Vehicle condition monitoring method, device, terminal equipment and medium based on automobile

Abstract

Embodiments of the present invention provide a vehicle condition monitoring method, apparatus, terminal device, and medium based on an automobile, belonging to the field of vehicle monitoring technology. The method includes: performing data change analysis on initial sensor data of an initial vehicle to obtain data change points; performing sensor association analysis based on the data change points to obtain target association relationships between the initial sensors; determining initial interference parameters based on the target association relationships and the initial sensor data; obtaining target sensor data of a target vehicle under a target sensor, and classifying the target sensor data to obtain a target operation type; determining a target interference parameter from the initial interference parameter based on the target operation type and a preset operation type; determining a data change range of the target vehicle under the sensor based on the target interference parameter; performing anomaly identification on the target sensor data based on the data change range to obtain target anomaly data; and performing condition monitoring on the target vehicle based on the target anomaly data to obtain a vehicle monitoring result.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle monitoring, and in particular to a method, device, terminal equipment and medium for monitoring vehicle conditions based on automobiles. Background Art

[0002] With rapid socioeconomic development, people's quality of life has significantly improved, and more and more families and individuals are choosing cars as their daily means of transportation. However, despite the convenience cars bring to our lives, they often lack timely and effective monitoring and detection of the vehicle's actual condition while driving. This problem directly increases driving risks, especially when faced with emergencies. Drivers often lack immediate access to vehicle health information, making it difficult to respond promptly and accurately.

[0003] Current technical solutions incorporate machine learning models to monitor vehicle conditions, but these models primarily rely on anomaly classification to detect potential faults or issues. These models often misjudge or miss abnormal vehicle conditions. For example, potential fault signals may be overlooked, or normal vehicle conditions may be misidentified as abnormal, leading to unnecessary repairs or safety hazards. Consequently, existing vehicle condition monitoring methods are prone to misjudgements or misses, making it impossible to accurately and timely monitor and identify the vehicle's actual condition, thereby increasing driving risks. Summary of the Invention

[0004] The main purpose of the embodiments of the present invention is to provide a vehicle condition monitoring method, device, terminal equipment and medium based on automobiles, aiming to solve the problem in related technologies that the actual condition of the vehicle cannot be accurately and timely monitored and discovered, thereby increasing driving risks.

[0005] In a first aspect, an embodiment of the present invention provides a vehicle condition monitoring method based on an automobile, comprising:

[0006] Obtaining initial sensor data corresponding to an initial sensor of an initial vehicle under a preset operation type, and performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type;

[0007] Performing sensor association analysis based on the data change points to obtain target association relationships between the initial sensors of the initial vehicle under the preset operation type;

[0008] Determining an initial interference parameter corresponding to the initial sensor according to the target association relationship and the initial sensing data;

[0009] Obtaining target sensor data corresponding to a target vehicle under a target sensor, and performing operation type classification according to the target sensor data to obtain a target operation type corresponding to the target sensor data;

[0010] Determining a target interference parameter corresponding to the target operation type from the initial interference parameter according to the target operation type and the preset operation type;

[0011] Determine the data variation range corresponding to the target vehicle under the target sensor according to the target interference parameter;

[0012] Performing abnormality identification on the target sensor data according to the data variation range to obtain target abnormality data corresponding to the target vehicle;

[0013] The vehicle condition of the target vehicle is monitored according to the target abnormal data to obtain a vehicle supervision result corresponding to the target vehicle.

[0014] In a second aspect, an embodiment of the present invention provides a vehicle condition monitoring device based on a car, comprising:

[0015] a data acquisition module, configured to obtain initial sensor data corresponding to an initial sensor of an initial vehicle under a preset operation type, and perform data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type;

[0016] A data analysis module, configured to perform sensor association analysis based on the data change points to obtain target association relationships corresponding to the initial sensors of the initial vehicle under the preset operation type;

[0017] a parameter processing module, configured to determine an initial interference parameter corresponding to the initial sensor according to the target association relationship and the initial sensing data;

[0018] A data classification module is used to obtain target sensing data corresponding to the target vehicle under the target sensor, and classify the operation type according to the target sensing data to obtain the target operation type corresponding to the target sensing data;

[0019] a parameter determination module, configured to determine a target interference parameter corresponding to the target operation type from the initial interference parameter according to the target operation type and the preset operation type;

[0020] A range determination module, configured to determine a data variation range corresponding to the target vehicle under the target sensor according to the target interference parameter;

[0021] an abnormality identification module, configured to identify abnormalities in the target sensor data according to the data variation range, and obtain target abnormality data corresponding to the target vehicle;

[0022] The result determination module is used to perform vehicle condition supervision on the target vehicle according to the target abnormal data to obtain a vehicle supervision result corresponding to the target vehicle.

[0023] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any one of the vehicle condition supervision methods based on automobiles provided in the specification of the present invention are realized.

[0024] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the vehicle condition supervision methods based on automobiles provided in the specification of the present invention.

[0025] The embodiment of the present invention provides a vehicle condition monitoring method, apparatus, terminal device and medium based on automobiles, the method comprising: obtaining initial sensor data corresponding to initial sensors of an initial vehicle under a preset operation type, and performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensors of the initial vehicle under the preset operation type; performing sensor association analysis based on the data change points to obtain target association relationships corresponding to initial sensors of the initial vehicle under the preset operation type, thereby timely discovering different target association relationships corresponding to initial sensors under different preset operation types, thereby providing good support for subsequent timely discovery of anomalies under different preset operation types; and then determining initial interference parameters corresponding to the initial sensors based on the target association relationships and the initial sensor data, thereby accurately determining the sensor The initial interference parameters of the sensor are calculated to reduce the impact of external interference on sensor data, improving data reliability. This allows for timely detection of anomalies under different preset operation types and the corresponding abnormal data to be identified. The target sensor data corresponding to the target vehicle under the target sensor is obtained, and the operation type is classified based on the target sensor data to obtain the target operation type corresponding to the target sensor data. The target interference parameters corresponding to the target operation type are determined from the initial interference parameters based on the target operation type and the preset operation type. The target interference parameters are used to determine the data variation range corresponding to the target vehicle under the target sensor. Based on the data variation range, anomalies are identified in the target sensor data to obtain target abnormal data corresponding to the target vehicle. The target vehicle is then monitored based on the target abnormal data to obtain vehicle monitoring results corresponding to the target vehicle. By performing detailed analysis and anomaly identification on the sensor data of the target vehicle under the target operation type, target abnormal data can be detected in a timely manner. This real-time monitoring mechanism not only accurately monitors the operating status of the target vehicle but also effectively reduces the risks that the vehicle may face during driving. This significantly improves driving safety and addresses the problem in related technologies that cannot accurately and timely monitor and detect the actual vehicle condition, thereby increasing driving risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flow chart of a vehicle condition monitoring method based on an automobile provided by an embodiment of the present invention;

[0028] Figure 2A schematic diagram of the module structure of a vehicle condition monitoring device based on an automobile provided by an embodiment of the present invention;

[0029] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0032] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0033] Embodiments of the present invention provide a vehicle-based vehicle condition monitoring method, apparatus, terminal device, and medium. The vehicle-based vehicle condition monitoring method can be applied to a terminal device, which can be an electronic device such as a tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. The terminal device can also be a server or a server cluster.

[0034] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0035] Please refer to Figure 1 , Figure 1 A flow chart of a vehicle condition monitoring method based on an automobile provided in an embodiment of the present invention.

[0036] like Figure 1 As shown, the automobile-based vehicle condition supervision method includes steps S101 to S108.

[0037] Step S101: obtaining initial sensor data corresponding to an initial sensor of an initial vehicle under a preset operation type, and performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type.

[0038] Exemplarily, the preset operation type refers to any operation type of vehicle operations that the initial vehicle can perform. For example, the preset operation type can be any vehicle operation such as vehicle acceleration, deceleration, gear shifting, lane changing, braking, etc.

[0039] For example, multiple initial sensors are installed at key parts of the initial vehicle, such as the engine, brake system, tires, battery, and suspension system. Initial sensor types include temperature sensors, pressure sensors, acceleration sensors, and vibration sensors, and the initial sensor data corresponding to the initial vehicle is collected, including but not limited to temperature, pressure, vibration, speed, fuel consumption, battery status, tire pressure, brake wear, and other data.

[0040] For example, data change analysis algorithms, such as time series-based analysis methods (e.g., sliding window analysis, differential analysis) or statistical methods (e.g., variance analysis, trend analysis), are used to analyze the initial sensor data to identify data change points in the initial sensor data. Data change points represent sudden changes or trend changes in sensor states under a preset vehicle operation type.

[0041] In some embodiments, performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensors of the initial vehicle under the preset operation type includes: determining an initial change point, and segmenting the initial sensor data according to the initial change point to obtain first sensor data and second sensor data; performing statistical data calculation on the first sensor data to obtain a corresponding first mean and first standard deviation, and performing statistical data calculation on the second sensor data to obtain a corresponding second mean and second standard deviation; determining a segmentation quality value corresponding to the initial change point based on the first mean, the first standard deviation, the second mean, and the second standard deviation; obtaining a maximum value corresponding to the segmentation quality value as a target quality value, and determining a distribution probability of the target quality value corresponding to the segmentation quality value; determining a segmentation state corresponding to the target quality value based on the distribution probability and a preset value, and determining the data change point from the initial change point based on the segmentation state and the target quality value; wherein the segmentation quality value is obtained according to the following formula:

[0042] ;

[0043] in, represents the segmentation quality value corresponding to the i-th initial change point, represents the first mean value corresponding to the first sensor data corresponding to the i-th initial change point, represents the second mean value corresponding to the second sensing data corresponding to the i-th initial change point; represents the first standard deviation corresponding to the first sensing data corresponding to the i-th initial change point; represents the second standard deviation corresponding to the second sensing data corresponding to the i-th initial change point; represents the number of data corresponding to the first sensing data corresponding to the i-th initial change point, represents the amount of data corresponding to the second sensing data corresponding to the i-th initial change point, Indicates taking the absolute value.

[0044] Exemplarily, a change point detection algorithm (such as CUSUM, PELT, Binary Segmentation, etc.) is used to scan the initial sensor data to identify possible initial change points, and then for each identified initial change point, the initial sensor data is cut into two parts: the data before the initial change point is determined as the first sensor data and the data after the initial change point is determined as the second sensor data.

[0045] Exemplarily, a statistical algorithm is used to calculate the mean of the first sensor data to obtain a first mean, and a statistical algorithm is used to calculate the standard deviation of the first sensor data to obtain a first standard deviation, and a statistical algorithm is used to calculate the mean of the second sensor data to obtain a second mean, and a statistical algorithm is used to calculate the standard deviation of the second sensor data to obtain a second standard deviation.

[0046] For example, the first mean, first standard deviation, second mean, and second standard deviation are used according to the following formula to obtain the segmentation quality value corresponding to the initial change point. The segmentation quality value measures the degree of difference in data distribution before and after the initial change point:

[0047] ;

[0048] in, represents the segmentation quality value corresponding to the i-th initial change point, represents the first mean value of the first sensor data corresponding to the i-th initial change point, represents the second mean value corresponding to the second sensor data corresponding to the i-th initial change point; represents the first standard deviation of the first sensor data corresponding to the i-th initial change point; represents the second standard deviation of the second sensor data corresponding to the i-th initial change point; represents the number of data corresponding to the first sensing data corresponding to the i-th initial change point, represents the number of data corresponding to the second sensing data corresponding to the i-th initial change point, Indicates taking the absolute value.

[0049] For example, the segmentation quality value can quantify the difference in data distribution before and after the initial change point by combining the first mean, first standard deviation, second mean, and second standard deviation. The first mean and second mean reflect the central tendency of the data, while the first standard deviation and second standard deviation reflect the degree of dispersion of the data. By combining the first mean, first standard deviation, second mean, and second standard deviation using the above formula, a more comprehensive assessment of changes in the data distribution can be achieved, providing an objective basis for the subsequent determination of data change points. The above formula comprehensively considers multiple statistical characteristics of the data distribution (mean, standard deviation, and data volume), rather than relying solely on the mean or standard deviation. This allows the segmentation quality value to better reflect the significance of the data before and after the change point. Therefore, by calculating and screening the segmentation quality value, significant change points in the data can be effectively identified and false change points can be eliminated. This makes data segmentation more robust and maintains high reliability even in complex real-world scenarios (such as those in the presence of noise or large data fluctuations).

[0050] For example, the segmentation quality value with the largest segmentation quality value among all initial change points is obtained as the target quality value. At the same time, the distribution probability of the target quality value among all segmentation quality values ​​is calculated, which can be a probability density function value or a cumulative distribution function value. Then, the segmentation state corresponding to the target quality value is determined based on the distribution probability and a preset value. If the distribution probability is higher than or equal to the preset value, the initial change point corresponding to the target quality value is considered to be a data change point. If the distribution probability is lower than the preset value, it is considered that no data change point exists in the initial sensor data. Then, the data change point can be determined as empty or as a preset flag, such as None, so that it can be accurately identified when performing subsequent correlation analysis based on the data change point.

[0051] Step S102: performing sensor association analysis based on the data change points to obtain target association relationships corresponding to the initial sensors of the initial vehicle under the preset operation type.

[0052] Exemplarily, the associated sensors of the initial vehicle that have changed or been affected under the preset operation type are obtained based on the data change point. Then, when the data change point is empty or a preset mark, it means that the sensors of the initial vehicle whose data change point is empty or a preset mark are not affected under the preset operation type. Then, the sensor data corresponding to the sensors whose data change points are not empty or not preset marks are associated with analysis to obtain the corresponding target association relationship.

[0053] For example, sensors associated with a preset operation type, such as a speed sensor, acceleration sensor, steering angle sensor, and accelerator pedal position sensor, are selected based on data change points, thereby obtaining relevant sensor data corresponding to the relevant sensors from the initial sensor data. Then, target association relationships between the relevant sensors are calculated using correlation coefficient analysis (such as the Pearson correlation coefficient and the Spearman rank correlation coefficient), mutual information analysis, principal component analysis (PCA), cluster analysis (such as K-means), and the like. The target association relationship is one of strong positive correlation, strong negative correlation, weak positive correlation, and weak negative correlation.

[0054] In some embodiments, the initial sensor includes at least a first sensor and a second sensor, the initial sensor data includes at least first sensor data corresponding to the first sensor and second sensor data corresponding to the second sensor, the data change points include at least a first change point corresponding to the first sensor data and a second change point corresponding to the second sensor data, and the sensor association analysis based on the data change points to obtain the target association relationship between the initial sensors of the initial vehicle under the preset operation type includes: obtaining a first acquisition time corresponding to the first change point from the first sensor data and a second acquisition time corresponding to the second change point from the second sensor data; obtaining a first time quantity corresponding to the first acquisition time and a second time quantity corresponding to the second acquisition time; determining an initial association matrix between the first sensor and the second sensor based on the first time quantity and the second time quantity; adjusting the initial association matrix based on the first change point and the second change point to obtain a target association matrix between the first sensor and the second sensor of the initial vehicle under the preset operation type; and performing sensor association analysis based on the target association matrix to obtain the target association relationship between the first sensor and the second sensor of the initial vehicle under the preset operation type.

[0055] Exemplarily, an initial sensor associated with a preset operation type is selected through data change points, the initial sensor includes at least a first sensor and a second sensor, the initial sensing data includes at least first sensing data corresponding to the first sensor and second sensing data corresponding to the second sensor, and the data change points include at least a first change point corresponding to the first sensing data and a second change point corresponding to the second sensing data.

[0056] Exemplarily, the first sensor data and the second sensor data respectively include multiple data change points, and then the first acquisition times corresponding to the multiple first change points are obtained from the first sensor data, and the second acquisition times corresponding to the multiple second change points are obtained from the second sensor data, and then data statistics are performed on the first acquisition time to obtain the first time quantity, and data statistics are performed on the second acquisition time to obtain the second time quantity.

[0057] For example, an initial correlation matrix is ​​constructed using the first time quantity and the second time quantity. The rows of the matrix represent the first sensor, and the columns represent the second sensor. Each element in the matrix represents the number of corresponding data change points of the first sensor and the second sensor at the corresponding time.

[0058] For example, the initial correlation matrix is ​​adjusted based on the temporal alignment of the first and second change points. For example, if the time difference between the first and second change points is significant, a time offset adjustment may need to be added to the matrix. The temporal characteristics of the first and second change points (e.g., magnitude of change, trend, etc.) are used to dynamically adjust the elements in the matrix. For example, sensor pairs with large magnitudes of change can have their association weights increased, thereby calibrating the adjusted initial correlation matrix to ensure that the elements in the matrix accurately reflect the true correlation relationship between the two sensors under the preset operation type. The elements in the matrix are then standardized or normalized to ensure that the correlation strengths between different sensors are comparable.

[0059] Exemplarily, the target association matrix is ​​used to perform further association analysis, such as calculating the correlation coefficient, mutual information or other statistical indicators to quantify the association strength between the two sensors, and then comparing the association strength with a preset strength, so as to determine the target association relationship between the first sensor and the second sensor based on the comparison result.

[0060] In some embodiments, the sensor association analysis performed according to the target association matrix to obtain the target association relationship corresponding to the first sensor and the second sensor of the initial vehicle under the preset operation type includes: obtaining a target characterization vector between the first sensor and the second sensor according to the target association matrix; performing weighted processing on the target characterization vector through a modulation function to obtain an initial characterization value corresponding to the first sensor and the second sensor; performing data adjustment on the initial characterization value through an adaptive filter to obtain a target characterization value corresponding to the first sensor and the second sensor; and determining the target association relationship corresponding to the first sensor and the second sensor according to the target characterization value.

[0061] For example, each row (or column) in the target association matrix represents the association relationship between a sensor and other sensors. The association rows (or columns) corresponding to the first and second sensors are extracted from the matrix to form two representation vectors. Each element in the representation vector represents the strength of the association between the first sensor (or second sensor) and other sensors (including the second sensor or the first sensor).

[0062] For example, a modulation function is designed to weight the elements in a representation vector. The modulation function takes the elements of the representation vector as input and outputs weighted numerical values. The modulation function can dynamically adjust weights based on sensor importance, correlation strength, or other factors. The modulation function is applied to the representation vector of a first sensor and the representation vector of a second sensor, respectively, to obtain two weighted initial representation numerical values. These weighted numerical values ​​can better reflect the correlation characteristics between the first and second sensors.

[0063] Exemplarily, an adaptive filter is used to perform data conditioning on the initial characterization values. The adaptive filter can dynamically adjust filtering parameters based on the input data. The filter input is the weighted initial characterization value, and the output is the adjusted target characterization value. The initial characterization value of the first sensor and the initial characterization value of the second sensor are filtered separately. The filter can automatically optimize the filtering effect based on data fluctuations, noise levels, or other factors, making the target characterization value more stable and accurate.

[0064] For example, the target representation values ​​of the first sensor and the second sensor are analyzed to evaluate the strength and consistency of the association between them. Based on the degree of difference or similarity between the target representation values, a target association relationship between the first sensor and the second sensor is determined. If the target representation values ​​are close or highly correlated, a strong association relationship is determined between them; otherwise, a weak association relationship is determined.

[0065] Specifically, the application of modulation functions and adaptive filters makes the calculation of associations more dynamic and flexible, adapting to different operation types and data characteristics. The data conditioning step effectively eliminates the influence of noise and outliers, making the target representation values ​​more stable and reliable, thereby improving the accuracy of association analysis. Furthermore, the determination of target associations provides strong support for more accurate monitoring of vehicle state changes and enhancing the efficiency and reliability of multi-sensor collaboration.

[0066] Step S103: determining an initial interference parameter corresponding to the initial sensor according to the target association relationship and the initial sensing data.

[0067] Exemplarily, the initial sensor includes at least a first sensor and a second sensor, and from the previously determined target association relationship, a sensor associated with the first sensor is found, namely, an associated sensor. Data corresponding to the associated sensor is extracted from the initial sensor data, referred to as the first data. At the same time, the sensor data corresponding to the first sensor is extracted, referred to as the second data. Ensure that the first data and the second data are aligned in time, that is, correspond to the same time point. Preprocess the first data and the second data, including denoising, filtering, normalization and other operations, to eliminate noise and outliers, ensure data accuracy, and then identify interference factors of the associated sensor on the first sensor, such as cross-sensitivity, environmental impact, etc., and select a suitable data adjustment method, such as linear regression, partial least squares method, Kalman filtering, etc., to adjust the second data according to the first data, and then use the first data as an adjustment factor to adjust the second data to eliminate or reduce the impact of the associated sensor on the first sensor. For example, by establishing a mathematical model between the first and second data, calculating an interference correction, and subtracting the correction from the second data, the degree of interference caused by the associated sensor on the first sensor, i.e., the initial interference parameter, can be calculated during the data adjustment process. The initial interference parameter can be an interference coefficient, interference percentage, or other quantitative indicator reflecting the degree of influence of the associated sensor on the first sensor. Similarly, the initial interference parameter corresponding to the second sensor is obtained according to the above process.

[0068] In some embodiments, the initial interference parameters include at least a first interference parameter corresponding to the first sensor and a second interference parameter corresponding to the second sensor, and the determining of the initial interference parameters corresponding to the initial sensor based on the target association relationship and the initial sensor data includes: determining a first associated sensor corresponding to the first sensor and a second associated sensor corresponding to the second sensor from the initial sensor based on the target association relationship; obtaining third sensor data corresponding to the first associated sensor and fourth sensor data corresponding to the second associated sensor; calculating the first data difference corresponding to adjacent time data in the first sensor data and calculating the second sensor data the first sensor data and the third sensor data are respectively calculated according to the first and second data difference values ​​corresponding to the adjacent time data; the first sensor data and the third sensor data are respectively calculated according to the first and second data difference values ​​corresponding to the adjacent time data; the first sensor data and the third sensor data are respectively calculated according to the first and second data difference values ​​corresponding to the adjacent time data; the first sensor data and the third sensor data are respectively calculated according to the first and second data difference values ​​corresponding to the adjacent time data; the first sensor data and the third sensor data are respectively calculated according to the first and second sample mean values ​​and the third sample variance; the first sensor data and the third sensor data are respectively determined according to the first and second sample mean values ​​and the third sample variance; the first sensor data and the third sensor data are respectively determined according to the first and second sample mean values ​​and the first sample variance; the first sensor data and the third sensor data are respectively determined according to the first and second sample mean values ​​and the first sample variance; the first sensor data and the third sensor data are respectively determined according to the first and second sample mean values ​​and the first sample variance; the first sensor data and the third sensor data are respectively determined according to the first and second sample mean values ​​and the first sample variance; the first sensor data and the third sensor data are respectively determined according to the first and third ... determining a third mean range corresponding to the third sensor data according to the third sample mean and the third sample variance in combination with a normal distribution; calculating a fifth sample mean and a fifth sample variance for the fifth data difference, and determining a fifth mean range corresponding to the first sensor data and the first associated sensor according to the fifth sample mean and the fifth sample variance in combination with a normal distribution; adjusting the first mean range according to the third mean range and the fifth mean range to obtain the first interference parameter corresponding to the first sensor; calculating a second sample mean and a second sample variance for the second data difference, and calculating a fourth sample mean for the fourth data difference. and fourth sample variance; determining a second mean range corresponding to the second sensor data according to the second sample mean and the second sample variance in combination with a normal distribution; determining a fourth mean range corresponding to the fourth sensor data according to the fourth sample mean and the fourth sample variance in combination with a normal distribution; calculating a sixth sample mean and a sixth sample variance for the sixth data difference, and determining a sixth mean range corresponding to the second sensor data and the second associated sensor according to the sixth sample mean and the sixth sample variance in combination with a normal distribution; adjusting the second mean range according to the fourth mean range and the sixth mean range to obtain the second interference parameter corresponding to the second sensor.

[0069] Exemplarily, the initial interference parameters include at least a first interference parameter corresponding to the first sensor and a second interference parameter corresponding to the second sensor.

[0070] Exemplarily, a first associated sensor associated with the first sensor and a second associated sensor associated with the second sensor are extracted from the target association relationship, and then third sensor data corresponding to the first associated sensor are extracted from the initial sensor data, and fourth sensor data corresponding to the second associated sensor are extracted from the initial sensor data.

[0071] Exemplarily, the differences between adjacent moment data are calculated for the first sensor data, the second sensor data, the third sensor data, and the fourth sensor data, respectively, to obtain the first data difference, i.e., the difference between adjacent moment data in the first sensor data, the second data difference, i.e., the difference between adjacent moment data in the second sensor data, the third data difference, i.e., the difference between adjacent moment data in the third sensor data, and the fourth data difference, i.e., the difference between adjacent moment data in the fourth sensor data.

[0072] Exemplarily, a fifth data difference value at the same time point is calculated for the first sensed data and the third sensed data, and a sixth data difference value at the same time point is calculated for the second sensed data and the fourth sensed data.

[0073] For example, a first sample mean is obtained by calculating the mean of the first data difference using a statistical algorithm, and a first sample variance is obtained by calculating the variance of the first data difference using a statistical algorithm. A third sample mean is obtained by calculating the mean of the third data difference using a statistical algorithm, and a third sample variance is obtained by calculating the sample variance of the third data difference using a statistical algorithm.

[0074] For example, a second sample mean is obtained by calculating the mean of the second data difference using a statistical algorithm, and a second sample variance is obtained by calculating the variance of the second data difference using a statistical algorithm. Furthermore, a fourth sample mean is obtained by calculating the mean of the fourth data difference using a statistical algorithm, and a fourth sample variance is obtained by calculating the variance of the fourth data difference using a statistical algorithm.

[0075] For example, a fifth sample mean is obtained by calculating the mean of the fifth data difference using a statistical algorithm, and a fifth sample variance is obtained by calculating the variance of the fifth data difference using a statistical algorithm. A sixth sample mean is obtained by calculating the mean of the sixth data difference using a statistical algorithm, and a sixth sample variance is obtained by calculating the variance of the sixth data difference using a statistical algorithm.

[0076] Exemplarily, a confidence interval corresponding to the first sample mean in the first sensor data is determined based on the first sample mean and the first sample variance in combination with a normal distribution, thereby determining a confidence level corresponding to the first sample mean based on the confidence interval. Furthermore, a first mean range corresponding to the first sensor data is determined based on the confidence level in combination with the first sample mean and the first sample variance. Similarly, a third mean range corresponding to the third sensor data is determined based on the third sample mean and the third sample variance in combination with a normal distribution. A second mean range corresponding to the second sensor data is determined based on the second sample mean and the second sample variance in combination with a normal distribution. A fourth mean range corresponding to the fourth sensor data is determined based on the fourth sample mean and the fourth sample variance in combination with a normal distribution. A fifth mean range between the first sensor data and the first associated sensor is determined based on the fifth sample mean and the fifth sample variance in combination with a normal distribution. A sixth mean range between the second sensor data and the second associated sensor is determined based on the sixth sample mean and the sixth sample variance in combination with a normal distribution.

[0077] Exemplarily, an adjustment factor is determined based on the relationship between the fifth mean range and the third mean range. For example, if the fifth mean range is larger, it indicates that the first sensor is more strongly interfered with by the first associated sensor, and the adjustment factor should be larger; otherwise, the adjustment factor should be smaller. The first mean range is combined with the adjustment factor to perform linear or nonlinear adjustment, thereby calculating an interference parameter based on the difference between the adjusted first mean range and the original first mean range. The first interference parameter is a quantitative indicator describing the degree of interference of the first sensor by the first associated sensor. This allows the first mean range to be adjusted based on the third and fifth mean ranges to obtain a first interference parameter corresponding to the first sensor. Similarly, the second mean range is adjusted based on the fourth and sixth mean ranges to obtain a second interference parameter corresponding to the second sensor.

[0078] Step S104: obtaining target sensing data corresponding to the target vehicle under the target sensor, and performing operation type classification according to the target sensing data to obtain a target operation type corresponding to the target sensing data.

[0079] For example, target sensing data corresponding to a target vehicle in motion under a target sensor is obtained, and then the data classification model is used to classify the operation type according to the target sensing data, thereby obtaining the target operation type corresponding to the target vehicle under the target sensing data. The target operation type can be any vehicle operation such as vehicle acceleration, deceleration, gear shifting, lane changing, braking, etc.

[0080] Step S105: Determine a target interference parameter corresponding to the target operation type from the initial interference parameter according to the target operation type and the preset operation type.

[0081] Exemplarily, the target operation type is compared with the preset operation type, so as to obtain the target interference parameter corresponding to when the target operation type is the same as the preset operation type from the initial interference parameter.

[0082] Step S106: determining a data variation range corresponding to the target vehicle under the target sensor according to the target interference parameter.

[0083] Exemplarily, the expected sensor data of the target vehicle under the target sensor corresponding to the target operation type is obtained, and then the maximum interference parameter and the minimum interference parameter corresponding to the target operation type are obtained from the target interference parameter, so as to obtain the data volume corresponding to the target sensor data and the standard deviation corresponding to the target sensor data, and then the data volume is squared to obtain the square root value, and then the standard deviation is divided by the square root value to obtain the target ratio, and then the target ratio is multiplied by the minimum interference parameter to obtain the minimum value and the target ratio is multiplied by the maximum interference parameter to obtain the maximum value, so as to subtract the minimum value from the expected sensor data to obtain the lower limit value of the data variation range, and add the maximum value to the expected sensor data to obtain the upper limit value of the data variation range.

[0084] Step S107: performing abnormality identification on the target sensing data according to the data variation range to obtain target abnormality data corresponding to the target vehicle.

[0085] Exemplarily, it is determined whether the target sensor data is within the data variation range. When the target sensor data is not within the data variation range, the corresponding target sensor data is determined as target abnormal data corresponding to the target vehicle.

[0086] In some embodiments, the abnormality identification of the target sensor data according to the data variation range to obtain the target abnormality data corresponding to the target vehicle includes: determining the target data expectation corresponding to the target sensor, and performing difference calculation based on the target sensor data and the target data expectation to obtain the deviation value corresponding to the target sensor data; determining the degree of deviation corresponding to the target sensor data according to the deviation value and the data variation range; determining initial abnormal data from the target sensor data according to the deviation degree, and obtaining the remaining sensor data after removing the initial abnormal data from the target sensor data; performing distance calculation on the initial abnormal data to obtain the distance information corresponding to the initial abnormal data and performing local density calculation on the initial abnormal data to obtain the density information corresponding to the initial abnormal data; Performing data distribution analysis on the distance information to obtain corresponding first probability distribution information and performing data distribution analysis on the density information to obtain corresponding second probability distribution information; fusing the first probability distribution information and the second probability distribution information to determine the target abnormality probability corresponding to the initial abnormal data; determining first abnormal data from the initial abnormal data according to the target abnormality probability; classifying the remaining sensor data using a support vector machine to obtain first classified data and second classified data corresponding to the remaining sensor data; calculating a first quantity corresponding to the first classified data and a second quantity corresponding to the second classified data, and determining second abnormal data corresponding to the remaining sensor data according to the first quantity and the second quantity; and merging the first abnormal data and the second abnormal data to obtain the target abnormal data corresponding to the target vehicle.

[0087] Exemplarily, the target data expectation corresponding to the target vehicle under the target sensor is obtained based on historical data, and then the target sensor data and the target data expectation are differentially calculated to obtain the deviation value corresponding to the target sensor data, thereby calculating the difference between the upper limit value and the lower limit value in the data change range to obtain the change difference, and then the deviation value and the change difference are ratio-calculated to obtain the degree of deviation corresponding to the target sensor data.

[0088] Exemplarily, a preset degree is determined, and when the deviation degree is greater than a preset threshold, the target sensor data corresponding to the deviation degree is determined as initial abnormal data.

[0089] For example, after obtaining the initial outlier data, the initial outlier data is removed from the target sensor data, thereby determining the remaining data as the residual sensor data. Distance calculation is then performed on the initial outlier data to obtain distance information for each initial outlier data point. The distance information reflects the difference in distribution between the initial outlier data point and the normal data point. Local density calculation is performed on the initial outlier data point to obtain density information for each initial outlier data point. The density information reflects the concentration of the initial outlier data point within its neighborhood.

[0090] For example, a data distribution analysis is performed on the distance information to obtain first probability distribution information corresponding to the initial abnormal data. A data distribution analysis is performed on the density information to obtain second probability distribution information corresponding to the initial abnormal data. The first probability distribution information is combined with the second probability distribution information to determine a target abnormality probability for the initial abnormal data. Based on the target abnormality probability, the first abnormal data with a higher target abnormality probability is screened out.

[0091] Exemplarily, a support vector machine (SVM) is used to classify the remaining sensor data to obtain first classified data and second classified data. A first quantity corresponding to the first classified data and a second quantity corresponding to the second classified data are then calculated. Based on the first quantity and the second quantity, a determination is made as to whether the remaining sensor data contains second abnormal data. When the difference between the first quantity and the second quantity is less than or equal to a preset value, it indicates that the remaining sensor data does not contain second abnormal data. When the difference between the first quantity and the second quantity is greater than a preset value, a first distance is calculated between the first classified data and the first abnormal data, and a second distance is calculated between the second classified data and the second abnormal data. The classified data corresponding to the smallest of the first and second distances is then determined as the second abnormal data.

[0092] Exemplarily, the first abnormal data and the second abnormal data are merged to obtain target abnormal data corresponding to the target vehicle.

[0093] Step S108: Perform vehicle condition monitoring on the target vehicle according to the target abnormal data to obtain a vehicle monitoring result corresponding to the target vehicle.

[0094] For example, a machine learning model or a deep learning model is used to monitor the target abnormal data to obtain a target vehicle condition corresponding to the target vehicle, for example, determining whether the target vehicle condition is one of low risk, medium risk, and high risk. Based on the target vehicle condition, a corresponding text is generated to alert the target associated user corresponding to the target vehicle, and the generated text is determined as the vehicle monitoring result corresponding to the target vehicle.

[0095] For example, abnormal data from a target vehicle, including various sensor data, is collected and labeled to indicate its risk level (low, medium, or high). Labels can be manually annotated based on historical data or generated by an expert system. This allows the selection of appropriate machine learning or deep learning models, such as logistic regression, support vector machines, random forests, or neural networks. The model is then trained using the abnormal data and corresponding labels, and its hyperparameters are adjusted to optimize performance. The real-time target abnormal data is then fed into the trained model to output the target vehicle's status, such as low, medium, or high risk. Different text generation rules are then defined based on the target vehicle's status. For example, for low risk, the generated text would read "The vehicle is operating normally and does not require special attention." For medium risk, the generated text would read "Some vehicle operating issues exist; inspection is recommended as soon as possible." For high risk, the generated text would read "Serious vehicle operating issues exist; stop and inspect immediately." The generated text is then identified as the vehicle monitoring result for the target vehicle.

[0096] In some embodiments, the vehicle condition supervision of the target vehicle based on the target abnormal data to obtain a vehicle supervision result corresponding to the target vehicle includes: using the data analysis layer of the abnormality recognition model to perform abnormal characterization on the target abnormal data to obtain an initial characterization vector corresponding to the target sensor; using the data fusion layer of the abnormality recognition model to perform data fusion on the initial characterization vector using the target association relationship to obtain a target characterization vector corresponding to the target vehicle; using the data classification layer of the abnormality recognition model to perform condition classification on the target characterization vector to obtain a target classification result corresponding to the target vehicle; and determining the vehicle supervision result corresponding to the target vehicle based on the target classification result and the target operation type.

[0097] Exemplarily, the anomaly recognition model includes a data analysis layer, a data fusion layer, and a data classification layer. The data analysis layer of the anomaly recognition model is used to perform anomaly characterization on the target anomaly data to obtain an initial characterization vector corresponding to the target sensor. The target association relationship is then used to integrate the initial characterization vectors from different sensors based on the data fusion layer, taking into account the correlation between them to form a comprehensive target characterization vector. For example, the data fusion layer uses stacking, weighted averaging, attention mechanisms, etc. to capture the relationship and synergy between sensors.

[0098] Exemplarily, the data classification layer makes a classification decision based on the target representation vector, determines the condition category of the vehicle and obtains a target classification result, which is one of low risk, medium risk and high risk.

[0099] For example, different text generation rules are defined based on the target classification results, such as low risk, medium risk, and high risk. For example, if the target classification result is combined with the target operation type and the target operation type is acceleration, then when the target classification result is low risk, the text "The vehicle's acceleration is normal and does not require special attention" is generated. When the target classification result is medium risk, the text "There are some problems with the vehicle's acceleration. It is recommended to check as soon as possible" is generated. When the target classification result is high risk, the text "There are serious problems with the vehicle's acceleration. Stop and check immediately" is generated, thereby determining the text as the vehicle supervision result corresponding to the target vehicle.

[0100] For example, after generating a corresponding text based on the target classification result and the target operation type, the vehicle monitoring results can be sent to the target user associated with the target vehicle via email or text message to achieve real-time monitoring of the target vehicle. Alternatively, the vehicle monitoring results can be converted into corresponding voice and then broadcasted as a voice message to achieve real-time monitoring of the target vehicle.

[0101] See also Figure 2 , Figure 2The embodiment of the present application provides a vehicle condition monitoring device 200 based on a car, and the vehicle condition monitoring device 200 based on a car includes a data acquisition module 201, a data analysis module 202, a parameter processing module 203, a data classification module 204, a parameter determination module 205, a range determination module 206, an anomaly recognition module 207, and a result determination module 208, wherein the data acquisition module 201 is used to obtain the initial sensor data corresponding to the initial sensor of the initial vehicle under the preset operation type, and perform data change analysis on the initial sensor data to obtain the data change points corresponding to the initial sensor of the initial vehicle under the preset operation type; the data analysis module 202 is used to perform sensor association analysis based on the data change points to obtain the target association relationship between the initial sensors of the initial vehicle under the preset operation type; the parameter processing module 203 is used to determine the target association relationship between the initial sensors of the initial vehicle under the preset operation type based on the target association relationship and the initial sensor data. The initial interference parameters corresponding to the initial sensor are determined based on the initial sensor data; a data classification module 204 is used to obtain the target sensor data corresponding to the target vehicle under the target sensor, and classify the operation type according to the target sensor data to obtain the target operation type corresponding to the target sensor data; a parameter determination module 205 is used to determine the target interference parameters corresponding to the target operation type from the initial interference parameters according to the target operation type and the preset operation type; a range determination module 206 is used to determine the data change range corresponding to the target vehicle under the target sensor according to the target interference parameters; an anomaly identification module 207 is used to identify anomalies on the target sensor data according to the data change range, and obtain target anomaly data corresponding to the target vehicle; a result determination module 208 is used to perform vehicle condition supervision on the target vehicle according to the target anomaly data to obtain a vehicle supervision result corresponding to the target vehicle.

[0102] In some embodiments, the automobile-based vehicle condition monitoring apparatus 200 may be applied to a terminal device.

[0103] It should be noted that, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described automobile-based vehicle condition monitoring device 200 can refer to the corresponding process in the aforementioned automobile-based vehicle condition monitoring method embodiment, and will not be repeated here.

[0104] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0105] like Figure 3As shown, the terminal device 300 includes a processor 301 and a memory 302 , and the processor 301 and the memory 302 are connected via a bus 303 , such as an I 2 C (Inter-Integrated Circuit) bus.

[0106] Specifically, processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or any conventional processor.

[0107] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0108] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0109] The processor is configured to run a computer program stored in a memory, and implement any one of the vehicle condition supervision methods based on automobiles provided in the embodiments of the present invention when executing the computer program.

[0110] In one embodiment, the processor is configured to run a computer program stored in the memory, and implement the following steps when executing the computer program:

[0111] Obtaining initial sensor data corresponding to an initial sensor of an initial vehicle under a preset operation type, and performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type;

[0112] Performing sensor association analysis based on the data change points to obtain target association relationships between the initial sensors of the initial vehicle under the preset operation type;

[0113] Determining an initial interference parameter corresponding to the initial sensor according to the target association relationship and the initial sensing data;

[0114] Obtaining target sensor data corresponding to a target vehicle under a target sensor, and performing operation type classification according to the target sensor data to obtain a target operation type corresponding to the target sensor data;

[0115] Determining a target interference parameter corresponding to the target operation type from the initial interference parameter according to the target operation type and the preset operation type;

[0116] Determine the data variation range corresponding to the target vehicle under the target sensor according to the target interference parameter;

[0117] Performing abnormality identification on the target sensor data according to the data variation range to obtain target abnormality data corresponding to the target vehicle;

[0118] The vehicle condition of the target vehicle is monitored according to the target abnormal data to obtain a vehicle supervision result corresponding to the target vehicle.

[0119] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned automobile-based vehicle condition supervision method embodiment, and will not be repeated here.

[0120] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any vehicle-based vehicle condition supervision method provided in the description of the embodiment of the present invention.

[0121] The storage medium may be an internal storage unit of the terminal device described in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the terminal device.

[0122] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the methods, systems, and devices disclosed above may be implemented as software, firmware, hardware, or any combination thereof. In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division between physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0123] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0124] The serial numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A vehicle condition monitoring method based on a car, characterized in that: The method comprises: Obtaining initial sensor data corresponding to an initial sensor of an initial vehicle under a preset operation type, and performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type; Performing sensor association analysis based on the data change points to obtain target association relationships between the initial sensors of the initial vehicle under the preset operation type; Determining an initial interference parameter corresponding to the initial sensor according to the target association relationship and the initial sensing data; Obtaining target sensor data corresponding to a target vehicle under a target sensor, and performing operation type classification according to the target sensor data to obtain a target operation type corresponding to the target sensor data; Determining a target interference parameter corresponding to the target operation type from the initial interference parameter according to the target operation type and the preset operation type; Determine the data variation range corresponding to the target vehicle under the target sensor according to the target interference parameter; Performing abnormality identification on the target sensor data according to the data variation range to obtain target abnormality data corresponding to the target vehicle; Performing vehicle condition monitoring on the target vehicle according to the target abnormal data to obtain a vehicle monitoring result corresponding to the target vehicle; The step of performing abnormality identification on the target sensor data according to the data variation range to obtain target abnormality data corresponding to the target vehicle includes: Determining target data expectations corresponding to the target sensor, and performing a difference calculation based on the target sensor data and the target data expectations to obtain a deviation value corresponding to the target sensor data; Determining a degree of deviation corresponding to the target sensor data according to the deviation value and the data variation range; determining initial abnormal data from the target sensor data according to the degree of deviation, and obtaining remaining sensor data after removing the initial abnormal data from the target sensor data; Performing distance calculation on the initial abnormal data to obtain distance information corresponding to the initial abnormal data and performing local density calculation on the initial abnormal data to obtain density information corresponding to the initial abnormal data; Performing data distribution analysis on the distance information to obtain corresponding first probability distribution information and performing data distribution analysis on the density information to obtain corresponding second probability distribution information; Fusing the first probability distribution information and the second probability distribution information to determine a target abnormality probability corresponding to the initial abnormal data; determining first abnormal data from the initial abnormal data according to the target abnormal probability; Classifying the remaining sensor data using a support vector machine to obtain first classification data and second classification data corresponding to the remaining sensor data; Calculating a first quantity corresponding to the first classified data and a second quantity corresponding to the second classified data, and determining second abnormal data corresponding to the remaining sensor data based on the first quantity and the second quantity; The first abnormal data and the second abnormal data are combined to obtain the target abnormal data corresponding to the target vehicle.

2. The method according to claim 1, characterized in that The performing data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type includes: Determining an initial change point, and cutting the initial sensor data according to the initial change point to obtain first sensor data and second sensor data; Performing statistical data calculation on the first sensor data to obtain a corresponding first mean and a first standard deviation, and performing statistical data calculation on the second sensor data to obtain a corresponding second mean and a second standard deviation; determining a segmentation quality value corresponding to the initial change point according to the first mean, the first standard deviation, the second mean, and the second standard deviation; Obtaining a maximum value corresponding to the segmentation quality value and determining it as a target quality value, and determining a distribution probability of the target quality value corresponding to the segmentation quality value; determining a segmentation state corresponding to the target quality value according to the distribution probability and a preset value, and determining the data change point from the initial change point according to the segmentation state and the target quality value; The segmentation quality value is obtained according to the following formula: ； in, represents the segmentation quality value corresponding to the i-th initial change point, represents the first mean value corresponding to the first sensor data corresponding to the i-th initial change point, represents the second mean value corresponding to the second sensing data corresponding to the i-th initial change point; represents the first standard deviation corresponding to the first sensing data corresponding to the i-th initial change point; represents the second standard deviation corresponding to the second sensing data corresponding to the i-th initial change point; represents the number of data corresponding to the first sensing data corresponding to the i-th initial change point, represents the amount of data corresponding to the second sensing data corresponding to the i-th initial change point, Indicates taking the absolute value.

3. The method according to claim 1, characterized in that The initial sensors include at least a first sensor and a second sensor, the initial sensor data include at least first sensor data corresponding to the first sensor and second sensor data corresponding to the second sensor, the data change points include at least a first change point corresponding to the first sensor data and a second change point corresponding to the second sensor data, and performing sensor association analysis based on the data change points to obtain a target association relationship corresponding to the initial sensors of the initial vehicle under the preset operation type includes: Obtaining a first acquisition time corresponding to the first change point from the first sensor data and obtaining a second acquisition time corresponding to the second change point from the second sensor data; Obtaining a first time quantity corresponding to the first acquisition time and a second time quantity corresponding to the second acquisition time; determining an initial correlation matrix corresponding to the first sensor and the second sensor according to the first time quantity and the second time quantity; Adjusting the initial correlation matrix according to the first change point and the second change point to obtain a target correlation matrix corresponding to the first sensor and the second sensor of the initial vehicle under the preset operation type; A sensor association analysis is performed according to the target association matrix to obtain a target association relationship corresponding to the first sensor and the second sensor of the initial vehicle under the preset operation type.

4. The method according to claim 3, characterized in that The performing sensor association analysis according to the target association matrix to obtain a target association relationship between the first sensor and the second sensor of the initial vehicle under the preset operation type includes: Obtaining a target representation vector between the first sensor and the second sensor according to the target association matrix; Performing weighted processing on the target characterization vector by a modulation function to obtain corresponding initial characterization values ​​between the first sensor and the second sensor; Performing data adjustment on the initial characterization value by using an adaptive filter to obtain a target characterization value corresponding to the first sensor and the second sensor; The target association relationship corresponding to the first sensor and the second sensor is determined according to the target representation value.

5. The method according to claim 3, characterized in that The initial interference parameters include at least a first interference parameter corresponding to the first sensor and a second interference parameter corresponding to the second sensor. Determining the initial interference parameters corresponding to the initial sensors according to the target association relationship and the initial sensing data includes: determining, from the initial sensors according to the target association relationship, a first associated sensor corresponding to the first sensor and a second associated sensor corresponding to the second sensor; Obtaining third sensor data corresponding to the first associated sensor and fourth sensor data corresponding to the second associated sensor; Calculating a first data difference corresponding to data at adjacent moments in the first sensing data and calculating a second data difference corresponding to data at adjacent moments in the second sensing data; Calculating a third data difference value corresponding to adjacent time data in the third sensor data and calculating a fourth data difference value corresponding to adjacent time data in the fourth sensor data; Calculating a fifth data difference between the first sensor data and the third sensor data at the same time, and calculating a sixth data difference between the second sensor data and the fourth sensor data at the same time; Calculating a first sample mean and a first sample variance for the first data difference, and calculating a third sample mean and a third sample variance for the third data difference; Determine a first mean range corresponding to the first sensor data according to the first sample mean and the first sample variance in combination with a normal distribution; Determine a third mean range corresponding to the third sensor data according to the third sample mean and the third sample variance in combination with a normal distribution; calculating a fifth sample mean and a fifth sample variance for the fifth data difference, and determining a fifth mean range corresponding to the first sensing data and the first associated sensor based on the fifth sample mean and the fifth sample variance in combination with a normal distribution; Adjusting the first mean value range according to the third mean value range and the fifth mean value range to obtain the first interference parameter corresponding to the first sensor; Calculating a second sample mean and a second sample variance for the second data difference, and calculating a fourth sample mean and a fourth sample variance for the fourth data difference; Determine a second mean range corresponding to the second sensor data according to the second sample mean and the second sample variance in combination with a normal distribution; Determining a fourth mean range corresponding to the fourth sensor data according to the fourth sample mean and the fourth sample variance in combination with a normal distribution; calculating a sixth sample mean and a sixth sample variance for the sixth data difference, and determining a sixth mean range corresponding to the second sensor data and the second associated sensor based on the sixth sample mean and the sixth sample variance in combination with a normal distribution; The second mean value range is adjusted according to the fourth mean value range and the sixth mean value range to obtain the second interference parameter corresponding to the second sensor.

6. The method according to claim 1, characterized in that The performing vehicle condition monitoring on the target vehicle according to the target abnormal data to obtain a vehicle monitoring result corresponding to the target vehicle includes: Using the data analysis layer of the anomaly recognition model to perform anomaly characterization on the target anomaly data to obtain an initial characterization vector corresponding to the target sensor; Using the data fusion layer of the anomaly recognition model to perform data fusion on the initial representation vector using the target association relationship to obtain a target representation vector corresponding to the target vehicle; Using the data classification layer of the anomaly recognition model to perform condition classification on the target representation vector to obtain a target classification result corresponding to the target vehicle; The vehicle supervision result corresponding to the target vehicle is determined according to the target classification result and the target operation type.

7. A vehicle condition monitoring device based on a car, characterized in that: include: a data acquisition module, configured to obtain initial sensor data corresponding to an initial sensor of an initial vehicle under a preset operation type, and perform data change analysis on the initial sensor data to obtain data change points corresponding to the initial sensor of the initial vehicle under the preset operation type; A data analysis module, configured to perform sensor association analysis based on the data change points to obtain target association relationships corresponding to the initial sensors of the initial vehicle under the preset operation type; a parameter processing module, configured to determine an initial interference parameter corresponding to the initial sensor according to the target association relationship and the initial sensing data; A data classification module is used to obtain target sensing data corresponding to the target vehicle under the target sensor, and classify the operation type according to the target sensing data to obtain the target operation type corresponding to the target sensing data; a parameter determination module, configured to determine a target interference parameter corresponding to the target operation type from the initial interference parameter according to the target operation type and the preset operation type; A range determination module, configured to determine a data variation range corresponding to the target vehicle under the target sensor according to the target interference parameter; An anomaly identification module is used to identify anomalies of the target sensor data according to the data variation range and obtain target anomaly data corresponding to the target vehicle; wherein, the identifying anomalies of the target sensor data according to the data variation range and obtaining target anomaly data corresponding to the target vehicle includes: determining the target data expectation corresponding to the target sensor, and performing difference calculation based on the target sensor data and the target data expectation to obtain a deviation value corresponding to the target sensor data; determining the degree of deviation corresponding to the target sensor data according to the deviation value and the data variation range; determining initial anomaly data from the target sensor data according to the deviation degree, and obtaining remaining sensor data after removing the initial anomaly data from the target sensor data; performing distance calculation on the initial anomaly data to obtain distance information corresponding to the initial anomaly data and performing local detection on the initial anomaly data Density calculation is performed to obtain density information corresponding to the initial abnormal data; data distribution analysis is performed on the distance information to obtain corresponding first probability distribution information and data distribution analysis is performed on the density information to obtain corresponding second probability distribution information; the first probability distribution information and the second probability distribution information are combined to determine the target abnormal probability corresponding to the initial abnormal data; first abnormal data is determined from the initial abnormal data according to the target abnormal probability; the remaining sensor data is classified using a support vector machine to obtain first classified data and second classified data corresponding to the remaining sensor data; a first quantity corresponding to the first classified data and a second quantity corresponding to the second classified data are calculated, and second abnormal data corresponding to the remaining sensor data are determined according to the first quantity and the second quantity; the first abnormal data and the second abnormal data are combined to obtain the target abnormal data corresponding to the target vehicle; The result determination module is used to perform vehicle condition supervision on the target vehicle according to the target abnormal data to obtain a vehicle supervision result corresponding to the target vehicle.

8. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the automobile-based vehicle condition supervision method according to any one of claims 1 to 6 when executing the computer program.

9. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the automobile-based vehicle condition supervision method according to any one of claims 1 to 6.

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