Fault detection method for pressure sensor in automatic driving watering cart

Abstract

The invention relates to the technical field of material traffic safety, and provides a fault detection method for a pressure sensor in an automatic driving watering cart, which comprises the following steps: acquiring vehicle data of the automatic driving watering cart through the sensor, and storing the data in a cloud server; when the automatic driving watering cart is in a normal working condition, an adjustment curve mathematical model of brake pressure and watering pressure is established; denoising and standardizing the vehicle data in the database; cloud data analysis is carried out on the multiple automatic driving watering carts working under the same working condition, and whether the corresponding pressure sensors have faults or not is judged; and the judgment result is fed back to a master control system of the faulty vehicle, and a driver or an automatic driving system is reminded to conduct maintenance or adjustment. According to the fault detection method, the problem of low operation efficiency caused by inaccurate watering pressure of the vehicle is avoided, and the potential safety hazard of vehicle driving caused by inaccurate braking pressure is eliminated.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic safety, and particularly relates to a method for detecting faults of a pressure sensor in an autonomous sprinkler truck. Background Art

[0002] As one of the common operating vehicles in the field of intelligent transportation, an autonomous sprinkler truck may encounter various faults during operation. Among them, if the sprinkler pressure sensor fails, it will cause uneven sprinkling or even insufficient water sprinkling amount, thereby affecting the road cleaning and dust suppression effects; in addition, once it malfunctions, it will cause abnormal vehicle braking, posing a certain threat to the driving safety of the vehicle, and may even induce major traffic safety accidents in severe cases. And simply judging whether the sprinkler pressure sensor or the braking pressure sensor is faulty only through the sensor, once it is identified that there is an inaccuracy in the pressure measurement of the sensor, it will cause heavy losses.

[0003] In view of this, the present invention is specifically proposed. Summary of the Invention

[0004] In order to solve the problems in the prior art, the method for detecting faults of a pressure sensor in an autonomous sprinkler truck proposed by the present invention can accurately judge whether there is a fault in the braking pressure sensor or the sprinkler pressure sensor of the vehicle, and effectively determine the specific value of the pressure measurement error, which is convenient for subsequent maintenance or adjustment.

[0005] The present invention provides a method for detecting faults of a pressure sensor in an autonomous sprinkler truck, including the following steps:

[0006] S1. Obtain the vehicle data of the autonomous sprinkler truck through the sensor, and at the same time store the obtained vehicle data in the cloud server;

[0007] S2. When the autonomous sprinkler truck is in normal working conditions, establish a mathematical model of the adjustment curve between the braking pressure and the sprinkler pressure;

[0008] S3. Establish a database with the vehicle data stored in S1, and at the same time perform denoising and normalization processing on the vehicle data in the database;

[0009] S4. Perform cloud data analysis on multiple autonomous sprinkler trucks operating under the same working conditions, and judge whether the corresponding pressure sensors are faulty;

[0010] S5. Feed back the judgment result of S4 to the main control system of the faulty vehicle to remind the driver or the autonomous driving system to perform maintenance or adjustment.

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

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

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

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

[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 braking pressure;

[0017] As the sprinkling operation progresses, the mass of the autonomous driving sprinkler truck will decrease as the amount of sprinkled water decreases, which is expressed by Equation (2) as:

[0018] m e =m s -ρV w (2)

[0019] Wherein, m e represents the current mass of the sprinkler truck; m s represents the initial mass of the sprinkler truck; ρ represents the water density; V w represents the amount of sprinkled water;

[0020] The relationship between the amount of sprinkled water, the sprinkling pressure, and the sprinkling time is expressed by Equation (3) as:

[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 amount of sprinkled water, which is calibrated and obtained through the physical characteristics of the sprinkler pump; P w represents the sprinkling pressure; t represents the sprinkling operation time;

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

[0024]

[0025] Wherein, a represents the braking deceleration.

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

[0027] Further, in S4, the specific steps for performing cloud data analysis on multiple autonomous driving sprinkler trucks operating under the same working conditions to determine whether the corresponding pressure sensors are faulty are as follows:

[0028] First, at the same time, collect the actual vehicle data corresponding to multiple autonomous driving sprinkler trucks. If the relationships between the braking pressure and the sprinkling pressure in the collected actual vehicle data all satisfy the adjustment curve mathematical model in S2, then the sensors in all the multiple autonomous driving sprinkler trucks have not failed;

[0029] If there is a situation where the relationship between the braking pressure and the sprinkling pressure in the collected actual vehicle data does not satisfy the adjustment curve mathematical model in S2, then the autonomous driving sprinkler truck corresponding to the unsatisfied relationship has a sensor fault;

[0030] Then, compare the sprinkling pressure value of the autonomous driving sprinkler truck corresponding to the unsatisfied relationship with the sprinkling pressure values of the other vehicles operating under the same working conditions. If the sprinkling pressure values are the same, then the braking pressure sensor of this vehicle is faulty;

[0031] If the sprinkling pressure values are different, then continue to compare the braking pressure values. If the braking pressure values are the same, then the sprinkling pressure sensor of this vehicle is faulty;

[0032] If both the compared sprinkling pressure value and the braking pressure value are different, then both the sprinkling pressure sensor and the braking pressure sensor of this vehicle are faulty.

[0033] Further, the specific steps for determining whether the pressure sensor corresponding to the autonomous driving sprinkler truck is faulty are as follows:

[0034] Given the sprinkling pressure error and the braking pressure error of the vehicle, which are expressed by Equation (5) as:

[0035]

[0036] where, ΔP b represents the braking pressure error; ΔP w represents the sprinkling pressure error; P r1 represents the actual braking pressure; P r2 represents the actual sprinkling pressure;

[0037] Substitute the actual braking pressure values and sprinkling pressure values corresponding to each of the multiple autonomous driving sprinkler trucks into Equation (5) respectively to obtain the braking pressure error ΔP b and the sprinkling pressure error ΔP w . If ΔP b or ΔPw If it is greater than the set threshold ε, there is a fault in the braking pressure sensor or the sprinkling pressure sensor in the corresponding autonomous driving sprinkler truck.

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

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

[0040] Compared with the prior art, the method for detecting faults in the pressure sensor in the autonomous driving sprinkler truck provided by the present invention detects the braking pressure sensor and the sprinkling pressure sensor of multiple autonomous driving sprinkler trucks operating under the same working conditions through V2X technology. By comparing the mathematical model of the pressure adjustment curve established under normal working conditions with the actually detected pressure measurement data, it can accurately identify whether there is a fault in the pressure sensor, solve the problem of inaccurate pressure measurement of a single sensor, avoid the problem of low operation efficiency caused by inaccurate sprinkling pressure of the vehicle, and eliminate the potential safety hazard of vehicle driving caused by inaccurate braking pressure. Description of the Drawings

[0041] Figure 1 It is a schematic flow chart of the method for detecting faults in the pressure sensor in the autonomous driving sprinkler truck of the present invention. Detailed Embodiments

[0042] The present invention will be further explained below in conjunction with the accompanying drawings of the specification and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0043] Embodiment 1

[0044] According to an embodiment of the present invention, as Figure 1 shown, the present invention provides a method for detecting faults in the pressure sensor in an autonomous driving sprinkler truck, which specifically includes the following steps:

[0045] S1. Obtain the vehicle data of the autonomous driving sprinkler truck through sensors, and at the same time store the obtained vehicle data in the cloud server.

[0046] Specifically, use the data acquisition equipment or auxiliary devices on the vehicle, such as: the Internet of Things data acquisition terminal, to collect the sensor data installed on the autonomous driving sprinkler truck in real time to obtain data including the braking pressure, sprinkling pressure, sprinkling time, water tank water volume, and braking deceleration of the vehicle. And store the obtained vehicle data in the cloud server through V2X technology (i.e., the Internet of Things technology).

[0047] S2. When the autonomous driving sprinkler is in normal working condition, establish a mathematical model of the adjustment curve between the braking pressure and the sprinkling pressure.

[0048] Specifically, according to the operating characteristics of the autonomous driving sprinkler, for example: as the water volume in the water tank decreases, the total mass of the sprinkler will also decrease accordingly. Under the same deceleration condition, the braking pressure will also decrease. Establish a mathematical model of the adjustment curve between the braking pressure and the sprinkling pressure.

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

[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 sprinkling operation progresses, the mass of the autonomous driving sprinkler will decrease as the sprinkling water volume decreases, which is expressed by Equation (2) as:

[0053] m e =m s -ρV w (2)

[0054] 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 water volume;

[0055] The relationship between the sprinkling water volume, the sprinkling pressure and the sprinkling time is expressed by Equation (3) as:

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

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

[0058] From Equations (1)-(3), the mathematical model of the adjustment curve between the braking pressure and the sprinkling pressure is obtained, which is expressed by Equation (4) as:

[0059]

[0060] Among them, a represents the braking deceleration.

[0061] S3. Establish a database with the vehicle data stored in S1, and at the same time, perform denoising and standardization processing on the vehicle data in the database.

[0062] Specifically, the cloud server receives and stores the data from multiple autonomous sprinkler trucks, establishes a unified database, and at the same time performs denoising and standardization processing on the data in the database through filtering algorithms to ensure the data quality. The filtering algorithms adopted in the present invention include: median filtering, mean filtering, and adaptive filtering, etc.

[0063] S4. Perform cloud data analysis on multiple autonomous sprinkler trucks operating under the same working conditions to determine whether the corresponding pressure sensors are faulty.

[0064] Specifically, if it is detected that there is a significant deviation between the actual braking pressure or sprinkling pressure data of a certain autonomous sprinkler truck and the established adjustment curve mathematical model under normal conditions, it can be preliminarily determined that there may be a measurement inaccuracy phenomenon in the sensor of this vehicle. Further, by comparing the sensor values of this vehicle with the sensor values of the other vehicles under the same working conditions in the cloud, the braking pressure or sprinkling pressure sensor failure can be further determined.

[0065] The specific operation is as follows:

[0066] First, at the same time, collect the actual vehicle data corresponding to multiple autonomous sprinkler trucks. If the relationship between the braking pressure and the sprinkling pressure in the collected actual vehicle data all satisfies Equation (4), then the sensors in the multiple autonomous sprinkler trucks are all free of faults; if there is any one or more cases where the relationship between the braking pressure and the sprinkling pressure in the collected actual vehicle data does not satisfy Equation (4), it is considered that there is a sensor fault in the autonomous sprinkler truck whose relationship does not satisfy.

[0067] Then, compare the sprinkling pressure value of the autonomous sprinkler truck whose relationship does not satisfy with the sprinkling pressure values of the other vehicles under the same working conditions. If the sprinkling pressure values are the same, the braking pressure sensor of this vehicle is faulty; if the sprinkling pressure values are different, continue to compare the braking pressure values. If the braking pressure values are the same, the sprinkling pressure sensor of this vehicle is faulty.

[0068] If both the compared sprinkling pressure value and braking pressure value are different, then both the sprinkling pressure sensor and the braking pressure sensor of this vehicle are faulty.

[0069] If it is detected that there is a significant deviation between the actual braking pressure or sprinkling pressure data of a certain autonomous driving sprinkler truck and the adjustment curve mathematical model established under normal conditions, it can be preliminarily judged that the sensors of the vehicle may have measurement inaccuracies. Further, by comparing the sensor values of the vehicle with those of the sensors of other vehicles under the same working conditions in the cloud, the faults of the braking pressure or sprinkling pressure sensors can be further determined. Thus, to determine whether there is a fault in the pressure sensor corresponding to the autonomous driving sprinkler truck, the following specific steps are included:

[0070] Given the sprinkling pressure error and braking pressure error of the vehicle, which are expressed by Equation (5) as:

[0071]

[0072] where, ΔP b represents the braking pressure error; ΔP w represents the sprinkling pressure error; P r1 represents the actual braking pressure; P r2 represents the actual sprinkling pressure;

[0073] Substitute the actual braking pressure values and sprinkling pressure values corresponding to multiple autonomous driving sprinkler trucks into Equation (5) respectively, to obtain the braking pressure error ΔP b and the sprinkling pressure error ΔP w . If ΔP b or ΔP w is greater than the set threshold ε, then there is a fault in the braking pressure sensor or sprinkling pressure sensor in the corresponding autonomous driving sprinkler truck. The set threshold ε can be set according to the specific working scenario of the sensor or the working accuracy requirements of different sensors. For example: Compare the sprinkling pressure sensor with a standard pressure source with a known pressure, and set the threshold ε to ±2%. If after multiple measurements, the deviation between the measured value of the sensor and the standard value continuously exceeds this threshold, it is very likely that the sensor is damaged or needs calibration.

[0074] S5. Through V2X technology, feedback the judgment result of S4 to the main control system of the faulty vehicle to remind 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 invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some 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 invention.

Claims

1. A fault detection method for a pressure sensor in an autonomous sprinkler truck, characterized in that, It includes the following steps: S1. Obtain the vehicle data of the autonomous driving sprinkler through sensors, and at the same time store the obtained vehicle data in the cloud server; S2. When the autonomous driving sprinkler is in normal working conditions, establish a mathematical model of the adjustment curve between the braking pressure and the sprinkling pressure; S3. Establish a database with the vehicle data stored in S1, and at the same time perform denoising and standardization processing on the vehicle data in the database; S4. Perform cloud data analysis on multiple autonomous driving sprinklers operating under the same working conditions to determine whether the corresponding pressure sensors are faulty; S5. Feed back the judgment result of S4 to the main control system of the faulty vehicle to remind the driver or the autonomous driving system to perform maintenance or adjustment.

2. The fault detection method of the pressure sensor in the automatic driving sprinkler according to claim 1, characterized in that, The vehicle data in S1 includes braking pressure, sprinkling pressure, sprinkling time, water volume in the water tank, and braking deceleration.

3. The fault detection method of the pressure sensor in the automatic driving sprinkler truck according to claim 1, characterized in that, Before obtaining the vehicle data of the autonomous driving sprinkler in S1, it is necessary to install the pressure sensor on the autonomous driving sprinkler.

4. The fault detection method of the pressure sensor in the automatic driving sprinkler according to claim 1, characterized in that, The specific steps for establishing the mathematical model of the adjustment curve between the braking pressure and the sprinkling pressure in S2 are as follows: According to the relationship between the braking force and the braking pressure, it is expressed by Equation (1) as: F b = k b P b (1) where 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; As the sprinkling operation progresses, the mass of the autonomous driving sprinkler will decrease as the sprinkling water volume decreases, which is expressed by Equation (2) as: m e = m s -ρV w (2) Among them, m e represents the current mass of the sprinkler truck; m s represents the initial mass of the sprinkler truck; ρ represents the water density; V w represents the amount of water sprayed; The relationship between the sprinkling water volume, the sprinkling pressure, and the sprinkling time is expressed by Equation (3) as: V w = k w P w t (3) Among them, k w represents a proportionality coefficient, indicating the positive correlation between the sprinkling pressure and the sprinkling water volume, which is calibrated and obtained through the physical characteristics of the sprinkling pump; P w represents the sprinkling pressure; t represents the sprinkling operation time; From Equations (1)-(3), the mathematical model of the adjustment curve between the braking pressure and the sprinkling pressure is obtained, which is expressed by Equation (4) as: Where, a represents the braking deceleration.

5. The fault detection method of the pressure sensor in the automatic driving sprinkler according to claim 1, characterized in that, In S3, the vehicle data in the database is denoised and standardized through a filtering algorithm.

6. The fault detection method of the pressure sensor in the automatic driving sprinkler according to claim 1, characterized in that, The specific steps for performing cloud data analysis on multiple autonomous driving sprinklers operating under the same working conditions in S4 to determine whether the corresponding pressure sensors are faulty are as follows: First, at the same time, collect the actual vehicle data corresponding to multiple autonomous driving sprinklers. If the relationship between the braking pressure and the sprinkling pressure in the collected actual vehicle data all satisfies the adjustment curve mathematical model in S2, then the sensors in the multiple autonomous driving sprinklers are not faulty; If there is a situation where the relationship between the braking pressure and the sprinkling pressure in the collected actual vehicle data does not satisfy the adjustment curve mathematical model in S2, then the autonomous driving sprinkler corresponding to the unsatisfied relationship has a sensor fault; Then, compare the sprinkling pressure value of the autonomous driving sprinkler corresponding to the unsatisfied relationship with the sprinkling pressure values of the other vehicles under the same operating conditions. If the sprinkling pressure values are the same, then the braking pressure sensor of this vehicle is faulty; If the sprinkling pressure values are different, then continue to compare the braking pressure values. If the braking pressure values are the same, then the sprinkling pressure sensor of this vehicle is faulty; If both the compared sprinkling pressure value and the braking pressure value are different, then both the sprinkling pressure sensor and the braking pressure sensor of this vehicle are faulty.

7. The fault detection method of the pressure sensor in the automatic driving sprinkler according to claim 6, characterized in that To determine whether the pressure sensor corresponding to the autonomous driving sprinkler is faulty, it specifically includes the following steps: Given the sprinkling pressure error and the braking pressure error of the vehicle, it is expressed by Equation (5) as: Among them, ΔP b represents the braking pressure error; ΔP w represents the sprinkler pressure error; P r1 represents the actual braking pressure; P r2 represents the actual sprinkler pressure; Substitute the actual braking pressure values and sprinkling pressure values corresponding to each of the multiple autonomous sprinkler trucks into Equation (5) respectively to obtain the braking pressure error ΔP b and the sprinkling pressure error ΔP w . If ΔP b or ΔP w is greater than the set threshold ε, there is a fault in the braking pressure sensor or the sprinkling pressure sensor in the corresponding autonomous sprinkler truck.

8. The fault detection method of the pressure sensor in the automatic driving sprinkler according to claim 1, characterized in that, In S1, the obtained vehicle data is stored in the cloud server through V2X technology.

9. The fault detection method of the pressure sensor in the automatic driving sprinkler 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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