Electric vehicle life cycle full-chain supervision method and system based on big data

Abstract

The invention relates to the technical field of electric bicycle supervision, and particularly discloses an electric bicycle life cycle full-chain supervision method and system based on big data, and the system comprises a traceability management module which is used for combining a bicycle body code, a battery code and a charger code of an electric bicycle into a traceability code through a traceability technology, the operation state data and the position information of the vehicle are collected through the Beidou central control module, the operation state of the vehicle in the using process is monitored through the vehicle monitoring module in combination with the operation state data of the vehicle, and the states of a battery and a motor in the using process of the vehicle can be analyzed; therefore, problems of a battery and a motor are avoided, the risk of a user in the using process is reduced, illegal events of the vehicle in the using process are supervised by combining the vehicle supervision module with the position information of the vehicle, illegal behaviors of the user can be monitored and early warned, and the user experience is improved. And the risk of the user in the use process is further reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle supervision, and in particular to a method and system for supervising the entire life cycle of an electric vehicle based on big data. Background Art

[0002] The rapid growth in the number of electric bicycles in my country has brought many problems to urban management, such as electric bicycle violations, charging problems, electric bicycle battery fire problems, motor operation problems, battery recycling pollution and environmental problems, and electric bicycle modifications. Therefore, an electric vehicle life cycle full-chain supervision system is needed to manage electric bicycles and avoid the above problems.

[0003] The traditional full-chain supervision system for the life cycle of electric vehicles usually includes a production supervision module, a sales supervision module, a registration and licensing module, a road monitoring and early warning module, and a recycling and processing module. It can realize functions such as production process monitoring of electric bicycle manufacturers, supervision of electric bicycle sales outlets, registration of electric bicycles, and issuance of license plates. It also uses intelligent monitoring equipment to monitor electric bicycles in real time, as well as scrapping of electric bicycles and recycling of used batteries, to achieve full-chain supervision of the life cycle of electric vehicles, thereby regulating market order and improving road safety.

[0004] The road monitoring and early warning module in the traditional electric vehicle life cycle full-chain supervision system generally monitors the usage trajectory and speed of electric bicycles to judge the risks of owners during use. However, since the internal batteries and motors of electric bicycles may age or wear out, this will also affect the risks of owners during use. If this situation is not supervised, it may cause damage to the vehicle or even battery spontaneous combustion, thereby greatly increasing the risks of owners during use. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for supervising the entire life cycle of electric vehicles based on big data to solve the following technical problems:

[0006] How to effectively supervise the operating status of electric bicycles.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method and system for supervising the entire life cycle of an electric vehicle based on big data, the system comprising:

[0009] The traceability management module is used to use the traceability technology to combine the body code, battery code and charger code of the electric bicycle into a traceability code, register the owner's personal information and the vehicle's license plate information when selling, and bind the traceability code of the electric bicycle through the user's mobile phone;

[0010] Beidou central control module, used to collect vehicle operation status data and vehicle location information, where the vehicle operation status data includes battery data and motor operation data;

[0011] The vehicle monitoring module includes a battery monitoring unit and a motor monitoring unit, which are used to monitor the operating status of the vehicle during charging and use by combining the battery status data of the vehicle and the motor operating status data;

[0012] The vehicle monitoring module is used to monitor illegal incidents during the use of the vehicle in combination with the vehicle's location information;

[0013] The environmental recycling module, by combining the data information registered in the traceability management module, reminds users to go to designated recycling points for battery recycling or vehicle scrapping procedures when the vehicle battery is about to expire or the vehicle reaches the scrap standard.

[0014] Furthermore, the monitoring process of the vehicle monitoring module includes:

[0015] By formula Calculate the battery status influence coefficient ω of the vehicle during the zth charging process z ;

[0016] And through the formula Calculate the battery status influence coefficient μ of the vehicle during the i-th use process i ;

[0017] Where z is any charging process, a is any data collection at a fixed time interval, b is the total number of collections, wd z is the average temperature of the battery during the zth charging process, dl az is the current intensity of the battery at the ath time point during the zth charging process, i is any use of the vehicle, wd i is the average battery temperature of the vehicle during the i-th use, wd y is the preset battery temperature, dl ai is the current intensity of the vehicle during the ath data collection in the i-th use process, dl y is the preset current intensity, hdl i is the total power consumption of the vehicle during its i-th use, lc i is the total mileage of the vehicle during its i-th use, hdl yis the preset power consumption per kilometer, and ρ is the influence coefficient of the battery life of the vehicle during the i-th use, which is set based on empirical fitting.

[0018] Furthermore, the monitoring process of the vehicle monitoring module also includes:

[0019] By calculating the battery status influence coefficient ω during the zth charging process z The battery status influence coefficient μ during the i-th use i Respectively with the preset battery influence coefficient threshold μ 01 Make a comparison;

[0020] If μ i ≥μ 01 , the system determines that there is a problem with the battery operation status of the vehicle during this use, and the battery needs to be repaired, and pushes the warning information to the user's mobile phone;

[0021] If μ i <μ 01 , the system determines that during this use, the vehicle's battery is in good operating condition and there is no problem;

[0022] If z ≥μ 01 , the system determines that there is a problem with the vehicle's battery status during this charging process, which means that the battery is faulty or the modified battery has been modified, and the battery needs to be repaired, and the warning information is pushed to the user's mobile phone;

[0023] If z <μ 01 , the system determines that the vehicle's battery is in good condition during this charging process, which means that there is no battery failure and it is not an original registered battery.

[0024] Furthermore, the monitoring process of the vehicle monitoring module also includes:

[0025] By formula Calculate the motor state influence coefficient δ during the i-th use of the vehicle i ;

[0026] Among them, dy ai is the voltage of the vehicle during the a-th data collection during the i-th use process, For all ai The average value, nj i is the average torque of the vehicle during its i-th use, nj y is the preset average torque, θ is the error influence coefficient, cz i is the load-bearing capacity of the vehicle during its i-th use, cz y The preset load-bearing capacity.

[0027] Furthermore, the monitoring process of the vehicle monitoring module also includes:

[0028] By calculating the motor state influence coefficient δ during the i-th use of the vehicle i and the preset motor state influence coefficient threshold δ 01 Make a comparison;

[0029] If δ i ≥δ 01 , the system determines that during this use, the vehicle's motor is in poor operating condition and there is an abnormality in the motor operation, and issues an early warning;

[0030] If δ i <δ 01 The system determines that during this use, the vehicle's motor is in good operating condition and there is no abnormality in the motor operation.

[0031] Furthermore, the monitoring process of the vehicle monitoring module also includes:

[0032] Through the historical monitoring records of the vehicle monitoring module, a change curve δ(t) of the vehicle motor state influence coefficient is established;

[0033] And through the formula Calculate the change γ of the motor state influence coefficient from the time the vehicle is put into use to the time it is used for the i-th time i ;

[0034] Then, the change of the motor state influence coefficient γ from the time the vehicle is put into use to the time when it is used for the i-th time is calculated. i The change threshold γ 01 Make a comparison;

[0035] If γ i ≥γ 01 ,The system determines that the change in the motor state influence coefficient is high, indicating that the internal structure of the motor may be aged, worn or damaged, and is prone to failure in subsequent use, and issues an early warning;

[0036] If γ i <γ 01 ,The change amount of the motor state influence coefficient is high, indicating that the motor has stable performance and good health and the risk of failure is low;

[0037] Among them, t1 is the first use of the vehicle after it is put into use, t i is the i-th use of the vehicle since it was put into use, n is the total number of times the vehicle has been used since it was put into use until the i-th use, For all δ i The average value of .

[0038] Furthermore, the supervision process of the vehicle supervision module includes:

[0039] The motor angular velocity is detected by the Beidou central control module and converted into speed. Based on this data, it can be shown whether the vehicle speed exceeds the national standard and whether there is any speeding behavior. If there is speeding behavior, it is considered to be an illegally modified vehicle, and a reminder record is generated in the system and fed back to the traffic management department;

[0040] Based on the vehicle route data displayed by the system, determine whether there is a wrong-way situation or abnormal lane usage, and generate a reminder record in the system and feedback to the traffic management department;

[0041] Based on the vehicle routes and usage time points displayed by the system, combined with the intersection indicator light data in the big data, it is determined whether there is a red light violation, and a reminder record is generated in the system and fed back to the traffic management department.

[0042] A method for supervising the entire life cycle of an electric vehicle based on big data, the method comprising:

[0043] S1: The traceability management module uses traceability technology to combine the body code, battery code and charger code of the electric bicycle into a traceability code, and registers the owner's personal information and vehicle license plate information during sales, and binds the traceability code of the electric bicycle through the user's mobile phone;

[0044] S2: Collect the vehicle's operating status data and vehicle location information through the Beidou central control module, where the vehicle's operating status data includes battery data and motor operating data;

[0045] S3: The vehicle monitoring module combines the battery status data and the motor operation status data of the vehicle to monitor the operation status of the vehicle during charging and use;

[0046] S4: The vehicle monitoring module combines the vehicle's location information to monitor illegal incidents during vehicle use;

[0047] S5: Through the environmental recycling module combined with the data information registered in the traceability management module, when the vehicle battery is about to expire or the vehicle reaches the scrap standard, the user is reminded to go to the designated recycling point to recycle the battery or go through the vehicle scrapping procedures.

[0048] Beneficial effects of the present invention:

[0049] (1) The present invention collects the vehicle's operating status data and vehicle location information through the Beidou central control module, and monitors the vehicle's operating status during use through the vehicle monitoring module combined with the vehicle's operating status data. The battery and motor status of the vehicle during use can be analyzed to avoid problems with the battery and motor, thereby reducing the risk of users during use. In addition, the vehicle supervision module is combined with the vehicle's location information to supervise illegal incidents during use of the vehicle. The user's illegal behavior can be monitored and warned, further reducing the risk of users during use.

[0050] (2) The present invention calculates the battery state influence coefficient ω of the vehicle during the zth charging process. z The battery status influence coefficient μ during the i-th use i Respectively with the preset battery influence coefficient threshold μ 01 Through this comparison method, it is possible to make an accurate judgment on whether there is a problem with the operating status of the vehicle battery during the use and charging process, thereby realizing the supervision of the vehicle battery and avoiding the situation where long-term use of the battery and lack of supervision of the battery status lead to battery aging and wear, increasing the risk of use by the car owner or battery spontaneous combustion during charging.

[0051] (3) The present invention calculates the motor state influence coefficient δ during the i-th use of the vehicle i and the preset motor state influence coefficient threshold δ 01 By comparing, the motor operating status during the use can be analyzed, and based on the analysis results, it can be further determined whether there is any abnormality in the motor operation. Through such a setting, effective monitoring of the motor status of the electric bicycle can be achieved, avoiding the situation where the vehicle is damaged due to lack of monitoring of the motor status, thereby increasing the risk of use for the owner.

[0052] (4) The present invention calculates the change γ of the motor state influence coefficient from the time the vehicle is put into use to the time when it is used for the i-th time i The change threshold γ 01 Through this comparison method, the change in the motor status influence coefficient can be judged. This data reflects the change in the performance of the motor since it was put into use, so the performance stability and health status of the motor can be further judged, thereby analyzing the possibility of future motor failures, realizing the supervision of the operating status of electric bicycles, and reducing the risks of owners during use.

[0053] (5) The present invention realizes full-chain supervision of the life cycle of electric bicycles based on multiple links such as the production, sales, operating status, illegal incident supervision and old battery recycling of electric bicycles, effectively ensuring the quality safety, use safety and environmental safety of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1 It is a schematic block diagram of a full-chain supervision system for the life cycle of electric vehicles based on big data in the present invention;

[0056] Figure 2 It is a flow chart of a method for supervising the entire chain of the electric vehicle life cycle based on big data in the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 As shown, in one embodiment, the present application provides a method and system for supervising the entire life cycle of an electric vehicle based on big data, the system comprising:

[0059] The traceability management module is used to use the traceability technology to combine the body code, battery code and charger code of the electric bicycle into a traceability code, register the owner's personal information and the vehicle's license plate information when selling, and bind the traceability code of the electric bicycle through the user's mobile phone;

[0060] Beidou central control module, used to collect vehicle operation status data and vehicle location information, where the vehicle operation status data includes battery data and motor operation data;

[0061] The vehicle monitoring module includes a battery monitoring unit and a motor monitoring unit, which are used to monitor the operating status of the vehicle during charging and use by combining the battery status data of the vehicle and the motor operating status data;

[0062] The vehicle monitoring module is used to monitor illegal incidents during the use of the vehicle in combination with the vehicle's location information;

[0063] The environmental recycling module, by combining the data information registered in the traceability management module, reminds users to go to the designated recycling point to recycle the battery or go through the vehicle scrapping procedures when the vehicle battery is about to expire or the vehicle reaches the scrap standard;

[0064] Through the above technical solution, this embodiment provides a traceability management module that uses traceability technology to synthesize the body code, battery code and charger code of the electric bicycle into a traceability code, and registers the owner's personal information and the vehicle's license plate information at the time of sale, and binds the traceability code of the electric bicycle through the user's mobile phone. When the user uses the electric bicycle, the Beidou central control module collects the vehicle's operating status data and vehicle location information, wherein the vehicle's operating status data includes battery data and motor operating data, and the vehicle monitoring module combines the vehicle's battery status data and motor operating status data to monitor the vehicle's operating status during charging and use, and the vehicle supervision module combines the vehicle's location information to supervise illegal incidents during the use of the vehicle, and through the environmental recycling module combined with the data information registered by the traceability management module, when the vehicle battery is about to expire or the vehicle reaches the scrap standard, remind the user to go to the designated recycling point for battery recycling or handle vehicle scrapping procedures;

[0065] Through the above technical solution, the sales and production supervision of electric bicycles can be realized through the traceability management module. After that, the Beidou central control module collects the vehicle's operating status data and vehicle location information, and the vehicle monitoring module combines the vehicle's operating status data to monitor the vehicle's operating status during use. The battery and motor status of the vehicle during use can be analyzed to avoid problems with the battery and motor, thereby reducing the risk of users during use. In addition, the vehicle supervision module combines the vehicle's location information to supervise illegal incidents during the use of the vehicle, and the user's illegal behavior can be monitored and warned, further reducing the risk of users during use.

[0066] The traceability management module will assign a unique license plate number and traceability code to each electric bicycle. The license plate number facilitates the traffic management department to identify and supervise the vehicle, while the traceability code realizes the traceability and management of the vehicle throughout its life cycle. Through the traceability code, users and regulatory authorities can query the manufacturer, production date, sales channel and other information of the vehicle to ensure the legality and traceability of the vehicle's source, and record the information of each electric bicycle in detail, such as brand, model, frame number, motor number, battery information, etc., and add all this information to the traceability code using traceability technology. These detailed information records enhance the intuitiveness and accuracy of vehicle information, making it convenient for users and regulatory authorities to query at any time. When users query the specific parameters of their own vehicles, they can quickly obtain accurate information through the platform. Regulatory authorities can also conduct accurate inspections based on these detailed information when conducting quality traceability, safety inspections, etc.

[0067] When using the traceability management module, user information, including name, ID number, contact information, etc., is strictly collected, and a real-name authentication system is implemented. This measure ensures the authenticity and accuracy of user information, and provides a reliable basis for subsequent vehicle management, safety warning and other functions. At the same time, real-name authentication also helps to improve users' sense of responsibility and reduce the occurrence of violations. The management unit can contact the car owner in time through the user's real-name information and urge him to correct the violation.

[0068] The environmental recycling module manages the recycling process of electric bicycles and registers the recycling information, which helps to ensure the order of the recycling market, prevent illegal recycling and dismantling, manage the recycling process of electric bicycles, and determine whether the vehicle has reached the scrap standard based on factors such as the age and mileage of the vehicle, and remind users to go through the scrapping procedures in a timely manner. Timely scrapping of old electric bicycles can reduce safety risks and improve road traffic safety. It is encouraged to gradually eliminate existing old electric bicycles through trade-in, discount repurchase, natural scrapping, and scrapping subsidies.

[0069] In addition, the vehicle monitoring module can monitor and manage the vehicle charger, motor, and battery in real time through the dedicated Beidou central control module. If the Beidou central control module detects that the data in any link does not meet the parameters during license registration, it will report warning information in real time. When the charger and battery are inconsistent with those during license registration, the Beidou central control module prohibits battery charging to prevent battery fires due to inconsistent electrical parameters.

[0070] The monitoring process of the vehicle monitoring module includes:

[0071] By formula Calculate the battery status influence coefficient μ of the vehicle during the i-th use process i ;

[0072] Where i is any use of the vehicle, wd i is the average battery temperature of the vehicle during the i-th use, wd y is the preset battery temperature, a is any data collection at a fixed time interval during one use, b is the total number of collections, dl ai is the current intensity of the vehicle during the ath data collection in the i-th use process, dl y is the preset current intensity, hdl i is the total power consumption of the vehicle during its i-th use, lc i is the total mileage of the vehicle during its i-th use, hdl y is the preset power consumption per kilometer, ρ is the influence coefficient of the battery life of the vehicle during the i-th use, which is set based on empirical fitting;

[0073] Through the above technical solution, this example provides the battery state influence coefficient μ during the i-th use process: i , can be obtained by formula Obviously, when the average battery temperature of the vehicle during the i-th use and the current intensity of the vehicle during the a-th data collection during the i-th use are higher, and the average power consumption of the vehicle during the i-th use is higher, then the battery state influence coefficient μ of the vehicle during the i-th use is i The higher the value, the worse the battery stability is during this use, and there may be problems with its operating status, which may pose a certain safety hazard. On the contrary, when the average battery temperature during the i-th use of the vehicle and the current intensity during the a-th data collection during the i-th use of the vehicle are closer to the preset values, and the average power consumption of the vehicle during the i-th use is lower, then the battery status influence coefficient μ of the vehicle during the i-th use is i The lower it is, the more stable the battery is, the better its operating condition is, and there is no potential safety hazard.

[0074] Through the above technical solution, by combining the battery operating status data of the vehicle during use, the operating status of the battery during the current use can be analyzed, thereby achieving supervision of the vehicle battery safety.

[0075] The monitoring process of the vehicle monitoring module also includes:

[0076] By calculating the battery status influence coefficient ω during the zth charging process z The battery status influence coefficient μ during the i-th use i Respectively with the preset battery influence coefficient threshold μ 01 Make a comparison;

[0077] If μ i ≥μ 01 , the system determines that there is a problem with the battery operation status of the vehicle during this use, and the battery needs to be repaired, and pushes the warning information to the user's mobile phone;

[0078] If μ i <μ 01 , the system determines that during this use, the vehicle's battery is in good operating condition and there is no problem;

[0079] If z ≥μ 01 , the system determines that there is a problem with the vehicle's battery status during this charging process, which means that the battery is faulty or the modified battery has been modified, and the battery needs to be repaired, and the warning information is pushed to the user's mobile phone;

[0080] If z <μ 01 , the system determines that the vehicle's battery is in good condition during this charging process, which means that the battery is not faulty and is not an original registered battery;

[0081] Through the above technical solution, this example calculates the battery state influence coefficient ω of the vehicle during the zth charging process z The battery status influence coefficient μ during the i-th use i Respectively with the preset battery influence coefficient threshold μ 01 Through this comparison method, it is possible to make an accurate judgment on whether there is a problem with the operating status of the vehicle battery during the use and charging process, thereby realizing the supervision of the vehicle battery and avoiding the situation where long-term use of the battery and lack of supervision of the battery status lead to battery aging and wear, increasing the risk of use by the car owner or battery spontaneous combustion during charging.

[0082] The monitoring process of the vehicle monitoring module also includes:

[0083] By formula Calculate the motor state influence coefficient δ during the i-th use of the vehicle i ;

[0084] Among them, dy ai is the voltage of the vehicle during the a-th data collection during the i-th use process, For all ai The average value, nj i is the average torque of the vehicle during its i-th use, nj y is the preset average torque, θ is the error influence coefficient, cz i is the load-bearing capacity of the vehicle during its i-th use, cz yis the preset load-bearing capacity;

[0085] Through the above technical solution, this example provides the motor state influence coefficient δ during the i-th use of the vehicle i , can be obtained by formula Calculated, where the formula The voltage fluctuation value during the i-th use of the vehicle can be calculated. Therefore, it is obvious that when the voltage fluctuation value and the average torque during the i-th use of the vehicle are larger, and the load-bearing capacity during the i-th use of the vehicle is smaller, the motor state influence coefficient δ during the i-th use of the vehicle is i The larger the value, the worse the motor operation status of the vehicle during this use. On the contrary, when the voltage fluctuation value and average torque of the vehicle during the i-th use are smaller and the load-bearing capacity of the vehicle during the i-th use is larger, the motor state influence coefficient δ of the vehicle during the i-th use is i The smaller it is, the better the motor is performing during use, which means the motor has no obvious signs of aging or wear.

[0086] The monitoring process of the vehicle monitoring module also includes:

[0087] By calculating the motor state influence coefficient δ during the i-th use of the vehicle i and the preset motor state influence coefficient threshold δ 01 Make a comparison;

[0088] If δ i ≥δ 01 , the system determines that during this use, the vehicle's motor is in poor operating condition and there is an abnormality in the motor operation, and issues an early warning;

[0089] If δ i <δ 01 , the system determines that during this use, the vehicle's motor is in good operating condition and there is no abnormality in the motor operation;

[0090] Through the above technical solution, this example calculates the motor state influence coefficient δ during the i-th use of the vehicle i and the preset motor state influence coefficient threshold δ 01 By comparing, the motor operating status during the use can be analyzed, and based on the analysis results, it can be further determined whether there is any abnormality in the motor operation. Through such a setting, effective monitoring of the motor status of the electric bicycle can be achieved, avoiding the situation where the vehicle is damaged due to lack of monitoring of the motor status, thereby increasing the risk of use for the owner.

[0091] The monitoring process of the vehicle monitoring module also includes:

[0092] Through the historical monitoring records of the vehicle monitoring module, a change curve δ(t) of the vehicle motor state influence coefficient is established;

[0093] And through the formula Calculate the change γ of the motor state influence coefficient from the time the vehicle is put into use to the time it is used for the i-th time i ;

[0094] Then, the change of the motor state influence coefficient γ from the time the vehicle is put into use to the time when it is used for the i-th time is calculated. i The change threshold γ 01 Make a comparison;

[0095] If γ i ≥γ 01 ,The system determines that the change in the motor state influence coefficient is high, indicating that the internal structure of the motor may be aged, worn or damaged, and is prone to failure in subsequent use, and issues an early warning;

[0096] If γ i <γ 01 ,The change amount of the motor state influence coefficient is high, indicating that the motor has stable performance and good health and the risk of failure is low;

[0097] Among them, t1 is the first use of the vehicle after it is put into use, t i is the i-th use of the vehicle since it was put into use, n is the total number of times the vehicle has been used since it was put into use until the i-th use, For all δ i The average value of

[0098] Through the above technical solution, this example provides the change γ of the motor state influence coefficient from the time the vehicle is put into use to the time it is used for the i-th time i , can be obtained by formula The calculation is then performed by calculating the change in the motor state influence coefficient γ from the time the vehicle is put into use to the time it is used for the i-th time i The change threshold γ 01 Through this comparison method, the change in the motor status influence coefficient can be judged. This data reflects the change in the performance of the motor since it was put into use, so the performance stability and health status of the motor can be further judged, thereby analyzing the possibility of future motor failures, realizing the supervision of the operating status of electric bicycles, and reducing the risks of owners during use.

[0099] The supervision process of the vehicle supervision module includes:

[0100] The motor angular velocity is detected by the Beidou central control module and converted into speed. Based on this data, it can be shown whether the vehicle speed exceeds the national standard and whether there is any speeding behavior. If there is speeding behavior, it is considered to be an illegally modified vehicle, and a reminder record is generated in the system and fed back to the traffic management department;

[0101] Based on the vehicle route data displayed by the system, determine whether there is a wrong-way situation or abnormal lane usage, and generate a reminder record in the system and feedback to the traffic management department;

[0102] Based on the vehicle usage routes and usage time displayed by the system, combined with the intersection indicator data in the big data, it is determined whether there is a red light violation, and a reminder record is generated in the system and fed back to the traffic management department;

[0103] Through the above technical solution, this example provides a supervision process of the vehicle supervision module, in which the Beidou central control module can detect the motor angular velocity and convert it into speed. When it exceeds the national standard limit of 25 kilometers per hour, it is determined to be illegal modification, and the warning information is reported to the platform in real time. Through such a setting, not only can it be supervised whether the user has speeding behavior, but also it can be judged whether the user has modified the car according to the maximum speed. By monitoring the vehicle route data, it can be judged whether the user is driving in the opposite direction, and according to the vehicle route and usage time displayed by the system, combined with the intersection indicator light data in the big data, it can be judged whether there is a red light violation. Through such a setting, after the data record is fed back to the traffic management department, it can provide the traffic management department with road enforcement data, thereby helping the traffic management department to increase road enforcement control.

[0104] See also Figure 2 As shown, a full-chain supervision method for the life cycle of electric vehicles based on big data, the method includes:

[0105] S1: The traceability management module uses traceability technology to combine the body code, battery code and charger code of the electric bicycle into a traceability code, and registers the owner's personal information and vehicle license plate information during sales, and binds the traceability code of the electric bicycle through the user's mobile phone;

[0106] S2: Collect the vehicle's operating status data and vehicle location information through the Beidou central control module, where the vehicle's operating status data includes battery data and motor operating data;

[0107] S3: The vehicle monitoring module combines the battery status data and the motor operation status data of the vehicle to monitor the operation status of the vehicle during charging and use;

[0108] S4: The vehicle monitoring module combines the vehicle's location information to monitor illegal incidents during vehicle use;

[0109] S5: Through the environmental recycling module combined with the data information registered in the traceability management module, when the vehicle battery is about to expire or the vehicle reaches the scrap standard, the user is reminded to go to the designated recycling point for battery recycling or to go through the vehicle scrapping procedures;

[0110] Through the above technical solution, this example provides a full-chain supervision method for the life cycle of electric vehicles based on big data. First, the body code, battery code and charger code of the electric bicycle are synthesized into a traceability code by using the traceability technology through the traceability management module. Then, the owner's personal information and the license plate information of the vehicle are registered at the time of sale, and the traceability code of the electric bicycle is bound through the user's mobile phone. When the vehicle is in use, the Beidou central control module collects the vehicle's operating status data and vehicle location information, and the vehicle monitoring module combines the vehicle's battery status data and motor operating status data to monitor the vehicle's operating status during charging and use. The vehicle supervision module combines the vehicle's location information to supervise illegal incidents during the use of the vehicle, and the environmental recycling module combines the data information registered in the traceability management module to remind the user to go to the designated recycling point for battery recycling or handle vehicle scrapping procedures when the vehicle battery is about to expire or the vehicle reaches the scrap standard.

[0111] Through this supervision method, full-chain supervision of the life cycle of electric bicycles can be achieved based on multiple links such as production, sales, operating status, illegal incident supervision, and recycling of old batteries, effectively ensuring the quality safety, use safety and environmental safety of electric vehicles.

[0112] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A full-chain supervision system for the life cycle of electric vehicles based on big data, characterized in that: The system comprises: The traceability management module is used to use the traceability technology to combine the body code, battery code and charger code of the electric bicycle into a traceability code, register the owner's personal information and the vehicle's license plate information when selling, and bind the traceability code of the electric bicycle through the user's mobile phone; Beidou central control module, used to collect vehicle operation status data and vehicle location information, where the vehicle operation status data includes battery data and motor operation data; The vehicle monitoring module includes a battery monitoring unit and a motor monitoring unit, which are used to monitor the operating status of the vehicle during charging and use by combining the battery status data of the vehicle and the motor operating status data; The vehicle monitoring module is used to monitor illegal incidents during the use of the vehicle in combination with the vehicle's location information; The environmental recycling module, by combining the data information registered in the traceability management module, reminds users to go to designated recycling points for battery recycling or vehicle scrapping procedures when the vehicle battery is about to expire or the vehicle reaches the scrap standard.

2. According to claim 1, a big data-based full-chain supervision system for electric vehicle life cycle, characterized in that: The monitoring process of the vehicle monitoring module includes: By formula Calculate the battery status influence coefficient ω of the vehicle during the zth charging process z ; And through the formula Calculate the battery status influence coefficient μ of the vehicle during the i-th use process i ; Where z is any charging process, a is any data collection at a fixed time interval, b is the total number of collections, wd z is the average temperature of the battery during the zth charging process, dl az is the current intensity of the battery at the ath time point during the zth charging process, i is any use of the vehicle, wd i is the average battery temperature of the vehicle during the i-th use, wd y is the preset battery temperature, dl ai is the current intensity of the vehicle during the ath data collection in the i-th use process, dl y is the preset current intensity, hdl i is the total power consumption of the vehicle during its i-th use, lc i is the total mileage of the vehicle during its i-th use, hdl y is the preset power consumption per kilometer, and ρ is the influence coefficient of the battery life of the vehicle during the i-th use, which is set based on empirical fitting.

3. The electric vehicle life cycle full-chain supervision system based on big data according to claim 2 is characterized in that: The monitoring process of the vehicle monitoring module also includes: By calculating the battery status influence coefficient ω during the zth charging process z The battery status influence coefficient μ during the i-th use i Respectively with the preset battery influence coefficient threshold μ 01 Make a comparison; If μ i ≥μ 01 , the system determines that there is a problem with the battery operation status of the vehicle during this use, and the battery needs to be repaired, and pushes the warning information to the user's mobile phone; If μ i <μ 01 , the system determines that during this use, the vehicle's battery is in good operating condition and there is no problem; If z ≥μ 01 , the system determines that there is a problem with the vehicle's battery status during this charging process, which means that the battery is faulty or the modified battery has been modified, and the battery needs to be repaired, and the warning information is pushed to the user's mobile phone; If z <μ 01 , the system determines that the vehicle’s battery is in good condition during this charging process, which means that there is no battery failure and it is not an original registered battery.

4. The electric vehicle life cycle full-chain supervision system based on big data according to claim 3 is characterized in that: The monitoring process of the vehicle monitoring module also includes: By formula Calculate the motor state influence coefficient δ during the i-th use of the vehicle i ; Among them, dy ai is the voltage of the vehicle during the ath data collection during the i-th use process, For all ai The average value, nj i is the average torque of the vehicle during its i-th use, nj y is the preset average torque, θ is the error influence coefficient, cz i is the load-bearing capacity of the vehicle during its i-th use, cz y The preset load-bearing capacity.

5. The electric vehicle life cycle full-chain supervision system based on big data according to claim 4 is characterized in that: The monitoring process of the vehicle monitoring module also includes: By calculating the motor state influence coefficient δ during the i-th use of the vehicle i and the preset motor state influence coefficient threshold δ 01 Make a comparison; If δ i ≥δ 01 , the system determines that during this use, the vehicle's motor is in poor operating condition and there is an abnormality in the motor operation, and issues an early warning; If δ i <δ 01 The system determines that during this use, the vehicle's motor is in good operating condition and there is no abnormality in the motor operation.

6. The electric vehicle life cycle full-chain supervision system based on big data according to claim 5 is characterized in that: The monitoring process of the vehicle monitoring module also includes: Through the historical monitoring records of the vehicle monitoring module, a change curve δ(t) of the vehicle motor state influence coefficient is established; And through the formula Calculate the change γ of the motor state influence coefficient from the time the vehicle is put into use to the time it is used for the i-th time i ; Then, the change of the motor state influence coefficient γ from the time the vehicle is put into use to the time when it is used for the i-th time is calculated. i The change threshold γ 01 Make a comparison; If γ i ≥γ 01 ,The system determines that the change in the motor state influence coefficient is high, indicating that the internal structure of the motor may be aged, worn or damaged, and is prone to failure in subsequent use, and issues an early warning; If γ i <γ 01 ,The change amount of the motor state influence coefficient is high, indicating that the motor has stable performance and good health and the risk of failure is low; Among them, t1 is the first use of the vehicle after it is put into use, t i is the i-th use of the vehicle since it was put into use, n is the total number of times the vehicle has been used since it was put into use until the i-th use, For all δ i The average value of .

7. The electric vehicle life cycle full-chain supervision system based on big data according to claim 6 is characterized in that: The supervision process of the vehicle supervision module includes: The motor angular velocity is detected by the Beidou central control module and converted into speed. Based on this data, it can be shown whether the vehicle speed exceeds the national standard and whether there is any speeding behavior. If there is speeding behavior, it is considered to be an illegally modified vehicle, and a reminder record is generated in the system and fed back to the traffic management department; Based on the vehicle route data displayed by the system, determine whether there is a wrong-way situation or abnormal lane usage, and generate a reminder record in the system and feedback to the traffic management department; Based on the vehicle routes and usage time points displayed by the system, combined with the intersection indicator light data in the big data, it is determined whether there is a red light violation, and a reminder record is generated in the system and fed back to the traffic management department.

8. A method for supervising the entire life cycle of an electric vehicle based on big data, the method adopting a system for supervising the entire life cycle of an electric vehicle based on big data as described in claims 1-7, characterized in that: The method comprises: S1: The traceability management module uses traceability technology to combine the body code, battery code and charger code of the electric bicycle into a traceability code, and registers the owner's personal information and vehicle license plate information during sales, and binds the traceability code of the electric bicycle through the user's mobile phone; S2: Collect the vehicle's operating status data and vehicle location information through the Beidou central control module, where the vehicle's operating status data includes battery data and motor operating data; S3: The vehicle monitoring module combines the battery status data and the motor operation status data of the vehicle to monitor the operation status of the vehicle during charging and use; S4: The vehicle monitoring module combines the vehicle's location information to monitor illegal incidents during vehicle use; S6: Through the environmental recycling module combined with the data information registered in the traceability management module, when the vehicle battery is about to expire or the vehicle reaches the scrap standard, the user is reminded to go to the designated recycling point to recycle the battery or go through the vehicle scrapping procedures.

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