CN115717986A

CN115717986A - Method and equipment for measuring fuming vehicle operation training parameters by adopting AR glasses

Info

Publication number: CN115717986A
Application number: CN202211311338.XA
Authority: CN
Inventors: 武明; 孙文选; 白海涛; 阎瑞; 安刚; 王颖辉; 张博; 姚伟召
Original assignee: Insititute Of Nbc Defence
Current assignee: Insititute Of Nbc Defence
Priority date: 2022-10-25
Filing date: 2022-10-25
Publication date: 2023-02-28
Anticipated expiration: 2042-10-25
Also published as: CN115717986B

Abstract

The embodiment of the invention provides a method and equipment for measuring smoking vehicle operation training parameters by using AR glasses, relates to the field of measurement, and solves the problem that infrared measurement accuracy of a smoking vehicle in an outdoor environment is poor in the prior art. According to the method, in each running process of the smoking vehicle, the current characteristic data of the smoking vehicle is obtained through the AR glasses, temperature measurement is carried out on each part of the smoking vehicle according to the current characteristic data of the smoking vehicle, the position of each AR glass is determined through a pre-trained model, therefore accurate temperature measurement of the part to be measured is achieved, and fault prediction of the smoking vehicle is obtained. According to the method, the fault problem of the equipment is checked in each operation process of the fuming vehicle, so that the fault detection efficiency of the equipment is improved, the uniform replacement of all parts of the equipment according to fixed time is avoided, and the utilization rate of the parts in the equipment is improved.

Description

Method and equipment for measuring operation training parameters of fuming vehicle by using AR glasses

Technical Field

The application relates to the field of measurement, in particular to a method and equipment for measuring fuming vehicle operation training parameters by adopting AR glasses.

Background

The smoking car is the equipment that uses under the outdoor environment, and its working process makes a large amount of smog to shelter from near environment, reduce visibility.

In the working process, because long-time work is needed, the smoke generating component is long-time, and the heat effect in the equipment leads to higher temperature per se under the high-power work. Because outdoor environment weather change is big, high fever, strong wind, sleet meteorology all can exist, and higher temperature work can lead to equipment to appear the thermal decay, needs staff frequent detection its operating temperature. Because the fuming vehicle runs in an outdoor environment, the fuming vehicle is not friendly to vehicle-mounted temperature measurement. In the prior art, temperature measurement is carried out through AR glasses, and the deviation of a measurement result is large in an outdoor environment.

Disclosure of Invention

In view of this, in order to solve the problem that the AR glasses have a large deviation of the measurement result in the outdoor environment, the embodiments of the present invention provide a method and an apparatus for measuring an operation training parameter of a smoke generating car by using the AR glasses.

The embodiment of the application provides a method for measuring fuming vehicle operation training parameters by adopting AR glasses, which comprises the following steps: when the smoking vehicle is in a running state, obtaining characteristic data of a heating component of the smoking vehicle in a running process, and determining the respective measuring position of each AR (augmented reality) glasses to which each smoking vehicle belongs in a preset positional relationship prediction model according to the characteristic data; sending the respective measurement position to each AR glasses, and receiving temperature detection data of the heat generating component on the smoking vehicle, which is fed back by each AR glasses at the measurement position; obtaining operation training parameters of the current operation of the smoking vehicle, wherein the operation training parameters of the current operation comprise current temperature data of a heating component in the smoking vehicle, and the current temperature data is determined by the position relation between the AR glasses and the heating component, wind speed data of the environment where the smoking vehicle is located, working power of equipment of the smoking vehicle, and distance information between the AR glasses and the heating component; judging whether a temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is smaller than a preset temperature data difference value threshold or not according to the current temperature data of the heating part; and if so, outputting the measured current temperature data of the heating component in the smoking vehicle, wherein the current temperature data is a temperature value.

Optionally, the determining, according to the feature data, the respective measurement position of each AR glasses to which each smoking vehicle belongs in a preset positional relationship prediction model includes: inputting the characteristic data of the heating part into a position relation prediction model trained in advance, and obtaining the position relation between the AR glasses and the heating part when the temperature data of the heating part is tested and output by the position relation prediction model; the position relation prediction model is used for inquiring information of the measured position of each AR glasses in the current environment matched with the characteristic data of the heat generating component according to the characteristic data of the heat generating component.

Optionally, the position relation prediction model is obtained by training according to the following method: inputting characteristic data of the heating part marked with the temperature detection target position information into an initial position relation prediction model to obtain temperature detection prediction position information of the heating part output by the initial position relation prediction model; and calculating an error value between the marked temperature detection target position information and the temperature detection prediction position information, and adjusting parameters of the initial position relation prediction model according to the error value to obtain a position relation prediction model for determining the temperature detection position information of the heating part.

Optionally, the number of the AR glasses is 4; after performing the step of receiving temperature detection data of the heat generating components on the smoking vehicle fed back by each of the AR glasses at the measurement location, the method further comprises: calculating the current temperature data according to the following formula by using the obtained 4 temperature detection data:

(ii) a Wherein, t ₁ First temperature data representative of an AR glasses test located at a first location of the smoking vehicle; t is t ₂ Second temperature data representative of an AR glasses test located at a second location of the smoking vehicle; t is t ₃ Third temperature data representative of an AR glasses test located at a third location of the smoking vehicle; t is t ₄ Fourth temperature data representative of an AR eyewear test located at a fourth location of the smoking vehicle.

Optionally, the distance information between the AR glasses and the heat generating component is determined by the following method: adopting a laser ranging sensor of the AR glasses to emit laser pulses to a heating part of the fuming vehicle, receiving reflected light returned by the heating part, and acquiring first time data from the emitting of the laser pulses to the receiving of the reflected light by the laser sensor; or, an infrared ranging sensor of the AR glasses is adopted to emit infrared light, and receive reflected light returned by the heating component, and second time data of the infrared light emitted from the infrared ranging sensor to the heating component and the reflected light received is acquired; determining distance information between the AR glasses and the heat generating component according to the first time data or the second time data.

Optionally, the method further includes: calculating a fault detection result of the smoking vehicle in the operation process according to the currently-operated operation training parameters; if the fault detection result indicates that no fault problem is found, sending a result message that no fault problem is found in the smoking vehicle to the target application; and if the fault detection result indicates that a fault problem exists, sending the current fault problem of the smoke generating vehicle and a maintenance scheme corresponding to the fault problem to the target application.

Optionally, the calculating a fault detection result of the smoking vehicle in the operation process according to the currently-operated operation training parameter includes: and if the temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is not less than a preset temperature data difference value threshold, determining a fault detection result of the fuming vehicle in the running process according to the current temperature data of the heating part.

Optionally, the currently running operation training parameters further include current video data of the heat generating component during running of the smoking vehicle, and current vibration data of the heat generating component; according to the currently running operation training parameters, calculating a fault detection result of the smoking vehicle in the running process, wherein the fault detection result comprises the following steps: according to the current video data of the heating components, the current vibration data of the heating components and the current temperature data of the heating components in the running process of the fuming vehicle, the fault detection result of the fuming vehicle in the running process is calculated.

Optionally, the calculating a fault detection result of the smoking vehicle in the operation process according to the current video data of the heat generating component, the current vibration data of the heat generating component, and the current temperature data of the heat generating component in the operation process of the smoking vehicle includes: comparing the current image data of the heating part with the standard image data of the heating part, and judging whether position error information exists in the position of the heating part in the current image data of the heating part; if the position error information exists, judging whether a vibration data difference value exists between the current vibration data and the standard vibration data of the heating part; and if the vibration data difference exists, calculating the fault level of the fuming vehicle in the operation process according to the position error information, the vibration data difference and the temperature data difference.

The embodiment of the application further provides equipment for measuring the operation training parameters of the fuming vehicle by adopting the AR glasses, and the equipment is characterized in that the method for measuring the operation training parameters of the fuming vehicle by adopting the AR glasses is adopted as described above.

Compared with the prior art, the method has the following advantages:

the embodiment of the application provides a method for measuring smoking vehicle operation training parameters by adopting AR glasses, which comprises the following steps: when a smoking vehicle is in a running state, obtaining characteristic data of a heating component of the smoking vehicle in a running process, and determining the respective measuring position of each AR (augmented reality) glasses to which each smoking vehicle belongs in a preset position relation prediction model according to the characteristic data; sending the respective measurement position to each AR glasses, and receiving temperature detection data of the heat generating component on the smoking vehicle, which is fed back by each AR glasses at the measurement position; obtaining operation training parameters of the current operation of the smoking vehicle, wherein the operation training parameters of the current operation comprise current temperature data of a heating component in the smoking vehicle, and the current temperature data is determined by the position relation between the AR glasses and the heating component, wind speed data of the environment where the smoking vehicle is located, working power of equipment of the smoking vehicle, and distance information between the AR glasses and the heating component; judging whether a temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is smaller than a preset temperature data difference value threshold or not according to the current temperature data of the heating part; and if so, outputting the measured current temperature data of the heating components in the smoking vehicle, wherein the current temperature data is a temperature value.

In the method, in each running process of the smoking vehicle, the current running operation training parameters of the smoking vehicle are obtained through the AR glasses, wherein the current running operation training parameters comprise current temperature data of the smoking vehicle, a plurality of temperature measurement positions of a heating part of the smoking vehicle are determined through a preset position relation prediction model, temperature detection data of the heating part fed back by the AR glasses are obtained at each temperature measurement position, then current temperature data of the heating part of the smoking vehicle are obtained according to the temperature detection data obtained through the AR glasses, temperature data difference calculation is carried out on the current temperature data detected and processed through the AR glasses and standard temperature data of the heating part, and if the temperature data difference is smaller than the preset temperature data difference, the current temperature detection data are determined to be the temperature data of the heating part of the smoking vehicle in the current running state. Here, a plurality of temperature detection positions of the heat generating component under test are determined by a preset positional relationship prediction model, and the influence of the environmental characteristic information of each of the plurality of temperature detection positions on the temperature data of the heat generating component is considered, where the environmental characteristic information of each temperature detection position includes information such as environmental wind speed data and distance information between the AR glasses and the heat generating component. According to the temperature data of the temperature detection positions, the current temperature data of the heating part in the operation process is determined, and the accuracy of the temperature data of the AR glasses test is improved.

Furthermore, the fault detection result of the fuming vehicle in the operation process is detected according to the obtained current temperature data of the heating part of the fuming vehicle, the accuracy of obtaining the current temperature data of the heating part is improved by the method, and on the basis, the accuracy of the fault detection result determined according to the current temperature data is also improved.

Furthermore, the operation training parameters of the current operation of the fuming vehicle further comprise current vibration data of the fuming vehicle, the current video data combines the current temperature data, the current vibration data and the current video data, the fault detection result of the fuming vehicle in the operation process is analyzed, and the accuracy of the fault detection result of the heating component is further improved. Moreover, after the fuming vehicle runs every time, the operation training parameter of the fuming vehicle running every time is obtained, the fault detection result of the fuming vehicle is analyzed according to the operation training parameter running every time, the fault detection efficiency of the equipment is improved, maintenance is conducted on specific fault parts of the equipment, and the utilization rate of all parts of the equipment which are uniformly replaced according to fixed time is also avoided, and the utilization rate of the parts in the equipment is improved.

Drawings

Fig. 1 is an application scene diagram of a method for measuring smoking vehicle operation training parameters by using AR glasses according to an embodiment of the present application.

Fig. 2 is a schematic view of a positional relationship between a smoking driving motor device of the smoking vehicle provided by the embodiment of the application and a smoking vehicle body.

Fig. 3 is a schematic diagram of temperature detection position distribution of a test smoking vehicle provided by the embodiment of the application.

Fig. 4 is a flowchart of a method for measuring smoking vehicle operation training parameters by using AR glasses according to a first embodiment of the present application.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.

The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The description used in this application and in the appended claims refers to the accompanying drawings in which: the terms "a," "an," "first," and "second," etc., are not intended to be limiting in number or order, but rather are used to distinguish one type of information from another.

In the working process, because long-time work is needed, the smoke generating component is long-time, and the heat effect in the equipment leads to higher temperature per se under the high-power work. Because outdoor environment weather changes greatly, high fever, strong wind, sleet weather all can exist, and higher temperature work can lead to equipment to appear the heat decay, needs staff frequently to detect its operating temperature. Because the fuming vehicle runs in an outdoor environment, the fuming vehicle is not friendly to vehicle-mounted temperature measurement. In the prior art, temperature measurement is carried out through AR glasses, and the deviation of a measurement result is large in an outdoor environment.

Equipment failure detection belongs to the necessary steps for the operation safety of equipment, and whether a moving part of the equipment has a failure or not is determined by detecting vibration data of the equipment. At present, equipment detection is generally carried out according to fixed detection time, and parts are replaced, so that part of equipment parts have faults but are not found in time, or part of equipment parts can be used but replaced, and the fault level of the parts of the equipment is upgraded or the utilization rate of the parts is low. Therefore, how to improve the efficiency of equipment detection is a problem to be solved.

To address the above issues, the present application provides a number of embodiments, discussed separately below.

Please refer to fig. 1, which is a view illustrating an application scenario of the method for measuring an operation training parameter of a smoking vehicle using AR glasses according to an embodiment of the present application.

The embodiment of the application is applied to when the fuming vehicle is in the running state, and the AR glasses are adopted to measure the operation training parameters of the fuming vehicle. The probability of failure of the heat decay of the smoking vehicle is then predicted from the currently running operating training parameters, in particular the temperature data.

Specifically, AR glasses (AR, i.e., "Augmented Reality"). The augmented reality technology is a technology for skillfully fusing virtual information and a real world, and a plurality of technical means such as multimedia, three-dimensional modeling, real-time tracking and registration, intelligent interaction, sensing and the like are widely applied, and virtual information such as characters, images, three-dimensional models, music, videos and the like generated by a computer is applied to the real world after analog simulation, and the two kinds of information supplement each other, so that the real world is enhanced.

In the embodiment of the present application, the AR glasses 100 include a camera 101, a central processing unit 102, a memory 103, a vibration data receiver 104, and a temperature sensor 105. Wherein, training personnel in the in-process of training in the fuming car, gather the current video data of the assembly of fuming car or part of waiting to detect the part through the camera 101 of AR glasses.

The server (here, the central processing unit 102) compares the current video data with the standard video data in the memory, determines whether the component to be detected has a fault phenomenon, and if so, sends a suggestion for guiding maintenance, and if the component to be detected still has a fault phenomenon after being guided to be maintained, performs manual maintenance. Then, the failure information of the component to be detected and the maintenance data corresponding to the failure information are stored in the memory.

In addition, on the basis of the video data, in order to further determine the fault problem of the component to be detected with the fault risk, the fault problem of the component to be detected is analyzed according to the current temperature data of the component to be detected.

Because the fuming vehicle contains multiple special equipment, the influence degree of vibration data and temperature data on the fault analysis factor is determined for analyzing the fault analysis factor of the special equipment of the fuming vehicle, the vibration data and the temperature data of the fuming vehicle are simultaneously analyzed, and the fault grade of the component to be detected is determined. The temperature measurement may be performed either before or after the vibration data measurement.

The method for acquiring the vibration data of the component to be detected of the smoking vehicle is as described in the description of fig. 2. In addition, the temperature data acquisition method and the analysis method can be described with reference to the following descriptions:

in order to promote the accuracy of the current temperature data of the fuming vehicle, when the embodiment of the application detects the current temperature data, the heating condition of the to-be-detected component of the fuming vehicle is considered, and the influence of the environment where the fuming vehicle is located on the temperature parameter of the fuming vehicle is also considered. Therefore, when testing the current temperature data of the smoking vehicle, the embodiment of the application determines the current temperature data according to the position relationship between the AR glasses and the heat generating component, the wind speed data of the environment where the smoking vehicle is located, the working power of the equipment of the smoking vehicle, and the distance information between the AR glasses and the heat generating component.

Firstly, determining the position information of temperature measuring equipment for testing the temperature data of the smoking vehicle in the current environment of the smoking vehicle through a pre-trained position relation prediction model.

As shown in fig. 3, which is a schematic diagram of a distribution of temperature detection positions for testing the temperature of a smoking vehicle according to an embodiment of the present application. The position relation prediction model determines that the temperature detection position information of the heat generating part of the smoking vehicle comprises a first temperature detection position 1, a second temperature detection position 2, a third temperature detection position 3 and a fourth temperature detection position 4. The position relation prediction model is used for determining temperature detection position information corresponding to the characteristic data of the smoking vehicle according to the characteristic data of the smoking vehicle. The characteristic data of the heat generating component in the smoking vehicle during operation can at least comprise one of the following data: the smoking vehicle comprises a smoking vehicle body, a heating part and AR glasses, wherein the smoking vehicle body is arranged on the smoking vehicle body, and the AR glasses are arranged on the smoking vehicle body.

And inputting the current characteristic data into the model, and obtaining the measurement position of each AR glasses around the speaking car. Therefore, more characteristic data with different values are trained, and the observation position of each AR glasses is obtained on the premise that the measured temperature data is closest to the actual temperature.

After the temperature test position relation between each AR glasses and the fuming vehicle is obtained, the temperature test data transmitted by the AR glasses at each temperature test position are respectively obtained, and the plurality of temperature test data are calculated according to the following formula 1 to obtain the final current temperature data T:

(formula 1)

Wherein, t ₁ First temperature data representative of an AR glasses test located at a first location of the smoking vehicle; t is t ₂ Second temperature data representative of an AR glasses test located at a second location of the smoking vehicle; t is t ₃ Third temperature data representative of an AR glasses test located at a third location of the smoking vehicle; t is t ₄ Fourth temperature data representative of an AR eyewear test located at a fourth location of the smoking vehicle. Above mentionedThe temperature data obtained from the temperature test position is related to the wind speed of the position, the distance between the position and the smoking vehicle and the equipment power of the smoking vehicle. Here, the coefficient preceding each temperature data is a fixed coefficient value obtained through a large number of historical test analysis calculations.

The position relation prediction model can be trained by adopting a logistic regression model through the following method:

firstly, establishing a plurality of groups of characteristic data, wherein each group comprises a plurality of characteristic data, including the actual temperature, the wind speed, the wind direction, the working power, the visibility and the like of the current heating component, the characteristic data are as described above, and the positions of the plurality of ARs and the distance between the AR and the equipment to be measured are continuously adjusted as the test data, so that the position of each AR is obtained under the condition that the accuracy between the measured temperature and the actual marked temperature is optimal.

And acquiring the characteristic data of the smoking vehicle marked with the temperature test target position, inputting the characteristic data of the smoking vehicle marked with the temperature test target position into an initial position relation prediction model, wherein the characteristic data comprises correct data and incorrect data, and until the temperature test prediction position information of the smoking vehicle output by the initial position relation prediction model is obtained and is equal to the position of each optimal AR in the front.

The temperature values which are measured and calculated by the AR intelligent glasses at each position are related, correct data and incorrect data do not need to be distinguished, and the information of the temperature test predicted position of the fuming vehicle output by the initial position relation prediction model is obtained through a logistic regression algorithm until the information of the temperature test predicted position of the fuming vehicle is equal to the position of each AR glass with the historically best measurement result. Wherein, the position can adopt a plane coordinate position taking the measured part as a center.

For example, parameters of characteristic data such as different outdoor wind speeds, temperatures and equipment power are simulated in a laboratory environment, the parameters are measured by detection points at different geometric positions, compensation data measured by a plurality of measurement points of AR glasses are analyzed, predicted compensation temperature data are obtained and added with an environment constant C, and the predicted temperature data are obtained; and comparing the predicted temperature data with the current actually-measured temperature data of the equipment, if the temperature difference between the predicted temperature data and the current actually-measured temperature data is smaller than a preset temperature difference threshold value, indicating that the predicted temperature data which is measured by the current measuring point and belongs to the actual temperature data of the measuring point in the real environment is close to the actual temperature data of the measuring point, respectively taking the current measuring points as the temperature measuring positions of the AR glasses, and storing the temperature measuring positions in a position relation prediction model.

The temperature measurement positions of the AR glasses are obtained in the laboratory environment in the mode and are used as the marked temperature test target positions of the smoking vehicle, and the initial position relation prediction model is trained.

And secondly, calculating a temperature difference value between the marked temperature test target position information and the temperature test prediction position information, and adjusting parameters of the initial position relation prediction model by taking the temperature difference value as a basis to obtain a trained position relation prediction model. Besides the position relation prediction model, the neural network can be adopted to train the most appropriate position of each AR under the variable of each characteristic data, so that the finally measured temperature is closest to the temperature of the part to be measured on the actual smoking vehicle.

The temperature data of each temperature detection position is determined by combining the temperature data of the current detection position tested by the AR glasses and the information of the distance between the current detection position and the heating component, the wind speed data of the current detection position and the power data of the heating component. Specifically, it is determined by the following formula 2:

(formula 2)

Wherein t represents temperature data of a current temperature detection position;

c is a temperature constant of the environment where the smoking vehicle is located;

d is distance temperature compensation data formed from the distance between the temperature test location and the smoking vehicle versus temperature data, which is determined by equation 3 below:

(formula 3)

The temperature testing positions are different, and the corresponding distance coefficients are different; for example, the first detection position: the left front of the heating component is 1 meter, and the first distance coefficient is 0.5; the second detection position: the left front is 1.5 meters, and the second distance coefficient is 0.4; the third detection position: the right front is 1.6 meters, and the third distance coefficient is 0.3; fourth detection position: the right front is 1.8 meters and the fourth distance coefficient is 0.2.

r is standard distance data of predetermined test temperature data, r _n Data representing the actual distance between the AR glasses and the smoking vehicle at the nth temperature test position.

W is wind speed temperature compensation data of the wind speed at the temperature test location to the temperature data of the smoking vehicle, which is determined by the following equation 4:

(n represents the nth temperature test position in the actual test environment) (equation 4)

The temperature testing positions are different, and the corresponding wind speed coefficients are different; s _n The wind speed data of the position tested by the AR glasses at the nth temperature testing position is obtained, s is standard wind speed data and belongs to a constant, and s is 60 for example.

P represents temperature data generated by the device of the smoking vehicle as a result of power generated by the device. For example, the current ambient temperature is 30 ℃, which is a normal temperature environment, the temperature generated by the power of the device is 100 ℃, and the power temperature compensation value is 0, so that the actual temperature of the device should be 100 ℃. If the detected temperature of the equipment is actually detected to be 100 ℃, the equipment is normal and compensation is not needed.

During subsequent measurement, according to the current environmental parameters, position information is sent to the AR glasses, current temperature data measured by each AR glasses is obtained, the temperature data measured by the AR glasses are integrated, such as an average value, the integrated temperature is compared with a preset temperature threshold, and if the integrated temperature exceeds the threshold, a possible fault is prompted.

The position of each AR glasses may be the same or different under different environmental parameters.

For example, the temperature data of the heat generating component of the smoking vehicle detected by the preset positional relationship prediction model needs 4 positions for the tester to carry AR glasses to test the temperature data at 4 positions. The 4 AR glasses test positions are respectively:

the first detection position: the left front of the heating component is 1 meter, and the first distance coefficient is 0.5; the second detection position: the left front is 1.5 meters, and the second distance coefficient is 0.4; the third detection position: the right front is 1.6 meters, and the third distance coefficient is 0.3; fourth detection position: the right front is 1.8 meters and the fourth distance coefficient is 0.2.

The 4 coefficients are obtained by comparing the temperature measured by the AR smart glasses at different positions with the environmental parameters and positions of the AR smart glasses during calibration in a laboratory and the data at standard power.

Wherein the temperature constant C is 23.5 ℃, and the wind speed coefficient is 0.5.

The current environment of the smoking vehicle is 23.5 ℃, so the power compensation temperature data of the smoking vehicle can be 0.

Then D is ₁ =0.5*（1/1） ² =0.5；D ₂ =0.4*（1/1.5） ² =0.17；

D ₃ =0.3*（1/1.6） ² =0.12；D ₄ =0.2*（1/1.8） ² =0.06。

W ₁ =0.5*（35/60） ² =0.17；W ₂ =0.5*（30/60） ² =0.125；

W ₃ =0.5*（30/60） ² =0.125；W ₄ =0.5*（25/60） ² =0.09。

For example, when the current ambient wind direction is toward the left front direction of the heat generating component of the smoke generating vehicle, and the smoke generating direction of the heat generating component is also in the left front direction, when the wind speed pushes the smoke generating component to move to the left front direction, the temperature data detected at the left front direction is higher than the normal temperature environment, and the temperature is higher as the temperature detection position is farther from the heat generating component, so that the temperature data at the left front direction is greater than the temperature data at the right front direction, and the temperature data at the first test position is greater than the temperature data at the second test position. The temperature data of the third temperature test location is greater than the temperature data of the fourth temperature test location.

Combining the above data, the temperature data for the four locations is as follows:

t ₁ =23.5+0.5+0.17+0=24.17℃；

t ₂ =23.5+0.17+0.125+0=23.795℃；

t ₃ =23.5+0.12+0.125+0=23.745℃；

t ₄ =23.5+0.06+0.09+0=23.65℃。

the current temperature data is:

=24.17+0.125*23.795-0.001*23.745 ² +0.00008*23.65 ³ =24.17+2.97-0.56+1.058=27.64℃。

therein, the temperature sensor 105 in the AR glasses comprises a laser ranging sensor comprising a laser transmitter and a laser receiver. The laser emitter emits laser pulses to a heat generating component of the smoking vehicle, wherein the heat generating component mainly refers to a rotating component in the smoking driving motor device and a moving component connected with the rotating component. After receiving the laser pulse, the heating component scatters in various directions, and part of scattered light returns to the laser receiver. The laser ranging sensor records time information of two processes of emitting laser pulses to the heating component and receiving scattered light returned by the heating component, and determines distance information between the AR glasses and the heating component according to the time information and the light speed.

In addition, the current light intensity data of the part to be detected is obtained through the ambient light data collected by the image collector of the AR glasses. Here, video data in the running process of the fuming vehicle is collected according to a camera of the AR glasses, and ambient light intensity data around the fuming driving motor device is obtained according to the video data.

In addition, the current video data in the running process of the fuming vehicle is detected, and the fault grade information of the part to be detected of the fuming vehicle can be analyzed by combining the current vibration data of the fuming vehicle after the current temperature data.

Specifically, a vibration sensor is arranged on the fuming vehicle, and the vibration sensor detects a current vibration signal of a component to be detected on the fuming vehicle and sends the current vibration signal to the vibration data receiver 104 of the AR glasses. The vibration data receiver 104 determines current vibration data of the part to be detected according to the received current vibration signal, and sends the current vibration data to the central processor 102. And the central processing unit 102 compares the current vibration data with the standard vibration data, and if the vibration data difference value between the current vibration data and the standard vibration data is greater than a preset vibration data difference value threshold, determining that the fault risk exists in the component to be detected.

Specifically, the vibration data difference of the component to be detected is calculated according to the following formula 5, and then the fault risk level of the component to be detected is analyzed according to the vibration data difference in combination with the current video data.

(i =1,2,3, \8230n) (formula 5)

Wherein i represents the number of times of detection of the current vibration data; x1 _i Representing the current vibration data of the part to be detected at the ith detection; z1 represents the vibration data difference.

Here, the vibration data of the component to be detected are obtained in the process of one-time operation of the fuming vehicle, and the vibration data difference value Z1 of the component to be detected is calculated according to the formula (1). And analyzing the fault risk level of the component to be detected according to the relation between the vibration data difference value and a preset vibration data difference value threshold value.

Because the smoking vehicle comprises part of special equipment, in the actual operation process of the smoking vehicle, the detected vibration data of the smoking vehicle usually comprises a double vibration process of vibration operation caused by starting of the smoking vehicle and vibration operation caused by a smoking driving motor of the smoking vehicle. Therefore, the vibration data during the operation of the smoking vehicle is vibration data caused by the dual vibration drive.

In order to avoid fuming car self to start the influence of the vibration signal that forms to fuming driving motor's vibration signal to influence the analysis and fuming various fault grade problems of waiting to detect the part among the driving motor device, it is specifically aroused by which vibration source to make clear of the fault information of the part of fuming car, this application embodiment is through setting up an elastic strain sensor between the driving motor device of fuming and the car body of fuming. As shown in fig. 2, it is a schematic view of a position relationship of the smoking driving motor device of the smoking vehicle provided in the embodiment of the present application in the body of the smoking vehicle. In fig. 2, the smoking driving motor device 203 is arranged on the smoking vehicle body 201, the elastic strain sensor 202 is arranged between the smoking vehicle body 201 and the smoking driving motor device 203, and the elastic strain sensor 202 can counteract the influence of the vibration of the smoking vehicle body 201 in the movement process on the vibration data of the smoking driving motor device 203.

When the smoking vehicle runs, the total vibration data V generated by the smoking vehicle comprises first vibration data V1 generated by the smoking vehicle body 201 and second vibration data V2 generated by the smoking motor driving device 203. Vibration data V detected by the elastic strain sensor 202 _x The difference between the total vibration data V generated for the smoking vehicle and the first vibration data V1 generated by the smoking vehicle body 201 can be calculated according to the following formula 6:

V _x = V-V1= (V1 + V2) -V1= V2 (formula 6).

Therefore, the vibration data V detected by the elastic strain sensor 202 _x I.e. the second vibration data V2 generated by the smoke generating motor driving device 203. The elastic strain sensor 202 sends the detected second vibration data V2 to the vibration data receiver 104 in the AR glasses. The vibration data receiver 104 analyzes the fault level of the smoking motor driving device 203 of the smoking vehicle according to the second vibration data V2. Wherein the smoking motor driving device 203 comprises a smoking motor component and a plurality of moving components connected with the smoking motor.

The fault information of the fuming driving electrode device in the running process of the fuming vehicle is analyzed by the server side according to the current video data, the current vibration data and the current temperature data in the running process of the fuming vehicle. And judging whether the position error information of the fuming driving motor device exists in the current video data according to a preset visual rule. And judging whether the current vibration data has vibration error data or not according to a preset vibration rule. And judging whether the current temperature data has temperature error data or not according to a preset temperature rule. And then, determining the fault probability of the fuming driving motor device with faults according to the judgment result, and determining maintenance decision information aiming at the fuming driving motor according to the fault probability.

Furthermore, according to the acquired current temperature data of the heating part of the fuming vehicle, the fault detection result of the fuming vehicle in the operation process is detected, the accuracy of acquiring the current temperature data of the heating part is improved, and on the basis, the accuracy of the fault detection result determined according to the current temperature data is also improved.

Furthermore, the operation training parameters of the current operation of the fuming vehicle further comprise current vibration data of the fuming vehicle, the current video data combines the current temperature data, the current vibration data and the current video data, and then the fault detection result of the fuming vehicle in the operation process is analyzed, so that the accuracy of the fault detection result of the heating component is further improved. Moreover, after the fuming vehicle runs at every time, the operation training parameters of the fuming vehicle running at every time are acquired, the fault detection result of the fuming vehicle is analyzed according to the operation training parameters running at every time, the fault detection efficiency of the equipment is improved, maintenance treatment is carried out on specific fault parts of the equipment, all parts of the equipment are prevented from being changed uniformly according to fixed time, and the utilization rate of the parts in the equipment is improved.

First embodiment

Corresponding to the application scene of the method for measuring the operation training parameters of the fuming vehicle by adopting the AR glasses, the first embodiment of the application provides a method for measuring the operation training parameters of the fuming vehicle by adopting the AR glasses. Since the first embodiment is basically similar to the application scenario of the method for measuring the operation training parameters of the smoking vehicle by using the AR glasses provided in the embodiment of the present application, please refer to the partial description of the application scenario of the method for measuring the operation training parameters of the smoking vehicle by using the AR glasses provided in the embodiment of the present application. The first embodiment described below is merely illustrative.

Please refer to fig. 4, which is a flowchart illustrating a method for measuring training parameters of smoking vehicle operation by using AR glasses according to a first embodiment of the present application. The method for measuring the operation training parameters of the smoking vehicle by adopting the AR glasses provided by the first embodiment of the application can be applied to a server side of equipment. The server of the device is a computer device that provides a program or software for providing a device operation failure detection service for a user to provide data services such as a data processing service and a data storage service, and a specific implementation manner is generally a server or a server cluster.

As shown in fig. 4, in step S401, when a smoking vehicle is in an operating state, feature data of a heat generating component of the smoking vehicle during operation is obtained, and a respective measurement position of each AR glasses to which each smoking vehicle belongs is determined in a preset positional relationship prediction model according to the feature data.

As shown in fig. 4, in step S402, the respective measurement positions are transmitted to the respective AR glasses, and temperature detection data of the heat generating component on the smoking vehicle, which is fed back at the measurement positions by the respective AR glasses, is received.

At this time, on the AR glasses, the detection position of the surface of the heat-generating component is recognized by image recognition, the AR glasses are focused on the detection position and the position is marked, so that the measurement of all the AR glasses is ensured at the same detection position. And integrating the temperature data measured by all the AR glasses to be used as the current temperature data of the actual detection of the part to be detected.

As shown in fig. 4, in step S403, an operation training parameter of the current operation of the smoking vehicle is obtained, where the operation training parameter of the current operation includes current temperature data of a heat generating component in the smoking vehicle, and the current temperature data is determined by a positional relationship between the AR glasses and the heat generating component, wind speed data of an environment where the smoking vehicle is located, power of device operation of the smoking vehicle, and distance information between the AR glasses and the heat generating component.

As shown in fig. 4, in step S404, it is determined whether a temperature data difference between the current temperature data of the heat-generating component and the standard temperature data of the heat-generating component is smaller than a preset temperature data difference threshold according to the current temperature data of the heat-generating component.

As shown in fig. 4, in step S405, if yes, the measured current temperature data of the heat generating component in the smoking vehicle is output, and the current temperature data is a temperature value.

The operation training parameters of the current operation of the components to be detected in the operation process of the smoking vehicle are detected, and the components to be detected comprise all the components to be detected in the motor driving device of the smoking vehicle, such as a rotating component of the motor driving device and a moving component connected with the rotating component.

Here, the obtaining of the currently running operation training parameters includes current temperature data of the smoking vehicle, wherein the current temperature data is determined by a positional relationship between the AR glasses and the heat generating component, wind speed data of an environment in which the smoking vehicle is located, power of equipment operation of the smoking vehicle, and distance information between the AR glasses and the heat generating component.

In order to promote the accuracy of the current temperature data of the fuming vehicle, when the embodiment of the application detects the current temperature data, the heating condition of the to-be-detected component of the fuming vehicle is considered, and the influence of the environment where the fuming vehicle is located on the temperature parameter of the fuming vehicle is also considered. Therefore, when the current temperature data of the smoking vehicle is tested, the current temperature data is determined according to the position relation between the AR glasses and the smoking vehicle, the wind speed data of the environment where the smoking vehicle is located and the distance information between the AR glasses and the smoking vehicle.

Wherein, the determining the respective measurement position of each AR glasses to which each smoking vehicle belongs in a preset positional relationship prediction model according to the characteristic data includes:

inputting the characteristic data of the heating part into a position relation prediction model trained in advance, and obtaining the position relation between the AR glasses and the heating part when the temperature data of the heating part is tested and output by the position relation prediction model; the position relation prediction model is used for inquiring information of the measured position of each AR glasses in the current environment matched with the characteristic data of the heat generating component according to the characteristic data of the heat generating component.

The position relation prediction model is obtained by training through the following method:

inputting the characteristic data of the heating part marked with the temperature detection target position information into an initial position relation prediction model to obtain the temperature detection prediction position information of the heating part output by the initial position relation prediction model; and calculating an error value between the marked temperature detection target position information and the temperature detection prediction position information, and adjusting parameters of the initial position relation prediction model according to the error value to obtain a position relation prediction model for determining the temperature detection position information of the heating part.

As shown in fig. 3, which is a schematic diagram of a distribution of temperature detection positions for testing the temperature of a smoking vehicle according to an embodiment of the present application. The position relation prediction model determines that the temperature detection position information of the heat generating component of the smoking vehicle comprises a first temperature detection position 1, a second temperature detection position 2, a third temperature detection position 3 and a fourth temperature detection position 4. The position relation prediction model is used for determining temperature detection position information corresponding to the characteristic data of the smoking vehicle according to the characteristic data of the smoking vehicle. The characteristic data of the heat generating component in the smoking vehicle in the operation process comprises the following steps: the vehicle that generates smoke is in the wind speed of the environment of car place of generating smoke, the position relation between AR glasses and the part that generates heat of car that generates smoke, specifically, the angle relation between the direction of generating smoke of the part that generates heat and the wind direction of current environment to and the wind direction of current environment.

Here, the number of the AR glasses is 4; after performing the step of receiving temperature detection data of the heat generating component on the smoking vehicle fed back by each of the AR glasses at the measurement position, the method further comprises:

calculating the current temperature data according to the following formula 1 by using the obtained 4 temperature detection data:

(formula 1)

Wherein, t ₁ First temperature data representative of an AR glasses test located at a first location of the smoking vehicle;

t ₂ second temperature data representative of an AR glasses test located at a second location of the smoking vehicle;

t ₃ third temperature data representative of an AR glasses test located at a third location of the smoking vehicle;

t ₄ fourth temperature data representative of an AR eyewear test located at a fourth location of the smoking vehicle.

The temperature data obtained from the different temperature test positions are related to the wind speed of the position, the distance between the position and the fuming vehicle and the equipment power of the fuming vehicle. Here, the coefficient preceding each temperature data is a fixed coefficient value obtained through a large number of historical test analysis calculations.

Firstly, establishing a plurality of groups of characteristic data, wherein each group comprises a plurality of characteristic data including the current actual temperature, the characteristic data are as described above, and the positions of a plurality of ARs and the distance between the AR and the equipment to be measured are continuously adjusted as the test data, so that the position of each AR under the condition of the best temperature measurement accuracy is obtained.

And acquiring characteristic data of the smoking vehicle with the temperature test target position marked, and inputting the characteristic data of the smoking vehicle with the temperature test target position marked into an initial position relation prediction model, wherein the characteristic data comprises correct data and incorrect data, until the temperature test prediction position information of the smoking vehicle output by the initial position relation prediction model is obtained and is equal to the position of each optimal AR in the front.

For example, parameters such as different outdoor wind speeds, temperatures, equipment power and the like are simulated in a laboratory environment, the parameters are measured by detection points at different geometric positions, compensation data measured by a plurality of measurement points of AR glasses are analyzed, predicted compensation temperature data are obtained and added with an environment constant C, and predicted temperature data are obtained; and comparing the predicted temperature data with the current actually measured temperature data of the equipment, if the temperature difference between the predicted temperature data and the current actually measured temperature data is smaller than a preset temperature difference threshold, representing that the predicted temperature data measured by the current measuring point belongs to the actual temperature data of the measuring point in the real environment, respectively taking each current measuring point as the temperature measuring position of the AR glasses, and storing the temperature measuring positions in a position relation prediction model.

And secondly, calculating a temperature difference value between the marked temperature test target position information and the temperature test prediction position information, and adjusting parameters of the initial position relation prediction model by taking the temperature difference value as a basis to obtain a trained position relation prediction model.

Further, the distance information between the AR glasses and the heat generating component is determined by: the method comprises the steps that a laser ranging sensor of the AR glasses is adopted to emit laser pulses to a heating part of the smoking vehicle, reflected light returned by the heating part is received, and first time data from emitting of the laser pulses to receiving of the reflected light are obtained; or, an infrared distance measuring sensor of the AR glasses is adopted to emit infrared light, reflected light returned by the heating component is received, and second time data of the infrared light emitted from the infrared distance measuring sensor to the heating component and the reflected light is obtained; determining distance information between the AR glasses and the heat generating component according to the first time data or the second time data.

Therein, the temperature sensor 105 in the AR glasses comprises a laser ranging sensor comprising a laser transmitter and a laser receiver. The laser emitter emits laser pulses to a heating component of the fuming vehicle, wherein the heating component mainly refers to a rotating component in the fuming driving motor device and a moving component connected with the rotating component. After receiving the laser pulse, the heat generating component scatters in various directions, and part of the scattered light returns to the laser receiver. The laser ranging sensor records time information of two processes of emitting laser pulses to the heating part and receiving scattered light returned by the heating part, and determines distance information between the AR glasses and the heating part according to the time information and the light speed.

The camera of the AR glasses records video data in the running process of the fuming vehicle, wherein the video data comprises RGB comprehensive numerical values of ambient light under the current environment.

After the smoking vehicle operation training parameters are obtained in the above manner, the method further includes:

calculating a fault detection result of the smoking vehicle in the operation process according to the currently-operated operation training parameters; if the fault detection result indicates that no fault problem is found, sending a result message that no fault problem is found in the fuming vehicle to the target application; and if the fault detection result indicates that a fault problem exists, sending the current fault problem of the smoking vehicle and a maintenance scheme corresponding to the fault problem to the target application.

Specifically, the detection can be performed in a first manner as follows:

according to the currently running operation training parameters, calculating a fault detection result of the smoking vehicle in the running process, wherein the fault detection result comprises the following steps:

and if the temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is not smaller than a preset temperature data difference value threshold, determining a fault detection result of the fuming vehicle in the running process according to the current temperature data of the heating part.

In addition, the currently running operation training parameters further comprise current video data of the heat generating component during the running process of the smoking vehicle and current vibration data of the heat generating component.

Therefore, the fault detection result of the smoking vehicle in the current operation process can be detected in the following second mode:

according to the current video data of the heating components, the current vibration data of the heating components and the current temperature data of the heating components in the running process of the fuming vehicle, the fault detection result of the fuming vehicle in the running process is calculated.

The calculating of the fault detection result of the fuming vehicle in the operation process according to the current video data of the heating component, the current vibration data of the heating component and the current temperature data of the heating component in the operation process of the fuming vehicle comprises:

comparing the current image data of the heating part with the standard image data of the heating part, and judging whether position error information exists in the position of the heating part in the current image data of the heating part; if the position error information exists, judging whether a vibration data difference value exists between the current vibration data and the standard vibration data of the heating part, and whether a temperature data difference value exists between the current temperature data and the standard temperature data of the heating part; and if the vibration data difference value and the temperature data difference value exist, calculating the fault level of the smoking vehicle in the operation process according to the position error information, the vibration data difference value and the temperature data difference value.

Wherein, confirm the fault class of the car of being fuming according to the vibration data, include: calculating a vibration error value between the current vibration data and the standard vibration data according to the following formula 5 according to the current vibration data, and determining the fault grade of the component to be detected according to the vibration error value:

(i =1,2,3, \8230n) (formula 5)

Wherein i represents the number of times of detection of the current vibration data; x1 _i Representing the current vibration data of the part to be detected at the ith detection; z1 represents the vibration data difference, i.e., standard deviation.

Here, the vibration data of the component to be detected is obtained for a plurality of times in the one-time operation process of the fuming vehicle, and the vibration data difference value Z1 of the component to be detected is calculated according to the formula 5. And analyzing the fault risk level of the component to be detected according to the relation between the vibration data difference value and a preset vibration data difference value threshold value.

In the actual operation process of the fuming vehicle, the fuming vehicle body starts to cause vibration work and the fuming driving motor causes vibration work, so that the vibration data in the operation process of the fuming vehicle is the vibration data caused by the double-vibration driving.

In order to determine which vibration source causes the failure information of the smoking component of the smoking vehicle, the embodiment of the application arranges an elastic strain sensor between the smoking driving motor device and the smoking vehicle body. As shown in fig. 2, the position relationship of the smoking driving motor device of the smoking vehicle provided by the embodiment of the application in the smoking vehicle body is schematically illustrated. In fig. 2, the smoking driving motor device 203 is arranged on the smoking vehicle body 201, the elastic strain sensor 202 is arranged between the smoking vehicle body 201 and the smoking driving motor device 203, and the elastic strain sensor 202 can counteract the influence of the vibration of the smoking vehicle body 201 in the movement process on the vibration data of the smoking driving motor device 203.

When the smoking vehicle runs, the total vibration data V generated by the smoking vehicle comprises first vibration data V1 generated by the smoking vehicle body 201 and second vibration data V2 generated by the smoking motor driving device 203. Vibration data V detected by the elastic strain sensor 202 _x The difference between the total vibration data V generated for the smoking vehicle and the first vibration data V1 generated by the smoking vehicle body 201 can be calculated according to the following formula 6: v _x V-V1= (V1 + V2) -V1= V2 (formula 6).

Therefore, the vibration data V detected by the elastic strain sensor 202 _x I.e. the second vibration data V2 generated by the smoke generating motor driving device 203. The elastic strain sensor 202 sends the detected second vibration data V2 to the vibration data receiver 104 in the AR glasses. The vibration data receiver 104 analyzes the failure level of the smoke generating motor driving device 203 of the smoke generating vehicle according to the second vibration data V2. Wherein the smoking motor driving device 203 comprises a smoking motor component and a plurality of moving components connected with the smoking motor.

And performing data screening processing on the current operation parameters before calculating a fault detection result of the equipment in the operation process according to the current operation parameters.

Specifically, data analysis is carried out on the current operating parameters, and if the operating parameter difference between the current operating parameters and preset standard operating parameters is larger than a first preset operating parameter difference and smaller than a second preset operating parameter difference, the current operating parameters are determined to be effective operating parameters, and equipment faults exist in the fuming vehicle corresponding to the current operating parameters; and if the current operating parameter is greater than a second preset operating parameter difference value, determining that the current operating parameter is an invalid operating parameter, and re-executing the step of obtaining the current operating parameter of the smoking vehicle acquired by the AR glasses in the operating process.

The embodiment of the application provides a method for monitoring the operation training state of a smoking vehicle by adopting AR glasses, which comprises the following steps: when the smoking vehicle is in an operating state, acquiring current operating parameters acquired by AR glasses in the operating process of the smoking vehicle, wherein the current operating parameters are acquired by the AR glasses used by training personnel; the current temperature data is determined by the position relation between the AR glasses and the smoking vehicle, the wind speed data of the environment where the smoking vehicle is located and the distance information between the AR glasses and the smoking vehicle; calculating a fault detection result of the fuming vehicle in the operation process according to the current operation parameters; if the fault detection result indicates that no fault problem is found, sending a result message that no fault problem is found in the fuming vehicle to the target application; and if the fault detection result indicates that a fault problem exists, sending the current fault problem of the smoke generating vehicle and a maintenance scheme corresponding to the fault problem to the target application.

The embodiment of the application provides a method for measuring smoking vehicle operation training parameters by adopting AR glasses, which comprises the following steps: when the smoking vehicle is in a running state, obtaining characteristic data of a heating component of the smoking vehicle in a running process, and determining the respective measuring position of each AR (augmented reality) glasses to which each smoking vehicle belongs in a preset positional relationship prediction model according to the characteristic data; sending the respective measurement position to each AR glasses, and receiving temperature detection data of the heat generating component on the smoking vehicle, which is fed back by each AR glasses at the measurement position; obtaining operation training parameters of the current operation of the smoking vehicle, wherein the operation training parameters of the current operation comprise current temperature data of a heating component in the smoking vehicle, and the current temperature data is determined by the position relation between the AR glasses and the heating component, wind speed data of the environment where the smoking vehicle is located, working power of equipment of the smoking vehicle and distance information between the AR glasses and the heating component; judging whether a temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is smaller than a preset temperature data difference value threshold or not according to the current temperature data of the heating part; and if so, outputting the measured current temperature data of the heating component in the smoking vehicle, wherein the current temperature data is a temperature value.

Based on the first embodiment, the embodiment of the application further provides equipment for measuring the operation training parameters of the smoking vehicle by using the AR glasses, and the method for measuring the operation training parameters of the smoking vehicle by using the AR glasses as described in the first embodiment is adopted.

Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the appended claims.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

1. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, computer readable Media does not include non-Transitory computer readable Media (transient Media), such as modulated data signals and carrier waves.

2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Claims

1. A method for measuring fuming vehicle operation training parameters by adopting AR glasses is characterized by comprising the following steps:

when a smoking vehicle is in a running state, obtaining characteristic data of a heating component of the smoking vehicle in a running process, and determining the respective measuring position of each AR (augmented reality) glasses to which each smoking vehicle belongs in a preset position relation prediction model according to the characteristic data;

sending the respective measurement position to each AR glasses, and receiving temperature detection data of the heat generating component on the smoking vehicle, which is fed back by each AR glasses at the measurement position;

obtaining operation training parameters of the current operation of the smoking vehicle, wherein the operation training parameters of the current operation comprise current temperature data of a heating component in the smoking vehicle, and the current temperature data is determined by the position relation between the AR glasses and the heating component, wind speed data of the environment where the smoking vehicle is located, working power of equipment of the smoking vehicle and distance information between the AR glasses and the heating component;

judging whether a temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is smaller than a preset temperature data difference value threshold or not according to the current temperature data of the heating part;

and if so, outputting the measured current temperature data of the heating component in the smoking vehicle, wherein the current temperature data is a temperature value.

2. The method according to claim 1, wherein the determining the respective measured position of each AR glasses to which each smoking vehicle belongs in a preset positional relationship prediction model according to the characteristic data comprises:

inputting the characteristic data of the heating part into a position relation prediction model trained in advance, and obtaining the position relation between the AR glasses and the heating part when the temperature data of the heating part is tested and output by the position relation prediction model;

the position relation prediction model is used for inquiring the information of the measured position of each AR glasses in the current environment matched with the characteristic data of the heat generating component according to the characteristic data of the heat generating component.

3. The method of claim 2, wherein the position relation prediction model is obtained by training as follows:

inputting the characteristic data of the heating part marked with the temperature detection target position information into an initial position relation prediction model to obtain the temperature detection prediction position information of the heating part output by the initial position relation prediction model;

and calculating an error value between the marked temperature detection target position information and the temperature detection prediction position information, and adjusting parameters of the initial position relation prediction model according to the error value to obtain a position relation prediction model for determining the temperature detection position information of the heating part.

4. The method of claim 1, wherein the number of AR glasses is 4; after performing the step of receiving temperature detection data of the heat generating component on the smoking vehicle fed back by each of the AR glasses at the measurement position, the method further comprises:

calculating the current temperature data T according to the following formula by using the obtained 4 temperature detection data:

t ₄ fourth temperature data representative of an AR glasses test located at a fourth location of the smoking vehicle.

5. The method of claim 1, wherein the distance information between the AR glasses and the heat generating component is determined by:

the method comprises the steps that a laser ranging sensor of the AR glasses is adopted to emit laser pulses to a heating part of the smoking vehicle, reflected light returned by the heating part is received, and first time data from emitting of the laser pulses to receiving of the reflected light are obtained; or, an infrared ranging sensor of the AR glasses is adopted to emit infrared light, and receive reflected light returned by the heating component, and second time data of the infrared light emitted from the infrared ranging sensor to the heating component and the reflected light received is acquired;

determining distance information between the AR glasses and the heat generating component according to the first time data or the second time data.

6. The method of claim 1, further comprising:

calculating a fault detection result of the smoking vehicle in the operation process according to the currently-operated operation training parameters;

if the fault detection result indicates that no fault problem is found, sending a result message that no fault problem is found in the smoking vehicle to the target application;

and if the fault detection result indicates that a fault problem exists, sending the current fault problem of the smoking vehicle and a maintenance scheme corresponding to the fault problem to the target application.

7. The method according to claim 6, wherein calculating the fault detection result of the smoking vehicle during operation according to the currently operating training parameters comprises:

and if the temperature data difference value between the current temperature data of the heating part and the standard temperature data of the heating part is not less than a preset temperature data difference value threshold, determining a fault detection result of the fuming vehicle in the running process according to the current temperature data of the heating part.

8. The method of claim 6, wherein the currently operating training parameters further include current video data of heat generating components during operation of the smoking vehicle, and current vibration data of the heat generating components;

9. The method of claim 8, wherein calculating the fault detection result of the smoking vehicle during operation based on current video data of the heat generating component, current vibration data of the heat generating component, and current temperature data of the heat generating component during operation of the smoking vehicle comprises:

comparing the current image data of the heating part with the standard image data of the heating part, and judging whether position error information exists in the position of the heating part in the current image data of the heating part or not;

if the position error information exists, judging whether a vibration data difference value exists between the current vibration data and the standard vibration data of the heating part;

and if the vibration data difference exists, calculating the fault level of the fuming vehicle in the operation process according to the position error information, the vibration data difference and the temperature data difference.

10. An apparatus for measuring smoking vehicle operation training parameters using AR glasses, wherein the method of measuring smoking vehicle operation training parameters using AR glasses according to any one of claims 1 to 9 is used.