Shared electric vehicle headgear supplementary prediction method and system based on data analysis

Abstract

The invention relates to the technical field of electric vehicle data processing, in particular to a shared electric vehicle headgear supplementary prediction method and system based on data analysis. According to the difference between the number of riding users and the number of shared electric vehicles in different time ranges of each day and the distribution of riding starting time, the electric vehicle use demand degree of each day is obtained; according to the electric vehicle use demand degree distribution of different days in each quarter and the correlation between the electric vehicle use demand degrees of different days and the riding electricity consumption, the quarter use intensity of each quarter is obtained; the riding electricity consumption and the headgear usage amount are adjusted by combining the putting area heat degree of each quarter and the battery health degree distribution of all the shared electric vehicles in different time ranges, and the headgear usage correction amount and the riding electricity consumption correction amount are obtained; and predicting the optimal supplementation time of the headgear supplementation. According to the method, the operation time for supplementing the headgear is optimized by analyzing the historical and accurate usage amount of the headgear and the riding electricity consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle data processing, and in particular to a method and system for predicting the replenishment of headgear of a shared electric vehicle based on data analysis. Background Art

[0002] The headgear of shared electric vehicles is an important component of the vehicle, and is mainly used to improve the hygiene of user use. Due to the frequent services of shared electric vehicles, there may be a shortage of headgear, and some vehicles have a surplus of headgear due to low frequency of use, resulting in waste of resources and affecting user experience; therefore, it is necessary to make appropriate adjustments to the replenishment of headgear.

[0003] In the prior art, the headgear is replenished by relying on the background warning given by the quantity threshold sensor in the equipment; however, since battery replacement is one of the core tasks of the operation and maintenance of shared electric vehicles, only considering the replenishment of headgear will lead to inconsistent vehicle operation and maintenance cycles, increase unnecessary duplication of work, and lack of effective data analysis methods, and the effect of headgear replenishment prediction is poor. Summary of the invention

[0004] In order to solve the technical problem that only considering the replenishment of headgear leads to inconsistent vehicle operation and maintenance cycles, lacks an effective data analysis method, and has a poor prediction effect of headgear replenishment, the purpose of the present invention is to provide a method and system for predicting the replenishment of shared electric vehicle headgear based on data analysis. The technical solutions adopted are as follows: The present invention proposes a method for predicting the replenishment of shared electric vehicle head covers based on data analysis, and the method comprises: Obtain the number of riding users in the deployment area at different times of the day in the past year, as well as the riding data of each shared electric vehicle, including the number of rides, riding power consumption, headgear usage, riding start time, and battery health; Based on the difference between the number of riding users and the number of shared electric vehicles at different times of the day, as well as the distribution of riding start times, the daily demand for electric vehicles is obtained; Based on the distribution of electric vehicle usage demand on different days in each quarter, and the correlation between the electric vehicle usage demand and riding power consumption on different days, the quarterly usage intensity of each quarter is obtained; based on the distribution of the number of rides of all shared electric vehicles at all times in each quarter, the popularity of the deployment area of ​​each quarter is obtained; According to the popularity of the launch area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the riding power consumption and headgear usage are adjusted to obtain the headgear usage correction amount and riding power correction amount; Based on the distribution of headgear usage correction and riding electricity correction of all shared electric vehicles at different time ranges, the optimal time for headgear replenishment is predicted.

[0005] Furthermore, the method for obtaining the electric vehicle use demand includes: Based on the difference between the number of riding users and the number of shared electric vehicles in each time range, as well as the distribution of riding start time, the electric vehicle utilization rate in each time range is obtained; Arrange the usage rates of electric vehicles at all time ranges within each day in chronological order to obtain a fitting curve for fitting the usage rates of electric vehicles; The average slope value between different adjacent data points on the fitting curve is obtained as the overall slope level; within the preset peak time range, the average slope value between different adjacent data points in all peak time ranges on the fitting curve is obtained as the peak slope level; the average slope value between different adjacent data points in other time ranges except the peak time range is obtained as the non-peak slope level; The ratio of the peak slope level to the off-peak slope level is obtained, and the product of the ratio result and the overall slope level is calculated as the daily electric vehicle use demand.

[0006] Furthermore, the method for obtaining the electric vehicle usage rate includes: Obtain the difference between the number of users and the number of shared electric vehicles within each time range as the quantity difference; Obtain the mean difference between different adjacent riding start times within each time range as the time difference level; The product of the quantity difference and the time difference level is obtained and negatively correlated to be mapped as the electric vehicle utilization rate within each time range.

[0007] Furthermore, the method for obtaining the quarterly usage intensity includes: According to the distribution of electric vehicle usage demand on different days in each quarter, the electric vehicle thermal value of each quarter is obtained; For each quarter, the sum of the riding electricity consumption of all shared electric vehicles at all times of the day is obtained as the total riding electricity consumption per day; the mean square error of the sequence composed of the electric vehicle usage demand and the total riding electricity consumption on different days is obtained, and negative correlation mapping is performed as the first trend coefficient; The product between the electric vehicle thermal value and the first trend coefficient of the corresponding quarter is obtained as the quarterly usage intensity of each quarter.

[0008] Furthermore, the method for obtaining the thermal value of the electric vehicle includes: The cumulative sum of the electric vehicle usage demand of all days in each quarter is obtained as the first cumulative sum of demand; the cumulative sum of the electric vehicle usage demand of all days in a historical year is obtained as the second cumulative sum of demand; The ratio between the first demand cumulative sum and the second demand cumulative sum is obtained as the electric vehicle thermal value for each quarter.

[0009] Furthermore, the method for obtaining the heat of the delivery area includes: Obtain the number of hot spots in the service range of the delivery area, where the hot spots include business districts, subways, and schools; The sum of the number of rides of each shared electric vehicle at all times of the day is obtained as the first sum; the average of the first sums of all shared electric vehicles on all days of each quarter is obtained as the average number of rides; The product of the average number of rides and the number of hot spots in the delivery area is obtained and normalized to obtain the delivery area heat in each quarter.

[0010] Furthermore, the method for obtaining the headgear usage correction amount and the power consumption correction amount includes: According to the popularity of the launch area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the battery replacement forecast correction coefficient for each quarter is obtained; For the headgear usage or riding electricity consumption as the data to be corrected, the battery replacement forecast correction coefficient of each quarter and the corresponding data to be corrected in each time range are multiplied together to obtain the headgear usage correction amount and riding usage correction amount in each time range.

[0011] Furthermore, the method for obtaining the battery swap prediction correction coefficient includes: For each quarter, the average value of the battery health of each shared electric vehicle at all times is obtained as the local battery health level; the average value of the local battery health levels of all shared electric vehicles is obtained as the overall battery health level; The quarterly usage intensity of each quarter is normalized and mapped, and the sum of the normalized mapping result and the heat of the deployment area is obtained as the weighted value; the product of the weighted value and the overall health level of the battery is obtained as the battery replacement prediction correction coefficient for each quarter.

[0012] Furthermore, the method for obtaining the optimal replenishment time includes: For the correction amount for headgear use or riding electricity use, the target data is taken as the sum of the target data of each shared electric vehicle at different times of the day, which is taken as the overall target data of each shared electric vehicle on a daily basis; the average of the overall target data of all shared electric vehicles on a daily basis is taken as the overall target average data of the day; Construct a fitting curve for fitting the overall average data of all the days, and select the intersection of the corresponding fitting curves between the headgear usage correction amount and the riding power correction amount; The mean slope of all adjacent data points on the fitting curve between adjacent intersections is obtained. If the difference between the corresponding mean slopes of two fitting curves between adjacent intersections is less than a preset difference threshold, the time range between the corresponding adjacent intersections is used as the optimal supplementary time for the headgear.

[0013] The present invention also proposes a shared electric vehicle head cover replenishment prediction system based on data analysis, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of the shared electric vehicle head cover replenishment prediction method based on data analysis.

[0014] The present invention has the following beneficial effects: The present invention obtains the daily demand for electric vehicle use according to the difference between the number of riding users and the number of shared electric vehicles at different time ranges of each day, as well as the distribution of riding start times, reflects the supply and demand relationship, and accurately quantifies the daily demand for electric vehicle use; obtains the quarterly use intensity of each quarter according to the distribution of the demand for electric vehicle use on different days in each quarter, and the correlation between the demand for electric vehicle use on different days and riding power consumption, comprehensively evaluates the riding activity of each quarter, and identifies the seasonal change trend; obtains the heat of the deployment area of ​​each quarter according to the distribution of the number of rides of all shared electric vehicles at all time ranges in each quarter, identifies high-heat areas, and optimizes vehicle deployment and headgear replenishment; adjusts the riding power consumption and headgear usage according to the heat of the deployment area of ​​each quarter, the quarterly use intensity, and the battery health distribution of all shared electric vehicles at different time ranges, obtains the headgear usage correction amount and the riding power correction amount, and improves the prediction accuracy of the riding power consumption and the headgear usage; predicts the optimal replenishment time for headgear replenishment according to the distribution of the headgear usage correction amount and the riding power correction amount of all shared electric vehicles at different time ranges. The present invention optimizes the operation time of replenishing the headgear by analyzing the accurate historical usage of the headgear and the power consumption during riding. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1A flowchart of a method for predicting the replenishment of head covers for shared electric vehicles based on data analysis provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining the use demand of an electric vehicle provided by an embodiment of the present invention; Figure 3 A flow chart of a method for obtaining quarterly usage intensity provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a shared electric vehicle head cover replenishment prediction method and system based on data analysis proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following is a detailed description of a method and system for predicting the replenishment of shared electric vehicle head covers based on data analysis provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a method flow chart of a method for predicting the replenishment of shared electric vehicle head covers based on data analysis provided by an embodiment of the present invention, specifically comprising: Step S1: Obtain the number of riding users in the deployment area at different times of the day in the past year, as well as the riding data of each shared electric vehicle, the riding data including the number of rides, riding power consumption, headgear usage, riding start time and battery health.

[0021] In an embodiment of the present invention, in order to meet the demand for headgear replenishment and improve the operation and maintenance efficiency of electric vehicles, it is necessary to analyze the riding data of shared electric vehicles; first, each shared electric vehicle is usually equipped with a GPS positioning module and Beidou positioning, a 4G / 5G communication module, a sensor, a control chip and a battery management system, which upload the riding data of the shared electric vehicle to the cloud server in real time through the Internet of Things technology; obtain the number of riding users in the deployment area at different times of the day in the past year, as well as the riding data of each shared electric vehicle, the riding data including the number of rides, riding power consumption, headgear usage, riding start time and battery health.

[0022] It should be noted that, in one embodiment of the present invention, the time range is one hour; historical one year's riding data is obtained for analysis; in other embodiments of the present invention, the time range can be set by the implementer according to the specific situation, and is not limited or elaborated here.

[0023] It should be noted that in the process of riding a shared electric vehicle, effective riding data has certain data support for the battery replacement and headgear replenishment needs of the shared electric vehicle; however, there will be some invalid data in the existing stored data, such as missing starting and ending points, too short riding distance, etc.; therefore, it is necessary to eliminate such invalid data to improve the prediction accuracy of the battery replacement and headgear replenishment needs of shared electric vehicles: in one embodiment of the present invention, first of all, in all the riding data in the history of one year, if there are data fields in the stored riding data that are empty or missing values, the riding distance is less than 20 meters, or the recorded content is exactly the same, they are all regarded as invalid data and are eliminated from the acquired data set.

[0024] It should be noted that, in one embodiment of the present invention, in order to facilitate the subsequent data processing, the riding data is standardized to eliminate the dimension of the data so that indicators of different units or orders of magnitude can be comprehensively analyzed and compared. Standardization can adopt existing methods such as Z-score standardization and min-max transformation. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0025] Step S2: According to the difference between the number of riding users and the number of shared electric vehicles at different time ranges every day, as well as the distribution of riding start time, the daily demand for electric vehicle use is obtained.

[0026] The number of cycling users at different times will be affected by many factors, such as seasons, weather, holidays, working hours, etc.; during rush hours, the number of cycling users may increase significantly, resulting in an increase in the utilization rate of electric vehicles; the number of shared electric vehicles put into use will also affect the utilization rate. If the number of shared electric vehicles put into use is insufficient, it may lead to a shortage of electric vehicles at certain times and a high utilization rate; conversely, it may lead to a low utilization rate; the distribution of cycling start times reflects the travel habits and needs of users. If a large number of users choose to ride in the morning before going to work, the utilization rate of electric vehicles in the morning may be higher; therefore, based on the difference between the number of cycling users and the number of shared electric vehicles at different times of the day, as well as the distribution of cycling start times, the daily demand for electric vehicles is obtained.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the electric vehicle usage demand degree can be found in Figure 2 , which shows a flow chart of a method for obtaining the use demand of an electric vehicle, including: Step S201: Obtain the electric vehicle utilization rate within each time range based on the difference between the number of riding users and the number of shared electric vehicles within each time range, as well as the distribution of riding start times.

[0028] The electric vehicle usage rate data directly reflects the degree of activity of electric vehicles in actual use. By monitoring and analyzing the electric vehicle usage rate at different time ranges, we can intuitively understand the demand for electric vehicles in different time periods.

[0029] Preferably, in one embodiment of the present invention, the method for obtaining the utilization rate of electric vehicles includes: Obtain the difference between the number of riding users and the number of shared electric vehicles within each time range as the quantity difference; Obtain the mean difference between different adjacent riding start times within each time range as the time difference level; The product of the quantity difference and the time difference level is obtained and negatively correlated to be mapped as the electric vehicle utilization rate within each time range.

[0030] It should be noted that the greater the data difference, the greater the difference between the number of riding users and the number of shared electric vehicles, the smaller the number of riding users relative to the number of shared electric vehicles, and the smaller the utilization rate of electric vehicles; the greater the time difference level, the greater the difference between adjacent riding start times, which means that the farther the adjacent riding start times are apart, the smaller the utilization rate of electric vehicles, which is a negative correlation; in some embodiments of the present invention, the product of the quantity difference and the time difference level can be negatively correlated by taking the inverse method, in which, in order to avoid the denominator of the formula being 0, it is necessary to manually add a set threshold, such as 0.01; it can also be The function performs negative correlation mapping on the product of the quantity difference and the time difference level. The specific means are well known to those skilled in the art and will not be elaborated here.

[0031] In one embodiment of the present invention, the formula for the utilization rate of electric vehicles is expressed as: ; in, Indicates The usage rate of electric vehicles within the time range; Indicates the number of cycling users; Indicates the number of shared electric vehicles; It represents the mean difference between different adjacent riding start times within each time range, that is, the time difference level; Take the absolute value.

[0032] In the formula for electric vehicle utilization rate, It represents the difference between the number of riding users and the number of shared electric vehicles within each time range. As the quantity difference, the larger the quantity difference, the smaller the number of riding users is than the number of shared electric vehicles, and the lower the utilization rate of electric vehicles; the smaller the quantity difference, the closer the number of riding users is to the number of shared electric vehicles, the more people have demand for shared electric vehicles, and the higher the utilization rate of electric vehicles; It represents the mean difference between different adjacent riding start times within each time range, that is, the time difference level. The smaller the time difference level, the closer the adjacent riding start times are, the greater the demand for shared electric vehicles, and the greater the utilization rate of electric vehicles.

[0033] Step S202: Arrange the usage rates of electric vehicles at all time ranges of each day in chronological order to obtain a fitting curve for fitting the usage rates of electric vehicles.

[0034] By arranging the usage rate data in chronological order, we can intuitively capture this temporal trend of change; by fitting the curve, we can smooth the data and reduce the impact of noise and outliers, thereby more accurately revealing the overall trend of electric vehicle use.

[0035] It should be noted that, in some embodiments of the present invention, curve fitting can be performed using existing fitting methods such as the least squares method and polynomial fitting method. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0036] Step S203: obtaining the average slope between different adjacent data points on the fitting curve as the overall slope level; obtaining the average slope between different adjacent data points in all peak time ranges on the fitting curve within the preset peak time range as the peak slope level; obtaining the average slope between different adjacent data points in other time ranges except the peak time range as the non-peak slope level; The slope reflects the rate of change between data points and can be used to evaluate the upward or downward trend of the curve. The overall level of the slope between all adjacent data points on the fitted curve is quantified by taking the average value, reflecting the average rate of change in the utilization rate of electric vehicles. The greater the overall slope level, the greater the upward trend in the utilization rate of electric vehicles and the greater the demand for use.

[0037] The peak slope level is used to evaluate changes in riding demand during peak hours, and the off-peak slope mean is used to evaluate changes in riding demand during off-peak hours. The larger the peak slope level value, the faster the growth in riding demand during peak hours, and it may be necessary to increase the number of vehicles or replenish headgear.

[0038] It should be noted that, in one embodiment of the present invention, the slope is calculated as the ratio of the ordinate difference to the abscissa difference between the data points on the fitting curve.

[0039] It should be noted that, in one embodiment of the present invention, the utilization rate of electric vehicles may vary significantly in different time ranges. For example, the utilization rate is higher during rush hour in the morning and evening, and lower at night. Therefore, the preset peak time ranges are 7:00-8:00, 8:00-9:00, 17:00-18:00, and 18:00-19:00; in other embodiments of the present invention, specific settings can be made according to specific circumstances, and no limitation or elaboration is made here.

[0040] Step S204: According to the deviation between the peak slope level and the off-peak slope level, and the overall slope level, the daily demand for electric vehicle use is obtained, and both the deviation and the overall slope level are positively correlated with the demand for electric vehicle use.

[0041] It should be noted that, in some embodiments of the present invention, the deviation between the peak slope level and the off-peak slope level can be calculated by calculating the ratio between the peak slope level and the off-peak slope level. The larger the ratio, the larger the peak slope level is compared to the off-peak slope level, and the higher the frequency of use of electric vehicles within the peak time range; it can also be calculated by the difference between the peak slope level and the off-peak slope level, and normalized and mapped to 0-1. The larger the result, the larger the peak slope level is compared to the off-peak slope level, and the greater the demand for use. The specific means are well known to those skilled in the art and will not be repeated here.

[0042] Step S3: According to the distribution of electric vehicle usage demand on different days in each quarter, and the correlation between the electric vehicle usage demand and riding electricity consumption on different days, the quarterly usage intensity of each quarter is obtained; according to the distribution of the number of rides of all shared electric vehicles within all time ranges in each quarter, the popularity of the deployment area of ​​each quarter is obtained.

[0043] The usage of shared electric vehicles varies in different quarters. By analyzing the distribution of usage demand on different days in each quarter, we can have a more comprehensive understanding of the overall usage of the quarter. There is a certain correlation between the demand for electric vehicles and the amount of electricity used for riding. During periods of high demand, the amount of electricity used for riding is usually also high. By analyzing the correlation, we can more accurately identify the impact of different seasons on the demand for electric vehicles and evaluate the quarterly usage intensity of each quarter. Based on the distribution of the demand for electric vehicles on different days in each quarter, and the correlation between the demand for electric vehicles on different days and the amount of electricity used for riding, we can obtain the quarterly usage intensity of each quarter.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining the quarterly usage intensity can be found in Figure 3 , which shows a flow chart of a method for obtaining quarterly usage intensity, including: Step S301: Obtain the electric vehicle thermal value for each quarter according to the electric vehicle usage demand distribution on different days in each quarter.

[0045] The electric vehicle thermal value is a quantitative representation of usage demand in that quarter and can reflect the riding activity in the region.

[0046] Preferably, in one embodiment of the present invention, the method for obtaining the thermal value of an electric vehicle includes: The cumulative sum of the electric vehicle usage demand of all days in each quarter is obtained as the first cumulative sum of demand; the cumulative sum of the electric vehicle usage demand of all days in a historical year is obtained as the second cumulative sum of demand; The ratio between the first demand cumulative sum and the second demand cumulative sum is obtained as the electric vehicle thermal value for each quarter.

[0047] Step S302: For each quarter, the sum of the riding electricity consumption within all time ranges of each day is obtained as the total riding electricity consumption per day; the mean square error of the sequence composed of the electric vehicle usage demand and the total riding electricity consumption on different days is obtained, and negative correlation mapping is performed as the first trend coefficient.

[0048] The total amount of electricity used in riding is a direct indicator to measure the daily usage intensity of electric vehicles. By summarizing the riding electricity consumption at all time ranges every day, it reflects the comprehensive value of the electric vehicle usage activity on that day.

[0049] The mean square error is a statistical method to measure the degree of difference between two sequences. It helps to quantify the correlation between the demand for electric vehicle use and the total electricity consumption. The larger the mean square error, the smaller the correlation and the smaller the first trend coefficient. The smaller the mean square error, the greater the correlation and the larger the first trend coefficient.

[0050] Step S303: Obtain the product of the electric vehicle thermal value and the first trend coefficient of the corresponding quarter as the quarterly usage intensity of each quarter.

[0051] In one embodiment of the present invention, the formula for quarterly usage intensity is expressed as: ; ; in, Indicates quarterly intensity of use by quarter; Indicates Electric vehicle thermal value for the quarter; Indicates The sequence of electric vehicle usage demand on different days in a quarter; Indicates The sequence of total electricity consumption of riding on different days in the quarter; Indicates Demand for electric vehicles per day; Indicates Demand for electric vehicles per day; Indicates Number of days in the quarter; Indicates the number of days in a year; Represents the mean square error function.

[0052] In the formula for quarterly usage intensity, Express Perform negative correlation mapping, that is, the first trend coefficient, To avoid the denominator of the formula being 0, the formula will be meaningless; Indicates The mean square error between the sequence composed of the electric vehicle usage demand and the sequence composed of the total riding electricity consumption on different days in the quarter. The larger the mean square error, the smaller the correlation between the electric vehicle usage demand and the total riding electricity consumption. The smaller the mean square error, the greater the correlation between the electric vehicle usage demand and the total riding electricity consumption. The greater the total riding electricity consumption, the greater the electric vehicle usage demand, the greater the electric vehicle thermal value, and the greater the quarterly usage intensity.

[0053] The distribution of riding times reflects the usage of shared electric vehicles in different time periods and regions. By analyzing the distribution of riding times in each quarter, high-heat areas and low-heat areas can be identified. The more riding times, the higher the heat. The heat of the deployment area is a comprehensive assessment of the riding activity in the area, which can reflect the demand intensity of the area. The heat of the deployment area in each quarter is obtained based on the distribution of riding times of all shared electric vehicles within all time ranges in each quarter.

[0054] Preferably, in one embodiment of the present invention, the method for obtaining the heat of the delivery area includes: Get the number of hotspots in the service area of ​​the delivery area, including business districts, subways, and schools; The sum of the number of rides of each shared electric vehicle at all times of the day is obtained as the first sum; the average of the first sums of all shared electric vehicles on all days of each quarter is obtained as the average number of rides; The product of the average number of rides and the number of hot spots in the delivery area is obtained and normalized to obtain the delivery area heat in each quarter.

[0055] It should be noted that the service scope of the deployment area can be obtained in advance by the implementation personnel based on relevant professional information, and the number of hot spots within the service scope can be analyzed through map software. The more hot spots there are, the greater the flow of people, the greater the demand for shared electric vehicles may be, and the greater the average number of rides in each quarter, the more popular the deployment area will be.

[0056] In one embodiment of the present invention, for each quarter, the formula for the popularity of the delivery area is expressed as: ;in, Indicates the popularity of the delivery area in each quarter; Indicates the number of hotspot areas corresponding to the delivery area; represents the average number of rides; Represents the normalization function.

[0057] Step S4: According to the popularity of the deployment area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the riding power consumption and the headgear usage are adjusted to obtain the headgear usage correction amount and the riding power correction amount.

[0058] New batteries or batteries in good health can provide higher endurance, so under the same power condition, these batteries support longer riding distances; aging batteries or batteries in poor health have significantly shorter riding distances under the same power condition, which may increase the frequency of vehicle battery replacement; the popularity of the deployment area and the quarterly usage intensity can reflect changes in riding demand and frequency, affecting power consumption and the speed of headgear usage. Power consumption and headgear usage are faster in high-heat areas, while those in low-heat areas are slower. By combining regional heat data, the riding power consumption and headgear usage in different regions can be corrected under different quarterly conditions; according to the popularity of the deployment area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the riding power consumption and headgear usage are adjusted to obtain the headgear usage correction amount and riding power correction amount.

[0059] Preferably, in one embodiment of the present invention, the method for obtaining the headgear usage correction amount and the power consumption correction amount includes: According to the popularity of the launch area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the battery replacement forecast correction coefficient for each quarter is obtained; Preferably, in one embodiment of the present invention, a method for obtaining a battery swap prediction correction coefficient includes: For each quarter, the average value of the battery health of each shared electric vehicle at all times is obtained as the local battery health level; the average value of the local battery health levels of all shared electric vehicles is obtained as the overall battery health level; The quarterly usage intensity of each quarter is normalized and mapped, and the sum of the normalized mapping result and the heat of the deployment area is obtained as the weighted value; the product of the weighted value and the overall health level of the battery is obtained as the battery replacement prediction correction coefficient for each quarter.

[0060] It should be noted that the greater the quarterly usage intensity, the more popular the deployment area, the greater the usage demand, the faster the power consumption, and the more positively correlated with the battery replacement prediction correction coefficient; the lower the overall health level of the battery, the significantly shortened riding distance, which may increase the frequency of vehicle battery replacement, and is negatively correlated with the battery replacement prediction correction coefficient.

[0061] In one embodiment of the present invention, the formula of the battery swap prediction correction coefficient is expressed as: ; in, Indicates Quarterly battery replacement forecast correction factor; Indicates The quarterly heat value of the delivery area; Indicates quarterly intensity of use by quarter; Indicates The overall health of shared electric vehicle batteries during the quarter; represents the hyperbolic tangent function.

[0062] For the headgear usage or riding electricity consumption as the data to be corrected, the battery replacement forecast correction coefficient of each quarter and the corresponding data to be corrected in each time range are multiplied together to obtain the headgear usage correction amount and riding usage correction amount in each time range.

[0063] Step S5: predicting the optimal time for replenishing the headgear based on the distribution of the headgear usage correction amount and the riding power correction amount of all shared electric vehicles within different time ranges.

[0064] Combined with the distribution of headgear usage corrections and riding electricity corrections in a historical year, it reflects the usage habits and demand characteristics of electric vehicles in the deployment area, which helps to improve the operation and maintenance efficiency of shared electric vehicles.

[0065] Preferably, in one embodiment of the present invention, the optimal replenishment time includes: For the correction amount for headgear use or the correction amount for riding electricity consumption as the target data, the sum of the target data of each shared electric vehicle at different time ranges every day is obtained as the target overall data of each shared electric vehicle every day; the mean of the target overall data of all shared electric vehicles every day is obtained as the target overall average data of every day; a fitting curve is constructed to fit the target overall average data of all days, and the intersection of the corresponding fitting curves between the correction amount for headgear use and the correction amount for riding electricity consumption is selected; The mean slope of all adjacent data points on the fitting curve between adjacent intersections is obtained. If the difference between the corresponding mean slopes of two fitting curves between adjacent intersections is less than a preset difference threshold, the time range between the corresponding adjacent intersections is used as the optimal supplementary time for the headgear.

[0066] It should be noted that the smaller the difference in the mean slopes, the more consistent the changing trends between the fitting curves, and the more similar the headgear usage and power consumption performance; in one embodiment of the present invention, the size of the preset difference threshold is set to 0.3; in other embodiments of the present invention, the size of the preset difference threshold can be set according to the specific circumstances, and is not limited or elaborated here.

[0067] After obtaining the optimal time to replenish the headgear, the number threshold sensor in the headgear mechanism is used to obtain the number of missing headgear for shared electric vehicles in the deployment area at the corresponding time in the next year, and then the demand value for headgear carried while replacing batteries in the area is obtained, so as to replenish the headgear more accurately and provide accurate operation and maintenance plans for operation and maintenance personnel to adapt to the high-frequency use of shared electric vehicles.

[0068] It should be noted that if the electricity consumption curve grows at a faster rate, it means that the riding demand in the deployment area is higher, but the headgear usage curve grows at a slower rate, which means that users in the area may be more inclined to use their own helmets or a riding method that does not rely on headgear. At this time, the headgear inventory should be dynamically adjusted to allocate excess headgear to areas with higher riding demand and headgear usage frequency to optimize resource utilization.

[0069] In summary, the present invention obtains the daily demand for electric vehicle use according to the difference between the number of riding users and the number of shared electric vehicles at different time ranges of the day, as well as the distribution of the start time of riding; obtains the quarterly usage intensity of each quarter according to the distribution of the demand for electric vehicle use on different days in each quarter, and the correlation between the demand for electric vehicle use on different days and the riding power consumption; adjusts the riding power consumption and headgear usage in combination with the popularity of the launch area of ​​each quarter and the battery health distribution of all shared electric vehicles at different time ranges, obtains the headgear usage correction amount and the riding power correction amount; predicts the optimal replenishment time for headgear replenishment. The present invention optimizes the operation time for headgear replenishment by analyzing the historically accurate headgear usage and riding power consumption.

[0070] The present invention also proposes a shared electric vehicle head cover replenishment prediction system based on data analysis, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any step of a shared electric vehicle head cover replenishment prediction method based on data analysis.

[0071] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for predicting the replenishment of shared electric vehicle headgear based on data analysis, characterized in that: The method comprises: Obtain the number of riding users in the deployment area at different times of the day in the past year, as well as the riding data of each shared electric vehicle, including the number of rides, riding power consumption, headgear usage, riding start time, and battery health; Based on the difference between the number of riding users and the number of shared electric vehicles at different times of the day, as well as the distribution of riding start times, the daily demand for electric vehicles is obtained; Based on the distribution of electric vehicle usage demand on different days in each quarter, and the correlation between the electric vehicle usage demand and riding power consumption on different days, the quarterly usage intensity of each quarter is obtained; based on the distribution of the number of rides of all shared electric vehicles at all times in each quarter, the popularity of the deployment area of ​​each quarter is obtained; According to the popularity of the launch area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the riding power consumption and headgear usage are adjusted to obtain the headgear usage correction amount and riding power correction amount; Based on the distribution of headgear usage correction and riding electricity correction of all shared electric vehicles at different time ranges, the optimal time for headgear replenishment is predicted.

2. According to the method for predicting the replenishment of shared electric vehicle headgear based on data analysis according to claim 1, it is characterized in that: The method for obtaining the electric vehicle use demand degree includes: Based on the difference between the number of riding users and the number of shared electric vehicles in each time range, as well as the distribution of riding start time, the electric vehicle utilization rate in each time range is obtained; Arrange the usage rates of electric vehicles at all time ranges within each day in chronological order to obtain a fitting curve for fitting the usage rates of electric vehicles; The average slope value between different adjacent data points on the fitting curve is obtained as the overall slope level; within the preset peak time range, the average slope value between different adjacent data points in all peak time ranges on the fitting curve is obtained as the peak slope level; the average slope value between different adjacent data points in other time ranges except the peak time range is obtained as the non-peak slope level; The ratio of the peak slope level to the off-peak slope level is obtained, and the product of the ratio result and the overall slope level is calculated as the daily electric vehicle use demand.

3. According to claim 2, a method for predicting the replenishment of shared electric vehicle headgear based on data analysis is characterized in that: The method for obtaining the electric vehicle utilization rate includes: Obtain the difference between the number of users and the number of shared electric vehicles within each time range as the quantity difference; Obtain the mean difference between different adjacent riding start times within each time range as the time difference level; The product of the quantity difference and the time difference level is obtained and negatively correlated to be mapped as the electric vehicle utilization rate within each time range.

4. According to the method for predicting the replenishment of shared electric vehicle headgear based on data analysis in claim 1, it is characterized in that: The method for obtaining the quarterly usage intensity includes: According to the distribution of electric vehicle usage demand on different days in each quarter, the electric vehicle thermal value of each quarter is obtained; For each quarter, the sum of the riding electricity consumption of all shared electric vehicles at all times of the day is obtained as the total riding electricity consumption per day; the mean square error of the sequence composed of the electric vehicle usage demand and the total riding electricity consumption on different days is obtained, and negative correlation mapping is performed as the first trend coefficient; The product between the electric vehicle thermal value and the first trend coefficient of the corresponding quarter is obtained as the quarterly usage intensity of each quarter.

5. A method for predicting the replenishment of shared electric vehicle headgear based on data analysis according to claim 4, characterized in that: The method for obtaining the thermal value of the electric vehicle comprises: The cumulative sum of the electric vehicle usage demand of all days in each quarter is obtained as the first cumulative sum of demand; the cumulative sum of the electric vehicle usage demand of all days in a historical year is obtained as the second cumulative sum of demand; The ratio between the first demand cumulative sum and the second demand cumulative sum is obtained as the electric vehicle thermal value for each quarter.

6. The method for predicting the replenishment of shared electric vehicle headgear based on data analysis according to claim 1 is characterized in that: The method for obtaining the heat of the delivery area includes: Obtain the number of hot spots in the service range of the delivery area, where the hot spots include business districts, subways, and schools; The sum of the number of rides of each shared electric vehicle at all times of the day is obtained as the first sum; the average of the first sums of all shared electric vehicles on all days of each quarter is obtained as the average number of rides; The product of the average number of rides and the number of hot spots in the delivery area is obtained and normalized to obtain the delivery area heat in each quarter.

7. The method for predicting the replenishment of shared electric vehicle headgear based on data analysis according to claim 1 is characterized in that: The method for obtaining the headgear usage correction amount and the power consumption correction amount includes: According to the popularity of the launch area in each quarter, the quarterly usage intensity and the battery health distribution of all shared electric vehicles at different time ranges, the battery replacement forecast correction coefficient for each quarter is obtained; For the headgear usage or riding electricity consumption as the data to be corrected, the battery replacement forecast correction coefficient of each quarter and the corresponding data to be corrected in each time range are multiplied together to obtain the headgear usage correction amount and riding usage correction amount in each time range.

8. The method for predicting the replenishment of shared electric vehicle headgear based on data analysis according to claim 7 is characterized in that: The method for obtaining the battery swap prediction correction coefficient includes: For each quarter, the average value of the battery health of each shared electric vehicle at all times is obtained as the local battery health level; the average value of the local battery health levels of all shared electric vehicles is obtained as the overall battery health level; The quarterly usage intensity of each quarter is normalized and mapped, and the sum of the normalized mapping result and the heat of the deployment area is obtained as the weighted value; the product of the weighted value and the overall health level of the battery is obtained as the battery replacement prediction correction coefficient for each quarter.

9. The method for predicting the replenishment of shared electric vehicle headgear based on data analysis according to claim 1 is characterized in that: The method for obtaining the optimal replenishment time includes: For the correction amount for headgear use or riding electricity use, the target data is taken as the sum of the target data of each shared electric vehicle at different times of the day, which is taken as the overall target data of each shared electric vehicle on a daily basis; the average of the overall target data of all shared electric vehicles on a daily basis is taken as the overall target average data of the day; Construct a fitting curve for fitting the overall average data of all the days, and select the intersection of the corresponding fitting curves between the headgear usage correction amount and the riding power correction amount; The mean slope of all adjacent data points on the fitting curve between adjacent intersections is obtained. If the difference between the corresponding mean slopes of two fitting curves between adjacent intersections is less than a preset difference threshold, the time range between the corresponding adjacent intersections is used as the optimal supplementary time for the headgear.

10. A shared electric vehicle headgear replenishment prediction system based on data analysis, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a shared electric vehicle head cover replenishment prediction method based on data analysis as described in any one of claims 1 to 9 are implemented.

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