Wireless vehicle state monitoring system

Abstract

The invention relates to the technical field of remote monitoring, in particular to a wireless vehicle state monitoring system which comprises a state change evaluation module, a data transmission adjustment module, a power anomaly detection module, a health state evaluation module and a risk early warning optimization module. According to the method, the vehicle operation parameters are obtained, the change amplitude and the rate of the parameters are analyzed in multiple time windows, the change amplitude and the rate are compared with the preset threshold values, the transmission frequency is adjusted according to the stability of the state, important data are updated in time, and the abnormality of the power system is detected by analyzing the data transmitted in real time. By matching specific data of engine torque and gear shifting modes, prediction of potential faults of the vehicle is more accurate, data of main components of the vehicle are integrated to be compared with health standards, accurate evaluation of the overall health state of the vehicle is achieved, risk early warning can be given out in advance, driving safety is optimized, and the driving safety is improved. And the service life and the operation efficiency of the vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring, and particularly to a wireless vehicle status monitoring system. Background Art

[0002] The technical field of remote monitoring involves using technologies such as wireless communication, computer network, cloud computing, and data analysis to perform non-contact data collection, transmission, storage, and analysis on the status, operating parameters, or environmental variables of target objects to achieve real-time or periodic monitoring. This field includes technologies such as wireless sensor networks, cellular communication, low-power wide-area networks, edge computing, and artificial intelligence analysis to improve the coverage, response speed, and data processing capabilities of monitoring systems. Remote monitoring is widely used in industrial equipment monitoring, smart city management, environmental monitoring, healthcare monitoring, and intelligent transportation, aiming to provide efficient data support to optimize management, enhance safety, and reduce operation and maintenance costs.

[0003] The wireless vehicle status monitoring system belongs to a specific application of remote monitoring technology, mainly used to monitor the operating status of vehicles in real time, including vehicle speed, engine operating parameters, tire pressure, braking system status, body vibration, and battery health status. The system uses wireless communication technology to transmit monitoring data to the cloud or local server, and combines data analysis algorithms to evaluate and warn the vehicle status to ensure the safe operation of the vehicle, improve operation and maintenance efficiency, and support remote fault diagnosis and predictive maintenance.

[0004] Traditional monitoring systems rely on fixed-frequency data uploads, which can cause waste of network resources, resulting in data delays or losses at critical moments. And data processing is usually carried out in the cloud, relying on continuous data transmission, which not only increases communication costs but also affects the integrity of data and the accuracy of analysis results in an unstable network environment. Moreover, it cannot capture rapidly changing operating parameters, such as rapidly changing engine speed or braking pressure, missing the opportunity to prevent major faults. This causes the monitoring system to be unable to provide necessary support at critical moments, affecting the safe operation and maintenance decision-making of vehicles. In the assessment of the vehicle's health status by traditional systems, it relies on indirect observations and historical data, lacking real-time and accuracy, which can lead to the neglect of potential vehicle problems in a rapidly changing driving environment, increasing the risk of accidents, and being unable to diagnose and handle critical vehicle faults in a timely manner, causing safety hazards and economic losses. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art and propose a wireless vehicle status monitoring system.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A wireless vehicle status monitoring system, the system includes: The state change evaluation module obtains the vehicle operation parameters, calls the parameter change data within multiple time windows, and calculates the stability level of each operation parameter based on the change amplitude, change rate, and tolerance threshold, generating state stability classification information; The data transmission adjustment module calculates the adjusted data transmission frequency of each operation parameter based on the state stability classification information, transmits the operation parameters to the cloud monitoring platform through a wireless network, and obtains the real-time transmission information of the monitoring data; The power anomaly detection module calls the real-time transmission information of the monitoring data, calculates the rolling resistance change rate and air resistance change rate per unit time, calculates the resistance energy consumption per unit time according to the vehicle speed and slope conditions, determines whether there is an anomaly in the power system, and obtains the power system anomaly deviation information; The health state evaluation module calculates the failure probability of each component based on the power system anomaly deviation information, combines the component impact weights, calculates the health score of the vehicle, and obtains the vehicle health evaluation information; The risk warning optimization module calculates the risk level based on the vehicle health evaluation information, issues a risk warning to the vehicle driver, and obtains the vehicle state warning information.

[0007] The improvement of the present invention is that the state stability classification information is specifically the mean change rate, fluctuation range, and amplitude change value, the real-time transmission information of the monitoring data includes the high-frequency transmission interval and data transmission result, the power system anomaly deviation information is specifically the rolling resistance change rate, air resistance change rate, energy consumption deviation amount, and power system anomaly state, the vehicle health evaluation information includes component failure risk and vehicle health score, and the vehicle state warning information includes vehicle risk level, influence range, and failure type.

[0008] The improvement of the present invention is that the state change evaluation module includes: The parameter change calculation sub-module obtains the vehicle operation parameters, calls the vehicle speed, engine speed, braking pressure, fuel quantity, road slope, humidity, and temperature change data within multiple time windows, calculates the change amplitude of each operation parameter within multiple time windows, and obtains the change amplitude data set of each operation parameter; The stability level calculation sub-module calculates the change rate of each operation parameter within the time window based on the change amplitude data set of each operation parameter, compares the change rate with the tolerance threshold, and uses the formula: ; Performs operations to obtain the stability level value, and obtains the operation parameter stability level data; Wherein, represents the stability level value, represents the operation parameter value within the time window ​ represents the value of the operating parameter for the previous time window, represents the total number of time windows, represents the maximum change value among all time windows, represents the tolerance threshold, is the base of the natural logarithm; The state stability classification sub-module calls the stability level data of the operating parameters, and classifies the stability of each operating parameter according to the preset classification criteria to establish state stability classification information.

[0009] The present invention is improved in that the data transmission adjustment module includes: The data comparison sub-module, based on the state stability classification information, calls the preset stability criteria, compares the stability classification value of each operating parameter with the preset stability criteria, obtains the stability deviation of each operating parameter, and obtains stability deviation data; The transmission frequency calculation sub-module calls the stability deviation data, combines the preset data transmission frequency of each operating parameter, and uses the formula: ; Performs an operation to obtain the adjusted data transmission frequency and establish data transmission adjustment parameters; Wherein, represents the adjusted data transmission frequency, represents the preset data transmission frequency, represents the stability classification value of the current operating parameter, represents the preset stability criteria, represents the transmission adjustment exponent; The data transmission monitoring sub-module calls the data transmission adjustment parameters, uses the vehicle-mounted wireless network to transmit the operating parameters to the cloud monitoring platform, and obtains the real-time transmission information of the monitoring data.

[0010] The present invention is improved in that the power anomaly detection module includes: The resistance change calculation sub-module calls the real-time transmission information of the monitoring data, extracts the vehicle driving speed, tire pressure, air density and wind speed data, and calculates the rolling resistance change rate and air resistance change rate per unit time to obtain resistance change rate data; The resistance energy consumption calculation sub-module calls the resistance change rate data, and according to the vehicle speed and slope conditions, uses the formula: ; Calculates the resistance energy consumption per unit time and establishes a resistance energy consumption impact parameter; Wherein, represents the resistance energy consumption per unit time, represents the rolling resistance change rate per unit time, represents the rate of change of air resistance per unit time, represents the vehicle driving speed, represents the vehicle mass, represents the acceleration due to gravity, represents the slope angle; The power deviation judgment sub-module calls the resistance energy consumption influence parameter, combines the engine torque output and the gearbox shifting mode data, calculates the deviation of the resistance change rate from the torque and the shifting mode, compares with the standard deviation threshold, judges whether there is an abnormality in the power system, and obtains the power system abnormality deviation information.

[0011] The improvement of the present invention is that the health status assessment module includes: The failure probability calculation sub-module calls the power system abnormality deviation information, obtains the data of the engine, gearbox, battery and braking system, compares with the standard operation data, and uses the formula: ; Performs operations to obtain the component failure probability data and establish the failure risk parameter; wherein, represents the probability of failure occurrence, represents the observed component operation data, represents the standard operation data of the component, represents the decimal compensation term to prevent the denominator from being zero, represents the component failure attenuation factor, represents the component operation time, is the base of the natural logarithm; The health score calculation sub-module calls the failure risk parameter, combines the influence weight of each component, calculates the overall vehicle health score, and obtains the vehicle health score; The health assessment sub-module calls the vehicle health score, compares with the preset assessment criteria of the health score, classifies the health status of the vehicle, and records the failure probability of each component to establish the vehicle health assessment information.

[0012] The improvement of the present invention is that the risk warning optimization module includes: The risk level calculation sub-module calls the vehicle health assessment information, obtains the failure type, occurrence probability and influence range, and uses the formula: ; Performs operations to obtain the vehicle risk level and establish the vehicle risk assessment data; wherein, represents the risk level of the entire vehicle, represents the component 's probability of failure occurrence, represents the component The scope of influence of the fault represents the vehicle health score represents the standard value of the health score represents the total number of vehicle components represents the component index; The warning information generation sub-module calls the vehicle risk assessment data, combines the preset risk level threshold, compares and classifies the warning levels, and obtains the vehicle status warning level; The warning information transmission sub-module calls the vehicle status warning level, uses the in-vehicle wireless network to send the warning information to the corresponding vehicle, including the risk level, potential faulty equipment, and failure probability of the vehicle, and issues a risk warning to the vehicle driver to obtain the vehicle status warning information.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by obtaining the vehicle operation parameters and analyzing the change amplitude, rate, and comparison with the preset threshold within multiple time windows, the stability of the vehicle status is effectively evaluated, the immediate status of the vehicle can be monitored more accurately, and the transmission frequency is adjusted according to the stability of the status, reducing unnecessary data transmission, optimizing the use of network resources, ensuring the timely update of important data, detecting abnormalities in the power system by analyzing the real-time transmitted data, and cooperating with the specific data of the engine torque and shift pattern, the prediction of potential vehicle faults is more accurate. By comparing the data of each main component of the vehicle with the health standard, an accurate assessment of the overall health status of the vehicle is achieved, risk warnings can be issued in advance, driving safety is optimized, and the service life and operation efficiency of the vehicle are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the status change assessment module of the present invention; Figure 3 is the flow chart of the data transmission adjustment module of the present invention; Figure 4 is the flow chart of the power abnormality detection module of the present invention; Figure 5 is the flow chart of the health status assessment module of the present invention; Figure 6 is the flow chart of the risk warning optimization module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0016] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0017] Please refer to Figure 1 , the present invention provides a technical solution: a wireless vehicle status monitoring system, the system includes: The status change evaluation module obtains vehicle operation parameters, which include vehicle speed, engine speed, braking pressure, fuel quantity, road gradient, humidity, temperature, etc. It calls the parameter change data within multiple time windows, calculates the change amplitude of each operation parameter, and calculates the stability level of each operation parameter based on the change amplitude, change rate, and tolerance threshold, and generates status stability classification information; The data transmission adjustment module compares the status stability classification information with a preset stability standard, combines the preset data transmission frequency of each operation parameter, calculates the adjusted data transmission frequency of each operation parameter, transmits the operation parameters to the cloud monitoring platform through a wireless network, and obtains the real-time transmission information of the monitoring data; The power anomaly detection module calls the real-time transmission information of the monitoring data, calculates the change rate of rolling resistance and the change rate of air resistance per unit time according to the vehicle driving speed, tire pressure, air density, and wind speed data, calculates the resistance energy consumption per unit time according to the vehicle speed and gradient conditions, calls the engine torque output and transmission shift mode data, and calculates the deviation of the change rate of resistance from the torque and shift mode to determine whether there is an anomaly in the power system and obtains the power system anomaly deviation information; The health status evaluation module is based on the power system anomaly deviation information, compares the standard operation data according to the engine, transmission, battery, and braking system data, calculates the failure probability of each component, combines the component impact weight, calculates the health score of the vehicle, and obtains the vehicle health evaluation information; The risk warning optimization module is based on the vehicle health evaluation information, calls the fault type, occurrence probability, and impact range, calculates the risk level, and issues a risk warning to the vehicle driver through wireless communication to obtain the vehicle status warning information.

[0018] The state stability classification information specifically includes the mean change rate, the fluctuation range, and the amplitude change value. The monitoring data real-time transmission information includes the high-frequency transmission interval and the data transmission result. The dynamic system abnormal deviation information specifically includes the rolling resistance change rate, the air resistance change rate, the energy consumption deviation, and the abnormal state of the dynamic system. The vehicle health assessment information includes the component failure risk and the vehicle health score. The vehicle state warning information includes the vehicle risk level, the influence range, and the fault type.

[0019] Please refer to Figure 2 , the state change assessment module includes: The parameter change calculation sub-module obtains the vehicle operation parameters, calls the vehicle speed, engine speed, brake pressure, fuel quantity, road gradient, humidity, and temperature change data within multiple time windows, calculates the change amplitude of each operation parameter within multiple time windows, and obtains the change amplitude data set of each operation parameter; After the parameter change calculation sub-module obtains the vehicle operation parameters, the parameter data of each time window is collected by in-vehicle sensors, stored in the data cache module and arranged in chronological order. The vehicle speed data of each time window is provided by the vehicle's wheel speed sensor, the engine speed data is obtained through the crankshaft position sensor, the brake pressure data is collected by the brake pressure sensor, the fuel quantity information comes from the fuel level sensor, the road gradient is calculated by the accelerometer combined with the vehicle GPS altitude information, and the humidity and temperature are measured by the environmental sensor. After the collected data is stored, it is parsed in chronological order, and the data of each operation parameter in the current time window and the previous time window is compared to calculate the change amplitude of the parameter. The change amplitude calculation method for each parameter is as follows: the vehicle speed change amplitude is calculated as the absolute value of the vehicle speed value in the current window minus the vehicle speed value in the previous window, the engine speed change amplitude is the absolute value of the engine speed value in the current window minus the engine speed value in the previous window, the brake pressure change amplitude is the absolute value of the brake pressure value in the current window and the brake pressure value in the previous window, the fuel quantity change amplitude is calculated in the same way, and the differential calculation is performed based on the fuel quantity value measured by the fuel sensor. The road gradient change amplitude is calculated from the gradient value in the current window and the gradient value in the previous window. The change amplitudes of humidity and temperature are calculated in the same way. After all the change amplitude values are calculated, the change amplitude data calculated for each time window is stored in the database, and finally, the change amplitude data set of each operation parameter is formed.

[0020] The stability level calculation sub-module calculates the change rate of each operation parameter within the time window based on the change amplitude data set of each operation parameter, and compares the change rate with the tolerance threshold using the formula: ; Performs the operation to obtain the stability level value and obtains the operation parameter stability level data; Among them, represents the stability level value, represents the operating parameter values within the time window and represents the operating parameter values of the previous time window represents the operating parameter values of the previous time window, represents the total number of time windows, represents the maximum change value among all time windows, represents the tolerance threshold, is the base of the natural logarithm; The stability level calculation sub-module calculates the change rate of each operating parameter within the time window based on the change amplitude dataset. When calculating, all time window data of the target parameter are extracted from the change amplitude dataset, and the change rate between adjacent time windows is calculated. The calculation method is the change amplitude value divided by the time window interval time. The calculated change rate data is stored in a temporary array for further comparison. After the change rate of each operating parameter is calculated, it is compared with the preset tolerance threshold. The tolerance threshold is set based on the operating stability experimental data of various vehicles. Through statistical analysis of a large amount of vehicle driving data, the maximum reasonable change range of different operating parameters under normal driving conditions is determined. The specific numerical settings are as follows: The tolerance threshold for vehicle speed change is set to 5 km / h / s, which is obtained by testing the vehicle speed change rate under different driving modes (smooth driving, accelerating driving, emergency braking, etc.) and selecting the 95th percentile as the threshold setting. The threshold for engine speed is set to 500 RPM / s, which is derived from the measured speed change range in the engine bench test. The threshold for braking pressure is set to 10 bar / s, which is calculated based on the braking system response time and braking torque. The threshold for fuel quantity is set to 0.5 L / s, which is obtained by testing the fuel consumption rate of different vehicles. The threshold for slope change is set to 2° / s, which comes from the experiment on the impact of terrain slope change on vehicle stability. The threshold for humidity change is set to 5%RH / s, which is due to the measurement error range of the humidity sensor and the influence of environmental changes. The threshold for temperature change is set to 2°C / s, which is obtained based on the experimental data of the temperature difference between the inside and outside of the vehicle. During the comparison process, for the change rate exceeding the threshold, a lower stability score is assigned, and for the change rate below the threshold, a higher stability score is assigned. The stability level value is calculated using the formula: ; where, is the stability level value, is the time window within the operating parameter values, is the operating parameter value of the previous time window, is the total number of time windows, is the maximum change value among all time windows, is the tolerance threshold. For example, in a computing instance, assume that the dataset of the speed change range of a certain vehicle is km / h, and the tolerance threshold is set to 5 km / h. Then the calculation process is as follows: ; The calculated stability level value is stored in the database for state stability classification.

[0021] The state stability classification sub-module calls the stability level data of the operating parameters, and classifies the stability of each operating parameter according to the preset classification criteria to establish state stability classification information; The state stability classification sub-module calls the stability level data of the operating parameters and classifies them according to the preset classification criteria. After the stability level data of different parameters are sorted and classified, they are classified according to different level intervals. For example, for vehicle speed stability classification, the following criteria can be set: is classified as "high stability", is classified as "medium stability", is classified as "low stability". For other parameters such as engine speed, braking pressure, fuel quantity, slope, humidity, temperature, etc., similar methods can be used for classification, as shown in Table 1.

[0022] Table 1 Classification Table of Operating Parameter Stability Levels This classification standard is based on vehicle operation stability test data and is set in combination with driving safety assessment. Among them, the stability classification standard is interrelated with the threshold setting. For example, the stability classification standard of vehicle speed directly depends on the previously set threshold, ensuring that the classification standards of different operating parameters are consistent with the actual vehicle operation characteristics. As shown in Table 1, all operating parameters are classified according to the stability level to obtain state stability classification information, and the classified data is stored in the database for further operation state assessment.

[0023] Please refer to Figure 3 , the data transmission adjustment module includes: Based on the state stability classification information, the data comparison sub-module calls the preset stability standard, compares the stability classification value of each operating parameter with the preset stability standard, obtains the stability deviation of each operating parameter, and gets the stability deviation data; Based on the state stability classification information, after the data comparison sub-module calls the preset stability criteria, it first extracts the stability level data of each operating parameter from the database and stores them classified by parameter type. When comparing the stability level data of each operating parameter with the preset stability criteria, the difference between the classification value of each operating parameter and the corresponding value of the stability criteria is calculated to obtain the stability deviation. For example, in the comparison of vehicle speed stability, if the stability classification value of the current operating parameter is 3.5 and the preset stability criteria is 4, then the calculated stability deviation is , similarly, for the engine speed, if the stability classification value is 2.0 and the preset standard is 3.0, then the deviation value is . All the calculated stability deviation data is stored in the stability deviation data set, as shown in Table 2. The setting basis of the preset stability criteria is the operating condition requirements of the vehicle power system. Specifically, the stability criteria for vehicle speed is determined based on the test data of vehicle driving smoothness. This test is based on the long-term operating data of urban roads, highways, and complex terrains, calculates the vehicle speed stability distribution under different road conditions, and selects the stability level distribution range within 70% of the operating time as the standard value. The stability criteria for engine speed is set based on the engine load fluctuation range. This load fluctuation range is calculated by regression of the relationship between engine fuel injection quantity and torque output, and the average value of the speed stability level corresponding to the fuel injection adjustment range within ±5% is selected as the standard value. The stability criteria for braking pressure is set based on the response characteristics of the vehicle braking system. Specifically, the braking pressure change of the vehicle under normal driving conditions is measured, and the average value within the 95% confidence interval is selected as the standard value. The standard values of other parameters such as fuel quantity, slope, humidity, and temperature are all set according to the 95% quantile interval statistically calculated from the sensor historical data to ensure the rationality of the standard values.

[0024] Table 2 Data Table of Operating Parameter Stability Deviation As shown in Table 2, after the stability deviation data of all operating parameters is calculated, it is stored in the stability deviation database for subsequent data transmission adjustment.

[0025] The transmission frequency calculation sub-module calls the stability deviation data and combines it with the preset data transmission frequency of each operating parameter, using the formula: ; Performs operations to obtain the adjusted data transmission frequency and establish data transmission adjustment parameters; Wherein, represents the adjusted data transmission frequency, represents the preset data transmission frequency, represents the stability classification value of the current operating parameter, represents a preset stability standard, represents the transmission adjustment index; After the transmission frequency calculation sub-module calls the stability deviation data and combines it with the preset data transmission frequencies of each operating parameter, it first extracts the stability deviations of each operating parameter from the database and obtains the corresponding preset data transmission frequencies. The preset transmission frequencies of each operating parameter are set according to system requirements. For example, the preset transmission frequency of vehicle speed can be set to 10 Hz, the engine speed to 5 Hz, the braking pressure to 8 Hz, the fuel quantity change to 2 Hz, the slope change to 4 Hz, the humidity change to 3 Hz, and the temperature change to 1 Hz. The extracted data is input into the formula: ; Among them, represents the adjusted data transmission frequency, represents the preset data transmission frequency, represents the stability grading value of the current operating parameter, represents the preset stability standard, represents the transmission adjustment index, The setting of is based on the influence of the change frequencies of different parameters on the data transmission requirements. Specifically, to The value range of is obtained by analyzing the sampling rate of vehicle operation data, and the value range is between . A low value is used for relatively stable parameters such as ambient temperature and humidity, and a high value is used for rapidly changing parameters such as vehicle speed, engine speed, and braking pressure. By calculating the data fluctuation frequencies of each operating parameter within the stability change range, the corresponding value is determined. For example, for vehicle speed, historical data shows that the standard deviation of its change rate is 1.2 km / h / s, then is set to 1.2. For engine speed, the standard deviation of change is 300 RPM / s, then ; Calculate the adjusted transmission frequencies of all operating parameters and store them in the data transmission adjustment parameter set, as shown in Table 3.

[0026] Table 3 Adjusted Data Transmission Frequency Table As shown in Table 3, the calculated data is stored in the data transmission adjustment parameter set for the data transmission monitoring sub-module to call.

[0027] The data transmission monitoring sub-module calls the data transmission adjustment parameters, and uses the in-vehicle wireless network to apply the adjusted data transmission frequency to the data transmission process of the corresponding operating parameters, transmit the operating parameters to the cloud monitoring platform, and obtain the real-time transmission information of the monitoring data.

[0028] After the data transmission monitoring sub-module calls the data transmission adjustment parameters, it extracts the adjusted data transmission frequencies of each operating parameter from the database and applies them to the vehicle data transmission system. When the system performs data transmission through the in-vehicle wireless network, each operating parameter is sent according to the adjusted transmission frequency to ensure that different parameters dynamically adjust the data upload rate according to their stability changes. During the data transmission process, the in-vehicle wireless module first packs the data of each operating parameter and sets the data sending interval according to the adjusted transmission frequency. For example, if the adjusted transmission frequency of the vehicle speed is 11.2 Hz, then data is sent once every seconds, that is, about 0.089 seconds. If the adjusted transmission frequency of the engine speed is 6.4 Hz, then data is sent once every seconds, that is, about 0.156 seconds. After calculating the data transmission time intervals of all data, the data is sent to the cloud monitoring platform through the in-vehicle wireless network. After the cloud receives the data, it records the data in real time according to the data transmission timestamp and generates the real-time monitoring information of the operating parameters, finally completing the data transmission monitoring process.

[0029] Please refer to Figure 4 , the power anomaly detection module includes: The resistance change calculation sub-module calls the real-time transmission information of the monitoring data, extracts the vehicle driving speed, tire pressure, air density and wind speed data, calculates the rolling resistance change rate and air resistance change rate per unit time, and obtains the resistance change rate data; After the resistance change calculation sub-module calls the real-time transmission information of the monitoring data, it first extracts the vehicle driving speed, tire pressure, air density and wind speed data from the database. The data of each parameter is collected and stored in real time by in-vehicle sensors. The vehicle driving speed is provided by the wheel speed sensor, the tire pressure is measured by the tire pressure monitoring system, the air density is calculated according to the atmospheric pressure, temperature and humidity, and the wind speed is measured by the wind speed sensor. After all the data is arranged in timestamp order, the rolling resistance change rate and air resistance change rate per unit time are calculated. When calculating the rolling resistance change rate, the tire pressure data is first called, and according to the rolling resistance calculation formula , where the rolling resistance coefficient is set with reference to the experimental test results of different types of tires. Usually, when the tire pressure is in the range of 220 kPa to 250 kPa, the value of a sedan is between 0.007 and 0.012. In this embodiment, 0.01 is taken. If the tire pressure is lower than 200 kPa, then may rise above 0.015. When the tire pressure is higher than 250 kPa it can drop to 0.006. The specific value is dynamically adjusted according to the tire pressure data. For example, if the mass of a vehicle is 1500 kg and the acceleration due to gravity is taken as 9.81 m / s2, and the tire pressure is 220 kPa corresponding to taking 0.01, then the rolling resistance is ; Assume that the rolling resistance in the previous time window is 140 N and the time window interval is 1 s, then the rolling resistance change rate is ; When calculating the air resistance change rate, the vehicle driving speed, air density, and wind speed data are called. The air resistance calculation formula is , where the air density is set according to the temperature and air pressure data. The air density calculation formula is , where represents the atmospheric pressure, is the air specific gas constant (287.05 J / (kg·K)), represents the ambient temperature (unit K). For example, when the air pressure is 101325 Pa and the temperature is 15°C (i.e., 288.15 K), the calculated kg / m3, and in this embodiment, kg / m3 is taken. The frontal surface area of the vehicle is taken as 2.5 m2, and the drag coefficient is taken as 0.3. If the current vehicle speed is 20 m / s and the vehicle speed in the previous time window is 18 m / s, then the air resistance is ; ; The air resistance change rate is ; All the calculated rolling resistance change rate and air resistance change rate data are stored in the resistance change rate database.

[0030] The resistance energy consumption calculation sub-module calls the resistance change rate data. According to the vehicle speed and slope conditions, the formula is used: ; Calculate the resistance energy consumption per unit time and establish the resistance energy consumption influence parameters; Among them, represents the resistance energy consumption per unit time, represents the rolling resistance change rate per unit time, represents the change rate of air resistance per unit time, represents the vehicle driving speed, represents the vehicle mass, represents the gravitational acceleration, represents the slope angle; After the resistance energy consumption calculation sub-module calls the resistance change rate data, it first extracts the rolling resistance change rate and the air resistance change rate , and combines the vehicle driving speed and the slope condition to calculate the resistance energy consumption per unit time. The calculation formula is: ; The slope angle is measured by an on-vehicle inclination sensor, and the measurement error is generally within . If the slope angle measured by the sensor is 5°, then 5° is used for calculation during calculation. Let the vehicle driving speed be m / s, the slope angle be , and the vehicle mass be kg. Calculate the resistance energy consumption: ; All calculated resistance energy consumption data are stored in the resistance energy consumption influence parameter database.

[0031] The power deviation judgment sub-module calls the resistance energy consumption influence parameters, combines the engine torque output and the gearbox shifting mode data, calculates the deviation between the resistance change rate and the torque and shifting mode, compares with the standard deviation threshold, judges whether there is an abnormality in the power system, and obtains the power system abnormal deviation information; After the power deviation judgment sub-module calls the resistance energy consumption influence parameters, it first extracts the resistance energy consumption data per unit time, and combines and compares the engine torque output and the gearbox shifting mode data, extracts the engine torque data from the database. Let the current engine output torque be 300 N·m, and the gearbox shifting mode is set to the 3rd gear. Calculate the deviation between the resistance change rate and the torque and shifting mode. Let the standard deviation threshold be 10%. The setting of this threshold is based on the industry standard of vehicle power system stability. Generally speaking, the allowable range of power deviation for commercial vehicles is relatively small (5%-8%), while that for passenger vehicles can be appropriately relaxed to 10%-12%. In this embodiment, 10% is used as the standard deviation threshold. If the preset standard torque output is 320 N·m, then calculate the torque deviation: ; The calculated deviation is below 10%, so it is determined that the power system is normal. If the calculated deviation is greater than 10%, it is determined that there is an abnormality in the power system. This 10% threshold applies to the matching relationship between engine torque and the transmission system. If this value exceeds 10%, it may mean abnormal shift logic, deviation in engine power output, or abnormal transmission loss. The calculated abnormal deviation information of the power system is stored in the database.

[0032] Please refer to Figure 5 , the health status assessment module includes: The fault probability calculation sub-module calls the abnormal deviation information of the power system, obtains the data of the engine, transmission, battery, and braking system, compares it with the standard operating data, and uses the formula: ; Performs operations to obtain component fault probability data and establish fault risk parameters; Among them, represents the probability of failure occurrence, represents the observed component operating data, represents the standard operating data of the component, represents the decimal compensation term to prevent the denominator from being zero, represents the component fault attenuation factor, represents the component operating time, is the base of the natural logarithm; After the fault probability calculation sub-module calls the abnormal deviation information of the power system, it first extracts the current operating data of the engine, transmission, battery, and braking system from the database, and compares it with the standard operating data of each component. The observed component operating data includes engine speed, oil temperature, transmission shift mode, battery voltage, and brake fluid pressure. The standard operating data is set by the vehicle manufacturer and corrected in combination with experimental data. After all the data is extracted, the probability of failure occurrence of each component is calculated, and the calculation formula is: ; Among them, represents the observed data, represents the standard data, is set to 0.01. The basis for setting this value is to ensure that when the standard data is extremely small or close to zero, the calculation will not be abnormal due to the denominator approaching zero. This value is adjusted according to the order of magnitude of the component standard operating value. For example, when the standard value of the engine speed is 3000 RPM, is set to 0.01, and for a smaller magnitude parameter such as the battery voltage of 12.6V, can be set to 0.001 to reduce calculation errors, Represents the fault attenuation factor, with a value range of [0.01, 0.05]. This value is set based on the statistical data of the historical faults of the components, reflecting the aging trend of the components. The engine will cause internal wear due to long-term operation, so its attenuation factor is set relatively large, such as 0.03. While the attenuation of the brake fluid pressure is relatively small, so it is set to 0.015. The larger the attenuation factor, the more obvious the influence of the fault probability on the operating time. is the operating time of the component. For example, the observed engine speed of a vehicle is 3100 RPM, the standard speed is 3000 RPM, the engine operating time is 200 hours, and the fault attenuation factor is set to 0.03, then calculate: ; Similarly, calculate the fault probabilities of the transmission, battery, and braking system, and store them in the fault risk parameter database, as shown in Table 4.

[0033] Table 4 Component Fault Probability Data Table All calculated fault probability data are stored in the fault risk parameter database.

[0034] The health score calculation sub-module calls the fault risk parameters, combines the influence weights of each component, calculates the overall vehicle health score, and obtains the vehicle health score; After the health score calculation sub-module calls the fault risk parameters, it first extracts the fault probability data of each component, and calculates the overall vehicle health score in combination with the influence weights of each component. The influence weights are set based on the influence degree of each component on the operation of the whole vehicle, and are determined by analyzing the historical fault data and key performance indicators. The engine fault has the most significant impact on the whole vehicle, so the weight is set to 0.4. The transmission fault may affect power transmission, and its weight is set to 0.3. The battery system fault mainly affects starting and power supply, and the weight is set to 0.2. While the braking system fault affects safety, but usually has redundant protection measures, so the weight is set to 0.1. When calculating the health score, multiply the fault probability of each component by the weight and sum them up: ; Substitute the fault probability data in Table 4: ; The calculated health score is stored in the vehicle health score database.

[0035] The health assessment sub-module calls the vehicle health score, compares it with the preset assessment criteria of the health score, classifies the health status of the vehicle, and records the fault probability of each component to establish vehicle health assessment information; After the health assessment sub-module calls the vehicle health score, it extracts the health score data and compares it with the preset health assessment criteria. The health assessment criteria are set based on the long-term monitoring data of the vehicle. Generally, when the vehicle health score is above 0.98, it is in a normal operating state; when it is between 0.95 and 0.98, it is in a slightly abnormal state, which means that there may be signs of early wear on some components; when it is between 0.90 and 0.95, it is in a moderately abnormal state, and regular maintenance is recommended; when it is below 0.90, it is in a severely abnormal state, and immediate repair may be required. The specific numerical range of the health assessment criteria is set with reference to industry safety standards and adjusted in combination with the vehicle service life and historical failure probability. Different score intervals correspond to different health states. For example: is "Healthy", is "Slightly Abnormal", is "Moderately Abnormal", is "Severely Abnormal". Classify the calculated 0.9974 as "Healthy", record the component failure probability data, and establish vehicle health assessment information and store it in the database.

[0036] Please refer to Figure 6 , the risk warning optimization module includes: The risk level calculation sub-module calls the vehicle health assessment information, obtains the failure type, occurrence probability, and impact range, and uses the formula: ; Calculate to obtain the vehicle risk level and establish vehicle risk assessment data; Among them, represents the risk level of the entire vehicle, represents the failure occurrence probability of component , represents the failure impact range of component , represents the vehicle health score, represents the standard value of the health score, represents the total number of vehicle components, represents the component index; After the risk level calculation sub-module calls the vehicle health assessment information, it first extracts the fault types, fault occurrence probabilities, and fault influence ranges of each component from the database. The fault type of each component is determined based on sensor detection results and historical data. For example, engine faults may include abnormal ignition systems, oil pressure fluctuations, or abnormal coolant temperatures. Transmission faults may include shifting delays or abnormal torque transmission. Battery faults may involve decreased charge and discharge efficiency or unstable voltages. Brake system faults may involve brake fluid leakage or insufficient braking force. After obtaining the fault occurrence probabilities of all components, the fault influence ranges of the components are extracted from the database. The influence range is quantified based on the degree of influence of the component on the overall operation safety of the vehicle. The set ranges are as follows: the influence range of engine faults is set to 0.8, the influence range of transmission faults is set to 0.7, the influence range of battery faults is set to 0.5, and the influence range of brake system faults is set to 0.9. After all data is extracted, the overall risk level of the vehicle is calculated. The calculation formula: ; Among them, the health score standard value is set to 1.0. The setting basis is the theoretical optimal health score of the vehicle in a fault-free state. This value does not change with the driving state of the vehicle and serves as the benchmark for calculating the vehicle health state. The actual health score is affected by the fault probability and component performance changes, and fluctuates with the vehicle operation time and failure rate. The numerical range , where the closer H is to 1, the closer the vehicle health state is to the ideal value. When H is close to 0, it indicates that the vehicle has serious faults and needs repair.

[0037] Let the calculated result of the above health score be , and the fault probabilities and influence ranges of each component are shown in Table 5 below.

[0038] Table 5 Vehicle Component Fault Data Table Substitute the data for calculation: ; The calculated vehicle risk level is stored in the risk assessment database.

[0039] The warning information generation sub-module calls the vehicle risk assessment data, combines the preset risk level thresholds, and compares the classified warning levels to obtain the vehicle status warning level; After the warning information generation sub-module calls the vehicle risk assessment data, it first extracts the calculated vehicle risk level and classifies it according to the preset risk level thresholds. The preset risk level thresholds are set according to the vehicle operation safety standards. The threshold setting range is: when When the vehicle risk level is determined to be "high risk", this value is based on accident statistical analysis, indicating a relatively high probability of functional damage to the vehicle components, which may affect driving safety. When it falls within a certain range, it is classified as "medium risk". In this range, the vehicle may experience a slight performance decline but can still continue to operate. When it is in another range, it is classified as "low risk". The vehicle may have minor wear but does not affect normal driving. When it is in a certain range, it is classified as "safe", indicating that the current state of the vehicle is good, and all components are operating stably. All risk thresholds depend on the tolerance range of the vehicle's key components and the calculation of the probability of operational failure, and are adjusted according to actual operating data. The calculated is classified, the current vehicle state is determined to be "safe", and the determination result is stored in the vehicle state warning database.

[0040] The warning information transmission sub-module calls the vehicle state warning level and uses the in-vehicle wireless network to send the warning information to the corresponding vehicle, including the vehicle's risk level, potential faulty equipment, and failure probability, to give a risk warning to the vehicle driver and obtain the vehicle state warning information; After the warning information transmission sub-module calls the vehicle state warning level, it first extracts the risk level, potential faulty components, and failure probability information from the database and generates a warning information. The warning information includes the vehicle identification number, the current risk level, the faulty components and their probabilities, etc. After the warning information is assembled, it calls the in-vehicle wireless network module to pack the data and send it to the corresponding vehicle, and at the same time generates a warning prompt on the vehicle dashboard display screen, and finally records the vehicle state warning information in the remote server database.

[0041] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A wireless vehicle status monitoring system, characterized in that: The system comprises: The state change assessment module obtains vehicle operating parameters, calls parameter change data in multiple time windows, calculates the stability level of each operating parameter based on the change amplitude, change rate and tolerance threshold, and generates state stability classification information; The data transmission adjustment module calculates the data transmission frequency of each operating parameter after adjustment based on the state stability classification information, transmits the operating parameter to the cloud monitoring platform through the wireless network, and obtains the real-time transmission information of the monitoring data; The power anomaly detection module calls the monitoring data to transmit information in real time, calculates the rolling resistance change rate and the air resistance change rate per unit time, calculates the resistance energy consumption per unit time according to the vehicle speed and slope conditions, determines whether there is a power system anomaly, and obtains power system abnormality deviation information; The health status assessment module calculates the failure probability of each component based on the abnormal deviation information of the power system, calculates the health score of the vehicle in combination with the component impact weight, and obtains the vehicle health assessment information; The risk warning optimization module calculates the risk level based on the vehicle health assessment information, issues risk warning to the vehicle driver, and obtains vehicle status warning information.

2. The wireless vehicle status monitoring system according to claim 1, characterized in that: The state stability grading information specifically includes the mean value of the rate of change, the fluctuation range and the amplitude change value; the real-time transmission information of the monitoring data includes the high-frequency transmission interval and the data transmission result; the abnormal deviation information of the power system specifically includes the rolling resistance change rate, the air resistance change rate, the energy consumption deviation and the abnormal state of the power system; the vehicle health assessment information includes the component failure risk and the vehicle health score; the vehicle state warning information includes the vehicle risk level, the impact range and the failure type.

3. The wireless vehicle status monitoring system according to claim 1, characterized in that: The state change assessment module comprises: The parameter change calculation submodule obtains vehicle operating parameters, calls the vehicle speed, engine speed, brake pressure, fuel volume, road slope, humidity and temperature change data in multiple time windows, calculates the change range of each operating parameter in multiple time windows, and obtains the change range data set of each operating parameter; The stability level calculation submodule calculates the change rate of each operating parameter within the time window based on the change amplitude data set of each operating parameter, and compares the change rate according to the tolerance threshold, using the formula: ； The stability level value is obtained by operation, and the stability level data of the operating parameters is obtained; in, Represents the stability level value, Represents time window The operating parameter values ​​within Represents the operating parameter value of the previous time window, Represents the total number of time windows, Represents the maximum change value in all time windows, represents the tolerance threshold, is the base of natural logarithms; The state stability grading submodule calls the operating parameter stability grade data, grades the stability of each operating parameter according to a preset grading standard, and establishes state stability grading information.

4. The wireless vehicle status monitoring system according to claim 1, characterized in that: The data transmission adjustment module comprises: The data comparison submodule calls a preset stability standard based on the state stability classification information, compares the stability classification value of each operating parameter with the preset stability standard, obtains the stability deviation of each operating parameter, and obtains stability deviation data; The transmission frequency calculation submodule calls the stability deviation data, combines the preset data transmission frequency of each operating parameter, and adopts the formula: ； Calculate and obtain the adjusted data transmission frequency, and establish data transmission adjustment parameters; in, Represents the adjusted data transmission frequency, Represents the preset data transmission frequency, Represents the stability classification value of the current operating parameters, represents the preset stability standard, stands for Transmission Adjustment Index; The data transmission monitoring submodule calls the data transmission adjustment parameters, and uses the vehicle-mounted wireless network to transmit the operating parameters to the cloud monitoring platform to obtain real-time transmission information of the monitoring data.

5. The wireless vehicle status monitoring system according to claim 1, characterized in that: The power anomaly detection module comprises: The resistance change calculation submodule calls the real-time transmission information of the monitoring data, extracts the vehicle speed, tire pressure, air density and wind speed data, calculates the rolling resistance change rate and the air resistance change rate per unit time, and obtains the resistance change rate data; The resistance energy consumption calculation submodule calls the resistance change rate data and uses the formula according to the vehicle speed and slope conditions: ； Calculate the resistance energy consumption per unit time and establish the resistance energy consumption influencing parameters; in, Represents the resistance energy consumption per unit time, Represents the rate of change of rolling resistance per unit time, Represents the rate of change of air resistance per unit time, represents the vehicle speed, Represents the vehicle mass, represents the acceleration due to gravity, represents the slope angle; The power deviation judgment submodule calls the resistance energy consumption influencing parameters, combines the engine torque output and the transmission shift mode data, calculates the deviation between the resistance change rate and the torque and shift mode, compares the standard deviation threshold, determines whether there is a power system abnormality, and obtains the power system abnormal deviation information.

6. The wireless vehicle status monitoring system according to claim 1, characterized in that: The health status assessment module includes: The fault probability calculation submodule calls the abnormal deviation information of the power system, obtains the data of the engine, transmission, battery and brake system, compares the standard operation data, and adopts the formula: ； Obtain component failure probability data through calculation and establish failure risk parameters; in, represents the probability of failure, represents the observed component operation data, Represents the standard operating data of the component, Represents the decimal compensation term to prevent the denominator from being zero, represents the component failure attenuation factor, Represents the component running time, is the base of natural logarithms; The health score calculation submodule calls the fault risk parameter, combines the influence weight of each component, calculates the overall health score of the vehicle, and obtains the vehicle health score; The health assessment submodule calls the vehicle health score, compares the preset assessment criteria of the health score, classifies the health status of the vehicle, records the failure probability of each component, and establishes vehicle health assessment information.

7. The wireless vehicle status monitoring system according to claim 1, characterized in that: The risk warning optimization module includes: The risk level calculation submodule calls the vehicle health assessment information to obtain the fault type, occurrence probability and impact range, using the formula: ； Calculate and obtain the vehicle risk level and establish vehicle risk assessment data; in, Represents the risk level of the entire vehicle, Representative parts The probability of failure, Representative parts The scope of the fault impact, Represents the vehicle health score, Represents the standard value of health score, Represents the total number of vehicle parts, Represents the component index; The warning information generation submodule calls the vehicle risk assessment data, combines the preset risk level threshold, compares the classification warning level, and obtains the vehicle status warning level; The warning information transmission submodule calls the vehicle status warning level and uses the on-board wireless network to send warning information to the corresponding vehicle, including the vehicle's risk level, potential faulty equipment and failure probability, to provide risk warning to the vehicle driver and obtain vehicle status warning information.

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