CN113240002B

CN113240002B - Internet of vehicles big data preprocessing system, device and method

Info

Publication number: CN113240002B
Application number: CN202110510732.5A
Authority: CN
Inventors: 王震坡; 刘鹏; 龙超华; 谢俊隽; 石文童; 李阳; 祁春玉
Original assignee: Beijing Bitnei Corp ltd
Current assignee: Beijing Bitnei Corp ltd
Priority date: 2021-05-11
Filing date: 2021-05-11
Publication date: 2024-03-19
Anticipated expiration: 2041-05-11
Also published as: CN113240002A

Abstract

The invention relates to a system, a device and a method for preprocessing big data of the Internet of vehicles. The system adopts a vehicle state processing module to divide running data continuously uploaded by a vehicle into a starting fragment set and a flameout fragment set, adopts the vehicle charging state processing module to determine the charging state of the starting fragment set and the charging state of the flameout fragment set, classifies the running data of the starting fragment set and the running data of the flameout fragment set according to the determined charging state of the starting fragment set and the determined charging state of the flameout fragment set to obtain a first fragment classification result, adopts a fault state processing module to judge whether the first fragment classification result has faults or not, and is also used for classifying the faults when the faults exist to obtain a second fragment classification result, thereby improving the data processing efficiency while meeting various intelligent network complex application scenes.

Description

Internet of vehicles big data preprocessing system, device and method

Technical Field

The invention relates to the technical field of data processing, in particular to a system, a device and a method for preprocessing big data of the Internet of vehicles.

Background

The intelligent and networking of automobiles has also been greatly advanced due to the development of dynamism, artificial intelligence and big data. The current large data of the running new energy automobile has mass basic data accumulation, but the continuous basic data storage occupies huge space, the business analysis calculation memory and the time consumption are serious, and the application of the basic data often needs three roles of a large data engineer, a vehicle engineer and a product engineer to participate, so that the technical threshold is high, and the application efficiency of the basic data is reduced.

Currently, some preprocessing technologies segment, extract and reorganize basic data uploaded by a vehicle according to service requirements or specific working conditions to form a preprocessed database for supporting subsequent analysis services. The technology can better support corresponding subsequent business analysis work, but lacks wide business scene applicability. When other business or working condition analysis is carried out, preprocessing is needed again, and the processed data lacks expansibility and wide business scene applicability.

Another method for preprocessing the basic data is to divide the basic data into driving segments, charging segments, parking segments and the like according to the states of the vehicles (such as driving, parking, charging and the like) so as to facilitate classification management. The preprocessing method aims at restoring the real vehicle condition, but because of the problem of a certain proportion of vehicle uploading data, the real working condition of the vehicle is complex and changeable, the actual processing logic is difficult to completely reflect the vehicle working condition, and the basic data still needs to be searched after the problem occurs, so that the efficiency of service application is reduced. On the other hand, the different segments lack of connection, when the different segments need to be comprehensively analyzed, the processing difficulty is increased, and the processing efficiency of the follow-up service is reduced.

Therefore, providing a vehicle networking big data processing method or system with high processing efficiency capable of meeting various intelligent vehicle networking complex application scenes is a technical problem to be solved in the field.

Disclosure of Invention

The invention aims to provide a system, a device and a method for preprocessing big data of the Internet of vehicles, which can improve the data processing efficiency while meeting the complex application scenes of various intelligent Internet of vehicles.

In order to achieve the above object, the present invention provides the following solutions:

an internet of vehicles big data preprocessing system, comprising:

the vehicle state processing module is used for dividing the running data continuously uploaded by the vehicle into a starting fragment set and a flameout fragment set; the operation data includes: vehicle state data, battery state data, motor state data, electronic control state data, and engine state data;

the vehicle charge state processing module is connected with the vehicle state processing module and is used for determining the charge state of the starting fragment set and the charge state of the flameout fragment set, and classifying the operation data of the starting fragment set and the operation data of the flameout fragment set according to the determined charge state of the starting fragment set and the determined charge state of the flameout fragment set to obtain a first fragment classification result; the first segment classification result includes: start state + park charge segment, start state + drive charge segment, start state + un-charge segment, flameout state + park charge segment, and flameout segment + un-charge segment;

The fault state processing module is connected with the vehicle charging state processing module and is used for judging whether the first state classification result has faults or not, and when the faults exist, the fault state processing module is also used for classifying the existing faults to obtain a second segment classification result; the second segment classification result includes: the method comprises the following steps of starting state+parking charge+non-fault section, starting state+parking charge+fault section, starting state+running charge+non-fault section, starting state+running charge+fault section, starting state+non-charge+non-fault section, starting state+non-charge+fault section, flameout state+parking charge+non-fault section, flameout state+parking charge+fault section, flameout section+non-charge+non-fault section and flameout section+non-charge+fault section.

Preferably, the method further comprises:

the abnormal value processing module is respectively connected with the vehicle state processing module, the vehicle charging state processing module and the fault state processing module, and is used for carrying out abnormal processing on the continuously uploaded running data of the vehicle when the vehicle state processing module divides the continuously uploaded running data of the vehicle into a starting fragment set and a flameout fragment set, and carrying out abnormal processing on the running data of the starting fragment set and the running data of the flameout fragment set when the vehicle charging state processing module carries out fragment classification on the running data of the starting fragment set and the running data of the flameout fragment set according to the determined charging state of the starting fragment set and the determined charging state of the flameout fragment set; the exception handling includes: deletion and correction.

Preferably, the outlier processing module includes:

a first exception handling unit for executing a first processing logic; the first processing logic is to: judging whether the vehicle state in the continuously uploaded running data of the vehicle is abnormal or invalid, if so, directly deleting the continuously uploaded running data of the vehicle; judging whether the vehicle state in the running data continuously uploaded by the vehicle is in a state except a starting state, a flameout state, an abnormal state and an invalid state, judging whether the vehicle state is the starting state or the flameout state according to the speed of the vehicle, the total current, the data acquisition time and the mileage difference between the running data, or judging whether the vehicle speed is 0 and the total current is less than or equal to 0, if the vehicle speed is 0 and the total current is less than or equal to 0, modifying the current state of the vehicle to be in the flameout state, and if the vehicle speed is not 0 and the total current is greater than 0, modifying the current state of the vehicle to be in the starting state;

a second exception handling unit for executing a second processing logic; the second processing logic is to: judging whether the charging state of the operation data of the starting fragment set or the charging state of the operation data of the flameout fragment set is abnormal or invalid, if so, directly deleting the operation data of the starting fragment set or the operation data of the flameout fragment set; when the charging state in the operation data of the flameout segment set is uncharged or charged, judging whether the operation data of the flameout segment set is flameout state+parking charging segment according to the speed, the total current and the SOC; and if the charging state in the operation data of the starting fragment set is uncharged or charged, not processing.

Preferably, the vehicle state processing module includes:

the first traversing unit is used for traversing the vehicle state field in the running data continuously uploaded by the vehicle, when the vehicle state of the traversed current information frame is 1 and the vehicle state of the next information frame connected with the current information frame time sequence is 2, recording the current information frame as a starting frame, recording the next information frame connected with the current information frame time sequence as a flameout frame, recording the starting frame as a condition start, and recording the flameout frame as a fragment start; traversing the data after the segment starts, if the vehicle state is 2, continuing traversing until the vehicle state is 1, marking the information frame with the vehicle state of 1 as a condition, and marking the previous frame data of the information frame with the vehicle state of 1 as the segment end;

the segment dividing unit is connected with the first traversing unit and is used for dividing the running data continuously uploaded by the vehicle into the starting segment set and the flameout segment set according to the condition start, the segment start, the condition end and the segment end.

Preferably, the method further comprises:

the data validity detection module is connected with the fault state processing module and is used for detecting and judging the vehicle state and the charging state of the operation data in the second segment classification result according to the vehicle speed, the total current and the SOC, and adding a data validity detection result field into each judgment result;

When the charging state is detected to be uncharged, and the total current in the continuous multiple pieces of operation data is a negative value, the SOC is increased, and the added data validity detection result field is that the charging state is wrong;

if the vehicle state is flameout, but the speed of the continuous multiple pieces of information is not 0, the total current is not 0, and the SOC is reduced, the added data validity detection result field is that the vehicle state is wrong;

if the mileage difference between the condition start and the fragment start is larger than the set mileage in the first fragment classification result, the added data validity detection result field is data loss;

if the mileage difference between the condition end and the segment end is larger than the set mileage in the first segment classification result, the added data validity detection result field is data loss.

Preferably, the vehicle state of charge processing module includes:

the second traversing unit is used for traversing the charge state field in the flameout segment set, marking the obtained first charge state 1 information frame as a segment charge start, marking the last charge state not equal to 1 information frame connected with the first charge state 1 information frame time sequence as a conditional charge start, continuing traversing, marking the obtained last charge state 1 information frame as a segment charge end, and marking the next charge state not equal to 1 information frame connected with the last charge state 1 information frame time sequence as a conditional charge end;

The first charging state determining unit is connected with the second traversing unit and is used for judging whether charging process data exist in the flameout segment set after the second traversing unit traverses the flameout segment set, if so, the charging state is marked as parking charging, and if not, the charging state is marked as uncharged;

a third traversing unit, configured to traverse the charge state field in the start segment set, record the obtained first charge state 1 information frame as a segment charge start, record the last charge state not 1 information frame connected to the first charge state 1 information frame time sequence as a conditional charge start, continue traversing, record the obtained last charge state 1 information frame as a segment charge end, and record the next charge state not 1 information frame connected to the last charge state 1 information frame time sequence as a conditional charge end;

the second charging state determining unit is connected with the third traversing unit and is used for judging whether charging process data exist in the starting fragment set after the third traversing unit traverses the starting fragment set, if so, the charging state is marked as starting charging, and if not, the charging state is marked as uncharged;

A fourth traversing unit, configured to traverse the charge state field in the start segment set, record the obtained first charge state information frame with 2 as a segment charge start, start the last charge state information frame with non-2 connected to the first charge state information frame with 2 as a condition charge, continue traversing, end the obtained last charge state information frame with 2 as a segment charge, and end the next charge state information frame with non-2 connected to the last charge state information frame with 2 as a condition charge;

and the third charging state determining unit is connected with the fourth traversing unit and is used for judging whether the starting fragment set has charging process data after the fourth traversing unit traverses the starting fragment set, if so, marking the charging state as running charging, and if not, marking the charging state as uncharged.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

according to the Internet of vehicles big data preprocessing system provided by the invention, the vehicle state processing module is used for dividing the running data continuously uploaded by the vehicle into the starting segment set and the flameout segment set, the vehicle charging state processing module is used for determining the charging state of the starting segment set and the charging state of the flameout segment set, and the running data of the starting segment set and the running data of the flameout segment set are subjected to segment classification according to the determined charging state of the starting segment set and the determined charging state of the flameout segment set, so that a first segment classification result is obtained, the fault state processing module is used for judging whether the first state classification result has a fault or not, and the fault is further used for classifying the segments when the fault exists, so that a second segment classification result is obtained. The invention firstly carries out segment division according to the state of the national standard vehicle, the state of charge and the fault state, and can further form a data list with important process data such as the whole vehicle, the battery, the motor and the like, so that the invention can improve the data processing efficiency while meeting the complex application scenarios of various intelligent network connection.

In addition, the invention also provides the following technical scheme corresponding to the Internet of vehicles big data preprocessing system:

the Internet of vehicles big data preprocessing device comprises the Internet of vehicles big data preprocessing system.

A big data preprocessing method of the Internet of vehicles comprises the following steps:

acquiring running data continuously uploaded by a vehicle;

dividing running data continuously uploaded by a vehicle into a starting fragment set and a flameout fragment set; the operation data includes: vehicle state data, battery state data, motor state data, electronic control state data, and engine state data;

determining the charging state of the starting fragment set and the charging state of the flameout fragment set, and classifying the operation data of the starting fragment set and the operation data of the flameout fragment set according to the determined charging state of the starting fragment set and the determined charging state of the flameout fragment set to obtain a first fragment classification result; the first segment classification result includes: start state + park charge segment, start state + drive charge segment, start state + un-charge segment, flameout state + park charge segment, and flameout segment + un-charge segment;

Judging whether the first state classification result has faults or not, and classifying the faults when the faults exist, so as to obtain a second segment classification result; the second segment classification result includes: the method comprises the following steps of starting state+parking charge+non-fault section, starting state+parking charge+fault section, starting state+running charge+non-fault section, starting state+running charge+fault section, starting state+non-charge+non-fault section, starting state+non-charge+fault section, flameout state+parking charge+non-fault section, flameout state+parking charge+fault section, flameout section+non-charge+non-fault section and flameout section+non-charge+fault section.

Preferably, the dividing the running data continuously uploaded by the vehicle into a start segment set and a flameout segment set specifically includes:

traversing a vehicle state field in the running data continuously uploaded by the vehicle, when the vehicle state of the traversed current information frame is 1 and the vehicle state of the next information frame connected with the current information frame time sequence is 2, recording the current information frame as a starting frame, recording the next information frame connected with the current information frame time sequence as a flameout frame, recording the starting frame as a condition, and recording the flameout frame as a fragment start; traversing the data after the segment starts, if the vehicle state is 2, continuing traversing until the vehicle state is 1, marking the information frame with the vehicle state of 1 as a condition, and marking the previous frame data of the information frame with the vehicle state of 1 as the segment end;

And dividing the running data continuously uploaded by the vehicle into the starting fragment set and the flameout fragment set according to the condition start, the fragment start, the condition end and the fragment end.

Preferably, the determining the charging state of the starting segment set and the charging state of the flameout segment set, and classifying the operation data of the starting segment set and the operation data of the flameout segment set according to the determined charging state of the starting segment set and the determined charging state of the flameout segment set, so as to obtain a first segment classification result, which specifically includes:

traversing the charge state field in the flameout segment set, marking the obtained information frame with the first charge state of 1 as the segment charge start, marking the information frame with the last charge state of not equal to 1, which is connected with the information frame with the first charge state of 1, as the condition charge start, continuing traversing, marking the obtained information frame with the last charge state of 1 as the segment charge end, and marking the information frame with the next charge state of not equal to 1, which is connected with the information frame with the last charge state of 1, as the condition charge end;

After traversing the flameout segment set, judging whether charging process data exist in the flameout segment set, if so, marking the charging state as parking charging, and if not, marking the charging state as uncharged;

traversing the charge state field in the starting fragment set, marking the obtained first charge state 1 information frame as a fragment charge start, marking the last charge state 1 information frame connected with the first charge state 1 information frame time sequence as a conditional charge start, continuing traversing, marking the obtained last charge state 1 information frame as a fragment charge end, and marking the next charge state 1 information frame connected with the last charge state 1 information frame time sequence as a conditional charge end;

after traversing the starting fragment set, judging whether charging process data exist in the starting fragment set, if so, marking the charging state as starting charging, and if not, marking the charging state as uncharged;

traversing the charge state field in the starting fragment set, marking the obtained first charge state information frame with the charge state of 2 as a fragment charge start, taking the last charge state information frame with the charge state of not 2 connected with the first charge state information frame time sequence as a conditional charge start, continuing traversing, taking the obtained last charge state information frame with the charge state of 2 as a fragment charge end, and taking the next charge state information frame with the charge state of not 2 connected with the last charge state information frame time sequence with the charge state of 2 as a conditional charge end;

After traversing the starting fragment set, judging whether charging process data exist in the starting fragment set, if so, marking the charging state as running charging, and if not, marking the charging state as uncharged.

The technical effects achieved by the internet of vehicles big data preprocessing device and method provided by the invention are the same as those achieved by the internet of vehicles big data preprocessing system, so that the technical effects are not repeated here.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a process flow diagram of a large data preprocessing system of the Internet of vehicles, which is provided by the invention;

FIG. 2 is a diagram of an example of vehicle operation data of the Internet of vehicles according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of flameout segment and startup segment division according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of abnormal vehicle state values according to an embodiment of the present invention;

Fig. 5 is a schematic diagram of parking charging in a flameout data segment set according to an embodiment of the present invention;

fig. 6 is a schematic diagram of parking charging in a startup data segment set according to an embodiment of the present invention;

fig. 7 is a schematic diagram of driving charging in a startup data segment set according to an embodiment of the present invention;

FIG. 8 is a frame diagram of the Internet of vehicles big data preprocessing device provided by the invention;

fig. 9 is a flowchart of a method for preprocessing internet of vehicles big data.

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.

Interpretation of technical terms:

SOC: the battery state of charge.

Basic data: the vehicle uploads continuous data in real time every 30s according to national standards, and the data volume is in the P level.

Fragment data: and the data size of the fragment data of the basic data subjected to preprocessing operations such as segmentation, extraction, recombination and the like is in the T level.

The vehicle states in national standard GBT32960 are divided into: 1, starting state; 2 flameout state; 3 other states; FE abnormal state; FF inactive state.

The state of charge in national standard GBT32960 is divided into: 1, stopping and charging; 2, running and charging; 3, charging; 4, finishing charging; FE abnormal state; FF inactive state.

Highest alarm level in national standard GBT 32960: 0 has no fault; 1 first-level fault; a 2-level fault; 3, three-level fault; FE abnormal state; FF inactive state.

Timing phase connection definition: the 2 information frames are time-sequentially connected, and no other information frame is in the middle.

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

Based on the processing flow chart of the internet of vehicles big data preprocessing system shown in fig. 1, the internet of vehicles big data preprocessing system provided by the invention comprises: a vehicle state processing module 131, a vehicle state of charge processing module 132, and a fault state processing module 133.

The vehicle state processing module 131 is configured to divide the running data continuously uploaded by the vehicle into a start segment set and a flameout segment set. The operation data includes: vehicle state data, battery state data, motor state data, electronic control state data, and engine state data.

In the actual running process, the inputs of the vehicle state processing module 131 are: continuous flow data meeting the requirements of national standard GBT32960 are uploaded, and as shown in FIG. 2, a vehicle state field in the continuous flow data is selected for processing. In a specific process, the related segment partitioning logic is: if the vehicle state of the vehicle uploading data is only started (i.e. vehicle state=1) and flameout (i.e. vehicle state=2), the following fragment processing is directly performed:

as shown in part (a) of fig. 3, a certain flameout segment is divided, in a continuous data sequence, a vehicle status field is traversed to find a start frame (vehicle status=1), and the next frame connected with the start frame in time sequence must be a flameout frame (vehicle status=2), then the start frame (vehicle status=1) is started, and the next flameout frame connected with the time sequence (vehicle status=2) is started as a segment. Continuing the traversal, when the next start frame (vehicle state=1) is encountered, as a condition end, the frame immediately before the condition end is a segment end. As shown in part (b) of fig. 3, which illustrates the division of a certain start segment, in the data sequence, a flameout frame (vehicle state=2) is found and the next frame following it in time must be a start frame (vehicle state=1). Then this flameout frame (vehicle state=2) is started as a condition and the next start frame (vehicle state=1) in the sequential phase is started as a segment as shown in part (b) of fig. 3. Continuing the reading, the next flameout frame (vehicle state=2) is encountered, and the condition ends.

The condition for setting the segment starts, the segment ends, and the condition ends at 4 time points. On the one hand, the data of vehicles, batteries, motors, electric control, engines and the like at the head and the tail of the segment can be extracted according to 4 points. On the other hand, the data validity of the whole segment can be judged according to the 4 point data.

In order to implement the specific logic of the above segment division, the vehicle state processing module 131 provided by the present invention further includes: the first traversing unit and the segment dividing unit connected with the first traversing unit.

The first traversing unit is used for traversing the vehicle state field in the running data continuously uploaded by the vehicle, when the vehicle state of the traversed current information frame is 1 and the vehicle state of the next information frame connected with the current information frame time sequence is 2, the current information frame is recorded as a starting frame, the next information frame connected with the current information frame time sequence is recorded as a flameout frame, the starting frame is recorded as a condition, and the flameout frame is recorded as a segment start. Traversing the data after the start of the segment, if the vehicle state is 2, continuing the traversing until the vehicle state is 1, marking the information frame with the vehicle state of 1 as a condition, and marking the previous frame data of the information frame with the vehicle state of 1 as the segment.

The segment dividing unit is used for dividing the running data continuously uploaded by the vehicle into a start segment set and a flameout segment set according to the condition start, the segment start, the condition end and the segment end.

Based on the specific processing logic and structure of the vehicle state processing module 131, it is able to precisely divide a large amount of continuous vehicle operation data into two main types of data sets, namely, a start state segment and a flameout state segment.

The vehicle state of charge processing module 132 is connected to the vehicle state of charge processing module 131, where the vehicle state of charge processing module 132 is configured to determine a state of charge of the starting segment set and a state of charge of the flameout segment set, and perform segment classification on the operation data of the starting segment set and the operation data of the flameout segment set according to the determined state of charge of the starting segment set and the determined state of charge of the flameout segment set, so as to obtain a first segment classification result. The first segment classification result includes: start state + park charge segment, start state + drive charge segment, start state + uncharged segment, flameout state + park charge segment, and flameout segment + uncharged segment.

I.e. whether the piece of data is a parking charge, a driving charge or an uncharged state, depending on the vehicle charge state. And logic judgment processing is performed for other or abnormal situations of the vehicle charge state. If the charging state is abnormal or invalid, the direct deletion does not influence the later service development because the state is extremely small in proportion, so that the direct discarding is adopted. If the vehicle state is parked and in an uncharged state, it is necessary to determine whether it needs to be corrected to a parked charge according to the speed, the total current and the time (state of charge=1). If the vehicle is parked and charging is completed, it is necessary to determine whether it needs to be corrected to a parked charge according to the speed, the total current, and the time (state of charge=1). If the vehicle state is driving, the uncharged state and the charged state are not processed.

In order to determine and classify the states of charge of the start segment and the flameout segment data sets, the vehicle state of charge processing module 132 preferably processes the states of charge as follows:

as shown in fig. 5, the charging state field in the flameout segment data set is traversed, and the information frame with the first charging state=1 is found as the segment charging start, and the time sequence is connected with the conditional charging start frame with the charging state not equal to 1. And (3) the last information frame with the charge state=1 in the flameout segment set is the segment charge ending, and the next time sequence is connected with the conditional charge ending with the charge state not equal to 1. After the traversal is finished, if a charging process is found, the charging process is marked as parking charging. If the entire segment has no charging process, it is marked as uncharged.

As shown in fig. 6, the charging status field in the segment data set is traversed, the first charging status=1 information frame is the segment charging start, and the time sequence is connected with the conditional charging start frame with the charging status not equal to 1. And (3) the last information frame with the charging state=1 in the starting fragment set is the fragment charging end, and the time sequence is connected with the conditional charging end with the next charging state not equal to 1. After the traversal is finished, if a charging process is found, the charging process is marked as parking charging. If the entire segment has no charging process, it is marked as uncharged.

As shown in fig. 7, the charge status field in the start fragment data set is traversed, the first charge status=2 information frame is the fragment charge start, and the time sequence is connected with the last charge status frame which is not equal to 2 and is the conditional charge start frame. And (3) the last information frame with the charging state=2 in the starting fragment set is the fragment charging end, and the next charging state which is connected in time sequence is not equal to 2 is the conditional charging end. After the traversal is finished, if the charging process is found, the driving charging is marked. If the entire segment has no charging process, it is marked as uncharged.

To implement the processing logic described above, the vehicle state of charge processing module 132 provided by the present invention preferably includes: the device comprises a second traversing unit, a first charging state determining unit, a third traversing unit, a second charging state determining unit, a fourth traversing unit and a third charging state determining unit.

The second traversing unit is used for traversing the charge state field in the flameout segment set, marking the obtained first charge state 1 information frame as the segment charge start, marking the last charge state not equal to 1 information frame connected with the first charge state 1 information frame time sequence as the condition charge start, continuing traversing, marking the obtained last charge state 1 information frame as the segment charge end, and marking the next charge state not equal to 1 information frame connected with the last charge state 1 information frame time sequence as the condition charge end.

The first charging state determining unit is connected with the second traversing unit and is mainly used for judging whether charging process data exist in the flameout segment set after the second traversing unit traverses the flameout segment set, if so, the charging state is marked as parking charging, and if not, the charging state is marked as uncharged.

The third traversing unit is used for traversing the charge state field in the starting fragment set, marking the obtained first charge state 1 information frame as the start of fragment charge, marking the last charge state 1 information frame connected with the first charge state 1 information frame time sequence as the start of conditional charge, continuing traversing, marking the obtained last charge state 1 information frame as the end of fragment charge, and marking the next charge state 1 information frame connected with the last charge state 1 information frame time sequence as the end of conditional charge.

The second charging state determining unit is connected with the third traversing unit and is mainly used for judging whether the starting fragment set has charging process data after the third traversing unit traverses the starting fragment set, if so, marking the charging state as starting charging, and if not, marking the charging state as uncharged.

The fourth traversing unit is used for traversing the charge state field in the starting fragment set, marking the obtained first charge state information frame with the charge state of 2 as the start of fragment charge, taking the last charge state information frame with the charge state of not 2 connected with the first charge state information frame time sequence as the start of conditional charge, continuing to traverse, taking the obtained last charge state information frame with the charge state of 2 as the end of fragment charge, and taking the next charge state information frame with the charge state of not 2 connected with the last charge state information frame time sequence of 2 as the end of conditional charge.

The third charging state determining unit is connected with the fourth traversing unit and is mainly used for judging whether the starting fragment set has charging process data after the fourth traversing unit traverses the starting fragment set, if so, the charging state is marked as running charging, and if not, the charging state is marked as uncharged.

Based on the specific processing logic and structure of the vehicle state of charge processing module 132 provided in the present invention, the states of charge in the start segment set and the flameout segment set divided by the vehicle state processing module 131 can be precisely divided to obtain the first segment classification result.

The fault state processing module 133 is connected to the vehicle charging state processing module 132, where the fault state processing module 133 is configured to determine whether a fault exists in the first state classification result, and when the fault exists, further configured to classify a segment of the existing fault to obtain a second segment classification result. The second segment classification result includes: the method comprises the following steps of starting state+parking charge+non-fault section, starting state+parking charge+fault section, starting state+running charge+non-fault section, starting state+running charge+fault section, starting state+non-charge+non-fault section, starting state+non-charge+fault section, flameout state+parking charge+non-fault section, flameout state+parking charge+fault section, flameout section+non-charge+non-fault section and flameout section+non-charge+fault section.

The fault alarm processing module classifies the data fragments of the input first state classification result according to the highest alarm level field in the vehicle uploading operation data. If the whole segment has no fault, the fault segment is a non-fault segment, and if the whole segment has at least one fault alarm message, the fault segment is a fault segment. The fault alarm processing module carries out alarm grade identification on the data fragments which are already classified according to the highest alarm grade of the vehicle, wherein the fault alarm processing module comprises 0-3 states which are respectively no fault, 1-level fault, 2-level fault and 3-level fault.

After the fault segment classification is performed to obtain a second segment classification result, the invention also needs to extract the data of 4 time points of time stamps, segment data items, SOC, mileage, total voltage, total current, single voltage, single extreme voltage, temperature, vehicle speed, braking and driving states, motors, engines and the like, which are shown in the figure 2. In addition, fields such as maximum and minimum values of information such as total voltage, total current, single voltage, single extreme voltage, temperature, vehicle speed, temperature difference, single voltage difference and the like in the whole segment are required to be extracted to generate a new data set. To achieve this, a data extraction module may be provided to connect with the fault alarm processing module.

In order to support a wide service application scenario conveniently, based on the data processed by the vehicle state processing module 131, the vehicle charging state processing module 132 and the fault state processing module 133, problem data caused by abnormal fields such as mileage, voltage, current, speed and the like may exist, and adverse effects may be generated on the post-service.

The data validity detection module 134 is connected to the fault state processing module 133, and is mainly configured to detect and determine a vehicle state and a charging state of the operation data in the second segment classification result according to the vehicle speed, the total current and the SOC, and add a data validity detection result field to each determination result. The added data validity detection result field mainly plays a role of identification, and is mainly used for detecting and judging the vehicle state and the charging state through the vehicle speed, the total current, the SOC and the like. And judging whether the whole segment has data loss, data insufficiency and data errors or not through the table display mileage, time interval and SOC values at 4 moments of condition start, segment end and condition end, thereby carrying out validity detection of the data segment.

When the state of charge is detected to be uncharged, and the total current in the continuous pieces of operation data is negative (representing charging), the SOC is increased, and the added data validity detection result field is the state of charge with errors.

If the vehicle state is flameout, but the vehicle speed of the continuous pieces of information is not 0, the total current is not 0, and the SOC is reduced, the added data validity detection result field is that the vehicle state is wrong.

Further, detecting the data loss of the whole segment through mileage and SOC corresponding to four time points of condition start, segment end and condition end, and marking the added identification field. In particular, the method comprises the steps of,

if the mileage difference between the condition start and the fragment start is greater than the set mileage in the first fragment classification result, the added data validity detection result field is data loss.

In the present invention, the number of miles is preferably set to 2 km, but the present invention is not limited thereto. The process of data identification using 2 km as error is:

if in any type of segment, the condition starts to have a mileage difference of more than 2 km from the segment start, it can be assumed that there is a loss of some data and the data loss is identified in the added field.

If the mileage between the end of the condition and the end of the segment is more than 2 km in any type of segment, then a partial loss of data may be considered and the data loss identified in the added field.

Since the vehicle state is in addition to the normal start-up and flameout states, other states (vehicle state=3), abnormal states (vehicle state=fe), and invalid states (vehicle state=ff) as shown in fig. 4 may also occur. Therefore, it is necessary to perform abnormality judgment for the information frames in which the above 3 states occur in the process. Further, since the charge state of the vehicle is in addition to normal parking charge and running charge, there is a possibility that an uncharged state (charge state=3), a charged completed state (charge state=4), an abnormal state (charge state=fe), and an invalid state (charge state=ff) as shown in fig. 6 may occur according to national standard specification data. Therefore, in order to process abnormal values in the operation data, the internet of vehicles big data preprocessing system provided by the invention also preferably comprises an abnormal value processing module.

The abnormal value processing module is respectively connected with the vehicle state processing module 131, the vehicle charging state processing module 132 and the fault state processing module 133, and is mainly used for performing abnormal processing on the continuously uploaded running data of the vehicle when the vehicle state processing module 131 divides the continuously uploaded running data of the vehicle into a starting segment set and a flameout segment set, and is used for performing abnormal processing on the running data of the starting segment set and the running data of the flameout segment set when the vehicle charging state processing module 132 classifies the running data of the starting segment set and the running data of the flameout segment set according to the determined charging state of the starting segment set and the determined charging state of the flameout segment set. Exception handling includes: deletion and correction.

Further, the outlier processing module provided by the invention comprises: a first exception handling unit and a second exception handling unit.

The first exception handling unit is configured to execute first processing logic. The first processing logic is: and judging whether the vehicle state in the continuously uploaded running data of the vehicle is abnormal or invalid, and if so, directly deleting the continuously uploaded running data of the vehicle. Judging whether the vehicle state in the running data continuously uploaded by the vehicle is in a state except a starting state, a flameout state, an abnormal state and an invalid state (namely, other states, the vehicle state=3), judging whether the vehicle state is the starting state or the flameout state according to the speed of the vehicle, the total current, the data acquisition time and the mileage difference between the running data, or judging whether the vehicle speed is 0 and the total current is less than or equal to 0, if the vehicle speed is 0 and the total current is less than or equal to 0, modifying the current state of the vehicle into the flameout state, and if the vehicle speed is not 0 and the total current is greater than 0, modifying the current state of the vehicle into the starting state.

The first abnormality processing unit is mainly designed for an abnormal value of the vehicle state processing module 131 in the process of performing the vehicle state processing. When the vehicle state is judged to be abnormal or invalid, the abnormal or invalid state is extremely small in proportion, so that the vehicle state is processed by adopting a strategy of direct deletion.

The second exception handling unit is to execute second processing logic. The second processing logic is: judging whether the charging state of the operation data of the starting fragment set or the charging state of the operation data of the flameout fragment set is abnormal or invalid, and if so, directly deleting the operation data of the starting fragment set or the operation data of the flameout fragment set. And when the charging state in the operation data of the flameout segment set is uncharged or charged, judging whether the operation data of the flameout segment set is flameout state+parking charging segment according to the speed, the total current and the SOC. If the charging state in the operation data of the starting fragment set is uncharged or charged, the processing is not performed.

I.e. deleted directly if the state of charge is abnormal or invalid.

If the vehicle state is flameout and the state of charge is uncharged or charging is completed, it is necessary to determine whether it needs to be corrected to a parking charge according to the speed, the total current and the SOC (state of charge=1). If the vehicle state is on and the charge state is uncharged or charging is complete, no processing is performed.

An alternative decision logic is: if the vehicle state is flameout and in an uncharged state, but there is an increase in SOC with a continuous plurality of information that the vehicle speed is 0 and the total current is negative, it is corrected to a parking charge (state of charge=1).

Another alternative decision logic is: if the vehicle state is flameout and in the charge completion state, but there is an increase in the SOC with a continuous pieces of information that the vehicle speed is 0 and the total current is negative, it is corrected to the parking charge (charge state=1).

The second abnormality processing unit is mainly a setting for an abnormal value that the vehicle state of charge processing module 132 has in performing data processing. Because the charge state is abnormal or the duty ratio of the invalid state is extremely small, the direct deletion does not influence the development of the later-stage service, so that a strategy of direct deletion is adopted.

Based on the abnormal value processing module provided by the invention, the abnormal value in the whole data processing process can be accurately processed, so that the overall efficiency of the data processing process can be ensured.

In addition, the invention also provides two technical schemes corresponding to the Internet of vehicles big data preprocessing system:

the internet of vehicles big data preprocessing device comprises a big data platform 12 and the internet of vehicles big data preprocessing system 13 provided by the invention, as shown in fig. 8.

The data acquisition module 112 in the intelligent network-connected automobile 11 acquires data of parts including a vehicle state, a battery state, a motor state, an electric control state, an engine state and the like through the sensor 111 and an internal bus. And then transmitted to the big data platform 12 through the data transmission module 113.

The big data platform 12 includes a data receiving module 121, a data storing module 122, and a data extracting module 123. The data receiving module 121 receives the data sent by the module and sends the data to the data storage module 122 for distributed storage. When the data is needed to be used, the data is extracted and sent to a data preprocessing system.

After the data preprocessing system inputs the extracted data, the data processing module 131, the charging state processing module 132 and the fault state processing module 133 begin to process the data sequentially, and finally the data validity detection module 134 judges the data validity of the segment according to the conditions of mileage, time, SOC and the like.

The data processed by the data preprocessing system is finally saved to form a processed database 14.

The continuous state data, including the vehicle, the battery, the motor, the engine, the electric control and the like, which are collected and sent to the cloud big data platform 12 by the equipment, are processed into a start state+parking charge+no fault section, a start state+parking charge+fault section, a start state+running charge+no fault section, a start state+running charge+fault section, a start state+no charge+no fault section, a start state+no charge+fault section, a flameout state+parking charge+no fault section, a flameout state+parking charge+fault section, a flameout section+no charge+no fault section, and a flameout section+no charge+fault section according to the vehicle state, the charge state and the fault state in the national standard field. For these 10 states, it is necessary to determine the validity of the data segment and whether there is a data loss or not according to the vehicle speed, the total current, the SOC, the mileage, the time interval, and the like.

The other is a method for preprocessing big data of the internet of vehicles, as shown in fig. 9, which comprises the following steps:

step 900: and acquiring running data continuously uploaded by the vehicle.

Step 901: the continuously uploaded running data of the vehicle is divided into a start segment set and a flameout segment set. The operation data includes: vehicle state data, battery state data, motor state data, electronic control state data, and engine state data.

Step 902: determining the charging state of the starting fragment set and the charging state of the flameout fragment set, and classifying the operation data of the starting fragment set and the operation data of the flameout fragment set according to the determined charging state of the starting fragment set and the determined charging state of the flameout fragment set to obtain a first fragment classification result. The first segment classification result includes: start state + park charge segment, start state + drive charge segment, start state + uncharged segment, flameout state + park charge segment, and flameout segment + uncharged segment.

Step 903: judging whether the first state classification result has faults or not, and classifying the existing faults by fragments when the faults exist to obtain a second fragment classification result. The second segment classification result includes: the method comprises the following steps of starting state+parking charge+non-fault section, starting state+parking charge+fault section, starting state+running charge+non-fault section, starting state+running charge+fault section, starting state+non-charge+non-fault section, starting state+non-charge+fault section, flameout state+parking charge+non-fault section, flameout state+parking charge+fault section, flameout section+non-charge+non-fault section and flameout section+non-charge+fault section.

The step 901 specifically includes:

traversing the vehicle state field in the running data continuously uploaded by the vehicle, when the vehicle state of the traversed current information frame is 1 and the vehicle state of the next information frame connected with the current information frame time sequence is 2, recording the current information frame as a starting frame, recording the next information frame connected with the current information frame time sequence as a flameout frame, recording the starting frame as a condition start, and recording the flameout frame as a fragment start. Traversing the data after the start of the segment, if the vehicle state is 2, continuing the traversing until the vehicle state is 1, marking the information frame with the vehicle state of 1 as a condition, and marking the previous frame data of the information frame with the vehicle state of 1 as the segment.

And dividing the running data continuously uploaded by the vehicle into a start segment set and a flameout segment set according to the condition start, the segment start, the condition end and the segment end.

The step 902 specifically includes:

traversing the charge state field in the flameout segment set, marking the obtained information frame with the first charge state of 1 as the segment charge start, marking the information frame with the last charge state of not equal to 1, which is connected with the information frame with the first charge state of 1, as the condition charge start, continuing traversing, marking the obtained information frame with the last charge state of 1 as the segment charge end, and marking the information frame with the next charge state of not equal to 1, which is connected with the information frame with the last charge state of 1, as the condition charge end.

After traversing the flameout segment set, judging whether charging process data exist in the flameout segment set, if so, marking the charging state as parking charging, and if not, marking the charging state as uncharged.

And traversing a charge state field in a start fragment set, marking the obtained information frame with the first charge state of 1 as a fragment charge start, marking the information frame with the last charge state of not 1 connected with the information frame with the first charge state of 1 as a conditional charge start, continuing traversing, marking the obtained information frame with the last charge state of 1 as a fragment charge end, and marking the information frame with the next charge state of not 1 connected with the information frame with the last charge state of 1 as a conditional charge end.

After traversing the starting fragment set, judging whether charging process data exist in the starting fragment set, if so, marking the charging state as starting charging, and if not, marking the charging state as uncharged.

And traversing a charging state field in the starting fragment set, marking the obtained first charging state information frame with the charging state of 2 as a fragment charging start, taking the last charging state information frame with the charging state of not 2 connected with the first charging state information frame with the charging state of 2 as a conditional charging start, continuing traversing, taking the obtained last charging state information frame with the charging state of 2 as a fragment charging end, and taking the next charging state information frame with the charging state of not 2 connected with the last charging state information frame with the charging state of 2 as a conditional charging end.

In summary, the technical scheme provided by the invention can solve the problems of high preprocessing technical threshold, low application efficiency and the like in the prior art caused by large basic data storage amount, heavy calculation memory consumption and long calculation time consumption of the vehicle operation under the condition of intelligent network connection complex application scene in the new energy automobile industry. In order to solve the problem of single service application scene of the prior preprocessing technology, the invention sets the following steps: firstly, dividing segments according to national standard vehicle states, charging states and fault states, secondly, setting segment condition starting, segment ending and condition ending, and finally, forming a data list according to the divided segment extraction condition starting, segment ending and condition ending time point data and important process data such as a whole vehicle, a battery, a motor and the like.

In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.

The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims

1. The big data preprocessing system of the Internet of vehicles is characterized by comprising:

The fault state processing module is connected with the vehicle charging state processing module and is used for judging whether the first segment classification result has faults or not, and when the faults exist, the fault state processing module is also used for classifying the existing faults to obtain a second segment classification result; the second segment classification result includes: start state+park charge+no-fault section, start state+park charge+fault section, start state+travel charge+no-fault section, start state+travel charge+fault section, start state+uncharged+no-fault section, start state+uncharged+faulty section, flameout state+park charge+no-fault section, flameout state+park charge+fault section, flameout section+uncharged+no-fault section, and flameout section+uncharged+faulty section;

the vehicle state processing module includes:

2. The internet of vehicles big data preprocessing system of claim 1, further comprising:

3. The internet of vehicles big data preprocessing system according to claim 2, wherein said outlier processing module comprises:

4. The internet of vehicles big data preprocessing system of claim 1, further comprising:

5. The internet of vehicles big data preprocessing system of claim 1, wherein the vehicle state of charge processing module comprises:

6. The internet of vehicles big data preprocessing device, characterized by comprising the internet of vehicles big data preprocessing system according to any one of claims 1-5.

7. The Internet of vehicles big data preprocessing method is characterized by comprising the following steps:

acquiring running data continuously uploaded by a vehicle;

judging whether the first segment classification result has faults or not, and classifying the faults when the faults exist, so as to obtain a second segment classification result; the second segment classification result includes: start state+park charge+no-fault section, start state+park charge+fault section, start state+travel charge+no-fault section, start state+travel charge+fault section, start state+uncharged+no-fault section, start state+uncharged+faulty section, flameout state+park charge+no-fault section, flameout state+park charge+fault section, flameout section+uncharged+no-fault section, and flameout section+uncharged+faulty section;

The method for dividing the running data continuously uploaded by the vehicle into a starting segment set and a flameout segment set specifically comprises the following steps:

8. The internet of vehicles big data preprocessing method according to claim 7, wherein the determining the charging state of the starting segment set and the charging state of the flameout segment set, and classifying the operation data of the starting segment set and the operation data of the flameout segment set according to the determined charging state of the starting segment set and the determined charging state of the flameout segment set, to obtain a first segment classification result, specifically includes: