Data interaction system between unmanned road sweeper and cloud platform based on hybrid encryption

Abstract

The present invention discloses a data interaction system between an unmanned road sweeper and a cloud platform based on hybrid encryption, which specifically relates to the field of communication interaction information security technology, including an authority authentication module, a data encryption module, an information processing module, a master-slave authentication control module, a horizontal trust management module and a communication scheduling module. It dynamically matches encryption strategies and key templates through task scoring and mapping models, supports offline identity authentication, horizontal trust signatures and channel switching mechanisms, and improves the security of data transmission, system fault tolerance and collaborative reliability in scenarios such as task switching and network anomalies. The present invention implements a task-driven dynamic encryption matching mechanism to improve data security and resource efficiency; constructs a multi-level authentication and role signature map to enhance the collaborative credibility and fault tolerance of equipment; introduces a priority index scheduling strategy to ensure that key data is transmitted first in a highly volatile network environment, thereby improving the overall stability and security of the system.

Description

Technical Field

[0001] The present invention relates to the field of communication interaction information security technology, and more specifically, to a data interaction system between an unmanned road sweeper and a cloud platform based on hybrid encryption. Background Art

[0002] In recent years, with the continuous advancement of intelligent urban management, unmanned street sweepers, as a new generation of urban service terminal equipment, have gradually been put into practical use in scenarios such as parks, communities, and municipal roads. These devices typically have capabilities such as path planning, obstacle recognition, autonomous driving, and task scheduling. They use sensors to sense environmental conditions and upload task data in real time to the platform for scheduling optimization and regulatory records.

[0003] At the same time, the cloud platform serves as the mission control center and data aggregation hub for the autonomous sweepers, assuming core functions such as multi-vehicle coordination, task dispatching, job logging, and algorithm updates. To achieve efficient management, sweepers must regularly or in real time upload various data types to the cloud platform, including but not limited to: operation trajectories, images and videos, equipment status, and task completion. The cloud platform, in turn, sends back task parameters, policy updates, and temporary instructions.

[0004] However, traditional data exchange mechanisms face the following challenges: Data during communication is easily intercepted or tampered with: Street sweepers are usually located in an open network environment and lack stable private network isolation, which exposes data during transmission to threats such as eavesdropping and man-in-the-middle attacks;

[0005] Lack of task adaptability in encryption mechanisms: Existing systems often use fixed encryption algorithms, making it difficult to flexibly adjust encryption strategies based on task sensitivity or network environments. This can easily lead to wasted computing resources or insufficient security levels.

[0006] Lack of fault tolerance in disconnected state: When communication between the road sweeper and the cloud platform is interrupted, the data caching mechanism or task authorization mechanism is imperfect, which can easily lead to task execution failure or safety hazards;

[0007] The scheduling process does not take into account the differences in the importance of data streams: During task scheduling and data upload, the value and urgency of different data cannot be distinguished, and there is a situation where important information is occupied by low-priority data.

[0008] Therefore, the present invention proposes a data interaction system between an unmanned road sweeper and a cloud platform based on hybrid encryption in order to solve the above problems. Summary of the Invention

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] The data interaction system between the unmanned road sweeper and the cloud platform based on hybrid encryption includes:

[0011] The authorization authentication module is configured to parse the device identity and short-term authentication token distributed by the cloud platform. When the platform connection is unavailable, the authentication is performed based on the cached signature token.

[0012] The data encryption module has a key pool mechanism. The device side pre-configures multiple levels of session keys and dynamically selects the key for encrypting vehicle data from the key pool based on the current task status.

[0013] The information processing module establishes a mapping relationship between task status and encryption level. When switching tasks, it automatically matches the corresponding encryption algorithm and key template according to the level of the current task. If the match fails, data transmission is blocked and security renegotiation is initiated.

[0014] The master-slave authentication control module sets a multi-level identity verification path. When the master authentication path fails, the slave authentication mechanism is automatically activated for local authorization, and the scope and time limit of the activation task are limited.

[0015] The horizontal trust management module generates role signature credentials for sweepers during task collaboration and proves the legitimacy and authorization time of their tasks to neighboring devices through a structured signature chain.

[0016] The communication scheduling module has a pre-loaded key channel and an instant key negotiation mechanism. It decides whether to trigger the master key negotiation process or use the historical session key to encrypt the initial state data based on the task sensitivity.

[0017] Among them, the task encryption level mapping model in the information processing module is constructed based on the task content, real-time requirements and information sensitivity, so that the data encryption strength changes dynamically with the task, thereby improving the task completion rate and data security of the cleaning operation.

[0018] In a preferred embodiment, the key pool in the data encryption module is hierarchically managed according to a tree-like hierarchical structure, with the lowest layer being the task-level key, the middle layer being the task group common key, and the top layer being the platform authorization key. The key call of each layer is controlled by the task scoring function F(Tscore, Slevel); the task scoring function is obtained by the weighted sum of the task complexity score Tscore and the sensitivity level score Slevel;

[0019] The task complexity score (Tscore) is calculated based on a combination of four indicators: task scheduling instruction length, logic depth, required vehicle resource types, and runtime. These indicators correspond to T1 to T4, and each indicator is normalized and weighted to form a total score. The sensitivity level score (Slevel) is derived from the level determination model results fed back by the information processing module. The score range is 1 to 4, corresponding to general, device-level, platform-level, and regulatory-level information, respectively.

[0020] When the encryption strategy is called, the result of the scoring function F is calculated and matched with the encryption security value corresponding to each layer of the key. The key level corresponding to the encryption security value closest to but not lower than the scoring function F is selected. If the result of function F is lower than the minimum key call threshold K1, the system enters the protection state, encryption is not performed, and the task data is not allowed to be uploaded.

[0021] In a preferred embodiment, the mapping relationship between task status and encryption level is constructed based on a state transition diagram, and any task status node is mapped with reference to three dimensions: real-time factor R1, information sensitivity S1 and task content importance N1, where the transition probability P(i→j) is obtained by calculating the Euclidean distance between the transition vector composed of the real-time factor R1, information sensitivity S1 and task content importance N1 and the preset standard vector. The transition probability P(i→j) represents the probability of transitioning from state i to state j. A standard security level vector set Vi (i∈{1,2,3}) is preset, and the level i corresponding to Vi with the smallest distance from the transition vector is selected, which is the "security level of the current task", and the "security level of the current task" corresponds to a unique level in the tree hierarchical structure. When any node switches to a task of another security level, the mapping mechanism forces a fallback to the key renegotiation state and clears the mapping cache.

[0022] In a preferred embodiment, the task window limit parameter Tauth is bound when the authentication mechanism is locally activated. This parameter is generated by the device self-test module when the authentication is started and is subtracted from the task time value Ttask. If |Tauth−Ttask| is greater than the task offset threshold R2, the authentication is determined to be invalid.

[0023] In a preferred embodiment, the dual-channel strategy in communication scheduling includes a key negotiation channel and a historical key fast call channel. The switching mechanism is controlled by the key critical factor Kdyn. When Kdyn is less than the negotiation trigger threshold B3, the historical key is called and the signature cache is used for the first round of handshake authentication.

[0024] When Kdyn is greater than or equal to B2, the platform negotiation key initialization process is carried out. A one-time negotiation parameter is introduced into the process. The parameter is generated by superimposing a hash random number, the vehicle's geographic location tag, and a timestamp. The dual-channel state machine is recorded by the status flag Sflag. When the Sflag value enters the renegotiation flag state, the task instruction push is automatically paused to avoid communication errors causing task synchronization errors.

[0025] In a preferred embodiment, the role signature credential structure includes a task number, permission granularity, a time limit label and a device ID hash, wherein the task number is temporarily allocated and generated by the edge node before each task collaboration, and the permission granularity is limited according to the task sensitive area. The role signature credential generates a device signature graph locally in the form of hash aggregation. The graph structure has a ring structure detection mechanism to prevent repeated authorization links from forming a permission amplification path; the signature graph maintenance cycle is bound to the task update cycle. When the collaborative device node exceeds the collaborative density threshold P4, a trust reduction algorithm is forcibly introduced to limit the horizontal chain expansion rate.

[0026] In a preferred embodiment, different encryption algorithms are provided in the key pool of each level, and the encryption algorithms include symmetric encryption and asymmetric encryption. The combination strategy of each encryption algorithm is determined based on the preset encryption cost function Cenc and authentication strength function Sauth. The functional relationship is: selection scheme = max{Cenc(i) / Sauth(i)}, where i is the set number of candidate algorithms. The combination scheme with the largest ratio is selected and applied to the current task. If all the ratios obtained at the current level are lower than the calculated security threshold Z5, the hierarchical encryption algorithm switch is initiated and two rounds of handshake confirmation are performed.

[0027] In a preferred embodiment, the sensitivity level determination mechanism includes an edge feature extraction unit and a task classification model, wherein the edge feature extraction unit collects three behavioral features of task data: format complexity, control instruction set distribution, and transmission timing mode, assigns scores F1, F2, and F3 to each feature, and normalizes and combines them into a task feature vector Vtask;

[0028] The task classification model is a shallow convolutional neural network structure that generates a classification decision boundary based on historical task data. It divides tasks into four sensitivity levels, C1 to C4, representing general information, device configuration information, platform key information, and regulatory interface information, respectively. The classification boundary is automatically updated through incremental training every fixed time window.

[0029] When a new task is input and its Vtask offset from the original classification boundary exceeds the adjustment threshold E, the model's classification confidence in the task decreases. Instead of directly assigning a level, the task is marked as "pending confirmation status" and submitted to the platform for manual label confirmation and judgment. The sensitivity level is then re-established and fed back to the information processing module for subsequent key matching calls.

[0030] In a preferred embodiment, the communication scheduling module configures a dynamic transmission priority mechanism, calculates the ratio of the result of the task scoring function F to the amount of data in the data packet, obtains the priority index Px, evaluates the Px value of each data to be uploaded at a fixed period, and selects the data with the highest priority index to enter the encryption channel first. When the Px value distribution fluctuates, that is, the standard deviation value is greater than the adjustment threshold V, the preset temporary current limiting strategy is triggered.

[0031] Technical effects and advantages of the present invention:

[0032] The present invention establishes a dynamic mapping relationship between task complexity, data sensitivity level and encryption strategy in the key pool by constructing a key hierarchy calling mechanism based on the task scoring function, so that the system can automatically select the encryption algorithm and key hierarchy with the best matching degree in different task scenarios, avoiding the waste of computing resources or insufficient security strength caused by the fixed encryption mechanism. At the same time, when the scoring result is lower than the security threshold, the system rejects data transmission and triggers renegotiation, effectively preventing low-security level tasks from being executed incorrectly. This mechanism not only improves the data confidentiality in the execution of unmanned sweeper tasks, but also optimizes the allocation efficiency of the vehicle's computing resources. It is particularly suitable for intelligent scheduling scenarios in which multiple tasks are executed in parallel and in weak network communication environments.

[0033] This invention introduces a multi-level identity verification path into the master-slave authentication control module, enabling local authentication mechanisms when the cloud platform connection fails or the network is interrupted. It also determines authentication validity by comparing the difference between the "task window limit parameter" and the "task time value," thereby ensuring the security and controllability of the task authorization process. Simultaneously, the horizontal trust management module generates role signature credentials based on the task number, permission granularity, time tag, and device ID hash, constructing a local signature graph to detect ring structures and coordinate permission convergence between devices, preventing permission amplification attacks or disguised device access, and improving the overall system trust security and task stability during multi-device collaborative execution.

[0034] The present invention introduces a calculation method based on the ratio of the task scoring function to the packet size in the communication scheduling module to form a priority index Px. This index is used to dynamically sort multiple types of data to be uploaded in the encrypted channel, thereby improving the transmission priority of critical mission data. Furthermore, when the system detects excessive fluctuations in the priority index distribution, that is, when the standard deviation exceeds the adjustment threshold, a temporary current limiting strategy is automatically triggered to limit the transmission cadence of low-priority data. This ensures the priority processing of highly sensitive, real-time mission data in scenarios where network resources are limited or fluctuating dramatically. This mechanism effectively enhances the intelligence of data scheduling strategies and reduces the risk of delays or packet loss for important tasks due to network congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0036] Figure 1 This is a schematic diagram of the data interaction system between the unmanned road sweeper and the cloud platform based on hybrid encryption in the present invention.

[0037] Figure 2 This is a flow chart of data interaction between the device side and the cloud platform in the present invention. DETAILED DESCRIPTION

[0038] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Reference Figure 1-2 The following examples were obtained:

[0040] Example 1: A data interaction system between an unmanned road sweeper and a cloud platform based on hybrid encryption, comprising:

[0041] The authorization authentication module is configured to parse device identities and short-lived authentication tokens distributed by the cloud platform. When the platform connection is unavailable, authentication is performed based on cached signature tokens. The module also parses device identities (such as unique IDs and public key certificates) distributed by the cloud platform. It processes short-lived authentication tokens to ensure that each communication access is authenticated. When the platform connection is unavailable, it performs temporary offline authentication based on locally cached signature tokens. This module addresses the issue of device startup being blocked in extreme post-disaster network environments, improves the system's resistance to connection dependency, and ensures minimum device availability. It also prevents counterfeit device access and ensures the integrity of the end-to-end trust chain for task scheduling.

[0042] The data encryption module features a key pool mechanism. Multiple levels of session keys are pre-configured on the device side. Keys for encrypting vehicle data are dynamically selected from the key pool based on the current task status. An internal key pool structure divides keys into multiple levels (e.g., task level, task group level, and platform level). The module dynamically calls the appropriate key based on the current task status. It supports switching between symmetric encryption (high efficiency) and asymmetric encryption (strong security). This prevents attacks such as illegal access to task data and man-in-the-middle tampering. It supports both "on-demand encryption" and "tiered encryption" strategies, balancing computing power and security. This ensures the privacy and integrity of transmitted data, including information reported by sweepers and video streams.

[0043] The information processing module establishes a mapping relationship between task status and encryption level. When switching tasks, it automatically matches the corresponding encryption algorithm and key template based on the current task's level. If the match fails, data transmission is blocked and a security renegotiation is initiated. The module analyzes the current task status (e.g., cleaning, inspection, rescue, emergency response), constructs a mapping model from "task status to encryption level," matches the key level, and selects an encryption template (algorithm type, key bit length, etc.). If the match fails, data flow is blocked and renegotiation is initiated. The "task execution logic" is embedded in the "secure communication strategy," ensuring that sensitive tasks (e.g., cleaning outside the hospital area and classified monitoring) use higher-level encryption strategies, thus achieving intelligent security adaptability in the communication system.

[0044] The master-slave authentication control module sets up a multi-level identity verification path. When the master authentication path fails, the slave authentication mechanism is automatically activated for local authorization, and the scope and validity period of the activation task are limited. It also sets up a master authentication path (platform online verification) and a slave authentication path (local verification). When master authentication fails, it automatically switches to slave authentication. The scope and validity period of slave authentication activation tasks are limited. This ensures that the system can operate in degraded conditions in scenarios such as platform network failure and regional network disconnection, improving the system's continuous availability and uninterrupted task capabilities. It also controls the scope of slave authentication authorization to prevent device identity theft.

[0045] The horizontal trust management module generates role signature credentials for sweepers during collaborative tasks and proves the legitimacy and authorization time of their tasks to neighboring devices through a structured signature chain. Sweepers generate role signature credentials during collaborative tasks, constructing a signature chain graph to establish mutual trust relationships across multiple workshops. The graph incorporates permission granularity and ring structure detection mechanisms to prevent link abuse. This module enables inter-device verification during collaborative tasks without real-time platform intervention, improving the security and efficiency of large-scale deployments of sweeper clusters, and supporting "distributed autonomous communication" to form a trusted edge network.

[0046] The communication scheduling module features preloaded key channels and real-time key negotiation mechanisms. Based on task sensitivity, it determines whether to trigger master key negotiation or use historical session keys to encrypt initial state data. It also manages key channel selection (preloaded vs. real-time negotiation), dynamically determines whether to trigger key negotiation based on task sensitivity, and manages communication priority, determining the order in which data is sent. This enables task priority control in bandwidth-constrained environments, ensuring that critical data is prioritized and secure channels are established first, thus preventing delays or failures of important commands due to network fluctuations.

[0047] Among them, the task encryption level mapping model in the information processing module is constructed based on the task content, real-time requirements and information sensitivity, so that the data encryption strength changes dynamically with the task, thereby improving the task completion rate and data security of the cleaning operation.

[0048] The key pool within the data encryption module is managed hierarchically in a tree-like structure. The lowest layer is the task-level key, the middle layer is the task group common key, and the top layer is the platform authorization key. The key call of each layer is controlled by the task scoring function F(Tscore, Slevel). The task scoring function is obtained by the weighted sum of the task complexity score Tscore and the sensitivity level score Slevel.

[0049] The task complexity score (Tscore) is calculated based on a combination of four indicators: task scheduling instruction length, logic depth, required vehicle resource types, and runtime. These indicators correspond to T1 to T4, and each indicator is normalized and weighted to form a total score. The sensitivity level score (Slevel) is derived from the level determination model results fed back by the information processing module. The score range is 1 to 4, corresponding to general, device-level, platform-level, and regulatory-level information, respectively.

[0050] When the encryption strategy is called, the result of the scoring function F is calculated and matched with the encryption security value corresponding to each layer of the key. The key level corresponding to the encryption security value closest to but not lower than the scoring function F is selected. If the result of function F is lower than the minimum key call threshold K1, the system enters the protection state, encryption is not performed, and the task data is not allowed to be uploaded.

[0051] The data encryption module of this invention features a key pool mechanism, which is managed hierarchically in a tree-like structure. The key pool consists of three logical tiers: the lowest tier contains task-level keys, the middle tier contains task group common keys, and the top tier contains platform authorization keys. Each tier's key set is pre-deployed on the device and includes corresponding key attribute tags, including key algorithm type (e.g., AES, RSA, SM4), bit strength, and security level number.

[0052] Key selection control mechanism: Before executing a task, the information processing module first generates the current task status based on the basic attributes of the task, and then calculates the result of the task scoring function F(Tscore, Slevel). The scoring function consists of two parts:

[0053] Tscore (Task Complexity Score): The Task Complexity Score is composed of the following four indicators, which are recorded as T1, T2, T3, and T4:

[0054] T1: Task scheduling instruction length (unit: bytes), reflecting the number of task call interfaces;

[0055] T2: Logic depth (unit: number of levels), that is, the number of nested control structure layers or branch logic;

[0056] T3: Types of on-board resources (unit: number of resource items), such as cameras, radars, communication units, etc.

[0057] T4: Run time (unit: seconds), which refers to the continuous execution time expected by the system scheduler.

[0058] Each metric undergoes maximum and minimum normalization (or Z-score standardization) to the range [0, 1]. The final weighted sum is: Tscore = w1·T1+w2·T2+w3·T3+w4·T4; w1 to w4 are the system's preset task type weights.

[0059] Slevel (Sensitivity Level Score): The sensitivity level score is determined by the task classification model in the information processing module, ranging from 1 to 4, corresponding to general information (C1), device configuration information (C2), platform key information (C3) and regulatory interface information (C4), with scores of 1, 2, 3 and 4 respectively.

[0060] The final scoring function result is: F(Tscore,Slevel)=α·Tscore+β·Slevel; where α and β are weighting coefficients, reflecting the system's relative emphasis on complexity and sensitivity levels.

[0061] Key loading process: After calculating the scoring function result, the system compares it with the cryptographic security values assigned to each key pool level. Each key level has a preset security threshold range. For example, Level 1 (task-level key) corresponds to a cryptographic security value range of [0.0, 1.5); Level 2 (task group common key) corresponds to a range of [1.5, 2.5); and Level 3 (platform authorization key) corresponds to a range of [2.5, 4.0]. The system searches from top to bottom, selecting the key level closest to the scoring function result, but not lower than it, and selects a key from that level to complete key loading.

[0062] For example, the current task is "An unmanned road sweeper performs key obstacle clearance tasks in the periphery of a post-disaster hospital." Its task attributes are as follows: T1 (instruction length): 1200 bytes, normalized to 0.75; T2 (logical depth): 5 layers, normalized to 0.6; T3 (resource types): camera, radar, 5G communication, normalized to 0.7; T4 (estimated runtime): 300 seconds, normalized to 0.9; weight settings: w1 = 0.3, w2 = 0.2, w3 = 0.2, w4 = 0.3; Tscore = 0.3 × 0.75 + 0.2 × 0.6 + 0.2 × 0.7 + 0.3 × 0.9 = 0.765; according to the information processing module, this task involves platform scheduling of sensitive data, Slevel = 3; system parameter settings α = 1, β = 0.8; F = 1 × 0.765 + 0.8 × 3 = 3.165. At this point, the system compares the score F=3.165 with the key pool levels:

[0063] The maximum security value of the task-level key layer 1 is 1.5 (inadequate); the maximum security value of the task group common key layer 2 is 2.5 (still insufficient); the platform authorized key layer 3 range is [2.5, 4.0], which is a match. Therefore, the system will select a key from the platform authorized key layer (layer 3) to encrypt the task data and sign it before uploading. If the scoring function result is lower than the minimum key call threshold K1 (for example, K1 = 0.6), the system enters a protected state, does not perform encryption, and does not allow the task data to be uploaded.

[0064] The mapping relationship between task status and encryption level is constructed based on the state transition diagram. Any task state node is mapped with reference to three dimensions: real-time factor R1, information sensitivity S1, and task content importance N1. The transition probability P(i→j) is obtained by calculating the Euclidean distance between the transition vector composed of real-time factor R1, information sensitivity S1, and task content importance N1 and the preset standard vector. The transition probability P(i→j) represents the probability of transitioning from state i to state j. A set of standard security level vectors Vi (i∈{1,2,3}) is preset. The level i corresponding to Vi with the smallest distance to the transition vector is selected, which is the "security level of the current task". The "security level of the current task" corresponds to a unique level in the tree hierarchical structure. When any node switches to a task of another security level, the mapping mechanism forces a fallback to the key renegotiation state and clears the mapping cache.

[0065] This invention uses an information processing module to establish a mapping relationship between task states and encryption levels, enabling unmanned road sweepers to dynamically match the appropriate encryption level, algorithm template, and key call path based on the specific characteristics of the task being performed. The core structure of this mapping relationship is constructed based on a state transition graph model. Each task state node is defined as a state node in the graph, and the encryption level associated with each node is determined through a feature mapping mechanism.

[0066] Task Status Feature Vector Construction: Each task status node constructs a status feature vector based on three dimensions, specifically: Real-time Factor R1: Indicates the task's sensitivity to response time, with higher values representing stronger real-time requirements; Information Sensitivity S1: Indicates the sensitivity level of the data processed in the task, such as whether it contains identity information, scheduling instructions, platform interfaces, etc.; Task Content Importance N1: Indicates the global scheduling priority of the task in the current cleaning cycle, involving path selection, core area cleaning, emergency response, etc. Real-time Factor R1, Information Sensitivity S1, and Task Content Importance N1 can all be obtained through expert assignment methods or other existing methods that achieve the same purpose. These methods will not be elaborated here. Together, they form a three-dimensional vector, denoted as: V0 = (R1, S1, N1), which is used to determine the "encryption security level" corresponding to the current task in the system.

[0067] Security Level Determination Mechanism and State Transition Control: The system pre-defines a set of standard security level vectors: V1, V2, and V3, corresponding to three security levels. Each standard level vector Vi is also a three-dimensional vector, representing the reference characteristic structure of level i. The system calculates the Euclidean distance between the current task vector V0 and each standard vector Vi. The Vi with the smallest distance is selected, and the corresponding number i is the security level of the current task. This level i has a preset mapping relationship with the tree-like key pool hierarchy in the data encryption module. For example: Security Level 1 (i=1) → Key Pool Level 1 (task-level key); Security Level 2 (i=2) → Key Pool Level 2 (task group key); Security Level 3 (i=3) → Key Pool Level 3 (platform authorization key).

[0068] Task state transition control logic: The system records the current task state as i. If the security level number mapped to the next task state is j, a state transition is considered to have occurred. The transition probability P(i→j) represents the probability of transitioning from state i to state j. In this embodiment, the system does not introduce an explicit transition matrix from the Markov model, but instead uses "whether the security level has changed" as the state transition trigger. When i≠j, the system immediately performs the following operations: clears the key mapping cache for the original task state; forces entry into the key renegotiation state; prohibits further data transmission along the old encryption channel; loads the key hierarchy corresponding to the new level j, and reselects the encryption algorithm template. This mechanism ensures that data is always processed and transmitted within the correct security level coverage, preventing the security risks of key level downgrade or mismatch during task state switching.

[0069] Taking the "task switching scenario" as an example: Assume that the current task of an unmanned road sweeper is "regional boundary inspection". The corresponding features are: R1=0.4 (medium-low real-time performance), S1=0.5 (data includes path data), N1=0.3 (low priority), and the calculated vector V0=(0.4, 0.5, 0.3); the preset safety level vectors in the system are: V1=(0.2, 0.4, 0.3), V2=(0.6, 0.6, 0.5), V3=(0.8, 0.9, 0.9); the calculated Euclidean distance is: D1≈0.22; D2≈0.33; D3≈0.78

[0070] If V1 (i=1) is selected, the current task security level is 1, corresponding to key pool level 1. If the task is switched to "Clearing the Core Area After a Disaster," with V0=(0.8, 0.9, 0.8), the system recalculates and determines it to security level 3 (i=3). Since 1≠3, a state jump and key renegotiation are triggered.

[0071] When the authentication mechanism is locally activated, the task window limit parameter Tauth is bound. This parameter is generated by the device self-test module when authentication is started and is subtracted from the task timeout value Ttask. If |Tauth−Ttask| is greater than the task offset threshold R2, the authentication is deemed to have failed.

[0072] The master-slave authentication control module of the present invention is designed to enable the unmanned sweeper to still have limited and secure data processing and task execution capabilities in the event of network connection failure or platform failure. The module sets up a multi-level identity verification path, including a master authentication path and a slave authentication path:

[0073] Main authentication path: The device identity legitimacy verification is performed by the cloud platform under standard circumstances;

[0074] Slave authentication path: This is a local authorization process performed by the local module based on historical authentication records and hardware signature cache when primary authentication fails or the platform is unreachable. This system uses a master-slave authentication linkage mechanism to ensure that devices can still perform critical tasks in the event of post-disaster network disconnection or edge communication failure. It also controls the scope and timeliness of execution, preventing the risk of identity fraud and unauthorized use.

[0075] Activation and constraint mechanism of the slave authentication mechanism: When the slave authentication mechanism is activated, the system binds a task window limit parameter called Tauth, which is generated by the device self-test module when the authentication is started.

[0076] The self-check module collects the following status information of the current device: local timestamp (synchronized with the last authentication); local system load status; residual value of the validity period of the last authentication; local security cache integrity check result. Based on the above information, the system generates Tauth, which represents the "maximum continuous execution window" during which the device is allowed to activate tasks at the current moment. The unit of Tauth is a time dimension (such as minutes, seconds, etc.), representing the "trust validity period" tolerated locally. At the same time, the system obtains the task time limit value Ttask pushed by the task scheduling module, which represents the estimated running time of the task from the current moment. The system makes a judgment through the following rules: If |Tauth - Ttask| > the task offset threshold R2, the local authentication is determined to be invalid. Here, R2 is the maximum offset tolerance preset by the system, such as 60 seconds, 5 minutes, etc., and different values can be set according to the task sensitivity level. This threshold is used to control whether the required execution duration of the task exceeds the locally tolerable trust period and prevent unauthorized actions caused by overly long tasks.

[0077] Behavior logic of authentication result: If the authentication is successful (|Tauth - Ttask| ≤ R2): The system generates a "temporary local authorization token"; starts the task execution process; the token has a time limit flag attached in the background; all subsequent data encryption and task result uploads carry this token for permission identification; once the platform reconnects, the system verifies whether the token has completed the task within the window period; if the task times out or the token expires, an abnormal task behavior log will be recorded. If the authentication fails (|Tauth - Ttask| > R2): The system refuses to execute the current task; generates a local warning message; records an audit log and notifies the platform (synchronized after reconnecting); clears the task instruction cache.

[0078] Suppose the driverless sweeper is currently unable to connect to the platform, and the device enters the secondary authentication process. The device self-check module generates Tauth = 180 seconds (3 minutes), representing that the maximum single-task execution window allowed by the local policy of this device is 180 seconds. The task scheduling module simultaneously pushes cleaning task X, and estimates Ttask = 210 seconds. The system calculates: |Tauth - Ttask| = |180 - 210| = 30 seconds; if the task offset threshold R2 set by the system is 60 seconds, then 30 seconds < R2, the authentication passes, and the system issues a "temporary local execution token" for this task, and the device enters the local task execution process. If the Ttask of another task Y is 300 seconds, the difference is 120 seconds, exceeding the offset threshold R2. At this time, the authentication fails, and the system refuses to execute task Y.

[0079] In communication scheduling, the dual-channel strategy includes a key negotiation channel and a historical key fast call channel. The switching mechanism is controlled by the key critical factor Kdyn. When Kdyn is less than the negotiation trigger threshold B3, the historical key is called and the first-round handshake authentication is performed using the signature cache;

[0080] When Kdyn is greater than or equal to B2, the platform negotiation key initialization process is carried out. A one-time negotiation parameter is introduced into the process. The parameter is generated by superimposing a hash random number, the vehicle's geographic location tag, and a timestamp. The dual-channel state machine is recorded by the status flag Sflag. When the Sflag value enters the renegotiation flag state, the task instruction push is automatically paused to avoid communication errors causing task synchronization errors.

[0081] The communication scheduling module is the core unit in this invention that enables secure and efficient data exchange between the vehicle and the cloud platform. Its responsibility is to switch between different key channels based on the sensitivity of the current task and the state of the communication environment, ensuring the security, stability, and resource efficiency of data transmission. This module features two transmission paths: a preloaded key channel and an on-the-fly key negotiation mechanism. These two mechanisms are used for: performing the first round of encryption based on historical authentication information for quick task startups; and triggering master key negotiation to establish a new secure channel for highly sensitive tasks or when the authentication status is unstable.

[0082] Dual-channel architecture and switching mechanism: Channel types: Key negotiation channel (primary channel): Based on a TLS-like architecture, it generates new keys through real-time negotiation with the cloud platform; supports the introduction of one-time negotiation parameters to enhance protection against replay attacks; suitable for high-sensitivity tasks or when the current connection authentication status is uncertain. Historical key fast recall channel (secondary channel): Utilizes historical session keys cached locally on the device; supports rapid communication recovery in weak network conditions; and completes the initial encryption and handshake process in conjunction with a local signature cache file.

[0083] Channel switching mechanism: The communication scheduling module uses a variable called the key critical factor Kdyn as the basis for dynamic channel switching. Kdyn is a floating-point value that can be obtained from a pre-trained machine learning model, such as a convolutional neural network model. The input data can include but is not limited to the following:

[0084] The current task sensitivity level (provided by the information processing module); device authentication status (master / slave authentication, token remaining time); current network quality score (packet loss rate, latency, bandwidth, etc.). The output data is the key critical factor Kdyn.

[0085] Specifically, Kdyn's generative model uses a multi-input convolutional neural network structure. The model has three main input channels, which receive feature information from different modules within the system:

[0086] Model input: Task sensitivity level indicator (provided by the information processing module): The value is 1-4, corresponding to the general level, equipment level, platform level, and regulatory level; it can be converted into a one-hot encoded vector input model.

[0087] Device authentication status metrics: including the current primary authentication / slave authentication flag; the remaining valid duration of the current device token (in seconds); the authentication path stability score (such as the number of successful handshakes in the past 24 hours).

[0088] Current network quality metrics: network packet loss rate (0 - 1); average latency (in milliseconds);

[0089] Real - time bandwidth utilization (0 - 1); network volatility (such as the standard deviation of latency).

[0090] After these data are normalized by the pre - processing module, they enter a three - way convolutional network structure as three groups of input vectors, and are concatenated and fully connected at the high - level feature fusion stage. Finally, a numerical value is output as Kdyn.

[0091] Model training principle and process: The training dataset of this model consists of historical task execution records and system status logs. The specific training process is as follows:

[0092] Data collection stage: Collect the history of communication channel selection during the execution of multiple rounds of sweeper task scheduling; each data record contains the task type, network status, authentication status, and channel decision result (using historical keys or platform negotiation); each data is finally labeled with a "recommended channel level label"; input feature vectors and expected outputs are constructed based on these data.

[0093] Model training stage: Iteratively train the model to minimize the distance between the actual output Kdyn and the "recommended channel level"; map the channel level label to a floating - point value to form a continuous objective function; at the same time, constrain the output Kdyn distribution to fluctuate within a set range (such as [0.5, 4.5]) for subsequent threshold judgment applications.

[0094] Model verification and deployment stage: Evaluate the model accuracy on the validation dataset; after tuning, deploy it to the device side.

[0095] Switching rules: If Kdyn < B3 (negotiation trigger threshold): The system determines that the current task is in the normal level or stable communication state; select to use the historical key channel; use the cached signature and the platform's most recent public key certificate for the first - round handshake authentication; encrypt the initial state data (such as startup state, task number, location coordinates, etc.). If Kdyn ≥ B2 (strong negotiation trigger threshold): The system immediately enters the key negotiation process and introduces a one - time negotiation parameter Pnonce as a handshake extension: Pnonce = Hash(random number || current geographical location label || timestamp) for the "extra identity label" field in the key negotiation message to prevent man - in - the - middle attacks. The thresholds B2 and B3 can overlap or be set at intervals, supporting personalized scenario - based policy configuration.

[0096] State management mechanism: The communication scheduling module uses the state flag variable Sflag to represent the current channel state. The state definitions are as follows: Sflag=S0: default idle state, no channel is started; Sflag=S1: in historical key call state; Sflag=S2: in key negotiation initialization state; Sflag=S3: negotiation fails, entering the communication blocking state; Sflag=S4: negotiation completed, establishing a new secure channel.

[0097] When Sflag=S2, if a handshake failure, parameter verification exception, timeout, or other abnormal conditions occur, the system enters Sflag=S3. In Sflag=S3, the system performs the following actions: suspends all task instruction pushes; intercepts new task scheduling messages; locks the encryption module and prohibits the use of expired keys; and resumes the renegotiation process.

[0098] Assume the current task is "Cleaning a Risky Area Under Platform Management," with a sensitivity assessment of Slevel 4 (Supervisory). The device's primary certification is still valid, but the network is experiencing intermittent connectivity. The system's real-time Kdyn value is 3.2, exceeding the platform's default value of B2 of 2.5. According to the rules, the communication scheduling module determines that the master key negotiation process should begin. It generates one-time negotiation parameters: random number R = 10393, geolocation tag L = GZ-HZ block 45, and timestamp T = 2025-04-08 09:35:11.

[0099] Pnonce = Hash(R||L||T), set Sflag = S2, and initiate a TLS-like handshake. After successful negotiation, the state changes to Sflag = S4, and normal communication begins. If the task is interrupted and a new task is reassigned, the system will determine whether to downgrade to a historical key channel based on the real-time updated Kdyn value.

[0100] It should be noted that: in each channel calling process, it is necessary to first determine the key pool level to be accessed based on the result of the current task scoring function F; in this level, the optimal encryption combination strategy is calculated based on the encryption cost function Cenc(i) and the authentication strength function Sauth(i). The channel only decides whether to directly obtain the policy key through the historical cache or enter the negotiation process to obtain a new session key.

[0101] During the execution of the sweeper involved in the present invention, there are task data of different levels and changeable network status. If the key negotiation process is always forced to be used, it will lead to: network congestion and increased probability of negotiation failure; large consumption of computing resources, which is especially not conducive to low-sensitivity, frequent, and fragmented tasks; long encryption channel establishment time, affecting real-time performance. On the contrary, if historical cached keys are used in all cases, it may also lead to: reduced security strength due to key reuse; certain high-sensitivity tasks are at risk of data leakage due to insufficient key levels. Therefore, it is necessary to introduce a dynamic judgment mechanism in the communication scheduling module to reasonably choose whether to use cached keys or negotiate new keys based on factors such as task scores, authentication status, and network quality, so as to achieve an adaptive balance between encryption strength and system efficiency.

[0102] In typical operating environments for autonomous road sweepers, such as park edges, underground passages, and post-disaster areas, unstable platform connections are common. If the system requires online key negotiation, temporary network outages will render the task impossible. By introducing a rapid recall mechanism for historical keys, combined with signature caching and an authentication fallback path, the initial phase of the task can be completed during short network outages, ensuring uninterrupted cleaning operations and system downtime. Furthermore, key renegotiation will resume after communication is restored, achieving an asynchronous, secure closed-loop.

[0103] Different task sensitivity levels (C1-C4) correspond to different data confidentiality requirements. The communication scheduling module derives the key pool hierarchy from the results of the task scoring function and determines the channel path based on the key criticality factor Kdyn. For tasks with low sensitivity and high transmission frequency (such as general location information and navigation status), using historical keys can meet security requirements without initiating a full key negotiation process each time, significantly reducing key calculation and handshake overhead.

[0104] The role signature credential structure includes the task number, permission granularity, timeliness label and device ID hash. The task number is temporarily allocated and generated by the edge node before each task collaboration. The permission granularity is restricted according to the task-sensitive area. The role signature credential generates a device signature graph locally through hash aggregation. The graph structure has a ring structure detection mechanism to prevent repeated authorization links from forming a permission amplification path; the signature graph maintenance cycle is bound to the task update cycle. When the collaborative device node exceeds the collaborative density threshold P4, the trust reduction algorithm is forcibly introduced to limit the horizontal chain expansion rate.

[0105] The horizontal trust management module of this invention is used to address security and trust issues during task collaboration between unmanned road sweepers. In traditional unmanned systems, inter-device identity authentication relies on a platform-centric verification mechanism. However, in scenarios with unstable networks or offline platforms, adjacent devices lack a verifiable identity and permission mechanism, creating the risk of misjudgment or impersonation during collaboration.

[0106] To solve this problem, the present invention introduces a role signature credential mechanism and a structured signature chain structure (graph) between sweepers. Through local generation and update, it realizes identity confirmation, permission verification and collaborative task authorization between devices without relying on the platform.

[0107] Role Signature Credential Structure Description: The horizontal trust management module generates a role signature credential for the sweeper before task collaboration. This credential serves as the local authentication basis for device identity, permissions, and task association. Its structure includes the following four core fields:

[0108] Task number: Each collaborative task is assigned a unique number, which is temporarily generated and broadcast by the edge nodes (such as small relay controllers) in the collaborative area during the task initialization phase;

[0109] Permission granularity: indicates the scope of the device's permissions in this collaborative task. Permission granularity is graded and restricted based on task-sensitive areas. For example, only obtaining navigation paths is allowed but uploading surveillance images is not allowed.

[0110] Time limit tag: indicates the validity period of the signature certificate, in units of absolute timestamp or task execution window;

[0111] Device ID hashing: Uses the SHA series of algorithms to hash the device's unique identification information (such as hardware fingerprint and public key digest) to ensure privacy protection and identity non-forgeability.

[0112] The signature certificate is cryptographically signed by the device's local security module and its authenticity and integrity can be verified by other devices.

[0113] Structured signature chain and graph generation mechanism: Role signature credentials are added to a locally maintained device signature graph. The graph consists of multiple nodes, each representing a role signature credential for a device. A hash aggregation mechanism is used when updating the graph, combining the hash digests of multiple credentials into a Merkle tree root node and storing it in the device's local signature chain. The graph has a ring structure detection mechanism. When a device is detected to form a circular authorization chain with itself or other devices (A trusts B, B trusts C, and C trusts A), it is identified as an "abnormal trust chain." The system triggers a trust cleansing process, canceling the credential chain with the weakest permissions to prevent the emergence of "privilege amplification paths."

[0114] Trust control strategy and dynamic adjustment: The system is equipped with a collaboration density threshold P4, which represents the maximum number of collaborative devices in a certain area or task segment. For example, setting P4=5 means that a maximum of 5 devices are allowed to establish a horizontal trust chain during a task collaboration process. When the number of collaborative devices exceeds the threshold P4, the system forcibly introduces a trust reduction algorithm. The algorithm in this prior art relies on evaluating the value indicators of each horizontal signature path (such as task overlap and device accessibility), giving priority to retaining paths with "recent successful communication + strong authority restrictions", and terminating signature interactions for redundant or repeated authorization paths, thereby preventing the chain from expanding indefinitely, leading to resource abuse or signature flooding. The maintenance cycle of the signature map is bound to the task update cycle, that is, each time a task is updated, the system will synchronously update the map, clear expired credentials, and add new authorization paths to form a periodic trust refresh.

[0115] Assume that six sweepers are deployed simultaneously in a cleaning area due to complex terrain. The task number is T20250408. The edge node generates a task number for the task and sets the permission granularity level to "navigation collaboration + environmental state sharing." Each sweeper generates a signature credential based on its role (master, auxiliary, follower): Sweeper A generates a credential containing the task number T20250408, permission granularity P2, expiration date 2025-04-08 10:00-10:15, and the device ID hash H(A); Sweeper B verifies the credential and generates its own credential, which it broadcasts to C; when C attempts to sign B, but B has already signed A, the system detects a closed loop from A to B to C to A; the graph algorithm deletes C's signature record and marks the path as an "abnormal path"; at this point, the collaborative density detection module discovers that six devices have established signature paths, exceeding the threshold P4=5; the system introduces a trust reduction algorithm, retaining only three trusted paths: master A, secondary B, and the nearby C. Other devices only retain the permission to passively receive data.

[0116] Different encryption algorithms are set in the key pool of each level, including symmetric encryption and asymmetric encryption. The combination strategy of each encryption algorithm is determined based on the preset encryption cost function Cenc and authentication strength function Sauth. The functional relationship is: selection scheme = max{Cenc(i) / Sauth(i)}, where i is the set number of candidate algorithms. The combination scheme with the largest ratio is selected and applied to the current task. If all the ratios obtained at the current level are lower than the calculated security threshold Z5, the hierarchical encryption algorithm switch is initiated and two rounds of handshake confirmation are performed.

[0117] Symmetric encryption methods include SM4, AES-128, and AES-256; asymmetric encryption methods include RSA-2048, ECC, and SM2. Each time a task is activated, the system screens all candidate encryption algorithm combinations at the corresponding level and selects the optimal solution based on the ratio of the encryption cost function Cenc(i) to the authentication strength function Sauth(i). The functional relationship is as follows: Selected Solution = max{Cenc(i) / Sauth(i)}; where i represents the candidate algorithm combination number.

[0118] The encryption cost function Cenc(i) is used to measure the resource overhead and encryption load required by the current candidate algorithm combination i, and can generally include the following existing technical parameters: unit data encryption time: such as the number of milliseconds required to encrypt 1MB of data, which comes from the open source encryption library benchmark; key length bit: the number of key bits required by the encryption algorithm, reflecting its key space; processor resource consumption rate: the proportion of CPU occupied during the task encryption process; power consumption rate: the power consumption corresponding to each encryption operation (such as mW / s); a reference calculation method is: Cenc(i)=Tenc(i)×P(i); where: Tenc(i): the average encryption time of the algorithm combination; P(i): the power consumption per unit time of the algorithm combination.

[0119] The authentication strength function Sauth(i) is used to evaluate the comprehensive capabilities of candidate algorithm combinations in terms of anti-attack, anti-replay, and anti-tampering. The evaluation parameters may include: the algorithm's public security level: such as RSA-2048 has a security strength of 112 bits and ECC has a security strength of 128 bits; the authentication mechanism integrity index: whether it supports complete identity authentication and integrity verification; the quantum attack resistance score: such as the relative vulnerability of symmetric algorithms to quantum attacks; the known attack frequency in the current environment: whether there are public vulnerabilities or cracking reports, etc.

[0120] A reference calculation method is: Sauth(i) = Slevel(i) × B(i); where: Slevel(i) is the basic security level of the algorithm combination recognized by the cryptography community (such as the NIST recommended level); B(i) is the algorithm security weight coefficient assigned by the platform or regulator. The encryption cost function Cenc(i) and the authentication strength function Sauth(i) can also be calculated using other existing methods that achieve the same measurement effect. This example is not exhaustive and only provides one possible implementation method.

[0121] The system evaluates the Cenc(i) / Sauth(i) ratios of all combinations in the candidate algorithm set and selects the combination with the largest ratio as the encryption scheme for the current task. If all candidate combinations at this level satisfy: max{Cenc(i) / Sauth(i)} < Z5; that is, the security is insufficient or the cost is too high, the system triggers: the hierarchical encryption algorithm switching mechanism: switch to a higher-level key pool level (such as task group level → platform level); perform two rounds of handshake confirmation: including device identity verification and key negotiation path verification; prevent resource abuse or delay caused by incorrect invocation of high-strength algorithms for low-level tasks.

[0122] The sensitive level determination mechanism includes an edge feature extraction unit and a task classification model. The edge feature extraction unit collects three behavioral characteristics of task data: format complexity, control instruction set distribution, and transmission timing pattern, and assigns scores F1, F2, and F3 to each characteristic respectively and normalizes them into a task feature vector Vtask.

[0123] The task classification model is a shallow convolutional neural network structure that generates a classification decision boundary based on historical task data and classifies tasks into four sensitive levels C1 to C4, representing general information, device configuration information, platform key information, and regulatory interface information respectively; the classification boundary is automatically incrementally trained and updated every fixed time window.

[0124] When a new task is input, if the offset of its Vtask from the original classification boundary exceeds the adjustment threshold E, that is, the model's confidence in classifying this task decreases, the task is not directly assigned a level, but is marked as "pending confirmation status", and is handed over to the platform for manual label confirmation and determination, and then its sensitive level is re-established and fed back to the information processing module for subsequent key matching and invocation.

[0125] Structure and implementation of the edge feature extraction unit: The edge feature extraction unit is located in the local system of the sweeper, and analyzes the data characteristics of the task to be executed in real time, and extracts three-dimensional indicators from the behavioral level, namely: format complexity (F1): Analyze the hierarchy and nesting depth of the task data structure, such as whether it contains nested JSON, multi-level XML, etc.; data with high complexity is more likely to contain sensitive information such as identifiable identity, geography, and status; the system presets several format type templates and assigns a complexity score according to the matching situation, ranging from 0 to 10.

[0126] Control instruction set distribution (F2): Analyze the types of instructions used in the task, such as navigation, device control, image acquisition, etc.; different instruction combinations represent the degree of invocation of the system's core functions by the task; the system maintains an instruction sensitivity table and assigns higher scores to high-sensitivity instructions.

[0127] Transmission Timing Mode (F3): Analyzes the frequency, latency fluctuation, and timing continuity of data packets. High-frequency and stable transmission is more likely to represent real-time data streams (such as images and videos) and carries a higher risk. The mean, standard deviation, and packet loss rate within a specific time window are calculated and mapped to a timing complexity score.

[0128] The three scoring values are normalized and combined into a task feature vector: Vtask=(F1_norm, F2_norm, F3_norm), which will serve as the input of the subsequent classification model.

[0129] The task classification model is a shallow convolutional neural network designed for compact size, high computational efficiency, and ease of deployment on the vehicle edge. The model performs convolution, pooling, and full connectivity on the Vtask input vector, outputting classification probabilities for the task within four sensitivity levels. The model output is a probability vector [p1, p2, p3, p4], with the category corresponding to the maximum value being the initial task classification level.

[0130] Classification offset and pending confirmation mechanism: When a new task arrives, the model calculates the closest category corresponding to its Vtask vector. If the model determines that its classification confidence is insufficient, that is, the offset between the vector and the center of the original classification boundary exceeds the set adjustment threshold E, the classification result is considered unstable, and the system enters the following process: the task is marked as "pending confirmation status"; it is prevented from entering the encryption process and data upload process; its feature vector and data summary are synchronously submitted to the platform; the platform performs manual label review; the platform label result is used to confirm the final sensitivity level of the task; the result is returned to the information processing module to guide the key level matching call; the sample is included in the next round of model incremental training data set. This process ensures that the model does not rashly classify when it cannot confirm, thereby improving system security.

[0131] Suppose a new task has the following characteristics: F1=7.5 (multi-layer nested data format); F2=8.0 (including advanced control instructions such as unlocking area fences); F3=6.8 (high-speed transmission timing); after normalization, Vtask=(0.75, 0.8, 0.68); the vector is input into the convolutional neural network model, and the output probabilities are: p1=0.05, p2=0.1, p3=0.35, p4=0.5 → the initial judgment level is the regulatory interface information corresponding to p4; the system calculates the Euclidean distance between the center vector corresponding to the task Vtask and the regulatory interface information to be 0.41, which is higher than the adjustment threshold E=0.35 set by the system. It is judged that the classification confidence has decreased, and it is marked as "pending confirmation" and submitted to the platform for manual confirmation.

[0132] The communication scheduling module configures a dynamic transmission priority mechanism, calculates the ratio of the result of the task scoring function F to the amount of data in the data packet, obtains the priority index Px, evaluates the Px value of each data to be uploaded at a fixed period, and selects the data with the highest priority index to enter the encryption channel first. When the Px value distribution fluctuates, that is, the standard deviation value is greater than the adjustment threshold V, the preset temporary current limiting strategy is triggered.

[0133] Priority Index Calculation Method: Each data block to be transmitted corresponds to a source task. The system first calculates the task's scoring function F (provided by the information processing module). Then, combined with the data block's data volume D, it calculates the data block's transmission priority index Px. The calculation method is as follows: Px = F / D, where: F: Task scoring function, which takes into account the task's complexity (Tscore) and sensitivity level (Slevel), as detailed earlier in this manual; D: Data volume of the data packet; Px: Priority index, which indicates the importance of the task's unit data; higher values indicate more important tasks.

[0134] Scheduling strategy and execution mechanism: Periodic scheduling evaluation: The communication scheduling module scans the queue of data to be uploaded at a fixed period (e.g., every 3 seconds). For each piece of data in the queue, the corresponding Px index is calculated and sorted from largest to smallest. The highest-priority data enters the encrypted channel first for encryption processing and upload. The remaining data is queued in order until the next scheduling cycle or when an idle channel becomes available. Px fluctuation detection and current limiting mechanism: The distribution of data packet priority index Px within the current scheduling cycle varies significantly. The system calculates its standard deviation σ (the standard deviation indicates the degree of fluctuation in the data set). If the standard deviation σ is greater than the preset adjustment threshold V, it indicates that the data priority fluctuates significantly, and high-priority tasks may be delayed due to low-priority traffic congestion. At this time, a temporary current limiting strategy is triggered, that is, only the top N (e.g., the top 3) pieces of data ranked by Px in the current cycle are allowed to enter the encrypted upload channel. The remaining data is temporarily suspended. A cooldown period (e.g., 1 second) is set before re-evaluation to prevent high fluctuations from causing resource scheduling chaos.

[0135] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0136] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0137] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0138] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The data interaction system between unmanned road sweeper and cloud platform based on hybrid encryption is characterized by: include: The authorization authentication module is configured to parse the device identity and short-term authentication token distributed by the cloud platform. When the platform connection is unavailable, the authentication is performed based on the cached signature token. The data encryption module has a key pool mechanism. The device side pre-configures multiple levels of session keys and dynamically selects the key for encrypting vehicle data from the key pool based on the current task status; The information processing module establishes a mapping relationship between task status and encryption level. When switching tasks, it automatically matches the corresponding encryption algorithm and key template according to the level of the current task. If the match fails, data transmission is blocked and security renegotiation is initiated. The master-slave authentication control module sets a multi-level identity verification path. When the master authentication path fails, the slave authentication mechanism is automatically activated for local authorization, and the scope and time limit of the activation task are limited. The horizontal trust management module generates role signature credentials for sweepers during task collaboration and proves the legitimacy and authorization time of their tasks to neighboring devices through a structured signature chain. The communication scheduling module has a pre-loaded key channel and an instant key negotiation mechanism. It decides whether to trigger the master key negotiation process or use the historical session key to encrypt the initial state data based on the task sensitivity. Among them, the task encryption level mapping model in the information processing module is constructed based on the task content, real-time requirements and information sensitivity, so that the data encryption strength changes dynamically with the task, thereby improving the task completion rate and data security of the cleaning operation.

2. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 1 is characterized in that: The key pool within the data encryption module is managed hierarchically in a tree-like structure. The lowest layer is the task-level key, the middle layer is the task group common key, and the top layer is the platform authorization key. The key call of each layer is controlled by the task scoring function F(Tscore, Slevel). The task scoring function is obtained by the weighted sum of the task complexity score Tscore and the sensitivity level score Slevel. The task complexity score (Tscore) is calculated based on a combination of four indicators: task scheduling instruction length, logic depth, required vehicle resource types, and runtime. These indicators correspond to T1 to T4, and each indicator is normalized and weighted to form a total score. The sensitivity level score (Slevel) is derived from the level determination model results fed back by the information processing module. The score range is 1 to 4, corresponding to general, device-level, platform-level, and regulatory-level information, respectively. When the encryption strategy is called, the result of the scoring function F is calculated and matched with the encryption security value corresponding to each layer of the key. The key level corresponding to the encryption security value closest to but not lower than the scoring function F is selected. If the result of function F is lower than the minimum key call threshold K1, the system enters the protection state, encryption is not performed, and the task data is not allowed to be uploaded.

3. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 2 is characterized in that: The mapping relationship between task status and encryption level is constructed based on a state transition diagram. For any task state node, a mapping is constructed based on three dimensions: real-time factor R1, information sensitivity S1, and task content importance N1. The transition probability P(i→j) is calculated by calculating the Euclidean distance between the transition vector formed by real-time factor R1, information sensitivity S1, and task content importance N1 and a preset standard vector. The transition probability P(i→j) represents the probability of transitioning from state i to state j. A set of standard security level vectors Vi (i∈{1,2,3}) is preset. The level i corresponding to the transition vector with the smallest distance Vi is selected as the "security level of the current task." The "security level of the current task" corresponds to a unique level in the tree hierarchy. When any node switches to a task of a different security level, the mapping mechanism forces a fallback to the key renegotiation state and clears the mapping cache.

4. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 3 is characterized in that: When the authentication mechanism is locally activated, the task window limit parameter Tauth is bound. This parameter is generated by the device self-test module when authentication is started and is subtracted from the task timeout value Ttask. If |Tauth−Ttask| is greater than the task offset threshold R2, the authentication is deemed to have failed.

5. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 4 is characterized in that: The dual-channel strategy in communication scheduling includes a key negotiation channel and a historical key fast call channel. The switching mechanism is controlled by the key critical factor Kdyn output by a pre-trained machine learning model. When Kdyn is less than the negotiation trigger threshold B3, the historical key is called and the signature cache is used for the first round of handshake authentication. When Kdyn is greater than or equal to B2, the platform negotiation key initialization process is carried out. A one-time negotiation parameter is introduced into the process. The parameter is generated by superimposing a hash random number, the vehicle's geographic location tag, and a timestamp. The dual-channel state machine is recorded by the status flag Sflag. When the Sflag value enters the renegotiation flag state, the task instruction push is automatically paused to avoid communication errors causing task synchronization errors.

6. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 5 is characterized in that: The role signature credential structure includes the task number, permission granularity, timeliness label and device ID hash. The task number is temporarily allocated and generated by the edge node before each task collaboration. The permission granularity is restricted according to the task-sensitive area. The role signature credential generates a device signature graph locally through hash aggregation. The graph structure has a ring structure detection mechanism to prevent repeated authorization links from forming a permission amplification path; the signature graph maintenance cycle is bound to the task update cycle. When the collaborative device node exceeds the collaborative density threshold P4, the trust reduction algorithm is forcibly introduced to limit the horizontal chain expansion rate.

7. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 6 is characterized in that: Different encryption algorithms are set in the key pool of each level, including symmetric encryption and asymmetric encryption. The combination strategy of each encryption algorithm is determined based on the preset encryption cost function Cenc and authentication strength function Sauth. The functional relationship is: selection scheme = max{Cenc(i) / Sauth(i)}, where i is the set number of candidate algorithms. The combination scheme with the largest ratio is selected and applied to the current task. If all the ratios obtained at the current level are lower than the calculated security threshold Z5, the hierarchical encryption algorithm switch is initiated and two rounds of handshake confirmation are performed.

8. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 7 is characterized in that: The sensitivity level determination mechanism includes an edge feature extraction unit and a task classification model. The edge feature extraction unit collects three behavioral characteristics of task data: format complexity, control instruction set distribution, and transmission timing mode. Each characteristic is assigned a score of F1, F2, and F3, and the scores are normalized and combined into a task feature vector Vtask. The task classification model is a shallow convolutional neural network structure that generates a classification decision boundary based on historical task data. It divides tasks into four sensitivity levels, C1 to C4, representing general information, device configuration information, platform key information, and regulatory interface information, respectively. The classification boundary is automatically updated through incremental training every fixed time window. When a new task is input and its Vtask offset from the original classification boundary exceeds the adjustment threshold E, the model's classification confidence in the task decreases. Instead of directly assigning a level, the task is marked as "pending confirmation status" and submitted to the platform for manual label confirmation and judgment. The sensitivity level is then re-established and fed back to the information processing module for subsequent key matching calls.

9. The hybrid encryption-based data interaction system between an unmanned road sweeper and a cloud platform according to claim 8 is characterized in that: The communication scheduling module configures a dynamic transmission priority mechanism, calculates the ratio of the result of the task scoring function F to the amount of data in the data packet, obtains the priority index Px, evaluates the Px value of each data to be uploaded at a fixed period, and selects the data with the highest priority index to enter the encryption channel first. When the Px value distribution fluctuates, that is, the standard deviation value is greater than the adjustment threshold V, the preset temporary current limiting strategy is triggered.

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