battery ratio prediction and analysis method according to claim 1, characterized in that Based on the total daily battery swapping times and the total daily service capacity, determine the vehicle battery matching situation, which specifically includes: Based on the total daily battery swapping times and the total daily service capacity, judge whether a new station needs to be built and / or new batteries need to be purchased, and obtain a first judgment result; Under the condition that the first judgment result is that a new station does not need to be built and / or new batteries do not need to be purchased, determine the theoretically required number of batteries and the actual configured number of batteries through a preset battery number processing method; Based on the theoretically required number of batteries and the actual configured number of batteries, judge whether new batteries need to be purchased, and obtain a second judgment result.

5. The method for predicting and analyzing the vehicle battery ratio according to claim 4, wherein Based on the total daily battery swapping times and the total daily service capacity, judge whether a new station needs to be built and / or new batteries need to be purchased, and obtain a first judgment result, which specifically includes: When the total daily battery swapping times are less than or equal to the total daily service capacity, the first judgment result is that a new station does not need to be built and / or new batteries do not need to be purchased; When the total daily battery swapping times are greater than the total daily service capacity, the first judgment result is that a new station needs to be built and / or new batteries need to be purchased.

6. The vehicle battery ratio prediction and analysis method according to claim 5, wherein, After the first judgment result is that a new station needs to be built and / or new batteries need to be purchased, the method further includes: Determine the number of new batteries to be equipped based on the total number of battery replacements, the preset theoretical total service capacity, and the daily service capacity of a single battery.

7. A method for predicting and analyzing the battery ratio of a vehicle according to claim 4, characterized in that, Based on the theoretically required number of batteries and the actually configured number of batteries, determine whether new batteries need to be purchased to obtain a second judgment result, which specifically includes: When the theoretically required number of batteries is greater than or equal to the actually configured number of batteries, the second judgment result is that new batteries need to be purchased; When the theoretically required number of batteries is less than the actually configured number of batteries, the second judgment result is that new batteries do not need to be purchased.

8. A method for predicting and analyzing the battery ratio of a vehicle according to claim 7, characterized in that, After the second judgment result is that new batteries need to be purchased, the method further includes: Based on the second judgment result, calculate the number of additional batteries that need to be purchased; wherein, the number of additional batteries is the difference between the theoretically required number of batteries and the actually configured number of batteries.

9. The vehicle battery ratio prediction and analysis method according to claim 1, wherein, After determining the theoretically required number of batteries and the actually configured number of batteries, the method further includes: Based on the actually configured number of batteries, determine the theoretical maximum service times of the configured batteries; According to the theoretical maximum service times of the configured batteries and the daily average number of battery replacements, determine the theoretical maximum number of service vehicles of the configured batteries.

10. A vehicle battery ratio estimation and analysis device, characterized in that The device includes: A battery replacement times module, configured to determine the number of battery replacement vehicles and the daily average number of battery replacements based on a preset battery replacement times algorithm through battery replacement parameter processing, and obtain the total daily battery replacement times according to the number of battery replacement vehicles and the daily average number of battery replacements; A service capacity module, configured to obtain the total daily service capacity through preset preprocessing of station-side data according to the station-side data table; A ratio module, configured to determine the vehicle-battery ratio based on the total daily battery replacement times and the total daily service capacity.

11. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the program, it implements the vehicle-battery ratio prediction and analysis method according to any one of claims 1 to 9.

12. A non-volatile computer storage medium stores computer-executable instructions, characterized in that, When the computer-executable instructions are executed by the processor, it implements the vehicle-battery ratio prediction and analysis method according to any one of claims 1 to 9.

Images