An intelligent vehicle audit method and system based on big data edge computing equipment

Abstract

The present invention discloses a smart vehicle audit method and system based on big data edge computing equipment, which relates to the field of smart vehicle technology, including collecting environmental information and vehicle operation status data through sensors on smart vehicles, and preprocessing the data; based on the identified situational features, fog nodes collaborate with cloud databases to generate audit strategies for specific situations; sending the audit strategy instruction set back to the smart vehicle, the vehicle implements strategy adjustments and feeds back effect data; the fog node receives feedback data to evaluate strategy performance, optimizes the existing audit strategy based on the evaluation results, and sends the optimized strategy to the smart vehicle. The present invention optimizes the personalized strategy of smart vehicles by integrating edge computing and cloud databases, greatly improving environmental adaptability, driving safety and decision-making efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent vehicles, and particularly to an intelligent vehicle auditing method and system based on big data edge computing devices. Background Art

[0002] As an important part of future transportation, intelligent vehicles have been rapidly promoted in recent years with the rapid development of artificial intelligence, Internet of Things, and big data technologies. Especially in the fields of vehicle environment perception, path planning, and decision-making control, intelligent vehicles have been able to achieve autonomous driving in complex traffic environments with the help of advanced technologies such as sensor fusion, machine learning, and edge computing. Edge computing, as an emerging technology, plays a crucial role in the application of intelligent vehicles. It reduces latency, improves the real-time performance and security of data processing by processing data near the data source. However, despite the significant progress made in edge computing and intelligent vehicle technologies, there are still some challenges and limitations.

[0003] The environmental perception and decision-making processes of intelligent vehicles mainly rely on the data collected by on-vehicle sensors and the auxiliary analysis of the cloud. However, traditional data processing methods often face problems such as data transmission latency, privacy protection, and bandwidth limitations, which limit the response speed and security of intelligent vehicles. In addition, due to the lack of effective data preprocessing and context recognition algorithms, intelligent vehicles often have difficulty accurately understanding driving situations in the face of complex and changing traffic environments, thus affecting the accuracy and timeliness of decision-making. Especially in the context of big data, how to efficiently process and analyze massive vehicle operation state data has become one of the bottlenecks restricting the development of intelligent vehicles. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent vehicle auditing method and system based on big data edge computing devices to solve the problems of slow response speed and inaccurate context understanding of intelligent vehicles in a big data environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an intelligent vehicle auditing method based on a big data edge computing device, which includes collecting environmental information and vehicle operation status data through sensors on the intelligent vehicle and preprocessing the data; after receiving the preprocessed data, the fog node performs a preliminary analysis; based on the preliminary analysis results, the fog node performs situation awareness analysis, constructs a driving situation view in combination with external data sources, and uses a machine learning model to identify situation features; based on the identified situation features, the fog node collaborates with the cloud database to generate an auditing strategy for a specific situation; sends the auditing strategy instruction set back to the intelligent vehicle, and the vehicle implements strategy adjustment and feeds back effect data; the fog node receives the feedback data to evaluate the strategy performance, and based on the evaluation results, optimizes the existing auditing strategy and sends the optimized strategy to the intelligent vehicle.

[0008] As a preferred solution of the intelligent vehicle auditing method based on the big data edge computing device of the present invention, wherein: the steps of collecting environmental information and vehicle operation status data through sensors on the intelligent vehicle and preprocessing the data are as follows:

[0009] Collect environmental information and vehicle operation status data through high-precision sensors equipped on the intelligent vehicle, and use Z-score to identify and remove measurement values that significantly deviate from the normal range;

[0010] For missing data caused by signal interference and hardware failures, use the K-nearest neighbor algorithm to predict the missing values;

[0011] Adopt UTC time to align all the original data collected by the sensors to the same time reference to ensure the consistency of the time of cross-sensor data;

[0012] Convert the encoding format of the original data to UTF-8 to improve data compatibility, and apply the maximum-minimum scaling method to scale the data to a specified range.

[0013] As a preferred solution of the intelligent vehicle auditing method based on the big data edge computing device of the present invention, wherein: the steps of the fog node performing a preliminary analysis after receiving the preprocessed data are as follows:

[0014] Adopt an adaptive weighted fusion algorithm to dynamically adjust the weights of the preprocessed data for data fusion, and the expression is:

[0015] ;

[0016] where, W i represents the weight of the data of the i-th sensor, is the variance of the sensor measurement error, and d i is the distance between the sensor and the target;

[0017] Define the environmental complexity C. Using the data after adaptive weighted fusion, evaluate the complexity of the current environment to guide the depth of subsequent data analysis and resource allocation. The expression is:

[0018] ;

[0019] where n is the total number of sensors, is the number of targets detected by the i-th sensor, is the average number of targets detected by all sensors;

[0020] Define the risk index R. Based on the environmental complexity and vehicle state, the expression is:

[0021] ;

[0022] where v is the vehicle speed, is the standard deviation of the speed, α is the absolute value of the acceleration, and A is the average value of the acceleration;

[0023] According to the risk index R, the fog node generates decision suggestions. At the same time, the analysis results are fed back to the vehicle control system and the cloud server for optimizing global traffic management and vehicle autonomous decision-making.

[0024] As a preferred solution of the intelligent vehicle auditing method based on the big data edge computing device described in the present invention, wherein: through the preliminary analysis results, the fog node conducts situation awareness analysis, constructs a driving situation view by combining external data sources, and uses a machine learning model to identify situation features. The specific steps are as follows:

[0025] Based on the preprocessed sensor data and preliminary analysis results, including the environmental complexity C and the risk index R, the fog node further integrates external data sources to form a comprehensive driving situation score J. The expression is:

[0026] ;

[0027] where, is the weight coefficient of the weather condition, w F is the traffic flow weight coefficient, w S is the weight coefficient of the road construction information, W t represents the quantization index of the weather condition, F t represents the quantization index of the traffic flow, refers to the influence degree of the road construction information;

[0028] Convert the situation score J into a specific situation view to help the autonomous driving system better understand the current driving environment;

[0029] Using the multi-dimensional feature fusion network MFFN, a comprehensive feature vector is extracted. MFFN contains multiple branches, each branch for a type of data. Features are extracted through a convolutional neural network and a long short-term memory network, and then merged into a unified feature representation through a gating mechanism. The expression is:

[0030] ;

[0031] Among them, represents the input vector of the h-th type of data, and F is the final scenario feature vector.

[0032] As a preferred solution of the intelligent vehicle auditing method based on the big data edge computing device of the present invention, wherein: based on the identified scenario features, the fog node cooperates with the cloud database to generate an auditing strategy for a specific scenario. The specific steps are as follows:

[0033] Introduce the feature enhancement function E to enhance the scenario feature vector F. The expression is:

[0034] ;

[0035] Among them, F' is the enhanced scenario feature vector, t is the current timestamp, and l is the vehicle position coordinate;

[0036] By designing the scenario correlation matrix A, evaluate the mutual influence between different scenario features. The expression is:

[0037] ;

[0038] Among them, A ij represents the correlation strength between the i-th and j-th scenario features, m is the dimension of the vector, α ik is the k-th element of the vector α i , and α jk is the k-th element of the vector α j ;

[0039] Based on the scenario correlation matrix A and the enhanced scenario feature vector F', construct the auditing strategy generation function G. The expression is:

[0040] ;

[0041] Among them, wi′ is the weight of the i-th scenario feature, G is the score of the auditing strategy, which reflects the pertinence and effectiveness of the strategy, and n represents the number of features;

[0042] Design the strategy adjustment function Φ to dynamically adjust the strategy according to the characteristics of the current scenario. The expression is:

[0043] ;

[0044] Among them, Φ is the adjusted audit strategy score, β controls the influence degree of G on the output of the whole function, γ controls the influence degree of R on the output of the whole function, and δ controls the influence degree of C on the output of the whole function.

[0045] As a preferred solution of the intelligent vehicle audit method based on big data edge computing devices according to the present invention, wherein: the steps of sending the audit strategy instruction set back to the intelligent vehicle, the vehicle implementing strategy adjustment and feeding back effect data are as follows:

[0046] Based on the audit strategy score Φ, the audit strategy is converted into an executable instruction set I, and the expression is:

[0047] ;

[0048] Among them, T is the instruction conversion function, is the current vehicle state information, is the environmental information;

[0049] Adopt the wireless communication protocol Comp to send the instruction set I to the intelligent vehicle. The vehicle control system adjusts the vehicle behavior according to the received instruction set I, and through the built-in monitoring system of the vehicle, the effect data D after executing the strategy is collected in real time e , and the expression is:

[0050] ;

[0051] Among them, V s ' is the vehicle state information after executing the strategy, and Q is the effect data collection function;

[0052] The collected effect data D e is uploaded to the fog node and the cloud database through the wireless communication protocol Comp.

[0053] As a preferred solution of the intelligent vehicle audit method based on big data edge computing devices according to the present invention, wherein: the fog node receives the feedback effect data to evaluate the strategy performance, and based on the evaluation result, optimizes the existing audit strategy and sends the optimized audit strategy to the intelligent vehicle. The specific steps are as follows:

[0054] The fog node evaluates and analyzes the feedback effect data, and the expression is:

[0055] ;

[0056] Among them, Ev is the strategy evaluation function, C s is the current strategy configuration, ρ i is the evaluation weight of the i-th effect data, M is the total number of effect data items, and D eiRepresents the specific value of the i-th effect data;

[0057] Define a policy effectiveness satisfaction level X. By comparing the size of the policy evaluation function Ev and the policy effectiveness satisfaction level X, judge the effectiveness of the audit policy;

[0058] When Ev ≥ X, it indicates that the execution effect of the audit policy is good, meeting or exceeding the expected goal, and no immediate adjustment is required;

[0059] When Ev < X, it indicates that the audit policy performs poorly in the current situation and fails to achieve the expected effect. Analyze and optimize it, and send the optimized audit policy to the intelligent vehicle through the wireless communication protocol Comp.

[0060] In a second aspect, the present invention provides an intelligent vehicle audit system based on a big data edge computing device, including a data collection and preprocessing module, an edge computing node module, a context recognition module, and a policy execution and optimization module; the data collection and preprocessing module is used to collect raw data from the intelligent vehicle and implement data cleaning, format conversion, and preliminary anomaly detection; the edge computing node module is used to receive and process the preprocessed data from the intelligent vehicle and perform preliminary analysis to quickly screen key information; the context recognition module is used to identify the characteristics of the driving context based on the preliminary analysis results by applying machine learning algorithms; the policy execution and optimization module is used to adjust the policy in real time, optimize the operation of the intelligent vehicle, and ensure high efficiency and safety.

[0061] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the intelligent vehicle audit method based on a big data edge computing device as described in the first aspect of the present invention is implemented.

[0062] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the intelligent vehicle audit method based on a big data edge computing device as described in the first aspect of the present invention is implemented.

[0063] The beneficial effects of the present invention are as follows: Through preliminary analysis and scenario awareness by the fog nodes of edge computing, combined with the support of the cloud database, the present invention generates and optimizes the audit policy. This process not only significantly improves the adaptability and safety of intelligent vehicles to complex environments, but also greatly improves the accuracy and efficiency of driving decisions, reduces potential risks, and improves the user experience. Description of the Drawings

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0065] Figure 1 It is a flowchart of the intelligent vehicle auditing method based on the big data edge computing device in Embodiment 1.

[0066] Figure 2 It is a flowchart of the intelligent vehicle auditing system based on the big data edge computing device in Embodiment 1. Detailed implementation manners

[0067] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention with reference to the drawings in the specification.

[0068] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0069] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0070] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides an intelligent vehicle auditing method based on a big data edge computing device, including the following steps:

[0071] S1. Collect environmental information and vehicle operation state data through sensors on the intelligent vehicle, and preprocess the data.

[0072] Furthermore, collect environmental information and vehicle operation state data through high-precision sensors equipped on the intelligent vehicle, including but not limited to millimeter-wave radar, lidar (LiDAR), vision cameras, ultrasonic sensors, GPS positioning modules, accelerometers, and gyroscopes;

[0073] Use Z-score to identify and remove measurement values that significantly deviate from the normal range. The expression is:

[0074] ;

[0075] Among them, X is the observed value, μ is the mean, and σ is the standard deviation. If |Z| > 3, then this value is considered an outlier;

[0076] For the missing data caused by signal interference and hardware failures, the nearest neighbor algorithm is used to predict the missing values;

[0077] Adopt UTC time to align all the original data collected by the sensors to the same time reference, so as to keep the time of cross-sensor data consistent;

[0078] Convert the encoding format of the original data to UTF-8 to improve data compatibility, and apply the min-max scaling method to scale the data to the specified range.

[0079] It should also be noted that in the intelligent vehicle system, different sensors may collect data at different time points. To ensure the accuracy of data analysis, it is necessary to align the data of all sensors to a unified time reference so as to correctly associate and analyze this data;

[0080] UTC is an accurate and widely accepted time standard for global time synchronization. Adopting UTC time as the time reference can ensure the global consistency of data, facilitate data exchange and comparison between different regions and systems. Usually, the central processing unit of an intelligent vehicle will record the local timestamp of each sensor data and convert it to UTC time, which may involve the calculation of time offsets and time zone conversions to ensure that all data is marked and stored according to UTC time.

[0081] S2. After the fog node receives the preprocessed data, it conducts a preliminary analysis.

[0082] Furthermore, the adaptive weighted fusion algorithm is used to dynamically adjust the weights of the preprocessed data for data fusion. The expression is:

[0083] ;

[0084] Among them, W i represents the weight of the data of the i-th sensor, is the variance of the sensor measurement error, and d i is the distance between this sensor and the target;

[0085] Define the environmental complexity C, and use the data after adaptive weighted fusion to evaluate the complexity of the current environment to guide the depth of subsequent data analysis and resource allocation. The expression is:

[0086] ;

[0087] where n is the total number of sensors, and N i is the number of targets detected by the i-th sensor, and is the average number of targets detected by all sensors;

[0088] Define the risk index R, based on the environmental complexity and vehicle state, the expression is:

[0089] ;

[0090] where v is the vehicle speed, is the standard deviation of the speed, α is the absolute value of the acceleration, and A is the average value of the acceleration;

[0091] According to the risk index R, the fog node generates decision suggestions, such as adjusting the vehicle speed, activating the warning system or recommending a driving route. At the same time, the analysis results are fed back to the vehicle control system and the cloud server for optimizing global traffic management and vehicle autonomous decision-making.

[0092] It should also be noted that by introducing the adaptive weighted fusion algorithm and the complexity evaluation formula, the refined management of the intelligent vehicle environment perception is realized. The value range of the risk index R is [0, ∞), where R < 1 indicates low risk and is suitable for normal driving; 1 < R < 10 indicates medium risk and requires cautious driving, and R ≥ 10 indicates high risk and preventive measures should be taken immediately.

[0093] S3. Through the preliminary analysis results, the fog node conducts scenario perception analysis, constructs a driving scenario view by combining external data sources, and uses a machine learning model to identify scenario features.

[0094] Furthermore, based on the preprocessed sensor data and preliminary analysis results, including the environmental complexity C and the risk index R, the fog node further integrates external data sources, such as weather forecasts, traffic flow, road construction information, etc., to form a comprehensive driving scenario score J, and the expression is:

[0095] ;

[0096] where, is the weight coefficient of the weather condition, w F is the weight coefficient of the traffic flow, w S is the weight coefficient of the road construction information, W t represents the quantization index of the weather condition, F t represents the quantization index of the traffic flow, refers to the influence degree of the road construction information;

[0097] Convert the situation score J into a specific situation view. For example, "moderate rainfall, low visibility, slight traffic congestion, no road construction, medium environmental complexity, high risk index" to help the autonomous driving system better understand the current driving environment;

[0098] Utilize the multi-dimensional feature fusion network MFFN to extract the comprehensive feature vector. MFFN contains multiple branches, and each branch processes a type of data (such as vision, radar, GPS, etc.). Features are extracted through convolutional neural networks and long short-term memory networks, and then merged into a unified feature representation through a gating mechanism. The expression is:

[0099] ;

[0100] Among them, represents the input vector of the h-th type of data, and F is the final situation feature vector.

[0101] It should also be noted that in intelligent vehicle technology, the multi-modal feature fusion network (MFFN) plays a crucial role. By integrating data from various sensors, it enhances the situation awareness ability of the autonomous driving system. MFFN is designed as a multi-branch structure, and each branch specializes in processing a type of data, such as visual images, radar signals, GPS positioning, etc. These branches use specialized data processing technologies. For example, convolutional neural networks (CNNs) are used for image data to extract visual features, while long short-term memory networks process sequence data such as time series radar signals. The features extracted by each branch are finally synthesized into a comprehensive feature vector through a gating mechanism. This mechanism not only effectively combines the information from different data sources but also optimizes the combination method of features through the adjustment of the gating, enabling the final obtained feature vector to comprehensively and accurately represent the situation of the current driving environment, thus greatly enhancing the autonomous driving system's understanding and judgment ability of the environment.

[0102] S4. Based on the identified situation features, the fog nodes cooperate with the cloud database to generate an auditing strategy for specific situations.

[0103] Furthermore, introduce the feature enhancement function E to enhance the situation feature vector F to make it contain more detailed driving environment details. This function considers factors such as time, location, and weather changes. The expression is:

[0104] ;

[0105] Among them, F' is the enhanced situation feature vector, t is the current timestamp, and l is the vehicle position coordinate;

[0106] By designing the situation correlation matrix A, evaluate the mutual influence between different situation features. The expression is:

[0107] ;

[0108] where A ij represents the correlation strength of the i-th and j-th situational features, m is the dimension of the vector, and α ik is the k-th element of the vector α i , and α jk is the k-th element of the vector α j ;

[0109] Based on the situational correlation matrix A and the enhanced situational feature vector F', construct the audit strategy generation function G, and the expression is:

[0110] ;

[0111] where w i ' is the personalized weight of the i-th situational feature, G is the score of the audit strategy, which reflects the pertinence and effectiveness of the strategy, and n represents the number of features;

[0112] Design the strategy adjustment function Φ to dynamically adjust the strategy according to the characteristics of the current situation, and the expression is:

[0113] ;

[0114] where Φ is the adjusted audit strategy score, β controls the influence degree of G on the output of the whole function, γ controls the influence degree of R on the output of the whole function, and δ controls the influence degree of C on the output of the whole function.

[0115] It should also be noted that in order to formulate an effective audit strategy, first, enrich the situational feature vector F through the feature enhancement function E so that it contains more detailed driving environment information, such as time, location, and weather changes, etc. Subsequently, use the situational correlation matrix A to evaluate the mutual influence between different situational features. This matrix is constructed by examining the correlation between features, which helps to understand how different driving situations jointly act on the driving environment. Based on these enhanced features and situational relevance, construct the audit strategy generation function G. This function combines the personalized weights of situational features and generates a score reflecting the pertinence and effectiveness of the strategy. Finally, introduce the strategy adjustment function Φ to dynamically adjust the strategy according to the characteristics of the current environment to ensure the real-time and adaptability of the strategy. This process ensures that the autonomous driving system can formulate and implement the most suitable audit strategy for various complex driving situations through precise mathematical modeling and algorithm adjustment.

[0116] S5. Send the audit strategy instruction set back to the intelligent vehicle, and the vehicle implements strategy adjustment and feeds back the effect data.

[0117] Further, based on the audit policy score Φ, the audit policy is converted into an executable instruction set I, and the expression is:

[0118] ;

[0119] where T is the instruction conversion function, is the current vehicle state information, is the environmental information;

[0120] The instruction set I is sent to the intelligent vehicle using the wireless communication protocol Comp. The vehicle control system adjusts the vehicle behavior, such as speed control and route planning, according to the received instruction set I, and collects the effect data D after the execution of the policy in real time through the built-in monitoring system of the vehicle e , and the expression is:

[0121] ;

[0122] where V s ' is the vehicle state information after the execution of the policy, and Q is the effect data collection function;

[0123] The collected effect data De is uploaded to the fog node and the cloud database through the wireless communication protocol Comp.

[0124] It should also be noted that in view of the security and real-time requirements of the instruction set transmission, an optimized version of the wireless communication protocol Comp is adopted. This protocol combines the advantages of 5G and satellite communication, and the expression is:

[0125] ;

[0126] where RT i is the round-trip time of the i-th communication path, ω i is the reliability weight of path i, and N is the total number of available communication paths;

[0127] The instruction set I is sent to the intelligent vehicle through the optimized wireless communication protocol Comp. After receiving it, the vehicle sends a reception confirmation signal ASK, and the expression is:

[0128] ;

[0129] where r is the reception confirmation function;

[0130] S6. The fog node receives the feedback effect data to evaluate the policy performance. Based on the evaluation results, the existing audit policy is optimized, and the optimized audit policy is sent to the intelligent vehicle.

[0131] Further, the fog node evaluates and analyzes the feedback effect data, and the expression is:

[0132] ;

[0133] where Ev is the policy evaluation function, C s is the current policy configuration, ρ i is the evaluation weight of the i-th effect data, M is the total number of items of effect data, and D ei represents the specific value of the i-th effect data;

[0134] Define a policy effectiveness satisfaction level X. By comparing the magnitudes of the policy evaluation function Ev and the policy effectiveness satisfaction level X, the effectiveness of the audit policy is judged.

[0135] When Ev ≥ X, it indicates that the execution effect of the audit policy is good, meeting or exceeding the expected goal, and no immediate adjustment is required.

[0136] When Ev < X, it indicates that the audit policy performs poorly in the current situation and fails to achieve the expected effect. Analyze and optimize it, and send the optimized audit policy to the intelligent vehicle through the wireless communication protocol Comp.

[0137] It should also be noted that the fog node receives the effect data returned from the vehicle to evaluate the performance of the current audit policy. Through the policy evaluation function Ev, it combines the current policy configuration Cs, the weights ρ of each item of effect data i and the corresponding data value D ei , calculates a quantified evaluation result. If this result reaches or exceeds the preset policy effectiveness satisfaction level X, it is considered that the policy execution effect is good and no adjustment is required; if it does not reach X, it indicates that the policy needs to be optimized. The fog node will adjust and optimize the policy according to these analysis results, and then send the optimized policy back to the intelligent vehicle through the wireless communication protocol Comp to ensure that the vehicle can adapt to the changing driving environment and improve the driving safety and efficiency. This process ensures that the audit policy can be adjusted in real time according to the actual effect data, thereby enhancing the adaptability and overall performance of the intelligent vehicle system.

[0138] This embodiment also provides an intelligent vehicle audit system based on a big data edge computing device, including:

[0139] Data acquisition and preprocessing module, edge computing node module, situation recognition module, policy execution and optimization module; the data acquisition and preprocessing module is used to collect raw data from intelligent vehicles and implement data cleaning, format conversion, and preliminary anomaly detection; the edge computing node module is used to receive and process the preprocessed data from intelligent vehicles, perform preliminary analysis, and quickly screen key information; the situation recognition module is used to identify the characteristics of driving situations based on the results of preliminary analysis by applying machine learning algorithms; the policy execution and optimization module is used to adjust policies in real time, optimize the operation of intelligent vehicles, and ensure efficiency and safety.

[0140] This embodiment also provides a computer device applicable to the situation of an intelligent vehicle auditing method based on a big data edge computing device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent vehicle auditing method based on a big data edge computing device as proposed in the above embodiment.

[0141] This computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of this computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator networks, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0142] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the intelligent vehicle auditing method based on big data edge computing devices as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0143] In summary, the present invention realizes the improvement of data quality and ensures a solid analysis foundation through the efficient collection and preprocessing of intelligent vehicle sensor data. In the preliminary analysis stage, dynamic data fusion and environmental risk assessment are carried out to generate immediate decision-making suggestions, enhancing driving safety. Scenario-aware analysis combines external data, and the MFFN is used to extract comprehensive features to deepen the system's understanding of the environment and improve decision-making intelligence. The generation and dynamic adjustment of auditing strategies ensure the pertinence and real-time nature of the strategies, adapting to complex situations. The closed-loop feedback mechanism optimizes the strategy performance, continuously improving the environmental adaptability and driving safety of intelligent vehicles, and promoting the efficiency and safety of intelligent transportation systems.

[0144] Example 2, referring to Table 1, is the second embodiment of the present invention. To further verify the advancement of the present invention, experimental simulation data of the intelligent vehicle auditing method based on big data edge computing devices is given.

[0145] Four intelligent vehicles of different models were selected, namely intelligent vehicle A, intelligent vehicle B, intelligent vehicle C, and intelligent vehicle D. Each intelligent vehicle was independently tested under the preset environmental complexity C, risk index R, and scenario score J conditions.

[0146] First, the objectives and hypotheses of the experiment were defined, clarifying the problems to be solved or the concepts to be verified through the experiment. This step ensured the directionality and effectiveness of the experimental design.

[0147] Secondly, four representative intelligent vehicles, namely A, B, C, and D, were selected, and the basic configurations and characteristics of each vehicle were recorded in detail to ensure the comparability and accuracy of the experimental results.

[0148] Next, different experimental conditions were set, including environmental complexity C, risk index R, etc., to simulate various situations that intelligent vehicles may encounter in the real world. Each intelligent vehicle was independently tested under these preset conditions.

[0149] Finally, the collected data was analyzed, the performances of different vehicles under various conditions were compared, the effectiveness of personalized strategy adjustment was evaluated, and suggestions for improving the performance and strategies of intelligent vehicles were put forward based on the experimental results.

[0150] Specifically, as shown in Table 1:

[0151] Table 1 Experimental Record Table

[0152]

[0153] Through the data analysis of the above table, the effectiveness and superiority of the method of the present invention can be clearly seen. By integrating advanced sensor technology, deep learning algorithms, and personalized strategy adjustment mechanisms, the present invention effectively improves the decision-making ability and driving safety of intelligent vehicles in complex environments. For example, by comparing the data of intelligent vehicles A and B, it can be clearly seen that when the environmental complexity C is relatively high, vehicle B still maintains a relatively high situation score J (7.9) and strategy execution effect score Ev (9.0) when the risk index R (4.1) is relatively high, which fully demonstrates the remarkable effect of the present invention in improving the ability of intelligent vehicles to adapt to complex environments.

[0154] Through the intelligent vehicle auditing method based on big data edge computing devices of the present invention, significant innovation and advantages are demonstrated in the environmental perception and decision optimization of intelligent vehicles, effectively improving the driving safety and efficiency of vehicles in complex environments. Compared with the prior art, the present invention can more accurately evaluate environmental risks and intelligently adjust driving strategies, thus providing strong technical support for the future development of intelligent transportation systems.

[0155] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent vehicle audit method based on big data edge computing equipment, characterized in that: include, Collect environmental information and vehicle operating status data through sensors on smart vehicles and pre-process the data; After receiving the preprocessed data, the fog node performs preliminary analysis; Based on the preliminary analysis results, the fog node conducts situational awareness analysis, builds a driving situation view in combination with external data sources, and uses machine learning models to identify situational features; Based on the identified situational features, the fog nodes collaborate with the cloud database to generate audit strategies for specific situations; The audit strategy instruction set is sent back to the intelligent vehicle, and the vehicle implements the strategy adjustment and provides feedback on the effect data; The fog node receives feedback data to evaluate the performance of the strategy, optimizes the existing audit strategy based on the evaluation results, and sends the optimized strategy to the smart vehicle; The environmental information and vehicle operation status data are collected through sensors on the smart vehicle, and the data is preprocessed. The specific steps are as follows: Collect environmental information and vehicle operation status data through high-precision sensors equipped on smart vehicles; Use Z-score to identify and remove measurements that are significantly out of the normal range. For missing data caused by signal interference and hardware failure, use the nearest neighbor algorithm to predict missing values. Using UTC time, all raw data collected by sensors are aligned to the same time base, so that the time of data across sensors is consistent; Convert the encoding format of the original data to UTF-8 to improve data compatibility, and apply the maximum and minimum scaling method to scale the data to the specified range; After receiving the preprocessed data, the fog node performs preliminary analysis. The specific steps are as follows: Adaptive weighted fusion algorithm is used to dynamically adjust the weight of preprocessed data for data fusion. The expression is: ； Among them, W i represents the weight of the i-th sensor data, is the variance of the sensor measurement error, d i is the distance between the sensor and the target; Define the environment complexity C, and use the adaptive weighted fusion data to evaluate the complexity of the current environment to guide the depth of subsequent data analysis and resource allocation. The expression is: ； Where n is the total number of sensors, N i is the number of targets detected by the i-th sensor, is the average number of targets detected by all sensors; Define the risk index R, based on the complexity of the environment and the vehicle status, and the expression is: ； Where v is the vehicle speed, is the standard deviation of velocity, α is the absolute value of acceleration, and A is the average value of acceleration; According to the risk index R, the fog node generates decision recommendations and feeds back the analysis results to the vehicle control system and cloud server to optimize global traffic management and vehicle autonomous decision-making.

2. The intelligent vehicle audit method based on big data edge computing equipment according to claim 1, characterized in that: Based on the preliminary analysis results, the fog node performs situational awareness analysis, combines external data sources to build a driving situation view, and uses a machine learning model to identify situational features. The specific steps are as follows: Based on the preprocessed sensor data and preliminary analysis results, including the environmental complexity C and the risk index R, the fog node further integrates external data sources to form a comprehensive driving scenario score J, which is expressed as: ； in, is the weight coefficient of weather conditions, w F is the traffic flow weight coefficient, w S is the weight coefficient of road construction information, W t A quantitative indicator of weather conditions, F t A quantitative indicator representing traffic flow, Refers to the impact degree of road construction information; Convert the scenario score J into a specific scenario view to help the autonomous driving system better understand the current driving environment; The multi-dimensional feature fusion network MFFN is used to extract the comprehensive feature vector. MFFN contains multiple branches. Each branch extracts features for a data type through convolutional neural network and long short-term memory network, and then merges them into a unified feature representation through the gating mechanism. The expression is: ； in, represents the input vector of the hth data type, and F is the final scenario feature vector.

3. The intelligent vehicle audit method based on big data edge computing equipment according to claim 2, characterized in that: Based on the identified situational features, the fog node collaborates with the cloud database to generate an audit strategy for a specific situation. The specific steps are as follows: The feature enhancement function E is introduced to enhance the scene feature vector F, and the expression is: ； Among them, F' is the enhanced scene feature vector, t is the current timestamp, and l is the vehicle position coordinate; By designing the situational association matrix A, the mutual influence between different situational features is evaluated, and the expression is: ； Among them, A ij represents the correlation strength between the i-th and j-th situational features, m is the dimension of the vector, and α ik is the vector α i The kth element of jk is the vector α j The kth element of ; Based on the situational association matrix A and the enhanced situational feature vector F', the audit strategy generation function G is constructed, and the expression is: ； Among them, w i ′ is the personalized weight of the i-th situational feature, G is the score of the audit strategy, which reflects the pertinence and effectiveness of the strategy, and n represents the number of features; Design a strategy adjustment function Φ to dynamically adjust the strategy based on the characteristics of the current situation. The expression is: ； Among them, Φ is the adjusted audit strategy score, β controls the influence of G on the output of the entire function, γ controls the influence of R on the output of the entire function, and δ controls the influence of C on the output of the entire function.

4. The intelligent vehicle audit method based on big data edge computing equipment according to claim 3 is characterized in that: The audit strategy instruction set is sent back to the intelligent vehicle, and the vehicle implements the strategy adjustment and feeds back the effect data. The specific steps are as follows: Based on the audit strategy score Φ, the audit strategy is converted into an executable instruction set I, which is expressed as: ； Where T is the instruction conversion function, is the current status information of the vehicle, for environmental information; The wireless communication protocol Comp is used to send the instruction set I to the intelligent vehicle. The vehicle control system adjusts the vehicle behavior according to the received instruction set I and collects the effect data D after executing the strategy in real time through the vehicle's built-in monitoring system. e , the expression is: ； Among them, V s ' is the vehicle status information after the strategy is executed, Q is the effect data collection function; The collected effect data D e , uploaded to the fog nodes and cloud database through the wireless communication protocol Comp.

5. The intelligent vehicle audit method based on big data edge computing equipment according to claim 4, characterized in that: The fog node receives the feedback effect data to evaluate the strategy performance, optimizes the existing audit strategy based on the evaluation results, and sends the audit-optimized strategy to the smart vehicle. The specific steps are as follows: The fog node evaluates and analyzes the feedback effect data, and the expression is: ； Among them, Ev is the strategy evaluation function, C s is the current policy configuration, ρ i is the evaluation weight of the i-th effect data, M is the total number of effect data, D ei Represents the specific value of the i-th effect data; Define a strategy effectiveness satisfaction level X, and judge the effectiveness of the audit strategy by comparing the strategy evaluation function Ev and the strategy effectiveness satisfaction level X; When Ev ≥ X, it indicates that the audit strategy is well implemented and meets or exceeds the expected goals, and no immediate adjustment is required; If Ev<X, it indicates that the audit strategy performs poorly in the current situation and fails to achieve the expected results. Analysis and optimization are performed, and the optimized audit strategy is sent to the smart vehicle through the wireless communication protocol Comp.

6. An intelligent vehicle audit system based on big data edge computing equipment, based on the intelligent vehicle audit method based on big data edge computing equipment according to any one of claims 1 to 5, characterized in that: Including data collection and preprocessing module, edge computing node module, situation recognition module, strategy execution and optimization module; The data acquisition and preprocessing module is used to collect raw data from smart vehicles and implement data cleaning, format conversion and preliminary anomaly detection; The edge computing node module is used to receive and process pre-processed data from smart vehicles, perform preliminary analysis, and quickly filter key information. The situation recognition module is used to identify the characteristics of the driving situation based on the preliminary analysis results and apply machine learning algorithms; The strategy execution and optimization module is used to adjust strategies in real time, optimize the operation of intelligent vehicles, and ensure efficiency and safety.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent vehicle audit method based on big data edge computing equipment described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent vehicle audit method based on a big data edge computing device as described in any one of claims 1 to 5 are implemented.

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