A low-battery monitoring and prediction method and system for shared vehicles

Abstract

The present invention provides a low-battery monitoring and prediction method and system for a shared vehicle, which relates to the technical field of shared vehicles. The method includes: obtaining vehicle model information of a target shared vehicle; obtaining a power consumption rate; connecting a data acquisition device to monitor the power of the target shared vehicle in real time; obtaining the charging station distribution location information of the target shared vehicle; using the power consumption rate and the charging station distribution location information as input quantities, and using the real-time power data of the target shared vehicle as input variables to build an objective function; obtaining a response result according to the objective function; and obtaining warning information when the response result is a response failure. The technical problem of not monitoring the remaining power of the vehicle in real time and providing a power warning for the vehicle in the prior art is solved. By monitoring the power of the shared vehicle in real time and predicting the remaining vehicle power in combination with the vehicle's operating characteristics and usage habits, a power warning is provided to ensure the vehicle's operating power.

Description

Technical Field

[0001] The present invention relates to the technical field of shared vehicles, and in particular to a low-battery monitoring and prediction method and system for a shared vehicle. Background Art

[0002] With the development of society, vehicles have become one of the most important means of transportation for people. As the number of vehicles increases, the shared vehicle industry has also quietly emerged. Shared vehicles are mainly electric vehicles. At present, the performance of power batteries and the reasonable management of battery energy are crucial to the power characteristics of vehicles. They directly determine the continuous driving mileage, acceleration performance and maximum climbing grade of electric vehicles. To extend the driving mileage of electric vehicles, it is necessary to increase the capacity of the battery or increase the battery efficiency. A good electric vehicle power monitoring system is the prerequisite for increasing the battery efficiency. Therefore, designing a special low-battery monitoring prediction method is of great significance to the development of shared vehicles. Summary of the invention

[0003] The present invention provides a low-battery monitoring and prediction method and system for a shared vehicle, which are used to solve the technical problem in the prior art that the remaining battery power of a vehicle cannot be monitored in real time and the battery power warning of the vehicle cannot be provided.

[0004] In view of the above problems, the present invention provides a low-battery monitoring and prediction method and system for a shared vehicle.

[0005] In a first aspect, the present invention provides a low-battery monitoring and prediction method for a shared vehicle, the method comprising: obtaining vehicle model information of a target shared vehicle; obtaining a power consumption rate based on the vehicle model information of the target shared vehicle, wherein the power consumption rate is the ratio of power consumption per driving distance to driving time of the target shared vehicle; connecting the data acquisition device to monitor the power of the target shared vehicle in real time, and uploading the real-time power data to the low-battery monitoring system; obtaining the charging station distribution location information of the target shared vehicle; constructing an objective function with the power consumption rate and the charging station distribution location information as input variables and the real-time power data of the target shared vehicle as input variables; obtaining a response result based on the objective function, wherein the response result includes a response failure and a response success; when the response result is a response failure, obtaining early warning information.

[0006] In a second aspect, the present invention provides a low-battery monitoring and prediction system for a shared vehicle, the system comprising: a vehicle model acquisition module, the vehicle model acquisition module being used to acquire vehicle model information of a target shared vehicle; a power consumption rate acquisition module, the power consumption rate acquisition module being used to obtain a power consumption rate based on the vehicle model information of the target shared vehicle, wherein the power consumption rate is a ratio of power consumption over a driving distance of the target shared vehicle to the driving duration; a power real-time monitoring module, the power real-time monitoring module being used to connect to the data acquisition device to monitor the power of the target shared vehicle in real time, and upload the real-time power data to the low-battery monitoring system system; a charging station location acquisition module, the charging station location acquisition module is used to obtain the charging station distribution location information of the target shared vehicle; an objective function construction module, the objective function construction module is used to build an objective function with the power consumption rate and the charging station distribution location information as input variables and the real-time power data of the target shared vehicle as input variables; a response result acquisition module, the response result acquisition module is used to obtain a response result according to the objective function, wherein the response result includes a response failure and a response success; a warning information acquisition module, the warning information acquisition module is used to obtain warning information when the response result is a response failure.

[0007] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0008] Due to the adoption of a low-power monitoring and prediction method for a shared vehicle, the vehicle model information of the target shared vehicle is obtained; according to the vehicle model information of the target shared vehicle, the power consumption rate is obtained, wherein the power consumption rate is the ratio of the power consumption of the driving distance of the target shared vehicle to the driving time; the data acquisition device is connected to monitor the power of the target shared vehicle in real time, and the real-time power data is uploaded to the low-power monitoring system; the charging station distribution location information of the target shared vehicle is obtained; the power consumption rate and the charging station distribution location information are used as input quantities, and the real-time power data of the target shared vehicle is used as input variables to build an objective function; according to the objective function, a response result is obtained, wherein the response result includes a response failure and a response success; when the response result is a response failure, an early warning information is obtained. The present invention monitors the power of the shared vehicle in real time, and predicts the remaining vehicle power in combination with the vehicle operation characteristics and usage habits. When the preset low power is reached, a power warning is issued, and pre-charging management is performed in combination with the distribution location of the vehicle charging station, which solves the technical problem that the vehicle remaining power cannot be monitored in real time and the vehicle power warning is issued in the prior art, thereby ensuring the vehicle operation power. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1A schematic flow chart of a low-battery monitoring and prediction method for a shared vehicle according to the present invention;

[0010] Figure 2 A schematic diagram of a charging station prediction process in a low-battery monitoring prediction method for a shared vehicle of the present invention;

[0011] Figure 3 A schematic diagram of a prediction charging station optimization process in a low-battery monitoring prediction method for a shared vehicle of the present invention;

[0012] Figure 4 It is a schematic diagram of the structure of a low-battery monitoring and prediction system for a shared vehicle according to the present invention.

[0013] Explanation of the accompanying drawings: vehicle model acquisition module 1, power consumption rate acquisition module 2, power real-time monitoring module 3, charging station location acquisition module 4, objective function construction module 5, response result acquisition module 6, warning information acquisition module 7. DETAILED DESCRIPTION

[0014] The present invention provides a low-power monitoring and prediction method for a shared vehicle, firstly obtaining the vehicle model information of the target shared vehicle; obtaining the power consumption rate according to the vehicle model information of the target shared vehicle, wherein the power consumption rate is the ratio of the power consumption of the driving distance of the target shared vehicle to the driving time; connecting the data acquisition device to monitor the power of the target shared vehicle in real time, and uploading the real-time power data to the low-power monitoring system; obtaining the charging station distribution location information of the target shared vehicle; taking the power consumption rate and the charging station distribution location information as input quantities, and taking the real-time power data of the target shared vehicle as input variables, building an objective function; according to the objective function, obtaining a response result, wherein the response result includes a response failure and a response success; when the response result is a response failure, obtaining an early warning information. The method solves the technical problem that the remaining power of the vehicle cannot be monitored in real time and the power warning of the vehicle cannot be performed in the prior art. By real-time monitoring of the power of the shared vehicle, and combining the vehicle operation characteristics and usage habits to predict the remaining vehicle power, when the preset low power is reached, a power warning is performed, and pre-charging management is performed in combination with the distribution location of the vehicle charging station, so as to ensure the vehicle operation power.

[0015] Embodiment 1

[0016] like Figure 1 As shown, this embodiment provides a low-battery monitoring and prediction method for a shared vehicle, the method is applied to a low-battery monitoring system for a shared vehicle, the system is communicatively connected to a data acquisition device, and the method includes:

[0017] Step S100: Obtaining vehicle model information of the target shared vehicle;

[0018] Specifically, the low-power monitoring system is used to monitor the power data of the shared vehicle in real time, and the data acquisition device is used to collect the real-time power data of the shared vehicle. The two are connected by communication to achieve mutual transmission of information. The target shared vehicle is the shared vehicle for which we want to monitor and predict power. The vehicle model information includes the vehicle's motor model, battery model, battery capacity, voltage, current, etc.

[0019] Step S200: obtaining a power consumption rate according to the vehicle model information of the target shared vehicle, wherein the power consumption rate is a ratio of the power consumption of the target shared vehicle for the driving distance to the driving time;

[0020] Specifically, the target shared vehicle model information is used as a benchmark to obtain the target shared vehicle's power consumption rate. Different vehicle models have different power consumption rates. The power consumption rate refers to the amount of electricity consumed by the vehicle per unit time, that is, the ratio of the target shared vehicle's driving distance power consumption to the driving time. For example, an electric car travels 270 kilometers in 3 hours. During this driving distance, it consumes a total of 45 kWh of electricity, so the car's power consumption is 15 kWh per hour. An electric bicycle travels 10 kilometers in half an hour and consumes about 0.25 kWh of electricity.

[0021] Step S300: connecting the data acquisition device to monitor the power of the target shared vehicle in real time, and uploading the real-time power data to the low power monitoring system;

[0022] Specifically, the data acquisition device is used to collect the real-time power of the shared vehicle. The data acquisition device is connected to monitor the power of the target shared vehicle in real time. The data acquisition device is communicated with the low-power monitoring system to realize information transmission. The data acquisition device can upload the collected power data to the low-power monitoring system. The low-power monitoring system monitors the power of the target shared vehicle in real time based on the real-time power data of the target shared vehicle, laying the foundation for subsequent remaining power prediction and pre-charging management.

[0023] Step S400: Obtaining the charging station distribution location information of the target shared vehicle;

[0024] Specifically, after obtaining the vehicle model information of the shared vehicle, the charging station that can be used to charge the target shared vehicle can be found according to the vehicle model of the shared vehicle, and the distribution location information of each charging station can be obtained, such as how many charging stations there are in a certain urban area, in which areas the charging stations are distributed, how many charging stations are distributed in each area, and the detailed location of each charging station.

[0025] Step S500: Taking the power consumption rate and the charging station distribution location information as input quantities and the real-time power data of the target shared vehicle as input variables, an objective function is constructed;

[0026] Specifically, the power consumption rate and the charging station distribution location information will not change, and the real-time power data of the target shared vehicle will change as the vehicle travels. The power consumption rate and the charging station distribution location information are used as input quantities, and the real-time power data of the target shared vehicle is used as the input variable to build an objective function. Through the objective function, according to the real-time power and power consumption rate of the vehicle, it is possible to determine how much remaining power the vehicle has and how long it can support the vehicle to travel. Then, according to the charging station distribution location information, it is possible to determine which charging station is suitable for the vehicle to charge. For example, the power consumption rate of a shared vehicle is 15 kWh per hour, and then the charging station distribution location of the vehicle in the city is obtained. At the current moment, the real-time remaining power of the vehicle is 20 kWh. The power consumption rate of 15 kWh per hour, the real-time power of 20 kWh, and the charging station distribution location are input into the objective function, and it can be concluded that the vehicle can travel for a maximum of 1 hour and 20 minutes. Then, based on this driving time, we can find a charging station with a suitable distance for charging.

[0027] Step S600: Obtain a response result according to the objective function, wherein the response result includes a response failure and a response success;

[0028] Specifically, through the objective function, based on the vehicle's real-time power and power consumption rate, it is possible to determine how much power the vehicle has left and how long it can support the vehicle's driving. Then, based on the distribution location information of the charging stations, it is possible to determine which charging station is suitable for the vehicle to charge. This is the response result to be obtained. The response results include response failure and response success. A successful response means that the remaining power of the target shared vehicle can support the vehicle to reach the charging station for charging. A suitable charging station can be selected based on the objective function. The response result is how long it will take for the target vehicle to go to the charging station for charging. A response failure means that based on the target vehicle's current remaining power and power consumption rate, it is impossible to reach the charging station for charging.

[0029] Step S700: When the response result is a response failure, obtain warning information.

[0030] Specifically, when the response fails, it means that the remaining power of the target shared vehicle cannot support it to reach the charging station for charging. At this time, an early warning needs to be issued to the target shared vehicle, and the early warning information is sent to the control terminal of the target shared vehicle. The driving user of the target shared vehicle can make corresponding responses according to the early warning information, such as calling the management company of the shared vehicle for rescue to tow the car to the charging station, or making an appointment for a mobile charging vehicle on the APP.

[0031] Furthermore, if Figure 2 As shown, step S600 of the embodiment of the present application also includes:

[0032] Step S610: When the response result is a successful response, the real-time positioning information of the target shared vehicle is obtained;

[0033] Step S620: inputting the real-time positioning information as a new variable into the objective function, and obtaining a secondary response result according to the objective function, wherein the secondary response result is a predicted charging station based on the real-time positioning information;

[0034] Step S630: recommending the predicted charging station to the control terminal of the target shared vehicle.

[0035] Specifically, when the response result is a successful response, the real-time positioning information of the target shared vehicle is obtained. The real-time positioning information is the location of the target vehicle at each moment, and the real-time positioning information is input into the objective function as a new variable. At this time, the input quantities of the objective function include the power consumption rate of the target shared vehicle and the distribution location information of the charging stations. The input variables include the real-time power data and real-time positioning information of the target shared vehicle. According to the power consumption rate and real-time power data, it can be judged how long and how far the remaining power of the target shared vehicle can support it to travel. It can also be judged when the target shared vehicle needs to be charged, and then based on the real-time positioning information of the target vehicle, it can be known. The distance between the target shared vehicle and each charging station is used to determine which charging station is most suitable, and the predicted charging station is sent to the control terminal of the target shared vehicle. For example, if the remaining power of the target shared vehicle can support the vehicle for up to 30 minutes, then according to the real-time location of the target shared vehicle, select a charging station closest to the target vehicle for charging. If the remaining power of the target shared vehicle can support the vehicle for up to 1 hour, then we can recommend the charging station that the target vehicle can reach within 1 hour to the control terminal of the target shared vehicle. The real-time driving user of the target shared vehicle can choose a suitable charging station for charging according to his or her own driving route and time.

[0036] Furthermore, step S800 of the embodiment of the present application further includes:

[0037] Step S810: determining whether the target shared vehicle is in a navigation state, and if the target shared vehicle is in a navigation state, obtaining real-time navigation information of the target shared vehicle;

[0038] Step S820: Determine a target route using the real-time navigation information and the real-time positioning information;

[0039] Step S830: Optimizing the predicted charging stations according to the target route and the charging station distribution location information.

[0040] Specifically, we can determine whether the target vehicle is in navigation state based on the navigation device on the car. If the target shared vehicle is in navigation state, we can obtain the real-time navigation information of the target shared vehicle. The real-time navigation information includes the driving purpose, driving route and time of the target shared vehicle. Combined with the real-time positioning information of the target shared vehicle, we can determine the driving route of the target shared vehicle. According to the target route and the distribution location information of the charging station, we can lock the charging stations near the entire driving route of the target shared vehicle. We can select the charging station that is more suitable for the target shared vehicle, thereby optimizing the predicted charging station.

[0041] Furthermore, step S630 of the embodiment of the present application further includes:

[0042] Step S631: recommending the predicted charging station to the control terminal of the target shared vehicle, and obtaining feedback information based on real-time driving users;

[0043] Step S632: If the feedback information from the real-time driving user is that the request failed, obtain a protection instruction;

[0044] Step S633: performing low power protection control on the power consumption module of the target shared vehicle according to the protection instruction.

[0045] Specifically, after the predicted charging station is sent to the control terminal of the target shared vehicle, the real-time driving user of the target vehicle will select the most suitable charging station for charging based on the actual situation and provide feedback. If the feedback information of the real-time driving user is that the request failed, it means that the target shared vehicle cannot reach the predicted charging station. At this time, a protection instruction will be obtained, and the power consumption module of the target shared vehicle will be controlled for low power protection according to the protection instruction. For example, undervoltage protection is to prevent insufficient battery power, and an anti-overdischarge protection circuit is set. When the battery voltage is lower than the value set by the controller, the undervoltage protection will be activated.

[0046] Furthermore, if Figure 3 As shown, step S830 of the embodiment of the present application also includes:

[0047] Step S831: Outputting the flow calculation result by performing flow identification on the charging station distribution location information;

[0048] Step S832: Determine the required driving time of the target route;

[0049] Step S833: predicting the vehicle occupancy of each charging station based on the flow calculation result and the required driving time, and obtaining a vehicle occupancy prediction result;

[0050] Step S834: Optimizing the predicted charging station according to the vehicle occupancy prediction result.

[0051] Specifically, the flow identification of the charging station distribution location information is to count the traffic flow of each charging station, determine the vehicle charging frequency of each charging station, mark it, and then output the flow calculation result. According to the target route, the required driving time of the target vehicle is determined. Based on the flow calculation result and the required driving time, the vehicle occupancy prediction is carried out for each charging station. It is predicted whether there is a location for the target shared vehicle to charge after the vehicle arrives at the charging station. Some charging stations may have a high charging frequency, and the target vehicle may not have a location to charge in the past. Based on this, the vehicle occupancy prediction result is obtained. When the driving time permits, a more suitable charging station is selected according to the vehicle occupancy prediction result, thereby optimizing the predicted charging station.

[0052] Furthermore, step S831 of the embodiment of the present application also includes:

[0053] Step S8311: Connect to the charging station management system to obtain the vehicle usage frequency of each charging station;

[0054] Step S8312: Generate a site flow curve by plotting the vehicle usage frequency;

[0055] Step S8313: Calculate the mean flow rate using the site flow curve to obtain the mean flow rate;

[0056] Step S8314: Output the flow calculation results corresponding to each charging station based on the mean flow.

[0057] Specifically, connect to the charging station management system, obtain the daily vehicle usage frequency of each charging station, select a period of time as the statistical period, which can be a month or a week, use time as the horizontal axis and vehicle usage frequency as the vertical axis, draw a curve chart of vehicle usage frequency, generate a site flow curve, and after generating the site flow curve, calculate the average value of the flow curve based on the daily site flow to obtain the mean flow. For example, obtain the daily vehicle usage frequency of a charging station in August, use date as the horizontal axis and vehicle usage frequency as the vertical axis, draw the site flow curve for August, and then calculate the daily average flow of the site in August based on the daily vehicle usage frequency in August. The calculation result is the mean flow we need.

[0058] Furthermore, step S900 in the embodiment of the present application also includes:

[0059] Step S910: performing flow periodicity characteristic analysis according to the site flow curve to obtain a marked cycle day, wherein the marked cycle day is a date at peak flow;

[0060] Step S920: obtaining the real-time date of the target shared vehicle;

[0061] Step S930: Compare the real-time date with the marked cycle day. If it is the marked cycle day, use the average flow rate of the marked cycle day as the flow rate calculation result of the corresponding charging station.

[0062] Specifically, the traffic flow curve of the site is used as a benchmark to analyze the periodic characteristics of the traffic flow. For example, with a week as a cycle, the traffic flow from Monday to Sunday is analyzed to determine which day has the highest traffic flow and obtain the marked cycle day. The marked cycle day is the date with peak traffic flow, that is, the vehicle usage frequency of the charging station is the highest on this day. The real-time date of the target shared vehicle is obtained, and the real-time date is compared with the marked cycle day to determine whether the real-time date is on the marked cycle day. If it is on the marked cycle day, the average traffic flow of the marked cycle day is calculated, and the average traffic flow of the marked cycle day is used as the traffic calculation result of the corresponding charging station. For example, the daily vehicle usage frequency of a charging station in September is obtained, and the site traffic curve of the charging station in September is drawn. With a week of seven days as a cycle, the traffic flow of a cycle is analyzed. It is found that the vehicle usage frequency of the charging station is the highest on Sundays of each week, so Sundays are marked and the average site traffic flow on Sundays is calculated separately. Then the real-time date of the target shared vehicle is obtained. If the real-time date of the target vehicle is also Sundays, then we use the average traffic flow on Sundays as the traffic calculation result of the charging station.

[0063] Embodiment 2

[0064] Based on the same inventive concept as the low battery monitoring prediction method of a shared vehicle in the aforementioned embodiment, Figure 4 As shown, the present application provides a low-battery monitoring and prediction system for a shared vehicle, the system is communicatively connected to a data acquisition device, and the system includes:

[0065] A vehicle model acquisition module, the vehicle model acquisition module is used to obtain vehicle model information of a target shared vehicle;

[0066] A power consumption rate acquisition module, the power consumption rate acquisition module is used to obtain the power consumption rate according to the vehicle model information of the target shared vehicle, wherein the power consumption rate is the ratio of the power consumption of the target shared vehicle to the driving time;

[0067] A real-time power monitoring module, which is used to connect to the data acquisition device to monitor the power of the target shared vehicle in real time and upload the real-time power data to the low-power monitoring system;

[0068] A charging station location acquisition module, which is used to obtain the charging station distribution location information of the target shared vehicle;

[0069] An objective function building module, the objective function building module is used to build an objective function with the power consumption rate and the charging station distribution location information as input variables and the real-time power data of the target shared vehicle as input variables;

[0070] A response result acquisition module, the response result acquisition module is used to acquire a response result according to the objective function, wherein the response result includes a response failure and a response success;

[0071] The warning information acquisition module is used to obtain warning information when the response result is a response failure.

[0072] Furthermore, the system further comprises:

[0073] A real-time positioning information acquisition module, wherein the real-time positioning information acquisition module is used to acquire the real-time positioning information of the target shared vehicle when the response result is a successful response;

[0074] A secondary response result acquisition module, the secondary response result acquisition module is used to input the real-time positioning information as a new variable into the objective function, and obtain a secondary response result according to the objective function, wherein the secondary response result is a predicted charging station based on the real-time positioning information;

[0075] A charging station recommendation module, wherein the charging station recommendation module is used to recommend the predicted charging station to the control terminal of the target shared vehicle.

[0076] Furthermore, the system further comprises:

[0077] A navigation state judgment module, the navigation state judgment module is used to judge whether the target shared vehicle is in a navigation state, and if the target shared vehicle is in a navigation state, obtain the real-time navigation information of the target shared vehicle;

[0078] A target route determination module, the target route determination module is used to determine a target route based on the real-time navigation information and the real-time positioning information;

[0079] A charging station prediction module is used to optimize the predicted charging station according to the target route and the charging station distribution location information.

[0080] Furthermore, the system further comprises:

[0081] A feedback information acquisition module, the feedback information acquisition module is used to recommend the predicted charging station to the control terminal of the target shared vehicle, and obtain feedback information according to the real-time driving user;

[0082] A protection instruction acquisition module, wherein the protection instruction acquisition module is used to acquire a protection instruction if the feedback information of the real-time driving user is that the request fails;

[0083] A low-battery protection control module is used to perform low-battery protection control on the power consumption module of the target shared vehicle according to the protection instruction.

[0084] Furthermore, the system further comprises:

[0085] A flow calculation module, the flow calculation module is used to output a flow calculation result by performing flow identification on the charging station distribution location information;

[0086] A driving time determination module, the driving time determination module is used to determine the required driving time of the target route;

[0087] A vehicle occupancy prediction module, the vehicle occupancy prediction module is used to predict the vehicle occupancy of each charging station based on the flow calculation result and the required driving time, and obtain a vehicle occupancy prediction result;

[0088] A predicted charging station optimization module is used to optimize the predicted charging station according to the vehicle occupancy prediction result.

[0089] Furthermore, the system further comprises:

[0090] A vehicle usage frequency acquisition module, which is used to connect to the charging station management system to obtain the vehicle usage frequency of each charging station;

[0091] A site flow curve generation module, the site flow curve generation module is used to generate a site flow curve by plotting a curve of the vehicle usage frequency;

[0092] A mean flow acquisition module, the mean flow acquisition module is used to calculate the mean flow based on the site flow curve to obtain the mean flow;

[0093] A flow calculation result acquisition module is used to output the flow calculation result corresponding to each charging station based on the average flow.

[0094] Furthermore, the system further comprises:

[0095] A flow periodicity characteristic analysis module, the flow periodicity characteristic analysis module is used to perform flow periodicity characteristic analysis according to the site flow curve to obtain an identification cycle day, wherein the identification cycle day is a date at peak flow;

[0096] A real-time date acquisition module, which is used to acquire the real-time date of the target shared vehicle;

[0097] A date comparison module is used to compare the real-time date with the identified cycle day. If it is the identified cycle day, the average flow rate of the identified cycle day is used as the flow calculation result of the corresponding charging station.

[0098] Through the above-mentioned detailed description of a low-battery monitoring and prediction method for a shared vehicle in this specification, those skilled in the art can clearly understand the low-battery monitoring and prediction method for a shared vehicle in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0099] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A low battery monitoring and prediction method for a shared vehicle, characterized in that: The method is applied to a low-battery monitoring system of a shared vehicle, the system is communicatively connected with a data acquisition device, and the method comprises: Obtain vehicle model information of the target shared vehicle; According to the vehicle model information of the target shared vehicle, a power consumption rate is obtained, wherein the power consumption rate is a ratio of the power consumption of the target shared vehicle to the driving time; Connecting the data acquisition device to monitor the power of the target shared vehicle in real time, and uploading the real-time power data to the low power monitoring system; Obtaining the charging station distribution location information of the target shared vehicle; Taking the power consumption rate and the charging station distribution location information as input variables and the real-time power data of the target shared vehicle as input variables, an objective function is constructed; According to the objective function, a response result is obtained, wherein the response result includes a response failure and a response success; When the response result is a response failure, obtaining warning information; When the response result is a successful response, obtaining the real-time positioning information of the target shared vehicle; Input the real-time positioning information as a new variable into the objective function, and obtain a secondary response result according to the objective function, wherein the secondary response result is a predicted charging station based on the real-time positioning information; Recommending the predicted charging station to the control terminal of the target shared vehicle, and obtaining feedback information based on real-time driving users; If the feedback information from the real-time driving user is that the request failed, obtaining a protection instruction; Performing low power protection control on the power consumption module of the target shared vehicle according to the protection instruction; The method further comprises: Determine whether the target shared vehicle is in a navigation state, and if the target shared vehicle is in a navigation state, obtain real-time navigation information of the target shared vehicle; Determining a target route using the real-time navigation information and the real-time positioning information; Optimizing the predicted charging stations according to the target route and the charging station distribution location information; The method further comprises: By performing flow identification on the charging station distribution location information, a flow calculation result is output; Determining a required driving time for the target route; Predicting vehicle occupancy at each charging station based on the flow calculation result and the required driving time, and obtaining a vehicle occupancy prediction result; The predicted charging station is optimized according to the vehicle occupancy prediction result.

2. The method according to claim 1, characterized in that The method further comprises: Connect to the charging station management system to obtain the vehicle usage frequency of each charging station; Generate a site traffic curve by plotting the vehicle usage frequency; Calculate the mean flow rate using the site flow curve to obtain the mean flow rate; The flow calculation results corresponding to each charging station are output based on the mean flow.

3. The method according to claim 1, characterized in that The method further comprises: According to the site traffic curve, the traffic periodicity characteristics are analyzed to obtain the identification cycle day, wherein the identification cycle day is the date at the peak traffic; Obtaining the real-time date of the target shared vehicle; The real-time date is compared with the marked cycle day. If it is the marked cycle day, the average flow rate of the marked cycle day is used as the flow rate calculation result of the corresponding charging station.

4. A low-battery monitoring and prediction system for a shared vehicle, characterized in that: The system is used to execute the method according to any one of claims 1 to 3, the system is communicatively connected with a data acquisition device, and the system comprises: A vehicle model acquisition module, the vehicle model acquisition module is used to obtain vehicle model information of a target shared vehicle; A power consumption rate acquisition module, the power consumption rate acquisition module is used to obtain the power consumption rate according to the vehicle model information of the target shared vehicle, wherein the power consumption rate is the ratio of the power consumption of the target shared vehicle to the driving time; A real-time power monitoring module, which is used to connect to the data acquisition device to monitor the power of the target shared vehicle in real time and upload the real-time power data to the low-power monitoring system; A charging station location acquisition module, which is used to obtain the charging station distribution location information of the target shared vehicle; An objective function building module, the objective function building module is used to build an objective function with the power consumption rate and the charging station distribution location information as input variables and the real-time power data of the target shared vehicle as input variables; A response result acquisition module, the response result acquisition module is used to acquire a response result according to the objective function, wherein the response result includes a response failure and a response success; A warning information acquisition module, wherein the warning information acquisition module is used to acquire warning information when the response result is a response failure; A real-time positioning information acquisition module, wherein the real-time positioning information acquisition module is used to acquire the real-time positioning information of the target shared vehicle when the response result is a successful response; A secondary response result acquisition module, the secondary response result acquisition module is used to input the real-time positioning information as a new variable into the objective function, and obtain a secondary response result according to the objective function, wherein the secondary response result is a predicted charging station based on the real-time positioning information; A charging station recommendation module, the charging station recommendation module is used to recommend the predicted charging station to the control terminal of the target shared vehicle; A feedback information acquisition module, the feedback information acquisition module is used to recommend the predicted charging station to the control terminal of the target shared vehicle, and obtain feedback information according to the real-time driving user; A protection instruction acquisition module, wherein the protection instruction acquisition module is used to acquire a protection instruction if the feedback information of the real-time driving user is that the request fails; A low-battery protection control module is used to perform low-battery protection control on the power consumption module of the target shared vehicle according to the protection instruction.

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