CN116110147A - Distributed time sequence data storage and aggregation analysis method - Google Patents
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Abstract
The invention discloses a distributed time sequence data storage and aggregation analysis method, and relates to the field of data processing; collecting vehicle driving information parameter data of each vehicle according to sampling time points of time sequence data, carrying out aggregation processing on the collected vehicle driving information parameter data of each vehicle to generate effective data, analyzing the effective data generated by aggregation, and generating latest time sequence data; the method comprises the steps of presetting a time sequence data standard threshold range, inquiring whether the latest time sequence data is abnormal according to the time sequence data standard threshold range, judging whether a vehicle breaks down, generating a fault report, receiving the fault report by a user, and performing troubleshooting and maintenance on the fault of the vehicle according to the fault report.
Description
Technical Field
The invention relates to the field of data processing, in particular to a distributed time sequence data storage and aggregation analysis method.
Background
Along with the development of technologies such as the internet of vehicles and wireless communication, the quantity of parameters required to be collected from vehicles is increased, and meanwhile, vehicle data required to be queried by users are updated continuously;
in the existing vehicle data query technology, data information required by a user is queried according to big data of the Internet of things, the method does not sort vehicle data source parameters, different vehicle parameters are ignored to have different importance, efficiency is low when the vehicle big data is queried, the existing vehicle data query method can only meet data query, analysis can not be performed on results after query results are obtained, failure problems of the vehicle can not be queried, a user is reminded of maintaining the failure problems of the vehicle, query monitoring of vehicle time sequence data is ignored, and the vehicle query monitoring requirements can not be met, so that the distributed time sequence data storage and aggregation analysis method is provided.
Disclosure of Invention
In order to solve the technical problems, the invention provides a distributed time sequence data storage and aggregation analysis method;
the aim of the invention can be achieved by the following technical scheme: a distributed time series data storage and aggregation analysis method, the method comprising:
step one: collecting vehicle driving information parameter data of each vehicle according to sampling time points of time sequence data;
step two: the collected vehicle driving information parameter data of each vehicle is aggregated to generate effective data;
step three: analyzing the effective data generated by aggregation to generate latest time sequence data;
step four: presetting a time sequence data standard threshold range, inquiring whether the latest time sequence data is abnormal according to the time sequence data standard threshold range, judging whether the vehicle has a fault or not, and generating a fault report;
step five: and the user receives the fault report and performs troubleshooting and maintenance on the faults of the vehicle according to the fault report.
Further, the vehicle driving information parameter data are obtained on the basis of the target time mark and the target vehicle mark;
the target time mark is a time mark corresponding to the vehicle driving information parameter data which is needed to be obtained by a user;
the target vehicle mark is a target vehicle corresponding to the vehicle driving information parameter data which needs to be obtained by a user;
the vehicle driving information parameter data comprise time sequence data of engine rotating speed, exhaust temperature and tire pressure;
the tire air pressure includes time series data of each tire air pressure of the vehicle.
Further, the process of aggregating the collected vehicle driving information parameter data of each vehicle includes:
processing the vehicle driving information parameter data according to a preset acquisition time format according to the set time point to respectively obtain preset acquisition time formatted time sequence data corresponding to the vehicle;
the preset acquisition time formatted time sequence data comprise vehicle driving information parameter data and acquisition time, the preset acquisition time comprises acquisition date and acquisition time, and each vehicle at least corresponds to one preset formatted time sequence data;
the preset acquisition time format is YYYY year-MM month-DD day, hh: mm: ss;
and carrying out aggregation processing on the formatted time sequence data of the preset acquisition time to generate effective data.
Further, the valid data comprises valid data A and valid data B;
the effective data A is effective data generated by carrying out aggregation processing on the formatted time sequence data of the preset acquisition time of the engine rotating speed and the exhaust temperature;
the effective data B is effective data generated by carrying out aggregation processing on the time sequence data formatted by the preset acquisition time of the tire air pressure;
and analyzing the effective data A and the effective data B generated by the aggregation processing.
Further, the process of analyzing the valid data a generated by the aggregation process includes:
the effective data A generated by aggregation has a plurality of engine speed effective data, and the engine speed effective data are analyzed to obtain an effective data average value of the engine speed
；
Analyzing a plurality of exhaust temperature effective data in the effective data A to obtain an effective data average value of the exhaust temperature
；
The average value of each effective data is obtained according to a calculation formula:
wherein N is n N pieces of engine speed effective data exist in the effective data a generated by aggregation,
representing the effective data average value of the engine speed, C n N pieces of effective data (n) of exhaust temperatures exist in the effective data (A) generated by aggregation,>
the effective data average value of the exhaust temperature is represented, and n is more than or equal to 1; p (P) n N tire pressure effective data exist in the effective data B generated by aggregation,
the effective data average value of the tire air pressure is represented, and n is more than or equal to 1;
W n ，U n and K n Representing the corresponding weight of each effective data, wherein n is more than or equal to 1;
and further aggregating, processing and storing the effective data mean value to generate the latest time sequence data.
Further, the process of analyzing the valid data B generated by the aggregation process includes:
the effective data B generated by aggregation has a plurality of wheel air pressure effective data, and the wheel air pressure effective data are analyzed to obtain the effective data average value of the wheel air pressure
。
Further, the average value of each effective data is aggregated and stored to generate the latest time sequence data;
the latest time sequence data is {
，/>
，/>
}。
Further, the process of judging whether the vehicle has a fault and generating a fault report includes:
the standard threshold ranges of the preset engine speed, the exhaust temperature and the tire pressure are respectively N Standard of 、C Standard of P Standard of Aggregating the data to generate a time sequence data standard threshold range;
the standard threshold range of the time sequence data is { N } Standard of ，C Standard of ，P Standard of }；
If the latest time sequence data is not within the time sequence data standard threshold range, the latest time sequence data is abnormal, the vehicle is in fault, a fault report is generated, and the fault report is sent to a user;
if the latest time sequence data is within the time sequence data standard threshold range, the latest time sequence data is not abnormal, and the vehicle can be judged to be not faulty, and a safety report is generated and sent to the user.
Further, the process of troubleshooting and repairing the fault of the vehicle according to the fault report comprises the following steps:
when the fault report received by the user is a primary fault report, the fault cause of the vehicle is known to be the problem of the engine speed;
when the fault report received by the user is a secondary fault report, the fault cause of the vehicle is known to be an exhaust temperature problem;
when the fault report received by the user is a three-level fault report, the fault cause of the vehicle is known to be the problem of wheel air pressure.
The vehicle fault problems of the received fault report are checked one by one, and the vehicle fault which is leaked and not checked is prevented from causing driving danger;
the user receives more than one fault report;
when the user receives the primary fault report and the secondary fault report, the fault cause of the vehicle is known to be the problems of the engine speed and the exhaust temperature;
when the user receives the secondary fault report and the tertiary fault report, the fault cause of the vehicle is known to be the problems of exhaust temperature and wheel air pressure;
when the user receives the first-level fault report and the third-level fault report, the fault cause of the vehicle is known to be the problems of the engine speed and the wheel air pressure;
when the user receives the first-level fault report, the second-level fault report and the third-level fault report, the fault cause of the vehicle is known to be the problems of the engine speed, the exhaust temperature and the wheel air pressure;
when a user receives the three-level report, the problem of the specific wheel air pressure cannot be judged, and the user is required to continuously send out a tire air pressure time sequence data acquisition request, and the tire air pressure preset acquisition time format time sequence data is subjected to aggregation processing to generate effective data B;
according to the effective data B and the standard threshold value range of tyre pressure respectively N Standard of The tire pressure problem at the specific position can be judged by comparing, so that the time for manually checking the wheels one by one is prolonged, the specific wheel pressure problem can be more accurately checked, and the tire pressure problem can be maintained, and the danger caused in the following driving process is prevented.
Compared with the prior art, the invention has the beneficial effects that: according to the sampling time points of the time series data, vehicle driving information parameter data of each vehicle are collected, the collected vehicle driving information parameter data of each vehicle are processed according to the preset collecting time formats, the vehicle driving information parameter data of each vehicle are processed according to the preset collecting time formats to respectively obtain preset collecting time formatted time series data corresponding to each vehicle, the effective data are generated through aggregation, the effective data generated through aggregation are analyzed to generate latest time series data, a preset time series data standard threshold range, whether the latest time series data are abnormal or not is inquired according to the time series data standard threshold range, accordingly whether the vehicle fails or not is judged, a failure report is generated and sent to a user, and the user timely checks and maintains the vehicle failure according to the failure report, so that the vehicle is free of failure and safe to run.
Drawings
Fig. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, a distributed time series data storage and aggregation analysis method includes the following steps:
step one: collecting vehicle driving information parameter data of each vehicle according to sampling time points of time sequence data;
the time sequence data are time-ordered data, are recorded and indexed according to a time dimension, and indicate a data column which records big data in the vehicle field and vehicle driving information parameters commonly used by vehicles according to a unified index and a standard time sequence;
receiving a vehicle time sequence data request initiated by a user, and acquiring vehicle driving information parameter data through a calling program programming interface (Application ProgrammingInterface, API);
the vehicle time sequence data request comprises a target time mark, a target vehicle mark and vehicle driving information parameter data;
the target time mark represents a time mark corresponding to the parameter data of the driving information of the vehicle which is required to be obtained by a user;
the target vehicle mark represents a target vehicle corresponding to the vehicle driving information parameter data which is required to be obtained by a user;
the vehicle driving information parameter data represents vehicle driving information parameter data obtained on the basis of a target time mark and a target vehicle mark;
it should be further noted that, in the implementation process, the target time stamp may be a specific time point or a specific time period;
the target vehicle mark can be a specific license plate number or a vehicle identification code;
the vehicle driving information parameter data comprise data of engine rotating speed, exhaust temperature and tire pressure;
the tire pressure is at the same time point, and time sequence data of the tire pressure of each vehicle are collected;
it should be further noted that, in the implementation process, the target time mark of the vehicle time sequence data request sent by the user is the preset collection time, the target vehicle mark is the target vehicle of the VAS code, and the collected vehicle driving information parameter data.
Step two: the collected vehicle driving information parameter data of each vehicle is aggregated to generate effective data;
processing the vehicle driving information parameter data of each vehicle according to a preset acquisition time format according to the set time point to respectively obtain preset acquisition time formatted time sequence data corresponding to each vehicle;
the preset acquisition time formatted time sequence data comprise vehicle driving information parameter data and acquisition time, the preset acquisition time comprises acquisition date and acquisition time, and each vehicle at least corresponds to one preset formatted time sequence data;
it should be further noted that, in the implementation process, the user may set the preset collection time according to the requirement, where the preset collection time format is yyyyy year-MM month-DD day, hh: mm: ss, for example: 2022, 12, 17, 12:00:23;
for example, for a target vehicle with a target vehicle signature of VAS code, the time series data for that vehicle is as follows in Table 1:
for a target vehicle with a target vehicle sign VAS code, the tire air pressure time sequence data of the vehicle is as follows in Table 2:
the tire air pressure time sequence data are collected and recorded according to the front, the right front, the left rear and the right rear;
as can be seen from table 1, the engine speed and exhaust temperature time series data of the vehicle are: the 1 st preset acquisition time format time sequence data is { engine speed: 1200 revolutions, exhaust temperature: 45 degrees, acquisition time: 0:00:00}, the other two are shown in table 1;
as can be seen from table 2, the tire pressure time series data of the vehicle are: the 1 st preset acquisition time format time sequence data are {230kpa, 241kpa, 231kpa, 250kpa }, and the other two are shown in table 2;
carrying out aggregation treatment on time sequence data in a preset acquisition time format of the engine rotating speed and the exhaust temperature to generate effective data A;
the tire pressure preset acquisition time format time sequence data are subjected to aggregation treatment to generate effective data B;
the valid data A and the valid data B form valid data of a target vehicle with a target vehicle sign of a VAS code.
Step three: analyzing the effective data generated by aggregation to generate the latest time sequence data:
in this embodiment, the time-series data storage management based on the aggregation processing also needs to perform statistical analysis data on each piece of effective data generated by the aggregation, where the statistical analysis data is a weighted average of the effective data through calculation, so as to obtain an effective data average value of the effective data;
n pieces of engine speed effective data exist in the effective data A generated by aggregation, and each piece of engine speed effective data is N n Wherein n is more than or equal to 1, and the effective data average value of the engine speed is
Effective data A is stored inValid data at n exhaust temperatures, each of which is C n Wherein n is more than or equal to 1, and the effective data average value of the exhaust temperature is +.>
；
N tire pressure effective data exist in the effective data B generated by aggregation, and each tire pressure effective data is P n Wherein n is more than or equal to 1, and the effective data average value of the tire air pressure is
；
The average value of each effective data is obtained according to a calculation formula:
wherein W is n ，U n And K n Representing the corresponding weight of each effective data, wherein n is more than or equal to 1;
the effective data average value is further aggregated, processed and stored to generate the latest time sequence data;
the latest time sequence data is {
，/>
，/>
}。
Step four: presetting a time sequence data standard threshold range, inquiring whether the latest time sequence data is abnormal according to the time sequence data standard threshold range, judging whether the vehicle has a fault or not, and generating a fault report;
the standard threshold ranges of the preset engine speed, the exhaust temperature and the tire pressure are respectively N Standard of 、C Standard of P Standard of Aggregating the data to generate a time sequence data standard threshold range;
the standard threshold range of the time sequence data is { N } Standard of ，C Standard of ，P Standard of -and corresponds to the latest timing data;
it should be further noted that, in the implementation process, the presetting of the time sequence data standard threshold value can be adjusted according to different seasons and different wheel states;
comparing the latest time sequence data according to the time sequence data standard threshold value, and judging whether the latest time sequence data is abnormal or not:
if the latest time sequence data is not within the time sequence data standard threshold range, the latest time sequence data is abnormal, the fault of the vehicle can be judged, a fault report is generated, and the fault report is sent to a user;
if the latest time sequence data is within the time sequence data standard threshold range, the latest time sequence data is not abnormal, the vehicle can be judged to be not faulty, a safety report is generated, and the safety report is sent to a user;
when the fault report received by the user is a primary fault report, the fault cause of the vehicle is known to be the problem of the engine speed;
when the fault report received by the user is a secondary fault report, the fault cause of the vehicle is known to be an exhaust temperature problem;
when the fault report received by the user is a three-level fault report, the fault cause of the vehicle is known to be the problem of wheel air pressure;
in actual conditions, the too high rotating speed of the long-term engine can cause overlarge torsion of various parts of the vehicle due to overheat temperature, so that service life is reduced, when abnormal exhaust temperature occurs, lubricating performance is deteriorated, light fractions in lubricating oil volatilize rapidly and are condensed on a cylinder, a piston ring or a piston ring groove, carbon deposition is caused, channel resistance is increased, power consumption is increased, when serious, the piston ring is clamped in the ring groove, the piston cannot work normally, the vehicle cannot be used normally, the danger is caused in the running process of the vehicle due to abnormal wheel air pressure, the problem of the wheel air pressure is solved, the service life of various parts is prolonged, power is reduced, and the running safety of the vehicle is ensured in the running process.
Step five: the user receives the fault report and performs troubleshooting and maintenance on the faults of the vehicle according to the fault report;
when a user receives a fault report, firstly checking the fault report, checking the fault grade, and secondly checking the vehicle fault problem caused by the fault;
the vehicle fault problems of the received fault report are checked one by one, and the vehicle fault which is leaked and not checked is prevented from causing driving danger;
the user receives more than one fault report;
when the user receives the primary fault report and the secondary fault report, the fault cause of the vehicle is known to be the problems of the engine speed and the exhaust temperature;
when the user receives the secondary fault report and the tertiary fault report, the fault cause of the vehicle is known to be the problems of exhaust temperature and wheel air pressure;
when the user receives the first-level fault report and the third-level fault report, the fault cause of the vehicle is known to be the problems of the engine speed and the wheel air pressure;
when the user receives the first-level fault report, the second-level fault report and the third-level fault report, the fault cause of the vehicle is known to be the problems of the engine speed, the exhaust temperature and the wheel air pressure;
it should be further noted that, in the implementation process, when the user receives the three-level report, it is unable to determine which specific wheel air pressure has a problem, and at this time, the user needs to continuously send out a request for acquiring time sequence data of tire air pressure, and aggregate the time sequence data of tire air pressure preset acquisition time format to generate effective data B;
according to the effective data B and the standard threshold value range of tyre pressure respectively N Standard of Comparing and judgingThe tire pressure problem occurs at the position of the broken tire, so that the time for manually checking the wheels one by one is prolonged, the problem of the tire pressure of the specific wheel can be accurately checked, the tire pressure problem is maintained, and the danger caused in the following driving process is prevented.
Working principle: according to the sampling time points of the time series data, vehicle driving information parameter data of each vehicle are collected, the collected vehicle driving information parameter data of each vehicle are processed according to the preset collecting time formats, the vehicle driving information parameter data of each vehicle are processed according to the preset collecting time formats to respectively obtain preset collecting time formatted time series data corresponding to each vehicle, the effective data are generated through aggregation, the effective data generated through aggregation are analyzed to generate latest time series data, a preset time series data standard threshold range, whether the latest time series data are abnormal or not is inquired according to the time series data standard threshold range, accordingly whether the vehicle fails or not is judged, a failure report is generated and sent to a user, and the user checks and maintains the vehicle failure according to the failure report, so that the vehicle does not fail and runs safely.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (9)
1. A distributed time series data storage and aggregation analysis method, the method comprising:
step one: collecting vehicle driving information parameter data of each vehicle according to sampling time points of time sequence data;
step two: the collected vehicle driving information parameter data of each vehicle is aggregated to generate effective data;
step three: analyzing the effective data generated by aggregation to generate latest time sequence data;
step four: presetting a time sequence data standard threshold range, inquiring whether the latest time sequence data is abnormal or not according to the time sequence data standard threshold range, judging whether the vehicle has a fault or not according to an inquiring result, and generating a fault report when the vehicle has the fault;
step five: and the user receives the fault report and performs troubleshooting and maintenance on the faults of the vehicle according to the fault report.
2. The distributed time series data storage and aggregation analysis method according to claim 1, wherein the vehicle driving information parameter data is obtained based on a target time stamp and a target vehicle stamp;
the target time mark is a time mark corresponding to the vehicle driving information parameter data which is needed to be obtained by a user;
the target vehicle mark is a target vehicle corresponding to the vehicle driving information parameter data which needs to be obtained by a user;
the vehicle driving information parameter data comprise time sequence data of engine rotating speed, exhaust temperature and tire pressure;
the tire air pressure includes time series data of each tire air pressure of the vehicle.
3. The method for storing and aggregating data according to claim 1, wherein the step of aggregating the collected vehicle running information parameter data of each vehicle comprises:
processing the vehicle driving information parameter data according to a preset acquisition time format according to the set time point to respectively obtain preset acquisition time formatted time sequence data corresponding to the vehicle;
and carrying out aggregation processing on the formatted time sequence data of the preset acquisition time to generate effective data.
4. A distributed time series data storage and aggregation analysis method according to claim 3, wherein the valid data comprises valid data a and valid data B;
the effective data A is effective data generated by carrying out aggregation processing on the formatted time sequence data of the preset acquisition time of the engine rotating speed and the exhaust temperature;
the effective data B is effective data generated by carrying out aggregation processing on the time sequence data formatted by the preset acquisition time of the tire air pressure;
and analyzing the effective data A and the effective data B generated by the aggregation processing.
5. The method for distributed time-series data storage and aggregation analysis according to claim 4, wherein the process of analyzing the valid data a generated by the aggregation process comprises:
the effective data A comprises a plurality of engine speed effective data, and the engine speed effective data are analyzed to obtain an effective data average value of the engine speed
；
Analyzing a plurality of exhaust temperature effective data in the effective data A to obtain an effective data average value of the exhaust temperature
。
6. The method for distributed time-series data storage and aggregation analysis according to claim 5, wherein the process of analyzing the valid data B generated by the aggregation process includes:
the effective data B generated by aggregation comprises a plurality of wheel air pressure effective data, and the wheel air pressure effective data are analyzed to obtain the effective data average value of the wheel air pressure
。
7. The method for storing and analyzing distributed time series data as claimed in claim 6, wherein each valid data average value is stored in an aggregation process to generate the latest time series data;
the latest time sequence data is {
，/>
，/>
}。
8. The distributed time series data storage and aggregation analysis method according to claim 7, wherein the process of judging whether the vehicle is malfunctioning comprises:
presetting a standard threshold range of engine speed, exhaust temperature and tire pressure, and polymerizing the standard threshold range to generate a time sequence data standard threshold range;
if the latest time sequence data is not within the time sequence data standard threshold range, the latest time sequence data is abnormal, the fault of the vehicle can be judged, a fault report is generated, and the fault report is sent to a user;
if the latest time sequence data is within the time sequence data standard threshold value range, the latest time sequence data is not abnormal, the vehicle is not in fault, and a safety report is generated and sent to a user.
9. The distributed time series data storage and aggregation analysis method according to claim 8, wherein the process of troubleshooting and repairing the vehicle according to the fault report comprises:
when the fault report received by the user is a primary fault report, the fault cause of the vehicle is known to be the problem of the engine speed;
when the fault report received by the user is a secondary fault report, the fault cause of the vehicle is known to be an exhaust temperature problem;
when the fault report received by the user is a three-level fault report, the fault cause of the vehicle is known to be the problem of wheel air pressure.
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