A traffic system global coordination management system and method

Abstract

The application discloses a kind of traffic system global coordination management system and method, including deployment in programmable terminal of vehicle and deployment in multiple cloud service nodes of cloud, programmable terminal and multiple cloud service nodes are communicated by federalization cooperative communication bus, wherein, programmable terminal includes software-defined vehicle platform, context awareness module and digital twin agent manager, cloud service node includes core service logic and data asset module, server digital twin agent generation module, federal computing engine and access control and audit module.The traffic system global coordination management system and method provided by the application realize the safe, trusted, privacy protection data cooperation and federal intelligence across system, promote traffic digital asset value realization, improve traffic efficiency and safety.

Description

Technical Field

[0001] This application relates to the field of intelligent connected vehicle technology, specifically to a comprehensive collaborative management system and method for a transportation system. Background Technology

[0002] With the deep integration of intelligent transportation and software-defined vehicles (SDV), transportation systems have generated massive amounts of high-value digital assets, including but not limited to: real-time vehicle operation data (including: location, speed, acceleration, and steering angle), high-precision maps and positioning information (including: centimeter-level precision maps and real-time positioning data), traffic flow models and predictive analysis (including: road network congestion index, traffic flow prediction models), control algorithms and optimization parameters (autonomous driving decision trees, path planning algorithms), user behavior preferences and travel patterns (driving habits, frequently used routes, preference settings), and infrastructure status and environmental perception data (road condition information, weather data, parking lot occupancy rate).

[0003] Currently, these digital assets are stored in various locations: vehicle terminals and in-vehicle computing platforms, multiple heterogeneous cloud platforms (OEM clouds, traffic management clouds, map service clouds, insurance company clouds, parking management clouds, energy management systems), or various third-party service systems and data brokers. This dispersed storage of digital assets creates serious "data silos," and this decentralized state has the following drawbacks:

[0004] 1. Technical interoperability challenges:

[0005] The lack of a unified standard leads to complex and highly heterogeneous interfaces;

[0006] The integration between different protocol stacks and data formats is difficult;

[0007] Semantic model incompatibility hinders meaningful data exchange;

[0008] Static pre-configured communication modes cannot adapt to the needs of dynamic scenarios;

[0009] Interface version fragmentation leads to high maintenance costs.

[0010] 2. Security and privacy issues:

[0011] Privacy regulations (GDPR, CCPA, China's Personal Information Protection Law) prevent the centralized aggregation of raw data;

[0012] Data sovereignty issues across jurisdictions;

[0013] Lack of fine-grained, contract-based access control mechanisms;

[0014] The use of data lacks auditability and accountability.

[0015] Traditional encryption methods cannot support the collaborative computing requirements where "data is available but not visible".

[0016] 3. Limitations of digital twin technology:

[0017] While SDV technology enables vehicle behavior to be defined and updated remotely in the cloud, making personalized services and fleet management possible, existing digital twin implementations have the following shortcomings:

[0018] Application limitations: Primarily used for state mirroring within a single system, lacking cross-system scalability;

[0019] Lack of communication protocols: In particular, there is a lack of secure and reliable cloud-based inter-system (C2C) collaborative communication protocols;

[0020] Lack of trust mechanisms: Cross-organizational data exchange lacks standardized trust establishment and verification mechanisms;

[0021] Update delay issue: Delayed model updates cause the virtual model to deviate from the state of the physical entity;

[0022] Static instantiation: Cannot support context-driven, dynamic, collaborative twin instantiation;

[0023] Contract enforcement deficiencies: There is a lack of mechanisms to embed data usage terms into the technical architecture.

[0024] 4. Economic and operational gap:

[0025] There is a lack of effective technical support for cross-regional and cross-institutional collaborative decision-making;

[0026] The mechanisms for realizing and monetizing the value of digital assets are imperfect;

[0027] Real-time, event-driven collaborative scenarios (emergency response, dynamic path optimization) cannot be implemented efficiently;

[0028] There is a lack of infrastructure to support the "data-as-a-service" business model in the transportation sector;

[0029] There is a lack of mechanisms for the distribution of benefits and cost accounting in multi-party collaboration.

[0030] In summary, existing transportation data assets lack security guarantees and are untrustworthy when collaborating with cross-system data, and there are also issues of privacy leaks. This leads to bottlenecks such as difficulty in realizing the value of transportation digital assets, low transportation efficiency, and insufficient security. Summary of the Invention

[0031] To address this, this application provides a comprehensive collaborative management system and method for transportation systems, thereby resolving the issues of lack of security, unreliability, and privacy leakage in existing technologies when traffic data assets are collaboratively managed across systems.

[0032] To achieve the above objectives, this application provides the following technical solution:

[0033] In a first aspect, a comprehensive collaborative management system for a transportation system includes a programmable terminal deployed in a vehicle and multiple cloud service nodes deployed in the cloud, wherein the programmable terminal communicates with the multiple cloud service nodes through a federated collaborative communication bus.

[0034] The programmable terminal includes a software-defined vehicle platform, a context-aware module, and a digital twin agent manager.

[0035] The software-defined vehicle platform is used to remotely deploy software using over-the-air (OTA) download technology and to reconstruct vehicle behavior.

[0036] The context-aware module is used to continuously collect, analyze and integrate multi-source vehicle data, and based on the multi-source vehicle data, infer the current scenario type in real time using a rule engine or machine learning model, assess the priority of collaborative needs, and predict the upcoming business scenarios.

[0037] The digital twin agent manager is used to trigger the agent instantiation process based on the business scenario predicted by the context-aware module, and to maintain the complete digital twin model of the vehicle.

[0038] The cloud service node includes a core service logic and data asset module, a server-side digital twin agent generation module, a federated computing engine, and an access control and auditing module.

[0039] The core service logic and data asset module is used for the business logic of the cloud system, manages the proprietary database and knowledge base, and provides computing and storage resources.

[0040] The server-side digital twin agent generation module is used to dynamically instantiate digital twin agents according to internal business needs or external collaboration requests.

[0041] The federated computing engine is used for collaboration between remote endpoints and integrates a secure multi-party computing library to support computation in an encrypted state.

[0042] The access control and auditing module is used to implement fine-grained permission management based on role-based access control or attribute-based access control.

[0043] As a preferred option, it also includes a cloud-based coordination coordinator, which is used for global coordination strategy formulation, workflow orchestration, proxy topology planning, consistency assurance, and fault recovery in complex cross-domain business processes deployed on three or more nodes.

[0044] Preferably, the software-defined vehicle platform covers the application layer, service layer, middleware layer and firmware layer when remotely deploying software, and supports incremental update and rollback mechanisms, and implements digital signature verification and multi-level secure boot.

[0045] Preferably, the context-aware module implements local differential privacy processing, location obfuscation, and data minimization principles during the data acquisition phase.

[0046] Preferably, when the digital twin agent manager maintains a complete digital twin model of the vehicle, it includes static features, dynamic states, capability descriptions, and service subscriptions.

[0047] Preferably, when the digital twin agent manager triggers the agent instantiation process, it extracts the minimum necessary data subset from the full model, selects a predefined agent template according to the business scenario type, and configures a three-layer structure to instantiate a temporary agent.

[0048] Preferably, the federated computing engine supports horizontal federated learning, vertical federated learning, and federated transfer learning paradigms, and integrates a secure multi-party computation library, homomorphic encryption, and secure aggregation technology.

[0049] Preferably, both the digital twin agent manager and the server-side digital twin agent generation module include a semantic layer, a data layer, and a contract layer. The semantic layer provides machine-readable and human-understandable data descriptions based on domain ontology and metadata models. The data layer is used to store or link data instances that actually need to be exchanged and provides data access interfaces. The contract layer is used to formally define key terms and automate their execution, and to ensure the consensus, immutability, and auditability of the contract through blockchain.

[0050] Preferably, the federated collaborative communication bus is used to provide identity authentication and trust management, agent discovery and registration services, secure connection establishment, message routing and transmission, data transmission optimization, auditing and traceability functions based on trusted data space technology, and supports three communication modes: vehicle-to-cloud, cloud-to-vehicle, and cloud-to-cloud.

[0051] Secondly, a method for comprehensive collaborative management of a transportation system, the method being applied to the comprehensive collaborative management system of the transportation system, comprising:

[0052] Step 1: The software-defined vehicle platform receives remotely deployed software rules or agent templates in advance, and the context-aware module or cloud service node continuously monitors various triggering conditions;

[0053] Step 2: The context awareness module identifies specific business scenarios requiring cross-system collaboration based on multi-source vehicle data and assesses whether to trigger the collaboration process;

[0054] Step 3: When the collaborative process is triggered, the initiator creates a digital twin agent manager and configures the digital twin agent manager to define the interaction ontology in the semantic layer, populate the actual content in the data layer, and define the usage terms in the contract layer. After allocating resources, it registers with the federated collaborative communication bus.

[0055] Step 4: The initiator discovers a respondent that can meet the requirements through broadcasting, querying, or subscribing; after discovering a respondent, both parties conduct a contract compatibility check, resolve conflicts through an automatic negotiation algorithm, sign the contract after reaching an agreement, and submit it to the blockchain for notarization.

[0056] Step 5: The initiator and the responder establish a TLS two-way authenticated encrypted channel and activate the use of smart contract monitoring data. Under the secure channel and contract monitoring, actual data exchange and business processing are carried out. The smart contract continuously monitors and handles default behavior.

[0057] Step 6: After the task is completed, send a completion confirmation and perform data cleanup and destroy temporary agents to release resources according to the contract terms;

[0058] Step 7: Collect performance data during the collaboration process, and evaluate and optimize it.

[0059] Compared with the prior art, this application has at least the following beneficial effects:

[0060] 1. This application provides a comprehensive collaborative management system for a transportation system, including a programmable terminal deployed in a vehicle and multiple cloud service nodes deployed in the cloud. The programmable terminal communicates with the multiple cloud service nodes via a federated collaborative communication bus. The programmable terminal includes a software-defined vehicle platform, a context-aware module, and a digital twin agent manager. The cloud service nodes include a core service logic and data asset module, a server-side digital twin agent generation module, a federated computing engine, and an access control and auditing module. This comprehensive collaborative management system for a transportation system provides secure, reliable, and privacy-preserving cross-system data collaboration and federated intelligence, promoting the realization of the value of transportation digital assets and improving traffic efficiency and safety.

[0061] 2. Both the digital twin agent manager and the server-side digital twin agent generation module include a semantic layer, a data layer, and a contract layer. This application, through a unified three-layer digital twin agent architecture, completely solves the interoperability problem between heterogeneous systems. Systems from different vendors and platforms can seamlessly collaborate without complex customized integration, significantly reducing system integration costs and shortening the time to launch new services. Attached Figure Description

[0062] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0063] Figure 1 An overall architecture diagram of a traffic system full-domain collaborative management system provided in Embodiment 1 of this application;

[0064] Figure 2 This is a schematic diagram of the deployment architecture and fault tolerance mechanism of a traffic system full-domain collaborative management system provided in Embodiment 1 of this application;

[0065] Figure 3 This is a schematic diagram of the privacy protection technology provided in Embodiment 1 of this application;

[0066] Figure 4 This is a schematic diagram of the digital twin agent architecture provided in Embodiment 1 of this application;

[0067] Figure 5 This is a schematic diagram of the federated collaborative communication bus architecture provided in Embodiment 1 of this application;

[0068] Figure 6 This is a flowchart of the intelligent charging collaboration provided in Embodiment 1 of this application;

[0069] Figure 7 This is a flowchart of the smart contract execution process provided in Embodiment 2 of this application;

[0070] Figure 8 This is a flowchart illustrating the cross-domain call process for a parking scenario provided in Embodiment 2 of this application. Detailed Implementation

[0071] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] In the description of this application: unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," "third," etc., in this application are intended to distinguish the objects referred to and do not have any special meaning in terms of technical connotation (e.g., they should not be construed as an emphasis on importance or order). Expressions such as "including," "comprising," and "having" also mean "not limited to" (certain units, components, materials, steps, etc.).

[0073] The terms used in this application, such as "upper," "lower," "left," "right," and "middle," are generally used to indicate the general relative positional relationship for the purpose of intuitive understanding by referring to the accompanying drawings, and are not absolute limitations on the positional relationship in the actual product.

[0074] Example 1

[0075] Please see Figure 1 and Figure 2 This embodiment provides a comprehensive collaborative management system for a transportation system, including a programmable terminal deployed in a vehicle and multiple cloud service nodes deployed in the cloud. The programmable terminal communicates with the multiple cloud service nodes through a federated collaborative communication bus.

[0076] The programmable terminal includes a software-defined vehicle platform, a context-aware module, and a digital twin agent manager.

[0077] Software-defined vehicle platform, used to remotely deploy software using over-the-air download technology and reconstruct vehicle behavior.

[0078] Specifically, the software-defined vehicle platform is the core computing platform deployed within the vehicle, serving as the intelligent terminal node of the entire system. It employs virtualized containers or hardware-isolated sandboxes to ensure physical isolation between remotely deployed business logic and the vehicle's core power / braking system. This platform possesses three key capabilities:

[0079] First, there's the capability for remote software deployment and execution: updates cover multiple layers from the application layer to the firmware layer. The application layer can update user applications and infotainment services; the service layer can deploy driver assistance functions and automatic parking modules; the middleware layer can update the communication protocol stack and data processing engine; and, under rigorous security verification, even electronic control unit firmware and sensor calibration parameters can be updated. The deployment mechanism uses over-the-air (OTA) download technology, supports incremental updates to reduce bandwidth consumption, and provides a rollback mechanism to handle update failures. All software components must be digitally signed for verification, public key infrastructure is used to ensure trusted origins, and a multi-level secure boot mechanism ensures code integrity.

[0080] Secondly, there is the ability to reconstruct vehicle behavior: through remote deployment, different driving strategies can be dynamically loaded, such as prioritizing fuel efficiency in economy mode and power response in sport mode; the parameters of the path planning algorithm can be updated to adapt to different traffic conditions; the human-machine interface and logic can be adjusted to provide a personalized user experience; and energy management strategies can be modified to optimize the charging time and cost of electric vehicles.

[0081] Finally, there's the security isolation and sandbox execution capability: virtualization technology is used to isolate the execution environment, preventing malware from affecting system stability. Critical safety functions such as braking and steering systems, along with updatable software, are protected by hardware isolation. Real-time monitoring and anomaly detection mechanisms continuously check system behavior to ensure compliance with ISO 26262 functional safety standards and ISO 21434 cybersecurity standards.

[0082] The context-aware module is used to continuously collect, analyze and integrate multi-source vehicle data. Based on the multi-source vehicle data, it uses a rule engine or machine learning model to infer the current scenario type in real time, assess the priority of collaborative needs, and predict upcoming business scenarios.

[0083] Specifically, the context-aware module continuously collects and analyzes multi-source information to provide input for collaborative scene recognition. This module integrates multiple perception data sources:

[0084] Vehicle status data includes: positioning information, which uses GPS or BeiDou systems to provide a standard accuracy of about two meters, or real-time dynamic carrier phase differential technology to provide centimeter-level accuracy; motion status data, which records speed, acceleration, and heading angle at a sampling rate of 100 Hz; driving mode, which indicates whether the current driving mode is manual or Level 2 to Level 5 autonomous driving; and vehicle health status, including fault codes and sensor operating status.

[0085] User identity and preference data includes driver or passenger authentication, which can be achieved through fingerprint recognition, facial recognition, mobile phone Bluetooth pairing, etc.; personal preference profile identifiers are associated with the user's seat position, music preferences, frequently used destinations, and other settings; and the list of subscribed services records the user's purchased insurance, membership parking, charging services, etc.

[0086] Environmental information includes: identification of surrounding landmarks to determine whether a vehicle has entered a parking lot, highway toll station, or specific business district; classification of traffic scenarios to identify whether the current situation is congested, smooth, or involved in an accident; monitoring of weather conditions such as rain, snow, fog, and slippery road surfaces that affect driving safety; and receiving traffic information broadcast by roadside units via vehicle-to-everything (V2X) communication.

[0087] The context-aware module's context reasoning engine, based on a rule engine or machine learning model, infers the current scenario type in real time, assesses the priority of collaborative needs, and predicts upcoming scenarios. For example, the system can predict that a vehicle will enter a parking lot entrance in five seconds and prepare the corresponding collaborative agent in advance.

[0088] Please see Figure 3Privacy protection mechanisms begin operating during the data acquisition phase. Local differential privacy processing adds noise to the data before it leaves the vehicle to protect sensitive information. Location obfuscation is based on the k-anonymity principle, ensuring that location data cannot uniquely identify an individual. Following the principle of data minimization, only the minimum data necessary to complete the task is collected.

[0089] The digital twin agent manager is used to trigger the agent instantiation process based on the business scenario predicted by the context-aware module and to maintain the complete digital twin model of the vehicle.

[0090] Specifically, the digital twin agent manager, acting as an intelligent gateway for the software-defined vehicle platform to interact with the external world, is responsible for the full lifecycle management of agents. Agent lifecycle management covers state transitions such as instantiation, activation, pause, and destruction; an automatic session timeout cleanup mechanism prevents agents from occupying resources indefinitely; resource reclamation and memory leak protection ensure system stability. Agent activity logs are recorded for subsequent auditing.

[0091] The digital twin agent manager maintains a complete digital twin model of the vehicle, including static features such as vehicle size, weight, configuration parameters, and vehicle identification number; dynamic status such as real-time location, speed, battery or fuel level, and sensor data streams; capability descriptions such as supported driving functions and automatic parking capabilities; and service subscriptions such as a list of currently active cloud services.

[0092] The dynamic proxy instantiation logic is the core function: when the context-aware module identifies a specific scenario, it triggers the proxy instantiation process. The digital twin proxy manager extracts the minimum necessary subset of data related to the scenario from the full model, following the principle of data minimization. It selects a predefined proxy template based on the scenario type, such as a parking request proxy, a charging query proxy, or an insurance data reporting proxy. It configures the three-layer structure of the proxy: setting the ontology model for the semantic layer, populating the actual content of the data layer, and defining the usage terms for the contract layer. It generates temporary unique identifiers for the proxy and allocates necessary computing resources such as CPU and memory quotas.

[0093] Multi-agent concurrency management supports the simultaneous operation of multiple agents. For example, a vehicle can simultaneously perform parking guidance and insurance data reporting. Priority arbitration between agents ensures that urgent tasks are handled first. Access control mechanisms prevent data conflicts between agents.

[0094] In the transportation system-wide collaborative management system provided in this embodiment, multiple independent cloud systems are allowed to access the federated bus as peer nodes, forming an open and scalable ecosystem.

[0095] The cloud system types include at least one of the following: vehicle manufacturer cloud, autonomous driving service cloud, smart parking management cloud, insurance company computing cloud, urban traffic management cloud, mobility service platform cloud, energy management cloud, and map service cloud. Each cloud system is connected to the federated bus as a peer node.

[0096] The OEM cloud provides services such as fleet management, remote diagnostics, and over-the-air (OTA) update distribution, serving as a bridge between vehicle manufacturers and users.

[0097] The autonomous driving service cloud provides advanced functions such as high-precision map updates, route planning services, and vehicle-to-everything (V2X) collaboration to support the safe operation of autonomous vehicles.

[0098] The smart parking management cloud provides one-stop parking services such as parking space inquiry, reservation, navigation, and billing, integrating resources from multiple parking lots.

[0099] The insurance company's cloud computing enables insurance premium calculation, risk assessment, and rapid claims processing based on usage, and pricing is based on actual driving behavior.

[0100] The city traffic management cloud is responsible for public transportation management functions such as traffic light control, traffic flow monitoring, and emergency dispatch, thereby optimizing urban traffic efficiency.

[0101] The cloud-based travel service platform provides services such as ride-hailing dispatch, carpooling matching, and multi-mode travel planning, integrating various travel modes such as buses, subways, and shared bicycles.

[0102] The energy management cloud manages the charging pile network, optimizes charging strategies, supports two-way energy trading between vehicles and the grid, and promotes the development of new energy vehicles.

[0103] Map service cloud provides services such as real-time traffic conditions, points of interest search, and crowdsourced map updates, and is the infrastructure for intelligent navigation.

[0104] Specifically, the cloud service nodes include the core service logic and data asset module, the server-side digital twin agent generation module, the federated computing engine, and the access control and auditing module.

[0105] The core service logic and data asset module is used for the business logic of the cloud system, manages the proprietary database and knowledge base, and provides computing and storage resources.

[0106] More specifically, the core service logic and data asset module implements the business logic of the cloud system, manages the proprietary database and knowledge base, and provides computing and storage resources. This is where the business value of the cloud system lies.

[0107] The server-side digital twin agent generation module is used to dynamically instantiate digital twin agents based on internal business needs or external collaboration requests.

[0108] More specifically, the server-side digital twin agent generation module dynamically instantiates digital twin agents based on internal business needs or external collaboration requests. Instantiation can be driven by various events: creating a response agent when receiving a collaboration request from a vehicle-side agent; creating a peer agent when receiving a collaboration request from other cloud agents; triggered by internal scheduled tasks, such as daily traffic flow prediction; or triggered by external events, such as traffic accident detection.

[0109] The proxy configuration capabilities of the server-side digital twin proxy generation module allow cloud systems to flexibly control the scope of exposed data. Users can choose to expose only aggregated statistical data rather than raw data to protect data privacy; they can define acceptable contract terms, such as minimum price and maximum data retention period; and they can set service quality parameters, such as response time commitments and maximum concurrent connections.

[0110] The federated computing engine is used for collaboration between remote endpoints and integrates a secure multi-party computation library to support computation in an encrypted state.

[0111] More specifically, the federated computing engine is specifically designed for cloud-to-cloud collaboration and is key to achieving collaborative intelligence under the protection of data sovereignty. This engine supports multiple federated learning paradigms: horizontal federated learning is suitable for scenarios with similar data features but different samples, such as collaboratively training traffic prediction models across multiple city traffic management clouds; vertical federated learning is suitable for scenarios with similar samples but different features, such as jointly analyzing driving data from vehicle manufacturer clouds and claims data from insurance clouds; and federated transfer learning is suitable for scenarios with different data distributions, utilizing source domain knowledge to improve target domain models.

[0112] The federated computing engine integrates a secure multi-party computation library, supporting computation in an encrypted state where each party cannot see the other's raw data. It supports homomorphic encryption, allowing direct mathematical operations on ciphertext, with the decrypted result identical to the plaintext result. It also supports secure aggregation, where model updates from multiple parties are aggregated in an encrypted state, making the contributions of individual participants undetectable.

[0113] The access control and auditing module is used to implement fine-grained permission management based on role-based or attribute-based access control.

[0114] Specifically, the access control and auditing module implements fine-grained permission management based on role-based or attribute-based access control. An immutable audit log integrated with the blockchain records all data access activities, complying with Article 30 of the GDPR's record retention requirements, and providing evidence for regulatory audits and dispute resolution.

[0115] This embodiment provides a comprehensive collaborative management system for a transportation system, which also includes an optional cloud-based collaborative coordinator for performing global collaborative strategy formulation, workflow orchestration, agent topology planning, consistency assurance, and fault recovery in complex cross-domain business processes deployed on three or more nodes.

[0116] Specifically, in complex, cross-domain business processes involving three or more nodes, the cloud-based coordination coordinator plays a crucial role. The main responsibilities of the cloud-based coordination coordinator include:

[0117] Global collaborative strategy formulation: Based on top-level business objectives, complex tasks are broken down into multiple sub-tasks and assigned to different cloud nodes. For example, prioritizing emergency vehicle passage requires coordination among traffic light control clouds, real-time route planning clouds, and public information dissemination clouds at multiple intersections along the route.

[0118] Workflow orchestration: Defines the order and parallel relationships of participation from various cloud nodes to ensure tasks are executed according to correct logic. Some steps can be performed in parallel to improve efficiency, while others must be executed sequentially to ensure correctness.

[0119] Agent topology planning: Determine which agents need to be interconnected to form a collaborative network. In complex scenarios, star, tree, or mesh topologies may be required.

[0120] Consistency guarantees: Ensure the atomicity, consistency, isolation, and durability of distributed transactions, or guarantee eventual consistency in scenarios with high performance requirements. When a step fails, it is necessary to roll back the completed steps or take compensatory measures.

[0121] Fault recovery: Handling abnormal situations such as node failure, network interruption, and timeout. System robustness is ensured through redundancy design, retry mechanisms, and degradation schemes.

[0122] In terms of implementation technology, the cloud-based collaborative coordinator uses a business process modeling and tagging method for process modeling, providing visual design tools. It employs the Saga pattern to handle distributed transactions, breaking down long transactions into multiple local transactions, each with a corresponding compensation transaction. Workflow scheduling engines such as Apache Airflow or Temporal are used for task orchestration and monitoring. Integration with blockchain smart contracts enables decentralized coordination, avoiding single points of failure and centralized trust issues.

[0123] Typical application scenarios include:

[0124] In scenarios where emergency vehicles are given priority passage, it is necessary to coordinate with the traffic signal control cloud along the route to adjust traffic lights in advance, the route planning cloud to calculate the optimal route in real time, and the public information release cloud to notify ordinary vehicles to give way, thus forming a green channel.

[0125] Multimodal travel planning scenarios require coordinating various resources: bus cloud for querying bus schedules and available seats, subway cloud for providing transfer information, shared bike cloud for displaying nearby vehicle distribution, and ride-hailing cloud for estimating waiting time and costs, to comprehensively provide users with the optimal travel solution.

[0126] City-level traffic optimization scenarios require coordination of multiple regional traffic management clouds to optimize traffic light timings over a larger scope, avoiding suboptimal overall performance due to localized optimization.

[0127] Please see Figure 4 In the comprehensive collaborative management system for a transportation system provided in this embodiment, both the digital twin agent manager and the server-side digital twin agent generation module include a three-layer structure: a semantic layer, a data layer, and a contract layer. The semantic layer provides machine-readable and human-understandable data descriptions based on the domain ontology and metadata model. The data layer is used to store or link data instances that actually need to be exchanged and provides data access interfaces. The contract layer is used to formally define key terms and automate their execution, and ensures the consensus, immutability, and auditability of the contract through blockchain.

[0128] Specifically, in this embodiment, each dynamically instantiated digital twin agent, whether originating from the vehicle or the cloud, follows the same three-layer abstraction structure, which is key to ensuring global interoperability.

[0129] First layer: Semantic layer

[0130] The semantic layer is implemented based on W3C's OWL (Web Ontology Language) or RDF (Resource Description Framework), solving the problems of "what to interact with" and "how to understand", and is used to eliminate the syntactic and semantic heterogeneity between different systems, providing machine-readable and human-understandable data descriptions.

[0131] The technical implementation employs an ontology engineering approach: The World Wide Web Consortium's Web Ontology Language is used to define ontology for the transportation domain, clearly defining the precise meanings of concepts, attributes, and relationships. Existing standard ontology, such as the sensor observation sampling actuator ontology and the pattern point organization vehicle ontology, is referenced to promote semantic interoperability between different systems. Classes are defined to describe entity types, such as vehicles, parking lots, and charging piles; attributes describe entity characteristics, such as length, capacity, and power; and relationships describe the connections between entities, such as a vehicle being located in a parking lot or the parking lot belonging to a management company.

[0132] The metadata model is based on the ISO 19115 geographic information metadata standard and uses serialization formats such as JSON-LD and RDF / XML. The metadata includes information such as data field names, data types, units, precision, and timestamp formats to ensure that the data is parsed correctly.

[0133] Semantic mapping provides mapping rules between different ontologies. Automakers may use proprietary data models, while industry standards require specific formats; semantic mapping enables automatic conversion. It also supports semantic validation, checking whether data conforms to the constraints defined in the ontology.

[0134] For example, the semantic description of vehicle dimensions includes: the type is defined as vehicle dimensions; the length attribute specifies the data type as a number, the unit as millimeters, and describes the vehicle length including the bumper, with a reasonable range of three thousand to six thousand millimeters; the width attribute specifies the unit as millimeters and describes the vehicle width including the rearview mirror; the geographic location attribute specifies the format as the WGS84 coordinate system, with an accuracy of plus or minus two meters.

[0135] Second layer: Data layer

[0136] The data layer stores or links to the actual data instances that need to be exchanged, provides data access interfaces, and the data content is strictly constrained by the semantic layer definition.

[0137] There are three data storage modes: embedded mode is suitable for small-scale static data, such as vehicle identification numbers, which are directly embedded in the agent to reduce network overhead; reference mode is suitable for large-scale data, which is linked to external data sources through a unified resource identifier and retrieved on demand; streaming mode is suitable for real-time data, which provides WebSocket or MQTT interfaces for continuous push updates.

[0138] Data interface types support multiple protocols: RESTful APIs are suitable for request-response patterns, are simple to use, and have wide support. gRPC is suitable for high-performance remote procedure calls, using binary serialization and the HTTP / 2 protocol. GraphQL is suitable for client-defined queries, allowing multiple resources to be retrieved in a single request. MQTT and AMQP are suitable for publish-subscribe patterns, enabling one-to-many communication.

[0139] Data security measures are multi-layered: the transport layer uses TLS 1.3 encryption to prevent eavesdropping and tampering. Data field-level encryption uses the AES-256 algorithm to protect sensitive information such as user identity. Access tokens are based on OAuth 2.0 and JSON WebToken to verify the requester's identity and permissions. Data anonymization and masking processes hide or replace sensitive fields when necessary.

[0140] For example, the vehicle data layer includes: embedded vehicle size data, with a length of 4,650 mm, a width of 1,850 mm, a height of 1,450 mm, and a wheelbase of 2,700 mm; a real-time location provides a WebSocket interface, using the WebSocket protocol, with an update frequency of one hertz, and requires Bearer token authentication; and a historical trajectory provides a RESTful API, using the GET method, with query parameters including start and end times.

[0141] Third layer: Contract layer

[0142] The contract layer defines the mandatory rules for "how data is used," and is the ultimate enforcer of data sovereignty and privacy protection, providing auditable proof of use. This is the most innovative part of this invention.

[0143] Core terms and conditions include:

[0144] The data usage purpose clause explicitly limits the data to a specific purpose. Predefined purposes include route planning, premium calculation, traffic flow prediction, academic research, and business analysis. The contract clearly specifies the permitted single use and strictly prohibits the reuse of data for other purposes. For example, location data provided by vehicles for parking guidance cannot be used for commercial advertising.

[0145] The access permissions and authentication terms specify a list of authorized parties, including the organization name, decentralized identity identifier, public key, and permitted operations such as read or subscribe. Authentication methods can include two-way TLS, OAuth 2.0 JWT, etc. Only explicitly authorized entities can access the data.

[0146] Privacy requirements clauses stipulate mandatory privacy protection measures: Anonymization requirements, such as k-anonymity, ensure that at least k individuals in the dataset have the same quasi-identifier, making unique identification impossible. Differential privacy sets a privacy budget, such as epsilon equal to 0.1, using a Laplace mechanism to add noise. Data minimization requires collecting only necessary fields. User consent mechanisms ensure explicit authorization for data use. The right to erasure, such as the right to be forgotten under the GDPR, allows users to request the deletion of their personal data, setting a retention period, such as thirty days. The contract explicitly lists compliance with regulations such as Section 25 of the GDPR (design privacy and default privacy) and Section 1798.100 of the CCPA (California Consumer Privacy Act).

[0147] Data lifecycle terms manage the time dimension of data: Retention period sets the maximum storage duration, such as 24 hours, with automatic deletion upon expiration, suitable for temporary data. Update frequency specifies the data refresh interval, such as immediate push for real-time changes. Version control retains historical versions when enabled, supporting auditing and rollback. Backup policy can be set to no backup, suitable for short-term temporary data.

[0148] The Service Level Agreement (SLA) terms define performance commitments: Availability is 99.9%, meaning a maximum downtime of approximately 43 minutes per month. Response time percentiles are 50 milliseconds at the 50th percentile, 200 milliseconds at the 95th percentile, and 500 milliseconds at the 99th percentile. Throughput is 1,000 requests per second. Data freshness is latency of less than one second.

[0149] Billing and value exchange terms support multiple business models: pricing models can be pay-per-query, subscription-based, free, or a hybrid model. Prices specify amounts, currencies such as fiat currencies, and billing units such as per API call. Billing cycles include monthly settlements. Settlement methods can be integrated with blockchain smart contracts to enable automated payments and receipts.

[0150] In terms of technical implementation, the choice of smart contract platform depends on the application scenario. Public blockchains such as Ethereum are suitable for public, decentralized scenarios where anyone can verify contract execution. Permissioned consortium blockchains such as Hyperledger are suitable for inter-enterprise collaboration, where participants need to authorize. Cross-chain platforms such as Polkadot are suitable for scenarios that require interoperability with multiple blockchains.

[0151] Contract execution engines use different programming languages ​​and virtual machines. The Ethereum Virtual Machine, written in Solidity, is widely compatible with contract applications. Chaincode is written in Go or Node.js and runs on Hyperledger Fabric. WebAssembly contracts support multiple programming languages ​​such as Rust and C++, offering excellent performance.

[0152] Contract verification mechanisms ensure contract security and reliability. Formal verification tools such as Mythril and Slither statically analyze contract code to uncover potential vulnerabilities. Contract auditing services involve a professional team manually reviewing contract logic. Runtime contract execution monitoring continuously checks contract behavior to prevent abnormal execution.

[0153] The contract negotiation process is a multi-step process. First, the initiating party's agent proposes a draft contract containing all desired terms. Second, the responding party's agent checks the compatibility of the terms, comparing each item against their own capabilities and limitations. If conflicts exist, an automatic negotiation algorithm is triggered. The algorithm is based on a priority-based concession strategy; for example, the price can be negotiated within the budget, and the data granularity can be downgraded to a level acceptable to both parties. Once both parties reach an agreement, the contract content is serialized and a hash value is calculated. Both parties digitally sign the contract hash value using their respective private keys. The signed contract is submitted to the blockchain, forming immutable evidence. After the contract is activated, the smart contract begins monitoring its execution. Any breach of contract, such as exceeding access limits, using the service for unauthorized purposes, or failing to meet service level commitments, will trigger automatic remedial measures. These remedial measures include suspending service, triggering predefined penalties, notifying relevant parties, and reporting to regulatory agencies.

[0154] Please see Figure 5In this embodiment, a comprehensive collaborative management system for a transportation system is provided. The federated collaborative communication bus, built upon trusted data space technology, serves as the system's "nerve center," providing comprehensive interconnectivity and ensuring all interactions occur in a secure, trusted, and auditable environment. The federated collaborative communication bus includes identity authentication and trust management, agent discovery and registration services, secure connection establishment, message routing and transmission, data transmission optimization, and auditing and traceability, and supports three communication paradigms.

[0155] Identity authentication and trust management:

[0156] A decentralized identity system ensures the verifiability of participating nodes. Each participating node possesses a unique decentralized identity identifier, conforming to the World Wide Web Consortium's DID standard. The DID document contains a public key for verifying signatures, a server endpoint indicating the network address, and an authentication method describing how to prove identity and control. The DID document is stored on a distributed ledger such as a blockchain or InterPlanetary File System, making it publicly verifiable and tamper-proof.

[0157] A hierarchical trust structure is established using a root of trust and a certificate chain. Industry-specific root CAs, such as the National Certification and Accreditation Center of the Ministry of Transport, serve as the highest trust anchor. Each participating node holds an X.509 certificate issued by the root of trust, forming a certificate chain. The system supports certificate revocation lists and online certificate status protocols to promptly revoke leaked or expired certificates.

[0158] Zero-knowledge proof authentication is suitable for privacy-sensitive scenarios. Nodes can prove that they belong to an authorized group without revealing their identity details. For example, a vehicle can prove that it is a legitimate product of a certain brand without disclosing its specific vehicle identification number (VIN).

[0159] Proxy discovery and registration service:

[0160] The service registry employs distributed service discovery protocols such as Consul, Etcd, or Zookeeper. When a proxy is published, it submits a capability description to the registry, including the type of service provided, data scope, quality parameters, and price. Semantic-based service matching is supported, which not only matches keywords but also understands the relationships between concepts, improving matching accuracy.

[0161] The discovery mechanism supports multiple modes. In broadcast discovery, the agent broadcasts a request message to the bus, and agents that can meet the request respond, suitable for dynamic environments. In query discovery, the agent actively queries the registry to obtain a list of service providers that meet the criteria, suitable for scenarios with clearly defined needs. In subscription discovery, the agent subscribes to notifications of the launch of specific types of services and automatically receives notifications when new services are released, suitable for long-term monitoring.

[0162] The service quality perception mechanism records metrics such as success rate, response time, and data quality of historical interactions. It recommends service providers based on reputation scores, helping initiators choose reliable partners. The scoring considers multiple dimensions such as reliability, performance, security, and user reviews, employing a weighted comprehensive algorithm.

[0163] Secure connection established:

[0164] The handshake protocol is based on TLS 1.3 mutual authentication, ensuring that both communicating parties have verified each other's identities. Building upon the standard TLS handshake, contract-level verification logic is integrated, establishing a connection only when both parties' contracts are compatible, thus avoiding wasted resources on invalid connections.

[0165] Key negotiation uses elliptic curve Diffie-Hellman key exchange, where both parties negotiate a shared key over an insecure channel. Forward secrecy is supported, ensuring that past session keys remain secure even if long-term keys are compromised, protecting historical communication content.

[0166] Channel isolation establishes independent encrypted channels between each pair of agents, using different session keys. Even if the key for one channel is leaked, it will not affect other channels. This prevents side-channel attacks such as timing attacks and power consumption analysis.

[0167] Message routing and transmission:

[0168] Routing strategies select the optimal path based on scenario requirements. Direct-connect routing is suitable for low-latency scenarios, allowing direct communication between agents without relays. Relay routing is suitable for cross-network segment scenarios, forwarding messages through bus nodes and resolving network address translation and firewall issues. Multi-path routing uses multiple paths simultaneously to transmit data, improving reliability and bandwidth utilization.

[0169] The message queue integrates with mature middleware such as Apache Kafka or RabbitMQ. It supports message persistence, ensuring messages are not lost even if the receiver is temporarily offline. It guarantees message order, ensuring messages are consumed in the order they were sent, especially in scenarios with high ordering requirements. A retransmission mechanism handles network failures, automatically retransmitting unacknowledged messages.

[0170] Flow control and congestion management prevent system overload. The token bucket algorithm limits the sending rate; each sender receives a certain number of tokens, which are consumed when sending messages and replenished at a fixed rate. Bandwidth allocation is dynamically adjusted based on real-time load conditions, prioritizing high-priority traffic.

[0171] Data transmission optimization:

[0172] Compression algorithms such as gzip, Brotli, and Zstandard reduce the amount of data transmitted, saving bandwidth and time. Choose the appropriate compression algorithm based on the data type; text data has a high compression ratio, while binary data may not be suitable for compression.

[0173] Incremental transmission only transmits the changed data portion, employing differential coding technology. For frequently updated data, such as vehicle location, only the change relative to the previous transmission is transmitted, significantly reducing bandwidth usage.

[0174] Edge caching caches frequently accessed data on edge nodes closer to the endpoint. When a user makes a request, it is preferentially retrieved from the edge cache, reducing backhaul traffic and latency. The caching strategy considers factors such as data timeliness, access frequency, and storage costs.

[0175] Auditing and Traceability:

[0176] An immutable log submits all interaction events to a blockchain such as Hyperledger Fabric or a consortium blockchain. The log includes timestamps, decentralized identity identifiers of the participants, interaction type (e.g., data read or write), data hashes (not raw data to protect privacy), and contract hashes associated with usage terms. The immutability of the blockchain ensures the log's authenticity and reliability; any modifications will be detected.

[0177] The log query interface supports regulatory audits and provides flexible query conditions such as time range, participants, and interaction type. It allows users to query their own data usage records, satisfying Article 15 of the GDPR regarding data access rights, enabling users to understand when, by whom, and in what manner their personal data was used.

[0178] The dispute resolution mechanism uses automated arbitration based on log evidence. When the parties disagree on contract execution, the smart contract automatically retrieves the blockchain logs, compares the actual actions with the contract terms, and determines the breaching party. It also integrates external arbitration services such as the Kleros decentralized arbitration platform, allowing a jury to vote and adjudicate complex disputes.

[0179] Supports three basic communication paradigms:

[0180] In the vehicle-to-cloud communication model, the vehicle actively initiates requests, and the cloud responds. Typical scenarios include vehicles requesting parking space information, reporting insurance data, querying charging station locations, and downloading map updates. A key characteristic is the short lifespan of the vehicle-side agent, which is destroyed after the request is completed, while the cloud agent may exist for an extended period serving multiple vehicles. Communication is triggered by vehicle-side events, such as user button clicks, entering a geofence, or battery levels falling below a threshold.

[0181] In cloud-to-vehicle communication, the cloud initiates the communication, and the vehicle responds. Typical scenarios include over-the-air downloads of new software, insurance policy adjustments to premiums, real-time route guidance to avoid congestion, and remote vehicle control such as preheating the air conditioning or unlocking doors. A key feature is ensuring the vehicle is online and capable of executing commands, using a push notification mechanism to trigger the vehicle's response. The cloud maintains the vehicle's online status and sends commands at appropriate times to avoid disturbing users, such as sending push notifications late at night.

[0182] The core innovation of this invention is the cloud-to-cloud communication model, which enables secure, reliable, and peer-to-peer collaboration between cloud systems. This model supports data sovereignty protection, allowing data to participate in collaborative computation without leaving the source domain, thus meeting privacy regulations and corporate data strategies; it supports model collaboration, achieving distributed intelligence through technologies such as federated learning and secure multi-party computation, where all parties share models rather than raw data; and it supports value circulation, enabling the monetization of digital assets and cross-platform service combinations, promoting new business models.

[0183] Typical applications include traffic light collaborative optimization among multiple traffic management clouds, where the globally optimal control strategy is trained through federated learning while protecting the data sovereignty of each party; insurance data exchange based on usage between vehicle manufacturer clouds and insurance clouds, where vehicle driving data does not leave the vehicle manufacturer cloud and the insurance cloud obtains risk assessment results through secure multi-party computation; and crowdsourced map update collaboration among multiple map service providers, where the original data contributed by each party is kept locally and only verified map updates are synchronized.

[0184] The transportation system-wide collaborative management system provided in this embodiment has the following core technologies:

[0185] 1. Software-defined vehicles as remotely programmable intelligent terminals

[0186] Software-defined vehicles are not only passive data sources but also active, reconfigurable endpoints. Their behavior, capabilities, and interaction interfaces can be dynamically defined and updated remotely by authorized cloud systems through secure deployment. This remote deployment capability covers multiple layers:

[0187] At the application level, user applications, infotainment services, and navigation interfaces can be dynamically updated to provide users with a personalized experience. At the service level, advanced services such as driver assistance functions, automatic parking modules, and fleet collaboration logic can be deployed or modified. At the middleware level, basic components such as communication protocol stacks, data processing engines, and security authentication modules can be updated. Even under strict security verification, electronic control unit firmware and sensor calibration parameters can be updated.

[0188] This capability allows vehicle behavior to be dynamically reconfigured based on different scenarios. For example, when entering a parking lot, the vehicle can automatically load a collaborative parking module; on highways, it can activate convoy collaborative driving functionality; and near charging stations, it can enable intelligent charging scheduling algorithms, such as... Figure 6 As shown.

[0189] The entire remote deployment process is protected by multiple layers of security mechanisms. Over-the-air (OTA) download technology is employed, supporting incremental updates and rollback mechanisms. All deployed software components must be digitally signed and verified, with a public key infrastructure (PKI) system ensuring a trustworthy origin. A multi-level secure boot mechanism ensures that only verified code can be executed. Critical safety functions such as braking and steering systems are hardware isolated from updatable software, complying with ISO 26262 functional safety standards and ISO 21434 cybersecurity standards.

[0190] 2. Digital twins as dynamic, standardized intelligent agents

[0191] This embodiment expands the concept of "digital twin" from the traditional static state mirror to dynamically instantiated, lightweight, task-specific intelligent agents. These agents are not long-term, complete virtual copies, but rather temporary entities created on demand according to specific collaborative scenarios and automatically destroyed upon completion of the task.

[0192] Each digital twin agent employs a standardized three-tier architecture design, which is a key innovation of this embodiment:

[0193] The semantic layer defines "what information is exchanged" and "how to understand that information." This layer employs domain ontology and metadata models to provide machine-readable and human-understandable data descriptions. The semantic layer references international standards such as the ISO 23150 digital twin framework, the AutoSAR automotive open system architecture, and the SENSORIS sensor interface specification. By precisely defining data field names, data types, units, precision, and timestamp formats, the semantic layer eliminates syntactic and semantic heterogeneity issues between different systems. For example, when a vehicle agent describes its length as "4,650 millimeters," while the parking system uses meters as the unit, the automatic conversion rules provided by the semantic layer ensure accurate understanding between the two parties.

[0194] The data layer provides the content or access interfaces that actually need to be exchanged. Data can take various forms: for small-scale static data such as vehicle identification numbers, it can be directly embedded in a proxy; for large-scale data such as historical trajectories, it uses URIs to link to external data sources; for real-time data such as current location, it provides streaming interfaces. The data layer supports multiple interface protocols, including RESTful APIs suitable for request-response patterns, gRPC suitable for high-performance calls, GraphQL suitable for client-defined queries, and MQTT and AMQP suitable for publish-subscribe patterns. All data transmission is protected by multiple layers of security, including transport layer encryption, data field-level encryption, access token verification, and data anonymization and masking when necessary.

[0195] The contract layer is the most innovative part of this invention. It defines the mandatory rules for "how data is used" and is the ultimate enforcer of data sovereignty and privacy protection. The contract layer is implemented based on smart contract technology, embedding data usage terms into the technical architecture to achieve automatic verification and execution.

[0196] The contract layer contains several key term types:

[0197] The data use purpose clause explicitly stipulates that data can only be used for specific purposes, such as route planning, premium calculation, and traffic flow prediction, and strictly prohibits data reuse for other purposes. The access permission and authentication clause specifies which organizations can access the data and employs mechanisms such as decentralized identity verification, public key authentication, and two-way TLS. The privacy handling requirements clause stipulates mandatory privacy protection measures, such as k-anonymity, differential privacy, data minimization principles, user consent mechanisms, and the right to erasure, ensuring compliance with Article 25 of the GDPR, the CCPA, and other regulations.

[0198] The data lifecycle terms manage the retention period for data, such as requiring certain sensitive data to be automatically deleted within 24 hours, and support version control and backup policies. The service level agreement terms define performance metrics such as system availability, response time, throughput, and data freshness. The billing and value exchange terms support various pricing models, including pay-per-query, subscription, and free options, and can be integrated with blockchain smart contracts for automatic settlement.

[0199] The execution of the contract layer relies on a smart contract platform. Public blockchains like Ethereum can be chosen for public scenarios, while permissioned blockchains like Hyperledger can be used for consortium scenarios. Contracts undergo formal verification before execution, and their execution is continuously monitored during runtime. Any breach of contract triggers automatic remedial measures, such as service suspension, fines, and notification of regulatory agencies.

[0200] Contract negotiation is an automated process. When two agents attempt to establish a connection, the initiator proposes a draft contract, and the responder checks the terms for compatibility. If conflicts arise, the system triggers an automated negotiation algorithm to find a compromise based on predefined priorities and concession rules. Once both parties reach an agreement, the contract hash is digitally signed and submitted to the blockchain, forming immutable evidence.

[0201] 3. Three-dimensional federated communication paradigm

[0202] This architecture achieves seamless integration of three basic communication paradigms:

[0203] Vehicle-to-cloud communication allows vehicles to proactively request services from the cloud. Typical scenarios include vehicles requesting parking space information, reporting insurance data, and querying charging station locations. In this mode, the vehicle-side agent typically has a short lifespan and is destroyed after the request is completed, while the cloud agent may exist for a long time to serve multiple vehicles.

[0204] Cloud-to-vehicle communication allows the cloud system to proactively push information or commands to the vehicle. Typical scenarios include over-the-air updates, insurance policy distribution, real-time route guidance, and remote vehicle control. This mode requires ensuring the vehicle is online and capable of execution, using a push notification mechanism to trigger a response on the vehicle.

[0205] The core innovation of this invention is cloud-to-cloud communication, which enables secure, reliable, and peer-to-peer collaboration between cloud systems. This model supports data sovereignty protection, allowing data to participate in collaborative computation without leaving the source domain; it supports model collaboration, achieving distributed intelligence through technologies such as federated learning and secure multi-party computation; and it supports value circulation, enabling the monetization of digital assets and cross-platform service combinations.

[0206] Typical applications of cloud-to-cloud communication include: coordinated optimization of traffic lights between multiple traffic management clouds, where the globally optimal signal control strategy is trained through federated learning while protecting the data sovereignty of each party; usage-based insurance data exchange between OEM clouds and insurance clouds, where vehicle driving data does not leave the OEM cloud and the insurance cloud obtains risk assessment results through secure multi-party computation; and crowdsourced map update collaboration between multiple map service providers, where the original data contributed by each party is kept locally and only verified map updates are synchronized.

[0207] The comprehensive collaborative management system for the transportation system provided in this embodiment has the following advantages:

[0208] 1. Breakthrough in interoperability

[0209] By employing a unified three-layer digital twin agent architecture, the interoperability challenges between heterogeneous systems are completely resolved. The semantic layer provides a standardized ontology model, eliminating syntactic and semantic barriers; the data layer supports multiple interface protocols to adapt to different technology stacks; and the contract layer ensures the technical execution of usage terms, establishing a foundation of trust. Systems from different vendors and platforms can seamlessly collaborate without complex customized integration. This significantly reduces system integration costs, shortens the time to market for new services, and promotes innovation and competition.

[0210] 2. Privacy and Security Protection

[0211] The innovative contract layer design embeds data usage terms into the technical architecture, achieving fine-grained, executable, and auditable privacy protection. Each data exchange is conducted under explicit contractual constraints, retaining full control over the data owner. Employing cutting-edge technologies such as federated learning and secure multi-party computation, it supports collaborative computing where "data is usable but not visible," allowing data to participate in global intelligence without leaving the local machine. This privacy protection paradigm complies with the most stringent privacy regulations such as GDPR and CCPA, without sacrificing the mining and utilization of data value. Blockchain-based notarization provides an immutable audit log, ensuring that any data misuse can be traced and held accountable, establishing a trusted data collaboration environment.

[0212] 3. Dynamism and flexibility

[0213] Unlike traditional static system integration, this embodiment achieves dynamic, context-driven collaboration. The digital twin agent is instantiated on demand, selecting the minimum necessary dataset based on the real-time scenario and destroying itself immediately after task completion, significantly reducing resource consumption and attack surface. The reconfigurability of the software-defined vehicle allows vehicle behavior to dynamically adjust according to the scenario; the same vehicle can play different roles in different times and spaces, maximizing the utilization of the vehicle's intelligent potential. The automatic contract negotiation mechanism adapts to rapidly changing business needs, establishing new collaborative relationships without manual intervention, improving system agility.

[0214] 4. Scalability and Openness

[0215] The federated collaborative communication bus provides an open access mechanism, allowing new cloud nodes and services to easily join the ecosystem. Based on standard interfaces and protocols, different organizations can maintain technological independence and business autonomy while participating in global collaboration, avoiding the monopoly and single point of failure risks of centralized platforms. The system supports comprehensive communication paradigms from vehicle to cloud, cloud to vehicle, and cloud to cloud, building a true end-to-end collaborative network. As the number of participants increases, the network effect grows exponentially, resulting in an overall value that increases exponentially.

[0216] 5. Economic value creation

[0217] This embodiment provides the technological infrastructure for realizing the value of digital assets in transportation. Through the billing and settlement mechanism at the contract layer, data and services can be accurately priced and monetized, promoting the development of the data element market. Federated computing allows multiple parties to co-create value while protecting data sovereignty; for example, a jointly trained artificial intelligence model may outperform any single-party model, and the benefits can be fairly distributed according to contributions. New business models such as "data as a service," "model as a service," and "algorithm as a service" become possible, giving rise to new industrial ecosystems. SMEs and startups can access the platform by providing specialized services without huge infrastructure investments, lowering the innovation threshold and promoting market competition and technological progress.

[0218] 6. Enhanced social benefits

[0219] In terms of traffic efficiency optimization, collaboration between vehicles and infrastructure, between vehicles, and between multiple systems can significantly reduce congestion, shorten travel time, and lower energy consumption and carbon emissions. Federal traffic flow prediction is more accurate than predictions from a single system, traffic light coordination optimization improves intersection efficiency, and priority passage for emergency vehicles saves lives.

[0220] In terms of safety enhancement, functions such as hazard warning sharing between vehicles, automatic accident alarms, and risk prediction based on big data reduce traffic accidents. Autonomous vehicles gain environmental awareness beyond that of individual vehicle sensors through collaborative perception, improving the safety of decision-making.

[0221] In terms of user experience improvement, personalized, seamless, and intelligent travel services enhance user satisfaction. Multi-modal travel planning integrates various modes of transportation, providing convenient door-to-door solutions. Personalized vehicle settings are synchronized to the cloud, allowing users to seamlessly switch between different vehicles. Insurance coverage based on usage enables safe drivers to enjoy lower premiums, incentivizing good driving behavior.

[0222] In terms of enhanced public governance, traffic management departments, through federalized data collaboration, can obtain a comprehensive understanding of traffic conditions without infringing on privacy, thus optimizing policy formulation. Emergency response is more efficient, with multiple departments working together to handle emergencies. Data-driven decision-making replaces experience-based decision-making, improving the scientific nature of governance.

[0223] 7. Technical feasibility and advancement

[0224] The core technologies relied upon in this embodiment, such as digital twins, federated learning, blockchain, smart contracts, and zero-knowledge proofs, have all been verified and applied in other fields. The innovation of this embodiment lies in the creative combination of these technologies applied to the transportation sector, constructing a complete system architecture and methodology. It is highly technically feasible, can be implemented based on existing mature technology stacks, and is forward-looking, pointing the way for the future development of intelligent transportation.

[0225] In summary, the transportation system-wide collaborative management system provided in this application realizes secure, reliable, and privacy-protected cross-system data collaboration and federated intelligence, promotes the realization of the value of transportation digital assets, and improves transportation efficiency and safety.

[0226] Example 2

[0227] This embodiment provides a method for comprehensive collaborative management of a transportation system. This method is applied to the comprehensive collaborative management system for a transportation system provided in Embodiment 1, covering the entire process from scenario triggering to task completion. It is applicable to all modes: vehicle-to-cloud, cloud-to-vehicle, and cloud-to-cloud. The method includes:

[0228] Step 1: Capability preparation and scenario monitoring. The software-defined vehicle platform receives remotely deployed software rules or agent templates in advance, and the context awareness module or cloud service node continuously monitors various triggering conditions.

[0229] Specifically, this step includes vehicle-side capability preloading and continuous context monitoring.

[0230] Vehicle-side capability preloading:

[0231] This step applies only to vehicle-to-cloud and cloud-to-vehicle modes. Software-defined vehicle platforms may receive relevant software rules or agent templates in advance to prepare for rapid response later. For example, when a vehicle leaves the factory or during periodic over-the-air updates, it may receive rules such as "triggering collaborative parking when entering a specific parking lot," logic such as "finding nearby charging stations when the battery level is below 15%," and scripts such as "activating the automatic parking agent when the user presses the one-click valet parking button."

[0232] These preloaded contents are stored locally on the vehicle, consuming minimal resources and using a lightweight template format. When the corresponding scenario is triggered, the template is quickly instantiated into a complete proxy, reducing cold start time.

[0233] Continuous context monitoring:

[0234] The vehicle-side context awareness module runs continuously, monitoring various triggering conditions.

[0235] Geographic location changes are a common trigger. Geofencing technology defines virtual boundaries, triggering events when a vehicle enters or leaves a specific area. For example, entering an airport parking lot triggers a parking guidance agent; entering a highway triggers a fleet coordination agent; and approaching a charging station triggers a charging reservation agent.

[0236] User actions directly reflect user needs. Clicking the "Start Navigation" button in the navigation system triggers the route planning agent; selecting "Find Nearby Restaurants" triggers the point of interest query agent; enabling the "Automatic Parking" function triggers the parking assistance agent.

[0237] Vehicle status changes indicate a need for intervention. A charge station search for a service agent is triggered when the battery level drops below 15%; abnormal tire pressure triggers a repair service recommendation for a service agent; and the engine malfunction indicator light illuminates, triggering a remote diagnostic service.

[0238] Time-based events support scheduled tasks. A commuter route query agent is triggered every morning at 8:00 AM to obtain the latest traffic conditions and recommend departure times; a vehicle health check agent is triggered every Sunday evening to generate a maintenance suggestion report; and an insurance data reporting agent is triggered at the beginning of each month to calculate the monthly premium.

[0239] The cloud system also monitors internal events. Scheduled tasks include hourly updates to the traffic flow prediction model and daily system backups. External events include receiving traffic accident alerts to trigger emergency response procedures and detecting deteriorating weather to trigger hazard warning broadcasts. Business logic triggers include detecting a vehicle's repeated traffic violations to trigger insurance premium adjustment notifications and discovering outdated map data to trigger update pushes.

[0240] The monitoring technology employs complex event processing engines such as Apache Flink CEP and Esper. These engines support event pattern matching, allowing the definition of complex conditions such as "a vehicle brakes suddenly three times within ten minutes" or "entering a congested area with insufficient fuel to reach its destination." They support time windows, aggregating events within a specific time range. They also support sequence patterns, detecting the order in which events occur. Finally, they support logical combination, using AND, OR, and NOT logical operators to combine multiple conditions.

[0241] Step 2: Collaborative scenario identification and triggering. The context awareness module identifies specific business scenarios that require cross-system collaboration based on multi-source vehicle data and assesses whether to trigger the collaborative process.

[0242] Specifically, scene recognition:

[0243] Based on contextual information, the system identifies specific business scenarios requiring cross-system collaboration. Scenario recognition is not simply rule matching, but intelligent reasoning that integrates multi-dimensional information.

[0244] The scenario classification system covers the main needs of the transportation sector:

[0245] Vehicle service scenarios directly serve vehicles and users. In collaborative automated parking, vehicles collaborate with parking management systems and other vehicles to achieve unattended automated parking. In charging station reservation and route guidance scenarios, vehicles check real-time charging station occupancy, reserve a time slot, and obtain the optimal route. In remote vehicle control scenarios, users can remotely preheat the vehicle, unlock doors, and turn on the air conditioning via a mobile application; the vehicle verifies the user's identity with the cloud and executes the commands. In personalized settings synchronization scenarios, users seamlessly switch between different vehicles, and settings such as seat position and music preferences are automatically synchronized.

[0246] In travel management scenarios, the system optimizes the user's travel experience. Multi-modal travel planning integrates various transportation modes such as cars, buses, subways, and shared bicycles, recommending the fastest or most economical combination for users. Real-time route optimization and replanning dynamically adjusts routes based on real-time traffic conditions, avoiding congested or accident-prone areas. In carpooling and ride-sharing coordination scenarios, the system matches passengers with similar carpooling needs, optimizes driver pick-up routes, and reduces empty runs and waiting time.

[0247] Safety and emergency response scenarios ensure traffic safety and emergency response. In the automatic traffic accident alarm and rescue scenario, when a collision is detected, the vehicle automatically sends its location and vehicle information to the emergency center, notifying nearby hospitals and traffic police. In the emergency vehicle priority passage scenario, the routes of ambulances, fire trucks, and police cars are prioritized, traffic lights along the route are adjusted in advance, and ordinary vehicles receive avoidance prompts. In the dangerous road section warning broadcast scenario, when the vehicle in front detects dangers such as icy roads or obstacles, it broadcasts a warning to the vehicle behind to slow down or change lanes via the vehicle network. In the vehicle theft tracking scenario, after the owner reports the theft, the system coordinates multiple surveillance cameras, parking lots, and toll stations to track the vehicle's location, assisting the police in solving the case.

[0248] Commercial service scenarios create business value. In insurance data reporting scenarios, vehicles periodically or in real-time report driving behavior data such as mileage, speed, and number of emergency braking incidents, allowing insurance companies to calculate personalized premiums. In vehicle-as-a-service subscription management scenarios, users subscribe monthly to features such as autonomous driving and advanced navigation; the system verifies the subscription status and activates the corresponding capabilities. In automatic parking or toll payment scenarios, tolls are automatically deducted when a vehicle leaves a parking lot or passes through a tollbooth, eliminating the need to stop to collect a card or queue for payment. In in-vehicle product recommendation and purchase scenarios, the system recommends nearby restaurants, gas stations, attractions, etc., based on passenger preferences and current location, supporting direct ordering.

[0249] Data collaboration scenarios facilitate the extraction of data value. In crowdsourced map updates, numerous vehicles contribute sensor data such as lane lines and traffic sign recognition results, which are then verified by map service providers to update high-precision maps. In federated traffic flow prediction scenarios, traffic management departments in multiple cities train a global traffic flow prediction model through federated learning without sharing raw data, improving prediction accuracy. In cross-regional travel origin-destination matrix construction scenarios, multiple transportation operators collaboratively analyze cross-city travel patterns to optimize long-distance transportation planning. In fleet collaborative optimization scenarios, multiple trucks from a logistics company collaboratively plan routes and schedules to reduce total transportation costs and carbon emissions.

[0250] Triggering decision:

[0251] After identifying the scenario, the system assesses whether to trigger a collaborative process.

[0252] Priority assessment considers multiple dimensions. Regarding urgency, security-related scenarios such as incident alarms have the highest priority and trigger immediately, while commercial service scenarios have lower priority and can be delayed. Regarding user preferences, explicitly expressed user needs, such as manually clicked navigation, take precedence over automatic system recommendations. Regarding cost-effectiveness, the benefits of collaboration, such as time or cost savings, must outweigh the costs, such as data usage fees and privacy risks.

[0253] Necessary prerequisite checks ensure collaborative feasibility. Regarding network connectivity quality, sufficient bandwidth and low latency are required; weak network signals may cause delays until signal recovery. Regarding system resource availability, the onboard computing platform's CPU, memory, and battery power must meet minimum requirements to avoid impacting driving safety functions. Regarding cloud service availability, the target cloud system must be online and not overloaded; its service health status can be checked in advance.

[0254] User authorization mechanisms protect privacy. If collaboration involves privacy-sensitive data such as location or driving behavior, the system requests user authorization, explaining the data's purpose and protection measures. Users can choose to allow, deny, or set conditions, such as allowing access only during specific times or in specific areas. Authorization records are kept for auditing purposes, and users can revoke authorization at any time.

[0255] The trigger decision algorithm considers the above factors, calculates the overall benefits and risks of collaboration, and triggers when a threshold is exceeded. The algorithm can employ multi-attribute decision-making methods such as the analytic hierarchy process (AHP) or fuzzy evaluation, or it can be based on machine learning to train a decision model.

[0256] Step 3: Dynamic digital twin agent instantiation. When the collaborative process is triggered, the initiator creates a digital twin agent manager and configures the semantic layer of the digital twin agent manager to define the interaction ontology, the data layer to fill in the actual content, and the contract layer to define the terms of use. After allocating resources, it registers with the federated collaborative communication bus.

[0257] Specifically, the initiator's agent creates:

[0258] When a decision is made to trigger collaboration, the initiating system dynamically creates a digital twin agent.

[0259] Taking a collaborative parking scenario initiated by the vehicle as an example, the vehicle-side agent manager receives the scenario trigger signal from the context awareness module and obtains the complete context information of the current vehicle.

[0260] The semantic layer configuration defines the ontology model for the interaction. The context points to the parking domain of the transportation ontology, the request type is specified as parking space allocation, and the required fields include vehicle size, estimated arrival time, user preferences, etc. These definitions ensure that the parking cloud can correctly understand the request.

[0261] The data layer configuration populates the actual content. Vehicle identifiers use decentralized identity identifiers to protect privacy, without directly exposing the vehicle identification number. Vehicle dimensions, including length 4650 mm, width 1850 mm, and height 1450 mm, are extracted from the vehicle's static model. Current location is obtained from GPS using the WGS84 coordinate system. Estimated arrival time is calculated based on current speed and distance, such as in ten minutes. User preferences are read from user profiles, such as a desire for shaded parking, proximity to elevators, and the need for charging stations.

[0262] The contract layer configuration defines the terms of use. The data usage is explicitly limited to route planning and parking space allocation, and may not be used for other purposes. The list of permitted recipients includes only decentralized identity identifiers specific to the parking management cloud; other systems have no access. The privacy policy requires anonymization of vehicle identifiers and immediate deletion of data after parking. The service level agreement requires a maximum response time of two seconds and a parking space location error of less than one meter. The pricing model is set to free in this scenario because the parking fee already includes service costs.

[0263] The proxy instantiation process first allocates resources, including memory for storing proxy data structures, CPU for executing proxy logic, and network bandwidth for data transmission. A temporary unique identifier, such as a UUID, is generated for subsequent communication referencing the proxy. The proxy is registered with the federated bus, submitting its capability description and service requirements so that other systems can discover it. An instantiation log is recorded, including timestamps, trigger scenarios, and lifecycle settings, for auditing and debugging.

[0264] Take the parking cloud response scenario initiated from the cloud as an example. After receiving the request from the vehicle, the parking cloud creates a response proxy.

[0265] Semantic layer matching of vehicle-side definitions ensures that both parties use the same ontology model, avoiding semantic conflicts.

[0266] The data layer provides parking space allocation results. The allocation algorithm selects the optimal location from available parking spaces based on vehicle size, arrival time, and user preferences. The results include the allocated parking space number (e.g., A3-025), location coordinates, floor information, parking space type (e.g., obstructed), and whether a charging station is available. The navigation route includes a detailed list of waypoints, turn instructions from the parking lot entrance to the designated parking space, a total distance of 850 meters, and an estimated travel time of three minutes.

[0267] The contract confirms acceptance of the vehicle-side terms. The data usage purpose is consistent with the vehicle-side, solely for this parking guidance. The data usage commitment explicitly states that vehicle trajectories will not be saved; only entry and exit times will be recorded for billing purposes. The service level guarantee includes a response time of 1.2 seconds, meeting the vehicle-side requirement of within two seconds, and a parking space availability guarantee of 95%, meaning that the provided parking spaces are indeed vacant.

[0268] Key parameters for agent configuration:

[0269] Set the agent's lifespan. Immediately destroyed upon task completion is suitable for one-time collaborations such as parking requests; both the vehicle-side and cloud-side agents are destroyed upon completion of the response. Fixed duration, such as automatic expiration after 24 hours, is suitable for temporary subscription services. Indefinite duration until manual destruction is suitable for long-term services such as fleet management agents.

[0270] The callback interface defines the notification address after the proxy completes the task. The vehicle-side proxy may specify the push interface of the in-vehicle application, displaying a notification to the user upon task completion. The cloud-side proxy may specify a webhook for a backend service, triggering subsequent business processes such as billing.

[0271] Fault tolerance strategies handle exceptional situations. Retry counts are set to a maximum of three retries to prevent task failure due to momentary network outages. Timeout handling involves abandoning the task if a response is delayed for more than ten seconds to avoid indefinite blocking. Degradation solutions include lower-priority backup services that automatically switch to when the primary service becomes unavailable.

[0272] Step 4: Agent discovery, matching and contract negotiation. The initiator discovers a respondent who can meet the requirements through broadcast, query or subscription mode. After discovering the respondent, both parties conduct contract compatibility checks, and resolve conflicts through an automatic negotiation algorithm. After reaching an agreement, the contract is signed and submitted to the blockchain for notarization.

[0273] Specifically, the proxy discovery process:

[0274] After the initiating agent is created, it needs to find a responding agent that can meet the requirements.

[0275] In broadcast mode, the vehicle-side agent broadcasts a service discovery message to the federated bus. The message type is identified as service discovery, the sender's decentralized identity identifier is such as the vehicle's DID, the agent identifier is such as agent parking request 001, the requested service type is such as parking space allocation service, the geographical range is such as the current location 39.9042 N 116.4074 E with a radius of 2 kilometers, the service quality requirement is such as a response time of less than 2 seconds, and the timestamp records the sending time.

[0276] The parking cloud agent responds to the broadcast. The message type identifier is the service provider, the sender's decentralized identity identifier such as the parking cloud's DID, the agent identifier such as agent parking allocation 002, the type of service provided such as parking space allocation service, the coverage area such as a radius of 5 kilometers including the area where the vehicle is located, the current capacity such as 300 available parking spaces, and the service quality such as an average response time of 1.5 seconds.

[0277] The vehicle-side agent may receive responses from multiple cloud-based agents, requiring the selection of the most suitable one. Selection criteria include prioritizing proximity, lower price, higher service quality, and better historical reputation. Multi-objective optimization algorithms, such as Pareto optimality, are employed to find the overall optimal solution.

[0278] In query mode, the vehicle-side agent directly queries the service registry. Query criteria include finding parking service providers in Beijing's Chaoyang District that offer prices below 10 yuan per hour and support charging stations. The registry returns a list of matching cloud agents and their detailed capability descriptions. The vehicle-side agent further filters based on the selection criteria, sorts the results, and selects the best one.

[0279] The subscription model is suitable for long-term engagement. The vehicle-side agent subscribes to specific types of services, such as charging station services along the commuting route. The agent is automatically notified when a new charging station becomes available or when existing charging station information is updated. This model reduces the overhead of repeated queries and improves response speed.

[0280] Contract negotiation mechanism:

[0281] After finding a candidate respondent, both parties enter the contract negotiation stage.

[0282] A contract compatibility check was conducted, comparing each clause of the contracts between the two parties. A data usage purpose check was performed to ensure both parties had a consistent understanding of the data's intended use. If the vehicle-side application declared it for route planning, while the cloud-based system identified it as commercial analysis, then incompatibility was deemed a breach, and negotiations were terminated.

[0283] Privacy requirements are checked to ensure sufficient privacy protection. If the vehicle requires k-anonymity (k=5), but the cloud can only provide k=3, then incompatibility exists. If the vehicle requires differential privacy (epsilon=0.1), but the cloud sets it to 1, privacy protection is weak, and incompatibility also exists.

[0284] Service level agreement (SLA) checks ensure performance commitments are met. If the vehicle requires a maximum response time of two seconds, while the cloud guarantees a response time of three seconds, this cannot be met and is incompatible.

[0285] Pricing checks ensure that costs are acceptable. A conflict exists if the maximum acceptable price on the vehicle side is 0.001 per transaction, while the cloud-based price is 0.005. However, pricing is usually negotiable and is marked as a negotiable conflict.

[0286] Automatic negotiation algorithms handle negotiable conflicts.

[0287] Price negotiation employs a compromise strategy. If the vehicle's maximum acceptable price is 0.001 each time, and the cloud's lowest price is 0.003 each time, a compromise price of 0.002 is proposed. If both parties agree, the price terms in the contract are updated, and the negotiation is successful. If still unsatisfactory, multiple rounds of negotiation can be conducted to gradually approach the target price, or other compensation can be introduced, such as a commitment to long-term cooperation in exchange for a discount.

[0288] Data granularity negotiation addresses precision conflicts. If the vehicle wants raw GPS data but is concerned about privacy risks, while the cloud can accept aggregated data, the negotiation downgrades the granularity to the lowest acceptable level for both parties, such as one-minute aggregation, five-minute aggregation, or hourly aggregation. Once the first mutually acceptable granularity is found, the contract is updated.

[0289] Time window negotiation addresses timeliness conflicts. If the vehicle side wants data deleted immediately, the cloud needs to retain it for 24 hours for auditing. A compromise retention period can be negotiated, such as automatic deletion after six hours, with encrypted storage to reduce risk.

[0290] Unnecessary conflicts lead to negotiation failure. If there are irreconcilable conflicts in the contract terms between the two parties (such as mismatch in legal compliance), the system will automatically trigger a collaborative termination mechanism and record it in the audit log. For example: if the vehicle requires the data to be used only for route planning, while the cloud insists that it can be used for business analysis, the conflict of purposes cannot be reconciled, and the connection will be terminated. If the vehicle requires GDPR compliance, while the cloud is located in a non-compliant region, the legal conflict cannot be resolved, and the connection will be terminated.

[0291] Contract signing and notarization will take place after successful negotiation.

[0292] Generate the final contract text, including a list of decentralized identity identifiers of the participants, the detailed content of the merged terms, the current timestamp recording the signing time, and a random number to prevent replay attacks.

[0293] Both parties' signatures ensure the contract's authenticity. The SHA-256 hash of the contract text is calculated to obtain a fixed-length digest. The vehicle-side agent signs the hash value using its private key, generating a digital signature. The cloud agent also signs it. The signature can be verified by anyone using their public key, confirming the signatory's identity and the integrity of the content.

[0294] Submitting to the blockchain ensures immutable evidence preservation. A blockchain transaction is constructed, identified as a contract creation, and includes the contract hash, signatures of both parties, and the encrypted contract details. This is then submitted to the blockchain network, where miners or consensus nodes verify the transaction and write it into a block. Once the contract is on the blockchain, any modification requires altering all subsequent blocks, which is virtually impossible in a decentralized network, guaranteeing the legal evidentiary value of the contract.

[0295] The contract hash value is returned to both parties' agents as proof of subsequent interactions. All data exchanges reference this contract hash value to ensure compliance with the agreed terms.

[0296] Step 5: Secure and controlled collaborative execution. The initiator and responder establish a TLS two-way authenticated encrypted channel and activate the use of smart contract monitoring data. Actual data exchange and business processing are carried out under the secure channel and contract monitoring. The smart contract continuously monitors and handles default behavior.

[0297] Specifically, the establishment of a safe passage:

[0298] Please see Figure 7 After the contract is signed, both parties establish an encrypted communication channel.

[0299] The TLS two-way authentication handshake process ensures the authenticity of both parties' identities. The vehicle-side agent sends a ClientHello message, including supported cipher suites and a random number. The parking cloud agent returns a ServerHello message, selecting a cipher suite, returning a random number, and sending the server certificate. The vehicle-side agent verifies the server certificate, checking if it was issued by a trusted CA, its validity period, and its revocation status. The vehicle-side agent sends the client certificate and a ClientKeyExchange message, including a pre-master key. The parking cloud agent verifies the client certificate, confirming the vehicle's legitimacy. Both parties calculate the session key based on the pre-master key and the random number. They exchange ChangeCipherSpec and Finished messages, enabling encryption and completing the handshake. This implementation uses the contract hash value as part of the handshake credential when establishing a TLS 1.3 two-way encrypted channel, ensuring that only the agent who signed the contract can establish communication.

[0300] Contract activation and monitoring ensure the terms are executed. Deploy the monitoring smart contract to the blockchain, using a data usage monitoring template. Configuration parameters include allowed data usage purposes such as route planning, maximum access frequency (vehicle data accessed only once per task), contract expiration time, and penalties for breach of contract such as service suspension and a 0.1% fine.

[0301] Real-time monitoring is initiated, with the smart contract subscribing to the data stream endpoints of the vehicle-side agent and the access logs of the cloud agent. Each time data is accessed from the cloud, the smart contract verifies whether the access complies with the contract terms. If any breach is detected, such as exceeding the access limit, using the data for unauthorized purposes, or data leakage to a third party, penalty measures are immediately triggered.

[0302] Data exchange and business processing:

[0303] Under secure channels and contract monitoring, both parties conduct actual data exchange and business processing.

[0304] In a vehicle-to-cloud scenario, the vehicle agent sends a parking request. The message header includes an agent identifier, a contract hash value associated with the terms of use, a current timestamp, and a digital signature from the vehicle for the message body to prevent tampering. The message body includes detailed vehicle size data, current location coordinates, and user preference settings. The entire message is encrypted using a session key and sent through a secure channel.

[0305] The parking cloud agent receives the message and decrypts it using the session key. It verifies the digital signature to confirm the message has not been tampered with and originates from the claimed sender. It checks the contract hash and queries the blockchain to confirm the contract's validity. It then calls the smart contract to check if the access complies with the terms of use, such as whether it exceeds the access limit. If verification fails, the request is rejected and logged; if successful, processing continues.

[0306] The business process executes a parking space allocation algorithm. Inputting the vehicle size filters for sufficiently large parking spaces; inputting user preferences prioritizes parking spaces with shelter, proximity to elevators, and availability of charging stations; inputting the current location and arrival time reserves parking spaces to prevent them from being occupied. The algorithm outputs the optimal parking space number and navigation route.

[0307] Generate a response message containing details of the allocated parking space and navigation route. Encrypt the message and send it to the vehicle-side agent via a secure channel.

[0308] The vehicle-side agent receives the response, decrypts and verifies it, and displays the parking space information and route to the user, or directly transmits it to the automatic parking system to start the parking process.

[0309] In a cloud-to-vehicle scenario, a parking cloud agent generates route guidance instructions. The message header includes the agent identifier, the target vehicle's decentralized identity identifier, the contract hash value, and the instruction type, such as navigation update. The message body includes the destination parking space number and GPS coordinates, a detailed list of waypoints including the coordinates and instructions for each turn, a description of the parking space's surrounding environment (e.g., a pillar to the left and a wall to the right), and a suggested driving speed (e.g., a speed limit of 10 km / h in the parking lot). The message is then sent after digital signature and encryption.

[0310] The vehicle-side agent receives the instruction, verifies that the sender is an authorized parking cloud provider, and checks the contract to confirm its right to send navigation instructions. It then decrypts and extracts the route data, transmitting it to the in-vehicle navigation system or autonomous driving system. The vehicle follows the route, displaying its real-time location and remaining distance. Upon reaching a parking space, it stops and sends a confirmation message to the cloud.

[0311] In a cloud-to-cloud scenario, multiple traffic management clouds collaboratively optimize traffic lights. The coordinator identifies a list of intersections requiring coordination and creates a federated learning task. Each cloud agent initializes its local model and trains it using local traffic flow data. After training, it calculates model parameter updates, such as gradients, without sending the original data.

[0312] A secure aggregation protocol is employed, where each agent encrypts its updates before sending them to the aggregation server. The server aggregates all updates in encrypted form and decrypts them to obtain the global update. This global update is then distributed to each agent to update its local model. This process iterates through multiple rounds until the model converges, demonstrating that the global model outperforms any single local model, thus achieving collaborative intelligence.

[0313] Throughout the process, the raw traffic flow data never leaves the respective cloud environments, protecting data sovereignty. The contract layer ensures that the model is used solely for signal optimization and not misused for other purposes. Audit logs record the parameter hashes for each training round, verifying the correctness of the computation process.

[0314] Contract execution monitoring and default handling:

[0315] Smart contracts continuously monitor data usage behavior.

[0316] Access control verifies that each data access request originates from an authorized party. It checks if the requester's decentralized identity identifier is in the contract's authorized list. It also verifies the validity of the requester's digital certificate and the correctness of its signature. If unauthorized access is attempted, it is denied and the breach is recorded.

[0317] Purpose verification is used to check whether data is being used for purposes permitted by the contract, through data flow analysis or audit logs. If data is detected being fed into unauthorized algorithms or systems, a breach alert is triggered. For example, location data used for route planning should not appear in an advertising recommendation system.

[0318] Access frequency is limited by recording the number of accesses. If the maximum number of accesses is exceeded as stipulated in the contract, further access will be blocked. If the contract allows one access, a second access will be denied and a breach will be recorded.

[0319] The data retention period monitors the data storage time and automatically triggers a deletion command upon expiration. If data is detected to be retained after the expiration date, a breach of contract is triggered. Verifiable deletion technology, such as encryption key destruction, is employed to ensure that the data cannot be decrypted even if copied.

[0320] The privacy protection measures are verified to check whether the privacy protection technologies required by the contract have been adopted. If the contract requires k-anonymity, verify whether the dataset satisfies the k value; if differential privacy is required, verify whether the noise addition conforms to the epsilon parameter. Compliance is proven without exposing the original data through cryptographic techniques such as zero-knowledge proofs.

[0321] Default handling measures are executed automatically. Service suspension is the lightest penalty; the smart contract calls the API to suspend the defaulting party's access until the problem is resolved. Penalty deduction: A predefined penalty amount is automatically deducted from the defaulting party's blockchain account and transferred to the injured party or a public fund. Notification: Default notices are sent to data owners and regulatory agencies, including details such as the type of default, time, and evidence. Legal proceedings: For serious defaults, litigation materials are automatically generated, including blockchain evidence, contract terms, and evidence of default, and submitted to arbitration institutions or courts.

[0322] Default records are permanently stored on the blockchain, affecting the defaulting party's reputation score and reducing their future cooperation opportunities.

[0323] Step 6: Collaborative task completion and agent destruction. After the task achieves its goal, a completion confirmation is sent, and data cleanup and destruction of temporary agents are performed to release resources in accordance with the contract terms.

[0324] Specifically, task completion confirmation:

[0325] Once the collaborative task has achieved its objective, it enters the final stage.

[0326] In a vehicle-to-cloud scenario, once the vehicle arrives at the designated parking space, parking is complete. The vehicle-side agent sends a task completion message to the cloud agent, including the actual arrival time, parking space confirmation, and user satisfaction feedback. The cloud agent updates the parking space occupancy status, initiates the billing process, and sends a confirmation reply to the vehicle-side agent.

[0327] In a cloud-to-vehicle scenario, the in-vehicle system confirms that it has received and applied the software update sent from the cloud. The vehicle agent sends an update success message, including the new version number and functional test results. The cloud agent records the update log, updates the vehicle profile, and prepares a rollback plan if any problems are detected.

[0328] In a cloud-to-cloud scenario, when a federated learning task reaches a predetermined number of iterations or a model accuracy threshold, the coordinator sends a task completion notification to all participating agents. Each agent then saves its final model and stops training.

[0329] Data cleansing and privacy protection:

[0330] According to the contract terms, data cleanup shall be performed. After the data cleanup is performed, the responding party shall submit a proof of data destruction to the bus for the initiator to audit.

[0331] Temporary data deletion includes one-time data such as vehicle location and route; this data is deleted from the cache and database immediately after the task is completed. Secure deletion technology is employed, repeatedly overwriting the storage media to prevent data recovery.

[0332] Session keys and data encryption keys are destroyed when no longer needed. Even if encrypted data is copied, it cannot be decrypted without the key, achieving data inaccessibility.

[0333] If data needs to be retained for statistical analysis, de-identification processes should remove or hash all personal identifiers such as vehicle identification numbers and user identities to ensure that the data cannot be associated with individuals.

[0334] Audit log archiving retains only essential audit logs, such as access time, data hash value, and contract hash value, and does not include the original data content. Logs are stored on a blockchain or an immutable storage system to meet legal compliance requirements such as GDPR, which require the retention of processing records, but without excessively retaining personal data.

[0335] Agent lifecycle management:

[0336] Once the task is completed, destroy the temporary agent and release the resources.

[0337] Resource reclamation releases the memory, CPU, network connection, and other resources occupied by the agent, returning them to the system resource pool for use by other tasks.

[0338] Unregister the service by removing the agent from the service registry, making it no longer discoverable or accessible. Close all active connections to the agent and notify connected parties that the agent has been destroyed.

[0339] The status archive saves a snapshot of the agent's final state, such as creation time, destruction time, number of tasks processed, and resource consumption statistics, for subsequent analysis and optimization.

[0340] The logging system submits the agent's complete activity logs to a centralized logging system or blockchain, supporting auditing, troubleshooting, and performance optimization.

[0341] For long-standing proxies such as cloud service proxies, instead of destroying them, they are put into a dormant state. Active activity ceases, reducing resource consumption, but state and configuration are retained, allowing them to be awakened at any time to serve new requests.

[0342] Step 7: Feedback and optimization. Collect performance data during the collaboration process and evaluate and optimize it.

[0343] Specifically, performance evaluation and feedback:

[0344] The system collects performance data during the collaboration process for evaluation and optimization.

[0345] Response time statistics are used to measure the end-to-end latency from request to response reception, comparing it to the commitments in the Service Level Agreement (SLA). If the latency exceeds the commitments, it is marked as a performance issue, and the causes are analyzed, such as network congestion, server overload, or inefficient algorithms.

[0346] The success rate is calculated by tracking the number of successful and failed collaborative tasks. If the success rate is lower than expected, the reasons for failure, such as contract negotiation failure, network interruption, or incorrect data format, are analyzed to improve system robustness.

[0347] Monitor resource consumption, including CPU usage, memory usage, network traffic, and storage space. Optimize resource allocation strategies to avoid resource waste or bottlenecks.

[0348] User satisfaction is collected through user feedback, such as parking experience ratings, route accuracy evaluations, and charging service satisfaction. User feedback is the most direct quality indicator, guiding product improvement.

[0349] Model update and optimization:

[0350] Based on the collected data, optimize the system model and strategies.

[0351] Scene recognition model optimization utilizes machine learning techniques, such as convolutional neural networks to analyze sensor data, recurrent neural networks to model time series data, and reinforcement learning to optimize decision-making strategies. Training data comes from historical collaboration records, labeling successful and failed cases, allowing the model to learn which contexts should trigger collaboration and which should be ignored.

[0352] The proxy template has been optimized by predefining more proxy templates based on common scenarios, thus speeding up instantiation. The template structure has also been optimized to reduce redundant fields and compress message size.

[0353] Analyze the reasons for failed negotiations to optimize contract terms and adjust the default values ​​and negotiable scope of the terms. For example, if price negotiations frequently fail, it may be necessary to adjust the pricing strategy or provide a more flexible billing model.

[0354] Routing policy optimization is based on network topology and traffic statistics to optimize message routing paths. Software-defined networking technology is used to dynamically adjust routing rules, avoid congested links, and select low-latency paths.

[0355] Security policies are updated based on newly discovered threats. For example, if a new attack pattern is detected, intrusion detection rules are upgraded; if a weakness in an encryption algorithm is discovered, a switch to stronger encryption is made.

[0356] Ecosystem Evolution:

[0357] The system supports open evolution, allowing new participants and new services to be introduced.

[0358] New cloud node access follows standard interfaces and protocols, allowing new service providers to easily connect to the federated bus. After completing identity authentication and trust verification, they register their service capabilities and begin collaborating with existing nodes. This openness fosters ecosystem growth and provides a richer portfolio of services.

[0359] New scenarios are supported. With innovation in the transportation sector, new collaborative scenarios may emerge, such as collaborative flying cars, collaborative unmanned delivery, and collaborative vehicle-road systems. The flexibility of the system architecture allows for the definition of new scenario templates, agent types, and contract terms without refactoring the core framework.

[0360] Standardization efforts will involve participation in the development of industry standards, and successful practices will be promoted as industry norms. Semantic layer ontology, contract layer terms, and communication protocols will be gradually standardized to improve cross-vendor and cross-platform interoperability.

[0361] Successful experiences in cross-domain expansion within the transportation sector can be extended to other areas such as smart energy vehicle and grid collaboration, multi-system linkage in smart cities, and industrial internet device collaboration. The paradigms of digital twin agents and federated collaboration are universally applicable, supporting broader digital transformation.

[0362] Example 1: Collaborative Automated Parking System

[0363] Scenario Description: A user is driving to a shopping mall and is about to arrive at the mall's underground parking entrance. The user wants the vehicle to automatically find a parking space and park, allowing them to enter the mall directly. After shopping, the user wants the vehicle to automatically drive to the pick-up point.

[0364] System Configuration:

[0365] Vehicle-side: Software-defined vehicles equipped with Level 3 autonomous driving capabilities, featuring a vehicle-side digital twin agent manager, context awareness module, and multi-sensor fusion system including GPS / BeiDou dual-mode positioning, LiDAR, cameras, and ultrasonic radar.

[0366] Cloud-based: The mall's smart parking management cloud manages 3,000 parking spaces and is equipped with parking space detection sensors, a route planning system, a billing system, a cloud-based digital twin agent generation module, and a federated collaborative communication bus interface.

[0367] Federation Bus: Provides services such as identity authentication, agent discovery, secure connection, and audit logs, and is deployed in a hybrid environment of shopping mall private cloud and public Internet.

[0368] Please see Figure 8 Implementation steps:

[0369] Step 1: Capability Preparation. The vehicle is pre-loaded with a collaborative parking agent template at the time of purchase or via over-the-air download, including protocols for communicating with parking lots, parking behavior rules, and safety constraints.

[0370] Step 2: Scene Monitoring and Recognition. The vehicle-side context awareness module continuously monitors the vehicle's location. When it detects that the vehicle has entered a geofence within a 500-meter radius of the shopping mall parking lot, a candidate scene is triggered. Further detection involves the user clicking the "Automatic Parking" button on the in-vehicle screen to confirm the user's intent. Based on the combined context of vehicle location, user operation, and the vehicle's autonomous driving capabilities, the scene is identified as a collaborative automatic parking scenario and given high priority.

[0371] Step 3: Instantiate the vehicle agent. The vehicle agent manager extracts relevant data from the complete digital twin model of the vehicle: vehicle dimensions are 4,800 mm long, 1,900 mm wide, and 1,500 mm high; current location is 39.91°N, 116.40°E; estimated arrival time at the parking lot entrance is 5 minutes; user preference is for a parking space near the elevator and equipped with a charging station.

[0372] Configure the semantic layer, adopt the parking domain ontology, define the request type as collaborative parking service, and the required fields include vehicle size, arrival time, and preference settings.

[0373] Configure the data layer, fill in the actual values, use decentralized identity identifiers to protect privacy for vehicle identifiers, and blur the location data to a precision of ten meters.

[0374] The contract layer is configured so that the purpose of data use is limited to parking space allocation and route guidance. The recipient is allowed to be a decentralized identity identifier of the shopping mall's parking cloud. Privacy requirements include anonymizing the vehicle identification number and deleting the data immediately after parking. The service level agreement requires a response time of less than three seconds, guaranteed parking space availability, and accurate route. Pricing accepts the shopping mall's standard parking fee and does not charge additional parking service fees.

[0375] Instantiate the agent, allocate memory and computing resources, generate agent identifiers, and register with the federation bus.

[0376] Step 4: Agent Discovery and Matching. The vehicle-side agent queries the service registry via the federated bus, searching for criteria such as providing collaborative parking services, covering the current location, and supporting autonomous vehicles. The registry returns a shopping mall parking cloud agent with capabilities including managing 3,000 parking spaces, 800 currently available parking spaces, supporting L3 level autonomous driving guidance, and an average response time of two seconds. The vehicle-side agent meets the requirements, and the cloud agent is selected.

[0377] Step 5: Contract Negotiation. The vehicle-side agent sends a draft contract to the cloud agent. The cloud agent checks compatibility: data usage purpose is consistent, privacy requirements are met, service level agreement response time is less than three seconds (two seconds is acceptable), and the pricing of ten yuan per hour for shopping mall parking is in line with market standards. The contract is compatible and no further negotiation is needed.

[0378] Both parties sign the contract, calculate the contract text hash value, the vehicle-side agent signs using the vehicle's private key, and the cloud agent signs using the mall's parking cloud private key. The contract is submitted to the blockchain, the transaction type is contract creation, and the contract hash value, both parties' signatures, and timestamp are recorded. After blockchain confirmation, the contract hash value is returned.

[0379] Step 6: Secure Connection Establishment. Both parties perform a TLS 1.3 mutual authentication handshake. The vehicle-side agent provides the vehicle certificate, and the cloud agent provides the mall parking cloud certificate, which are mutually verified. The session key is negotiated, and an encrypted channel is established. A smart contract is deployed to monitor data usage, ensuring compliance with contract terms.

[0380] Step 7: Data Exchange and Business Processing. The vehicle-side agent sends a parking request message via an encrypted channel, including vehicle dimensions, current location, estimated arrival time, user preferences, etc. The message is encrypted using a session key and includes a digital signature.

[0381] The cloud-based agent receives and decrypts the verification data, then invokes the parking space allocation algorithm. The algorithm inputs vehicle dimensions to filter parking spaces with a length greater than five meters and a width greater than two meters; user preferences to filter spaces near elevators and equipped with charging stations; and estimated arrival time to reserve a parking space for five minutes. The algorithm outputs the optimal parking space number B2-088, located on the second basement level near the elevator entrance, equipped with a 7kW charging station.

[0382] Generate a navigation route from the current vehicle location to the parking lot entrance, passing the entrance toll station, driving along the B-zone passage, reaching the second basement level, and turning left to find parking space 088. The route includes a detailed list of waypoints, GPS coordinates and driving instructions for each turn, with a total distance of approximately 900 meters and an estimated travel time of four minutes.

[0383] The cloud agent sends a response message containing parking space information and navigation route, encrypted before transmission. The vehicle agent receives and decrypts the message, displaying the parking space location and route to the user for confirmation. Upon user agreement, the vehicle enters autonomous driving mode and follows the route.

[0384] Step 8: Collaborative Parking Execution. The vehicle enters the parking lot, passes through the toll booth, and its license plate is scanned for identification. It travels along the B-zone lane, with onboard LiDAR and cameras monitoring the surrounding environment in real time and comparing the route with the cloud-based path to ensure accuracy. Upon reaching the second basement level, parking space number 088 is detected; there are no obstacles nearby, and the space is indeed vacant.

[0385] The automatic parking algorithm is activated, and the vehicle slowly drives into the parking space. The steering wheel, accelerator, and brakes are precisely controlled, and parking is successful. The vehicle sends a parking completion message to the cloud agent, including the actual parking time, parking space confirmation, and successful charging station connection.

[0386] Step 9: Task Completion and Data Cleanup. The cloud agent updates the parking space occupancy status, marking parking space 088 as occupied and starting timer and billing. Vehicle location trajectory data is deleted, retaining only the entry time and parking space number for billing purposes.

[0387] The vehicle-side agent is destroyed, releasing the occupied memory and computing resources, and deregistered from the federated bus. A task completion log is retained, recording successful parking, time taken (four minutes), and high user satisfaction.

[0388] Step 10: User Shopping and Car Retrieval. Two hours after shopping at the mall, the user requests to retrieve their car via the mobile app. This triggers a new collaborative process: the parking cloud agent generates a retrieval instruction and sends it to the vehicle. The vehicle automatically starts, disconnects from the charging station, drives out of the parking space, and proceeds to the mall's pick-up point. The user gets into the car, and the system automatically calculates the parking fee of twenty yuan, deducting it via mobile payment.

[0389] Technical effects:

[0390] This embodiment achieves seamless collaboration between vehicles and parking lots, significantly improving the user experience. Users no longer need to search for parking spaces, saving time and effort. Automated parking reduces human error, improving parking safety and efficiency. Intelligent parking space allocation optimizes parking lot resource utilization, reduces vehicle cruising time within the parking lot, and lowers carbon emissions.

[0391] Data privacy is fully protected: vehicle identification numbers are anonymized, location data is obfuscated, and data is deleted immediately after parking. A contractual layer ensures that data is used solely for parking services and not misused for commercial advertising or other purposes. Blockchain audit logs provide immutable evidence, allowing users to query their data usage records.

[0392] The system is flexible and scalable, allowing new parking lots to easily connect to the federated bus and provide collaborative parking services. Vehicles from different brands can use the service as long as they support the standard protocol, promoting industry interoperability.

[0393] Example 2: Urban Traffic Flow Prediction Based on Federated Learning

[0394] Scenario Description: A provincial traffic management department aims to improve the accuracy of traffic flow prediction across the province and optimize traffic light control and travel guidance. The province comprises five major cities, each with its own independent traffic management cloud platform, possessing local traffic flow data. Due to data sovereignty and privacy regulations, cities are unwilling to upload raw data to the provincial center. A technical solution is needed to achieve cross-city collaborative intelligence while protecting data locality.

[0395] System Configuration:

[0396] Cloud nodes: Five city traffic management clouds, each deployed in the data center of the respective city's traffic management bureau, manage local road network traffic flow data, historical records, and traffic light control systems. Each cloud is equipped with a federated computing engine, supports federated learning algorithms, deploys a cloud-based digital twin agent generation module, and connects to a federated collaborative communication bus.

[0397] Coordinator: The provincial transportation coordination center deploys a cloud-based collaborative coordinator, which is responsible for the global coordination, workflow orchestration, and model aggregation of federated learning tasks.

[0398] Federal Bus: A trusted data space covering the entire province, providing services such as secure communication, identity authentication, and audit logs.

[0399] Implementation steps:

[0400] Step 1: Task Initiation. The provincial coordinator identifies the requirement: to train a unified traffic flow prediction model for the entire province, used for traffic flow prediction in the next hour. The federated learning task is defined as follows: the model architecture is a long short-term memory neural network; input features include historical traffic flow, time, weather, holidays, etc.; the output is the traffic flow for each road segment in the next hour.

[0401] Step 2: Agent Instantiation and Registration. The coordinator sends task invitations to the cloud environments of the five cities. Each city's traffic management cloud evaluates the task, confirms its willingness to participate, and considers factors such as the model's value to local businesses, computational resource consumption, and privacy risks. Once each city agrees to participate, it instantiates a cloud-based digital twin agent.

[0402] The proxy configuration semantic layer adopts a traffic domain ontology, defines the data type as traffic flow time series, and the model type as a Long Short-Term Memory (LSTM) network. The configuration data layer does not expose raw traffic flow data, only providing a model training interface, accepting global model parameters, and returning local model update quantities. The configuration contract layer limits the purpose of data use to traffic flow prediction, prohibiting its use for other purposes. Privacy protection employs secure aggregation and differential privacy. Participants include five city clouds and a provincial coordinator. The service level agreement promises that each training round will be completed within ten minutes, and revenue distribution is based on data contribution and model improvement.

[0403] Each agent registers with the federal bus and submits a description of its capabilities, including the size of the dataset (e.g., city A has one year of historical data covering 500 road segments), computing power (e.g., GPU servers), and privacy protection level.

[0404] Step 3: Contract Negotiation. The coordinator agent proposes a global contract draft, defining the federated learning protocol as a federated averaging algorithm, the privacy budget differential privacy epsilon equal to one, the number of training rounds as fifty, and the model convergence criterion as the validation set loss not decreasing for three consecutive rounds.

[0405] Each city's agent checks the contract's compatibility. City C raises concerns about the privacy budget being too high and requests a reduction to 0.5%. The coordinator agrees and updates the contract. All participants sign the contract and submit it to the blockchain for record-keeping.

[0406] Step 4: Execution of Federated Learning.

[0407] Initialization phase: The coordinator agent initializes the global model, randomly sets the neural network parameters, and distributes them to the city agents.

[0408] Local Training Phase: Each city agent receives the global model and trains the model using local traffic flow data. City A loads its local dataset, divides it into training and validation sets, and runs a model training algorithm such as stochastic gradient descent, iterating for several rounds (e.g., ten rounds), calculating the model parameter updates, i.e., the gradients. Differential privacy noise is added; based on the Laplace's law of epsilon equal to 0.5, noise is added to the gradients to protect data privacy. Cities B, C, D, and E also perform local training.

[0409] Secure aggregation phase: Each city agent encrypts its update data using homomorphic encryption or secret sharing techniques, and sends the encrypted update data to the coordinator. The coordinator aggregates the update data from all cities in the encrypted state, calculating an average or weighted average, with weights based on the size of each city's dataset. The aggregation result is then decrypted to obtain the global model update data.

[0410] Model update phase: The coordinator updates the global model using the global update value, obtaining new model parameters, and distributes them to each city agent. Each city agent updates its local model, preparing for the next round of training.

[0411] Iterative execution: Repeat local training, safe aggregation, and model updates for fifty rounds or until the model converges.

[0412] Step 5: Model Validation and Deployment. After training, the coordinator agent validates the global model performance on independent test sets, calculating prediction errors such as mean absolute percentage error. Compared to the single-city model, the global federated model shows a 20% reduction in error, demonstrating significant synergistic effects.

[0413] Each city's agent downloads the final global model and deploys it to their local traffic management system for real-time traffic flow prediction. Based on the prediction results, they optimize traffic light control strategies, proactively alleviate congestion, and issue travel advice to the public.

[0414] Step 6: Revenue Distribution and Audit. According to the contract terms, the coordinator calculates the contribution of each city, considering factors such as dataset size, data quality's contribution to model performance improvement, and computational resource investment. Cooperative revenue is then distributed; revenue can be monetary, such as provincial government subsidies, or priority access rights, such as priority access to provincial resources.

[0415] The blockchain audit log records the parameter hash value, aggregation process, and model version for each training round. Any participant can verify the correctness of the training process and ensure that there is no cheating behavior such as submitting false gradients.

[0416] Step 7: Task Completion and Agent Destruction. Upon completion of the federated learning task, the coordinator agent sends a task completion notification to each city agent. Each agent saves the final model, deletes intermediate training data, and releases computing resources. They are then deregistered from the federated bus, ending their lifecycle.

[0417] Technical effects:

[0418] This embodiment enables intelligent traffic collaboration across cities, training a high-quality global model while strictly protecting data sovereignty and privacy. The original traffic flow data from each city never leaves its local area, meeting data security and privacy regulations and eliminating legal and political obstacles to data sharing.

[0419] Federated learning technology achieves "data usable but not visible," allowing cities to share model intelligence rather than raw data, resulting in a win-win situation. The global model outperforms any single-city model, and all cities benefit from collaboration, with improved prediction accuracy, enhanced traffic management efficiency, reduced congestion, and increased public satisfaction.

[0420] The contract layer ensures fairness and transparency, revenue distribution is based on objective contribution assessments, and blockchain audit logs provide verifiable evidence, avoiding trust issues. The system's successful demonstration provides a reference for federated collaboration in other fields, such as multi-hospital joint training of disease diagnostic models in healthcare and multi-bank collaborative fraud prevention in financial risk control.

[0421] Example 3: Emergency vehicle priority passage coordination

[0422] Scenario Description: A traffic accident has occurred in a city, requiring an ambulance to rush to the scene. To shorten the rescue time, it is necessary to coordinate the traffic light systems at multiple intersections along the route to create a green channel for the ambulance; at the same time, ordinary vehicles along the route should be notified to give way, optimizing the ambulance's route.

[0423] System Configuration:

[0424] On the vehicle side: Ambulances are equipped with a software-defined vehicle platform, have an emergency vehicle priority passage agent template installed, and have vehicle-to-everything (V2X) communication capabilities, enabling them to communicate with traffic management cloud and other vehicles.

[0425] Cloud nodes: City traffic management cloud, managing 500 traffic light intersections citywide, deploying signal control systems and route optimization systems. Medical emergency center cloud, managing ambulance dispatch, tracking hospital distribution and real-time ambulance locations. Public information dissemination cloud, capable of broadcasting messages to vehicles within the area.

[0426] Coordinator: Emergency Collaboration Coordinator, deployed in the city's emergency command center, is responsible for the workflow orchestration and coordination of multi-cloud systems.

[0427] Federation Bus: A city-level trusted data space that supports emergency priority communication, ensuring low latency and high reliability.

[0428] Implementation steps:

[0429] Step 1: Emergency Trigger. An accident occurs, and a citizen calls for emergency medical assistance. The emergency center receives the call and determines the accident location as 39.85°N, 116.35°E. The injured person's condition is serious and requires immediate treatment. The emergency center selects the nearest ambulance, currently located at the hospital at 39.90°N, 116.40°E, approximately 8 kilometers from the accident scene, with an estimated travel time of 20 minutes.

[0430] Step 2: The coordinator initiates the emergency response process. The emergency center cloud sends a priority passage request to the emergency coordinator, including the ambulance identifier, current location, destination, and highest emergency level. The coordinator identifies this as a priority passage scenario for emergency vehicles, assigning it the highest priority, and immediately initiates the coordination process.

[0431] Step 3: Multiple Agent Instantiation. The coordinator instantiates the central coordinating agent, responsible for global task decomposition and monitoring.

[0432] The ambulance client instantiates an emergency vehicle agent. The semantic layer defines it as an emergency vehicle priority passage service. The data layer provides real-time location, speed, and expected route. The contract layer uses the data for emergency rescue purposes. It allows traffic management cloud and public information cloud to access location data. The ambulance number is anonymized for privacy protection. The service level agreement requires real-time update latency of less than one second. The pricing is free because it is a public emergency service.

[0433] The traffic management cloud instantiates a signal control agent to adjust traffic lights along the route. The public information cloud instantiates a broadcast agent to notify ordinary vehicles to give way. Each agent registers with the federated bus and is allocated communication resources based on emergency priority.

[0434] Step 4: Route Planning and Task Decomposition. The coordinator invokes the route optimization algorithm, inputting the ambulance's current location and the accident scene location, to calculate the optimal route. Considering real-time traffic conditions, it avoids congested sections, selecting the fastest route through ten traffic light intersections, with an estimated travel time of twelve minutes.

[0435] The coordinator breaks down the tasks: Task 1: Priority control of traffic lights, handled by the traffic management cloud, adjusting traffic lights at ten intersections to ensure a green light when the ambulance arrives; Task 2: Notification of ordinary vehicles, handled by the public information cloud, broadcasting avoidance messages to vehicles within a 500-meter radius along the ambulance's route; Task 3: Dynamic route optimization, with the coordinator continuously monitoring and replanning in real time if road conditions change or the ambulance deviates from the route.

[0436] Step 5: Rapid Contract Negotiation. Due to the time constraints of the emergency scenario, a simplified negotiation process is adopted. The coordinator proposes a standard emergency contract, which all participants have pre-signed in an emergency collaboration framework agreement containing general terms. Each agent automatically accepts the contract, signs it, and submits it to the blockchain; the entire process is completed within two seconds.

[0437] Step 6: Collaborative execution.

[0438] Traffic light control: The traffic management cloud agent receives the real-time location and route of ambulances and calculates their arrival time at each intersection. Thirty seconds in advance, the traffic lights are adjusted: the red light time is shortened for the direction currently red, and the green light time is extended or turns green earlier for the direction of the ambulance. After the ambulance passes, the traffic lights return to normal control mode, minimizing impact on traffic in other directions. This process is implemented sequentially at ten intersections, ensuring unimpeded passage for ambulances with green lights throughout their journey.

[0439] Notification for regular vehicles: The public information cloud agent broadcasts a message to vehicles along the route stating that an ambulance is on an emergency mission and is expected to pass your location in one minute. Drivers are advised to give way and to pull over or change lanes. The message is broadcast via the vehicle network, and in-vehicle navigation systems display a warning, prompting drivers to give way.

[0440] Dynamic route optimization: The coordinator continuously receives ambulance location updates and monitors traffic changes. If a new accident causing congestion is detected at the intersection ahead, the route is replanned in real time, using a suboptimal alternative route. Traffic light control and vehicle notification policies are updated to ensure the ambulance always takes the fastest route.

[0441] Step 7: Mission Complete. The ambulance arrived at the accident scene within ten minutes, saving ten minutes compared to normal time. The injured received timely treatment, and their lives were saved. The ambulance agent sent a mission complete message, the coordinator notified all cloud agents to cease coordination, traffic lights returned to normal, and the broadcast of avoidance messages stopped.

[0442] Step 8: Data Archiving and Analysis. The coordinator archives a complete record of the emergency event, including trigger time, ambulance trajectory, traffic light adjustment logs, route replanning records, and task completion time. Blockchain audit logs provide tamper-proof evidence for post-event analysis and liability determination.

[0443] Analysis of this emergency response revealed that rescue time was reduced by 50%, there were no secondary accidents, and public cooperation was high. Lessons learned will be summarized to optimize emergency coordination strategies and update proxy templates and coordination algorithms.

[0444] Technical effects:

[0445] This embodiment demonstrates the efficient collaboration of multi-cloud systems in emergency scenarios. Through an inter-cloud collaboration coordinator, heterogeneous systems such as traffic management cloud, emergency center cloud, and public information cloud seamlessly cooperate to form a unified emergency response system.

[0446] An emergency prioritization mechanism ensures that critical tasks are handled first, and the federated bus allocates dedicated resources for emergency communications, guaranteeing low latency and high reliability. A simplified contract negotiation process adapts to urgent time requirements, and pre-signed framework agreements accelerate response times.

[0447] Real-time dynamic collaboration capabilities support route replanning, real-time traffic light adjustments, and dynamic vehicle notifications, adapting to rapidly changing on-site conditions. The flexibility of digital twin agents allows for rapid instantiation and destruction, resulting in high resource utilization efficiency.

[0448] The system offers significant social benefits, shortening emergency response time, increasing rescue success rates, and protecting public life and property. Its successful application provides a technological model for smart city emergency management and can be extended to other emergency response fields such as fire fighting, policing, and disaster prevention.

[0449] For details on the specific implementation of each module in a comprehensive collaborative management method for a transportation system, please refer to the above description of the limitations of a comprehensive collaborative management system for a transportation system, which will not be repeated here.

[0450] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

Claims

1. A comprehensive collaborative management system for a transportation system, characterized in that, It includes a programmable terminal deployed in a vehicle and multiple cloud service nodes deployed in the cloud, wherein the programmable terminal communicates with the multiple cloud service nodes through a federated collaborative communication bus; The programmable terminal includes a software-defined vehicle platform, a context-aware module, and a digital twin agent manager. The software-defined vehicle platform is used to remotely deploy software using over-the-air (OTA) download technology and to reconstruct vehicle behavior. The context-aware module is used to continuously collect, analyze and integrate multi-source vehicle data, and based on the multi-source vehicle data, infer the current scenario type in real time using a rule engine or machine learning model, assess the priority of collaborative needs, and predict the upcoming business scenarios. The digital twin agent manager is used to trigger the agent instantiation process based on the business scenario predicted by the context-aware module, and to maintain the complete digital twin model of the vehicle. The cloud service node includes a core service logic and data asset module, a server-side digital twin agent generation module, a federated computing engine, and an access control and auditing module. The core service logic and data asset module is used for the business logic of the cloud system, manages the proprietary database and knowledge base, and provides computing and storage resources. The server-side digital twin agent generation module is used to dynamically instantiate digital twin agents according to internal business needs or external collaboration requests. The federated computing engine is used for collaboration between remote endpoints and integrates a secure multi-party computing library to support computation in an encrypted state. The access control and auditing module is used to implement fine-grained permission management based on role-based access control or attribute-based access control.

2. The traffic system full-domain collaborative management system according to claim 1, characterized in that, It also includes a cloud-based coordination coordinator, used for global coordination strategy formulation, workflow orchestration, proxy topology planning, consistency assurance, and fault recovery in complex cross-domain business processes deployed on three or more nodes.

3. The traffic system full-domain collaborative management system according to claim 1, characterized in that, The software-defined vehicle platform is used to remotely deploy software, covering the application layer, service layer, middleware layer, and firmware layer, and supports incremental updates and rollback mechanisms, as well as digital signature verification and multi-level secure boot.

4. The traffic system full-domain collaborative management system according to claim 1, characterized in that, The context-aware module implements local differential privacy processing, location obfuscation, and data minimization principles during the data acquisition phase.

5. The traffic system full-domain collaborative management system according to claim 1, characterized in that, When the digital twin agent manager maintains the complete digital twin model of the vehicle, it includes static features, dynamic states, capability descriptions, and service subscriptions.

6. The traffic system full-domain collaborative management system according to claim 1, characterized in that, When the digital twin agent manager triggers the agent instantiation process, it extracts the minimum necessary data subset from the full model, selects a predefined agent template according to the business scenario type, and configures a three-layer structure to instantiate a temporary agent.

7. The traffic system full-domain collaborative management system according to claim 1, characterized in that, The federated computing engine supports horizontal federated learning, vertical federated learning, and federated transfer learning paradigms, and integrates a secure multi-party computation library, homomorphic encryption, and secure aggregation technology.

8. The traffic system full-domain collaborative management system according to claim 1, characterized in that, Both the digital twin agent manager and the server-side digital twin agent generation module include a semantic layer, a data layer, and a contract layer. The semantic layer provides machine-readable and human-understandable data descriptions based on domain ontology and metadata models. The data layer is used to store or link actual data instances that need to be exchanged and provides data access interfaces. The contract layer is used to formally define key terms and automate their execution, and ensures consensus, immutability, and auditability of the contract through blockchain.

9. The traffic system full-domain collaborative management system according to claim 1, characterized in that, The federated collaborative communication bus is used to provide identity authentication and trust management, agent discovery and registration services, secure connection establishment, message routing and transmission, data transmission optimization, auditing and traceability functions based on trusted data space technology, and supports three communication modes: vehicle-to-cloud, cloud-to-vehicle, and cloud-to-cloud.

10. A method for comprehensive collaborative management of a transportation system, characterized in that, The method is applied to the traffic system full-domain collaborative management system according to any one of claims 1-9, comprising: Step 1: The software-defined vehicle platform receives remotely deployed software rules or agent templates in advance, and the context-aware module or cloud service node continuously monitors various triggering conditions; Step 2: The context awareness module identifies specific business scenarios requiring cross-system collaboration based on multi-source vehicle data and assesses whether to trigger the collaboration process; Step 3: When the collaborative process is triggered, the initiator creates a digital twin agent manager and configures the digital twin agent manager to define the interaction ontology in the semantic layer, populate the actual content in the data layer, and define the usage terms in the contract layer. After allocating resources, it registers with the federated collaborative communication bus. Step 4: The initiator discovers a respondent that can meet the requirements through broadcasting, querying, or subscribing; after discovering a respondent, both parties conduct a contract compatibility check, resolve conflicts through an automatic negotiation algorithm, sign the contract after reaching an agreement, and submit it to the blockchain for notarization. Step 5: The initiator and the responder establish a TLS two-way authenticated encrypted channel and activate the use of smart contract monitoring data. Under the secure channel and contract monitoring, actual data exchange and business processing are carried out. The smart contract continuously monitors and handles default behavior. Step 6: After the task is completed, send a completion confirmation and perform data cleanup and destroy temporary agents to release resources according to the contract terms; Step 7: Collect performance data during the collaboration process, and evaluate and optimize it.

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