System for AI-supported analysis, forecasting and verification of vehicle data with blockchain-based data integrity

Abstract

System for AI-supported analysis, forecasting and verification of vehicle data with blockchain-based data integrity, characterized in that one or more artificial intelligence units collect, analyze and process vehicle, usage and operational data to predict maintenance needs, safety risks and optimization potentials, whereby all analysis results are stored in a blockchain structure and cryptographically verified.

Description

[0001] The present invention relates to a complete technical system for the artificially intelligent analysis, prediction, and verification of vehicle data using cryptographic methods, in particular so-called hash verification methods and a data architecture based on chain logic ("blockchain technology"). The aim of the invention is to create a consistently verifiable, self-learning, and tamper-resistant infrastructure for all data-relevant processes in the vehicle and mobility environment.

[0002] A technical problem arises when modern vehicles increasingly generate complex data streams from sensors, control units, communication modules, and external services. This data determines a vehicle's safety, efficiency, and environmental impact. However, existing systems store and process this data in centralized databases whose integrity and authenticity cannot be technically verified. Manipulation, subsequent modification, or misinterpretation of the underlying data cannot be ruled out with mathematical certainty. Furthermore, there is no standardized mechanism to ensure that all involved parties—vehicle, workshop, manufacturer, insurer, or authority—access the same verified data.The object of the invention is therefore to create a standardized, interoperable system that automatically records all vehicle-relevant data, evaluates it on the basis of learning-capable mathematical models and proves its authenticity through cryptographic methods.

[0003] The fundamental principle of the invention is based on the combination of artificial intelligence, machine learning, and a blockchain-based data verification layer. The system forms a closed technical control loop in which data from the vehicle is acquired, analyzed, evaluated, and permanently stored. Before further processing, each piece of information is assigned a mathematically derived test value, a so-called "hash value," which serves as a unique digital fingerprint of the original data. Even minor changes to the input result in a completely different test value, thus enabling the immediate detection of manipulation or data loss.

[0004] The calculations, forecasts, and assessments generated by the artificial intelligence are also assigned a hash value and stored with a timestamp in a chained data structure (blockchain). In the context of this invention, a blockchain is understood to be a chronologically structured data storage system in which each entry ("block") contains the checksum of the previous block. This chaining prevents subsequent changes from going unnoticed.

[0005] The artificial intelligence architecture used in the invention consists of multi-layered neural networks, i.e., mathematical models with several processing levels. Each level receives input signals, weights them, and forwards them to the next level. By repeatedly adjusting the weighting factors, the system learns to recognize relationships between input and output data. This process is called stepwise feedback of the weights (also known as "error feedback"). The goal of the learning process is to minimize the deviation between the predicted and the actual result.

[0006] These networks are trained using real and simulated vehicle data consisting of sensor readings, system states, driving profiles, and maintenance entries. All training data is normalized and weighted according to relevance in a preprocessing module. After each completed training cycle, the resulting model is cryptographically verified: The input data, model parameters, and generated results are all assigned a hash value, which is stored in the blockchain. This allows for the verification of the data basis for any given model at any time.

[0007] Data flow and verification logic: The system features multi-stage data processing. First, the sensors and control units continuously acquire measured values. These are checked in the preprocessing module, tagged with metadata, and passed to the analysis unit. Before each storage or transfer, a hash value is generated, which is stored in the blockchain along with a timestamp. The analysis unit compares current measurement data with historical patterns, detects deviations, generates forecasts about future events, and assesses data quality. Every calculation, every intermediate step, and every result is again tagged with a hash value, creating a continuous, mathematically closed verification chain.

[0008] The generated forecasts, status reports, and recommendations for action are digitally signed and stored decentrally. Each signature is uniquely linked to the blockchain and can be verified by authorized parties. This creates a technical trust system in which all information about a vehicle is mathematically verifiable and tamper-proof.

[0009] Security and integrity are ensured by the system's use of additional cryptographic mechanisms beyond hash verification, including asymmetric encryption and digital signatures. Each participant, such as a vehicle, a workshop, or a testing institution, is assigned its own unique digital key. This allows the origin of a data record to be clearly determined and prevents any unauthorized modification. This architecture creates a decentralized trust space where integrity is guaranteed not by a single institution, but by the mathematical structure of the system itself.

[0010] Technical benefits: By combining artificial intelligence, mathematical modeling, and cryptographic evidence preservation, a new level of data security is created in the automotive sector. The system independently detects technical anomalies, documents them immutably, and enables predictive maintenance. All vehicle data is processed in a manner that is both legally verifiable and technically traceable. This results in a completely new level of transparency, traceability, and trust throughout the entire vehicle lifecycle.

[0011] This describes the basic technical framework of the invention. Based on this, the individual claims follow, which explain the specific functional modules and technical features of the system in detail.

[0012] The invention relates to a complete technical system for the artificial intelligence-based analysis, prediction, and verification of vehicle data with blockchain-based data integrity, as described in claim 1. The starting point is the increasing complexity of digital vehicle systems and the need to manage safety-relevant, economic, and operational data reliably and in a tamper-proof manner. The invention provides a unified method in which all data generated by the vehicle are continuously recorded, evaluated, mathematically verified, and stored in a tamper-proof structure. As described in claim 1, the system consists of a central AI analysis instance, a data verification logic, a blockchain storage layer, and a communication and application architecture that networks all vehicle components and external partners.

[0013] According to claim 2, all sensor data, diagnostic data, and communication events of the vehicle are acquired via standardized interfaces. This input architecture ensures cross-manufacturer interoperability of the data. The data is pre-processed, filtered, and forwarded in real time to the AI ​​analysis instance via encrypted protocols. According to claim 3, the system analyzes this input data based on historical and current values, generates forecasts regarding wear, maintenance requirements, or safety risks, and derives recommendations for action. Each forecast is assigned a hash value and stored in the blockchain, thus creating tamper-proof proof of all calculation results.

[0014] As described in claim 4, the artificial intelligence of this invention is self-learning. It uses neural networks and machine learning to recognize new patterns from the archived data. The system analyzes deviations between predicted and actual behavior, adjusts internal parameters, and thus continuously improves the model quality. Claim 5 stipulates that all calculations are verified by cryptographic hashing methods. Every data change generates a new checksum, which is documented in the blockchain, thereby preventing subsequent manipulation.

[0015] According to claim 6, the system calculates a dynamic trust score, the so-called TrustScore, which assesses the vehicle's condition, reliability, and usage history. This score forms the technical basis for insurance, leasing, or valuation processes. Claim 7 concerns continuous safety monitoring, in which the AI ​​continuously examines sensor values ​​and communication streams for safety-relevant deviations and automatically documents any detected anomalies. According to claim 8, all internal system processes are secured by hash verification. Both data processing and model updates are authenticated by cryptographic signatures, resulting in mathematically verifiable integrity.

[0016] As described in claim 9, the system detects anomalies based on defined thresholds and machine learning-based pattern recognition. Each identified deviation is stored with a timestamp and test value to enable subsequent evaluations. According to claim 10, an aggregation unit is used that converts data from various sources into a uniform format. This normalization creates the prerequisite for consistent, comparable analyses.

[0017] According to protection claim 11, data protection is ensured through pseudonymization and encryption. Personal data is stored separately and can only be re-identified using a special authorization key. Protection claim 12 describes the communication interface through which authorized partners, such as workshops, insurance companies, or manufacturers, can access aggregated analysis results without disclosing the actual raw data.

[0018] This defines the foundation of the system according to the invention: an AI-supported vehicle data architecture that mathematically secures and documents every input, calculation and output.

[0019] As described in claim 13, the system provides a visual output that displays analysis and forecast results in real time. All results generated by the artificial intelligence are presented as color-coded status indicators, charts, and trend lines. This visualization serves not only user-friendliness but also technical traceability, as each piece of displayed information is linked to a hash value and stored in the blockchain. In this way, every graphical or numerical output can be mathematically verified at any time. According to claim 14, the invention includes a learning and evaluation module that continuously analyzes the quality of the training data used. Only data sources whose confidence index exceeds a predefined threshold are included in the training process.This self-regulating data quality assurance ensures the stability of the models and prevents over-adjustments or misinterpretations.

[0020] According to claim 15, all intermediate results, model versions, and parameter changes are stored in a decentralized blockchain. Each model update generates a new block with a hash value and timestamp. This complete logging makes all development and learning steps traceable and ensures that the origin of each model can be uniquely verified. As described in claim 16, the system includes a data protection and access protocol that records every interaction with the data. Every write, read, or verification operation is accompanied by a cryptographic checksum and archived in an audit-proof manner.

[0021] According to claim 17, a communication architecture connects all units of the system—the vehicle, the cloud analytics, external partner APIs, and the blockchain infrastructure—via encrypted data channels. This architecture is based on modular protocols that enable the exchange of new data sources without compromising the integrity of the overall system. Claim 18 describes the use of an optimization unit that evaluates all active AI models at regular intervals. It checks performance indicators such as precision, recall, and error rates and makes automatic parameter adjustments to continuously improve the efficiency of the models.

[0022] As described in Claim 19, the usage analysis performs an evaluation of individual driving behavior. Driving style, environmental conditions, and mechanical stresses are correlated by artificial intelligence to generate predictive maintenance recommendations. This information flows directly into insurance and warranty calculations, thus increasing transparency for all parties involved. Claim 20 summarizes the combined effect of the previous modules and describes a self-learning, fully integrated network that ensures data integrity, functional monitoring, and trustworthiness assessment without human intervention.

[0023] As described in claim 21, the system extends its application to the field of autonomous driving. By fusing cameras, radar, lidar, and ultrasonic sensors, the vehicle's surroundings are captured three-dimensionally. The AI ​​interprets this sensor data in real time and generates driving decisions based on it. Safety-critical conditions are automatically detected, and the results are stored with blockchain verification. According to claim 22, this technology is further enhanced by an augmented reality interface that projects relevant driving and safety information directly into the driver's field of vision, thereby reducing reaction times and increasing road safety.

[0024] According to claim 23, the invention includes driver state and emotion recognition. For this purpose, biometric sensors, cameras, and heart rate measurements are used to identify fatigue, stress, or distraction. The AI ​​reacts situationally by issuing warnings or activating driving assistance systems. According to claim 24, an AI-supported voice and gesture assistant is used in the vehicle, which learns the user's preferences and personalizes the control of comfort and safety functions.

[0025] As described in claim 25, the system includes a biometric access module that identifies authorized users based on individual characteristics such as voice, fingerprint, or facial recognition. This prevents unauthorized use of the vehicle, and all access events are immutably stored in the blockchain.

[0026] These enhancements transform the technical architecture of the invention into a complete, adaptive vehicle data infrastructure that not only verifies data but also actively increases safety, comfort, and efficiency.

[0027] As described in claim 26, the invention integrates an AI-supported energy management system that dynamically analyzes charging times, electricity prices, and grid loads. The artificial intelligence takes into account the user's individual driving schedule, the battery status, and external grid signals to calculate the optimal charging time. All charging and discharging processes are authenticated via hash-based checksums and documented in the blockchain, making energy flows transparent and verifiable. According to claim 27, the system can interact bidirectionally with the power grid. Vehicles feed excess energy back into the grid when needed; the artificial intelligence controls these processes based on grid frequency, energy price, and vehicle priority, thus ensuring both grid stability and energy efficiency.

[0028] According to claim 28, an enhanced battery management system monitors cell temperature, voltage, and charging cycles. The AI ​​detects anomalies in cell behavior and automatically adjusts charging profiles to extend battery life. As described in claim 29, the system utilizes swarm intelligence by having multiple vehicles anonymously exchange their status and traffic data. The resulting data density enables the artificial intelligence to optimize traffic flow, reduce energy consumption, and identify safety-relevant patterns at an early stage.

[0029] According to claim 30, the system is capable of providing software and model updates wirelessly ("over the air"). Each update is cryptographically signed and blockchain-verified, thus preventing unauthorized software modifications. In this way, the vehicle's intelligence remains up-to-date at all times without the need for physical intervention.

[0030] As described in Claim 31, the concept extends to a community data network that collects anonymized information about road conditions, construction sites, and hazards. This data is analyzed by AI and weighted according to its reliability before being stored as validated information blocks and made accessible to other vehicles. Claim 32 relates to cybersecurity monitoring. An intrusion detection system detects attacks on control units and communication interfaces through pattern recognition, initiates countermeasures, and logs every security-relevant event in the blockchain.

[0031] According to claim 33, the invention monitors the integrity of the vehicle software. All control units have digital signatures that are verified during the startup process. If checksums and signatures do not match, the system activates a security mode that isolates the affected components. As described in claim 34, vehicle history data—such as mileage, maintenance records, or accident data—is provided with blockchain signatures to ensure its authenticity. Each change generates a new, traceable block, thus preventing forgery.

[0032] According to claim 35, in the event of detected manipulation or a safety-critical error, a quarantine mode is activated, which deactivates the affected functions and puts the vehicle into a safe state. The activation of this mode is also documented in a tamper-proof manner.

[0033] As described in claim 36, the artificial intelligence of the invention is also integrated into manufacturing processes. Production facilities, robotic systems, and quality inspections are controlled by adaptive algorithms that detect and correct deviations in real time. Claim 37 complements this with visual quality control: High-resolution cameras capture surfaces and components; neural networks evaluate the images, detect irregularities, and archive the results immutably.

[0034] According to claim 38, a predictive maintenance and monitoring system is used for production facilities. It analyzes vibrations, temperatures, and operating times, predicts failures, and automatically schedules maintenance intervals. According to claim 39, AI optimizes global supply chains and warehousing processes by calculating transport times, material availability, and production capacity utilization. Integrating this data into the blockchain creates a transparent and verifiable supply and production network.

[0035] As outlined in claim 40, the system verifies the authenticity of vehicle parts and components using digital certificates. Spare parts are marked with unique verification codes, the authenticity of which is verified via the blockchain. This reliably prevents the use of counterfeit parts, and the entire maintenance chain remains traceable.

[0036] Claims 26 to 40 transform the invention into an integrated platform that combines energy management, production monitoring, cybersecurity and data authenticity in a unified AI-supported framework.

[0037] As described in claim 41, the invention comprises an AI-based accident and emergency management system that reacts automatically in the event of an incident. Upon impact, the artificial intelligence analyzes the force, direction, and speed of the impact, assesses the deformation zones, and calculates the potential risk of injury to the occupants. The results are transmitted to emergency services in real time, while the system simultaneously deactivates safety-relevant vehicle functions. All event data is assigned hash values ​​and stored immutably in the blockchain, resulting in objective and verifiable accident documentation.

[0038] According to claim 42, the system is extended to include a virtual service assistant that plans maintenance appointments based on diagnosed vehicle conditions, makes recommendations, and automates service processes. The AI ​​recognizes patterns in maintenance behavior and generates suggestions for spare parts, repair times, and cost estimates. These processes are fully digital and continuously verified.

[0039] As described in claim 43, the system enables remote diagnostics, allowing authorized workshops to access relevant diagnostic data via secure API connections. Error codes are automatically analyzed, evaluated, and interpreted by the AI. This allows problems to be identified even before a workshop visit. Claim 44 relates to dynamic quote generation. The system combines the results of the remote diagnostics with current spare parts prices, workshop capacities, and repair times. From this, it generates an individual, data-driven repair quote, which is digitally approved and documented.

[0040] According to claim 45, every maintenance procedure, intervention, and analysis result is recorded in a digital vehicle file. This file constitutes the complete, blockchain-verified history of a vehicle and can serve as proof for authorities, insurance companies, or buyers. According to claims 46 and 47, the invention extends this logic to fleet management and real-time vehicle monitoring. The system detects anomalies, failures, and safety risks within an entire vehicle fleet, automatically evaluates them, and derives recommendations for action.

[0041] As described in Claim 48, leasing, rental, and sales processes are fully digitally documented. Each transaction receives a unique signature, making the condition of a vehicle at the time of handover mathematically verifiable. According to Claim 49, all software updates, system patches, and security certificates are digitally signed. These signatures ensure that only authorized software components may be executed in the vehicle. Modifications to security-relevant files are technically impossible.

[0042] According to claim 50, the invention includes a comprehensive compliance framework. This framework continuously monitors all data flows, interactions, AI models, and blockchain entries and validates their conformity with legal, ethical, and security standards. Deviations are automatically detected, logged, and forwarded to authorized control bodies. This ensures complete legal certainty and technical integrity in operation.

[0043] The system's functionality is based on a continuous cycle of acquisition, processing, verification, and optimization. All relevant vehicle data is collected in real time by sensors, control units, and communication modules and transmitted to the AI ​​via secure channels. The AI ​​processes this information, recognizes patterns, generates forecasts, and verifies their consistency with previous states. Every result, every calculation, and every change generates a new hash value, which is stored in the blockchain. In this way, an immutable, chronological data chain is created that documents the entire lifecycle of a vehicle.

[0044] In practical terms, this means that all processes – from vehicle production and operation to maintenance and resale – are tamper-proof and traceable. Artificial intelligence detects irregularities before they lead to malfunctions, ensuring that maintenance is preventive rather than reactive. Workshops can perform remote diagnostics, fleet operators can monitor the condition of their vehicles in real time, and insurance companies and authorities gain access to tamper-proof records of vehicle conditions and events.

[0045] Blockchain technology serves not only as storage but also as a technical layer of trust between all participants. Each data block contains a mathematically calculated hash value that confirms the authenticity of the information. By linking these blocks, a closed, tamper-proof information network is created, forming the basis for digital vehicle identities.

[0046] The interplay of artificial intelligence, machine learning, and blockchain technology results in an autonomous, adaptive, and verifiable vehicle ecosystem. The system is capable of independently detecting errors, improving forecasts, documenting processes, and demonstrating integrity without requiring human intervention.

[0047] In summary, the invention provides a holistic technical concept for securing, automating, and documenting all vehicle-related processes. It combines mathematically verifiable data backup, adaptive analysis mechanisms, and legally compliant traceability in a single system. This lays the foundation for a new generation of digital mobility, in which transparency, security, and trust are technically guaranteed. The system according to the invention enables the acquisition, analysis, and storage of vehicle data with unprecedented quality and integrity, thus forming a key technological basis for the automotive industry of the future.

Claims

[1] System for AI-supported analysis, forecasting and verification of vehicle data with blockchain-based data integrity, characterized by , that one or more artificial intelligence units collect, analyze and process vehicle, usage and operational data to predict maintenance needs, safety risks and optimization potential, with all analysis results being stored in a blockchain structure and cryptographically verified. [2] System according to claim 1, characterized by that the AI ​​units access a distributed data infrastructure which receives sensor data, diagnostic data, driving behavior data and operating parameters via standardized interfaces, structures and processes them, and feeds them into real-time analysis. [3] System according to any of the preceding claims, characterized bythat a forecasting mechanism is integrated which makes precise predictions about maintenance cycles, vehicle conditions and wear parts based on current and historical data sets, which are immutably documented in the blockchain. [4] System according to any of the preceding claims, characterized by that the artificial intelligence is trained through continuous machine learning to recognize data patterns and permanently improve forecast accuracy. [5] System according to any of the preceding claims, characterized by , that a verification unit compares all incoming data sources against blockchain entries via cryptographic checksums to ensure data integrity and authenticity. [6] System according to any of the preceding claims, characterized by, that a TrustScore is generated which assesses the technical condition, reliability, maintenance history and risk of a vehicle and is updated in real time. [7] System according to any of the preceding claims, characterized by that a security monitoring unit is integrated which detects critical conditions, anomalies or malfunctions, generates warning messages and stores the events as verified data records in the blockchain. [8] System according to any of the preceding claims, characterized by that all calculations, testing processes and AI evaluations are secured using hash-based verification methods to prevent manipulation and subsequent changes. [9] System according to any of the preceding claims, characterized by that the AI ​​automatically detects anomalies in driving, sensor or diagnostic data and stores them as verified events with timestamps and context information in the blockchain. [10] System according to any of the preceding claims, characterized by , that an aggregation unit is implemented which unifies, normalizes and transforms data from different sources into structured datasets to create consistent foundations for AI-based evaluations and forecasts. [11] System according to any of the preceding claims, characterized by that personal data is protected before storage by anonymization or pseudonymization in order to comply with data protection regulations and at the same time ensure the traceability of technical processes. [12] System according to any of the preceding claims, characterized by that an API interface exists through which authorized third parties, such as workshops, insurance companies or manufacturers, can retrieve analysis results without gaining access to the raw data. [13] System according to any of the preceding claims, characterized bythat analysis and forecast results are displayed visually in real time on user interfaces or mobile devices to enable an immediate assessment of the vehicle's condition. [14] System according to any of the preceding claims, characterized by that a continuous learning module is integrated, which monitors the quality, relevance and weighting of the training data and independently optimizes AI models. [15] System according to any of the preceding claims, characterized by that all results, assessments and model parameters generated by the AI ​​are stored as immutable data records in the blockchain, thus ensuring audit-proof documentation. [16] System according to any of the preceding claims, characterized by , that a data protection protocol monitors and records all data access, transfers and processing, thus creating complete transparency over every interaction. [17] System according to any of the preceding claims, characterized by that a communication architecture is implemented which enables the secure exchange of AI models and analysis results between distributed system units. [18] System according to any of the preceding claims, characterized by that an optimization unit is integrated which regularly checks, improves and adapts the AI ​​models to new data types or vehicle models. [19] System according to any of the preceding claims, characterized by , that a usage analysis is carried out, which evaluates driving behavior, vehicle load and operating parameters and uses the results to improve future AI predictions. [20] System according to any one of the preceding claims, characterized by, that the combined effect of AI analysis, forecasting, verification and blockchain documentation creates a closed, self-learning data network that enables transparent, objective and automated vehicle valuation. [21] System according to any of the preceding claims, characterized by that the vehicle is equipped with multimodal sensor fusion, which combines cameras, radar, lidar and ultrasonic sensors to enable AI-based environmental analysis and real-time driving decisions. [22] System according to any one of the preceding claims, characterized by that the AI ​​offers enhanced driver assistance, which displays information directly in the driver's field of vision via augmented reality (AR) and reacts contextually to traffic situations. [23] System according to any of the preceding claims, characterized bythat driver condition and emotion monitoring is integrated, which uses biometric sensors, facial recognition or heart rate analysis to detect the driver's physical and mental state and adjusts the vehicle behavior accordingly. [24] System according to any of the preceding claims, characterized by , that an in-car AI assistant is present, which recognizes the user's individual preferences via voice and gesture control and automatically adjusts vehicle functions. [25] System according to any of the preceding claims, characterized by that biometric access control and behavior-based authentication are integrated, which allow vehicle access and use only for authorized persons. [26] System according to any of the preceding claims, characterized bythat an intelligent charging management system (Smart Charging) is in place, which optimizes charging times, energy prices and network loads using AI and securely documents the processes in the blockchain. [27] System according to any of the preceding claims, characterized by , that Vehicle-to-Grid (V2G) functionalities are implemented, which enable bidirectional energy feedback and AI-controlled grid stabilization. [28] System according to any of the preceding claims, characterized by that the battery management system is AI-based, analyzes charging cycles, monitors cell health, and optimizes battery lifespan through predictive algorithms. [29] System according to any of the preceding claims, characterized by that the vehicle has a swarm intelligence system which exchanges information from connected vehicles to improve traffic flow, safety and energy efficiency. [30] System according to any one of the preceding claims, characterized by that over-the-air (OTA) updates are planned for AI models, which are distributed decentrally, blockchain-verified, and automatically integrated to improve system performance. [31] System according to any of the preceding claims, characterized by that a community data network is integrated, which collects anonymized information about road conditions, traffic obstacles and available parking spaces and transmits it to connected vehicles in real time. [32] System according to any of the preceding claims, characterized by , that an intrusion detection system (IDS) is integrated, which detects cyberattacks and unauthorized access to vehicle systems in real time and automatically initiates countermeasures. [33] System according to any of the preceding claims, characterized by, that a tamper protection system is implemented which checks the software integrity, firmware versions and security certificates of the control units (ECUs) and documents changes using blockchain technology. [34] System according to any of the preceding claims, characterized by that a blockchain-based data origin check is carried out, which verifies mileage readings, maintenance entries and accident data, thus ensuring a falsification-proof vehicle history. [35] System according to any of the preceding claims, characterized by , that in the event of detected security breaches, a quarantine mode is automatically activated, isolating affected components and transferring the vehicle to a safe fallback operation. [36] System according to any of the preceding claims, characterized bythat vehicle production is AI-controlled, with robot manufacturing processes being optimized through adaptive learning models and manufacturing tolerances being corrected in real time. [37] System according to any of the preceding claims, characterized by , that real-time quality control in manufacturing is carried out using computer vision algorithms to automatically detect material defects, surface deviations and assembly defects. [38] System according to any of the preceding claims, characterized by , that predictive maintenance mechanisms are applied to production facilities to detect machine failures early and to proactively plan maintenance work. [39] System according to any of the preceding claims, characterized by , that AI-supported supply chain and inventory optimization is implemented, which creates demand forecasts, automates replenishment processes and prevents bottlenecks. [40] System according to any one of the preceding claims, characterized by that parts authentication takes place in warehouse and workshop processes, in which components are checked for originality and origin using blockchain verification. [41] System according to any of the preceding claims, characterized by that an AI-supported accident and emergency management system is integrated, which automatically analyzes the extent of damage, direction of impact and probability of injury in the event of collisions and transmits relevant data to rescue services. [42] System according to any of the preceding claims, characterized by that a virtual service assistant is available that identifies maintenance needs, suggests workshop appointments, and creates AI-based repair recommendations. [43] System according to any of the preceding claims, characterized by that a remote diagnostics unit is implemented which analyzes vehicle data in real time, identifies causes of faults and generates AI-supported repair suggestions. [44] System according to any of the preceding claims, characterized by , that a dynamic repair quote generation system is provided, which automatically generates an individual price quote based on spare part prices, workshop capacities and diagnostic results. [45] System according to any one of the preceding claims, characterized by that a digital vehicle file is integrated, which stores all maintenance, repair and diagnostic data immutably and makes it accessible to authorized partners. [46] System according to any of the preceding claims, characterized by that a fleet management dashboard is integrated, which combines risk, usage and maintenance data and uses AI to generate recommendations for increasing efficiency. [47] System according to any of the preceding claims, characterized by, that real-time fleet monitoring is planned, which detects anomalies, failures and risk events and sends automated notifications to those responsible. [48] ​​System according to any of the preceding claims, characterized by that audit-proof documentation of all vehicle and usage data for leasing, residual value and resale processes is carried out in the blockchain. [49] System according to any of the preceding claims, characterized by , that security certificates and signed OTA patch logs are created to permanently document software versions, security updates and changes in a traceable manner. [50] System according to any one of the preceding claims, characterized by that an end-to-end compliance framework is integrated, which monitors and validates all AI models, data flows and blockchain entries, and ensures compliance with legal and security standards.