CN117371635B - Mobile battery replacement energy storage data analysis management system and method based on artificial intelligence - Google Patents

Mobile battery replacement energy storage data analysis management system and method based on artificial intelligence Download PDF

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CN117371635B
CN117371635B CN202311530021.XA CN202311530021A CN117371635B CN 117371635 B CN117371635 B CN 117371635B CN 202311530021 A CN202311530021 A CN 202311530021A CN 117371635 B CN117371635 B CN 117371635B
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李明
丁晶
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Zhejiang Kuaige New Energy Technology Co ltd
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Abstract

The invention relates to the technical field of mobile battery replacement supervision, in particular to a mobile battery replacement energy storage data analysis and management system and method based on artificial intelligence, comprising the steps of capturing a target driving record; judging and identifying characteristic driving records showing the using habit of a user on a target new energy electric car battery; capturing, monitoring and extracting a characteristic use allowance range presented on battery use when a user drives a target new energy electric car; carrying out probability prediction evaluation on the possibility that a user charges a target new energy electric car when going to a charging station in a new driving record; and according to the probability value of the new driving record, determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending the optimal driving route for the user, and determining whether to perform early warning prompt of mobile charging and battery replacement for the user in the driving process.

Description

Mobile battery replacement energy storage data analysis management system and method based on artificial intelligence
Technical Field
The invention relates to the technical field of mobile power conversion supervision, in particular to a mobile power conversion energy storage data analysis and management system and method based on artificial intelligence.
Background
The pure electric vehicle has great difference in structure with the fuel oil vehicle, the whole pure electric vehicle has the highest cost, and is also the most concerned, the service life of the battery pack directly determines the whole service life of the pure electric vehicle, the state of the battery pack directly determines the state of the pure electric vehicle, and even the value retention rate; the over-discharge principle of the battery of the pure electric vehicle is the same as that of the battery of the mobile phone, namely, the battery is stored for a long time under the condition of extremely low electric quantity, so that a large amount of lithium ions in the battery are permanently deactivated, the voltage of the whole battery is far lower than a driving voltage value, and the battery body is permanently disabled.
In the process of driving a new energy electric car, the user is influenced by objective battery allowance, namely the battery display allowance is too low to complete a driving journey, or the influence of subjective battery allowance, namely the psychological early warning of the battery allowance which does not reach an overdischarge state at present, is often caused to temporarily decide to go to a charging station to cause the phenomenon that the driving plan is influenced, so that the user trip efficiency is reduced, and meanwhile, the situation that the electric car battery is unfavorable to the health sometimes occurs.
Disclosure of Invention
The invention aims to provide a mobile battery-replacement energy storage data analysis management system and method based on artificial intelligence, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the mobile battery replacement energy storage data analysis and management method based on artificial intelligence comprises the following steps:
step S100: every time a user is detected to start a target new energy electric car, inquiring and acquiring a destination position A of the target of the user in a driving journey to be started, capturing a departure position B of the current target new energy electric car, every time the user is detected to drive the target new energy electric car to finish space movement from A to B, and generating a driving record of the user on the target new energy electric car;
step S200: among all the history driving records of the user, the history driving record which is captured in the corresponding driving journey of the user and contains the charge of the new energy electric vehicle to the charging station is set as the target driving record;
step S300: respectively combing driving information of each target driving record, and judging and identifying characteristic driving records showing the using habit of a user on the target new energy electric car battery in all the target driving records;
step S400: capturing, monitoring and extracting a characteristic use allowance range presented on battery use when a user drives a target new energy electric car from all characteristic driving records;
step S500: each time when the situation that a user starts a target new energy electric car and a new driving record is about to be generated is detected, probability prediction evaluation is carried out on the possibility that the user goes to a charging station to charge the target new energy electric car in the new driving record;
step S600: and according to the probability value of the new driving record, determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending the optimal driving route for the user, and determining whether to perform early warning prompt of mobile charging and battery replacement for the user in the driving process.
Further, step S300 includes:
step S301: acquiring the driving mileage number M of a new energy electric vehicle driven by a user in each target driving record, acquiring each performance state parameter of a new energy electric vehicle battery at the beginning of each target driving record, and acquiring the minimum battery quantity W required to be consumed by the new energy electric vehicle driven by the user for completing the driving mileage number M according to each performance state parameter; obtaining a maximum battery residual quantity F corresponding to a battery of the target new energy electric vehicle when the battery is in an overdischarge state near zero, and obtaining a battery residual quantity Q displayed by the target new energy electric vehicle when each target driving record starts;
step S302: when a certain target driving record meets Q-W is more than or equal to alpha F, wherein alpha represents an adjustment coefficient, and 2> alpha >1; acquiring a running route L1 actually completed by a user in a certain target driving record, acquiring a running duration t1 used by the user for completing the running route L for an optimal running route L2 recommended by the user according to a corresponding departure position and a corresponding destination position by a navigation system of a target new energy electric car, and acquiring a running duration t2 estimated by the navigation system when the optimal running route L2 is recommended for the user;
step S303: comparing the routes of L2 and L1, capturing all target road sections which are not overlapped with L2 in L1, and accumulating the total distance of all the target road sections to obtain J1; marking all target road sections which can be led to a charging station selected by a user in all target road sections, and accumulating the total distance J2 of the target road sections with the marks; calculating a characteristic value beta= |t2-t1|× (J2/J1) corresponding to a certain target driving record; if the characteristic value beta of a certain target driving record is larger than the threshold value, extracting the certain target driving record as the characteristic driving record;
when the battery of the new energy electric car is in an overdischarge state, the performance of the battery is often damaged, and from the data presentation of the residual battery, the residual battery displayed when the zero is nearly in the overdischarge state is often a residual value which can enable a user to perceive that the residual current of the battery is lower, and in actual application, when the battery of the vehicle is lower than 20%, the phenomenon of overdischarge of the battery possibly occurs; if it is detected that a certain target driving record meets Q-W is greater than or equal to alpha F, it means that the vehicle battery allowance display always presents a safer and more sufficient state in the driving process, so that the possibility that the action of charging the trolley battery, which occurs at the moment, is influenced by the objective battery allowance is smaller, for example, the obvious battery allowance is insufficient to support the complete driving path, the possibility that the user is related to the use habit of the trolley battery is higher, for example, a safe range exists for the battery allowance of the user in the actual driving process of the vehicle;
the process can also realize that the target driving records of the charging behavior generated due to the convenience of the user driving route are eliminated, and the target driving records of the charging behavior generated due to the influence of the subjective judgment of the user are reserved.
Further, step S400 includes:
step S401: respectively extracting a running route S1 actually completed by a user in any characteristic driving record, acquiring an optimal running route S2 recommended to the user by a navigation system of a target new energy electric car according to a corresponding departure position and a corresponding destination position, and acquiring all target road sections containing marks in the S1 after the S2 and the S1 are subjected to route comparison;
step S402: taking a target road section closest to a corresponding departure position among all target road sections containing marks as a characteristic road section, capturing a battery residual quantity Q1 displayed by a target new energy electric car when a user drives the target new energy electric car to start to travel the characteristic road section in any characteristic driving record, and taking the battery residual quantity Q1 as a first characteristic battery residual quantity of any characteristic driving record;
step S403: capturing the battery residual quantity Q2 displayed by the target new energy electric car when the user drives the target new energy electric car to the selected charging station, and taking the battery residual quantity Q2 as a second characteristic battery residual quantity of any characteristic driving record;
step S404: traversing the first and second characteristic battery residuals of all the characteristic driving records to respectively capture the minimum value Q1 in all the first characteristic battery residuals min And a minimum Q2 in all second characteristic battery residuals min Generating a user-to-trolley battery characteristic use margin range Q2 min ，Q1 min ]。
Further, step S500 includes:
step S501: extracting the characteristic use margin range [ Q2 ] of the electric car battery by the user min ，Q1 min ]The method comprises the steps of carrying out a first treatment on the surface of the The navigation system of the new energy electric car acquires the battery allowance Y displayed by the new energy electric car when the new driving record starts according to the starting position and the ending position of the corresponding new driving record input by the user and the optimal driving route G recommended by the user;
step S502: setting a unit driving period Te, capturing the average power consumption and the average speed which are presented by a user from the start of a driving stroke corresponding to a new driving record to the completion of the first unit driving period Te, and respectively acquiring the battery allowance of a target new energy electric car from Y to Q2 according to the average power consumption min Driving time Tr for desired driving 1 And from Y to Q1 min Driving time Tr for desired driving 2 ；
Step S503: acquiring full Tr of user driving at average speed 1 At this time, the total distance X1 traveled in the optimal travel route G is obtained, and the user is driving full Tr at an average speed 2 At that time, the total distance of travel X2 is completed in the optimal travel route G; acquiring the total distance E of the optimal driving route G;
step S504: calculating the probability delta=1/[ (X1/e+x2/E)/2 ] of the user in the new driving record that the user goes to the charging station to charge the target new energy electric car;
the larger the [ (X1/E+X2/E)/2 ], the smaller the corresponding 1/[ (X1/E+X2/E)/2 ], indicating that the closer the end of the driving range is, the lower the likelihood of charging the target new energy electric vehicle to the charging station correspondingly occurs within the allowable time range.
Further, step S600 includes:
step S601: when delta of the new driving record is larger than a threshold value, feedback prompt is carried out to the management terminal, position distribution information of optional charging stations in the driving process is supplemented, and a consideration factor range of a navigation system of the target new energy electric car when an optimal driving route is recommended for a user is supplemented, so that a new optimal driving route is generated;
step S602: and (3) carrying out position early warning prompt on the charging stations which can get through in advance in the process of running according to the new optimal running path by the user.
In order to better realize the method, the invention also provides a mobile battery-replacement energy storage data analysis management system, which comprises: the system comprises a driving record extraction management module, a target driving record screening module, a characteristic driving record judging and identifying module, a characteristic use allowance range identification extraction module, a probability prediction evaluation management module and a mobile power-change early warning prompt management module;
the driving record extraction management module is used for inquiring and acquiring the destination position A of a target in a driving journey to be started by a user every time the target new energy electric car is detected to be started by the user, capturing the departure position B of the current target new energy electric car, and generating a driving record of the target new energy electric car by the user every time the user is detected to drive the target new energy electric car to finish space movement from A to B;
the target driving record screening module is used for setting the historical driving record which is captured in the corresponding driving journey of the user and contains the charge of the target new energy electric vehicle to the charging station as the target driving record in all the historical driving records of the user;
the characteristic driving record judging and identifying module is used for respectively carrying out driving information carding on each target driving record, and judging and identifying the characteristic driving records showing the using habit of a user on the target new energy electric car battery in all the target driving records;
the characteristic use allowance range identification and extraction module is used for capturing, monitoring and extracting the characteristic use allowance range presented on the battery use when a user drives a target new energy electric car from all characteristic driving records;
the probability prediction evaluation management module is used for performing probability prediction evaluation on the possibility that the user charges the target new energy electric vehicle when the user starts the target new energy electric vehicle and is about to generate a new driving record in the new driving record;
and the mobile battery-changing early warning prompt management module is used for determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending the optimal driving route for the user according to the probability value of the new driving record, and determining whether to carry out mobile battery-charging early warning prompt on the user in the driving process.
Further, the probability prediction evaluation management module comprises a record capturing unit and a probability prediction evaluation unit;
a record capturing unit for capturing a new driving record generated by a user;
and the probability prediction evaluation unit is used for performing probability prediction evaluation on the possibility that the user charges the target new energy electric vehicle when the user starts the target new energy electric vehicle and is about to generate a new driving record in the new driving record.
Further, the mobile power-changing early-warning prompt management module comprises an consideration factor range adjusting unit and a mobile power-changing early-warning prompt unit;
the consideration factor range adjusting unit is used for determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending an optimal driving route for a user according to the probability value of the new driving record;
the mobile battery replacement early warning prompt unit is used for determining whether to carry out early warning prompt of mobile battery replacement in the driving process for the user.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the characteristic analysis is carried out on the historical driving record containing the charge of the target new energy electric car from the charging station by the user in the corresponding driving route, the judgment and identification on the characteristic driving record showing the use habit of the battery of the target new energy electric car by the user are realized, the capturing and monitoring and the extraction are carried out on the characteristic use allowance range shown by the user on the battery use, the probability prediction evaluation is carried out on the possibility that the target new energy electric car is charged by the charging station when the user starts a new driving route, the personalized management on the mobile power change of the user is realized, the phenomenon that the driving plan is influenced because the user temporarily decides the charging station due to the battery allowance is reduced, and the health supervision on the electric car battery is realized while the trip efficiency of the user is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an artificial intelligence based mobile battery replacement energy storage data analysis and management method;
FIG. 2 is a schematic diagram of the mobile power conversion energy storage data analysis management system based on artificial intelligence.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: the mobile battery replacement energy storage data analysis and management method based on artificial intelligence comprises the following steps:
step S100: every time a user is detected to start a target new energy electric car, inquiring and acquiring a destination position A of the target of the user in a driving journey to be started, capturing a departure position B of the current target new energy electric car, every time the user is detected to drive the target new energy electric car to finish space movement from A to B, and generating a driving record of the user on the target new energy electric car;
step S200: among all the history driving records of the user, the history driving record which is captured in the corresponding driving journey of the user and contains the charge of the new energy electric vehicle to the charging station is set as the target driving record;
step S300: respectively combing driving information of each target driving record, and judging and identifying characteristic driving records showing the using habit of a user on the target new energy electric car battery in all the target driving records;
wherein, step S300 includes:
step S301: acquiring the driving mileage number M of a new energy electric vehicle driven by a user in each target driving record, acquiring each performance state parameter of a new energy electric vehicle battery at the beginning of each target driving record, and acquiring the minimum battery quantity W required to be consumed by the new energy electric vehicle driven by the user for completing the driving mileage number M according to each performance state parameter; obtaining a maximum battery residual quantity F corresponding to a battery of the target new energy electric vehicle when the battery is in an overdischarge state near zero, and obtaining a battery residual quantity Q displayed by the target new energy electric vehicle when each target driving record starts;
step S302: when a certain target driving record meets Q-W is more than or equal to alpha F, wherein alpha represents an adjustment coefficient, and 2> alpha >1; acquiring a running route L1 actually completed by a user in a certain target driving record, acquiring a running duration t1 used by the user for completing the running route L for an optimal running route L2 recommended by the user according to a corresponding departure position and a corresponding destination position by a navigation system of a target new energy electric car, and acquiring a running duration t2 estimated by the navigation system when the optimal running route L2 is recommended for the user;
step S303: comparing the routes of L2 and L1, capturing all target road sections which are not overlapped with L2 in L1, and accumulating the total distance of all the target road sections to obtain J1; marking all target road sections which can be led to a charging station selected by a user in all target road sections, and accumulating the total distance J2 of the target road sections with the marks; calculating a characteristic value beta= |t2-t1|× (J2/J1) corresponding to a certain target driving record; if the characteristic value beta of a certain target driving record is larger than the threshold value, extracting the certain target driving record as the characteristic driving record;
for example, L2 is route-aligned with L1, and all target links that are captured in L1 that do not coincide with L2 include link a in L1, link b in L1, and link c in L1; in summary, the total distance j1=a+b+c obtained by accumulating all the target road segments;
step S400: capturing, monitoring and extracting a characteristic use allowance range presented on battery use when a user drives a target new energy electric car from all characteristic driving records;
wherein, step S400 includes:
step S401: respectively extracting a running route S1 actually completed by a user in any characteristic driving record, acquiring an optimal running route S2 recommended to the user by a navigation system of a target new energy electric car according to a corresponding departure position and a corresponding destination position, and acquiring all target road sections containing marks in the S1 after the S2 and the S1 are subjected to route comparison;
step S402: taking a target road section closest to a corresponding departure position among all target road sections containing marks as a characteristic road section, capturing a battery residual quantity Q1 displayed by a target new energy electric car when a user drives the target new energy electric car to start to travel the characteristic road section in any characteristic driving record, and taking the battery residual quantity Q1 as a first characteristic battery residual quantity of any characteristic driving record;
step S403: capturing the battery residual quantity Q2 displayed by the target new energy electric car when the user drives the target new energy electric car to the selected charging station, and taking the battery residual quantity Q2 as a second characteristic battery residual quantity of any characteristic driving record;
step S404: traversing the first and second characteristic battery residuals of all the characteristic driving records to respectively capture the minimum value Q1 in all the first characteristic battery residuals min And a minimum Q2 in all second characteristic battery residuals min Generating a user-to-trolley battery characteristic use margin range Q2 min ，Q1 min ]；
Step S500: each time when the situation that a user starts a target new energy electric car and a new driving record is about to be generated is detected, probability prediction evaluation is carried out on the possibility that the user goes to a charging station to charge the target new energy electric car in the new driving record;
wherein, step S500 includes:
step S501: extracting the characteristic use margin range [ Q2 ] of the electric car battery by the user min ，Q1 min ]The method comprises the steps of carrying out a first treatment on the surface of the The navigation system of the new energy electric car acquires the battery allowance Y displayed by the new energy electric car when the new driving record starts according to the starting position and the ending position of the corresponding new driving record input by the user and the optimal driving route G recommended by the user;
step S502: setting a unit driving period Te, capturing the average power consumption and the average speed which are presented by a user from the start of a driving stroke corresponding to a new driving record to the completion of the first unit driving period Te, and respectively acquiring the battery allowance of a target new energy electric car from Y to Q2 according to the average power consumption min Driving time Tr for desired driving 1 And from Y to Q1 min Driving time Tr for desired driving 2 ；
Step S503: acquiring full Tr of user driving at average speed 1 At this time, the total distance X1 traveled in the optimal travel route G is obtained, and the user is driving full Tr at an average speed 2 At that time, the total distance of travel X2 is completed in the optimal travel route G; acquiring the total distance E of the optimal driving route G;
step S504: calculating the probability delta=1/[ (X1/e+x2/E)/2 ] of the user in the new driving record that the user goes to the charging station to charge the target new energy electric car;
step S600: according to the probability value of the new driving record, whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending the optimal driving route for the user is determined, and whether to perform early warning prompt of mobile charging and battery replacement for the user in the driving process is determined;
wherein, step S600 includes:
step S601: when delta of the new driving record is larger than a threshold value, feedback prompt is carried out to the management terminal, position distribution information of optional charging stations in the driving process is supplemented, and a consideration factor range of a navigation system of the target new energy electric car when an optimal driving route is recommended for a user is supplemented, so that a new optimal driving route is generated;
step S602: and (3) carrying out position early warning prompt on the charging stations which can get through in advance in the process of running according to the new optimal running path by the user.
In order to better realize the method, the invention also provides a mobile battery-replacement energy storage data analysis management system, which comprises: the system comprises a driving record extraction management module, a target driving record screening module, a characteristic driving record judging and identifying module, a characteristic use allowance range identification extraction module, a probability prediction evaluation management module and a mobile power-change early warning prompt management module;
the driving record extraction management module is used for inquiring and acquiring the destination position A of a target in a driving journey to be started by a user every time the target new energy electric car is detected to be started by the user, capturing the departure position B of the current target new energy electric car, and generating a driving record of the target new energy electric car by the user every time the user is detected to drive the target new energy electric car to finish space movement from A to B;
the target driving record screening module is used for setting the historical driving record which is captured in the corresponding driving journey of the user and contains the charge of the target new energy electric vehicle to the charging station as the target driving record in all the historical driving records of the user;
the characteristic driving record judging and identifying module is used for respectively carrying out driving information carding on each target driving record, and judging and identifying the characteristic driving records showing the using habit of a user on the target new energy electric car battery in all the target driving records;
the characteristic use allowance range identification and extraction module is used for capturing, monitoring and extracting the characteristic use allowance range presented on the battery use when a user drives a target new energy electric car from all characteristic driving records;
the probability prediction evaluation management module is used for performing probability prediction evaluation on the possibility that the user charges the target new energy electric vehicle when the user starts the target new energy electric vehicle and is about to generate a new driving record in the new driving record;
the probability prediction evaluation management module comprises a record capturing unit and a probability prediction evaluation unit;
a record capturing unit for capturing a new driving record generated by a user;
the probability prediction evaluation unit is used for performing probability prediction evaluation on the possibility that the user charges the target new energy electric vehicle when the user starts the target new energy electric vehicle and is about to generate a new driving record in the new driving record;
the mobile battery-changing early warning prompt management module is used for determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending an optimal driving route for a user according to the probability value of the new driving record, and determining whether to carry out mobile battery-charging early warning prompt on the user in the driving process;
the mobile power-changing early-warning prompt management module comprises an consideration factor range adjustment unit and a mobile power-changing early-warning prompt unit;
the consideration factor range adjusting unit is used for determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending an optimal driving route for a user according to the probability value of the new driving record;
the mobile battery replacement early warning prompt unit is used for determining whether to carry out early warning prompt of mobile battery replacement in the driving process for the user.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (2)

1. The mobile battery replacement energy storage data analysis and management method based on artificial intelligence is characterized by comprising the following steps of:
step S100: every time a user is detected to start a target new energy electric car, inquiring and acquiring a destination position A of the target of the user in a driving journey to be started, capturing a departure position B of the current target new energy electric car, every time the user is detected to drive the target new energy electric car to finish space movement from A to B, and generating a driving record of the user on the target new energy electric car;
step S200: among all the history driving records of the user, the history driving record which is captured in the corresponding driving journey of the user and contains the charge of the new energy electric vehicle to the charging station is set as the target driving record;
step S300: respectively combing driving information of each target driving record, and judging and identifying characteristic driving records showing the using habit of a user on the target new energy electric car battery in all the target driving records;
step S400: capturing, monitoring and extracting a characteristic use allowance range presented on battery use when a user drives a target new energy electric car from all characteristic driving records;
step S500: each time when the situation that a user starts a target new energy electric car and a new driving record is about to be generated is detected, probability prediction evaluation is carried out on the possibility that the user goes to a charging station to charge the target new energy electric car in the new driving record;
step S600: according to the probability value of the new driving record, whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending the optimal driving route for the user is determined, and whether to perform early warning prompt of mobile charging and battery replacement for the user in the driving process is determined;
the step S300 includes:
step S301: acquiring the driving mileage number M of a new energy electric vehicle driven by a user in each target driving record, acquiring each performance state parameter of a new energy electric vehicle battery at the beginning of each target driving record, and acquiring the minimum battery quantity W required to be consumed by the new energy electric vehicle driven by the user for completing the driving mileage number M according to each performance state parameter; obtaining a maximum battery residual quantity F corresponding to a battery of the target new energy electric vehicle when the battery is in an overdischarge state near zero, and obtaining a battery residual quantity Q displayed by the target new energy electric vehicle when each target driving record starts;
step S302: when a certain target driving record meets Q-W is more than or equal to alpha F, wherein alpha represents an adjustment coefficient, and 2> alpha >1; acquiring a running route L1 actually completed by a user in the certain target driving record, acquiring an optimal running route L2 recommended to the user by a navigation system of the target new energy electric vehicle according to the corresponding departure position and the corresponding destination position, acquiring a running duration t1 used by the user for completing the running route L1, and acquiring a running duration t2 estimated by the navigation system when the optimal running route L2 is recommended to the user;
step S303: comparing the routes of L2 and L1, capturing all target road sections which are not overlapped with L2 in L1, and accumulating the total distance of all the target road sections to obtain J1; marking all target road sections which can be led to a charging station selected by a user in all target road sections, and accumulating the total distance J2 of the target road sections with the marks; calculating a characteristic value beta= |t2-t1|× (J2/J1) corresponding to a certain target driving record; if the characteristic value beta of a certain target driving record is larger than a threshold value, extracting the certain target driving record as a characteristic driving record;
the step S400 includes:
step S401: respectively extracting a running route S1 actually completed by a user in any characteristic driving record, acquiring an optimal running route S2 recommended to the user by a navigation system of a target new energy electric car according to a corresponding departure position and a corresponding destination position, and acquiring all target road sections containing marks in the S1 after the S2 and the S1 are subjected to route comparison;
step S402: taking a target road section closest to the corresponding departure position among all target road sections containing marks as a characteristic road section, capturing a battery residual quantity Q1 displayed by a target new energy electric vehicle when a user starts to drive the target new energy electric vehicle to travel the characteristic road section in the arbitrary characteristic driving record, and taking the battery residual quantity Q1 as a first characteristic battery residual quantity of the arbitrary characteristic driving record;
step S403: capturing the battery residual quantity Q2 displayed by the target new energy electric car when the user drives the target new energy electric car to the selected charging station, and taking the battery residual quantity Q2 as a second characteristic battery residual quantity of the arbitrary characteristic driving record;
step S404: traversing the first and second characteristic battery residuals of all the characteristic driving records to respectively capture the minimum value Q1 in all the first characteristic battery residuals min And a minimum Q2 in all second characteristic battery residuals min Generating a user-to-trolley battery characteristic use margin range Q2 min ，Q1 min ]；
The step S500 includes:
step S501: extracting the characteristic use margin range [ Q2 ] of the electric car battery by the user min ，Q1 min ]The method comprises the steps of carrying out a first treatment on the surface of the The navigation system of the new energy electric car acquires the battery allowance Y displayed by the new energy electric car when the new driving record starts according to the starting position and the ending position which are input by the user and correspond to the new driving record, and the optimal driving route G recommended by the user;
step S502: setting a unit driving period Te, capturing the average power consumption and the average speed which are presented by a user from the beginning of the driving stroke corresponding to the new driving record to the full of the first unit driving period Te, and respectively acquiring the battery allowance of the target new energy electric car from Y to Q2 according to the average power consumption min Driving time Tr for desired driving 1 And from Y to Q1 min Driving time Tr for desired driving 2 ；
Step S503: acquiring that the user is driving full Tr according to the average speed 1 When the user is driving at the average speed, the total distance X1 of the optimal driving route G is obtained 2 When the travel route G is the optimal travel route G, the total distance X2 traveled is completed; acquiring the total distance E of the optimal driving route G;
step S504: calculating the probability delta=1/[ (X1/e+x2/E)/2 ] of the user in the new driving record that the user goes to the charging station to charge the target new energy electric car;
the step S600 includes:
step S601: when delta of the new driving record is larger than a threshold value, feedback prompt is carried out to the management terminal, position distribution information of optional charging stations in the driving process is supplemented, and a consideration factor range of a navigation system of the target new energy electric car when an optimal driving route is recommended for a user is supplemented, so that a new optimal driving route is generated;
step S602: and carrying out position early warning prompt on the charging stations which can get through in advance in the process of running according to the new optimal running path.
2. A mobile power conversion energy storage data analysis management system, the system comprising: the system comprises a driving record extraction management module, a target driving record screening module, a characteristic driving record judging and identifying module, a characteristic use allowance range identification extraction module, a probability prediction evaluation management module and a mobile power-change early warning prompt management module;
the driving record extraction management module is used for inquiring and acquiring the destination position A of a target in a driving journey to be started by a user every time the target new energy electric car is detected to be started by the user, capturing the departure position B of the current target new energy electric car, and generating a driving record of the target new energy electric car by the user every time the user is detected to drive the target new energy electric car to complete space movement from A to B;
the target driving record screening module is used for setting the historical driving record which is captured in the corresponding driving journey of the user and contains the charging of the target new energy electric car to the charging station as the target driving record in all the historical driving records of the user;
the characteristic driving record judging and identifying module is used for respectively carrying out driving information carding on each target driving record, and judging and identifying the characteristic driving records showing the using habit of a user on the target new energy electric car battery in all the target driving records;
the characteristic use allowance range identification and extraction module is used for capturing, monitoring and extracting the characteristic use allowance range presented on the battery use when a user drives a target new energy electric car from all characteristic driving records;
the probability prediction evaluation management module is used for performing probability prediction evaluation on the possibility that a user goes to a charging station to charge the target new energy electric car in the new driving record when detecting that the user starts the target new energy electric car and is about to generate a new driving record;
the mobile battery-changing early warning prompt management module is used for determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending an optimal driving route for a user according to the probability value of the new driving record, and determining whether to carry out early warning prompt of mobile battery-changing in the driving process of the user;
the probability prediction evaluation management module comprises a record capturing unit and a probability prediction evaluation unit;
the record capturing unit is used for capturing a new driving record generated by a user;
the probability prediction evaluation unit is used for performing probability prediction evaluation on the possibility that the user charges the target new energy electric car when the user starts the target new energy electric car and is about to generate a new driving record in the new driving record;
the mobile power-changing early-warning prompt management module comprises an consideration factor range adjusting unit and a mobile power-changing early-warning prompt unit;
the consideration factor range adjusting unit is used for determining whether to adjust the consideration factor range of the navigation system of the target new energy electric car when recommending an optimal driving route for a user according to the probability value of the new driving record;
the mobile battery replacement early warning prompt unit is used for determining whether to carry out early warning prompt of mobile battery replacement in the driving process of a user;
acquiring the driving mileage number M of a new energy electric vehicle driven by a user in each target driving record, acquiring each performance state parameter of a new energy electric vehicle battery at the beginning of each target driving record, and acquiring the minimum battery quantity W required to be consumed by the new energy electric vehicle driven by the user for completing the driving mileage number M according to each performance state parameter; obtaining a maximum battery residual quantity F corresponding to a battery of the target new energy electric vehicle when the battery is in an overdischarge state near zero, and obtaining a battery residual quantity Q displayed by the target new energy electric vehicle when each target driving record starts;
when a certain target driving record meets Q-W is more than or equal to alpha F, wherein alpha represents an adjustment coefficient, and 2> alpha >1; acquiring a running route L1 actually completed by a user in the certain target driving record, acquiring an optimal running route L2 recommended to the user by a navigation system of the target new energy electric vehicle according to the corresponding departure position and the corresponding destination position, acquiring a running duration t1 used by the user for completing the running route L1, and acquiring a running duration t2 estimated by the navigation system when the optimal running route L2 is recommended to the user;
comparing the routes of L2 and L1, capturing all target road sections which are not overlapped with L2 in L1, and accumulating the total distance of all the target road sections to obtain J1; marking all target road sections which can be led to a charging station selected by a user in all target road sections, and accumulating the total distance J2 of the target road sections with the marks; calculating a characteristic value beta= |t2-t1|× (J2/J1) corresponding to a certain target driving record; if the characteristic value beta of a certain target driving record is larger than a threshold value, extracting the certain target driving record as a characteristic driving record;
respectively extracting a running route S1 actually completed by a user in any characteristic driving record, acquiring an optimal running route S2 recommended to the user by a navigation system of a target new energy electric car according to a corresponding departure position and a corresponding destination position, and acquiring all target road sections containing marks in the S1 after the S2 and the S1 are subjected to route comparison;
taking a target road section closest to the corresponding departure position among all target road sections containing marks as a characteristic road section, capturing a battery residual quantity Q1 displayed by a target new energy electric vehicle when a user starts to drive the target new energy electric vehicle to travel the characteristic road section in the arbitrary characteristic driving record, and taking the battery residual quantity Q1 as a first characteristic battery residual quantity of the arbitrary characteristic driving record;
capturing the battery residual quantity Q2 displayed by the target new energy electric car when the user drives the target new energy electric car to the selected charging station, and taking the battery residual quantity Q2 as a second characteristic battery residual quantity of the arbitrary characteristic driving record;
traversing the first characteristic battery allowance and the second characteristic battery allowance of all the characteristic driving records, respectively capturing the minimum value Q1min in all the first characteristic battery allowance and the minimum value Q2min in all the second characteristic battery allowance, and generating a characteristic use allowance range [ Q2min, Q1min ] of a user on the trolley battery;
extracting a characteristic use allowance range [ Q2min, Q1min ] of a user on the trolley battery; the navigation system of the new energy electric car acquires the battery allowance Y displayed by the new energy electric car when the new driving record starts according to the starting position and the ending position which are input by the user and correspond to the new driving record, and the optimal driving route G recommended by the user;
setting a unit driving period Te, capturing average power consumption and average speed which are presented by a user from the start of a driving stroke corresponding to the new driving record to the completion of the first unit driving period Te, and respectively acquiring driving duration Tr1 required to be driven when the battery allowance of a target new energy electric car is reduced from Y to Q2min and driving duration Tr2 required to be driven when the battery allowance is reduced from Y to Q1min according to the average power consumption;
acquiring a total distance X1 of a user completing the traveling in the optimal traveling route G when the user drives the full Tr1 according to the average speed, and acquiring a total distance X2 of the user completing the traveling in the optimal traveling route G when the user drives the full Tr2 according to the average speed; acquiring the total distance E of the optimal driving route G;
calculating the probability delta=1/[ (X1/e+x2/E)/2 ] of the user in the new driving record that the user goes to the charging station to charge the target new energy electric car;
when delta of the new driving record is larger than a threshold value, feedback prompt is carried out to the management terminal, position distribution information of optional charging stations in the driving process is supplemented, and a consideration factor range of a navigation system of the target new energy electric car when an optimal driving route is recommended for a user is supplemented, so that a new optimal driving route is generated;
and carrying out position early warning prompt on the charging stations which can get through in advance in the process of running according to the new optimal running path.
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