CN114611893A - Vehicle starting method based on cloud self-checking - Google Patents
Vehicle starting method based on cloud self-checking Download PDFInfo
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Abstract
The invention discloses a vehicle starting method based on cloud self-checking, which is characterized in that vehicle operation data are collected in a cloud server in real time through a vehicle-mounted terminal, dynamic analysis and calculation are carried out on the data, and fine-grained parameters in a point check item, such as current, voltage and the like in operation, are analyzed and evaluated through a cloud terminal. And scoring the vehicle according to the degree and time of deviation of the vehicle from a normal value in operation, and obtaining a cloud self-checking score according to the weight of each parameter so as to guide a driver or a vehicle user to evaluate each parameter of the vehicle before the vehicle is started. The driver or the vehicle user can carry out warranty or normal use on the vehicle according to the cloud detection result and feedback, so that a more professional detection result is obtained. And a temperature detection sensor is added in the vehicle-mounted terminal to monitor the local temperature, and the calculation algorithm is dynamically adjusted through the temperature, so that the influence of the local temperature on the detection result is eliminated.
Description
Technical Field
The invention relates to the technical field of industrial vehicles, in particular to a vehicle starting method based on cloud self-checking.
Background
In the use process of industrial vehicles, point inspection before driving is an effective means for inspecting vehicle performance and vehicle problems. The method is characterized in that a driver or a user of the vehicle checks parameters of the industrial vehicle, such as an oil level, a lifting system of a forklift, whether a gear is abnormal, whether a foot brake is normal, whether a water-oil tank level is normal and the like before starting the vehicle, so that the situation that the vehicle is started abnormally does not hurt personnel or equipment when the vehicle is started is ensured, and the safety accidents are effectively avoided; the conventional inspection work for the industrial vehicle is to roughly check the vehicle condition before a driver or a vehicle operator starts the vehicle or inspect various parameters according to the instruction of a mobile phone APP, and although some conventional inspection item abnormalities such as the tire use condition and the lifting system condition can be avoided in the prior art, the use conditions of some fine-grained parameters such as current parameters, voltage parameters and other operation parameters cannot be detected. The abnormality may not appear during the inspection, but some potential safety hazards appear in the actual use process.
Therefore, it is a problem worthy of research to provide a vehicle starting method based on cloud self-check, in which a cloud collects real-time data of a running vehicle and dynamically analyzes and adjusts the data.
Disclosure of Invention
The invention aims to provide a vehicle starting method based on cloud self-checking, which is used for collecting real-time data of a running vehicle at a cloud end and dynamically analyzing and adjusting the data.
The purpose of the invention is realized as follows:
the vehicle starting method based on the cloud self-checking comprises the following steps: step 1: a vehicle-mounted terminal is arranged on a vehicle, terminal data are acquired through CAN communication, and the acquired data are transmitted to a cloud server through a 4G or 5G network; step 2: detecting the temperature of a region in real time according to a temperature sensor installed on a vehicle so as to eliminate the influence of the temperature on the whole detection method; and 3, step 3: the cloud server analyzes and dynamically adjusts the received real-time data in the step 1 to obtain parameters of each self-checking phase; and 4, step 4: estimating the parameters of each self-checking item in the step 3 according to the degree of deviation of each self-checking item from the normal value, and estimating and scoring according to the weight of each self-checking item in the whole self-checking algorithm to obtain a final score; and 5: the method comprises the following steps that a driver or a vehicle user carries out one-touch cloud self-inspection before starting a vehicle, and a cloud server scores the vehicle according to previous operation parameters and returns a self-inspection result; step 6: the vehicle user or the driver carries out the next operation on the vehicle according to the self-checking result returned by the cloud end and the related suggestion item, if the vehicle is not overhauled or normally used, the vehicle is carried out; and 7: if the spot check is not successful, the step 4 and the step 5 can be repeated.
The self-checking algorithm in the step 4 is as follows:
p1 traction parameter bisection = dynamically calculating the degree of deviation of the traction detection parameter from the normal value;
p2 hoisting parameter halving = dynamically calculating the degree of hoisting detection parameter deviation from a normal value;
……
ps 1: the larger each deviation normal value is, the more the uploaded data is continuously corrected into a single halving;
ps 1: TCV: the temperature sensor detects the temperature, and the influence of the temperature on the dynamic adjustment of various parameters is added, so that the detection of different areas is disinfected, and the equal division standard of each area is ensured;
final score =
Wherein n is represented as: the number of parameter items required to be checked by self-test is as follows: only the rotating speed of the traction motor and the rotating speed of the pump motor are available, and n is 2;
w: the weighted average value of the weights occupied by the parameter items required by the self-checking is obtained;
TCV: the temperature correction value is obtained by comparing the local temperature acquired by the sensor with the temperature set by the standard, and the amount is set for eliminating the influence of the local temperature on the algorithm;
taking the average value of the previous data as an initial value, calculating the degree of deviation of the latest data from a normal value when the latest data reaches the cloud, wherein the larger the degree of deviation from the normal value is, the smaller the dynamic adjustment weight setting value is; the temperature correction value is compared with a standard temperature value by adopting a locally acquired temperature value, the larger the deviation degree of the temperature correction value from the standard temperature value is, the smaller the weight of the temperature correction value is, and the self-checking score is dynamically corrected; self-defining detection parameters, and dynamically adding or deleting self-detection parameters by self-defining the self-detection parameters.
Has the positive and beneficial effects that: according to the invention, the vehicle operation data is collected in real time through the vehicle-mounted terminal in the cloud server, and the data is dynamically analyzed and calculated to guide a driver or a vehicle user to evaluate various parameters of the vehicle before the vehicle is started. The driver or the vehicle user can carry out warranty or normal use on the vehicle according to the cloud detection result and feedback, so that a more professional detection result is obtained.
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FIG. 1 is a schematic structural diagram of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
The vehicle starting method based on the cloud self-checking comprises the following steps: step 1: the vehicle-mounted terminal is arranged on the vehicle, terminal data are collected through CAN communication, and the collected data are transmitted to the cloud server through a 4G or 5G network.
Step 2: according to the temperature sensor installed on the vehicle, the regional temperature is detected in real time, so that the influence of the temperature on the whole detection method is eliminated.
And step 3: and (3) the cloud server analyzes and dynamically adjusts the received real-time data in the step (1) to obtain parameters of each self-checking phase.
And 4, step 4: estimating the parameters of each self-checking item in the step 3 according to the degree of deviation of each self-checking item from the normal value, and estimating and scoring according to the weight of each self-checking item in the whole self-checking algorithm to obtain a final score; the self-checking algorithm is as follows:
p1 traction parameter bisection = dynamically calculating the degree of deviation of the traction detection parameter from the normal value;
p2 hoisting parameter halving = dynamically calculating the degree of hoisting detection parameter deviation from a normal value;
……
ps 1: the larger each deviation normal value is, the more the uploaded data is continuously corrected into a single halving;
ps 1: TCV: the temperature sensor detects the temperature, and the influence of the temperature on the dynamic adjustment of various parameters is added, so that the detection of different areas is disinfected, and the equal division standard of each area is ensured;
final score =
Wherein n is represented as: the number of parameter items required to be checked by self-test is as follows: only the rotating speed of the traction motor and the rotating speed of the pump motor are available, and n is 2;
w: the weighted average value of the weights occupied by the parameter items required by the self-checking is obtained;
TCV: the temperature correction value is obtained by comparing the local temperature acquired by the sensor with the temperature set by the standard, and the amount is set for eliminating the influence of the local temperature on the algorithm;
taking the average value of the previous data as an initial value, calculating the degree of deviation of the latest data from a normal value when the latest data reaches the cloud end each time, wherein the larger the degree of deviation from the normal value is, the smaller the dynamic adjustment weight setting value is; the temperature correction value is compared with a standard temperature value by adopting a locally acquired temperature value, the larger the deviation degree of the temperature correction value from the standard temperature value is, the smaller the weight of the temperature correction value is, and the self-checking score is dynamically corrected; self-defining detection parameters, namely dynamically adding or deleting self-detection parameters by self-defining the self-detection parameters, so that the self-detection application range is improved; the temperature correction value is added in the algorithm, the local temperature is dynamically collected, the influence on the algorithm is corrected, the condition that each area uses a unified standard algorithm is guaranteed, and the reliability of the algorithm is improved.
And 5: the method comprises the steps that a driver or a vehicle user carries out one-touch cloud self-checking before starting the vehicle, and a cloud server scores the vehicle according to previous operation parameters and returns a self-checking result.
Step 6: and (4) carrying out next operation on the vehicle by a vehicle user or a driver according to the self-checking result returned by the cloud end and the related suggestion item, if the vehicle is overhauled or normally used, carrying out the next operation on the vehicle.
And 7: if the spot check is not successful, the step 4 and the step 5 can be repeated.
The vehicle operation data are collected in real time in the cloud server through the vehicle-mounted terminal, dynamic analysis and calculation are carried out on the data, and fine-grained parameters in the point inspection item, such as current, voltage and the like in operation, are analyzed and evaluated through the cloud terminal. And scoring the vehicle according to the degree and time of deviation of the vehicle from a normal value in operation, and obtaining a cloud self-checking score according to the weight of each parameter so as to guide a driver or a vehicle user to evaluate each parameter of the vehicle before the vehicle is started. The driver or the vehicle user can carry out warranty or normal use on the vehicle according to the cloud detection result and feedback, so that a more professional detection result is obtained. And a temperature detection sensor is added in the vehicle-mounted terminal to monitor the local temperature, the calculation algorithm is dynamically adjusted through the temperature, the influence of the local temperature on the detection result is eliminated, and the cloud detection method can be used under any condition.
The traditional self-checking method is delivered to the cloud server to analyze the self-checking parameters in real time according to the real vehicle running parameters, and the aim of accurately analyzing the self-checking parameters is fulfilled according to data training; the vehicle operation data monitoring system is not limited to health monitoring of vehicle operation data, the problem that the traditional self-inspection cannot find is solved, the possibility of accidents in the use process is reduced, and the vehicle operation data monitoring system is used as a first line of defense for a manager or a vehicle user to find problems.
According to the invention, the vehicle operation data is collected in real time through the vehicle-mounted terminal in the cloud server, and the data is dynamically analyzed and calculated to guide a driver or a vehicle user to evaluate various parameters of the vehicle before the vehicle is started. The driver or the vehicle user can carry out warranty or normal use on the vehicle according to the cloud detection result and feedback, so that a more professional detection result is obtained.
Claims (2)
1. A vehicle starting method based on cloud self-checking is characterized in that: the method comprises the following steps: step 1: a vehicle-mounted terminal is arranged on a vehicle, terminal data are acquired through CAN communication, and the acquired data are transmitted to a cloud server through a 4G or 5G network; step 2: detecting the temperature of a region in real time according to a temperature sensor installed on a vehicle so as to eliminate the influence of the temperature on the whole detection method; and 3, step 3: the cloud server analyzes and dynamically adjusts the received real-time data in the step 1 to obtain parameters of each self-checking phase; and 4, step 4: estimating the parameters of each self-checking item in the step 3 according to the degree of deviation of each self-checking item from the normal value, and estimating and scoring according to the weight of each self-checking item in the whole self-checking algorithm to obtain a final score; and 5: the method comprises the following steps that a driver or a vehicle user carries out one-touch cloud self-inspection before starting a vehicle, and a cloud server scores the vehicle according to previous operation parameters and returns a self-inspection result; step 6: the vehicle user or the driver carries out the next operation on the vehicle according to the self-checking result returned by the cloud end and the related suggestion item, if the vehicle is not overhauled or normally used, the vehicle is carried out; and 7: if the spot check is not successful, the step 4 and the step 5 can be repeated.
2. The vehicle starting method based on the cloud self-test as claimed in claim 1, wherein: the self-checking algorithm in the step 4 is as follows:
p1 traction parameter bisection = dynamically calculating the degree of deviation of the traction detection parameter from the normal value;
p2 hoisting parameter halving = dynamically calculating the degree of hoisting detection parameter deviation from a normal value;
……
ps 1: the larger each deviation normal value is, the more the uploaded data is continuously corrected into a single halving;
ps 1: TCV: the temperature sensor detects the temperature, and the influence of the temperature on the dynamic adjustment of various parameters is added, so that the detection of different areas is disinfected, and the equal division standard of each area is ensured;
final score =
，
Wherein n is represented as: the number of parameter items required to be checked by self-test is as follows: only the rotating speed of the traction motor and the rotating speed of the pump motor are available, and n is 2;
w: the weighted average value of the weights occupied by the parameter items required by the self-checking is obtained;
TCV: the temperature correction value is obtained by comparing the local temperature acquired by the sensor with the temperature set by the standard, and the amount is set for eliminating the influence of the local temperature on the algorithm;
taking the average value of the previous data as an initial value, calculating the degree of deviation of the latest data from a normal value when the latest data reaches the cloud end each time, wherein the larger the degree of deviation from the normal value is, the smaller the dynamic adjustment weight setting value is; the temperature correction value is compared with a standard temperature value by adopting a locally acquired temperature value, the larger the deviation degree of the temperature correction value from the standard temperature value is, the smaller the weight of the temperature correction value is, and the self-checking score is dynamically corrected; self-defining detection parameters, and dynamically adding or deleting self-detection parameters by self-defining the self-detection parameters.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110954167A (en) * | 2019-12-11 | 2020-04-03 | 云南电网有限责任公司昆明供电局 | Line inspection robot fault maintenance and self-checking data acquisition method |
| CN112799380A (en) * | 2021-01-04 | 2021-05-14 | 中车青岛四方车辆研究所有限公司 | Auxiliary system self-checking system and method suitable for unmanned train |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110954167A (en) * | 2019-12-11 | 2020-04-03 | 云南电网有限责任公司昆明供电局 | Line inspection robot fault maintenance and self-checking data acquisition method |
| CN112799380A (en) * | 2021-01-04 | 2021-05-14 | 中车青岛四方车辆研究所有限公司 | Auxiliary system self-checking system and method suitable for unmanned train |
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