Real-time data stream analysis method and system for international market of automobiles

Abstract

This invention relates to the field of distributed streaming data processing and dynamic reconfiguration of cross-domain processing logic, and particularly to a real-time data stream analysis method and system for the international automotive market. The method includes: acquiring pure physical kinematic state data of the vehicle and pre-authorized original environmental data from a pre-defined system; calculating and generating a transient symmetric key; encrypting the original environmental data into ciphertext data and writing it into a memory buffer; inputting the kinematic data into a gradient descent optimization model, fine-tuning the feature transformation computation graph weight matrix and bias terms, and outputting a new rule domain matching operator path; reading the hardware clock cycle value and injecting it into the header of the ciphertext data tensor to generate a routing marker, guiding the data flow to the new operator path; after confirming the path switch, sending an overwrite command to replace the transient symmetric key with an all-zero physical state, blocking the decryption and restoration path of the remaining ciphertext data. This invention solves the problem of feature data leakage or computational interruption caused by differences in feature transformation standards during cross-regional driving.

Description

Technical Field

[0001] This invention relates to the field of distributed streaming data processing and cross-domain processing logic dynamic reconfiguration technology, and particularly to a real-time data stream analysis method and system for the international automotive market. Background Technology

[0002] Real-time data stream analytics technology for the international automotive market involves the instant processing and logical operations of continuous and massive data sequences generated by sensors. The core components of a real-time data stream analytics system for the international automotive market include a distributed streaming computing engine, data acquisition operators, and a memory buffer. The distributed streaming computing engine, as the system's core scheduling hub, is responsible for breaking down complex data processing tasks into multiple parallel micro-units and distributing them to in-vehicle terminals or edge nodes for low-latency computation. Data acquisition operators are the basic functional units that execute specific business logic, specifically responsible for filtering, format conversion, and preprocessing raw sensor data. To buffer the mismatch between input and processing rates, the system expands temporary storage space through a memory buffer to store the raw feature data to be processed, maintaining the continuity of the data link. When vehicles move between different geographical regions globally, the real-time data stream analytics system for the international automotive market must reconstruct the processing logic in real time based on the rule boundary information fed back by geofencing.

[0003] Existing cross-regional vehicle-mounted streaming processing technologies supporting dynamic perception of multi-regional data rule matching present the following technical pain points: When a vehicle crosses a regional boundary, the vehicle-mounted sensor faces differentiated feature conversion rules for image information, point cloud data, and biometric information from different management domains. Due to the millisecond-level real-time evolution differences in the definition of feature fields in the control rules of each region, if the data acquisition operators inside the distributed streaming computing engine adopt a hard-coded method, it is impossible to achieve non-stop synchronous updates of the rule matching logic. As a result, when the computing node processes high-frequency concurrent data streams, the unprocessed original feature data cached in the memory buffer space may be directly transmitted to the backend cloud server due to operator logic failure, or the computing task may be forcibly interrupted due to the system protection mechanism triggered by logic verification failure. For example, when a vehicle enters a second region requiring panoramic semantic segmentation processing from the first region that performs facial partial blurring standard, the standard image of the first region stored in memory may be incorrectly identified as the matching data of the second region and sent out due to operator update delay, thus causing a serious risk of instantaneous escape of original feature data. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a real-time data stream analysis method and system for the international automotive market. This invention solves the technical problem that, due to the millisecond-level real-time evolution differences in the feature conversion standards of vehicle sensing data across different rule domains globally, the streaming computing engine cannot synchronously update the safety rule matching logic of the safety processing operator during cross-regional driving, resulting in the instantaneous leakage of original feature data in the memory buffer space or the forced interruption of the computing task.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows:

[0006] Firstly, the real-time data stream analysis method for the international automotive market provided by this invention includes:

[0007] Step 1: Acquire pure physical kinematic state data of the vehicle and receive raw environmental data collected according to the preset system authorization;

[0008] Step 2: Calculate and generate a transient symmetric key, and use the transient symmetric key to convert the original environment data into ciphertext data and write it into the memory buffer space;

[0009] Step 3: Input the vehicle's pure physical kinematic state data into the gradient descent optimization model, calculate the network weight update gradient, use the network weight update gradient to fine-tune the weight matrix and bias terms of the preset feature transformation calculation graph, and output the fine-tuned new rule domain matching operator path.

[0010] Step 4: Read the hardware clock cycle value, expand the feature dimension in the tensor header of the ciphertext data and inject the hardware clock cycle value to generate a routing mark, and guide the ciphertext data to the new rule domain matching operator path according to the routing mark;

[0011] Step 5: After confirming that the ciphertext data flows to the new rule domain matching operator path, send an overwrite level pulse command to the storage space storing the transient symmetric key to replace the transient symmetric key with an all-zero physical state, thereby blocking the decryption and restoration path of the ciphertext data remaining in the memory buffer space.

[0012] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein the step of inputting the vehicle's pure physical kinematic state data into the gradient descent optimization model to calculate the network weight update gradient includes:

[0013] The global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed and yaw angle included in the vehicle's pure physical kinematic state data are extracted to form a real-time dynamic vector; the global positioning coordinates are converted into a relative distance scalar with a preset regional boundary line as the absolute origin using the Earth curvature model;

[0014] The relative distance scalar and the chassis wheel speed pulse frequency are used to calculate and generate a high-dimensional spacetime manifold tensor.

[0015] The relative distance scalar is mapped to the dynamic learning rate parameter of the gradient descent optimization model;

[0016] Using the high-dimensional spatiotemporal manifold tensor and the dynamic learning rate parameter, backpropagation is performed in the gradient descent optimization model to output the network weights and update the gradient.

[0017] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein the extraction of global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle from the vehicle's pure physical kinematic state data to form a real-time dynamic vector includes:

[0018] By removing the user identity-related attributes from the collected global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle, anonymized physical parameters are obtained.

[0019] The sampling frequency of the anonymized physical parameters is adjusted using the relative distance scalar;

[0020] When the distance from the boundary line of the region is less than a preset distance threshold, the sampling frequency is increased to increase the dimensionality density of the real-time dynamic vector.

[0021] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein the step of calculating and generating a transient symmetric key, and using the transient symmetric key to convert the original environmental data into ciphertext data and write it into a memory buffer space, includes:

[0022] Before the original environment data is written into the memory buffer space, a key generation request is initiated;

[0023] Based on the electronic geofence feature encoding of the legal area where the vehicle is currently located, the transient symmetric key is output;

[0024] When a control instruction to write the original environmental data into the memory buffer space is captured, streaming encrypted computation is activated using an idle clock cycle.

[0025] The plaintext environment pixels and point cloud 3D coordinates included in the original environment data are converted into a ciphertext sequence through bitwise XOR logic, and the ciphertext sequence is used as the ciphertext data.

[0026] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein converting the plaintext environmental pixels and point cloud 3D coordinates included in the original environmental data into a ciphertext sequence through bitwise XOR logic, includes:

[0027] During direct memory access, the instantaneous minute deviation value of the yaw angle included in the pure physical kinematic state data of the vehicle is extracted;

[0028] The instantaneous minute deviation value is injected as a scrambling factor into the initial vector of the bitwise XOR logic;

[0029] The transient symmetric key, after being injected with the scrambling factor, is used to perform transparent streaming encryption on the continuously input original environmental data to obtain the ciphertext sequence; the converted ciphertext sequence is then written into the memory buffer space.

[0030] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein reading the hardware clock cycle value, expanding the feature dimension in the tensor header of the encrypted data, and injecting the hardware clock cycle value to generate a routing tag, includes:

[0031] When a precise out-of-bounds low-level signal is received, a system management interrupt is triggered to take over the current execution privileges.

[0032] Read the high-precision event count value to obtain the absolute hardware clock cycle value at the moment of boundary crossing, and use it as the hardware clock cycle value;

[0033] The tensor structure of the currently unprocessed ciphertext data is dynamically reduced in dimensionality to release the reserved data space.

[0034] The privileged tag feature dimension, including the hardware clock cycle value, is written into the header of the data placeholder space to generate the routing tag.

[0035] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein guiding the encrypted data stream to the new rule domain matching operator path according to the routing marker includes:

[0036] Analyze the privileged marker feature dimensions;

[0037] The value of the privileged marker feature dimension is compared with the preset rule domain switching time threshold;

[0038] Under the premise of comparison and matching, the address pointer of the new rule domain matching operator path is injected into the data processing layer;

[0039] By using instruction-level conditional branch activation instructions, the subsequently passed ciphertext data is routed to the execution branches of the new rule domain matching operator path, including deep blurring of faces or erasure of vehicle license plates.

[0040] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, after confirming that the encrypted data stream matches the operator path of the new rule domain, sends an overwrite level pulse instruction to the storage space storing the transient symmetric key, including:

[0041] Send a security message confirming the boundary violation with a digital signature;

[0042] After verifying the legality of the boundary violation confirmation security message, the system extracts the chassis wheel speed pulse frequency from the vehicle's pure physical kinematics data and calculates the feature destruction frequency. Further, the system extracts the velocity component from the vehicle's pure physical kinematics data and constructs a coupled feedback function between the evolution frequency and the encrypted data flow rate. The boundary crossing time is obtained by calculating the ratio of the preset physical buffer width to the velocity component, and the difference between the boundary crossing time and the system hardware execution delay is configured as the trigger delay for sending the overwrite level pulse command. After the system detects a precise boundary violation signal and the trigger delay has elapsed, the hardware control unit immediately sends an overwrite level pulse command to the storage space storing the transient symmetric key according to the feature destruction frequency.

[0043] Furthermore, the real-time data stream analysis method for the international automotive market described in this invention, wherein replacing the transient symmetric key with an all-zero physical state to block the decryption and restoration path of the ciphertext data remaining in the memory buffer space, includes:

[0044] Clear any remaining key copies in the internal cache and broadcast a global cache invalidation signal;

[0045] By replacing the transient symmetric key with a physical-level annihilation of a completely zero physical state, the mathematical reverse path for decrypting and restoring the remaining ciphertext data is blocked.

[0046] The remaining encrypted data is directly converted into high-entropy random thermal noise at the underlying layer;

[0047] Also includes:

[0048] Before sending the overwrite level pulse command, the velocity component in the vehicle's pure physical kinematic state data is extracted to construct a coupled feedback function between the evolution frequency and the ciphertext data flow rate;

[0049] The boundary crossing time is obtained by calculating the ratio of the preset physical buffer band width to the velocity component, and the difference between the boundary crossing time and the system hardware execution delay is configured as the trigger delay for sending the overwrite level pulse command.

[0050] After the trigger delay is reached, the overwrite level pulse command is sent according to the characteristic destruction frequency;

[0051] Using the hardware clock cycle value as a synchronization anchor, the activation time of the new rule domain matching operator path and the physical failure time of the ciphertext data remaining in the memory buffer space are controlled to be in the same clock cycle at the global clock level.

[0052] Secondly, the real-time data stream analysis system for the international automotive market provided by this invention is applied to the real-time data stream analysis method for the international automotive market as described above, including:

[0053] The data acquisition module is used to acquire pure physical kinematic state data of the vehicle and receive raw environmental data collected according to the preset system authorization.

[0054] An encryption writing module is used to calculate and generate a transient symmetric key, and use the transient symmetric key to convert the original environment data into ciphertext data and write it into a memory buffer space;

[0055] The model fine-tuning module is used to input the pure physical kinematic state data of the vehicle into the gradient descent optimization model, calculate the network weight update gradient, use the network weight update gradient to fine-tune the weight matrix and bias terms of the preset feature transformation calculation graph, and output the fine-tuned new rule domain matching operator path.

[0056] The routing guidance module is used to read the hardware clock cycle value, expand the feature dimension in the tensor header of the ciphertext data and inject the hardware clock cycle value to generate a routing mark, and guide the ciphertext data to the path of the new rule domain matching operator according to the routing mark;

[0057] The overwrite blocking module is used to send an overwrite level pulse command to the storage space storing the transient symmetric key after confirming that the ciphertext data flows to the path of the new rule domain matching operator, thereby replacing the transient symmetric key with an all-zero physical state and blocking the decryption and restoration path of the ciphertext data remaining in the memory buffer space.

[0058] Beneficial effects of this invention:

[0059] This invention provides a real-time data stream analysis method and system for the international automotive market. By establishing a smooth evolution mechanism for a differentiable computational graph based on a regional spatiotemporal manifold, and utilizing a gradient descent optimization model to fine-tune the weight matrix and bias terms of the feature transformation computational graph at the parameter level and perform structural pruning, it effectively eliminates the cold start latency and memory jitter caused by switching existing hard-coded operators. The streaming access layer, combined with a memory microarchitecture-level inline encryption mechanism, calls a hardware random number generator to generate a regionally unique transient symmetric key. Transparent hardware streaming encryption is implemented on the physical channel through which raw environment data is written to dynamic random access memory, forcibly converting all raw feature data residing in the memory buffer space into ciphertext bound to the current management domain. This physically eliminates the data security risks caused by plaintext flow of features without feature transformation within the system. The data stream tensor dimensionality reduction elastic routing technology, relying on instruction set architecture-level timestamps, directly injects hardware clock cycle values ​​into the tensor header of the ciphertext data, achieving lossless elastic routing switching of feature transformation logic at the pipeline instruction level, avoiding computational task interruptions caused by bus suspension or backpressure pauses. By combining the absolute isolation of illegal data based on transient key hardware annihilation, and using microsecond-level privileged instructions to drive the dedicated encryption key physical register to perform level clearing, the remaining unprocessed historical sensor ciphertext data degenerates into high-entropy random thermal noise due to the physical annihilation of the decryption key. In conjunction with the coupling feedback function to dynamically configure the trigger delay of the overwrite level pulse instruction, the activation time of the new rule domain matching operator path and the physical failure time of the remaining ciphertext data are highly synchronized at the global clock level, completely cutting off the physical path of unauthorized transmission of historical feature information when crossing the management boundary. Attached Figure Description

[0060] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0061] Figure 1 This is a flowchart illustrating the real-time data stream analysis method for the international automotive market according to the present invention. Detailed Implementation

[0062] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0063] Please see Figure 1 In a first aspect, the real-time data stream analysis method for the international automotive market provided by this invention includes:

[0064] Step 1: Acquire pure physical kinematic state data of the vehicle and receive raw environmental data collected according to the preset system authorization;

[0065] Step 2: Calculate and generate a transient symmetric key, and use the transient symmetric key to convert the original environment data into ciphertext data and write it into the memory buffer space;

[0066] Step 3: Input the vehicle's pure physical kinematic state data into the gradient descent optimization model, calculate the network weight update gradient, use the network weight update gradient to fine-tune the weight matrix and bias terms of the preset feature transformation calculation graph, and output the fine-tuned new rule domain matching operator path.

[0067] Step 4: Read the hardware clock cycle value, expand the feature dimension in the tensor header of the ciphertext data and inject the hardware clock cycle value to generate a routing mark, and guide the ciphertext data to the new rule domain matching operator path according to the routing mark;

[0068] Step 5: After confirming that the ciphertext data flows to the new rule domain matching operator path, send an overwrite level pulse command to the storage space storing the transient symmetric key to replace the transient symmetric key with an all-zero physical state, thereby blocking the decryption and restoration path of the ciphertext data remaining in the memory buffer space.

[0069] In its specific implementation, the real-time data stream analysis method for the international automotive market provided by this invention acquires pure physical kinematic state data of the vehicle and receives raw environmental data collected according to preset system authorization. The pure physical kinematic state data of the vehicle includes wheel speed pulse frequency and vehicle speed extracted by the chassis domain controller, as well as yaw angle information output by the inertial measurement unit. The raw environmental data mainly refers to the dense point cloud sequence output by the onboard LiDAR, and the environmental image frames initially captured by the high-definition camera array according to preset safety control and feature conversion requirements. During vehicle operation, the underlying sensor interface continuously pulls the above two types of data streams from the vehicle control bus and perception network at a high frequency, transforming the dynamic displacement at the physical level and the surrounding three-dimensional spatial topology into a continuous input source for the digital world, providing the most basic boundary calculation material for subsequent rule matching determination and feature conversion processing.

[0070] A transient symmetric key is calculated and generated. This key is then used to convert the original environmental data into ciphertext data and write it to a memory buffer. Upon receiving the original environmental data input signal, the trusted execution environment built into the vehicle edge computing node immediately drives a hardware random number generator to extract the thermal noise entropy value at the current timestamp to generate the transient symmetric key. The lifecycle of the transient symmetric key is strictly bound to the vehicle's trajectory within the current single management domain. The underlying driver directly intercepts the write operation of the original environmental data at the memory bus physical layer, using the transient symmetric key to perform a high-frequency bitwise XOR operation on the pixel matrix of the environmental image frame and the 3D coordinates of the point cloud sequence. After the XOR operation, the pixel matrix and 3D coordinates become disordered and aliased, forming ciphertext data whose features cannot be recovered using conventional image processing algorithms. The system allocates the ciphertext data to a memory buffer with privileged access control attributes for resident processing, thus cutting off the physical possibility of plaintext data flow as soon as the data is written to disk.

[0071] The vehicle's pure physical kinematics data is input into the gradient descent optimization model to calculate the network weight update gradient. This gradient is then used to fine-tune the weight matrix and bias terms of the preset feature transformation computation graph, outputting a finely tuned new rule domain matching operator path. The gradient descent optimization model is built on the control plane of the streaming engine. After receiving wheel speed pulse frequency and six-axis attitude angle information, it maps the minute displacements of physical coordinates into high-dimensional tensor input feature vectors. The model uses the topological distance between the currently executed safety processing operator structure and the standard operator structure required by the preset target management region as the loss function, continuously deriving the network weight update gradient through backpropagation. During the vehicle's movement across physical buffer zones between adjacent regions, the system performs multiple rounds of high-frequency iterations on the preset feature transformation computation graph to be activated in memory based on the network weight update gradient. The weight matrix and bias terms of the preset feature transformation computation graph undergo smooth deformation during iteration, with the original license plate blurring operator gradually evolving into the face depth occlusion operator required by the target management region, ultimately generating a finely tuned new rule domain matching operator path adapted to the new rule domain feature transformation standard.

[0072] The system reads the hardware clock cycle value, expands the feature dimension in the tensor header of the encrypted data, and injects the hardware clock cycle value to generate a routing mark. Based on the routing mark, the encrypted data is guided to the new rule domain matching operator path. When the satellite positioning module, in conjunction with the inertial measurement unit, determines that the vehicle's physical coordinates precisely cross the management boundary line, the central processing unit triggers the underlying privileged interrupt mechanism and reads the high-precision event count value from the motherboard's high-precision event counter register as the hardware clock cycle value. The data access layer then forcibly expands the tensor header of the encrypted data queuing for processing in the memory buffer by a privileged mark feature dimension and writes the absolute hardware clock cycle value into the privileged mark feature dimension to generate the routing mark. The stream computing engine's scheduler compares the clock value in the routing mark with the system's preset rule domain switching time threshold, freezes the address pointer pointing to the initial operator path, and accurately distributes all subsequent encrypted data with the new routing mark to the finely tuned new rule domain matching operator path for processing.

[0073] After confirming that the encrypted data flow is directed to the new rule domain matching operator path, an overwrite level pulse command is sent to the storage space storing the transient symmetric key. This replaces the transient symmetric key with an all-zero physical state, blocking the decryption and restoration path of the remaining encrypted data in the memory buffer space. When the system's underlying data flow monitoring probe detects that the new rule domain matching operator path has successfully output the first processing result conforming to the new rule domain feature transformation standard, it sends an out-of-bounds confirmation message to the main control chip of the trusted execution environment. Upon receiving the message, the main control chip bypasses the operating system's regular file management layer and directly injects the highest-priority overwrite level pulse command into the static random access memory (SRAM) hardware array storing the transient symmetric key. The pulse command forcibly emptys the physical capacitor state in the SRAM and forcibly replaces the digital matrix of the transient symmetric key with an all-zero physical state. Due to the physical annihilation of the decryption key, the remaining encrypted data in the memory buffer space that has not yet been processed by the preceding operators instantly loses its mathematical reverse path for decryption and restoration, directly degenerating into high-entropy random thermal noise with no logical connection in the underlying storage medium.

[0074] The system extracts global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle from the vehicle's pure physical kinematics data to form a real-time dynamic vector. Global positioning coordinates encompass longitude, latitude, and altitude data received from satellite positioning systems, reflecting the absolute spatial position in the real physical world. The chassis wheel speed pulse frequency directly maps to the number of tire rolls on the road surface. Using an Earth curvature model, the global positioning coordinates are converted into a relative distance scalar with a preset regional boundary line as the absolute origin. The Earth curvature model incorporates a constant Earth radius for curvature compensation calculations, reducing the spherical three-dimensional coordinates to a one-dimensional relative distance scalar. The relative distance scalar represents the physical length of the vehicle's distance from the regional boundary line. A high-dimensional spatiotemporal manifold tensor is generated by calculating the relative distance scalar and chassis wheel speed pulse frequency. The dimension matrix of the high-dimensional spatiotemporal manifold tensor coheses both spatial proximity and physical speed. The relative distance scalar is then mapped to the dynamic learning rate parameter of a gradient descent optimization model. A smaller relative distance scalar value indicates that the vehicle is closer to the boundary line, and a larger dynamic learning rate parameter output by the system's internal mathematical mapping function. Using a high-dimensional spatiotemporal manifold tensor, backpropagation calculations are performed in the gradient descent optimization model based on the dynamic learning rate parameter, outputting the network weights to update the gradient. During the construction of the gradient descent optimization model, the system establishes a comprehensive training space containing a historical benchmark sample set and a target rule domain standard set. Anonymized physical parameters are extracted during vehicle movement to construct an input feature tensor, and a cross-rule domain operator topology transfer loss function is defined. When the relative distance scalar continuously changes with the vehicle's physical displacement, the optimization model uses the chain rule to calculate the gradient components in real time. The underlying algorithm logic injects a penalty term representing the boundary constraints into the weight matrix, driving asymmetric updates to the neuron connection strength of the feature transformation computation graph. Based on the method of directly driving model parameter evolution using physical motion vectors, the neural network architecture produces smooth topological deformation during vehicle crossing of the physical buffer zone, avoiding the computational link jitter caused by existing discrete switching.

[0075] In cross-regional logistics transportation scenarios, transport trucks drive at high speeds toward regional boundary nodes. The dynamic learning rate parameter increases exponentially as the physical distance decreases, driving backpropagation to calculate the high-frequency continuous output of network weights to update the gradient.

[0076] Anonymized physical parameters are obtained by removing user identity-related attributes from the collected global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle. These user identity-related attributes include vehicle identification code, driver account identification number, and in-vehicle media access control physical address. After the physical state data undergoes a cleaning logic to remove user identity-related attributes, it transforms into anonymized physical parameters that purely reflect motion trends, severing the mapping link between the trajectory and a specific natural person's identity. The sampling frequency of the anonymized physical parameters is adjusted using a relative distance scalar. The adjustment logic is executed synchronously according to the distance decay function; a decrease in the relative distance scalar value triggers a proportional increase in the sampling frequency value. When the distance to the boundary line is less than a preset distance threshold, the sampling frequency is increased to increase the dimensionality density of the real-time dynamic vector. The preset distance threshold is set as the actual physical width of the physical buffer zone at the boundary of the management domain. In cross-regional driving scenarios, when a passenger vehicle enters a range of two kilometers from the boundary line, the sampling frequency jumps from once per second to hundreds of times per second. The high-frequency sampled anonymized physical parameters continuously expand the density of the dimensional matrix in the real-time dynamic vector.

[0077] Before the raw environmental data is written to the memory buffer, a key generation request is initiated. The raw environmental data includes a 3D point cloud coordinate matrix constructed by LiDAR scanning and a red, green, and blue color pixel array captured by an optical camera array. When the memory direct access channel detects the input byte stream of the raw environmental data, it triggers a low-level hardware interrupt and initiates the key generation request. Based on the electronic geofence feature code of the legal area where the vehicle is currently located, a transient symmetric key is output. The electronic geofence feature code embeds the hash value of the vertex coordinates of the polygon corresponding to the geographical boundary of the management area, which, combined with the thermal noise entropy value at the low-level hardware level, continuously generates a transient symmetric key with a region-specific identifier. When the control command to write the raw environmental data to the memory buffer is captured, streaming encryption computation is activated using an idle clock cycle. The low-level bus arbitration logic identifies the idle write gap in the memory and seamlessly inserts the streaming encryption computation task. The plaintext environmental pixels and the 3D coordinates of the point cloud included in the raw environmental data are converted into a ciphertext sequence through bitwise XOR logic, and the ciphertext sequence is used as the ciphertext data. In the scenario of environmental monitoring at regional border ports, the plaintext environmental pixels of roadside facilities encounter bitwise XOR logic operations the moment they enter the computing layer, causing the three-dimensional coordinate values ​​to be randomly flipped and solidified into a ciphertext sequence without any semantic information.

[0078] During direct memory access, the instantaneous minute deviation of the yaw angle, included in the vehicle's pure physical kinematics data, is extracted. This yaw angle is affected by road surface bumps or changes in lateral wind resistance, resulting in highly irregular instantaneous minute deviations. These minute deviations are essentially highly unpredictable thermodynamic random perturbation variables in the real physical world. These minute deviations are injected as scrambling factors into the initial vector of the bitwise XOR logic. The bitwise XOR logic originally uses a static initial vector to induce a regular encryption pattern; the injection of the scrambling factor breaks the static property of the initial vector, causing the starting point of each encryption operation to be a completely random walk distribution. Using the transient symmetric key after the scrambling factor injection, transparent streaming encryption computation is performed on the continuously input raw environmental data to obtain the ciphertext sequence. Transparent streaming encryption computation relies on a hardware gate array to synchronously complete encryption and decryption permutations during data bus transmission, without causing additional delays to the main data flow task. The converted ciphertext sequence is then written into the memory buffer space. In complex road conditions across regions, every bump and pothole encountered by the chassis becomes a scrambling factor in the encryption process. The ciphertext sequence carries the natural encryption barrier generated by the physical road conditions and is stored in the memory buffer space.

[0079] Upon receiving a precise boundary violation signal at the underlying level, a system management interrupt is triggered, taking over the current execution privileges. This underlying level signal, verified by both inertial navigation and satellite positioning, is triggered by pulling up a pin on the system motherboard, indicating that the vehicle's physical coordinates have substantially crossed the management boundary. The system management interrupt has the highest hardware execution priority, forcibly suspending all running user-mode and kernel-mode threads within the operating system. The high-precision event count value is read to obtain the absolute hardware clock cycle value at the moment of the boundary violation, serving as the hardware clock cycle value. The high-precision event count value operates independently of the operating system software clock, continuously recording the total absolute oscillation cycles of the central processing unit since power-on. The tensor structure of the currently processed encrypted data is dynamically reduced in dimensionality to release reserved data space. The dynamic dimensionality reduction logic truncates and discards non-critical data bits at the end of the encrypted data's tensor matrix, forcibly freeing up a fixed-byte data space without allocating new memory addresses. A privileged marker feature dimension, including the hardware clock cycle value, is written to the beginning of the data space to generate a routing marker. In cross-regional convoy driving scenarios, the routing markers generated by the lead vehicle the instant it crosses the boundary line and the encrypted data form an inseparable physical binding relationship in the memory address.

[0080] The privileged marker feature dimension is analyzed. The hardware decoding logic strips away the outer wrapper frame format data packet of the routing marker, extracting the absolute hardware clock cycle value information carried internally. The value of the privileged marker feature dimension is compared with a preset rule domain switching time threshold. The preset rule domain switching time threshold is fixed in the underlying read-only memory chip, limiting the extremely short time judgment window for switching of multi-region rule matching logic. Under the premise of comparison and matching, the address pointer of the new rule domain matching operator path is injected into the data processing layer. The address pointer contains the physical starting base address of the fine-tuned operator in the memory buffer space. Using the instruction-level conditional branch activation instruction, the subsequently incoming encrypted data is routed to the execution branch of the new rule domain matching operator path, which includes deep face blurring or vehicle license plate erasure. The conditional branch activation instruction directly modifies the jump target of the program counter in the hardware pipeline decoding stage, guiding the massive data flow to the specific business execution branch. In the port scenario of crossing the area with strict data security control regulations, encrypted data containing pedestrian facial features is directly poured into the underlying execution branch of deep face blurring according to the address pointer.

[0081] A boundary violation confirmation security message with a digital signature is sent. This message includes a digital signature generated by an asymmetric encryption algorithm to prevent malicious tampering during transmission within the system bus. After verifying the message's validity, the chassis wheel speed pulse frequency is extracted from the vehicle's pure physical kinematics data, and the feature destruction frequency is calculated. The digital signature verification indicates that the new rule domain feature conversion logic has stably taken over the data flow. A higher chassis wheel speed pulse frequency indicates a faster current vehicle speed, and the feature destruction frequency is set to a higher frequency level accordingly. An overwrite level pulse command is sent to the storage space based on the feature destruction frequency. The sending period of the overwrite level pulse command is determined by the feature destruction frequency, and the pulse signal directly controls the internal charge erasure pin of the storage chip. In extreme driving scenarios involving high-speed tollbooth crossings, the extremely high wheel speed pulse frequency drives the feature destruction frequency to rise synchronously, and the overwrite level pulse command is continuously sent at a high frequency to the corresponding physical address in the storage space.

[0082] The remaining key copy in the internal cache is cleared and a global cache invalidation signal is broadcast. The internal cache includes the CPU's L1 and L2 caches. The global cache invalidation signal is forcibly synchronized and cleared across all computing cores according to the hardware consistency protocol. A physical-level annihilation process, using a transient symmetric key to replace the remaining ciphertext with a zero physical state, blocks the mathematical reverse path for decrypting and restoring the remaining ciphertext data. The physical capacitor storage unit corresponding to the transient symmetric key is completely cleared of its internal charge state by an overwrite level pulse, and the ciphertext data is completely removed from the mapping dictionary required for the decryption function. The remaining ciphertext data is directly converted into high-entropy random thermal noise at the underlying level. The velocity component in the vehicle's pure physical kinematics data is extracted to construct a coupled feedback function between the evolution frequency and the ciphertext data flow rate. The boundary crossing time is obtained by calculating the ratio of the preset physical buffer band width to the velocity component. The difference between the boundary crossing time and the system hardware execution delay is configured as the trigger delay for sending the overwrite level pulse command. The boundary crossing time represents the absolute physical operation time required for the vehicle to pass through the managed boundary buffer band; after deducting the system hardware execution delay, a targeted trigger delay is obtained. By using the hardware clock cycle value as a synchronization anchor, the activation time of the new rule domain matching operator path and the physical failure time of the ciphertext data remaining in the memory buffer space are controlled to be within the same clock cycle at the global clock level. In international patrol scenarios involving dense movement across multiple regions, the coupled feedback function adjusts the trigger latency in real time to cope with frequent acceleration and deceleration actions, and the global clock level forces the physical failure command of the preceding rule domain data and the activation command of the new rule domain operator to occur within the exact same physical microsecond.

[0083] Real-time data stream analysis technology for the international automotive market faces the challenge of rule adaptation during cross-regional driving. When a vehicle crosses a regional boundary, onboard sensors encounter differentiated feature transformation rules for image information and point cloud data from different management domains. Because the definitions of feature fields in the control rules of each region evolve in real time, the hard-coded security processing operators within the distributed streaming computing engine cannot achieve non-stop synchronous updates of the rule matching logic. When computing nodes process high-frequency concurrent data streams, unprocessed raw feature data cached in the memory buffer space is easily transmitted directly to the cloud server due to operator logic failure, or the computing task is forcibly interrupted due to the system protection mechanism being triggered by logic verification failure. To address the aforementioned technical pain points, the real-time data stream analysis method for the international automotive market provided by this invention constructs a dynamic feature transformation mechanism through deep collaboration between underlying hardware and cryptographic logic.

[0084] In specific application scenarios, the system performs actions such as acquiring pure physical kinematic state data of the vehicle and receiving raw environmental data collected according to preset system authorization. The pure physical kinematic state data includes low-level motion parameters such as wheel speed pulse frequency and yaw angle extracted by the chassis domain controller, while the raw environmental data includes point cloud sequences output by the onboard LiDAR and image frames acquired by the optical camera array. When receiving the raw environmental data input signal, the system's underlying layer drives an internal hardware random number generator to extract physical thermal noise and generate a transient symmetric key. The lifecycle of the transient symmetric key is strictly bound to the vehicle's trajectory within the current single management domain. The underlying driver intercepts the write operation of the raw environmental data and uses the transient symmetric key to perform transparent streaming encryption calculations on the pixel matrix of the image frame and the three-dimensional coordinates of the point cloud sequence. After encryption, the plaintext raw environmental data undergoes disordered aliasing, forming ciphertext data whose features cannot be restored using conventional image processing logic. The system allocates the ciphertext data to a memory buffer space with privileged access control attributes for resident processing.

[0085] While the data is encrypted on the disk, the system inputs the vehicle's pure physical kinematics data into the gradient descent optimization model. The gradient descent optimization model is built on the control plane of the streaming computing engine. After receiving the underlying motion parameters, it maps the minute displacements of the physical coordinates into high-dimensional tensor input feature vectors. The gradient descent optimization model uses the topological distance between the currently executed safety processing operator structure and the standard operator structure required by the target management region as the loss function, and continuously derives the network weight update gradient through the backpropagation algorithm. During the vehicle's movement across the physical buffer zone of adjacent region boundaries, the system performs hundreds or thousands of high-frequency iterations on the pre-set feature transformation computation graph dormant in memory based on the network weight update gradient. The weight matrix and bias terms of the pre-set feature transformation computation graph undergo continuous deformation during iteration. The original local feature blurring operator gradually evolves into the panoramic depth occlusion operator required by the target management region, ultimately generating a finely tuned new rule domain matching operator path adapted to the new rule domain feature transformation standard.

[0086] After the operator path fine-tuning is completed, the system reads the hardware clock cycle value. When the underlying sensor probe determines that the vehicle's physical coordinates have substantially crossed the management authority boundary line, the central processing unit triggers a privileged interrupt mechanism and reads the high-precision event count value from the motherboard's high-precision event counter register to obtain the absolute hardware clock cycle value at the moment of boundary crossing, which is then used as the hardware clock cycle value. The data access layer then forcibly expands the tensor header of the encrypted data waiting to be processed in the memory buffer space with a privileged marker feature dimension, and writes the hardware clock cycle value into the privileged marker feature dimension to generate a routing marker. The stream computing engine's scheduler freezes the addressing pointer pointing to the initial operator path by comparing the clock value carried by the routing marker with the system's preset rule domain switching time threshold. All subsequent encrypted data with updated clock values ​​are directly injected into the new rule domain to match the operator path, guided by the addressing pointer.

[0087] After confirming that the ciphertext data flows to the new rule domain matching operator path, the underlying monitoring logic sends an overwrite level pulse command to the storage space storing the transient symmetric key. This storage space is a static random access memory (SRAM) hardware array within the trusted execution environment. The overwrite level pulse command directly manipulates the internal charge erasure pins of the memory chip, forcibly emptying the internal physical capacitor state and replacing the digital matrix of the transient symmetric key with a completely zero physical state. The decryption key undergoes physical annihilation, causing the remaining ciphertext data in the memory buffer space that has not yet been processed by the preceding operators to instantly lose its mathematical reverse path for decryption and reconstruction. The remaining ciphertext data directly degenerates into high-entropy random thermal noise in the underlying storage medium.

[0088] Based on the aforementioned real-time data stream analysis method for the international automotive market, the system is configured with multiple hardware and logic modules operating in deep collaboration. The data acquisition module acquires the vehicle's pure physical kinematics state data and receives raw environmental data collected according to preset system authorization. The encryption and writing module calculates and generates a transient symmetric key, uses this key to convert the raw environmental data into ciphertext data, and writes it to a memory buffer. The model fine-tuning module inputs the vehicle's pure physical kinematics state data into the gradient descent optimization model, calculates the network weight update gradient, uses this gradient to fine-tune the weight matrix and bias terms of the preset feature transformation computation graph, and outputs the fine-tuned new rule domain matching operator path. The routing guidance module reads the hardware clock cycle value, expands the feature dimension in the tensor header of the ciphertext data, injects the hardware clock cycle value to generate a routing marker, and guides the ciphertext data flow to the new rule domain matching operator path based on the routing marker. After confirming that the ciphertext data flows to the new rule field matching operator path, the overwrite blocking module sends an overwrite level pulse command to the storage space storing the transient symmetric key, replacing the transient symmetric key with an all-zero physical state, thus blocking the decryption and restoration path of the remaining ciphertext data in the memory buffer space.

[0089] Vehicle kinematics data reflects the vehicle's absolute physical motion trend in real three-dimensional space. Specifically, it includes wheel speed pulse frequencies extracted by the chassis domain controller, longitudinal and lateral accelerations output by the inertial measurement unit, yaw angle, and global positioning coordinates received via a satellite positioning antenna. The underlying motion parameters are rigorously cleaned before being fed into the computation node, completely removing user identity attributes such as driver account identifiers and in-vehicle multimedia access control addresses. In cross-regional logistics transportation scenarios, even minor vibrations of the heavy-duty truck chassis and changes in wheel angles are faithfully recorded and converted into pure physical quantities. Subsequent calculations perform topology calculations based on the anonymized physical parameters, completely severing the mapping link between the driving trajectory and a specific individual's identity.

[0090] Raw environmental data refers to the multimodal physical scene information of the vehicle's surroundings initially captured by the vehicle-mounted perception network according to preset system security authorization standards. Specifically, this includes a 3D point cloud coordinate matrix constructed by vehicle-mounted LiDAR scanning, and a red, green, and blue color pixel array captured by a high-definition optical camera array. Environmental sensing probes continuously capture road markings, surrounding vehicle outlines, and pedestrian features at extremely high frame rates under complex road conditions. The unprocessed plaintext environmental pixels and 3D point cloud coordinate matrix directly present a true reflection of the physical world. Commuter vehicles operating across regions record massive amounts of road network features daily at regional border crossings; the raw, unprocessed data often contains controlled feature attributes that are highly likely to conflict with data management rules in different regions.

[0091] The transient symmetric key is independently derived from the trusted execution environment built into the vehicle-mounted edge computing node. The generation mechanism deeply integrates the electronic geofence feature encoding of the vehicle's current legal area and forcibly incorporates the thermal noise entropy value extracted by the underlying hardware true random number generator. The electronic geofence feature encoding embeds the hash value of the vertex coordinates of the polygon corresponding to the geographic boundary of the management area. By introducing highly unpredictable thermodynamic random perturbation variables from the physical world, the generated symmetric key possesses extremely high cryptographic strength and management domain uniqueness. Because its lifecycle is strictly bound to the driving trajectory within a single management domain, once the system's preset boundary is crossed, the physical capacitor state associated with the key will be forcibly depleted.

[0092] The encrypted data is derived from the aforementioned plaintext environment pixels and point cloud 3D coordinates through transparent streaming encryption computation performed by the underlying gate array hardware. In the direct memory access transmission path, the bus listening logic of the memory controller inserts bit-by-bit XOR logic during idle clock cycles. The initial vector of the bit-by-bit XOR logic is dynamically injected with the instantaneous minute deviation value of the chassis yaw angle as a scrambling factor. The originally clearly distinguishable image pixels and 3D point cloud coordinates completely lose their original spatial semantic features after being disordered and aliased. The transformed, irregular data stream is allocated to a memory buffer space with privileged access control attributes and locked there.

[0093] The feature transformation computation graph is the underlying neural network topology within the distributed streaming computing engine, responsible for executing specific feature security processing business logic. To eliminate system cold start latency caused by hard-coded operator switching, the model fine-tuning module uses network weights to update gradients and perform multiple rounds of smooth deformation on the weight matrix and bias terms of the preset feature transformation computation graph. In cross-regional platooning scenarios, the original operator network nodes executing local license plate fuzzing rules iterate continuously within the short time window when the vehicle passes through the region boundary buffer zone. The initial neural network structure parameters are gradually pruned and interpolated as the spatial coordinates shift, gradually evolving into the face depth occlusion operator required by the target region. After the evolution is finalized, the finely tuned new rule domain matching operator path is output.

[0094] The gradient descent optimization model is built on the control plane of the streaming engine and is specifically designed to evaluate the topological distance between the currently executing safety processing operator structure and the standard operator structure of the target management region. The model accepts a high-dimensional spatiotemporal manifold tensor as input. This tensor is generated by fusing the relative distance scalar output from the Earth curvature model's dimensionality reduction with the chassis wheel speed pulse frequency. The relative distance scalar maps the physical length of the vehicle's distance from the region boundary and, as a dynamic learning rate parameter, directly influences the convergence speed of the gradient descent algorithm. The closer the truck gets to the region boundary, the more exponentially the dynamic learning rate parameter increases, driving the backpropagation calculation to aggressively update the gradient using network weights.

[0095] Routing tags are used to accurately control the forward propagation direction of massive concurrent data streams at the memory physical addressing level. When the underlying hardware probe confirms that the physical coordinates substantially cross the management authority boundary, the central processing unit triggers a privileged interrupt mechanism and reads the total absolute time oscillation count from the motherboard's high-precision event counter register to obtain the hardware clock cycle value. The data access layer performs dynamic dimensionality reduction by truncating non-critical data bits of the tensor structure of the currently processed ciphertext data. The freed-up data space is used to forcibly write the dimension containing the privileged tag feature. The privileged tag feature dimension contains the hardware clock cycle value, which cannot be tampered with by the software application layer. Instruction-level conditional branch activation instructions directly modify the program counter's jump target using the comparison results from the decoding stage, guiding subsequent incoming data streams to specific execution branches.

[0096] The overwrite level pulse instruction directly acts on the static random access memory (SRAM) hardware array within the trusted execution environment. Once the digital signature verification confirms that the new rule field feature conversion logic has stably taken over the data flow, the main control chip calculates the feature destruction frequency based on the chassis wheel speed pulse frequency. The pulse signal, following the calculated frequency, bypasses the conventional operating system's file management layer and directly applies a high-level current to the physical pin storing the transient symmetric key. The physical capacitance state inside the SRAM is forcibly drained, and the digital matrix of the transient symmetric key is unconditionally replaced with a full-zero physical state. The physical-level annihilation of the decryption key at the silicon crystal layer permanently disables the corresponding encryption mechanism.

[0097] The residual ciphertext data specifically refers to historical sensor ciphertext information that has been written into the memory buffer space but has not yet been consumed by the preceding operators during the extremely short transition period of dynamic reconstruction of multi-region rule matching logic. The physical annihilation operation causes the transient symmetric key to completely disappear. The system clears the remaining key copy in the internal cache and broadcasts a global cache invalidation signal, making reverse decryption impossible globally. The information residing in the memory buffer space is completely deprived of the mapping dictionary support required by the decryption function. The binary code stream, having lost its mathematical decoding channel, directly degenerates in the underlying storage medium into high-entropy random thermal noise with no logical connection, exhibiting a state of maximized information entropy.

[0098] In the implementation of real-time data stream analysis methods for the international automotive market, to meet the requirements of transparency and full disclosure of underlying algorithm logic for data processing invention patents, transforming the vehicle's pure physical kinematic state data into a computable mathematical model is the fundamental core step in achieving dynamic reconstruction of cross-domain processing logic. The global positioning coordinates included in the acquired vehicle pure physical kinematic state data are input into the Earth curvature model. The Earth curvature model uses the principles of spherical trigonometry to project spherical coordinates into a one-dimensional straight-line distance in Euclidean space. The specific calculation formula for converting global positioning coordinates into a relative distance scalar with a preset regional boundary line as the absolute origin is expressed as follows:

[0099]

[0100] In the above formula, Represents a relative distance scalar. Represents the Earth's radius constant. This represents the current vehicle latitude value in global positioning coordinates. This represents the current longitude value of the vehicle in global positioning coordinates. The reference latitude value represents the default region boundary line as the absolute origin. The reference longitude value represents the absolute origin of the preset regional boundary line. The relative distance scalar, in physical meters, precisely quantifies the dynamic approximation degree of the vehicle approaching the boundary of a specific management area. To achieve the fusion of low-level features of multidimensional data, the computational processing layer performs tensor quantization reconstruction on the relative distance scalar and parameters such as chassis wheel speed pulse frequency, vehicle speed, and yaw angle. The tensor quantization reconstruction process maps physical quantities of different dimensions uniformly into a high-dimensional matrix space. To eliminate gradient calculation imbalance caused by differences in numerical magnitudes between different physical dimensions, the computational processing layer performs normalization mapping based on extreme values ​​or mean square error on the relative distance scalar, vehicle speed, chassis wheel speed pulse frequency, and yaw angle before tensor quantization construction, so that the values ​​of various input features are converged to the dimensionless interval [0,1], thereby generating a high-dimensional spatiotemporal manifold tensor. The specific matrix expression formula for generating the high-dimensional spatiotemporal manifold tensor is as follows:

[0101]

[0102] In the above formula, Represents the high-dimensional spacetime manifold tensor. Represents the normalized relative distance scalar. This represents the normalized vehicle speed. This represents the normalized chassis wheel speed pulse frequency. This represents the normalized yaw rate. The high-dimensional spatiotemporal manifold tensor, as the fundamental data structure of the gradient descent optimization model, carries the full spatiotemporal slice information of the vehicle in the physical world. To address the slow convergence or local oscillations caused by existing static learning rates, the computational processing layer maps the relative distance scalar to the dynamic learning rate parameter of the gradient descent optimization model. The dynamic learning rate parameter follows an inverse distance decay mapping logic, making the model update step more aggressive as the vehicle gets closer to the region boundary. The specific calculation formula for mapping the dynamic learning rate parameter is expressed as follows:

[0103]

[0104] In the above formula, Represents the dynamic learning rate parameter. The base learning rate constant represents the initial configuration of the system. Represents the spatial distance attenuation coefficient. Represents a relative distance scalar. This represents an exponential function with the natural constant as its base. When performing backpropagation calculations in a gradient descent optimization model using a high-dimensional spacetime manifold tensor with dynamic learning rate parameters, the core objective of the system is to minimize the structural difference loss function between the current operator and the standard operator. The backpropagation algorithm outputs the network weights and updates the gradients using the chain rule of differentiation. The specific formula for calculating the partial derivatives is expressed as follows:

[0105]

[0106] In the above formula, Represents the gradient of network weight updates. Represents the dynamic learning rate parameter. The loss function represents the difference between the currently executed security processing operator topology and the target management authority region standard operator topology. The weight matrix and bias terms represent the predefined feature transformation computation graph. This represents the partial derivative matrix of the loss function with respect to the weight matrix and bias terms. The weight matrix and bias terms of the preset feature transformation computation graph are continuously fine-tuned by updating the gradient using network weights, resulting in a smooth mathematical topological evolution of the feature transformation network structure as the physical distance decreases.

[0107] In the data encryption process, to prevent symmetric encryption algorithms from being attacked with known plaintext due to fixed initialization vectors, the encryption writing module introduces highly random physical noise during direct memory access. The instantaneous minute deviation of the yaw angle, included in the vehicle's pure physical kinematics data, is extracted and injected as a scrambling factor into the initialization vector of the bitwise XOR logic. The specific formula for updating the initialization vector of the bitwise XOR logic and performing transparent streaming encryption calculation is expressed as follows:

[0108]

[0109] In the above formula, This represents the initial vector for the bitwise XOR logic after the scrambling factor is injected. This represents the baseline initialization vector generated by the pseudo-random number generator. This represents the binary bitwise XOR operator. The scrambling factor is the instantaneous, minute deviation of the yaw angle. This represents the scalar amplification factor used to align the data bit width. This represents the ciphertext sequence after conversion. This represents the plaintext environment pixels and point cloud 3D coordinates included in the original environment data. This represents the transient symmetric key generated by computation. After the plaintext environment pixels and point cloud 3D coordinates undergo a double XOR perturbation, the physical visual features are completely broken down and solidified into a ciphertext sequence. To eliminate the time offset error caused by hardware instruction stacking, the control logic needs to establish an absolute synchronization anchor point at the time level. The velocity component is extracted from the vehicle's pure physical kinematics data to construct a coupled feedback function between the evolution frequency and the ciphertext data flow rate. The ratio of the preset physical buffer width to the velocity component is calculated to obtain the boundary crossing time. The difference between the boundary crossing time and the system hardware execution delay is configured as the trigger delay for sending the overwrite level pulse command. The specific calculation formula is expressed as follows:

[0110]

[0111] In the above formula, This represents the trigger delay for sending the overwrite level pulse command. This represents the preset physical buffer band width. The velocity component represents the vehicle's purely physical kinematic state data. This represents a comprehensive evaluation value of the system hardware execution latency of the underlying bus and storage controller.

[0112] The mathematical model and data processing path are verified and illustrated using a specific numerical example of a cross-regional freight vehicle traveling to a regional border port. The system obtains the vehicle's latitude as 39 degrees North and longitude as 116 degrees East, with the Earth's radius constant set to 6,371,000 meters. Substituting these values ​​into the Earth's curvature model, the relative distance scalar is calculated to be 2,000 meters. The vehicle's speed is 20 meters per second, the chassis wheel speed pulse frequency is 60 Hz, and the yaw angle is 0.2 radians. These values ​​are simultaneously filled into a 2x2 high-dimensional spatiotemporal manifold tensor matrix. The initial system configuration sets the base learning rate constant to 0.01 and the spatial distance decay coefficient to 0.001. Substituting these values ​​into the exponential decay formula, the dynamic learning rate parameter is calculated to be approximately 0.00135. The system drives the gradient descent optimization model according to the dynamic learning rate parameter of 0.00135, outputting network weights to update the gradient at a frequency of 50 times per second, fine-tuning the weight matrix and bias terms of the preset feature transformation calculation graph. In the encryption phase, the underlying probe captures a minute instantaneous deviation of 0.05 radians in the yaw angle. This 0.05 radians is multiplied by a scalar amplification factor of 256 and used as a scrambling factor. This factor is then injected into the initial vector via a bitwise XOR operation, causing plaintext environmental pixels containing pedestrian faces to be instantly and randomly replaced with a semantically meaningless ciphertext sequence. As the vehicle continues to advance, the system configuration reveals a preset physical buffer width of 2000 meters. The velocity component in the vehicle's pure physical kinematics data remains at 20 meters per second. The overall system hardware execution latency is estimated at 0.1 seconds. Substituting these values ​​into the trigger delay formula, the trigger delay for sending the overwrite level pulse command is calculated to be 99.9 seconds. 99.9 seconds after detecting the vehicle's precise boundary crossing signal, the system sends the overwrite level pulse command to the storage space precisely, causing the transient symmetric key to become a completely zero physical state. The activation of the new rule domain matching operator path and the irreversible destruction of the remaining ciphertext data achieve perfect millisecond-level alignment in timing.

[0113] To verify the system performance of this invention under complex cross-regional driving conditions, the experimental environment was built on an onboard computing platform equipped with trusted execution environment hardware. The comparative scheme selected for testing was the conventional hard-coded operator reload switching technology. By simulating a vehicle traversing a pre-set physical buffer zone of 2000 meters in width at a constant speed of 20 meters per second, the performance of the feature transformation computation graph smooth evolution mechanism involved in this invention and the conventional scheme were compared. Test data shows that the conventional hard-coded scheme generates a computational hang time of approximately 350 to 500 milliseconds during operator reload, leading to severe data accumulation in the memory buffer and triggering overflow risks. In contrast, this invention utilizes the gradient descent optimization model to update the gradient of the network weights for parameter-level fine-tuning, reducing the system processing latency during operator switching to less than 2 milliseconds. The computing engine maintained a stable data processing throughput during the evolution, without any forced interruption of the computational task due to operator updates.

[0114] In a comparative experiment on data security isolation, the effectiveness of destroying residual data in the memory buffer was thoroughly tested. Using a memory forensics analysis tool, snapshot scans of the storage space were performed at different time points after a vehicle crossed a boundary signal. Experimental results confirmed that conventional software-level zero-overwrite mode suffers from physical data erasure delays exceeding 120 milliseconds due to limitations imposed by the operating system's underlying driver scheduling and file system level. This invention, by initiating an absolute isolation mechanism based on transient key hardware annihilation, directly drives the reset of a dedicated hardware register state by sending an overwrite level pulse command with a digital signature. Experiments showed that the transient symmetric key completed physical annihilation within 0.5 microseconds after triggering the confirmation message. Information entropy detection of the residual memory data revealed that the binary bit sequence after key annihilation exhibited a maximized discrete state, making it impossible for any decryption algorithm to recover the original environmental pixel or point cloud coordinate information. The experimental conclusions fully demonstrate that this invention, through physical-level transient annihilation, fundamentally cuts off the physical path of unauthorized transmission of historical feature information.

[0115] Specialized tests were conducted to assess the accuracy and synchronization of feature conversion standard switching between different rule domains. By injecting routing markers with absolute hardware timestamps, the elastic routing accuracy of encrypted data in the streaming computing engine was monitored. In over 10,000 stress tests simulating crossing management boundaries of different administrative regions, the routing markers achieved 100% accuracy in guiding encrypted data flow to the matching operator path of the new rule domain. Test results show that the activation time of the matching operator path of the new rule domain and the physical failure time of the transient symmetric key of the preceding rule domain are perfectly aligned within the same hardware clock cycle at the system global clock level. No frame of data was misdelivered or rule matching logic drifted during the rule domain switching transition period. A series of experimental data supports the technical advantages of this invention in handling real-time data stream analysis in the international automotive market, achieving both millisecond-level rule evolution and high availability of the computing system.

[0116] In real-time data stream analysis methods for the international automotive market, the gradient descent optimization model is built on the control plane of a distributed streaming computing engine. The system establishes a comprehensive training space containing a historical benchmark sample set and a standard set of target rule domains. The model uses the topological distance between the currently executed safety processing operator structure and the standard operator structure required by the preset requirements of the target management authority region as the loss function. The system constructs an input feature tensor by extracting anonymized physical parameters during vehicle operation and defines a cross-rule domain operator topology transfer loss function. The underlying algorithm logic injects penalty terms representing management boundary constraints into the weight matrix of the feature transformation computation graph, driving asymmetric updates to the neuron connection strength of the feature transformation computation graph, thus transforming the vehicle's pure physical kinematic state data into a computable mathematical model.

[0117] The data processing path of the gradient descent optimization model begins with the acquisition and tensor reconstruction of the vehicle's purely physical kinematic state data. The computational processing layer integrates the relative distance scalar calculated using the Earth's curvature model with the chassis wheel speed pulse frequency, vehicle speed, and yaw angle, mapping them to a high-dimensional matrix space to generate a high-dimensional spatiotemporal manifold tensor as the model's fundamental data structure. The model is configured with a dynamic learning rate parameter that follows an inverse distance decay mapping logic. This dynamic learning rate parameter makes the model update step size more aggressive as the vehicle approaches the region boundary. The high-dimensional spatiotemporal manifold tensor is backpropagated within the gradient descent optimization model according to the dynamic learning rate parameter. The backpropagation algorithm uses the chain rule to solve for the gradient components in real time, outputting the network weights to update the gradient. The computing system updates the gradient based on the network weights and continuously fine-tunes the weight matrix and bias terms of the preset feature transformation computation graph within the driving time window when the vehicle crosses the physical buffer zone. In this iterative closed loop, the system monitors the loss function value in real time. When the topological distance loss value drops to less than the preset convergence threshold, or when the bottom probe determines that the vehicle's physical coordinates have crossed the management authority boundary, the system forcibly terminates the backpropagation iteration step, fixes the current network weight parameters, and outputs the fine-tuned new rule domain matching operator path, effectively preventing the risk of computational dead loop and memory overflow during the operator fine-tuning process.

[0118] Physical-level annihilation refers to an irreversible destruction mechanism implemented on the underlying hardware architecture of the encryption key. The main control chip of the Trusted Execution Environment (TEE) bypasses the operating system's conventional file management layer and directly injects a highest-priority overwrite pulse instruction into the static random access memory (SRAM) hardware array storing the transient symmetric key. This overwrite pulse instruction forcibly emptys the physical capacitance states within the SRAM, unconditionally replacing the digital matrix of the transient symmetric key with a completely zero physical state. The decryption key is completely destroyed at the silicon crystal level. The binary code stream, having lost its mathematical decoding channel, degenerates directly into high-entropy random thermal noise in the underlying storage medium, exhibiting a state of maximized information entropy. This physically blocks the mathematical reverse path of decryption and reconstruction.

[0119] Tensor header forced operation refers to a low-level memory operation that dynamically expands the feature dimension of the front end of the multidimensional matrix structure of ciphertext data during streaming data processing. When the CPU reads the absolute hardware clock cycle value at the moment of out-of-bounds access, the data access layer performs dynamic dimensionality reduction by truncating non-critical data bits of the tensor structure of the ciphertext data to be processed. This dynamic dimensionality reduction logic, without requesting new memory addresses from the operating system, forcibly frees up a fixed-byte data space at the very front of the data tensor, i.e., the tensor header. The system then writes the absolute hardware clock cycle value into the newly expanded privileged flag feature dimension to generate a routing flag. The hardware decoding logic subsequently parses the privileged flag feature dimension and uses instruction-level conditional branches to activate instructions, guiding massive concurrent data flows to specific feature processing execution branches.

[0120] The high-dimensional spacetime manifold tensor is a mathematical matrix constructed by fusing multiple physical quantities with different dimensions. It integrates the dual physical properties of spatial proximity and physical motion velocity, serving as the foundational basis for underlying model analysis. Deeply bound to it is the dynamic learning rate parameter, which is the adaptive core variable controlling the step size of weight updates. The system's internal mathematical mapping function utilizes an exponential function with a natural constant base to construct an exponential decay model. As the vehicle approaches the region boundary, the relative distance scalar value gradually decreases, and the dynamic learning rate parameter output by the system increases exponentially. This exponentially increasing value drives the backpropagation calculation to output extremely aggressive network weight update gradients, accelerating the smooth topological deformation of the neural network architecture of the feature transformation computation graph.

[0121] Embodiment 1 of this invention: In a real-world business scenario involving cross-regional driving, a passenger vehicle is moving from a first region where license plate erasure is performed to a second region requiring deep facial blurring. The onboard edge computing node acquires the vehicle's pure physical kinematics state data in real time and simultaneously receives raw environmental data collected according to preset system authorization. The system's underlying layer, upon receiving the instruction to write plaintext environmental pixels and point cloud 3D coordinates into the memory buffer, initiates a key generation request, outputting a transient symmetric key based on the electronic geofence feature encoding of the first region where the vehicle is currently located. The streaming access layer uses the generated transient symmetric key to perform transparent streaming encryption computation, converting the raw environmental data into a ciphertext sequence and securely residing it in the memory buffer. To cope with the upcoming rule domain switch, the computation plane inputs the vehicle's pure physical kinematics state data into the gradient descent optimization model, updating the gradient through the calculated network weights and performing hundreds or thousands of fine-tuning operations on the preset feature transformation computation graph. The weight matrix and bias term originally responsible for license plate erasure undergo continuous physical deformation within the time window of the vehicle crossing the buffer zone, gradually evolving and outputting the fine-tuned new rule domain matching operator path for the second region. When the inertial navigation component emits a precise out-of-bounds low-level signal, the central processing unit immediately reads the absolute hardware clock cycle value at the moment of the out-of-bounds event. The data access layer forcibly expands the feature dimension in the tensor header of the currently processed ciphertext data and injects the read hardware clock cycle value into the feature dimension to generate a unique routing marker. Subsequent incoming ciphertext data is seamlessly guided to the execution branch of the deep face blur by the instruction-level conditional branch activation instruction based on the generated routing marker. Once the monitoring logic confirms that the ciphertext data has successfully flowed to the new rule domain matching operator path, the trusted execution environment main control chip sends an overwrite level pulse instruction to the static random access memory array storing the transient symmetric key. The pulse instruction directly replaces the physical capacitor state corresponding to the transient symmetric key with a zero physical state, blocking the decryption and restoration path of the ciphertext data remaining in the memory buffer space.

[0122] Embodiment 2 of this invention: Facing the complex situation of cross-regional logistics transport fleets speeding across regional boundaries, the computing system requires extremely high dynamic adaptability to balance underlying computing power and data processing controllability. The data acquisition module extracts global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle from the vehicle's pure physical kinematic state data, constructing a real-time dynamic vector from the extracted underlying physical quantities. To eliminate the risk of unauthorized feature association, the cleaning logic removes user identity identifiers from the collected global positioning coordinates and vehicle speed data, obtaining purely anonymized physical parameters. The computing core uses the Earth curvature model to convert global positioning coordinates into relative distance scalars with a preset regional boundary line as the absolute origin, and fuses the relative distance scalars with the chassis wheel speed pulse frequency to generate a high-dimensional spatiotemporal manifold tensor. The relative distance scalar not only represents the physical length of the actual vehicle from the regional boundary line, but is also directly mapped to the dynamic learning rate parameter of the gradient descent optimization model. As a heavy truck rapidly approaches the boundary node of a region, the sharp decrease in the relative distance scalar triggers internal feedback logic. This not only increases the sampling frequency of the anonymized physical parameters to increase the dimensionality density of the real-time dynamic vector, but also drives the dynamic learning rate parameter to rise exponentially. The high-dimensional spatiotemporal manifold tensor is backpropagated in the gradient descent optimization model according to the rapidly increasing dynamic learning rate parameter, continuously outputting network weights to update gradients at an unprecedented speed.

[0123] Embodiment 3 of the present invention: When conducting cross-regional patrols in areas with extremely poor road conditions and multiple boundaries, the severe bumps experienced by the chassis are extracted and converted into scrambling factors for transparent streaming encryption computation. The computation module extracts the instantaneous minute deviation value of the yaw angle from the vehicle's pure physical kinematic state data within the direct memory access transmission path. This instantaneous minute deviation value, as a highly unpredictable thermodynamic random perturbation variable, is forcibly injected as a scrambling factor into the initial vector of the bitwise XOR logic. The underlying gate array uses the transient symmetric key after injecting the scrambling factor to perform transparent streaming encryption computation on the continuously input raw environmental data, ensuring that the starting point of each encryption operation is in a completely random wandering distribution state. As the vehicle crosses the management authority boundary, the front-end hardware routing node parses the privileged marker feature dimension carried in the tensor header of the ciphertext data and performs a low-level comparison between the value of the privileged marker feature dimension and a preset rule domain switching time threshold. After successfully matching and injecting the address pointer of the new rule domain matching operator path into the data processing layer, the trusted execution environment main control chip sends a security message confirming the boundary violation with a digital signature. After the underlying architecture verifies the validity of the confirmation message, it extracts the chassis wheel speed pulse frequency to calculate the feature destruction frequency and sends an overwrite level pulse command to the storage space according to the feature destruction frequency. To smooth out timing errors caused by hardware execution latency, the coupling feedback function calculates the ratio of the preset physical buffer width to the speed component to obtain the boundary crossing time, and configures the difference between the boundary crossing time and the hardware latency as the trigger delay of the pulse command. The control logic uses the injected hardware clock cycle value as an absolute synchronization anchor point, forcibly controlling that the activation time of the new rule domain matching operator path and the physical failure time of the ciphertext data remaining in the memory buffer space are in the same clock cycle at the global clock level.

Claims

1. A real-time data stream analysis method for the international automotive market, characterized in that: include: Step 1: Acquire pure physical kinematic state data of the vehicle and receive raw environmental data collected according to the preset system authorization; Step 2: Calculate and generate a transient symmetric key, and use the transient symmetric key to convert the original environment data into ciphertext data and write it into the memory buffer space; Step 3: Input the vehicle's pure physical kinematic state data into the gradient descent optimization model, calculate the network weight update gradient, use the network weight update gradient to fine-tune the weight matrix and bias terms of the preset feature transformation calculation graph, and output the fine-tuned new rule domain matching operator path. Step 4: Read the hardware clock cycle value, expand the feature dimension in the tensor header of the ciphertext data and inject the hardware clock cycle value to generate a routing mark, and guide the ciphertext data to the new rule domain matching operator path according to the routing mark; Step 5: After confirming that the ciphertext data flows to the new rule domain matching operator path, send an overwrite level pulse command to the storage space storing the transient symmetric key to replace the transient symmetric key with an all-zero physical state, thereby blocking the decryption and restoration path of the ciphertext data remaining in the memory buffer space.

2. The real-time data stream analysis method for the international automotive market according to claim 1, characterized in that, The step of inputting the vehicle's pure physical kinematic state data into the gradient descent optimization model to calculate the network weight update gradient includes: The global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed and yaw angle included in the vehicle's pure physical kinematic state data are extracted to form a real-time dynamic vector; the global positioning coordinates are converted into a relative distance scalar with a preset regional boundary line as the absolute origin using the Earth curvature model; The relative distance scalar and the chassis wheel speed pulse frequency are used to calculate and generate a high-dimensional spacetime manifold tensor. The relative distance scalar is mapped to the dynamic learning rate parameter of the gradient descent optimization model; Using the high-dimensional spatiotemporal manifold tensor and the dynamic learning rate parameter, backpropagation is performed in the gradient descent optimization model to output the network weights and update the gradient.

3. The real-time data stream analysis method for the international automotive market according to claim 2, characterized in that, The extracted vehicle's pure physical kinematics data, including global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle, constitutes a real-time dynamic vector, comprising: By removing the user identity-related attributes from the collected global positioning coordinates, chassis wheel speed pulse frequency, vehicle speed, and yaw angle, anonymized physical parameters are obtained. The sampling frequency of the anonymized physical parameters is adjusted using the relative distance scalar; When the distance from the boundary line of the region is less than a preset distance threshold, the sampling frequency is increased to increase the dimensionality density of the real-time dynamic vector.

4. The real-time data stream analysis method for the international automotive market according to claim 3, characterized in that, The calculation generates a transient symmetric key, and the raw environment data is converted into ciphertext data and written to a memory buffer using the transient symmetric key, including: Before the original environment data is written into the memory buffer space, a key generation request is initiated; Based on the electronic geofence feature encoding of the legal area where the vehicle is currently located, the transient symmetric key is output; When a control instruction to write the original environmental data into the memory buffer space is captured, streaming encrypted computation is activated using an idle clock cycle. The plaintext environment pixels and point cloud 3D coordinates included in the original environment data are converted into a ciphertext sequence through bitwise XOR logic, and the ciphertext sequence is used as the ciphertext data.

5. The real-time data stream analysis method for the international automotive market according to claim 4, characterized in that, The step of converting the plaintext environment pixels and point cloud 3D coordinates included in the original environment data into a ciphertext sequence through bitwise XOR logic includes: During direct memory access, the instantaneous minute deviation value of the yaw angle included in the pure physical kinematic state data of the vehicle is extracted; The instantaneous minute deviation value is injected as a scrambling factor into the initial vector of the bitwise XOR logic; The transient symmetric key, after being injected with the scrambling factor, is used to perform transparent streaming encryption on the continuously input original environmental data to obtain the ciphertext sequence; the converted ciphertext sequence is then written into the memory buffer space.

6. The real-time data stream analysis method for the international automotive market according to claim 5, characterized in that, The step of reading the hardware clock cycle value, expanding the feature dimension in the tensor header of the ciphertext data, and injecting the hardware clock cycle value to generate a routing tag includes: When a precise out-of-bounds low-level signal is received, a system management interrupt is triggered to take over the current execution privileges. Read the high-precision event count value to obtain the absolute hardware clock cycle value at the moment of boundary crossing, and use it as the hardware clock cycle value; The tensor structure of the currently unprocessed ciphertext data is dynamically reduced in dimensionality to release the reserved data space. The privileged tag feature dimension, including the hardware clock cycle value, is written into the header of the data placeholder space to generate the routing tag.

7. The real-time data stream analysis method for the international automotive market according to claim 6, characterized in that, The step of guiding the ciphertext data flow to the new rule domain matching operator path according to the routing tag includes: Analyze the privileged marker feature dimensions; The value of the privileged marker feature dimension is compared with the preset rule domain switching time threshold; Under the premise of comparison and matching, the address pointer of the new rule domain matching operator path is injected into the data processing layer; By using instruction-level conditional branch activation instructions, the subsequently passed ciphertext data is routed to the execution branches of the new rule domain matching operator path, including deep blurring of faces or erasure of vehicle license plates.

8. The real-time data stream analysis method for the international automotive market according to claim 7, characterized in that, After confirming that the ciphertext data stream is directed to the new rule domain matching operator path, a write-level pulse instruction is sent to the storage space storing the transient symmetric key, including: Send a security message confirming the boundary violation with a digital signature; After verifying the legality of the boundary violation confirmation security message, extract the chassis wheel speed pulse frequency included in the vehicle's pure physical kinematic state data and calculate the feature destruction frequency; The overwrite level pulse command is sent to the storage space according to the specified destruction frequency.

9. The real-time data stream analysis method for the international automotive market according to claim 8, characterized in that, The step of replacing the transient symmetric key with an all-zero physical state to block the decryption and restoration path of the ciphertext data remaining in the memory buffer space includes: Clear any remaining key copies in the internal cache and broadcast a global cache invalidation signal; By replacing the transient symmetric key with a physical-level annihilation of a completely zero physical state, the mathematical reverse path for decrypting and restoring the remaining ciphertext data is blocked. The remaining encrypted data is directly converted into high-entropy random thermal noise at the underlying layer; Also includes: Before sending the overwrite level pulse command, the velocity component in the vehicle's pure physical kinematic state data is extracted to construct a coupled feedback function between the evolution frequency and the ciphertext data flow rate; The boundary crossing time is obtained by calculating the ratio of the preset physical buffer band width to the velocity component, and the difference between the boundary crossing time and the system hardware execution delay is configured as the trigger delay for sending the overwrite level pulse command. After the trigger delay is reached, the overwrite level pulse command is sent according to the characteristic destruction frequency; Using the hardware clock cycle value as a synchronization anchor, the activation time of the new rule domain matching operator path and the physical failure time of the ciphertext data remaining in the memory buffer space are controlled to be in the same clock cycle at the global clock level.

10. A real-time data stream analysis system for the international automotive market, applied to the real-time data stream analysis method for the international automotive market as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to acquire pure physical kinematic state data of the vehicle and receive raw environmental data collected according to the preset system authorization. An encryption writing module is used to calculate and generate a transient symmetric key, and use the transient symmetric key to convert the original environment data into ciphertext data and write it into a memory buffer space; The model fine-tuning module is used to input the pure physical kinematic state data of the vehicle into the gradient descent optimization model, calculate the network weight update gradient, use the network weight update gradient to fine-tune the weight matrix and bias terms of the preset feature transformation calculation graph, and output the fine-tuned new rule domain matching operator path. The routing guidance module is used to read the hardware clock cycle value, expand the feature dimension in the tensor header of the ciphertext data and inject the hardware clock cycle value to generate a routing mark, and guide the ciphertext data to the path of the new rule domain matching operator according to the routing mark; The overwrite blocking module is used to send an overwrite level pulse command to the storage space storing the transient symmetric key after confirming that the ciphertext data flows to the path of the new rule domain matching operator, thereby replacing the transient symmetric key with an all-zero physical state and blocking the decryption and restoration path of the ciphertext data remaining in the memory buffer space.

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