A fault detection method for pressure sensor in an autonomous driving watering cart

Abstract

The application relates to the field of traffic safety technology and provides a fault detection method for a pressure sensor in an automatic driving watering vehicle, which comprises the following steps: obtaining vehicle data of the automatic driving watering vehicle through a sensor, and storing the data in a cloud server; when the automatic driving watering vehicle is in a normal working condition, a mathematical model of an adjustment curve of braking pressure and watering pressure is established; the vehicle data in a database is denoised and standardized; cloud data analysis is carried out on multiple automatic driving watering vehicles under the same working condition, whether the corresponding pressure sensors of the automatic driving watering vehicles exist faults is judged; and the judgment result is fed back to the main control system of the fault vehicle, reminding the driver or the automatic driving system to carry out maintenance or adjustment. The fault detection method avoids the problem of low working efficiency caused by inaccurate watering pressure of the vehicle, and eliminates the vehicle driving safety hidden danger caused by inaccurate braking pressure.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of traffic safety, and particularly relates to a fault detection method for a pressure sensor in an automatic driving watering vehicle. BACKGROUND

[0002] The automatic driving watering vehicle is one of common working vehicles in the field of intelligent traffic, and may encounter various faults in the working process. If the watering pressure sensor is faulty, the watering will be uneven, and even the watering amount will be insufficient, thereby affecting the road cleaning and dust reduction effect. In addition, once the watering pressure sensor is out of order, the vehicle braking will be abnormal, which will pose a certain threat to the driving safety of the vehicle, and in severe cases, will induce major traffic safety accidents. If only the sensor is used to determine whether the watering pressure sensor or the watering pressure sensor is faulty, once the pressure measurement of the identification sensor is inaccurate, a major loss will be caused.

[0003] Therefore, the application is proposed. SUMMARY

[0004] In order to solve the problems in the prior art, the fault detection method for the pressure sensor in the automatic driving watering vehicle can accurately determine whether the braking pressure sensor or the watering pressure sensor of the vehicle is faulty, and effectively determine the specific value of the pressure measurement error, so as to facilitate subsequent maintenance or adjustment.

[0005] The application provides a fault detection method for a pressure sensor in an automatic driving watering vehicle, comprising the following steps:

[0006] S1, acquiring vehicle data of the automatic driving watering vehicle through a sensor, and storing the acquired vehicle data in a cloud server;

[0007] S2, establishing an adjustment curve mathematical model of braking pressure and watering pressure when the automatic driving watering vehicle is in a normal working condition;

[0008] S3, establishing a database by using the vehicle data stored in S1, and performing denoising and standardization processing on the vehicle data in the database;

[0009] S4, performing cloud data analysis on a plurality of automatic driving watering vehicles operating in the same working condition, and determining whether the respective corresponding pressure sensors are faulty;

[0010] S5, feeding back the determination result of S4 to a main control system of a faulty vehicle, and reminding a driver or an automatic driving system to perform maintenance or adjustment.

[0011] Further, the vehicle data in S1 includes braking pressure, watering pressure, watering time, water tank water amount, and braking deceleration.

[0012] Further, in S1, before acquiring the vehicle data of the autonomous sprinkler, the pressure sensor needs to be installed on the autonomous sprinkler.

[0013] Further, in S2, the specific steps for establishing the adjustment curve mathematical model of the brake pressure and the sprinkling pressure are:

[0014] According to the relationship between the braking force and the brake pressure, it is expressed by formula (1):

[0015] F b =k b P b (1)

[0016] wherein k b represents the proportional parameter of the braking system, which is related to the physical properties of the brake; F b represents the braking force; P b represents the brake pressure;

[0017] As the sprinkling operation proceeds, the mass of the autonomous sprinkler will decrease with the decrease of the sprinkling amount, which is expressed by formula (2):

[0018] m e =m s -ρV w (2)

[0019] wherein m e represents the current mass of the sprinkler; m s represents the initial mass of the sprinkler; ρ represents the water density; V w represents the sprinkling amount;

[0020] The relationship between the sprinkling amount, the sprinkling pressure and the sprinkling time is expressed by formula (3):

[0021] V w =k w P w t (3)

[0022] wherein k w represents the proportional coefficient, representing the positive correlation between the sprinkling pressure and the sprinkling amount, which is calibrated through the physical properties of the sprinkling pump; P w represents the sprinkling pressure; t represents the sprinkling operation time;

[0023] From formula (1)-(3), the adjustment curve mathematical model of the brake pressure and the sprinkling pressure is obtained, which is expressed by formula (4):

[0024]

[0025] wherein a represents the braking deceleration.

[0026] Further, in S3, the vehicle data in the database is denoised and standardized by a filtering algorithm.

[0027] Further, in S4, cloud data analysis is performed on multiple automatic driving watering trucks under the same working condition to determine whether the corresponding pressure sensors of each automatic driving watering truck have faults, and the specific steps are as follows:

[0028] Firstly, at the same time, the actual vehicle data corresponding to multiple automatic driving watering trucks is collected. If the relationship between the braking pressure and the watering pressure in the collected actual vehicle data satisfies the adjustment curve mathematical model of S2, the sensors in the multiple automatic driving watering trucks are all normal.

[0029] If the relationship between the braking pressure and the watering pressure in the collected actual vehicle data does not satisfy the adjustment curve mathematical model of S2, the corresponding automatic driving watering truck has a sensor fault.

[0030] Then, the watering pressure value of the corresponding automatic driving watering truck that does not satisfy the relationship is compared with the watering pressure values of the other vehicles under the same working condition. If the watering pressure values are the same, the braking pressure sensor of the vehicle has a fault.

[0031] If the watering pressure values are different, the braking pressure values are further compared. If the braking pressure values are the same, the watering pressure sensor of the vehicle has a fault.

[0032] If the compared watering pressure values and braking pressure values are all different, the watering pressure sensor and the braking pressure sensor of the vehicle have faults.

[0033] Further, determining whether the corresponding pressure sensor of the automatic driving watering truck has a fault includes the following steps:

[0034] Given the watering pressure error and the braking pressure error of the vehicle, they are represented by formula (5) as follows:

[0035]

[0036] where ΔP b represents the braking pressure error; ΔP w represents the watering pressure error; P r1 represents the actual braking pressure; and P r2 represents the actual watering pressure.

[0037] The actual braking pressure values and the actual watering pressure values corresponding to multiple automatic driving watering trucks are respectively brought into formula (5) to obtain the braking pressure error ΔP b and the watering pressure error ΔP w . If ΔP b or ΔPw greater than a set threshold value ε, then the corresponding brake pressure sensor or watering pressure sensor in the automatic driving watering vehicle is faulty.

[0038] Further, the vehicle data obtained in S1 is stored in a cloud server through V2X technology.

[0039] Further, the judgment result of S4 is fed back to the main control system of the faulty vehicle in S5 through V2X technology.

[0040] Compared with the prior art, the fault detection method of the pressure sensor in the automatic driving watering vehicle provided by the present application detects the brake pressure sensor and the watering pressure sensor of multiple automatic driving watering vehicles operating under the same working condition through V2X technology, compares the pressure adjustment curve mathematical model established under normal working condition with the actually detected pressure measurement data, to accurately identify whether the pressure sensor is faulty, solves the problem of inaccurate pressure measurement of a single identification sensor, avoids the problem of low working efficiency caused by inaccurate watering pressure of the vehicle, and also eliminates the vehicle driving safety hazards caused by inaccurate brake pressure. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is a flowchart of the fault detection method of the pressure sensor in the automatic driving watering vehicle of the present application. DETAILED DESCRIPTION

[0042] The present application will be further explained in conjunction with the drawings and specific embodiments in the description, obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0043] Embodiment one

[0044] According to the embodiments of the present application, as shown in the drawings, the present application provides a fault detection method of a pressure sensor in an automatic driving watering vehicle, which specifically comprises the following steps: Figure 1

[0045] S1, obtaining vehicle data of the automatic driving watering vehicle through the sensor, and storing the obtained vehicle data in a cloud server.

[0046] Specifically, the data acquisition equipment or auxiliary device on the vehicle, such as the Internet of Things data acquisition terminal, is used to collect the sensor data installed on the automatic driving watering vehicle in real time to obtain data including the brake pressure, watering pressure, watering time, water tank water volume and brake deceleration of the vehicle. And the obtained vehicle data is stored in a cloud server through V2X technology (i.e. Internet of Things technology).​

[0047] S2, the automatic driving watering cart is in a normal working condition, and an adjustment curve mathematical model of braking pressure and watering pressure is established.

[0048] Specifically, according to the operation characteristics of the automatic driving watering cart, such as: as the water tank water volume decreases, the total mass of the watering cart also decreases, and under the same deceleration, the braking pressure also decreases, the adjustment curve mathematical model between the braking pressure and the watering pressure is established.

[0049] According to the relationship between the braking force and the braking pressure, it is expressed by formula (1):

[0050] F b =k b P b (1)

[0051] Wherein, k b represents the proportional parameter of the braking system, which is related to the physical properties of the brake; F b represents the braking force; P b represents the braking pressure;

[0052] As the watering operation proceeds, the mass of the automatic driving watering cart decreases with the decrease of the watering volume, which is expressed by formula (2):

[0053] m e =m s -ρV w (2)

[0054] Wherein, m e represents the current mass of the watering cart; m s represents the initial mass of the watering cart; ρ represents the water density; V w represents the watering volume;

[0055] The relationship between the watering volume, the watering pressure and the watering time is expressed by formula (3):

[0056] V w =k w P w t (3)

[0057] Wherein, k w represents the proportional coefficient, representing the positive correlation between the watering pressure and the watering volume, which is calibrated through the physical characteristics of the watering pump; P w represents the watering pressure; t represents the watering operation time;

[0058] From formula (1)-(3), the adjustment curve mathematical model of the braking pressure and the watering pressure is obtained, which is expressed by formula (4):

[0059]

[0060] wherein a represents the braking deceleration.

[0061] S3, a database is established with the vehicle data stored in S1, and the vehicle data in the database is denoised and standardized.

[0062] Specifically, the cloud server receives and stores data from multiple automatic driving watering trucks, establishes a unified database, and denoises and standardizes the data in the data through a filtering algorithm to ensure data quality. The filtering algorithm used in the application includes median filtering, mean filtering, and adaptive filtering.

[0063] S4, cloud data analysis is performed on multiple automatic driving watering trucks operating under the same working condition to determine whether the corresponding pressure sensor of each vehicle has a fault.

[0064] Specifically, if the actual braking pressure or watering pressure data of an automatic driving watering truck is significantly deviated from the adjustment curve mathematical model established under normal conditions, it can be preliminarily judged that the sensor of the vehicle may have a measurement error. Further, by comparing the sensor values of the vehicle with the sensor values of the remaining vehicles under the same working condition in the cloud, the braking pressure or watering pressure sensor fault can be further determined.

[0065] The specific operation is as follows:

[0066] First, at the same time, the actual vehicle data corresponding to multiple automatic driving watering trucks is collected. If the relationship between the braking pressure and the watering pressure in the collected actual vehicle data satisfies formula (4), the sensors in the multiple automatic driving watering trucks are all normal. If the relationship between the braking pressure and the watering pressure in the collected actual vehicle data does not satisfy formula (4) in any one or more cases, it is considered that the corresponding automatic driving watering truck has a sensor fault.

[0067] Then, the watering pressure value of the corresponding automatic driving watering truck that does not satisfy the relationship is compared with the watering pressure values of the remaining vehicles under the same working condition. If the watering pressure values are the same, the braking pressure sensor of the vehicle has a fault. If the watering pressure values are different, the braking pressure values are compared. If the braking pressure values are the same, the watering pressure sensor of the vehicle has a fault.

[0068] If the compared watering pressure values and braking pressure values are all different, the watering pressure sensor and the braking pressure sensor of the vehicle have faults.

[0069] If the actual brake pressure or watering pressure data of an autonomous watering vehicle is detected to have significant deviation from the adjustment curve mathematical model established under normal conditions, it can be preliminarily judged that the sensor of the vehicle may have a measurement error. Further, by comparing the sensor values of the vehicle with the sensor values of the remaining vehicles under the same working conditions in the cloud, the brake pressure or watering pressure sensor fault can be further determined. Thus, whether the corresponding pressure sensor of the autonomous watering vehicle has a fault is determined, specifically including the following steps:

[0070] The watering pressure error and the brake pressure error of the known vehicle are represented by formula (5) as follows:

[0071]

[0072] wherein ΔP b represents the brake pressure error; ΔP w represents the watering pressure error; P r1 represents the actual brake pressure; P r2 represents the actual watering pressure;

[0073] The actual brake pressure value and the actual watering pressure value of each of the plurality of autonomous watering vehicles are respectively brought into formula (5) to obtain the brake pressure error ΔP b and the watering pressure error ΔP w If ΔP b or ΔP w is greater than the set threshold value ε, the brake pressure sensor or the watering pressure sensor in the corresponding autonomous watering vehicle has a fault. The set threshold value ε can be set according to the specific working scene of the sensor or the working accuracy requirement of different sensors. For example: the watering pressure sensor is compared with the standard pressure source with known pressure for measurement, and the threshold value ε is set to ±2%. If the deviation between the measurement value of the sensor and the standard value continuously exceeds this threshold value after multiple measurements, it is likely that the sensor has been damaged or needs to be calibrated.

[0074] S5. The judgment result of S4 is fed back to the main control system of the fault vehicle through the V2X technology, reminding the driver or the autonomous driving system to perform maintenance or adjustment.

[0075] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting a fault of a pressure sensor in an automatic driving sprinkler truck, characterized in that: The following steps are involved: S1. Obtain vehicle data of the autonomous sprinkler truck through sensors and store the obtained vehicle data in a cloud server. S2. When the autonomous water sprinkler truck is in normal working condition, a mathematical model of the adjustment curve of the braking pressure and the water sprinkler pressure is established; S3, establishing a database with the vehicle data stored in S1, and performing denoising and standardization on the vehicle data in the database; S4. Perform cloud-based data analysis on multiple autonomous sprinkler trucks operating under the same working conditions to determine whether their corresponding pressure sensors are faulty. The specific steps are as follows: First, collect actual vehicle data corresponding to multiple autonomous sprinkler trucks at the same time. If the relationship between brake pressure and sprinkler pressure in the collected actual vehicle data satisfies the adjustment curve mathematical model S2, then the sensors in the multiple autonomous sprinkler trucks are not faulty; If the relationship between the braking pressure and the watering pressure in the actual vehicle data collected does not satisfy the adjustment curve mathematical model of S2, then the corresponding autonomous driving watering truck has a sensor fault; Then, the watering pressure value of the autonomous driving sprinkler truck that does not meet the corresponding relationship is compared with the watering pressure values ​​of other vehicles with the same operating conditions. If the watering pressure values ​​are the same, then the brake pressure sensor of the vehicle is faulty. If the watering pressure values ​​are different, the brake pressure values ​​are compared. If the brake pressure values ​​are the same, the watering pressure sensor of the vehicle is faulty. If the compared watering pressure values ​​and brake pressure values ​​are different, then both the watering pressure sensor and the brake pressure sensor of the vehicle are faulty; S5: Feedback the judgment result of S4 to the main control system of the faulty vehicle to remind the driver or the automatic driving system to perform repairs or adjustments.

2. The fault detection method for the pressure sensor in the automatic driving sprinkler truck according to claim 1 is characterized in that: The vehicle data in S1 includes brake pressure, watering pressure, watering time, water level in the water tank, and brake deceleration.

3. The fault detection method for the pressure sensor in the automatic driving sprinkler truck according to claim 1 is characterized in that: In S1, before obtaining the vehicle data of the autonomous driving sprinkler truck, a pressure sensor needs to be installed on the autonomous driving sprinkler truck.

4. The method for detecting a fault of a pressure sensor in an automatic driving sprinkler truck according to claim 1, wherein: The specific steps for establishing the mathematical model of the adjustment curve of the braking pressure and the watering pressure in S2 are: According to the relationship between braking force and braking pressure, it can be expressed as follows by formula (1): （1） in, Represents the proportional parameter of the braking system, which is related to the physical properties of the brake; Indicates braking force; Indicates brake pressure; As the watering operation proceeds, the mass of the autonomous watering truck decreases as the amount of water sprayed decreases, which can be expressed by formula (2): （2） in, Indicates the current quality of the sprinkler; Indicates the initial sprinkler mass; Indicates water density; Indicates the amount of watering; The relationship between the amount of water sprayed, the watering pressure and the watering time is expressed by formula (3): （3） in, It represents the proportionality coefficient, which represents the positive correlation between the sprinkler pressure and the sprinkler volume, and is obtained by calibration based on the physical characteristics of the sprinkler pump; Indicates the sprinkler pressure; Indicates the watering operation time; From equations (1) to (3), we can obtain the mathematical model of the adjustment curve of the braking pressure and the watering pressure, which is expressed by equation (4): （4） in, Indicates braking deceleration.

5. The method for detecting faults of a pressure sensor in an automatic driving sprinkler truck according to claim 1, characterized in that: In S3, the vehicle data in the database is denoised and standardized using a filtering algorithm.

6. The method for detecting faults of a pressure sensor in an automatic driving sprinkler truck according to claim 4, characterized in that: Determine whether the pressure sensor corresponding to the autonomous sprinkler truck is faulty, specifically including the following steps: The water spraying pressure error and brake pressure error of the known vehicle are expressed by formula (5): （5） in, Indicates brake pressure error; Indicates the sprinkler pressure error; Indicates the actual brake pressure; Indicates the actual sprinkler pressure; Substitute the actual braking pressure values ​​and watering pressure values ​​corresponding to multiple autonomous sprinkler trucks into formula (5) to obtain the braking pressure error and sprinkler pressure error ,like or Greater than the set threshold , then there is a fault in the brake pressure sensor or the watering pressure sensor in the corresponding autonomous driving sprinkler truck.

7. The method for detecting faults of a pressure sensor in an automatic driving sprinkler truck according to claim 1, characterized in that: In S1, the vehicle data acquired through V2X technology is stored in the cloud server.

8. The method for detecting faults of a pressure sensor in an automatic driving sprinkler truck according to claim 1, characterized in that: In S5, the judgment result of S4 is fed back to the main control system of the faulty vehicle through V2X technology.

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