Data anomaly analysis method and system for electronic equipment detection

Abstract

The invention relates to the technical field of data error detection, in particular to a data anomaly analysis method and system for electronic equipment detection. According to the method, error detection is carried out on data of the layer from the level of an equipment data sensing layer; and based on the first intelligent driving processing data and the first abnormal index, obtaining second intelligent driving processing data from the equipment data processing layer, so that on the basis of the first abnormal index, obtaining the second intelligent driving processing data with data relevance from the first intelligent driving processing data; on the basis of linkage of the first abnormal index, factors which can cause data errors in an equipment data processing layer can be acquired more accurately compared with direct acquisition of the second intelligent driving data; finally, a third abnormal index is obtained according to the first abnormal index and the second abnormal index, the influence of multi-factor data error detection in intelligent driving can be comprehensively considered, and therefore the data error detection capacity of intelligent driving is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data error detection, and in particular to a data anomaly analysis method and system for electronic device detection. Background Art

[0002] With the development of the times, the trend of intelligent driving is becoming stronger. At present, due to the safety requirements of intelligent driving and the huge amount of data processing, intelligent driving is extremely dependent on the accuracy of data. If there is an error in the data accuracy of intelligent driving, it may lead to serious consequences. At present, there are few methods for intelligent driving data error detection, and the data error detection ability of intelligent driving is poor. Summary of the invention

[0003] In order to solve the technical problem of poor data error detection capability of intelligent driving, the purpose of the present invention is to provide a data anomaly analysis method for electronic device detection. The technical solution adopted is as follows:

[0004] Acquire first intelligent driving processing data from a device data perception layer;

[0005] Acquire a first abnormality indicator according to the first intelligent driving processing data;

[0006] Based on the first intelligent driving processing data and the first abnormal indicator, obtaining second intelligent driving processing data from the device data processing layer;

[0007] Acquire a second abnormality indicator according to the second intelligent driving processing data;

[0008] Acquire a third abnormality indicator according to the first abnormality indicator and the second abnormality indicator;

[0009] The data abnormality result of the intelligent driving system is determined according to the third abnormality indicator.

[0010] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein the step of obtaining the first intelligent driving processing data from the device data perception layer comprises the following steps:

[0011] Determine the time node interval for single batch data collection;

[0012] In the time node interval, sensor data acquired by multiple sensors are collected based on time synchronization, wherein the data collection type and data collection quantity of the sensors in each time node interval are the same.

[0013] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein obtaining a first abnormality indicator according to the first intelligent driving processing data comprises the following steps:

[0014] Acquire a preset standard batch sensor data volume, wherein the duration of the time node interval of the standard batch sensor data volume is the same as the duration of the time node interval corresponding to the batch to be compared, wherein the standard batch sensor data volume and the sensor data collection quantity of the batch to be compared are the same, wherein the data collection type corresponding to the standard batch sensor data volume and the sensor data collection type of the batch to be compared are the same;

[0015] Acquire the data integrity corresponding to the comparison batch according to the ratio of the data collection quantity of the sensor data of the to-be-compared batch to the sensor data quantity of the standard batch;

[0016] Obtaining the degree of data deviation of the sensor data of the batch to be compared during the continuous collection process;

[0017] Obtaining the correlation of the degree of difference between the data corresponding to the different data collection types according to the degree of data deviation, and determining the data consistency according to the correlation of the degree of difference;

[0018] The first abnormality indicator is obtained according to the data integrity and the data consistency.

[0019] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein obtaining the data deviation degree of the sensor data of the batch to be compared during the continuous collection process comprises the following steps:

[0020] Generating a data deviation comparison combination according to the data collection types of the batch to be compared, wherein every two different data collection types correspond to one data deviation comparison combination;

[0021] Obtaining a difference set of the to-be-compared batch according to the data deviation comparison combination, wherein one of the difference sets corresponds to a monitoring angle of the vehicle;

[0022] The data deviation degree is determined according to the difference set.

[0023] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein obtaining the correlation of the degree of difference between the data corresponding to the different data collection types according to the degree of data deviation, and determining the data consistency according to the correlation of the degree of difference, comprises the following steps:

[0024] According to the data deviation degree and the monitoring angle of the vehicle, obtaining the correlation of the difference degree between the data corresponding to the data collection type;

[0025] The data consistency is determined based on the correlation of the degree of difference between the data corresponding to the data collection type and the number of the data deviation comparison combinations.

[0026] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein obtaining a second abnormality indicator according to the second intelligent driving processing data comprises the following steps:

[0027] Acquire a data delay anomaly index according to the second intelligent driving processing data;

[0028] Acquire a data transmission reliability index according to the second intelligent driving processing data;

[0029] The second abnormality indicator is obtained according to the data delay abnormality index and the data transmission reliability index.

[0030] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein obtaining a data delay abnormality index according to the second intelligent driving processing data comprises the following steps:

[0031] Acquire a data quality set from the second intelligent driving data, wherein the data quality set is determined and obtained by the first abnormality indicator;

[0032] Acquire a data processing time set from the second intelligent driving data, wherein the data processing time set is used to reflect a data transmission delay;

[0033] The data delay anomaly index is obtained according to the data quality set and the data processing time set.

[0034] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein obtaining a data transmission reliability index according to the second intelligent driving processing data comprises the following steps:

[0035] Acquire a vehicle body posture adjustment instruction and a vehicle body posture adjustment vector in the device data processing layer according to the second intelligent driving processing data;

[0036] Determining an executed vehicle body posture adjustment vector according to the vehicle body posture adjustment instruction and the vehicle body posture adjustment vector;

[0037] The data transmission reliability index is acquired according to the vehicle body posture adjustment instruction, the vehicle body posture adjustment vector and the executed vehicle body posture adjustment vector.

[0038] According to the above aspects and any possible implementation, an implementation is further provided;

[0039] After determining the data abnormality result of the intelligent driving system according to the third abnormality indicator, the method further includes the following steps:

[0040] generating vehicle behavior adjustment data according to the data abnormality result;

[0041] The vehicle behavior adjustment data is sent to the device data control layer to generate a vehicle behavior adjustment instruction according to the vehicle behavior adjustment data, and the vehicle behavior is controlled according to the vehicle behavior adjustment instruction.

[0042] In addition, in a data anomaly analysis system for electronic device detection according to the present invention, the system comprises:

[0043] A first data processing module, used to obtain first intelligent driving processing data from a device data perception layer;

[0044] A second data processing module, configured to obtain a first abnormality indicator according to the first intelligent driving processing data;

[0045] a third data processing module, configured to obtain second intelligent driving processing data from a device data processing layer based on the first intelligent driving processing data and the first abnormality indicator;

[0046] a fourth data processing module, configured to obtain a second abnormality indicator according to the second intelligent driving processing data;

[0047] a fifth data processing module, configured to obtain a third abnormality indicator according to the first abnormality indicator and the second abnormality indicator;

[0048] The sixth data processing module is used to determine the data abnormality result of the intelligent driving system according to the third abnormality indicator.

[0049] Furthermore, the first data processing module is further specifically configured to:

[0050] Determine the time node interval for single batch data collection;

[0051] In the time node interval, sensor data acquired by multiple sensors are collected based on time synchronization, wherein the data collection type and data collection quantity of the sensors in each time node interval are the same.

[0052] Furthermore, the second data processing module is further specifically configured to:

[0053] Acquire a preset standard batch sensor data volume, wherein the duration of the time node interval of the standard batch sensor data volume is the same as the duration of the time node interval corresponding to the batch to be compared, wherein the standard batch sensor data volume and the sensor data collection quantity of the batch to be compared are the same, wherein the data collection type corresponding to the standard batch sensor data volume and the sensor data collection type of the batch to be compared are the same;

[0054] Acquire the data integrity corresponding to the comparison batch according to the ratio of the data collection quantity of the sensor data of the to-be-compared batch to the sensor data quantity of the standard batch;

[0055] Obtaining the degree of data deviation of the sensor data of the batch to be compared during the continuous collection process;

[0056] Obtaining the correlation of the degree of difference between the data corresponding to the different data collection types according to the degree of data deviation, and determining the data consistency according to the correlation of the degree of difference;

[0057] The first abnormality indicator is obtained according to the data integrity and the data consistency.

[0058] Furthermore, the second data processing module is further specifically configured to:

[0059] Generating a data deviation comparison combination according to the data collection types of the batch to be compared, wherein every two different data collection types correspond to one data deviation comparison combination;

[0060] Obtaining a difference set of the to-be-compared batch according to the data deviation comparison combination, wherein one of the difference sets corresponds to a monitoring angle of the vehicle;

[0061] The data deviation degree is determined according to the difference set.

[0062] Furthermore, the second data processing module is further specifically configured to:

[0063] According to the data deviation degree and the monitoring angle of the vehicle, obtaining the correlation of the difference degree between the data corresponding to the data collection type;

[0064] The data consistency is determined based on the correlation of the degree of difference between the data corresponding to the data collection type and the number of the data deviation comparison combinations.

[0065] Furthermore, the fourth data processing module is further specifically configured to:

[0066] Acquire a data delay anomaly index according to the second intelligent driving processing data;

[0067] Acquire a data transmission reliability index according to the second intelligent driving processing data;

[0068] The second abnormality indicator is obtained according to the data delay abnormality index and the data transmission reliability index.

[0069] Furthermore, the fourth data processing module is further specifically configured to:

[0070] Acquire a data quality set from the second intelligent driving data, wherein the data quality set is determined and obtained by the first abnormality indicator;

[0071] Acquire a data processing time set from the second intelligent driving data, wherein the data processing time set is used to reflect a data transmission delay;

[0072] The data delay anomaly index is obtained according to the data quality set and the data processing time set.

[0073] Furthermore, the fourth data processing module is further specifically configured to:

[0074] Acquire a vehicle body posture adjustment instruction and a vehicle body posture adjustment vector in the device data processing layer according to the second intelligent driving processing data;

[0075] Determining an executed vehicle body posture adjustment vector according to the vehicle body posture adjustment instruction and the vehicle body posture adjustment vector;

[0076] The data transmission reliability index is acquired according to the vehicle body posture adjustment instruction, the vehicle body posture adjustment vector and the executed vehicle body posture adjustment vector.

[0077] Furthermore, the fourth data processing module is further specifically configured to:

[0078] generating vehicle behavior adjustment data according to the data abnormality result;

[0079] The vehicle behavior adjustment data is sent to the device data control layer to generate a vehicle behavior adjustment instruction according to the vehicle behavior adjustment data, and the vehicle behavior is controlled according to the vehicle behavior adjustment instruction.

[0080] The present invention has the following beneficial effects: the present invention first obtains the first intelligent driving processing data from the device data perception layer, so as to perform error detection on the data of the layer from the level of the device data perception layer; then, the first abnormal indicator is obtained according to the first intelligent driving processing data, so as to obtain the first factor that may cause data error at the perception layer level; then, based on the first intelligent driving processing data and the first abnormal indicator, the second intelligent driving processing data is obtained from the device data processing layer, and on the basis of the first abnormal indicator, the second intelligent driving processing data with data correlation may be obtained from the first intelligent driving processing data; then, the second abnormal indicator is obtained according to the second intelligent driving processing data, and on the basis of the linkage with the first abnormal indicator, the factor that may cause data error in the device data processing layer may be obtained more accurately than directly obtaining the second intelligent driving data; finally, the third abnormal indicator is obtained according to the first abnormal indicator and the second abnormal indicator, and the data abnormal result of the intelligent driving system is determined according to the third abnormal indicator, so as to comprehensively consider the data error detection influence of multiple factors in intelligent driving, thereby significantly improving the data error detection capability of intelligent driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0082] Figure 1 A flowchart of a data anomaly analysis method for electronic device detection provided by an embodiment of the present invention;

[0083] Figure 2 A flowchart of step S10 of a data anomaly analysis method for electronic device detection provided by one embodiment of the present invention;

[0084] Figure 3 A flowchart of step S20 of a data anomaly analysis method for electronic device detection provided by one embodiment of the present invention;

[0085] Figure 4 A flowchart of step S20 of another method for analyzing data anomalies for electronic device detection provided by one embodiment of the present invention;

[0086] Figure 5 A flowchart of step S20 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention;

[0087] Figure 6A flowchart of step S40 of a data anomaly analysis method for electronic device detection provided by one embodiment of the present invention;

[0088] Figure 7 A flowchart of step S40 of another data anomaly analysis method for electronic device detection provided by one embodiment of the present invention;

[0089] Figure 8 A flowchart of step S40 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention;

[0090] Fig. 9 A flowchart of step S40 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention;

[0091] Fig.10 A principle block diagram of a data anomaly analysis system for electronic device detection provided by one embodiment of the present invention;

[0092] Fig.11 A schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the data anomaly analysis method and system for electronic device detection proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0094] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0095] The following is a detailed description of a data anomaly analysis method and system for electronic device detection provided by the present invention in conjunction with the accompanying drawings.

[0096] See also Figure 1 , which shows a flow chart of a data anomaly analysis method for electronic device detection provided by an embodiment of the present invention, which specifically includes the following steps:

[0097] S10: Obtain first intelligent driving processing data from a device data perception layer.

[0098] Among them, currently intelligent driving can be divided into a device data perception layer, a device data processing layer and a device data control layer. In the embodiment of the present invention, the first intelligent driving processing data can be obtained from various types of sensors in the device data perception layer to perform error detection on the data under the device data perception layer.

[0099] S20: Obtain a first abnormality indicator according to the first intelligent driving processing data.

[0100] In one embodiment, the first abnormality indicator corresponds to the first intelligent driving processing data, and the first abnormality indicator reflects the abnormality of the internal data of the sensor in the device data perception layer.

[0101] S30: Based on the first intelligent driving processing data and the first abnormal indicator, obtain second intelligent driving processing data from the device data processing layer.

[0102] In one embodiment, the second intelligent driving processing data is obtained in the device data layer, and it should be noted that the second intelligent driving processing data needs to be obtained based on the first abnormal indicator, that is, the second intelligent driving processing data and the first intelligent driving processing data are actually linked. The data quality of the first intelligent driving processing data will affect the structure of the second intelligent driving processing data. The advantage of such a linkage design is that the data error problems occurring in the device data perception layer can be correctly transmitted and fed back to the device data control layer, thereby correctly combining the data problems at the two levels to obtain more accurate data detection results.

[0103] S40: Obtain a second abnormality indicator according to the second intelligent driving processing data.

[0104] In one embodiment, the second abnormality indicator corresponds to the second dynamic driving processing data, and the second abnormality indicator reflects the data delay and data processing abnormality existing in the device data processing layer under the premise of data error in the device data perception layer.

[0105] S50: Obtain a third abnormality indicator according to the first abnormality indicator and the second abnormality indicator.

[0106] In one embodiment, by combining the data anomalies existing in the first abnormality index and the second abnormality index, an overall abnormality index reflecting the intelligent driving system can be obtained, that is, a third abnormality index.

[0107] S60: Determine the data abnormality result of the intelligent driving system according to the third abnormality indicator.

[0108] It can be understood that for the intelligent driving system, the monitoring data of various environmental detection equipment, such as laser radar, camera, etc., are integrated and processed to a certain extent in the embodiments of the present invention. These are the most important data for intelligent driving. In this regard, the external environmental data collected in real time by the intelligent driving system can be obtained. In actual use, the relevant data inside the sensor will analyze and process the external monitoring data. The relevant data such as the current, voltage, temperature, vibration, etc. inside the sensor are also important indicators for evaluating the abnormality of the equipment, and these data also need to be further collected and analyzed.

[0109] In the field of automobile manufacturing, the device data perception layer is responsible for collecting external environmental data in real time through various sensors installed on the car. The stability and continuity of data collection have a crucial impact on the accuracy and efficiency of the subsequent planning and control process. The device data processing layer is responsible for real-time analysis, judgment and planning evaluation of the external environmental data collected by the device data perception layer. Its final planning results will be directly transmitted to the device data control layer to achieve precise control of the vehicle's driving trajectory. The coherence and response speed of the overall data flow have a significant impact on the actual driving performance of the car. Therefore, in order to ensure the stable operation of the intelligent driving system, it is necessary to conduct a comprehensive and in-depth analysis of the vehicle monitoring result data flow to evaluate the possible data anomalies in the intelligent driving system and take corresponding measures to adjust it accordingly.

[0110] During the testing phase of the intelligent driving system, each data layer will generate a large amount of data. In order to facilitate subsequent in-depth analysis, the first task is to preprocess the data. For the data of the device data perception layer, a time synchronization strategy can be adopted to ensure the consistency of the data of each sensor, followed by data cleaning, noise and redundancy removal, and fusion of multi-sensor data to obtain more comprehensive and accurate information. After receiving the data collected by the device data perception layer, the device data processing layer can perform obstacle prediction and trajectory smoothing through the corresponding data processing model, and then format the decision information to improve planning efficiency and decision quality. In the device data control layer, data preprocessing focuses on the precise analysis of execution commands, the optimal setting of control parameters, and the filtering of feedback information to ensure the accurate execution of control instructions and the efficient and stable response of the system. From the above, we can see that the specific division of labor of the intelligent driving system at different levels is different, and the amount of data involved is huge. How to ensure the accuracy of data is crucial for the intelligent driving system.

[0111] Understandably, when evaluating data anomalies in a car's intelligent driving system, the first task is to conduct a comprehensive and rigorous review of the various data collected by the device data perception layer. During normal operation of the intelligent driving system, its built-in sensors will continuously and finely monitor and identify the surrounding environment. However, once the sensor encounters a failure or deviation in the recognition process, the collected data may deviate from the normal range, thereby misleading the subsequent device data processing layer and the device data control layer, leading to erroneous decision instructions. Similarly, the data under the device data processing layer also needs to be comprehensively and rigorously reviewed, and the occurrence of data errors may also result in erroneous decision instructions.

[0112] In steps S10-S60, firstly, the first intelligent driving processing data is obtained from the device data perception layer, so as to perform error detection on the data of the layer from the level of the device data perception layer; then, the first abnormal indicator is obtained according to the first intelligent driving processing data, so as to obtain the first factor that may cause data error at the perception layer level; then, based on the first intelligent driving processing data and the first abnormal indicator, the second intelligent driving processing data is obtained from the device data processing layer, and on the basis of the first abnormal indicator, the second intelligent driving processing data with data correlation may be obtained from the first intelligent driving processing data; then, the second abnormal indicator is obtained according to the second intelligent driving processing data, and on the basis of the linkage with the first abnormal indicator, the factor that may cause data error in the device data processing layer may be obtained more accurately than directly obtaining the second intelligent driving data; finally, the third abnormal indicator is obtained according to the first abnormal indicator and the second abnormal indicator, and the data abnormal result of the intelligent driving system is determined according to the third abnormal indicator, so as to comprehensively consider the data error detection influence of multiple factors in intelligent driving, thereby significantly improving the data error detection capability of intelligent driving.

[0113] In the embodiment of the present invention, in order to accurately evaluate the degree of data anomaly in the device data perception layer, two key indicators need to be examined: one is the integrity level of the collected data, which is directly related to whether the data fully and completely reflects the environmental conditions; the other is the frequency of high-difference data during the collection process, which can effectively reveal the degree of abnormal data fluctuations. By comprehensively using these two evaluation indicators, the abnormal conditions of data collection in the device data perception layer can be judged more scientifically and systematically, providing solid data support for the stable operation of the intelligent driving system.

[0114] Figure 2 This is a flowchart of step S10 of a method for analyzing data anomalies for electronic device detection provided by one embodiment of the present invention.

[0115] Specifically, in step S10, i.e., the step of acquiring the first intelligent driving processing data from the device data perception layer, the following steps are specifically included:

[0116] S11: Determine the time node interval for collecting single batch data.

[0117] S12: In a time node interval, sensor data acquired by a plurality of sensors are collected based on time synchronization, wherein the type and quantity of data collected by the sensors in each time node interval are the same.

[0118] The device data perception layer collects monitoring data from multiple types of sensors. In the preprocessing stage, the monitoring data of each type of sensor can be synchronized in time, and the type and quantity of relevant sensor data at each time node should be consistent. Specifically, the data collected within a preset time period can be collected as a single batch.

[0119] Figure 3 This is a flowchart of step S20 of a method for analyzing data anomalies for electronic device detection provided by one embodiment of the present invention.

[0120] Further, in step S20, i.e., the step of obtaining the first abnormality indicator according to the first intelligent driving processing data, the following steps are specifically included:

[0121] S211: Obtain a preset standard batch sensor data volume, wherein the duration of the time node interval of the standard batch sensor data volume is the same as the duration of the time node interval corresponding to the batch to be compared, wherein the standard batch sensor data volume and the data collection quantity of the sensors of the batch to be compared are the same, wherein the data collection type corresponding to the standard batch sensor data volume and the data collection type of the sensors of the batch to be compared are the same.

[0122] S212: Obtaining data integrity corresponding to the comparison batch according to the ratio of the data collection quantity of the sensor data of the to-be-compared batch to the sensor data quantity of the standard batch.

[0123] S213: Obtain the degree of data deviation of the sensor data of the batch to be compared during the continuous collection process.

[0124] S214 obtains the correlation of the degree of difference between data corresponding to different data collection types according to the degree of data deviation, and determines the data consistency according to the correlation of the degree of difference;

[0125] S215: Obtain a first abnormality indicator according to data integrity and data consistency.

[0126] Figure 4 This is a flowchart of step S20 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention.

[0127] Further, in step S20, i.e., the step of obtaining the first abnormality indicator according to the first intelligent driving processing data, the following steps are specifically included:

[0128] S221: Generate a data deviation comparison combination according to the data collection types of the batch to be compared, wherein every two different data collection types correspond to a data deviation comparison combination.

[0129] S222: Obtain a difference set of the batch to be compared according to the data deviation comparison combination, wherein a difference set corresponds to a monitoring angle of the vehicle.

[0130] S223: Determine the degree of data deviation according to the difference set.

[0131] Figure 5 This is a flowchart of step S20 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention.

[0132] Further, in step S20, i.e., the step of obtaining the first abnormality indicator according to the first intelligent driving processing data, the following steps are specifically included:

[0133] S231: Obtaining correlation of difference between data corresponding to data collection types according to the degree of data deviation and the monitoring angle of the vehicle.

[0134] S232: Determine data consistency based on the correlation of the degree of difference between the data corresponding to the data collection type and the number of data deviation comparison combinations.

[0135] Specifically, each step of the above data anomaly analysis for the device data perception layer can be understood in conjunction with the following description.

[0136] In one embodiment, the data collected every second can be regarded as a collection batch, and the ratio of the amount of collected data in the current batch to be compared to the amount of collected data in the standard batch is used as the integrity of the current collection batch data, which is recorded as OT. i .

[0137] Understandably, from the perspective of data integrity, the same batch of data has been thoroughly analyzed. However, in the actual data collection process, if a sensor is subject to strong external interference or fails, although the data integrity of its batch is maintained, there will be significant differences between the data collected, which will have a certain impact on the subsequent analysis and identification work. Therefore, it is necessary to carry out consistency assessment work for the data collected in the same area of ​​the device data perception layer.

[0138] For the consistency evaluation of the same batch of data, we focus on multiple types of sensors in the same collection area for in-depth analysis. When the external monitoring environment changes, all types of sensors will respond accordingly. In view of the differences in data collected by different sensors, it is not feasible to directly compare different collection results to evaluate consistency. To this end, in an embodiment of the present invention, the consistency of the data is indirectly judged by analyzing the degree of deviation of the current batch of data to be compared during the continuous collection process, and evaluating the correlation between the degree of difference between the various types of data in the batch. Specifically, when the degree of correlation between the data is high, it means that the consistency of the data collected in the same area is relatively ideal. It can be specifically expressed by the following formula:

[0139]

[0140] In the above formula, i represents the batch of collected data, and n represents the monitoring angle of the current vehicle. i,n Indicates the data consistency of the nth monitoring level of the current i-th batch of data. MS n Indicates the number of sensor types for the current nth monitoring angle. n ,2) represents the number of pairwise combinations of the nth monitoring angle. 1,n A batch of difference sets representing the monitoring results of the first sensor in the pth combination of the nth monitoring angle. 2,n A batch difference set represents the monitoring results of the second sensor in the pth combination of the nth monitoring angle. Perform batch analysis on the data of any monitoring angle, obtain the difference of the continuous collection content of a single sensor in the batch, and form a difference set. Cov(p 1,n ,p 2,n ) represents the covariance of its two difference sets. Indicates p 1,n The standard deviation of the set of differences, Indicates p 2,n The standard deviation of the set of differences.

[0141] Further, Indicates the correlation degree of the difference set of two sensor data at any current angle. When the external environment changes, any sensor at the same angle will receive external signal data, and the difference between the continuously collected contents is similar. For this, the data collected by a single sensor is used to construct a difference set. By evaluating the correlation between the difference sets within a single batch according to the sensors, the larger the result, the higher the consistency of the current single angle sensor. For this, multiple sensors at an angle within any batch are matched and analyzed in pairs to obtain the consistency degree of their data.

[0142] UJ iIndicates the consistency of the current i-th batch of monitoring data. MO represents the number of angles monitored by the current sensor. The consistency of all monitoring angles in a batch is averaged to obtain the consistency of the entire batch of data.

[0143] It can be understood that by analyzing a single batch of data at the device data perception layer, the integrity and consistency of the corresponding batch of data are obtained. When the integrity of a single batch of data is high, the corresponding consistency is also relatively good, and the quality of the data in this batch is considered to be relatively high. Afterwards, in the evaluation of the overall data collected by the device data perception layer, the overall quality and stability of all batches of data in the data collection stage can be analyzed and obtained.

[0144] It can be understood that when the overall data quality is high during the collection process, the abnormal index of the device data perception layer data is relatively low. When the data quality difference within consecutive batches during the collection process is greater, it indicates that the abnormal index of the device data perception layer data at this time is higher. According to the above description, the overall data abnormality index of the device data perception layer is obtained:

[0145]

[0146] In the above formula, FJ i is the batch data quality set, which represents the quality of the i-th batch of data (specifically, it can be expressed as a percentage coefficient). i Indicates the consistency of the current i-th batch of monitoring data. i Indicates the integrity of the current i-th batch of monitoring data. norm indicates normalization. When the integrity of a batch of monitoring data is higher and the consistency of the data is higher, the quality of the current batch of data is considered to be higher.

[0147] GJ represents the abnormal index of the device data perception layer data. μ FJ represents the quality mean of the monitoring data at the device data perception layer, that is, FJ i The average value of the sum from the ith to the nth can be obtained. M represents the number of batches of monitoring data at the device data perception layer. ΔFJ i Indicates the difference between the data quality of the i-th batch and the previous batch. For example, when i=3, ΔFJ 3 Indicates the data quality of batch 3 compared to ΔFJ 2 (i.e. the previous data quality). It means that the quality differences of all batches of monitoring data are averaged. It can be understood that the larger the result is, the greater the difference between the continuous data obtained by the current device data perception layer, and the abnormal index of the data is relatively increased. The final ratio with the data quality mean can obtain the difference degree of the device data perception layer data.

[0148] Figure 6 This is a flowchart of step S40 of a method for analyzing data anomalies for electronic device detection provided by one embodiment of the present invention.

[0149] Further, in step S40, i.e., the step of obtaining the second abnormality indicator according to the second intelligent driving processing data, the following steps are specifically included:

[0150] S411: Obtain a data delay anomaly index according to the second intelligent driving processing data.

[0151] S412: Obtain a data transmission reliability index according to the second intelligent driving processing data.

[0152] S413: Obtain a second abnormality index according to the data delay abnormality index and the data transmission reliability index.

[0153] Figure 7 This is a flowchart of step S40 of another data anomaly analysis method for electronic device detection provided by an embodiment of the present invention.

[0154] Further, in step S40, i.e., the step of obtaining the second abnormality indicator according to the second intelligent driving processing data, the following steps are specifically included:

[0155] S421: Obtain a data quality set from the second intelligent driving data, wherein the data quality set is determined and obtained by the first abnormality indicator.

[0156] S422: Obtain a data processing time set from the second intelligent driving data, wherein the data processing time set is used to reflect a data transmission delay.

[0157] S423: Obtain a data delay anomaly index according to the data quality set and the data processing time set.

[0158] Figure 8 This is a flowchart of step S40 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention.

[0159] Further, in step S40, i.e., the step of obtaining the second abnormality indicator according to the second intelligent driving processing data, the following steps are specifically included:

[0160] S431: Acquire the vehicle body posture adjustment instruction and vehicle body posture adjustment vector in the data processing layer of the device according to the second intelligent driving processing data.

[0161] S432: Determine the executed vehicle body posture adjustment vector according to the vehicle body posture adjustment instruction and the vehicle body posture adjustment vector.

[0162] S433: Acquire a data transmission reliability index according to the vehicle body posture adjustment instruction, the vehicle body posture adjustment vector, and the executed vehicle body posture adjustment vector.

[0163] Fig. 9 This is a flowchart of step S40 of another method for analyzing data anomalies for electronic device detection provided by an embodiment of the present invention.

[0164] Further, in step S40, i.e., the step of obtaining the second abnormality indicator according to the second intelligent driving processing data, the following steps are specifically included:

[0165] S441: Generate vehicle behavior adjustment data according to the data abnormality result.

[0166] S442: Send the vehicle behavior adjustment data to the device data control layer to generate a vehicle behavior adjustment instruction according to the vehicle behavior adjustment data, and control the vehicle behavior according to the vehicle behavior adjustment instruction.

[0167] Specifically, each step of the above data anomaly analysis for the device data processing layer can be understood in conjunction with the following description.

[0168] In the previous analysis of the first abnormal indicator, the data captured by each acquisition device was deeply discussed, and on this basis, the first abnormal indicator displayed by the data of the device data perception layer was evaluated. It can be understood that the core function of the device data perception layer is to accept diversified data transmitted from the outside world. Once the data is successfully received, it lays a solid foundation for the subsequent in-depth analysis and instruction issuance of the device data processing layer and the device data control layer. Among them, it should be noted that, unlike the device data perception layer, the abnormal conditions of the data at the device data processing layer are often manifested in the delay phenomenon of the data processing and conversion process, the differences in the data synchronization process, and the irregular changes in the path device data processing layer.

[0169] In one embodiment, the present invention will deeply analyze the time deviation of the current data delay phenomenon in order to accurately judge the data abnormality at the data processing level of the data device. It can be understood that the significant data delay phenomenon actually deeply reveals the potential abnormality at the data processing level of the data device. In addition, the consistency of data during the transmission process between systems at all levels also has an impact on the actual operation effect of the intelligent driving control system that cannot be underestimated.

[0170] In the intelligent driving system, the device data perception layer collects data about the external environment in real time and transmits the collected data to the device data processing layer for analysis and judgment of vehicle behavior. When the quality of the collected data is high, the overall difference of each type of monitoring data changes little in a continuous time, so the data delay is relatively stable when analyzing and planning vehicle trajectories. However, when the data quality of the data transmitted by the device data perception layer is low in a continuous time, the device data processing layer needs to spend more time analyzing when planning and analyzing vehicle trajectories, resulting in a relatively large delay.

[0171] In the process of evaluating data delay differences, the key factor of data transmission quality at the device data perception layer needs to be comprehensively considered. According to research and analysis, there is a clear correlation between data quality and delay: when data quality reaches a high level, the delay time is usually shorter; conversely, when data quality decreases, the delay time increases accordingly. However, it is worth noting that if a large delay is observed under high data quality conditions, this often indicates that there is some abnormality in the data. In view of this, a systematic analysis is conducted using the dual indicators of data delay and data quality to accurately calculate the delay anomaly index of the data. This can be specifically reflected in the following formula.

[0172]

[0173] In the above formula, CU represents the delay anomaly index of the data processing layer of the current data device. i Represents the batch data quality set, YC i Represents the processing time of batch data in the device data processing layer. Cov(FJ i ,YC i ) represents the covariance of its data quality set and the corresponding data processing time set. The standard deviation that indicates the quality of the batch data. Indicates the standard deviation of the processing time of the data processing layer of the batch data device.

[0174] in, Indicates the degree of correlation between the current data quality and data processing time. When the data quality is higher, it is considered that its processing time is relatively low. When the correlation result of the two sets of data is smaller, it means that the delay anomaly of the data processing layer of the data device is smaller. When the correlation result of the two sets of data is closer to 0, it means that the current correlation degree of the two sets of data is lower, that is, the delay anomaly index of the data processing layer of the data device is higher.

[0175] In the data processing flow, the device data processing layer first processes the data and outputs specific instructions for adjusting the body posture based on the analysis and calculation results. These instructions are then passed to the device data control layer, which further converts them into directly executable vehicle control commands. The vehicle then makes corresponding posture adjustments based on these commands. In this process, the device data control layer plays a vital role, as it needs to accurately guide the adjustment of the body based on the instructions of the device data processing layer.

[0176] However, it is worth noting that potential faults in the data transmission link or incomplete feedback signals from the actuators may cause deviations or interruptions in the execution of instructions, thus causing abnormalities at the data level. In order to effectively address this problem, the present invention comprehensively evaluates the consistency and accuracy of data transmission by analyzing the degree of deviation between the actual posture change of the vehicle body and the expected posture change of the equipment data processing layer.

[0177]

[0178] In the above formula, PL represents the consistency of the current data transmission. MZ represents the number of body posture adjustment instructions issued by the device data processing layer. KZ m Represents the vehicle body posture adjustment vector under the device data processing layer, wherein the vehicle body posture adjustment vector refers to the vector that the vehicle body posture needs to be adjusted compared with the previous moment. It can be understood that the movement angle of the vehicle body posture adjustment can be specifically represented by a vector. GH m Indicates the vehicle body posture adjustment vector actually completed after the mth instruction is executed.

[0179] in, The cosine similarity evaluation is performed on the expected adjustment results of the body posture issued by the data processing layer of the device and the actual body posture change results. When the result is closer to 1, it means that the current completion degree is higher, that is, the consistency of data transmission is higher. Therefore, the completion status of all body posture adjustment instructions is evaluated, and the results are averaged to obtain the transmission consistency of the data processing layer of the data device.

[0180] In the above process, the data equipment data processing level was deeply analyzed, and two key indicators, the data abnormality delay coefficient and the consistency of data transmission, were successfully obtained. When a high data delay abnormality indicator appears in the intelligent driving control system, the system will face the risk of not being able to respond quickly to external changes. At the same time, if the degree of consistency in the data transmission process is low, this will lead to an increase in the deviation of the vehicle body posture adjustment, which may cause a series of problems. Based on the above detailed analysis results, we further focus on the data equipment data processing level to obtain the current abnormality of the data equipment data processing level. It is specifically reflected in the following formula:

[0181]

[0182] In the above formula, CJ represents the data anomaly index of the data processing layer of the data device. PL represents the consistency of the current data transmission. CU represents the delay anomaly index of the data processing layer of the current data device. norm is normalized. When the delay anomaly index of the data processing layer of the data device is higher and the consistency of the data transmission is lower, it is considered that the data anomaly index of the current data processing layer of the data device is higher.

[0183] In the above process, a detailed analysis of data anomaly indicators was carried out for the two core links of the intelligent driving control system, data collection and data processing. Specifically, the focus was on the abnormality of the monitoring data related to electronic equipment. The abnormal conditions of these data have a significant impact and potential interference on the safety of vehicle intelligent driving. To this end, the overall data anomaly indicators of the current vehicle intelligent driving system were comprehensively evaluated and obtained through the two dimensions of data collection and data processing.

[0184] PJ=norm(CJ+GJ)

[0185] In the above formula, PJ represents the data anomaly index of the current intelligent driving control system. CJ represents the data anomaly index of the data processing layer of the data device. GJ represents the data anomaly index of the device data perception layer. Norm represents normalization processing. Specifically, the anomalies of the data acquisition layer and the data device data processing layer are evaluated respectively, and the corresponding anomaly indexes are obtained respectively.

[0186] In the present invention, in the process of rigorously constructing the test system of the intelligent driving system, its complex data processing flow is scientifically divided into two modules: the device data perception layer and the device data processing layer. For these two independent data levels, a precise data flow acquisition strategy is implemented to ensure that all information flows in a single data layer can be accurately captured and stored in a specially designed database. Subsequently, by deeply analyzing the key data stored in the database, the abnormal indicators of the intelligent driving system are comprehensively evaluated, which significantly improves the data error detection capability of intelligent driving.

[0187] The present invention provides a data anomaly analysis system for electronic device detection, such as Fig.10 As shown, it includes the following modules:

[0188] A first data processing module 10, configured to obtain first intelligent driving processing data from a device data perception layer;

[0189] A second data processing module 20, configured to obtain a first abnormality indicator according to the first intelligent driving processing data;

[0190] A third data processing module 30, configured to obtain second intelligent driving processing data from the device data processing layer based on the first intelligent driving processing data and the first abnormality indicator;

[0191] A fourth data processing module 40, configured to obtain a second abnormality indicator according to the second intelligent driving processing data;

[0192] A fifth data processing module 50, configured to obtain a third abnormality indicator according to the first abnormality indicator and the second abnormality indicator;

[0193] The sixth data processing module 60 is used to determine the data abnormality result of the intelligent driving system according to the third abnormality indicator.

[0194] Furthermore, the first data processing module 10 is further specifically configured to:

[0195] Determine the time node interval for single batch data collection;

[0196] In a time node interval, sensor data acquired by a plurality of sensors are collected based on time synchronization, wherein the data collection type and data collection quantity of the sensors in each time node interval are the same.

[0197] Furthermore, the second data processing module 20 is further specifically configured to:

[0198] Acquire a preset standard batch sensor data volume, wherein the duration of the time node interval of the standard batch sensor data volume is the same as the duration of the time node interval corresponding to the batch to be compared, wherein the standard batch sensor data volume and the sensor data collection quantity of the batch to be compared are the same, wherein the data collection type corresponding to the standard batch sensor data volume and the sensor data collection type of the batch to be compared are the same;

[0199] The data integrity corresponding to the comparison batch is obtained according to the ratio of the data collection quantity of the sensor data of the to-be-compared batch to the sensor data quantity of the standard batch;

[0200] Obtain the data deviation degree of the sensor data of the batch to be compared during the continuous collection process;

[0201] According to the degree of data deviation, the correlation between the degree of difference between the data corresponding to different data collection types is obtained, and the data consistency is determined according to the correlation between the degree of difference;

[0202] A first abnormality indicator is obtained based on data integrity and data consistency.

[0203] Furthermore, the second data processing module 20 is further specifically configured to:

[0204] Generate a data deviation comparison combination according to the data collection type of the batch to be compared, wherein every two different data collection types correspond to one data deviation comparison combination;

[0205] Obtaining a difference set of the batch to be compared according to the data deviation comparison combination, wherein a difference set corresponds to a monitoring angle of the vehicle;

[0206] Determine the degree of data deviation based on the difference set.

[0207] Furthermore, the second data processing module 20 is further specifically configured to:

[0208] According to the degree of data deviation and the monitoring angle of the vehicle, obtain the correlation of the degree of difference between the data collected and the corresponding data;

[0209] The data consistency is determined based on the correlation of the degree of difference between the data corresponding to the data collection type and the number of data deviation comparison combinations.

[0210] Furthermore, the fourth data processing module 40 is further specifically configured to:

[0211] Acquire a data delay anomaly index according to the second intelligent driving processing data;

[0212] Acquire a data transmission reliability index according to the second intelligent driving processing data;

[0213] A second abnormality index is obtained according to the data delay abnormality index and the data transmission reliability index.

[0214] Furthermore, the fourth data processing module 40 is further specifically configured to:

[0215] Acquire a data quality set from the second intelligent driving data, wherein the data quality set is determined and obtained by the first abnormal indicator;

[0216] Acquire a data processing time set from the second intelligent driving data, wherein the data processing time set is used to reflect a data transmission delay;

[0217] The data delay anomaly index is obtained based on the data quality set and the data processing time set.

[0218] Furthermore, the fourth data processing module 40 is further specifically configured to:

[0219] Acquire the vehicle body posture adjustment instruction and the vehicle body posture adjustment vector in the data processing layer of the device according to the second intelligent driving processing data;

[0220] Determining an executed body posture adjustment vector according to the body posture adjustment instruction and the body posture adjustment vector;

[0221] A data transmission reliability index is obtained according to the vehicle body posture adjustment instruction, the vehicle body posture adjustment vector and the executed vehicle body posture adjustment vector.

[0222] Furthermore, the sixth data processing module 60 is further specifically configured to:

[0223] Generate vehicle behavior adjustment data based on data anomaly results;

[0224] The vehicle behavior adjustment data is sent to the device data control layer to generate a vehicle behavior adjustment instruction according to the vehicle behavior adjustment data, and the vehicle behavior is controlled according to the vehicle behavior adjustment instruction.

[0225] In the present invention, firstly, the first intelligent driving processing data is obtained from the device data perception layer, so as to perform error detection on the data of the layer from the level of the device data perception layer; then, the first abnormal indicator is obtained according to the first intelligent driving processing data, so as to obtain the first factor that may cause data error at the perception layer level; then, based on the first intelligent driving processing data and the first abnormal indicator, the second intelligent driving processing data is obtained from the device data processing layer, and on the basis of the first abnormal indicator, the second intelligent driving processing data with data correlation can be obtained from the first intelligent driving processing data; then, the second abnormal indicator is obtained according to the second intelligent driving processing data, and on the basis of the linkage with the first abnormal indicator, the factor that may cause data error in the device data processing layer can be more accurately obtained than directly obtaining the second intelligent driving data; finally, the third abnormal indicator is obtained according to the first abnormal indicator and the second abnormal indicator, and the data abnormal result of the intelligent driving system is determined according to the third abnormal indicator, so that the influence of multiple factors on data error detection in intelligent driving can be comprehensively considered, thereby significantly improving the data error detection capability of intelligent driving.

[0226] The present invention also provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the data anomaly analysis method for electronic device detection as described in the embodiment is implemented.

[0227] The invention also provides a computer device. Fig.11 Schematic diagram of a computer device in an embodiment of the present invention. Fig.11 As shown, the computer device 110 includes a processor 111, a memory 112, and a computer readable instruction 113 stored in the memory 112 and executable on the processor 111. When the processor 111 executes the computer readable instruction 113, each step of the data anomaly analysis method for electronic device detection is implemented.

[0228] Exemplarily, the computer readable instructions 113 may be divided into one or more modules / units, one or more modules / units are stored in the memory 112, and are executed by the processor 111 to implement the present invention. One or more modules / units may be a series of computer readable instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer readable instructions 113 in the computer device 110.

[0229] The computer device 110 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device may include, but is not limited to, a processor 111 and a memory 112. Those skilled in the art will appreciate that Fig.11 This is only an example of the computer device 110 and does not constitute a limitation of the computer device 110. The computer device 110 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0230] The processor 111 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits.

[0231] (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0232] The memory 112 may be an internal storage unit of the computer device 110, such as a hard disk or memory of the computer device 110. The memory 112 may also be an external storage device of the computer device 110, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 110. Further, the memory 112 may also include both an internal storage unit of the computer device 110 and an external storage device. The memory 112 is used to store computer-readable instructions and other programs and data required by the computer device. The memory 112 may also be used to temporarily store data that has been output or is to be output.

[0233] The embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0234] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0235] In the embodiments of the present invention, the server may be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0236] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0237] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and the computer-readable instructions can be executed by the processor. The steps of the above-mentioned various method embodiments. Among them, the computer-readable instructions include computer-readable instruction codes, and the computer-readable instruction codes can be in the form of source code, object code, executable files or some intermediate forms. The computer-readable medium may include: any entity or device that can carry computer-readable instruction codes, recording media, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media.

[0238] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0239] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0240] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A data anomaly analysis method for electronic device detection, It is characterized in that The method comprises: Acquire first intelligent driving processing data from a device data perception layer; Acquire a first abnormality indicator according to the first intelligent driving processing data; Based on the first intelligent driving processing data and the first abnormal indicator, obtaining second intelligent driving processing data from the device data processing layer; Acquire a second abnormality indicator according to the second intelligent driving processing data; Acquire a third abnormality indicator according to the first abnormality indicator and the second abnormality indicator; The data abnormality result of the intelligent driving system is determined according to the third abnormality indicator.

2. The data anomaly analysis method for electronic device detection according to claim 1, It is characterized in that The step of obtaining the first intelligent driving processing data from the device data perception layer includes the following steps: Determine the time node interval for single batch data collection; In the time node interval, sensor data acquired by multiple sensors are collected based on time synchronization, wherein the data collection type and data collection quantity of the sensors in each time node interval are the same.

3. The data anomaly analysis method for electronic device detection according to claim 2, It is characterized in that The step of obtaining a first abnormality indicator according to the first intelligent driving processing data comprises the following steps: Acquire a preset standard batch sensor data volume, wherein the duration of the time node interval of the standard batch sensor data volume is the same as the duration of the time node interval corresponding to the batch to be compared, wherein the standard batch sensor data volume and the sensor data collection quantity of the batch to be compared are the same, wherein the data collection type corresponding to the standard batch sensor data volume and the sensor data collection type of the batch to be compared are the same; Acquire the data integrity corresponding to the comparison batch according to the ratio of the data collection quantity of the sensor data of the to-be-compared batch to the sensor data quantity of the standard batch; Obtaining the degree of data deviation of the sensor data of the batch to be compared during the continuous collection process; Obtaining the correlation of the degree of difference between the data corresponding to the different data collection types according to the degree of data deviation, and determining the data consistency according to the correlation of the degree of difference; The first abnormality indicator is obtained according to the data integrity and the data consistency.

4. The data anomaly analysis method for electronic device detection according to claim 3, It is characterized in that The step of obtaining the data deviation degree of the sensor data of the batch to be compared during the continuous collection process comprises the following steps: Generating a data deviation comparison combination according to the data collection types of the batch to be compared, wherein every two different data collection types correspond to one data deviation comparison combination; Obtaining a difference set of the to-be-compared batch according to the data deviation comparison combination, wherein one of the difference sets corresponds to a monitoring angle of the vehicle; The data deviation degree is determined according to the difference set.

5. The data anomaly analysis method for electronic device detection according to claim 4, It is characterized in that The step of obtaining the correlation of the degree of difference between the data corresponding to the different data collection types according to the degree of data deviation, and determining the data consistency according to the correlation of the degree of difference, comprises the following steps: According to the data deviation degree and the monitoring angle of the vehicle, obtaining the correlation of the difference degree between the data corresponding to the data collection type; The data consistency is determined based on the correlation of the degree of difference between the data corresponding to the data collection type and the number of the data deviation comparison combinations.

6. The data anomaly analysis method for electronic device detection according to claim 1, It is characterized in that The step of obtaining a second abnormality indicator according to the second intelligent driving processing data comprises the following steps: Acquire a data delay anomaly index according to the second intelligent driving processing data; Acquire a data transmission reliability index according to the second intelligent driving processing data; The second abnormality indicator is obtained according to the data delay abnormality index and the data transmission reliability index.

7. The data anomaly analysis method for electronic device detection according to claim 6, It is characterized in that The step of obtaining a data delay abnormality index according to the second intelligent driving processing data comprises the following steps: Acquire a data quality set from the second intelligent driving data, wherein the data quality set is determined and obtained by the first abnormality indicator; Acquire a data processing time set from the second intelligent driving data, wherein the data processing time set is used to reflect a data transmission delay; The data delay anomaly index is obtained according to the data quality set and the data processing time set.

8. The data anomaly analysis method for electronic device detection according to claim 6, It is characterized in that The step of obtaining a data transmission reliability index according to the second intelligent driving processing data comprises the following steps: Acquire a vehicle body posture adjustment instruction and a vehicle body posture adjustment vector in the device data processing layer according to the second intelligent driving processing data; Determining an executed vehicle body posture adjustment vector according to the vehicle body posture adjustment instruction and the vehicle body posture adjustment vector; The data transmission reliability index is acquired according to the vehicle body posture adjustment instruction, the vehicle body posture adjustment vector and the executed vehicle body posture adjustment vector.

9. The data anomaly analysis method for electronic device detection according to claim 1, It is characterized in that After determining the data abnormality result of the intelligent driving system according to the third abnormality indicator, the method further includes the following steps: generating vehicle behavior adjustment data according to the data abnormality result; The vehicle behavior adjustment data is sent to the device data control layer to generate a vehicle behavior adjustment instruction according to the vehicle behavior adjustment data, and the vehicle behavior is controlled according to the vehicle behavior adjustment instruction.

10. A data anomaly analysis system for electronic equipment detection, It is characterized in that The system comprises: A first data processing module, used to obtain first intelligent driving processing data from a device data perception layer; A second data processing module, configured to obtain a first abnormality indicator according to the first intelligent driving processing data; a third data processing module, configured to obtain second intelligent driving processing data from a device data processing layer based on the first intelligent driving processing data and the first abnormality indicator; a fourth data processing module, configured to obtain a second abnormality indicator according to the second intelligent driving processing data; a fifth data processing module, configured to obtain a third abnormality indicator according to the first abnormality indicator and the second abnormality indicator; The sixth data processing module is used to determine the data abnormality result of the intelligent driving system according to the third abnormality indicator.

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