Vehicle state monitoring system based on big data cloud platform

Abstract

The invention discloses a vehicle state monitoring system based on a big data cloud platform, and belongs to the technical field of vehicle monitoring of the big data cloud platform. An edge calculation module; a cloud platform data processing unit; a digital twin module; a data security module; an open interface module; a task collaboration and data flow management module; by constructing a multi-mode sensor unit including environment perception, position state, visual perception, inertial measurement and the like, the problems that a traditional system is single in data source and weak in cooperative capability are solved; a block chain encryption and decentralized data storage mechanism is introduced into a data security module, and intelligent contract control and a cross-platform data sharing mechanism are combined, so that data tamper-proofing, access traceability and authorization level-to-level management under multi-party participation are realized; and the adaptive capacity of the system in the fields of government affairs, finance, manufacturing and the like with relatively high requirements on data security is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle monitoring in a big data cloud platform, and more specifically, it relates to a vehicle status monitoring system based on a big data cloud platform. Background Art

[0002] Modern intelligent vehicles are generally equipped with rich sensors, controllers, and communication modules, resulting in an explosive growth in the original data collected, processed, and uploaded. In this context, big data cloud platforms are widely used in vehicle status analysis, fault diagnosis, predictive maintenance, and driving behavior modeling, forming a data-driven state perception closed-loop management mechanism, which has become a key basic support technology for promoting the intelligent upgrade of the transportation system. However, there are still several bottlenecks restricting the performance and universality of existing vehicle status monitoring systems. Firstly, most systems lack a structured fusion mechanism for multi-modal sensing data, resulting in serious information islands and unable to achieve collaborative analysis of multi-dimensional states such as vehicle structure, electricity, environment, and behavior. Secondly, the edge processing ability is weak, and key events often rely on centralized processing in the cloud, with a relatively high response latency, which is not conducive to real-time fault warning and triggering of local control strategies. Thirdly, current platforms mostly adopt linear upload links and lack visual expression of vehicle operating states and simulation of behavior evolution. Fourthly, vehicle data faces challenges of trust deficiency and privacy protection in cross-platform interactions, and traditional centralized data management methods are difficult to meet the needs of multi-party collaboration, hierarchical authorization, and compliant sharing. Therefore, we have designed a vehicle status monitoring system based on a big data cloud platform. Summary of the Invention

[0003] To achieve the above object, the present invention provides the following technical solutions: A vehicle status monitoring system based on a big data cloud platform, comprising: A multi-modal sensor unit for collecting internal and external status data of the vehicle; An edge computing module for preprocessing the collected data, screening for anomalies, and uploading the data to the cloud platform; A cloud platform data processing unit, including a data fusion, intelligent adaptive analysis, and fault prediction module; A digital twin module for establishing a virtual model corresponding to the physical vehicle and performing real-time dynamic mapping; A data security module, based on blockchain technology, to achieve data encryption, decentralized storage, and cross-platform secure sharing; Open interface module, supporting interconnection and interoperability with external systems such as vehicle manufacturing, maintenance, and traffic management; Task collaboration and data flow management module, used to overall coordinate the above modules; Furthermore, the multi-modal sensor unit includes: Environmental sensing module, used to collect vehicle external and internal environmental parameters, including: Temperature sensor, used to detect the engine compartment, coolant, and external environment temperature; Humidity sensor, used to monitor the air humidity inside and outside the vehicle; Air pressure or pressure sensor, used to detect tire pressure, oil pressure, and brake system pressure; Dynamic state sensing module, used to collect vehicle motion and working condition information, including: Acceleration sensor, used to detect the vehicle's acceleration and vibration conditions; Gyroscope, used to obtain vehicle angular velocity or steering change information; Rotation angle sensor, used to monitor engine and wheel speed information; Position and positioning module, used to achieve vehicle positioning and motion trajectory monitoring, including: Beidou positioning unit, used to provide real-time position information; Inertial measurement unit, including three-axis accelerometer and three-axis gyroscope, used to estimate attitude and motion state when GPS signal is poor; Visual information acquisition module, used to collect vehicle surrounding images and auxiliary information, including: At least one camera, used to capture front, rear, and surrounding environment images; Optional infrared camera or lidar, used to supplement visual data in low light or extreme environments; Signal acquisition and preprocessing unit, used to perform preliminary processing on the signals collected by the above modules, including: Data acquisition circuit unit, used to convert analog or digital signals into standardized data and perform filtering, amplification, and noise suppression; Digital signal processor, used to collect, preprocess, and compress data in real time; Local storage cache unit, used to perform short-term storage of data and timestamp synchronization; Communication interface unit, used to achieve data transmission between the sensor module and other modules inside the vehicle and the edge computing module, including: Wired interface, such as CAN bus or Ethernet interface; Wireless interface, such as Wi-Fi, Bluetooth, or 5G communication upload unit.

[0004] Furthermore, the edge computing module includes: A data reception and input unit, which is used to receive vehicle state data collected by a multi-modal sensor unit and perform unified time management and data buffering; A data preprocessing unit, which is used to perform standardization processing on the received raw data, including filtering, amplification, denoising and normalization operations, so as to improve data quality; An anomaly screening unit, which is used to identify anomalies in the preprocessed data based on set rules or lightweight models, and judge whether there are anomaly events or fault signs; A local cache and data storage unit, which is used to temporarily store anomaly event data or key data, and ensure data is not lost when communication is interrupted or the network is unstable; A communication upload unit, which is used to upload the preprocessed data or anomaly identification results to the cloud platform data processing unit by wired or wireless means.

[0005] Furthermore, the cloud platform data processing unit includes: A data fusion module, which is used to perform time alignment, spatial association and structure unification on data uploaded by multi-modal sensor units of multiple vehicles, solve the problems of data source heterogeneity and spatio-temporal misalignment, and achieve multi-source data fusion; An intelligent adaptive analysis module, which is used to deeply analyze the vehicle state based on inputs such as historical operation data, vehicle behavior patterns, and environmental factors, through adaptive modeling, clustering analysis or classification algorithms, and identify operation trends, behavior characteristics and potential risks; A fault prediction module, which is used to build a prediction model based on multi-dimensional time series data and state variables, identify and probabilistically evaluate possible faults in advance, and output fault types, severity levels and prediction time windows; A model training and updating module, which is used to train or optimize the intelligent analysis model and the fault prediction model in combination with historical data, and perform personalized parameter fine-tuning or model redeployment according to vehicle usage scenarios; A scheduling and response module, which is used to generate actionable control instructions, maintenance suggestions or policy feedback from the analysis and prediction results, and issue them to the edge computing module or the vehicle internal control unit through the communication upload unit to achieve remote closed-loop management.

[0006] Furthermore, the data twin module includes: An entity modeling unit, which is used to build corresponding three-dimensional geometric models and functional topology models based on the physical structure, system configuration and component attributes of the vehicle, so as to establish a digital vehicle basic model; A data mapping and synchronization unit, which is used to receive vehicle state data uploaded by the edge computing module and dynamically map the state data to the corresponding virtual vehicle model, so as to achieve real-time synchronization between the physical vehicle and the virtual model; A state simulation and prediction unit, which is used to perform running state evolution simulation and potential fault trend prediction based on the current vehicle state and historical data by using rule reasoning, machine learning or time series prediction algorithms; A visualization interaction unit, which is used to graphically present the running state, historical trajectory and key parameter change information of the virtual vehicle, and support functions such as panoramic display, local magnification, multi-dimensional switching and playback of the vehicle; A feedback and control interface unit, which is used to generate control instructions from the simulation results or prediction and warning information, and send them to the edge computing module or vehicle control system through a communication link to achieve remote monitoring and dynamic adjustment.

[0007] Furthermore, the data security module includes: A data encryption unit, which is used to encrypt the collected vehicle state data, edge computing results and cloud processing results to prevent data theft and tampering during transmission or storage. The encryption algorithms include symmetric encryption and asymmetric encryption; A blockchain evidence storage unit, which is used to record key event data or data summary information in the form of hash into the blockchain ledger to build an immutable data trusted chain structure and improve data traceability; A decentralized data storage unit, which is used to distribute and store the collected data in multiple nodes after sharding to improve the system's disaster tolerance and access efficiency. The storage structure supports IPFS or a hybrid cloud-edge collaborative architecture; A permission control and smart contract unit, which is used to authorize and control the access of external entities (such as manufacturers, repair shops, management platforms, etc.) to vehicle data through smart contracts based on predefined policies. The access permissions support hierarchical setting and automatic trigger mechanisms; A cross-platform data sharing unit, which is used to achieve trusted circulation and secure sharing of data between multiple platforms and systems, and support data call log recording and access behavior tracking to ensure the security and compliance of data during cross-domain transmission.

[0008] Furthermore, the open interface module includes: An external system access adaptation unit, which is used to identify and be compatible with the heterogeneous communication protocols and data structures adopted by external systems such as vehicle manufacturing, repair, and traffic management, and realize the parsing and conversion of interface protocols. The protocols include but are not limited to OBD, UDS, DoIP, REST API, MQTT, etc.; An interface standard management unit, which is used to maintain and manage the technical specifications and version information of various docking interfaces, and support protocol mapping, interface upgrade and compatibility adaptation between different systems; The permission and identity authentication unit is used to authenticate the access behavior of external systems and control permissions. The authentication mechanisms include OAuth2.0, PKI, or role-based access control methods, and support access log recording and audit tracking; The two-way data exchange unit is used to achieve two-way communication between the system and external platforms, including pushing vehicle operation status data to the manufacturer platform, repair service platform, or traffic management system, or receiving control instructions, task scheduling information, or warning notices issued by external platforms to achieve closed-loop control linkage; The interface call monitoring and visualization management unit is used to monitor the interface call status, response latency, and communication exceptions in real time, support interface flow limiting, fusing, and fault tolerance mechanisms, and provide graphical call auditing and performance statistics functions.

[0009] Furthermore, the task collaboration and data flow management module includes: The data link management unit is used to build a data transmission path between the multi-modal sensor unit, edge computing module, cloud platform data processing unit, and digital twin module, and support the compression, buffering, classification, and forwarding scheduling of data streams; The task scheduling control unit is used to dynamically allocate the execution location of analysis tasks or prediction models based on the system computing resource load, network status, and model type, and achieve the migration and scheduling of models between the edge side and the cloud platform; The event priority response unit is used to set priorities for system tasks or data channels according to the fault level, abnormal indicators, or external task types, and achieve the priority processing and scheduling execution of high-priority events; The closed-loop control feedback unit is used to feedback the cloud platform processing results, digital twin prediction results, or external platform control instructions to the edge computing module or vehicle control system to achieve cross-module closed-loop control; The operation status monitoring and adaptive optimization unit is used to monitor the operation status of each functional module and communication link, and adjust the data flow strategy and task scheduling parameters based on performance feedback.

[0010] In summary, the present invention has the following beneficial effects: By constructing a multi-modal sensor unit including environmental perception, position status, visual perception, inertial measurement, etc., the problems of single data source and weak collaboration ability of traditional systems are solved; By cooperating with the signal acquisition and preprocessing unit, unified acquisition, synchronous processing, and standardized conversion of different types of signals can be achieved, providing high-quality input for subsequent cloud modeling and twin mapping; By introducing blockchain encryption and decentralized data storage mechanisms into the data security module, and combining smart contract control and cross-platform data sharing mechanisms, data anti-tampering, access traceability, and authorized hierarchical management under the participation of multiple parties are achieved, enhancing the adaptability of the system in fields with high data security requirements such as government affairs, finance, and manufacturing; By setting up a task collaboration and data flow management module to overall coordinate the data transmission paths, task priorities, and control feedback among various functional modules, ensuring the system maintains operational stability and response consistency in complex task scenarios, and further optimizing the system resource allocation and linkage execution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. 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.

[0012] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Embodiment

[0014] The following further elaborates on the present invention Figure 1 in detail.

[0015] Please refer to Figure 1 , the present invention provides a technical solution: a vehicle status monitoring system based on a big data cloud platform, as Figure 1 shown, including: A multi-modal sensor unit for collecting vehicle internal and external status data; An edge computing module for preprocessing the collected data, screening for anomalies, and uploading the data to the cloud platform; A cloud platform data processing unit including data fusion, intelligent adaptive analysis, and fault prediction modules; A digital twin module for establishing a virtual model corresponding to the physical vehicle and performing real-time dynamic mapping; The data security module, based on blockchain technology, realizes data encryption, decentralized storage, and cross-platform secure sharing; The open interface module supports interoperability with external systems such as vehicle manufacturing, maintenance, and traffic management; The task coordination and data flow management module is used to overall coordinate the above modules; In this embodiment: The multi-modal sensor unit can comprehensively cover various working condition changes during vehicle operation, including mechanical, electrical, behavioral, and environmental factors, providing high-dimensional feature input for subsequent calculation models, and through redundant design and fusion algorithms, the robustness and anti-interference ability of the system are also enhanced; By setting up the edge computing module, the system has a certain pre-processing ability, which can quickly identify risk events such as overheating, sudden braking, and yaw at the data generation source, thereby improving the system response efficiency and reducing the cloud communication load at the same time.

[0016] Such as Figure 1 As shown, the multi-modal sensor unit includes: The environmental sensing module is used to collect vehicle external and internal environment parameters, including: The temperature sensor is used to detect the engine compartment, coolant, and external environment temperature; The humidity sensor is used to monitor the air humidity inside and outside the vehicle; The air pressure or pressure sensor is used to detect tire pressure, oil pressure, and braking system pressure; The dynamic state sensing module is used to collect vehicle motion and working condition information, including: The acceleration sensor is used to detect the vehicle's acceleration and vibration conditions; The gyroscope is used to obtain vehicle angular velocity or steering change information; The angle sensor is used to monitor engine and wheel speed information; The position and positioning module is used to realize vehicle positioning and motion trajectory monitoring, including: The Beidou positioning unit is used to provide real-time position information; The inertial measurement unit, including a three-axis accelerometer and a three-axis gyroscope, is used to estimate the attitude and motion state when the GPS signal is poor; The visual information acquisition module is used to collect vehicle surrounding images and auxiliary information, including: At least one camera is used to capture front, rear, and surrounding environment images; Optional infrared cameras or lidar are used to supplement visual data in low-light or extreme environments; The signal acquisition and preprocessing unit is used to preliminarily process the signals collected by the above modules, including: The data acquisition circuit unit is used to convert analog or digital signals into standardized data and perform filtering, amplification, and noise suppression; The digital signal processor is used to collect, preprocess, and compress data in real time; The local storage cache unit is used for short-term data storage and timestamp synchronization; The communication interface unit is used to realize data transmission between the sensor module and other modules inside the vehicle and the edge computing module, including: Wired interfaces, such as CAN bus or Ethernet interface; Wireless interfaces, such as Wi-Fi, Bluetooth, or 5G communication upload unit.

[0017] In this embodiment: Among the above, the inertial measurement unit is composed of a three-axis accelerometer and a three-axis gyroscope, which can estimate the vehicle's position, speed, and attitude in the absence of external positioning signals. Through inertial navigation algorithms, it can achieve short-term autonomous positioning and dynamic trajectory restoration. It should be added that among the above, the visual information acquisition module supports front-view, rear-view, and panoramic cameras, and at the same time is equipped with lidar, which can be used to generate three-dimensional point cloud data of the environment, and can realize enhanced environmental perception redundancy under extreme weather conditions such as low light or rain and snow. Regarding the setting of the steering angle sensor, it is used to monitor the rotation angle of the steering mechanism in real time, and it can cooperate with vehicle speed information to judge the curvature radius, driving stability, and driving behavior, thereby providing a basis for steering system fault warning; By setting the environmental sensing module, the system can effectively sense the internal and external environmental conditions of the vehicle through the sensors such as temperature, humidity, and pressure during actual use, thereby improving driving safety; Moreover, through the fusion of dynamic parameters such as acceleration, steering angle, and gyroscope, the recognition of the vehicle's motion posture and driving behavior can be realized, and thus the functions such as fatigue detection and collision recognition preprocessing can be effectively implemented; Therefore, through cooperation with the subsequent edge computing module, the above content enables the sensor unit to support local risk preliminary judgment and warning output, and issue a brake prompt or control strategy within one second before an accident, thereby providing a trigger basis for active and passive safety functions.

[0018] Such as Figure 1 As shown, the edge computing module includes: The data reception and input unit is used to receive the vehicle state data collected by the multi-modal sensor unit and perform unified time management and data buffering; The data preprocessing unit is used to perform standardized processing on the received raw data, including filtering, amplification, denoising, and normalization operations to improve data quality; Anomaly screening unit, which is used to identify anomalies in the preprocessed data based on set rules or lightweight models, and determine whether there are abnormal events or fault signs; Local cache and data storage unit, which is used to temporarily store abnormal event data or key data, and ensure data is not lost when communication is interrupted or the network is unstable; Communication upload unit, which is used to upload the preprocessed data or anomaly identification results to the cloud platform data processing unit by wired or wireless means; In this embodiment, since vehicle status monitoring involves a large amount of data, high frequency, and complex sensor types, if completely relying on cloud processing will lead to significant delays. Therefore, preliminary processing and screening are completed locally through edge computing, which can greatly reduce communication delays and computing burdens, so as to meet the low-latency decision-making requirements in the high-speed driving environment. And through the local cache mechanism of the edge module, data loss caused by temporary network disconnection or platform anomalies can be prevented, and at the same time, it has CRC check and data integrity check functions, and then the uploaded data can be ensured to be accurate and error-free to support subsequent model training and traceability.

[0019] As Figure 1 shown, the cloud platform data processing unit includes: Data fusion module, which is used to perform time alignment, spatial association, and structure unification on the data uploaded by the multi-modal sensor units of multiple vehicles, solve the problems of data source heterogeneity and spatio-temporal misalignment, and realize multi-source data fusion; Intelligent adaptive analysis module, which is used to deeply analyze the vehicle status based on inputs such as historical operation data, vehicle behavior patterns, and environmental factors, and identify operation trends, behavior characteristics, and potential risks through adaptive modeling, clustering analysis, or classification algorithms; Fault prediction module, which is used to build a prediction model based on multi-dimensional time series data and state variables, identify and probabilistically evaluate possible faults in advance, and output fault types, severity levels, and prediction time windows; Model training and update module, which is used to train or optimize the intelligent analysis model and fault prediction model in combination with historical data, and perform personalized parameter fine-tuning or model redeployment according to vehicle usage scenarios; Scheduling and response module, which is used to generate actionable control instructions, maintenance suggestions, or policy feedback from the analysis and prediction results, and issue them to the edge computing module or vehicle internal control unit through the communication upload unit to achieve remote closed-loop management; In this embodiment, the data fusion module is used to normalize and integrate heterogeneous data uploaded from edge computing nodes. The fusion objects include vehicle attitude data, visual image summaries, environmental feature parameters, etc. The fusion process introduces time series synchronization, spatial alignment, and semantic matching mechanisms, and uses algorithms such as cooperative Kalman filtering and multi-modal attention mechanisms to eliminate redundancy and complete missing data, improving information consistency. By introducing a combination architecture of multi-modal fusion, adaptive analysis, deep prediction, intelligent training, and linkage response, an intelligent decision-making center is formed. This module is particularly suitable for high-load and multi-variable operating environments such as urban buses, intelligent logistics fleets, and autonomous driving test platforms.

[0020] As Figure 1 shown, the data twin module includes: An entity modeling unit, which is used to build corresponding three-dimensional geometric models and functional topology models based on the physical structure, system configuration, and component attributes of the vehicle, so as to establish a digital vehicle basic model; A data mapping and synchronization unit, which is used to receive the vehicle status data uploaded by the edge computing module and dynamically map the status data to the corresponding virtual vehicle model to achieve real-time synchronization between the physical vehicle and the virtual model; A status simulation and prediction unit, which is used to perform operation status evolution simulation and potential fault trend prediction based on the current vehicle status and historical data, using rule reasoning, machine learning, or time series prediction algorithms; A visualization interaction unit, which is used to graphically present the operation status, historical trajectory, and key parameter change information of the virtual vehicle, and support functions such as panoramic display, local magnification, multi-dimensional switching, and playback of the vehicle; A feedback and control interface unit, which is used to generate control instructions based on the simulation results or prediction and warning information, and send them to the edge computing module or the vehicle control system through the communication link to achieve remote monitoring and dynamic adjustment.

[0021] As Figure 1 shown, the data security module includes: A data encryption unit, which is used to encrypt the collected vehicle status data, edge computing results, and cloud processing results to prevent data theft and tampering during transmission or storage. The encryption algorithms include symmetric encryption and asymmetric encryption; A blockchain evidence storage unit, which is used to record key event data or data summary information in the form of hashes into the blockchain ledger to build an immutable data trusted chain structure and improve data traceability; A decentralized data storage unit, which is used to slice and distribute the collected data to multiple nodes for storage, improving the system's disaster tolerance and access efficiency. The storage structure supports IPFS or a hybrid cloud-edge collaborative architecture; The permission control and smart contract unit is used to authorize and control the access of external entities (such as manufacturers, repair plants, management platforms, etc.) to vehicle data through smart contracts based on predefined policies. The access permissions support hierarchical setting and an automatic trigger mechanism; The cross-platform data sharing unit is used to achieve the trusted circulation and secure sharing of data between multiple platforms and systems, support data call logging and access behavior tracking to ensure the security and compliance of data during cross-domain transmission.

[0022] Such as Figure 1 shown, the open interface module includes: The external system access adaptation unit is used to identify and be compatible with the heterogeneous communication protocols and data structures adopted by external systems such as vehicle manufacturing, repair, and traffic management, and realize the parsing and conversion of interface protocols. The protocols include but are not limited to OBD, UDS, DoIP, REST API, MQTT, etc.; The interface standard management unit is used to maintain and manage the technical specifications and version information of various docking interfaces, and support protocol mapping, interface upgrade, and compatibility adaptation between different systems; The permission and identity authentication unit is used to authenticate the access behavior of external systems and control permissions. The authentication mechanism includes OAuth2.0, PKI, or role-based access control methods, and supports access logging and audit tracking; The bidirectional data exchange unit is used to achieve two-way communication between the system and external platforms, including pushing vehicle operation status data to the manufacturer platform, repair service platform, or traffic management system, or receiving control instructions, task scheduling information, or warning notices issued by external platforms to achieve control closed-loop linkage; The interface call monitoring and visualization management unit is used to monitor the interface call status, response latency, and communication anomalies in real time, support interface flow control, circuit breaker, and fault tolerance mechanisms, and provide graphical call audit and performance statistics functions.

[0023] Such as Figure 1 shown, the task collaboration and data flow management module includes: The data link management unit is used to build a data transmission path between the multimodal sensor unit, edge computing module, cloud platform data processing unit, and digital twin module, and support the compression, buffering, classification, and forwarding scheduling of data streams; The task scheduling control unit is used to dynamically allocate the execution location of analysis tasks or prediction models based on the system computing resource load, network status, and model type, and realize the migration and scheduling of models between the edge side and the cloud platform; An event priority response unit, configured to set priorities for system tasks or data channels according to fault levels, abnormal indicators, or external task types, so as to implement the preferential processing and scheduling execution of high-priority events; A closed-loop control feedback unit, configured to feedback the cloud platform processing result, digital twin prediction result, or external platform control instruction to the edge computing module or vehicle control system, so as to implement cross-module closed-loop control; An operating state monitoring and adaptive optimization unit, configured to monitor the operating states of the various functional modules and communication links, and adjust the data flow strategy and task scheduling parameters based on performance feedback.

[0024] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0025] The above has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and the descriptions in the specification are only used to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and all these changes and improvements fall within the scope of the present invention claimed.

Claims

1. A vehicle status monitoring system based on a big data cloud platform, characterized in that: include: Multimodal sensor unit; Edge computing module; Cloud platform data processing unit; Digital twin module; Data security module; Open interface module; Task coordination and data flow management module.

2. A vehicle status monitoring system based on a big data cloud platform according to claim 1, characterized in that: The multimodal sensor unit comprises: Environmental sensor module, used to collect vehicle external and internal environmental parameters, including: Temperature sensors, humidity sensors, and barometric or pressure sensors; Dynamic state sensing module, used to collect vehicle motion and working condition information, including: Accelerometer, gyroscope and angle sensor; Position and location module, used to realize vehicle positioning and motion trajectory monitoring, including: Beidou positioning unit and inertial measurement unit; The visual information acquisition module is used to collect vehicle surrounding images and auxiliary information, including: Cameras and lidar for capturing images of the front, back, and surrounding environment; The signal acquisition and preprocessing unit is used to perform preliminary processing on the signals collected by the above modules, including: Data acquisition circuit unit, digital signal processor and local storage cache unit; Communication interface unit, comprising: Wired interface and wireless interface.

3. The vehicle status monitoring system based on the big data cloud platform according to claim 2 is characterized in that: The edge computing module includes: Data receiving and input unit; Data preprocessing unit; Abnormal screening unit; Local cache and data storage unit; Communication upload unit.

4. The vehicle status monitoring system based on the big data cloud platform according to claim 3 is characterized in that: The cloud platform data processing unit includes: Data fusion module; Intelligent adaptive analysis module; Fault prediction module; Model training and updating module; Scheduling and response module.

5. The vehicle status monitoring system based on the big data cloud platform according to claim 4 is characterized in that: The data twin module includes: Solid Modeling Unit; Data mapping and synchronization unit; State simulation and prediction unit; Visualization interaction unit; Feedback and control interface unit.

6. The vehicle status monitoring system based on the big data cloud platform according to claim 5 is characterized in that: The data security module comprises: Data encryption unit; Blockchain evidence storage unit; Decentralized data storage unit; Permission control and smart contract unit; Cross-platform data sharing unit.

7. The vehicle status monitoring system based on the big data cloud platform according to claim 6 is characterized in that: The open interface module comprises: External system access adapter unit; Interface standards management unit; Permission and identity authentication unit; Bidirectional data exchange unit; Interface call monitoring and visualization management unit.

8. The vehicle status monitoring system based on the big data cloud platform according to claim 5 is characterized in that: The task coordination and data flow management module includes: Data link management unit; Task scheduling control unit; Incident Priority Response Unit; Closed-loop control feedback unit; Operation status monitoring and adaptive optimization unit.

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