A fault diagnosis method for a multi-scene mobile portable emergency self-service payment robot

Abstract

The application discloses a kind of multi-scene mobile portable emergency self-service payment robot fault diagnosis method, it is related to the field of fault diagnosis, solve traffic self-service payment robot cannot analyze the increase of processing time caused by environmental change, and cannot independently monitor the execution time of payment process Problem, method is: based on test environment data and normal work data, the environmental disturbance coefficient of the position to which test payment robot belongs is calculated, and the sampling frequency of test payment robot is set according to environmental disturbance coefficient;The time used by the payment sub-process of test payment robot is tested through high-temperature test environment and low-temperature test environment, and the standard time of test payment robot in all payment sub-processes is obtained;The sampling frequency of formal payment robot is set based on real-time environment data, and whether formal payment robot exists fault is judged according to real-time detection data, and the fault diagnosis of traffic self-service payment robot is realized by the application.

Description

Technical Field

[0001] This invention belongs to the field of equipment fault diagnosis technology, specifically a fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot. Background Technology

[0002] Self-service payment robots are intelligent terminal devices deployed in traffic scenarios such as toll stations. They are used to automatically pay vehicle tolls with little or no human intervention. By integrating functional modules such as license plate recognition, toll card reading, fee calculation, order generation, and payment feedback, they can automate the entire toll payment process, thereby improving traffic efficiency, reducing labor costs, and minimizing errors caused by manual operation.

[0003] In the existing technology, existing payment robots have difficulty analyzing the increase in processing time caused by environmental changes. This makes it difficult to detect abnormal states of payment robots that are still running but are close to the failure boundary in a timely manner. Furthermore, existing solutions mostly use "payment success / failure" or "device online / offline" as the basis for anomaly judgment, without independently monitoring the execution time of each sub-process in the payment process.

[0004] Therefore, this invention proposes a fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot. Summary of the Invention

[0005] The purpose of this invention is to propose a fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot, so as to solve the problems mentioned in the background art.

[0006] The technical problem to be solved by this invention is:

[0007] How to diagnose faults in self-service transportation payment robots based on environmental changes.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot, the method comprising:

[0010] Step S100: Calculate the environmental disturbance coefficient of the test payment robot's location based on test environment data and normal operation data, and set the sampling frequency of the test payment robot according to the environmental disturbance coefficient.

[0011] Step S200: Test the time taken by the payment sub-process of the test payment robot in high temperature test environment and low temperature test environment respectively, and obtain the standard time of the test payment robot in all payment sub-processes.

[0012] Step S300: Set the sampling frequency of the formal payment robot based on real-time environmental data, and determine whether there is a fault in the formal payment robot based on real-time detection data.

[0013] Furthermore, the test environment data includes the test wind speed, test temperature, and test precipitation at the location of the test payment robot;

[0014] Normal operating data includes the wind speed threshold, optimal temperature range, and precipitation threshold when the payment robot is operating normally.

[0015] Further, step S100 includes the following sub-steps:

[0016] Step S101: Calculate the working wind speed coefficient for the location of the payment robot based on the wind speed threshold and the test wind speed. The specific calculation process is as follows:

[0017] Step S1011: When the test wind speed is less than the wind speed threshold, the working wind speed coefficient of the test payment robot is set to zero.

[0018] Step S1012: When the test wind speed is greater than or equal to the wind speed threshold, subtract the wind speed threshold from the test wind speed and then divide by the wind speed threshold.

[0019] If the calculation result is less than or equal to one, the calculation result will be used as the working wind speed coefficient of the test payment robot.

[0020] If the calculation result is greater than one, then the result will be used as the working wind speed coefficient for testing the payment robot.

[0021] Step S102: Calculate the operating temperature coefficient of the test payment robot's location using the optimal temperature range and test temperature. The calculation process is as follows:

[0022] Step S1021: When the test temperature is within the optimal temperature range, the working temperature coefficient of the test payment robot is set to zero.

[0023] Step S1022: When the test temperature is greater than the right endpoint of the optimal temperature range, proceed to step S1023.

[0024] When the test temperature is lower than the left end of the optimal temperature range, proceed to step S1024;

[0025] Step S1023: If the test temperature is greater than twice the right endpoint of the optimal temperature range, then the working temperature coefficient of the test payment robot is assigned according to the value of the test temperature.

[0026] If the test temperature is less than or equal to twice the right endpoint of the optimal temperature range, then subtract the right endpoint of the optimal temperature range from the test temperature, then divide by the right endpoint of the optimal temperature range, and use the result as the working temperature coefficient of the test payment robot.

[0027] Step S1024: If the test temperature is less than twice the left endpoint of the optimal temperature range, then the working temperature coefficient of the test payment robot is assigned a value based on the magnitude of the test temperature.

[0028] If the test temperature is greater than or equal to twice the left endpoint of the optimal temperature range, then subtract the test temperature from the left endpoint of the optimal temperature range, divide by the absolute value corresponding to the left endpoint of the optimal temperature range, and use the calculation result as the working temperature coefficient of the test payment robot.

[0029] Furthermore, step S100 also includes the following sub-steps:

[0030] Step S103: Calculate the working precipitation coefficient for the location of the test payment robot based on the precipitation threshold and the test precipitation. The calculation process is as follows:

[0031] Step S1031: When the test precipitation is less than or equal to the precipitation threshold, the working precipitation coefficient of the test payment robot is set to zero.

[0032] When the tested precipitation SS is greater than the precipitation threshold but less than or equal to the upper limit of precipitation, the working precipitation coefficient of the test payment robot's location is calculated.

[0033] Step S1032: When the tested precipitation is greater than the upper limit of precipitation, the working precipitation coefficient is assigned based on the tested precipitation.

[0034] Step S104: Sum the working wind speed coefficient, working temperature coefficient, and working precipitation coefficient to calculate the environmental disturbance coefficient of the test payment robot's location.

[0035] Step S105: Set the sampling frequency of the test payment robot according to the environmental disturbance coefficient, and then proceed to step S300. The specific process of setting the sampling frequency is as follows:

[0036] When the environmental disturbance coefficient is less than or equal to one and greater than or equal to zero, the sampling frequency of the test payment robot is set to the first sampling frequency.

[0037] When the environmental disturbance coefficient is less than or equal to two and greater than one, the sampling frequency of the test payment robot is set to the second sampling frequency.

[0038] When the environmental disturbance coefficient is less than three and greater than two, the sampling frequency of the test payment robot will be set to the third sampling frequency.

[0039] When the environmental disturbance coefficient is greater than or equal to three, the test payment robot stops working; wherein, the first sampling frequency is longer than the second sampling frequency, and the second sampling frequency is longer than the third sampling frequency.

[0040] Furthermore, the payment sub-process includes a license plate entry sub-process, a data reading sub-process, a fee calculation sub-process, and an order feedback sub-process;

[0041] The standard duration includes the first standard duration, the second standard duration, the third standard duration, and the fourth standard duration.

[0042] Further, step S200 includes the following sub-steps:

[0043] Step S201: Construct high-temperature and low-temperature test environments for the test payment robot;

[0044] The high-temperature test environment is as follows: wind speed is set to the wind speed threshold, temperature is set to twice the right end value of the optimal temperature range, and precipitation is set to the maximum precipitation value.

[0045] The low-temperature test environment is as follows: wind speed is set to the wind speed threshold, temperature is set to twice the left end value of the optimal temperature range, and precipitation is set to the maximum precipitation value.

[0046] Step S202: Place the fault-free test payment robot in a high-temperature test environment and proceed to step S203;

[0047] Step S203: Set a vehicle detector at the payment entrance of the test payment robot. When a vehicle triggers the vehicle detector, it is determined that the user has started to pay. At the same time, the test payment robot stops changing the sampling frequency and then proceeds to step S204.

[0048] Step S204: When the user starts paying, the license plate image of the vehicle is obtained. At the same time, the current time node is recorded as the initial recognition time node. Then, the license plate number of the vehicle in the license plate image is recognized. The time node when the license plate number is recognized is recorded as the termination recognition time node. Then, the termination recognition time node is subtracted from the initial recognition time node to calculate the first high temperature duration corresponding to the license plate number of the vehicle recognized by the payment robot. The process of recognizing the license plate number is recorded as the license plate entry sub-process.

[0049] Step S205: Obtain vehicle access card data through vehicle access card identification. At the same time, record the time node when the user of the vehicle puts in the vehicle access card as the insertion time node, and record the time node when the test payment robot identifies the access card data as the detection time node. Then, subtract the insertion time node from the detection time node to calculate the second high temperature duration used by the test payment robot to identify the access card data, and record the process of the test payment robot identifying the vehicle access card data as the data reading sub-process.

[0050] The data on the toll card includes the vehicle's license plate number, vehicle type, and the toll station number at the entrance toll station when the vehicle enters the highway.

[0051] Furthermore, step S200 also includes the following sub-steps:

[0052] Step S206: Compare the license plate number of the vehicle in the toll card data with the license plate number of the vehicle in the license plate image;

[0053] If the license plate number on the vehicle pass is the same as the license plate number in the license plate image, then the current time node is recorded as the initial time node for calculation, and the process proceeds to step S207.

[0054] If the license plate number on the vehicle pass does not match the license plate number in the license plate image, the license plate is determined to be abnormal and an abnormal signal is generated and sent to the staff.

[0055] Step S207: Calculate the highway toll incurred by the vehicle while traveling on the highway, and record the time node when the calculation is completed as the calculation end time node. At the same time, test the toll payment robot to generate the highway toll payment code.

[0056] Step S208: Subtract the calculation start time from the calculation end time to calculate the third high temperature duration corresponding to the highway toll for the vehicle calculated by the test payment robot, and record the process of calculating the highway toll for the vehicle traveling on the highway as the cost calculation sub-process.

[0057] Step S209: The vehicle owner pays the highway toll payment code generated by the test payment robot, and the time node when the user completes the scan is recorded as the scan time node. At the same time, the test payment robot generates a payment order and uploads it to the payment terminal. Then, the payment result is obtained and the user's payment feedback information is generated. The time node when the payment feedback information is generated is recorded as the feedback time node. The fourth high temperature duration of the test payment robot is calculated by subtracting the scan time node from the feedback time node. The process of generating the user's payment feedback information is recorded as the order feedback subprocess.

[0058] Step S210: After generating the user's payment feedback information, determine that the user has finished paying. Combine the license plate entry subprocess, data reading subprocess, fee calculation subprocess and order feedback subprocess into the payment subprocess of the test payment robot.

[0059] Step S211: Place the fault-free test payment robot in a low-temperature test environment, and repeat steps S203 to S209 to obtain the first low-temperature duration, second low-temperature duration, third low-temperature duration and fourth low-temperature duration of the test payment robot.

[0060] Step S212: Compare the first high temperature duration with the first low temperature duration, the second high temperature duration with the second low temperature duration, the third high temperature duration with the third low temperature duration, and the fourth high temperature duration with the fourth low temperature duration, and take the smaller value of the comparison results as the standard duration of the test payment robot in the corresponding payment sub-process; wherein, if the two are equal, any value is taken as the standard duration.

[0061] The smaller of the first high temperature duration and the first low temperature duration is used as the first standard duration; the smaller of the second high temperature duration and the second low temperature duration is used as the second standard duration; the smaller of the third high temperature duration and the third low temperature duration is used as the third standard duration; and the smaller of the fourth high temperature duration and the fourth low temperature duration is used as the fourth standard duration.

[0062] Furthermore, the real-time detection data includes the total number of vehicles that pay through the official payment robot, and the detection time for all vehicles when they pay through the official payment robot.

[0063] The detection time includes the first detection time of the license plate entry subprocess, the second detection time of the data reading subprocess, the third detection time of the cost calculation subprocess, and the fourth detection time of the order feedback subprocess.

[0064] Further, step S300 includes the following sub-steps:

[0065] Step S301: Obtain the real-time wind speed, real-time temperature and real-time precipitation at the location of the official payment robot to be detected. Repeat steps S101 to S104 to calculate the environmental disturbance coefficient at the location of the official payment robot and set the sampling frequency of the official payment robot according to the environmental disturbance coefficient.

[0066] Step S302: Set the sampling duration and collect real-time detection data of the formal payment robot according to the sampling frequency within the sampling duration;

[0067] Step S303: Determine if there is a fault in the license plate entry sub-process. The determination process is as follows:

[0068] Step S3031: Compare each of the first detection durations with the first standard duration;

[0069] If the first detection duration is greater than or equal to the first standard duration, the corresponding license plate entry subprocess is recorded as the first abnormal subprocess, and the number of abnormalities in the first abnormal subprocess is counted, and then the process proceeds to step S304.

[0070] If there is no first detection duration greater than or equal to the first standard duration, then the license plate entry sub-process is determined to be fault-free;

[0071] Step S3032: Divide the number of abnormalities by the total number of vehicles to calculate the first abnormality ratio of the official payment robot;

[0072] Step S3033: When the first abnormal ratio is greater than or equal to the first ratio threshold, it is determined that there is a fault in the license plate input sub-process. At the same time, a fault signal for the license plate input sub-process is sent to the staff, and the fault diagnosis of the formal payment robot is stopped.

[0073] When the first abnormal ratio is less than the first ratio threshold, proceed to step S304.

[0074] Furthermore, step S300 also includes the following sub-steps:

[0075] Step S304: Determine if there is a fault in the data reading sub-process. The determination process is as follows:

[0076] Step S3041: Compare each of the second detection durations with the second standard duration;

[0077] If there is a second detection duration greater than or equal to the second standard duration, the corresponding data reading sub-process is recorded as the second abnormal sub-process, and the number of abnormalities in the second abnormal sub-process is counted, and then the process proceeds to step S3042.

[0078] If there is no second detection duration greater than or equal to the second standard duration, the data reading sub-process is deemed to be fault-free.

[0079] Step S3042: Divide the number of anomalies in the second anomaly subprocess by the total number of vehicles to calculate the proportion of the second anomaly corresponding to the data reading subprocess of the formal payment robot.

[0080] Step S3043: When the second abnormal ratio is greater than or equal to the second ratio threshold, it is determined that there is a fault in the data reading subprocess. At the same time, a fault signal for the data reading subprocess is sent to the staff, and the fault diagnosis of the formal payment robot is stopped.

[0081] When the second abnormal ratio is less than the second ratio threshold, proceed to step S305;

[0082] Step S305: Determine if there is a fault in the cost calculation sub-process. The determination process is as follows:

[0083] Step S3051: Compare each of the third detection durations with the third standard duration;

[0084] If there is a third detection duration greater than or equal to the third standard duration, the corresponding cost calculation subprocess is recorded as the third abnormal subprocess, and the number of abnormalities in the third abnormal subprocess is counted, and then the process proceeds to step S3052.

[0085] If there is no third detection duration greater than or equal to the third standard duration, the cost calculation subprocess is deemed to be fault-free.

[0086] Step S3052: Divide the number of anomalies in the third anomaly subprocess by the total number of vehicles to calculate the proportion of the third anomaly corresponding to the fee calculation subprocess of the formal payment robot.

[0087] Step S3053: When the third abnormal ratio is greater than or equal to the third ratio threshold, it is determined that there is a fault in the fee calculation subprocess. At the same time, a fault signal for the fee calculation subprocess is sent to the staff, and the fault diagnosis of the formal payment robot is stopped.

[0088] When the third abnormality ratio is less than the third ratio threshold, proceed to step S306;

[0089] Step S306: Determine if there is a fault in the order feedback sub-process. The determination process is as follows:

[0090] Step S3061: Compare each of the fourth detection durations with the fourth standard duration;

[0091] If there is a fourth detection duration greater than or equal to the fourth standard duration, the corresponding order feedback subprocess is recorded as the fourth abnormal subprocess, and the number of abnormalities in the fourth abnormal subprocess is counted, and then the process proceeds to step S3062.

[0092] If there is no fourth detection duration greater than or equal to the fourth standard duration, the order feedback subprocess is deemed to be without fault.

[0093] Step S3062: Divide the number of anomalies in the fourth anomaly subprocess by the total number of vehicles to calculate the proportion of the fourth anomaly corresponding to the order feedback subprocess of the formal payment robot.

[0094] Step S3063: When the fourth abnormal ratio is greater than or equal to the fourth ratio threshold, it is determined that there is a fault in the order feedback subprocess, and at the same time, an order feedback subprocess fault signal is sent to the staff.

[0095] When the fourth abnormality ratio is less than the fourth ratio threshold, it is determined that all payment sub-processes of the formal payment robot are fault-free.

[0096] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0097] 1. This invention accurately calculates the environmental disturbance coefficient of the test payment robot's location based on test environment data and normal operation data, and sets the sampling frequency of the test payment robot according to the environmental disturbance coefficient;

[0098] 2. This invention tests the time taken by the payment robot in the payment sub-process by constructing high-temperature and low-temperature test environments respectively, and obtains the standard time of the payment robot in all payment sub-processes.

[0099] 3. This invention sets the sampling frequency of the formal payment robot based on real-time environmental data, and combines the standard duration of all payment sub-processes and real-time detection data to accurately determine whether there is a fault in the formal payment robot, thereby realizing fault diagnosis of the traffic self-service payment robot. Attached Figure Description

[0100] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0101] Figure 1 This is a flowchart of the method of the present invention;

[0102] Figure 2 This is an example diagram showing the top view of the vehicle detector in this invention;

[0103] Figure 3 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0104] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0105] Example 1, please refer to Figure 1 and Figure 2As shown, the technical solution provided by this invention is: a fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot. This embodiment achieves fault diagnosis of the payment robot by determining whether there is a fault in all payment sub-processes. First, the working coefficient of the payment robot in different natural environments is calculated, and the environmental disturbance coefficient of the payment robot is calculated based on the working coefficient. The sampling frequency of the test payment robot is then set based on the environmental disturbance coefficient. Then, a first test environment and a second test environment are constructed, and the time taken by the test payment robot in all payment sub-processes is tested. The time with the smaller value in the same payment sub-process is selected as the standard time. Finally, the sampling frequency of the formal payment robot is set based on the real-time environmental data of the environment to which the payment robot belongs, and the presence of a fault in the formal payment robot is determined based on the real-time detection data.

[0106] In this embodiment, the fault diagnosis method for the emergency self-service payment robot is as follows:

[0107] Step S100: Calculate the environmental disturbance coefficient of the test payment robot's location based on test environment data and normal operation data, and set the sampling frequency of the test payment robot according to the environmental disturbance coefficient.

[0108] The test environment data specifically includes the test wind speed, test temperature, and test precipitation at the location of the test payment robot. Specifically, the test wind speed can be adjusted using a blower; the test temperature can be adjusted using a high and low temperature test chamber; and the test precipitation can be adjusted using a rainfall simulator. The actual test precipitation is the cumulative precipitation over the previous hour. The normal operation data specifically includes the wind speed threshold, optimal temperature range, and precipitation threshold for the test payment robot during normal operation. Normal operation data for the test payment robot can be obtained from a database. The test payment robot analyzed in this embodiment is specifically used to collect tolls from vehicles exiting the highway and is a mobile payment robot.

[0109] In this embodiment, step S100 includes the following sub-steps:

[0110] Step S101: Calculate the working wind speed coefficient for the location of the payment robot based on the wind speed threshold and the test wind speed. The specific calculation process is as follows:

[0111] Step S1011: When the test wind speed is less than the wind speed threshold, the working wind speed coefficient of the test payment robot is set to zero.

[0112] Among them, when the working wind speed coefficient increases, it indicates that the adverse effect of wind speed on the test payment robot increases;

[0113] Step S1012: When the test wind speed is greater than or equal to the wind speed threshold, subtract the wind speed threshold from the test wind speed and then divide by the wind speed threshold.

[0114] If the calculation result is less than or equal to one, the calculation result will be used as the working wind speed coefficient of the test payment robot.

[0115] If the calculation result is greater than one, then the result will be used as the working wind speed coefficient for testing the payment robot.

[0116] In practice, if the calculation result is ∈ (1, 20), the working wind speed coefficient is assigned the value X1; if the calculation result is ∈ [20, 50), the working wind speed coefficient is assigned the value X2; if the calculation result is ∈ [50, +∞), the working wind speed coefficient is assigned the value X3, and 0 < X1 < X2 < X3.

[0117] It should be noted that when the test wind speed directly acts on the test payment robot, the internal components of the test payment robot will generate micro-vibrations. Long-term accumulation of micro-vibrations will lead to problems such as loosening of internal components or poor wiring contact.

[0118] Step S102: Calculate the operating temperature coefficient of the test payment robot's location using the optimal temperature range and test temperature. The calculation process is as follows:

[0119] Step S1021: When the test temperature is within the optimal temperature range, the working temperature coefficient of the test payment robot is set to zero.

[0120] When the test temperature falls within the optimal temperature range, it can include the left and right endpoints of the optimal temperature range.

[0121] Step S1022: When the test temperature is greater than the right endpoint of the optimal temperature range, proceed to step S1023.

[0122] When the test temperature is lower than the left end of the optimal temperature range, proceed to step S1024;

[0123] Step S1023: If the test temperature is greater than twice the right endpoint of the optimal temperature range, then the working temperature coefficient of the test payment robot is assigned according to the value of the test temperature.

[0124] In practice, when the right endpoint of the optimal temperature range is 5℃, its double value is 10. If the test temperature ∈ [11, 20], the working temperature coefficient is assigned the value Y1. If the test temperature ∈ [21, 30], the working temperature coefficient is assigned the value Y2. If the test temperature ∈ [31, +∞), the working temperature coefficient is assigned the value Y3, where 0 < Y1 < Y2 < Y3.

[0125] If the test temperature is less than or equal to twice the right endpoint of the optimal temperature range, then subtract the right endpoint of the optimal temperature range from the test temperature, then divide by the right endpoint of the optimal temperature range, and use the result as the working temperature coefficient of the test payment robot.

[0126] Step S1024: If the test temperature is less than twice the left endpoint of the optimal temperature range, then the working temperature coefficient of the test payment robot is assigned a value based on the magnitude of the test temperature.

[0127] In practice, when the right endpoint of the optimal temperature range is -5℃, its double value is -10. If the test temperature ∈ (-∞, -31], the working temperature coefficient is assigned the value Y3. If the test temperature ∈ [-30, -21], the working temperature coefficient is assigned the value Y2. If the test temperature ∈ [-20, -11], the working temperature coefficient is assigned the value Y1, 0 < Y1 < Y2 < Y3.

[0128] If the test temperature is greater than or equal to twice the left endpoint of the optimal temperature range, then subtract the test temperature from the left endpoint of the optimal temperature range, divide by the absolute value corresponding to the left endpoint of the optimal temperature range, and use the calculation result as the working temperature coefficient of the test payment robot.

[0129] Excessive testing temperature will accelerate the aging of electronic components inside the payment robot, while excessively low testing temperature may lead to decreased battery activity and weakened lubrication performance of mechanical parts.

[0130] Step S103: Calculate the working precipitation coefficient for the location of the test payment robot based on the precipitation threshold and the test precipitation. The calculation process is as follows:

[0131] Step S1031: When the test precipitation is less than or equal to the precipitation threshold, the working precipitation coefficient of the test payment robot is set to zero.

[0132] When the tested precipitation SS is greater than the precipitation threshold YZ, but less than or equal to the upper limit of precipitation, the working precipitation coefficient EL for the location of the test payment robot is calculated using the following formula:

[0133] ;

[0134] Step S1032: When the tested precipitation is greater than the upper limit of precipitation, the working precipitation coefficient is assigned based on the tested precipitation.

[0135] In practice, if the measured precipitation is ∈ (the upper limit of precipitation, 20), the working wind speed coefficient is assigned to N1; if the measured precipitation is ∈ [20, 50), the working wind speed coefficient is assigned to N2; if the measured precipitation is ∈ [50, +∞), the working wind speed coefficient is assigned to N3, 0 < N1 < N2 < N3.

[0136] Specifically, excessive rainfall during testing may reduce the protective performance of the test payment robot's shell, and rainwater may seep into the interior, causing short circuits. At the same time, excessive humidity will cause the metal parts of the test payment robot to rust.

[0137] Step S104: Sum the working wind speed coefficient, working temperature coefficient, and working precipitation coefficient to calculate the environmental disturbance coefficient of the test payment robot's location.

[0138] Step S105: Set the sampling frequency of the test payment robot according to the environmental disturbance coefficient, and then proceed to step S300. The specific process of setting the sampling frequency is as follows:

[0139] When the environmental disturbance coefficient is less than or equal to one and greater than or equal to zero, the sampling frequency of the test payment robot is set to the first sampling frequency.

[0140] When the environmental disturbance coefficient is less than or equal to two and greater than one, the sampling frequency of the test payment robot is set to the second sampling frequency.

[0141] When the environmental disturbance coefficient is less than three and greater than two, the sampling frequency of the test payment robot will be set to the third sampling frequency.

[0142] When the environmental disturbance coefficient is greater than or equal to three, the test payment robot stops working; wherein, the first sampling frequency is longer than the second sampling frequency, and the second sampling frequency is longer than the third sampling frequency;

[0143] In practice, the first sampling frequency can be once every 20 minutes for the test payment robot; the second sampling frequency can be once every 10 minutes for the test payment robot; and the third sampling frequency can be once every 5 minutes for the test payment robot.

[0144] Step S200: Test the time taken by the payment sub-process of the test payment robot in high temperature test environment and low temperature test environment respectively, and obtain the standard time of the test payment robot in all payment sub-processes.

[0145] The payment sub-process includes a license plate entry sub-process, a data reading sub-process, a fee calculation sub-process, and an order feedback sub-process; the standard duration includes a first standard duration, a second standard duration, a third standard duration, and a fourth standard duration; in reality, the test payment robot in step S200 and the test payment robot in step S100 are only of the same model, not the same payment robot; both the test payment robot in step S200 and the test payment robot in step S100 are fault-free payment robots;

[0146] In this embodiment, step S200 includes the following sub-steps:

[0147] Step S201: Construct high-temperature and low-temperature test environments for the test payment robot;

[0148] The specific high-temperature test environment is as follows: the wind speed is set to the wind speed threshold, the temperature is set to twice the right end value of the optimal temperature range, and the precipitation is set to the maximum precipitation value.

[0149] The specific low-temperature test environment is as follows: the wind speed is set to the wind speed threshold, the temperature is set to twice the left end value of the optimal temperature range, and the precipitation is set to the upper limit value of precipitation.

[0150] In practice, the test payment robot used for testing has been tested in advance and confirmed to be in a fault-free state; the construction of the first and second test environments can be achieved by adjusting the wind speed with a blower, adjusting the temperature with a high and low temperature test chamber, and adjusting the rainfall with a rainfall simulator.

[0151] Step S202: Place the fault-free test payment robot in a high-temperature test environment and proceed to step S203;

[0152] Step S203: Set a vehicle detector at the payment entrance of the test payment robot. When a vehicle triggers the vehicle detector, it is determined that the user has started to pay. At the same time, the test payment robot stops changing the sampling frequency and then proceeds to step S204.

[0153] Among them, the vehicle detector can be a ground inductive loop or lidar, etc.

[0154] Step S204: When the user starts paying, the license plate image of the vehicle is obtained. At the same time, the current time node is recorded as the initial recognition time node. Then, the license plate number of the vehicle in the license plate image is recognized. The time node when the license plate number is recognized is recorded as the termination recognition time node. Then, the termination recognition time node is subtracted from the initial recognition time node to calculate the first high temperature duration corresponding to the license plate number of the vehicle recognized by the payment robot. The process of recognizing the license plate number is recorded as the license plate entry sub-process.

[0155] Among them, when the vehicle has a temporary license plate, the user can identify it through the temporary license plate recognition camera of the payment robot; the user can also capture the license plate image of the vehicle through a camera deployed in the test payment robot; and the user can identify the license plate number of the vehicle in the license plate image through a license plate recognition algorithm.

[0156] Step S205: Obtain vehicle access card data through vehicle access card identification. At the same time, record the time node when the user of the vehicle puts in the vehicle access card as the insertion time node, and record the time node when the test payment robot identifies the access card data as the detection time node. Then, subtract the insertion time node from the detection time node to calculate the second high temperature duration used by the test payment robot to identify the access card data, and record the process of the test payment robot identifying the vehicle access card data as the data reading sub-process.

[0157] Specifically, the data on the toll card includes the vehicle's license plate number, vehicle type, and the toll station number at the entrance toll station when the vehicle enters the expressway; the vehicle type is specifically divided into vehicles with nine seats or less, vehicles with ten to nineteen seats, and vehicles with twenty to thirty-nine seats; when a vehicle enters the expressway through the entrance toll station, the entrance toll station writes the vehicle's license plate number, vehicle type, and the entrance toll station number into the vehicle toll card.

[0158] Step S206: Compare the license plate number of the vehicle in the toll card data with the license plate number of the vehicle in the license plate image;

[0159] If the license plate number on the vehicle pass is the same as the license plate number in the license plate image, then the current time node is recorded as the initial time node for calculation, and the process proceeds to step S207.

[0160] If the license plate number on the vehicle pass does not match the license plate number in the license plate image, the license plate is determined to be abnormal and an abnormal signal is generated and sent to the staff.

[0161] Step S207: Calculate the highway toll incurred by the vehicle while traveling on the highway, and record the time node when the calculation is completed as the calculation end time node. At the same time, test the toll payment robot to generate the highway toll payment code.

[0162] The calculation process for highway tolls is as follows:

[0163] Obtain the toll station number of the toll station to which the test payment robot belongs. Based on the toll station number of the entrance toll station and the toll station number of the toll station to which the test payment robot belongs, obtain the vehicle's mileage. Then, based on the vehicle type, query the toll per kilometer for the corresponding vehicle and multiply the mileage by the toll per kilometer to calculate the highway toll incurred by the vehicle on the highway.

[0164] In practice, the toll station number of the toll station to which the test payment robot belongs is set manually; the mileage between the entrance toll station and the toll station to which the test payment robot belongs can be obtained from the database and used as the vehicle's mileage; in this embodiment, the highway toll payment code is specifically the QR code generated by the test payment robot.

[0165] Step S208: Subtract the calculation start time from the calculation end time to calculate the third high temperature duration corresponding to the highway toll for the vehicle calculated by the test payment robot, and record the process of calculating the highway toll for the vehicle traveling on the highway as the cost calculation sub-process.

[0166] Step S209: The vehicle owner pays the highway toll payment code generated by the test payment robot, and the time node when the user completes the scan is recorded as the scan time node. At the same time, the test payment robot generates a payment order and uploads it to the payment terminal. Then, the payment result is obtained and the user's payment feedback information is generated. The time node when the payment feedback information is generated is recorded as the feedback time node. The fourth high temperature duration of the test payment robot is calculated by subtracting the scan time node from the feedback time node. The process of generating the user's payment feedback information is recorded as the order feedback subprocess.

[0167] The payment terminal can be a third-party payment platform or a bank server, etc.; the payment feedback information is specifically a prompt message indicating whether the user's payment was successful or failed.

[0168] Step S210: After generating the user's payment feedback information, determine that the user has finished paying. Combine the license plate entry subprocess, data reading subprocess, fee calculation subprocess and order feedback subprocess into the payment subprocess of the test payment robot.

[0169] Step S211: Place the fault-free test payment robot in a low-temperature test environment, and repeat steps S203 to S209 to obtain the first low-temperature duration, second low-temperature duration, third low-temperature duration and fourth low-temperature duration of the test payment robot.

[0170] Step S212: Compare the first high temperature duration with the first low temperature duration, the second high temperature duration with the second low temperature duration, the third high temperature duration with the third low temperature duration, and the fourth high temperature duration with the fourth low temperature duration, and take the smaller value of the comparison results as the standard duration of the test payment robot in the corresponding payment sub-process; wherein, if the two are equal, any value is taken as the standard duration.

[0171] The smaller of the first high temperature duration and the first low temperature duration is used as the first standard duration; the smaller of the second high temperature duration and the second low temperature duration is used as the second standard duration; the smaller of the third high temperature duration and the third low temperature duration is used as the third standard duration; and the smaller of the fourth high temperature duration and the fourth low temperature duration is used as the fourth standard duration. There is no comparative relationship between the first standard duration, the second standard duration, the third standard duration, and the fourth standard duration.

[0172] Step S300: Set the sampling frequency of the formal payment robot based on real-time environmental data, and determine whether there is a fault in the formal payment robot based on real-time detection data.

[0173] Specifically, the real-time detection data includes the total number of vehicles that pay through the official payment robot, and the detection time for all vehicles when paying through the official payment robot. The detection time includes the first detection time of the license plate entry subprocess, the second detection time of the data reading subprocess, the third detection time of the fee calculation subprocess, and the fourth detection time of the order feedback subprocess.

[0174] In this embodiment, step S300 includes the following sub-steps:

[0175] Step S301: Obtain the real-time wind speed, real-time temperature and real-time precipitation at the location of the official payment robot to be detected. Repeat steps S101 to S104 to calculate the environmental disturbance coefficient at the location of the official payment robot and set the sampling frequency of the official payment robot according to the environmental disturbance coefficient.

[0176] Step S302: Set the sampling duration and collect real-time detection data of the formal payment robot according to the sampling frequency within the sampling duration;

[0177] In practice, the sampling time can be one hour;

[0178] Step S303: Determine if there is a fault in the license plate entry sub-process. The determination process is as follows:

[0179] Step S3031: Compare each of the first detection durations with the first standard duration;

[0180] If the first detection duration is greater than or equal to the first standard duration, the corresponding license plate entry subprocess is recorded as the first abnormal subprocess, and the number of abnormalities in the first abnormal subprocess is counted, and then the process proceeds to step S304.

[0181] If there is no first detection duration greater than or equal to the first standard duration, then the license plate entry sub-process is determined to be fault-free;

[0182] Step S3032: Divide the number of abnormalities by the total number of vehicles to calculate the first abnormality ratio of the official payment robot;

[0183] Step S3033: When the first abnormal ratio is greater than or equal to the first ratio threshold, it is determined that there is a fault in the license plate input sub-process. At the same time, a fault signal for the license plate input sub-process is sent to the staff, and the fault diagnosis of the formal payment robot is stopped.

[0184] When the first abnormal ratio is less than the first ratio threshold, proceed to step S304;

[0185] The first ratio threshold is preset based on historical data. It should be noted that the environment in which the formal payment robot operates may cause delays in license plate image recognition, such as water accumulation on the license plate surface during rainy days. When there is a delay in the license plate input sub-process, the efficiency of the formal payment robot in recognizing the license plate image decreases or errors occur, causing subsequent payment sub-processes to fail to operate normally.

[0186] Step S304: Determine if there is a fault in the data reading sub-process. The determination process is as follows:

[0187] Step S3041: Compare each of the second detection durations with the second standard duration;

[0188] If there is a second detection duration greater than or equal to the second standard duration, the corresponding data reading sub-process is recorded as the second abnormal sub-process, and the number of abnormalities in the second abnormal sub-process is counted, and then the process proceeds to step S3042.

[0189] If there is no second detection duration greater than or equal to the second standard duration, the data reading sub-process is deemed to be fault-free.

[0190] Step S3042: Divide the number of anomalies in the second anomaly subprocess by the total number of vehicles to calculate the proportion of the second anomaly corresponding to the data reading subprocess of the formal payment robot.

[0191] Step S3043: When the second abnormal ratio is greater than or equal to the second ratio threshold, it is determined that there is a fault in the data reading subprocess. At the same time, a fault signal for the data reading subprocess is sent to the staff, and the fault diagnosis of the formal payment robot is stopped.

[0192] When the second abnormal ratio is less than the second ratio threshold, proceed to step S305;

[0193] Specifically, real-time rainfall will affect the humidity of the environment in which the formal payment robot is located. When the humidity is too high, it will increase the time consumed by the data reading sub-process, and at the same time, it indicates that there are problems such as decreased card reader sensitivity, poor contact of communication interface or abnormal data parsing program in the data reading sub-process.

[0194] Step S305: Determine if there is a fault in the cost calculation sub-process. The determination process is as follows:

[0195] Step S3051: Compare each of the third detection durations with the third standard duration;

[0196] If there is a third detection duration greater than or equal to the third standard duration, the corresponding cost calculation subprocess is recorded as the third abnormal subprocess, and the number of abnormalities in the third abnormal subprocess is counted, and then the process proceeds to step S3052.

[0197] If there is no third detection duration greater than or equal to the third standard duration, the cost calculation subprocess is deemed to be fault-free.

[0198] Step S3052: Divide the number of anomalies in the third anomaly subprocess by the total number of vehicles to calculate the proportion of the third anomaly corresponding to the fee calculation subprocess of the formal payment robot.

[0199] Step S3053: When the third abnormal ratio is greater than or equal to the third ratio threshold, it is determined that there is a fault in the fee calculation subprocess. At the same time, a fault signal for the fee calculation subprocess is sent to the staff, and the fault diagnosis of the formal payment robot is stopped.

[0200] When the third abnormality ratio is less than the third ratio threshold, proceed to step S306;

[0201] When the ambient temperature of the formal payment robot is too high or too low, the processing speed of the internal processor of the formal payment robot decreases or the database query response is delayed during the fee calculation sub-process, which leads to an increase in the time consumed by the fee calculation sub-process.

[0202] Step S306: Determine if there is a fault in the order feedback sub-process. The determination process is as follows:

[0203] Step S3061: Compare each of the fourth detection durations with the fourth standard duration;

[0204] If there is a fourth detection duration greater than or equal to the fourth standard duration, the corresponding order feedback subprocess is recorded as the fourth abnormal subprocess, and the number of abnormalities in the fourth abnormal subprocess is counted, and then the process proceeds to step S3062.

[0205] If there is no fourth detection duration greater than or equal to the fourth standard duration, the order feedback subprocess is deemed to be without fault.

[0206] Step S3062: Divide the number of anomalies in the fourth anomaly subprocess by the total number of vehicles to calculate the proportion of the fourth anomaly corresponding to the order feedback subprocess of the formal payment robot.

[0207] Step S3063: When the fourth abnormal ratio is greater than or equal to the fourth ratio threshold, it is determined that there is a fault in the order feedback subprocess, and at the same time, an order feedback subprocess fault signal is sent to the staff.

[0208] When the fourth abnormality ratio is less than the fourth ratio threshold, it is determined that all payment sub-processes of the formal payment robot are fault-free.

[0209] The fourth proportional threshold is preset based on historical data, and there is no numerical comparison relationship between the first, second, third, and fourth proportional thresholds.

[0210] Example 2: This embodiment of the invention also provides an electronic device for running the fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot; see also... Figure 3 The schematic diagram of an electronic device provided by the embodiment of the present invention shown above includes a memory and a processor. The memory is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor to realize the above-mentioned fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot.

[0211] Furthermore, Figure 3 The electronic device shown also includes a communication bus and a communication interface, with the processor, communication interface and memory connected via the communication bus;

[0212] The memory may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The communication bus can be an ISA bus, PCI bus, or EISA bus, etc. The communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one double-headed arrow, but this does not mean that there is only one communication bus or one type of communication bus.

[0213] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0214] In embodiment three, this invention also provides a computer storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot. For specific implementation details, please refer to the method embodiment, which will not be repeated here.

[0215] The computer program product of the fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot provided in this embodiment of the invention includes a computer storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0216] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and / or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0217] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0218] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot, characterized in that, The methods include: Step S100: Calculate the environmental disturbance coefficient of the test payment robot's location based on test environment data and normal operation data, and set the sampling frequency of the test payment robot according to the environmental disturbance coefficient. Step S100 includes the following sub-steps: Step S101: Calculate the working wind speed coefficient for the location of the payment robot based on the wind speed threshold and the test wind speed. The specific calculation process is as follows: Step S1011: When the test wind speed is less than the wind speed threshold, the working wind speed coefficient of the test payment robot is set to zero. Step S1012: When the test wind speed is greater than or equal to the wind speed threshold, subtract the wind speed threshold from the test wind speed and then divide by the wind speed threshold. If the calculation result is less than or equal to one, the calculation result will be used as the working wind speed coefficient of the test payment robot. If the calculation result is greater than one, then the result will be used as the working wind speed coefficient for testing the payment robot. Step S102: Calculate the operating temperature coefficient of the test payment robot's location using the optimal temperature range and test temperature. The calculation process is as follows: Step S1021: When the test temperature is within the optimal temperature range, the working temperature coefficient of the test payment robot is set to zero. Step S1022: When the test temperature is greater than the right endpoint of the optimal temperature range, proceed to step S1023. When the test temperature is lower than the left end of the optimal temperature range, proceed to step S1024; Step S1023: If the test temperature is greater than twice the right endpoint of the optimal temperature range, then the working temperature coefficient of the test payment robot is assigned according to the value of the test temperature. If the test temperature is less than or equal to twice the right endpoint of the optimal temperature range, then subtract the right endpoint of the optimal temperature range from the test temperature, then divide by the right endpoint of the optimal temperature range, and use the result as the working temperature coefficient of the test payment robot. Step S1024: If the test temperature is less than twice the left endpoint of the optimal temperature range, then the working temperature coefficient of the test payment robot is assigned a value based on the magnitude of the test temperature. If the test temperature is greater than or equal to twice the left endpoint of the optimal temperature range, then subtract the test temperature from the left endpoint of the optimal temperature range, divide by the absolute value corresponding to the left endpoint of the optimal temperature range, and use the calculation result as the working temperature coefficient of the test payment robot. Step S103: Calculate the working precipitation coefficient for the location of the test payment robot based on the precipitation threshold and the test precipitation. The calculation process is as follows: Step S1031: When the test precipitation is less than or equal to the precipitation threshold, the working precipitation coefficient of the test payment robot is set to zero. When the tested precipitation SS is greater than the precipitation threshold but less than or equal to the upper limit of precipitation, the working precipitation coefficient of the test payment robot's location is calculated. Step S1032: When the tested precipitation is greater than the upper limit of precipitation, the working precipitation coefficient is assigned based on the tested precipitation. Step S104: Sum the working wind speed coefficient, working temperature coefficient, and working precipitation coefficient to calculate the environmental disturbance coefficient of the test payment robot's location. Step S105: Set the sampling frequency of the test payment robot according to the environmental disturbance coefficient, and then proceed to step S300. The specific process of setting the sampling frequency is as follows: When the environmental disturbance coefficient is less than or equal to one and greater than or equal to zero, the sampling frequency of the test payment robot is set to the first sampling frequency. When the environmental disturbance coefficient is less than or equal to two and greater than one, the sampling frequency of the test payment robot is set to the second sampling frequency. When the environmental disturbance coefficient is less than three and greater than two, the sampling frequency of the test payment robot will be set to the third sampling frequency. When the environmental disturbance coefficient is greater than or equal to three, the test payment robot stops working; wherein, the first sampling frequency is longer than the second sampling frequency, and the second sampling frequency is longer than the third sampling frequency; Step S200: Test the time taken by the payment sub-process of the test payment robot in high temperature test environment and low temperature test environment respectively, and obtain the standard time of the test payment robot in all payment sub-processes. The high-temperature test environment is as follows: wind speed is set to the wind speed threshold, temperature is set to twice the right end value of the optimal temperature range, and precipitation is set to the maximum precipitation value. The low-temperature test environment is as follows: wind speed is set to the wind speed threshold, temperature is set to twice the left end value of the optimal temperature range, and precipitation is set to the maximum precipitation value. Step S300: Set the sampling frequency of the formal payment robot based on real-time environmental data, and determine whether there is a fault in the formal payment robot based on real-time detection data.

2. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 1, characterized in that, The test environment data includes the test wind speed, test temperature, and test precipitation at the location of the test payment robot; Normal operating data includes the wind speed threshold, optimal temperature range, and precipitation threshold when the payment robot is operating normally.

3. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 1, characterized in that, The payment sub-process includes a license plate entry sub-process, a data reading sub-process, a fee calculation sub-process, and an order feedback sub-process; The standard duration includes the first standard duration, the second standard duration, the third standard duration, and the fourth standard duration.

4. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 3, characterized in that, Step S200 includes the following sub-steps: Step S201: Construct high-temperature and low-temperature test environments for the test payment robot; Step S202: Place the fault-free test payment robot in a high-temperature test environment and proceed to step S203; Step S203: Set a vehicle detector at the payment entrance of the test payment robot. When a vehicle triggers the vehicle detector, it is determined that the user has started to pay. At the same time, the test payment robot stops changing the sampling frequency and then proceeds to step S204. Step S204: When the user starts paying, the license plate image of the vehicle is obtained. At the same time, the current time node is recorded as the initial recognition time node. Then, the license plate number of the vehicle in the license plate image is recognized. The time node when the license plate number is recognized is recorded as the termination recognition time node. Then, the termination recognition time node is subtracted from the initial recognition time node to calculate the first high temperature duration corresponding to the license plate number of the vehicle recognized by the payment robot. The process of recognizing the license plate number is recorded as the license plate entry sub-process. Step S205: Obtain vehicle access card data through vehicle access card identification. At the same time, record the time node when the user of the vehicle puts in the vehicle access card as the insertion time node, and record the time node when the test payment robot identifies the access card data as the detection time node. Then, subtract the insertion time node from the detection time node to calculate the second high temperature duration used by the test payment robot to identify the access card data, and record the process of the test payment robot identifying the vehicle access card data as the data reading sub-process. The data on the toll card includes the vehicle's license plate number, vehicle type, and the toll station number at the entrance toll station when the vehicle enters the highway.

5. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 4, characterized in that, Step S200 further includes the following sub-steps: Step S206: Compare the license plate number of the vehicle in the toll card data with the license plate number of the vehicle in the license plate image; If the license plate number on the vehicle pass is the same as the license plate number in the license plate image, then the current time node is recorded as the initial time node for calculation, and the process proceeds to step S207. If the license plate number on the vehicle pass does not match the license plate number in the license plate image, the license plate is determined to be abnormal and an abnormal signal is generated and sent to the staff. Step S207: Calculate the highway toll incurred by the vehicle while traveling on the highway, and record the time node when the calculation is completed as the calculation end time node. At the same time, test the toll payment robot to generate the highway toll payment code. Step S208: Subtract the calculation start time from the calculation end time to calculate the third high temperature duration corresponding to the highway toll for the vehicle calculated by the test payment robot, and record the process of calculating the highway toll for the vehicle traveling on the highway as the cost calculation sub-process. Step S209: The vehicle owner pays the highway toll payment code generated by the test payment robot, and the time node when the user completes the scan is recorded as the scan time node. At the same time, the test payment robot generates a payment order and uploads it to the payment terminal. Then, the payment result is obtained and the user's payment feedback information is generated. The time node when the payment feedback information is generated is recorded as the feedback time node. The fourth high temperature duration of the test payment robot is calculated by subtracting the scan time node from the feedback time node. The process of generating the user's payment feedback information is recorded as the order feedback subprocess. Step S210: After generating the user's payment feedback information, determine that the user has finished paying. Combine the license plate entry subprocess, data reading subprocess, fee calculation subprocess and order feedback subprocess into the payment subprocess of the test payment robot. Step S211: Place the fault-free test payment robot in a low-temperature test environment, and repeat steps S203 to S209 to obtain the first low-temperature duration, second low-temperature duration, third low-temperature duration and fourth low-temperature duration of the test payment robot. Step S212: Compare the first high temperature duration with the first low temperature duration, the second high temperature duration with the second low temperature duration, the third high temperature duration with the third low temperature duration, and the fourth high temperature duration with the fourth low temperature duration, and take the smaller value of the comparison results as the standard duration of the test payment robot in the corresponding payment sub-process; wherein, if the two are equal, any value is taken as the standard duration. The smaller of the first high temperature duration and the first low temperature duration is used as the first standard duration; the smaller of the second high temperature duration and the second low temperature duration is used as the second standard duration; the smaller of the third high temperature duration and the third low temperature duration is used as the third standard duration; and the smaller of the fourth high temperature duration and the fourth low temperature duration is used as the fourth standard duration.

6. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 5, characterized in that, The real-time detection data includes the total number of vehicles that pay through the official payment robot, as well as the detection time for each vehicle when paying through the official payment robot. The detection time includes the first detection time of the license plate entry subprocess, the second detection time of the data reading subprocess, the third detection time of the cost calculation subprocess, and the fourth detection time of the order feedback subprocess.

7. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 6, characterized in that, Step S300 includes the following sub-steps: Step S301: Obtain the real-time wind speed, real-time temperature and real-time precipitation at the location of the official payment robot to be detected. Repeat steps S101 to S104 to calculate the environmental disturbance coefficient at the location of the official payment robot and set the sampling frequency of the official payment robot according to the environmental disturbance coefficient. Step S302: Set the sampling duration and collect real-time detection data of the formal payment robot according to the sampling frequency within the sampling duration; Step S303: Determine if there is a fault in the license plate entry sub-process. The determination process is as follows: Step S3031: Compare each of the first detection durations with the first standard duration; If the first detection duration is greater than or equal to the first standard duration, the corresponding license plate entry subprocess is recorded as the first abnormal subprocess, and the number of abnormalities in the first abnormal subprocess is counted, and then the process proceeds to step S3032. If there is no first detection duration greater than or equal to the first standard duration, then the license plate entry sub-process is determined to be fault-free; Step S3032: Divide the number of abnormalities by the total number of vehicles to calculate the first abnormality ratio of the official payment robot; Step S3033: When the first abnormal ratio is greater than or equal to the first ratio threshold, it is determined that there is a fault in the license plate input sub-process. At the same time, a fault signal for the license plate input sub-process is sent to the staff, and the fault diagnosis of the formal payment robot is stopped. When the first abnormal ratio is less than the first ratio threshold, proceed to step S304.

8. The fault diagnosis method for a multi-scenario mobile portable emergency self-service payment robot according to claim 7, characterized in that, Step S300 further includes the following sub-steps: Step S304: Determine if there is a fault in the data reading sub-process. The determination process is as follows: Step S3041: Compare each of the second detection durations with the second standard duration; If there is a second detection duration greater than or equal to the second standard duration, the corresponding data reading sub-process is recorded as the second abnormal sub-process, and the number of abnormalities in the second abnormal sub-process is counted, and then the process proceeds to step S3042. If there is no second detection duration greater than or equal to the second standard duration, the data reading sub-process is deemed to be fault-free. Step S3042: Divide the number of anomalies in the second anomaly subprocess by the total number of vehicles to calculate the proportion of the second anomaly corresponding to the data reading subprocess of the formal payment robot. Step S3043: When the second abnormal ratio is greater than or equal to the second ratio threshold, it is determined that there is a fault in the data reading subprocess. At the same time, a fault signal for the data reading subprocess is sent to the staff, and the fault diagnosis of the formal payment robot is stopped. When the second abnormal ratio is less than the second ratio threshold, proceed to step S305; Step S305: Determine if there is a fault in the cost calculation sub-process. The determination process is as follows: Step S3051: Compare each of the third detection durations with the third standard duration; If there is a third detection duration greater than or equal to the third standard duration, the corresponding cost calculation subprocess is recorded as the third abnormal subprocess, and the number of abnormalities in the third abnormal subprocess is counted, and then the process proceeds to step S3052. If there is no third detection duration greater than or equal to the third standard duration, the cost calculation subprocess is deemed to be fault-free. Step S3052: Divide the number of anomalies in the third anomaly subprocess by the total number of vehicles to calculate the proportion of the third anomaly corresponding to the fee calculation subprocess of the formal payment robot. Step S3053: When the third abnormal ratio is greater than or equal to the third ratio threshold, it is determined that there is a fault in the fee calculation subprocess. At the same time, a fault signal for the fee calculation subprocess is sent to the staff, and the fault diagnosis of the formal payment robot is stopped. When the third abnormality ratio is less than the third ratio threshold, proceed to step S306; Step S306: Determine if there is a fault in the order feedback sub-process. The determination process is as follows: Step S3061: Compare each of the fourth detection durations with the fourth standard duration; If there is a fourth detection duration greater than or equal to the fourth standard duration, the corresponding order feedback subprocess is recorded as the fourth abnormal subprocess, and the number of abnormalities in the fourth abnormal subprocess is counted, and then the process proceeds to step S3062. If there is no fourth detection duration greater than or equal to the fourth standard duration, the order feedback subprocess is deemed to be without fault. Step S3062: Divide the number of anomalies in the fourth anomaly subprocess by the total number of vehicles to calculate the proportion of the fourth anomaly corresponding to the order feedback subprocess of the formal payment robot. Step S3063: When the fourth abnormal ratio is greater than or equal to the fourth ratio threshold, it is determined that there is a fault in the order feedback subprocess, and at the same time, an order feedback subprocess fault signal is sent to the staff. When the fourth abnormality ratio is less than the fourth ratio threshold, it is determined that all payment sub-processes of the formal payment robot are fault-free.

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