Charging optimization method and equipment for charging bicycle and medium

Abstract

The invention discloses a charging optimization method and device for a charging bicycle and a medium, and the method comprises the steps: obtaining the historical power supply information of a power distribution network and the information of the charging bicycle in a to-be-optimized region range, building a power demand model, and training the power demand model based on the historical power supply information and the information of the charging bicycle; determining a charging influence factor according to a bicycle use condition in the charging bicycle information and a corresponding user habit, so as to combine the charging influence factor into a trained power demand model; inputting the real-time charging and power supply data into the power demand model, and predicting a power consumption peak in a future time period in combination with the charging influence factor; determining to-be-charged bicycles, reverse power supply bicycles and corresponding total power supply electric quantity which have charging requirements in the power utilization peak; and reselecting a charging address for the to-be-charged bicycle based on the peak region range, and reversely supplying the power supply total electric quantity corresponding to the reverse power supply bicycle to the power distribution network so as to realize charging optimization.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a charging optimization method, device and medium for a charging bicycle. Background Art

[0002] With the enhancement of environmental awareness and the continuous development of science and technology, rechargeable bicycles, as a green, low-carbon and convenient means of transportation, not only have the environmental protection characteristics of zero emissions and low noise, but are also deeply loved by consumers because of their simple operation and low cost.

[0003] However, with the surge in the number of rechargeable bicycles, charging problems have gradually become prominent. Most users choose to charge their rechargeable bicycles after get off work for the sake of usage habits and convenience. Since a large number of rechargeable bicycles are charged in the same period of time, the distribution network needs to bear huge instantaneous loads, which may cause local grid voltage fluctuations, line overloads and other problems, and even affect the stable operation of the entire power system. Especially in areas where the power grid infrastructure is relatively weak, the impact of charging peak electricity consumption on the distribution network is more obvious, which brings considerable pressure to the power supply. Summary of the invention

[0004] The embodiments of the present application provide a charging optimization method, device and medium for a charging bicycle to solve the above-mentioned technical problems.

[0005] On the one hand, an embodiment of the present application provides a charging optimization method for a charging bicycle, comprising:

[0006] Obtain historical power supply information of the distribution network and information on charging bicycles within the area to be optimized, and build a power demand model to train the power demand model based on the historical power supply information and charging bicycle information;

[0007] Determine a charging influence factor according to the vehicle usage and corresponding user habits in the charging bicycle information, so as to combine the charging influence factor into the trained power demand model; wherein the user habits include driving habits and charging habits;

[0008] Input the real-time charging and supply data into the power demand model, and combine it with the charging influencing factors to predict the peak power consumption in the future time period; the peak power consumption includes the peak time and the peak area range;

[0009] According to the current user demand and the current charging bicycle information, determine the bicycles to be charged, the bicycles that can be reversely powered, and the total power supply corresponding to the bicycles that can be reversely powered during the peak power consumption period;

[0010] Based on the peak area range, the charging address is reselected for the bicycles to be charged, and the total power that can be supplied by the reverse-powered bicycles is reversely supplied to the distribution network to achieve charging optimization.

[0011] In one implementation of the present application, the charging influencing factor is determined according to the vehicle usage in the charging bicycle information and the corresponding user habits, specifically including:

[0012] Based on the preset sensors on the charging bicycle, the real-time parameters of the bicycle are obtained, and the charging behavior data of the charging pile and the online usage data are obtained; the real-time parameters of the bicycle include the travel distance, speed, acceleration, and battery power; the charging behavior data include the start time, end time, charging amount, and charging power of each charge; and the online usage data include the user's charging preference, charging frequency, and travel habits;

[0013] Analyze the user's charging frequency and charging amount in different time periods, as well as the user's selection ratio of fast charging and slow charging to determine the user's charging speed preference, and analyze the user's charging behavior in different locations to determine the user's charging scene preference to obtain the user's charging habits; charging scenes include home, company, and public places;

[0014] Statistics are collected on the user's average daily mileage, and the user's driving speed distribution and charging behavior at different power levels are analyzed to determine the user's range requirements, driving type, and charging behavior preferences, so as to obtain the user's driving habits; the driving type includes aggressive and stable types, and the charging behavior preference is used to indicate the bicycle power level when the user chooses to charge and stop charging;

[0015] Cluster users according to their charging and driving habits to identify different types of user groups and their corresponding charging characteristics;

[0016] The user type and the corresponding charging characteristics are input into the trained regression model to predict the user's corresponding charging needs and charging behaviors, and determine the weight coefficients of the corresponding charging influencing factors; among which, the charging influencing factors include power demand factor, charging speed preference factor, time flexibility factor, charging scenario preference factor and battery health management factor.

[0017] In one implementation of the present application, based on the peak area range, a charging address is reselected for the bicycle to be charged, specifically including:

[0018] According to the actual location of the peak area, determine the distribution of charging piles within a specified radius outside the peak area, the charging power of each charging pile, and the current usage status, so as to determine at least one available charging pile in the non-peak area; wherein the available charging pile is within the specified radius outside the peak area;

[0019] For an available charging pile, according to the current position of the bicycle to be charged and the actual position of the available charging pile, determine an estimated driving path between the bicycle to be charged and the available charging pile, and determine at least one road section type in the estimated driving path and the road section length corresponding to each road section;

[0020] Obtain the current remaining battery power of the bicycle to be charged and the historical driving data corresponding to each road section, so as to determine the speed of the bicycle to be charged under the battery power and the corresponding road section type according to the historical driving data;

[0021] According to the type of road section in the estimated driving path between the bicycle to be charged and each available charging pile, the length of each road section and the corresponding speed of the bicycle to be charged, the estimated arrival time and path cost of the bicycle to be charged to each available charging pile are calculated;

[0022] The actual distance, estimated arrival time and path cost between each available charging pile and the bicycle to be charged are sent to the user of the bicycle to be charged, and feedback information from the user is received;

[0023] The target charging address preferred by the user is determined through the feedback information, so that the bicycle to be charged can be charged through the target available charging pile at the target charging address.

[0024] In one implementation of the present application, the total power supply corresponding to the reversely powered bicycle is reversely supplied to the distribution network to achieve charging optimization, specifically including:

[0025] Determine the electricity demand during peak hours and peak areas;

[0026] Obtain the current load of the distribution network, and when the current load exceeds the preset maximum load, determine the corresponding load compensation coefficient according to the power demand;

[0027] According to the battery power of each reverse-powered bicycle in the total power supply, and in combination with the load compensation coefficient, a reverse power supply plan corresponding to the reverse-powered bicycle is formulated; wherein the reverse power supply plan includes a reverse power supply time period, a reverse power supply power, and a reverse power supply duration;

[0028] According to the reverse power supply time period, reverse power supply power and reverse power supply duration, the battery power corresponding to each reverse power supply bicycle is reversely supplied to the distribution network.

[0029] In one implementation of the present application, after the total power supply corresponding to the reversely powered bicycle is reversely supplied to the distribution network, the method further includes:

[0030] During the reverse power supply process, the battery status of the reverse power supply bicycle is monitored in real time, so as to stop the reverse power supply of the corresponding reverse power supply bicycle when the battery power is less than the preset travel power threshold;

[0031] receiving the user's demand update information in real time, so as to stop the reverse power supply of the corresponding reverse power supply bicycle when the demand update information indicates that there is a need to use the bicycle within a specified time period;

[0032] Determine whether the remaining power of the reverse power supply bicycle meets the demand for use. If not, charge the reverse power supply bicycle with the demand for use after the peak of power consumption ends.

[0033] During the reverse power supply process, the current load of the distribution network is monitored in real time to dynamically adjust the reverse power supply plan according to the current load of the distribution network and the reverse power supply results of the reverse-powered bicycle.

[0034] In one implementation of the present application, historical power supply information of the distribution network and information of charging bicycles within the area to be optimized are obtained, and a power demand model is constructed to train the power demand model based on the historical power supply information and the charging bicycle information, specifically including:

[0035] Determine the scope of the area to be optimized with charging optimization requirements, obtain the information of charging bicycles within the area to be optimized, and obtain the historical power supply information of the distribution network;

[0036] The historical power supply information includes at least the power supply capacity, grid load, and transmission loss. The charging bicycle information includes bicycle registration information and actual bicycle charging data. The bicycle registration information includes vehicle specifications, distribution location, and battery capacity. The actual bicycle charging data includes charging time, charging power, charging amount, and charging habits.

[0037] Based on battery capacity, charging power, and charging efficiency, analyze the charging needs of charging bicycles in different time periods, and based on power supply capacity, grid load, and transmission loss, determine the load characteristics of the distribution network in different seasons and time periods;

[0038] A power demand model is constructed to combine the charging demand of charging bicycles and the load characteristics of the distribution network. The power demand model is trained through historical power supply information and charging bicycle information until the power demand output by the power demand model matches the pre-marked power demand, completing the training of the power demand model.

[0039] In one implementation of the present application, based on current user demand and current charging bicycle information, determining the bicycles to be charged, the bicycles that can be reversely powered, and the total power supply corresponding to the bicycles that can be reversely powered that have charging demand during peak power consumption, specifically includes:

[0040] According to the user's driving and charging habits, the corresponding user profile is determined, and combined with the current timestamp and battery power, if the user profile shows that the user has a tendency to drive within the specified time period and the battery power is lower than the preset travel power threshold, the corresponding charging bicycle is determined to be a bicycle to be charged during the peak power consumption period;

[0041] Based on the user profile, a candidate reverse power supply bicycle with reverse charging potential during peak power consumption is determined, and a reverse power supply request is sent to the candidate reverse power supply bicycle; wherein the reverse charging potential is used to indicate that there is no driving tendency within a specified time period, and the battery power is greater than a preset driving power threshold;

[0042] Receive feedback information from the selected reverse power supply bicycle in response to the reverse power supply request to determine the reverse power supply bicycles that allow reverse power supply according to the feedback information, and calculate the total power supply during peak power consumption according to the battery power corresponding to each reverse power supply bicycle.

[0043] In one implementation of the present application, real-time charging and supply data is input into the power demand model, and combined with charging influencing factors, the peak power consumption in the future time period is predicted, specifically including:

[0044] Acquire real-time power supply data of the distribution network and real-time charging data of the charging bicycles, so as to input the real-time power supply data, real-time charging data and charging influencing factors into the power demand model;

[0045] Based on real-time charging supply data and charging influencing factors, combined with historical electricity consumption data, weather information, holiday schedules and the current timestamp, the specific peak time period, expected power consumption and peak fluctuation area range of peak electricity consumption in the future time period are predicted.

[0046] On the other hand, the embodiment of the present application further provides a charging optimization device for a charging bicycle, the device comprising:

[0047] at least one processor;

[0048] and, a memory communicatively coupled to the at least one processor;

[0049] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the charging optimization method for a rechargeable bicycle as described above.

[0050] On the other hand, an embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions. When the computer executable instructions are executed, a charging optimization method for a rechargeable bicycle as described above is implemented.

[0051] The present application provides a charging optimization method, device and medium for a charging bicycle, which at least have the following beneficial effects:

[0052] By obtaining the historical power supply information of the distribution network and the information of charging bicycles in the area to be optimized, and constructing a power demand model for training, it can more accurately reflect the supply and demand relationship between the distribution network and charging bicycles. The combination of charging influencing factors further improves the prediction ability of the model, making the prediction of peak power consumption in future time periods more accurate and reliable; according to current user needs and current charging bicycle information, it can accurately identify bicycles to be charged that have charging needs during peak power consumption, as well as reverse-powered bicycles with reverse power supply potential, which helps to reasonably allocate charging resources, avoid overloading of charging stations or idle resources, and improve the utilization rate of charging facilities; by predicting peak power consumption, it can Plan charging behavior in advance to avoid concentrated charging during peak hours, thereby reducing the load pressure on the distribution network. At the same time, the total power that can be supplied by the reverse-powered bicycles can be reversed to the distribution network to provide additional power support for the grid, which helps to balance supply and demand and enhance the stability of the grid. Reselecting charging addresses for bicycles to be charged can guide users to avoid peak hours and congested areas, reduce waiting time, improve charging efficiency, meet users' personalized needs, and improve user satisfaction with charging services. By optimizing charging behavior, reducing energy waste and carbon emissions, and utilizing the battery energy storage characteristics of reverse-powered bicycles, auxiliary services are provided to the distribution network, improving the flexibility and efficiency of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0054] Figure 1 A schematic diagram of a charging optimization method for a charging bicycle provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of the internal structure of a charging optimization device for a charging bicycle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0057] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0058] Figure 1 A schematic flow chart of a charging optimization method for a rechargeable bicycle provided in an embodiment of the present application.

[0059] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.

[0060] It should be noted that the server may be a single device or a system consisting of multiple devices, that is, a distributed server, and this application does not make any specific limitation on this.

[0061] like Figure 1 As shown, a charging optimization method for a charging bicycle provided in an embodiment of the present application includes:

[0062] 101. Obtain historical power supply information of the distribution network and information on charging bicycles within the area to be optimized, and construct a power demand model to train the power demand model based on the historical power supply information and the charging bicycle information.

[0063] Specifically, in one embodiment of the present application, historical power supply information of the distribution network and information of charging bicycles within the area to be optimized are obtained, and a power demand model is constructed to train the power demand model based on the historical power supply information and the charging bicycle information, specifically including:

[0064] Determine the scope of the area to be optimized with charging optimization requirements, obtain the information of charging bicycles within the area to be optimized, and obtain the historical power supply information of the distribution network;

[0065] The historical power supply information includes at least the power supply capacity, grid load, and transmission loss. The charging bicycle information includes bicycle registration information and actual bicycle charging data. The bicycle registration information includes vehicle specifications, distribution location, and battery capacity. The actual bicycle charging data includes charging time, charging power, charging amount, and charging habits.

[0066] Based on battery capacity, charging power, and charging efficiency, analyze the charging needs of charging bicycles in different time periods, and based on power supply capacity, grid load, and transmission loss, determine the load characteristics of the distribution network in different seasons and time periods;

[0067] A power demand model is constructed to combine the charging demand of charging bicycles and the load characteristics of the distribution network. The power demand model is trained through historical power supply information and charging bicycle information until the power demand output by the power demand model matches the pre-marked power demand, completing the training of the power demand model.

[0068] In one embodiment, in order to optimize the charging strategy of charging bicycles, improve the power supply efficiency of the distribution network, and reduce transmission losses, the city management department decided to build a power demand model to accurately predict and schedule the charging needs of charging bicycles. The city management department first determined the scope of the area to be optimized with charging optimization needs, which includes multiple shared bicycle parking spots and charging stations. Through the intelligent parking management system and the distribution network monitoring system, the management department obtained the information of charging bicycles within the area to be optimized, including bicycle registration information (such as vehicle specifications, distribution location, battery capacity) and actual bicycle charging data (such as charging time, charging power, charging amount, and user charging habits). At the same time, the management department also obtained the historical power supply information of the distribution network, including key indicators such as power supply capacity, grid load, and transmission loss.

[0069] Based on the acquired bicycle battery capacity, charging power and charging efficiency data, the management department analyzed the charging demand of rechargeable bicycles in different time periods. For example, it was found that the charging demand was higher during the peak hours in the morning and evening on weekdays, while it was relatively lower during the noon and night. At the same time, combined with the historical power supply information of the distribution network, the management department determined the load characteristics of the distribution network in different seasons and time periods. For example, in summer, the grid load is higher due to the increased use of air conditioners; while in winter, the load may increase due to heating demand.

[0070] The management department built a power demand model that aims to predict future power demand by combining the charging demand of charging bicycles and the load characteristics of the distribution network. To train the model, the management department used historical power supply information and charging bicycle information as training data. This data was input into the model, and the pre-labeled power demand was set as the target output. Through continuous iterative training, the model gradually learned how to predict power demand based on the input data. The accuracy of the model was evaluated by comparing the power demand output by the model with the pre-labeled power demand. After multiple training and adjustments, when the power demand output by the model matched the pre-labeled power demand, the management department believed that the model had been trained and could be used for actual power demand forecasting and charging strategy optimization.

[0071] In one embodiment, in the central business district of a smart city, the management department identified the area as an area to be optimized, and obtained the charging bicycle information and historical power supply information of the distribution network in the area. Through analysis, it was found that 8 to 9 a.m. and 5 to 6 p.m. on weekdays are the peak hours for charging demand, and the load of the power grid during these hours is also relatively high.

[0072] The management department then built a power demand model and trained it using historical data. After training, the model was able to accurately predict power demand in different time periods, taking into account factors such as the charging habits of charging bicycles, battery capacity, charging power, and the power supply capacity, load and transmission losses of the distribution network.

[0073] Based on the prediction results of the model, the management department has formulated an optimized charging strategy, such as charging some bicycles in advance before the peak period to reduce the grid load during the peak period; increasing the charging power during the off-peak period to improve the charging efficiency. The implementation of these strategies has effectively improved the power supply efficiency of the distribution network, reduced transmission losses, and also met the charging needs of users.

[0074] 102. Determine a charging influence factor according to the vehicle usage in the charging bicycle information and the corresponding user habits, so as to combine the charging influence factor into the trained power demand model.

[0075] It should be noted that the user habits in the embodiments of the present application include driving habits and charging habits.

[0076] Specifically, in one embodiment of the present application, the charging influencing factor is determined according to the vehicle usage in the charging bicycle information and the corresponding user habits, specifically including:

[0077] Based on the preset sensors on the charging bicycle, the real-time parameters of the bicycle are obtained, and the charging behavior data of the charging pile and the online usage data are obtained; the real-time parameters of the bicycle include the travel distance, speed, acceleration, and battery power; the charging behavior data include the start time, end time, charging amount, and charging power of each charge; and the online usage data include the user's charging preference, charging frequency, and travel habits;

[0078] Analyze the user's charging frequency and charging amount in different time periods, as well as the user's selection ratio of fast charging and slow charging to determine the user's charging speed preference, and analyze the user's charging behavior in different locations to determine the user's charging scene preference to obtain the user's charging habits; charging scenes include home, company, and public places;

[0079] Statistics are collected on the user's average daily mileage, and the user's driving speed distribution and charging behavior at different power levels are analyzed to determine the user's range requirements, driving type, and charging behavior preferences, so as to obtain the user's driving habits; the driving type includes aggressive and stable types, and the charging behavior preference is used to indicate the bicycle power level when the user chooses to charge and stop charging;

[0080] Cluster users according to their charging and driving habits to identify different types of user groups and their corresponding charging characteristics;

[0081] The user type and the corresponding charging characteristics are input into the trained regression model to predict the user's corresponding charging needs and charging behaviors, and determine the weight coefficients of the corresponding charging influencing factors; among which, the charging influencing factors include power demand factor, charging speed preference factor, time flexibility factor, charging scenario preference factor and battery health management factor.

[0082] In one embodiment, data collection and analysis are performed on a smart charging bicycle system to achieve accurate user behavior prediction and charging demand management. Through the GPS module, speed sensor, accelerometer and battery management system (BMS) installed on the bicycle, real-time data such as driving distance (such as 50 kilometers per day), speed (average speed 20km / h), acceleration (maximum acceleration 1.5m / s2) and battery power (remaining power 30%) are collected. Connect with the smart charging pile network in the city to collect information such as the start time of each charge (such as 8 am on weekdays), end time (9 am), charging amount (full to 100%, i.e. 4Ah), charging power (fast charging mode, power up to 400W). Through user APP registration information, charging records and user feedback, the user's charging preference (prefer fast charging), charging frequency (3 times a week), and travel habits (daily commuting) are obtained.

[0083] Through analysis, it was found that a certain user charged more frequently during lunch and evening on weekdays, and the proportion of fast charging selection reached 70%. Based on this, it was judged that the user tended to charge quickly to meet emergency travel needs. At the same time, the proportion of charging behavior at home and company scenes was 60% and 30% respectively, and charging was less in public places. According to statistics, the user's average daily mileage is 40 kilometers, and the speed is faster during the morning commuting period. It belongs to the aggressive driving type, and the user tends to start charging when the power is less than 20%. The user was clustered with other users with similar charging habits and driving habits, and it was identified that this is a user group that "commutes mainly and prefers fast charging". Its charging characteristics include high charging frequency, fast charging preference, and high charging activities on weekdays and around residence.

[0084] The above user types and their charging characteristics are input into the pre-trained regression model. The model comprehensively considers the power demand factor (high), charging speed preference factor (high), time flexibility factor (medium), charging scene preference factor (home> company> public places) and battery health management factor (focus on battery life), and predicts that the user will have a higher charging demand during weekday lunch and evening in the next week, and will be more inclined to choose fast charging services.

[0085] 103. Input the real-time charging and supply data into the power demand model, and combine it with the charging influencing factors to predict the peak power consumption in the future time period.

[0086] It should be noted that the peak electricity consumption in the embodiment of the present application includes peak time and peak area range.

[0087] Specifically, in one embodiment of the present application, the real-time charging and supply data is input into the power demand model, and combined with the charging influencing factors, the peak power consumption in the future time period is predicted, specifically including:

[0088] Acquire real-time power supply data of the distribution network and real-time charging data of the charging bicycles, so as to input the real-time power supply data, real-time charging data and charging influencing factors into the power demand model;

[0089] Based on real-time charging supply data and charging influencing factors, combined with historical electricity consumption data, weather information, holiday schedules and the current timestamp, the specific peak time period, expected power consumption and peak fluctuation area range of peak electricity consumption in the future time period are predicted.

[0090] In one embodiment, the real-time power supply data, charging data, and charging influencing factors are used as input parameters and input into a pre-trained power demand model. In the prediction process, the model combines real-time charging and supply data and charging influencing factors, and also considers historical power consumption data, weather information, holiday arrangements, and the current timestamp. Factors such as temperature, humidity, and rainfall affect people's travel and electricity usage habits. In addition, electricity consumption patterns tend to be different during holidays. By comprehensively analyzing this information, it is possible to predict the specific peak time period of peak power consumption in the future time period, that is, when the power consumption will reach its peak. It is also possible to predict the expected power consumption, that is, the expected total power consumption during the peak period, and the peak fluctuation area range, that is, which areas may be greatly affected by the peak power consumption.

[0091] Specifically, in the energy management center of a smart city, the power supply data of the distribution network and the charging data of the charging bicycles are monitored and analyzed in real time. It is currently a weekday afternoon, and the weather forecast shows that there will be light rain in the evening, and tomorrow is the weekend. The system combines these real-time data, historical power consumption data, weather information, holiday schedules, and the current timestamp, and inputs the relevant information into the power demand model.

[0092] After calculation, the model predicts that in the next 24 hours, 7pm to 9pm will be the peak period for electricity consumption, and it is expected that electricity consumption will reach the peak of the day. Due to the influence of light rain and weekends, some residential and commercial areas may become fluctuating areas of peak electricity consumption. Based on this prediction result, the energy management center can adjust the power supply strategy of the distribution network in advance, such as increasing backup power supply, optimizing power dispatching, and reminding users to arrange electricity consumption reasonably, so as to ensure the stable operation of the distribution network and meet the electricity demand of users. At the same time, for the management of charging bicycles, the charging strategy can also be adjusted according to the prediction results, such as charging more bicycles before the peak period to reduce the charging pressure during the peak period.

[0093] 104. According to current user demand and current charging bicycle information, determine the bicycles to be charged that have charging demand during peak hours, the bicycles that can be powered reversely, and the total power that can be supplied by the bicycles that can be powered reversely.

[0094] Specifically, in one embodiment of the present application, according to the current user demand and the current charging bicycle information, determining the bicycle to be charged, the bicycle capable of reverse power supply, and the total power supply corresponding to the bicycle capable of reverse power supply that has charging demand during the peak power consumption, specifically includes:

[0095] According to the user's driving and charging habits, the corresponding user profile is determined, and combined with the current timestamp and battery power, if the user profile shows that the user has a tendency to drive within the specified time period and the battery power is lower than the preset travel power threshold, the corresponding charging bicycle is determined to be a bicycle to be charged during the peak power consumption period;

[0096] Based on the user profile, a candidate reverse power supply bicycle with reverse charging potential during peak power consumption is determined, and a reverse power supply request is sent to the candidate reverse power supply bicycle; wherein the reverse charging potential is used to indicate that there is no driving tendency within a specified time period, and the battery power is greater than a preset driving power threshold;

[0097] Receive feedback information from the selected reverse power supply bicycle in response to the reverse power supply request to determine the reverse power supply bicycles that allow reverse power supply according to the feedback information, and calculate the total power supply during peak power consumption according to the battery power corresponding to each reverse power supply bicycle.

[0098] In one embodiment, in a smart city traffic management system, in order to optimize the use of power resources, improve the utilization rate of charging bicycles, and reduce the burden on the power grid during peak hours of power consumption, the system implements a charging and reverse power supply strategy based on user profiles and battery status.

[0099] By collecting and analyzing users' driving and charging habits, a unique user profile is generated for each user. These profiles include information such as the user's daily travel mode, preferred travel time, charging frequency, and charging period. The current timestamp is 5 pm on weekdays, and the system begins to check the user profiles of all registered users and the battery power of the associated charging bicycles. For users whose user profiles show a tendency to drive between 6 pm and 8 pm on weekdays (a specified time period) and whose charging bicycle battery power is lower than the preset travel power threshold, for example, enough power to support a short trip, the system marks these charging bicycles as bicycles to be charged. The system then sends charging reminders to these users, suggesting that they charge their bicycles before the peak of electricity consumption arrives to ensure that their travel needs can be met.

[0100] At the same time, the system also selects users who have no tendency to drive during peak power consumption hours (such as 6pm to 8pm) based on user portraits, and whose rechargeable bicycle battery power is greater than the preset driving power threshold, that is, the power is sufficient to support a long-distance trip or multiple short trips. The rechargeable bicycles of these users are regarded by the system as candidate reverse-powered bicycles with reverse charging potential. The system sends a reverse power supply request to these candidate reverse-powered bicycles, asking users whether they are willing to reversely supply the battery power of their bicycles to the power grid during peak power consumption hours.

[0101] After receiving the reverse power supply request, the user of the selected reverse power supply bicycle can choose whether to allow reverse power supply according to his own wishes and send feedback information to the system. The system receives and processes this feedback information, and determines those charging bicycles that allow reverse power supply, that is, reverse power supply bicycles. Subsequently, the system calculates the total amount of electricity that these bicycles can provide during peak power consumption hours based on the battery power corresponding to each reverse power supply bicycle. This total amount of electricity that can be supplied is then used by the system to optimize the power dispatch of the power grid, reduce the burden during peak power consumption hours, and may provide users with certain power feedback or discounts.

[0102] For example, in a residential area of ​​a smart city, the system identified several bicycles to be charged and several reverse power supply bicycles to be selected by analyzing user portraits and battery status. Among them, Mr. Zhang's charging bicycle was marked as a bicycle to be charged because the battery power was lower than the preset travel power threshold and his user portrait showed that he had the habit of driving at night. The system then sent him a charging reminder.

[0103] Ms. Li's rechargeable bicycle has sufficient power, and her user profile shows that she has no plans to drive at night, so her bicycle is considered by the system as a candidate for reverse power supply. The system sends her a reverse power supply request, and after Ms. Li agrees, her bicycle is determined to be a reverse power supply bicycle.

[0104] Based on the battery power of the reverse-powered bicycles of Ms. Li and other users, the system calculated the total power that these bicycles can provide during peak hours and used it to optimize the power dispatch of the power grid. This not only reduces the burden on the power grid, but also brings a certain amount of power feedback to users such as Ms. Li.

[0105] 105. Based on the peak area range, reselect the charging address for the bicycle to be charged, and reversely supply the total power that can be supplied by the reverse-powered bicycle to the distribution network to achieve charging optimization.

[0106] Specifically, in one embodiment of the present application, based on the peak area range, reselecting a charging address for the bicycle to be charged specifically includes:

[0107] According to the actual location of the peak area, determine the distribution of charging piles within a specified radius outside the peak area, the charging power of each charging pile, and the current usage status, so as to determine at least one available charging pile in the non-peak area; wherein the available charging pile is within the specified radius outside the peak area;

[0108] For an available charging pile, according to the current position of the bicycle to be charged and the actual position of the available charging pile, determine an estimated driving path between the bicycle to be charged and the available charging pile, and determine at least one road section type in the estimated driving path and the road section length corresponding to each road section;

[0109] Obtain the current remaining battery power of the bicycle to be charged and the historical driving data corresponding to each road section, so as to determine the speed of the bicycle to be charged under the battery power and the corresponding road section type according to the historical driving data;

[0110] According to the type of road section in the estimated driving path between the bicycle to be charged and each available charging pile, the length of each road section and the corresponding speed of the bicycle to be charged, the estimated arrival time and path cost of the bicycle to be charged to each available charging pile are calculated;

[0111] The actual distance, estimated arrival time and path cost between each available charging pile and the bicycle to be charged are sent to the user of the bicycle to be charged, and feedback information from the user is received;

[0112] The target charging address preferred by the user is determined through the feedback information, so that the bicycle to be charged can be charged through the target available charging pile at the target charging address.

[0113] In one embodiment, in a smart city charging management system, in order to optimize the user's charging experience, the system provides charging pile selection recommendations in non-peak areas for bicycles to be charged based on the charging demand and peak area distribution in the city. First, by analyzing the charging demand data in the city, peak charging areas such as business centers and office areas are identified. Then, based on the actual location of the peak area, the system determines the distribution of charging piles within a specified radius (for example, 5 kilometers) outside the peak area. The system collects the charging power (such as fast charging, slow charging) and the current usage status (such as idle, in use, faulty, etc.) of these charging piles, and filters out the available charging piles within the specified radius of the non-peak area.

[0114] For a selected available charging station, the system uses a map navigation algorithm to determine the estimated driving path between the current location of the bicycle to be charged and the actual location of the charging station. The system further analyzes the estimated driving path to determine at least one type of road section (such as urban roads, highways, alleys, etc.) and the length of each road section.

[0115] The system obtains the remaining battery power of the bicycle to be charged. At the same time, the system retrieves the corresponding historical driving data for each road section type, such as the average speed and acceleration performance at different power levels. Based on the historical driving data, the system determines the speed of the bicycle to be charged at the current battery power and the corresponding road section type.

[0116] The system calculates the estimated arrival time of the bicycle to be charged at each available charging station based on the type of road section in the expected driving path between the bicycle to be charged and each available charging station, the length of each road section and the corresponding vehicle speed. At the same time, the system also considers the path cost, such as driving distance, time cost, possible traffic congestion and other factors, and assigns a comprehensive cost score to each path.

[0117] The system sends the actual distance, estimated arrival time, and path cost between each available charging pile and the bicycle to be charged to the corresponding user through a mobile application or an onboard device. The user selects the target charging address based on this information, combined with his or her needs and preferences. After that, the system receives the user's feedback information, determines the target charging address selected by the user, and then finds the corresponding target available charging pile through the target charging address. After the bicycle to be charged navigates to the target available charging pile, the system performs charging connection and charging process management to ensure safe and efficient charging of the bicycle.

[0118] For example, on a weekday afternoon, user Xiao Li needs to charge his electric bicycle, but the charging piles in the city center are in peak hours and are in short supply. Based on Xiao Li's current location, the system analyzes the distribution of charging piles in the nearby area and finds several idle slow charging piles. It should be noted that in order to avoid users going to distant places for charging, the embodiment of the present application searches for idle available charging piles within a radius of 2 kilometers from the center. The system further calculates the estimated driving time and path cost for Xiao Li to reach each charging pile, and considers the impact of different road sections (such as urban roads and alleys) on vehicle speed. Finally, the system sends this information to Xiao Li, and Xiao Li chooses a charging pile that is a little farther away but has a lower path cost and a suitable estimated arrival time for charging. In this way, the system effectively alleviates the charging pressure in peak areas and improves the user's charging experience and efficiency.

[0119] In one embodiment of the present application, the total power supply corresponding to the reversely powered bicycle is reversely supplied to the distribution network to achieve charging optimization, specifically including:

[0120] Determine the electricity demand during peak hours and peak areas;

[0121] Obtain the current load of the distribution network, and when the current load exceeds the preset maximum load, determine the corresponding load compensation coefficient according to the power demand;

[0122] According to the battery power of each reverse-powered bicycle in the total power supply, and in combination with the load compensation coefficient, a reverse power supply plan corresponding to the reverse-powered bicycle is formulated; wherein the reverse power supply plan includes a reverse power supply time period, a reverse power supply power, and a reverse power supply duration;

[0123] According to the reverse power supply time period, reverse power supply power and reverse power supply duration, the battery power corresponding to each reverse power supply bicycle is reversely supplied to the distribution network.

[0124] In one embodiment, in order to effectively cope with the power demand during the peak period, the system utilizes the battery energy storage characteristics of the reverse-powered bicycle and develops a reverse power supply plan. First, by monitoring the real-time load data of the distribution network and combining the historical power consumption pattern analysis, it is determined that 5 to 7 pm on a certain weekday is the peak power consumption period. Further, through the geographic information system (GIS) and smart meter data, the peak area range is identified, such as commercial areas and residential areas, and the total power demand in the area is calculated. It is expected that the demand will increase by 30% during the peak period.

[0125] The system obtains the current load of the distribution network in real time and finds that the current load is close to 90% of the preset maximum load and is expected to exceed the maximum load during peak hours. Based on the increase in electricity demand in the peak area, the system calculates the required load compensation and determines the load compensation factor accordingly, assuming it is 1.2, indicating that an additional 20% of electricity is needed to compensate for the load gap during peak hours.

[0126] The system queries the battery power of each reverse-powered bicycle in the total power supply. These bicycles are parked at smart parking stations, have sufficient battery power and are connected to the distribution network. Combined with the load compensation factor, the system formulates a reverse power supply plan. In the plan, bicycles with sufficient battery power are selected, and the reverse power supply time period is set from 5 pm to 7 pm. The reverse power supply power is dynamically adjusted according to the output capacity of the bicycle battery and the demand of the distribution network, and the reverse power supply duration matches the peak period.

[0127] According to the reverse power supply plan, the system uses the intelligent control unit to reversely supply the battery power corresponding to each reverse-powered bicycle to the distribution network. During the reverse power supply period, the system monitors the reverse power supply power and duration in real time to ensure that the power supply is stable and does not exceed the safe output range of the bicycle battery. At the same time, the system also considers the impact of reverse power supply on the life of the bicycle battery. Through the optimization algorithm, while ensuring the demand of the distribution network, the loss of battery life is minimized.

[0128] For example, in a commercial area of ​​a certain city, the system identified that the peak period of electricity consumption was from 5 to 7 p.m., and the load was expected to exceed the maximum load. The system queried the reverse-powered bicycles in the nearby smart parking stations and found that the bicycles with sufficient power could participate in the reverse power supply. Therefore, the system formulated a reverse power supply plan, selected some bicycles, and set the reverse power supply time period, power, and duration. During peak hours, these bicycles reversely supplied power to the distribution network as planned, effectively alleviating the load pressure on the distribution network and ensuring the stability of the power supply. In this way, the system not only utilized the idle bicycle battery resources, but also improved the flexibility and reliability of the distribution network.

[0129] In one embodiment of the present application, after the total power supply corresponding to the reversibly powered bicycle is reversely supplied to the power distribution network, the method further includes:

[0130] During the reverse power supply process, the battery status of the reverse power supply bicycle is monitored in real time, so as to stop the reverse power supply of the corresponding reverse power supply bicycle when the battery power is less than the preset travel power threshold;

[0131] receiving the user's demand update information in real time, so as to stop the reverse power supply of the corresponding reverse power supply bicycle when the demand update information indicates that there is a need to use the bicycle within a specified time period;

[0132] Determine whether the remaining power of the reverse power supply bicycle meets the demand for use. If not, charge the reverse power supply bicycle with the demand for use after the peak of power consumption ends.

[0133] During the reverse power supply process, the current load of the distribution network is monitored in real time to dynamically adjust the reverse power supply plan according to the current load of the distribution network and the reverse power supply results of the reverse-powered bicycle.

[0134] In one embodiment, in order to meet the power demand of the distribution network during peak hours and ensure that the user's travel needs are not affected, the system implements a set of refined reverse power supply management strategies. During the reverse power supply process, the system monitors the battery power of each bicycle in real time through the battery management system (BMS) installed on the reverse-powered bicycle. When the battery power of a bicycle drops below the preset travel power threshold (for example, the minimum power to ensure that the user can complete a short trip), the system automatically stops the reverse power supply of the bicycle to avoid excessive discharge of the battery and affect the user's subsequent use.

[0135] The system receives real-time updates on user needs through the user's mobile phone APP or the terminal at the smart parking station. If the user submits a demand for a bicycle within a specified time period (such as during the execution of the reverse power supply plan), the system immediately identifies and stops the reverse power supply of the corresponding reverse power supply bicycle to ensure that the bicycle can be used for travel by the user.

[0136] For bicycles that stop reverse power supply due to user demand, the system determines whether the remaining power can meet the user's demand. If the remaining power is insufficient, the system will automatically arrange for these bicycles to go to the nearest charging station for charging after the peak power consumption ends, ensuring that they can quickly restore full power to meet the user's subsequent travel needs.

[0137] During the reverse power supply process, the system also monitors the current load of the distribution network in real time. According to the real-time load changes of the distribution network and the reverse power supply results of the reverse-powered bicycles (such as actual power supply power, duration, etc.), the system dynamically adjusts the reverse power supply plan. For example, if the load of the distribution network suddenly drops, the system may reduce the number of reverse-powered bicycles or reduce the reverse power supply power to avoid over-powering; conversely, if the load continues to rise, the system may increase the number of reverse-powered bicycles or increase the reverse power supply power to better meet the needs of the distribution network.

[0138] In one embodiment, in a commercial area of ​​a city, the system is implementing a reverse power supply plan, using the batteries of shared bicycles to provide power support to the distribution network. Suddenly, the system receives an alert that the battery power of a bicycle is about to drop to the travel power threshold, so it immediately stops the reverse power supply of the bicycle. At the same time, the system also receives a reservation information from a user who needs to use a bicycle in the next hour, and the system quickly stops the reverse power supply of the bicycle and ensures that it is available.

[0139] In addition, the system also monitors the load of the distribution network in real time and finds that the load has decreased. Therefore, the system dynamically adjusts the reverse power supply plan and reduces the number of bicycles that are reversely powered to avoid waste of resources caused by excessive power supply. For bicycles that stop reverse power supply due to user demand and have insufficient power, the system automatically arranges them to go to the charging station for charging after the peak power consumption ends, ensuring that they can quickly restore full power and provide convenience for users' subsequent travel.

[0140] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a charging optimization device for a charging bicycle, the structure of which is as follows: Figure 2 shown.

[0141] Figure 2 This is a schematic diagram of the internal structure of a charging optimization device for a charging bicycle provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0142] at least one processor;

[0143] and, a memory communicatively coupled to the at least one processor;

[0144] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable the at least one processor to:

[0145] Obtain historical power supply information of the distribution network and information on charging bicycles within the area to be optimized, and build a power demand model to train the power demand model based on the historical power supply information and charging bicycle information;

[0146] Determine a charging influence factor according to the vehicle usage and corresponding user habits in the charging bicycle information, so as to combine the charging influence factor into the trained power demand model; wherein the user habits include driving habits and charging habits;

[0147] Input the real-time charging and supply data into the power demand model, and combine it with the charging influencing factors to predict the peak power consumption in the future time period; the peak power consumption includes the peak time and the peak area range;

[0148] According to the current user demand and the current charging bicycle information, determine the bicycles to be charged, the bicycles that can be reversely powered, and the total power supply corresponding to the bicycles that can be reversely powered during the peak power consumption period;

[0149] Based on the peak area range, the charging address is reselected for the bicycles to be charged, and the total power that can be supplied by the reverse-powered bicycles is reversely supplied to the distribution network to achieve charging optimization.

[0150] The present application also provides a non-volatile computer storage medium storing computer executable instructions. When the computer executable instructions are executed, they can:

[0151] Obtain historical power supply information of the distribution network and information on charging bicycles within the area to be optimized, and build a power demand model to train the power demand model based on the historical power supply information and charging bicycle information;

[0152] Determine a charging influence factor according to the vehicle usage and corresponding user habits in the charging bicycle information, so as to combine the charging influence factor into the trained power demand model; wherein the user habits include driving habits and charging habits;

[0153] Input the real-time charging and supply data into the power demand model, and combine it with the charging influencing factors to predict the peak power consumption in the future time period; the peak power consumption includes the peak time and the peak area range;

[0154] According to the current user demand and the current charging bicycle information, determine the bicycles to be charged, the bicycles that can be reversely powered, and the total power supply corresponding to the bicycles that can be reversely powered during the peak power consumption period;

[0155] Based on the peak area range, the charging address is reselected for the bicycles to be charged, and the total power that can be supplied by the reverse-powered bicycles is reversely supplied to the distribution network to achieve charging optimization.

[0156] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0157] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, 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.

[0158] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0159] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0160] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0164] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0165] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0166] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0167] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A charging optimization method for a charging bicycle, characterized in that: The method comprises: Acquire historical power supply information of the distribution network and information of charging bicycles within the area to be optimized, and construct a power demand model to train the power demand model based on the historical power supply information and the charging bicycle information; Determine a charging influence factor according to the vehicle usage and corresponding user habits in the charging bicycle information, so as to combine the charging influence factor into the trained power demand model; wherein the user habits include driving habits and charging habits; Input the real-time charging and supply data into the power demand model, and combine the charging influencing factors to predict the peak power consumption in the future time period; wherein the peak power consumption includes the peak time and the peak area range; According to the current user demand and the current charging bicycle information, determine the bicycles to be charged that have charging demand during the peak power consumption period, the bicycles that can be powered reversely, and the total power supply that can be supplied by the bicycles that can be powered reversely; Based on the peak area range, a charging address is reselected for the bicycle to be charged, and the total power supply corresponding to the reversely powered bicycle is reversely supplied to the distribution network to achieve charging optimization.

2. The charging optimization method for a charging bicycle according to claim 1, characterized in that: According to the vehicle usage and corresponding user habits in the charging bicycle information, the charging influencing factors are determined, including: Based on the preset sensors on the charging bicycle, the real-time parameters of the bicycle are obtained, and the charging behavior data of the charging pile and the online usage data are obtained; wherein the real-time parameters of the bicycle include the travel distance, speed, acceleration, and battery power; the charging behavior data include the start time, end time, charging amount, and charging power of each charging; and the online usage data includes the user's charging preference, charging frequency, and travel habits; Analyze the user's charging frequency and charging amount in different time periods, as well as the user's selection ratio of fast charging and slow charging to determine the user's charging speed preference, and analyze the user's charging behavior in different locations to determine the user's charging scene preference, so as to obtain the user's charging habits; wherein the charging scenes include home, company, and public places; The average daily mileage of the user is counted, and the distribution of the user's driving speed and the charging behavior at different power levels are analyzed to determine the user's endurance requirements, driving type and charging behavior preference, so as to obtain the user's driving habits; the driving type includes aggressive type and stable type, and the charging behavior preference is used to indicate the power level of the bicycle when the user chooses to charge and stop charging; Clustering users according to their charging habits and driving habits to identify different types of user groups and the charging characteristics corresponding to the user groups; The user type and the corresponding charging characteristics are input into the trained regression model to predict the user's corresponding charging demand and charging behavior, and determine the weight coefficient of the corresponding charging influencing factor; wherein the charging influencing factor includes power demand factor, charging speed preference factor, time flexibility factor, charging scenario preference factor and battery health management factor.

3. The charging optimization method for a charging bicycle according to claim 1, characterized in that: Based on the peak area range, reselecting a charging address for the bicycle to be charged specifically includes: According to the actual location of the peak area, determine the distribution of charging piles within a specified radius outside the peak area, the charging power of each charging pile, and the current usage status, so as to determine at least one available charging pile in the non-peak area; wherein the available charging pile is within the specified radius outside the peak area; For an available charging pile, according to the current position of the bicycle to be charged and the actual position of the available charging pile, determine an estimated driving path between the bicycle to be charged and the available charging pile, and determine at least one road section type in the estimated driving path and the road section length corresponding to each road section; Obtaining the current remaining battery power of the bicycle to be charged and the historical driving data corresponding to each road section, so as to determine the speed of the bicycle to be charged under the battery power and the corresponding road section type according to the historical driving data; Calculate the estimated arrival time and path cost of the bicycle to be charged at each available charging pile according to the type of road section in the estimated driving path between the bicycle to be charged and each available charging pile, the length of each road section, and the corresponding speed of the bicycle to be charged; Sending the actual distance, estimated arrival time and path cost between each available charging pile and the bicycle to be charged to the user corresponding to the bicycle to be charged, and receiving feedback information from the user; The target charging address preferred by the user is determined through the feedback information, so as to charge the bicycle to be charged through the target available charging pile at the target charging address.

4. The charging optimization method for a charging bicycle according to claim 1, characterized in that: Reversely supplying the total power supply corresponding to the reversely powered bicycle to the power distribution network to achieve charging optimization, specifically including: Determine the power demand within the peak time and the peak area during the peak power consumption; Obtaining the current load condition of the distribution network, and determining a corresponding load compensation coefficient according to the power demand when the current load exceeds a preset maximum load; According to the battery power of each reverse-powered bicycle in the total power supply, and in combination with the load compensation coefficient, a reverse power supply plan corresponding to the reverse-powered bicycle is formulated; wherein the reverse power supply plan includes a reverse power supply time period, a reverse power supply power, and a reverse power supply duration; According to the reverse power supply time period, the reverse power supply power and the reverse power supply duration, the battery power corresponding to each of the reverse power supply bicycles is respectively used to reversely supply power to the power distribution network.

5. The charging optimization method for a charging bicycle according to claim 1, characterized in that: After the total power supply corresponding to the reversely powered bicycle is reversely supplied to the power distribution network, the method further includes: During the reverse power supply process, the battery status of the reverse power supply bicycle is monitored in real time, so as to stop the reverse power supply of the corresponding reverse power supply bicycle when the battery power is less than the preset travel power threshold; receiving the user's demand update information in real time, so as to stop the reverse power supply of the corresponding reverse power supply bicycle when the demand update information indicates that there is a demand for the bicycle within a specified time period; Determine whether the remaining power of the reverse power supply bicycle meets the vehicle use demand. If not, charge the reverse power supply bicycle with vehicle use demand after the peak power consumption ends. During the reverse power supply process, the current load of the distribution network is monitored in real time, so as to dynamically adjust the reverse power supply plan according to the current load of the distribution network and the reverse power supply result of the reverse power supply bicycle.

6. The charging optimization method for a charging bicycle according to claim 1, characterized in that: Acquiring historical power supply information of the distribution network and information of charging bicycles within the area to be optimized, and constructing a power demand model to train the power demand model based on the historical power supply information and the charging bicycle information, specifically including: Determine the scope of the area to be optimized with charging optimization requirements, obtain information on charging bicycles within the area to be optimized, and obtain historical power supply information of the distribution network; The historical power supply information includes at least power supply capacity, grid load, and transmission loss; the charging bicycle information includes bicycle registration information and actual bicycle charging data; the bicycle registration information includes vehicle specifications, distribution location, and battery capacity; the actual bicycle charging data includes charging time, charging power, charging amount, and charging habits; Based on battery capacity, charging power, and charging efficiency, analyze the charging needs of charging bicycles in different time periods, and based on power supply capacity, grid load, and transmission loss, determine the load characteristics of the distribution network in different seasons and time periods; A power demand model is constructed to combine the charging demand of the charging bicycle and the load characteristics of the distribution network, and the power demand model is trained through the historical power supply information and the charging bicycle information until the power demand output by the power demand model matches the pre-marked power demand, thereby completing the training of the power demand model.

7. The charging optimization method for a charging bicycle according to claim 1, characterized in that: According to the current user demand and the current charging bicycle information, the bicycles to be charged, the bicycles capable of reverse power supply and the total power supply corresponding to the bicycles capable of reverse power supply that have charging demand during the peak power consumption are determined, specifically including: According to the user's driving and charging habits, the corresponding user profile is determined, and combined with the current timestamp and battery power, if the user profile shows that the user has a tendency to drive within a specified time period and the battery power is lower than the preset travel power threshold, the corresponding charging bicycle is determined to be a bicycle to be charged during the peak power consumption period; Based on the user portrait, determine a candidate reverse power supply bicycle with reverse charging potential during the peak power consumption period, and send a reverse power supply request to the candidate reverse power supply bicycle; wherein the reverse charging potential is used to indicate that there is no driving tendency within a specified time period, and the battery power is greater than a preset driving power threshold; Receive feedback information from the selected reverse power supply bicycle in response to the reverse power supply request to determine the reverse power supply bicycles that allow reverse power supply according to the feedback information, and calculate the total power supply during the peak power consumption period according to the battery power corresponding to each reverse power supply bicycle.

8. The charging optimization method for a charging bicycle according to claim 1, characterized in that: The real-time charging and supply data is input into the power demand model, and combined with the charging influencing factors, the peak power consumption in the future time period is predicted, specifically including: Acquire real-time power supply data of the power distribution network and real-time charging data of the charging bicycle, so as to input the real-time power supply data, the real-time charging data and the charging influencing factor into the power demand model; According to the real-time charging power supply data and the charging influencing factors, and in combination with historical power consumption data, weather information, holiday schedules and the current timestamp, the specific peak time period, expected power consumption and peak fluctuation area range of the peak power consumption in the future time period are predicted.

9. A charging optimization device for a charging bicycle, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the charging optimization method for a charging bicycle as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed, a charging optimization method for a charging bicycle as described in any one of claims 1 to 8 is implemented.

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