An automatic driving scene data sharing method, device, equipment and medium

Abstract

This application provides a method, apparatus, device, and medium for data sharing in autonomous driving scenarios, effectively addressing various limitations of existing data sharing schemes in autonomous driving scenarios. The method includes: processing multi-source heterogeneous data from autonomous driving scenarios acquired by the data layer through a base layer to obtain target multi-source data; performing multi-dimensional evaluation of the target multi-source data to obtain evaluation data; processing the target multi-source data and evaluation data through a sharing layer to dynamically determine the target transaction points of the target multi-source data in the sharing platform; the sharing platform has a built-in point incentive mechanism based on blockchain and smart contracts; determining whether the transaction points obtained by the application layer meet the transaction conditions; if so, deducting an amount equivalent to the target transaction points from the user account through the smart contract built into the sharing layer, and granting the application layer access permission to the target multi-source data, thereby achieving secure and reliable sharing of autonomous driving scenarios.

Description

Technical Field

[0001] This application relates to the field of data sharing technology, and more specifically, to a data sharing method, apparatus, device, and medium for autonomous driving scenarios. Background Technology

[0002] The development of autonomous driving technology relies on massive and diverse scenario data. Among these, low-probability, high-value long-tail scenario data, such as extreme weather and traffic accidents, are crucial for overcoming technological bottlenecks and improving the safety of autonomous driving. However, current industry data application models have many limitations, making it difficult to meet the needs of large-scale and efficient technology research and development. Currently, the mainstream data application models in the industry are mainly divided into three categories: First, enterprises build their own private data loops, with data from different enterprises forming "data silos" that cannot achieve data interoperability and reuse; second, publicly available academic datasets, whose scale, timeliness, and scenario coverage are limited, making it difficult to match the actual needs of commercial research and development; and third, rudimentary point-to-point data transactions, which suffer from problems such as unclear compliance boundaries, lack of pricing standards, and insufficient continuous incentive mechanisms, making large-scale promotion impossible.

[0003] While the industry has widely recognized the importance of data co-construction and sharing in autonomous driving scenarios, existing data models have failed to build a sustainable, scalable, secure, and reliable industry-level data element circulation ecosystem. Current technologies struggle to promote the efficient and secure circulation of high-value data within the industry ecosystem while ensuring compliance throughout the data lifecycle. Specifically: First, the phenomenon of data silos is severe. Constrained by commercial competition and compliance risks, various entities are unwilling or afraid to share core data, leading to high overall social collection costs and making it difficult for individual enterprises to accumulate sufficient long-tail scenario data. Second, the personal and important data contained within the data are strictly protected by law. Traditional sharing methods lack reliable technical safeguards and auditing mechanisms, making it difficult to prove compliance and blurring the rights, responsibilities, and benefits of participating parties. Third, the lack of scientific measurement methods makes it impossible to accurately assess data contribution, data quality, and application effectiveness, hindering the establishment of a fair and transparent market-based incentive and distribution mechanism. Fourth, inconsistent data formats, interfaces, and quality standards result in high costs for data fusion and reuse, and the lack of a collaborative platform connecting data, computing power, algorithms, and toolchains leads to low industry collaboration efficiency. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, apparatus, device and medium for data sharing in autonomous driving scenarios, so as to solve the various limitations of existing data sharing schemes in autonomous driving scenarios.

[0005] In a first aspect, embodiments of this application provide a method for sharing data in autonomous driving scenarios, applied to a data sharing system built on blockchain and privacy computing technologies. The data sharing system includes a data layer, a foundation layer, a sharing layer, and an application layer. The method includes: The multi-source heterogeneous data of the autonomous driving scenario obtained by the data layer is input to the base layer, and the multi-source heterogeneous data is processed for compliance to obtain the target multi-source data. The target multi-source data is evaluated in multiple dimensions to obtain evaluation data, and after the evaluation is completed, the target multi-source data and its corresponding evaluation data are input into the sharing layer. The target multi-source data and the evaluation data are processed through the shared layer to dynamically determine the target transaction points of the target multi-source data in the shared platform built into the shared layer; wherein, the shared platform is configured with a points incentive mechanism based on blockchain and smart contracts; In response to the application layer's request to use the target multi-source data, it is determined whether the transaction points held by the user account associated with the application layer, obtained based on the points incentive mechanism, meet the transaction conditions corresponding to the target transaction points. If the conditions are met, the smart contract built into the sharing layer will deduct an amount equivalent to the target transaction points from the user account and grant the application layer access to the target multi-source data to achieve data sharing in the autonomous driving scenario.

[0006] This application's embodiments construct a layered architecture comprising a data layer, a foundation layer, a shared layer, and an application layer, organically combining compliance processing, multi-dimensional evaluation, dynamic points-based pricing, and smart contract-based transaction settlement. Compared with existing technologies, this application has the following beneficial technical effects: First, by using compliance processing at the foundational layer and blockchain-based evidence storage technology at the sharing layer, a reliable technical guarantee and auditing mechanism are provided for the entire data lifecycle. This solves the technical problems of insufficient compliance and unclear division of rights, responsibilities and benefits in traditional sharing methods, ensuring the security and reliability of the data circulation process.

[0007] Secondly, through a multi-dimensional evaluation model and a dynamic points-based pricing mechanism, it provides a scientific means of quantifying data contribution, data quality, and application value, solving the problem of data being difficult to measure and fairly price, and establishing a technology-driven, fair, and transparent market-based incentive and distribution system.

[0008] Furthermore, the layered sharing mechanism of the sharing platform (such as the points market pool and the bilateral negotiation pool) and the automated transaction process based on smart contracts effectively reduce the cost of data integration and reuse, promote the efficient and secure circulation of high-value data, thereby breaking down "data silos" and reducing the data collection cost for the entire society.

[0009] Finally, by building a collaborative platform that connects data, computing power, algorithms, and toolchains, and by using an incentive mechanism to guide multi-party participation, the efficiency of industry collaboration has been significantly improved, providing a technological foundation for building a sustainable, scalable, secure, and reliable industry-level data circulation ecosystem.

[0010] In conjunction with the first aspect, this application provides a first possible implementation of the first aspect, wherein determining whether the transaction points held by the user account associated with the application layer, obtained based on the points incentive mechanism, meet the transaction conditions corresponding to the target transaction points includes: Identify the data type of the target multi-source data, wherein the data type includes at least the points market pool data type or the bilateral negotiation pool data type; Based on the identified data type, the corresponding method is called from among the preset multiple methods for determining transaction points; The invoked transaction point determination method is executed to determine the target transaction points corresponding to the target multi-source data. This implementation achieves differentiated circulation of high-value data and general data by dynamically adapting pricing strategies based on data type, balancing data security and circulation efficiency.

[0011] In conjunction with the first aspect, this application provides a second possible implementation of the first aspect, wherein the data type includes at least the points market pool data type; The invoked transaction score determination method is executed to determine the target transaction score corresponding to the target multi-source data, including: Identify the data provider for the target multi-source data, and retrieve the historical transaction data of the data provider from the sharing platform; Based on the historical transaction data, the evaluation data, and preset expert opinion rules, the initial transaction score of the target multi-source data is dynamically adjusted to obtain the target transaction score. This implementation method provides an automated and adjustable pricing mechanism for market pool data by combining historical behavior, objective evaluation, and expert experience.

[0012] In conjunction with the first aspect, this application provides a third possible implementation of the first aspect, wherein the data type includes at least a bilateral negotiation pool data type; Executing the invoked transaction score determination method to determine the target transaction score corresponding to the target multi-source data further includes: The sharing platform performs privacy-preserving computation technology to perform encrypted matching between the data requester's needs and the data provider's data resources, in order to identify potential users of the target multi-source data. Provide a trusted online negotiation room based on a trusted execution environment for both parties who are successfully matched to conduct multi-dimensional negotiations; Based on the negotiation results generated in the online trusted negotiation room and the guarantee mechanism of the sharing platform based on smart contracts, the target transaction points are determined. This implementation method, by introducing privacy computing and a trusted execution environment, provides a secure negotiation and transaction environment that is "usable but not visible" for the most valuable and sensitive data, solving the pain point of not daring to share core data.

[0013] In conjunction with the first aspect, this application provides a fourth possible implementation of the first aspect, wherein the target multi-source data is evaluated in multiple dimensions to obtain evaluation data, including: For the target multi-source data, quality assessment dimensions, value assessment dimensions, and contribution assessment dimensions are set respectively; different assessment dimensions correspond to different sets of assessment parameters. Based on the aforementioned evaluation parameter set, preset evaluation models corresponding to the quality evaluation dimension, the value evaluation dimension, and the contribution evaluation dimension are configured and run to perform multi-dimensional evaluation and generate evaluation data containing scores for each dimension and a comprehensive score. This implementation method constructs a comprehensive data evaluation system, providing a scientific and objective basis for subsequent points-based pricing and rights allocation.

[0014] In conjunction with the first aspect, this application provides a fifth possible implementation of the first aspect, wherein granting the application layer access permission to the target multi-source data includes: The target multi-source data is uploaded to the sharing platform, and the target multi-source data is verified from multiple dimensions to obtain verification results; If the verification result is successful, an uploading event is generated based on the verification result, and the target multi-source data is uploaded. The uploading event is also pushed to application layer users who have subscribed to the relevant data types via the event notification mechanism of the sharing platform. This implementation method ensures the uniqueness of the uploaded data, the reasonableness of pricing, and the continuity of compliance by adding multiple verification steps before uploading, and improves the efficiency of data flow through the subscription notification mechanism.

[0015] In conjunction with the first aspect, this application provides a sixth possible implementation of the first aspect, wherein, after granting the application layer access permission to the target multi-source data, the following is included: Configure corresponding points acquisition sub-mechanisms based on the different roles of users in the data flow process. The roles include data providers, data processors, and data users. In response to a user performing a preset action corresponding to their role, a corresponding points acquisition sub-mechanism is run to enable the user's account to acquire corresponding transaction points, and the points acquisition record is written to the consortium blockchain corresponding to the sharing platform for storage. This implementation method, through role-based points incentives, precisely drives the activity of each link in the "supply-addition-use" process in the data ecosystem, forming a positive cycle ecosystem.

[0016] Secondly, embodiments of this application provide an autonomous driving scenario data sharing device, applied to a data sharing system, the data sharing system comprising a data layer, a foundation layer, a sharing layer, and an application layer, the device comprising: The input module is used to input multi-source heterogeneous data of autonomous driving scenarios obtained by the data layer into the base layer, and to perform compliance processing on the multi-source heterogeneous data to obtain target multi-source data. The evaluation module is used to perform multi-dimensional evaluation on the target multi-source data, obtain evaluation data, and input the target multi-source data and its corresponding evaluation data into the shared layer after the evaluation is completed. The processing module is used to process the target multi-source data and the evaluation data through the sharing layer to dynamically determine the target transaction points of the target multi-source data in the sharing platform built into the sharing layer; wherein, the sharing platform is configured with a points incentive mechanism based on blockchain and smart contracts; The response module is used to respond to the use request for the target multi-source data initiated by the application layer, and to determine whether the transaction points held by the user account associated with the application layer and obtained based on the points incentive mechanism meet the transaction conditions corresponding to the target transaction points. The granting module is used to, if the conditions are met, deduct an amount equivalent to the target transaction points from the user account through the smart contract built into the sharing layer, and grant the application layer access permission to the target multi-source data, so as to realize data sharing in autonomous driving scenarios.

[0017] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of any of the autonomous driving scenario data sharing methods described above are performed.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of any of the autonomous driving scenario data sharing methods described above. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a data sharing method for autonomous driving scenarios provided in an embodiment of this application is shown. Figure 2 An architecture diagram of the data sharing system provided in an embodiment of this application is shown; Figure 3 A schematic diagram of the hierarchical sharing mechanism provided in an embodiment of this application is shown; Figure 4 This paper shows a structural block diagram of an autonomous driving scenario data sharing device provided in an embodiment of this application; Figure 5 A structural block diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0022] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0024] The development of autonomous driving technology relies heavily on massive amounts of scenario data, especially low-probability, high-value long-tail scenario data. Currently, the industry suffers from problems such as data silos, insufficient public datasets, and flaws in rudimentary peer-to-peer transactions. Furthermore, a secure and reliable ecosystem for the sharing of autonomous driving data has not yet been established, and issues related to compliance, metrology, and inconsistent standards persist, hindering industry development.

[0025] Based on this, embodiments of this application provide a method, apparatus, device, and medium for sharing data in autonomous driving scenarios, which are described below through embodiments.

[0026] Example 1 To facilitate understanding of this embodiment, a method for sharing autonomous driving scenario data disclosed in this application will first be described in detail. For example... Figure 1 The diagram illustrates a process flow chart for a data sharing method in an autonomous driving scenario. This application provides a data sharing method for an autonomous driving scenario, applied to a data sharing system. The data sharing system includes a data layer, a foundation layer, a sharing layer, and an application layer. The method includes: S101. Input the multi-source heterogeneous data of the autonomous driving scenario obtained by the data layer to the base layer, and perform compliance processing on the multi-source heterogeneous data to obtain the target multi-source data. S102. Perform multi-dimensional evaluation on the target multi-source data to obtain evaluation data, and input the target multi-source data and its corresponding evaluation data into the shared layer after the evaluation is completed. S103. The target multi-source data and the evaluation data are processed through the shared layer to dynamically determine the target transaction points of the target multi-source data in the shared platform built into the shared layer; wherein, the shared platform is configured with a points incentive mechanism based on blockchain and smart contracts. S104. In response to the use request for the target multi-source data initiated by the application layer, determine whether the transaction points held by the user account associated with the application layer and obtained based on the points incentive mechanism meet the transaction conditions corresponding to the target transaction points. S105. If satisfied, the user account is deducted an amount equivalent to the target transaction points through the smart contract built into the sharing layer, and the application layer is granted access to the target multi-source data to achieve data sharing in the autonomous driving scenario.

[0027] In step S101, the data sharing system described in this application is built upon blockchain technology, privacy computing, and smart contract technology. It aims to construct an industry data element market that is rule-controlled, value-measurable, and trustworthy in its circulation. It comprises a data layer, a foundation layer, a sharing layer, and an application layer. The overall architecture of the data sharing system is as follows: Figure 2 As shown, the data layer receives data from data providers in the autonomous driving scenario, enabling the aggregation of multi-source heterogeneous data. Data providers include, but are not limited to: vehicle manufacturers (OEMs), autonomous driving technology companies, research institutions and universities, component suppliers, official agencies, and traffic management departments. The aggregated data includes various types such as raw vehicle-side data, raw vehicle-road-cloud data, labeled data, simulation data, and generated data, in formats including JPG, PCD, BIN, JSON, and TXT. The data layer also requires data from industry regulatory authorities on key supply chain capacity control policies, and credit data from credit reporting agencies on supplier credit ratings, historical default records, and the health of their financial chains.

[0028] The base layer performs compliance processing on the received multi-source heterogeneous data to obtain the target multi-source data. Specifically, this includes: 1) converting various data formats into a standard format recognized by the sharing layer through a unified access gateway module based on middleware such as Apache NiFi and Kafka Connect; 2) de-identifying sensitive information using a k-anonymity algorithm based on generalization levels, and generalizing quasi-identifiers such as timestamps and geographic locations. For example, generalizing precise GPS coordinates to a specific street area, and generalizing timestamps accurate to milliseconds to minutes; 3) injecting differential privacy noise to defend against privacy attacks. Through these processes, data availability is ensured while complying with data security regulations. The data de-identification algorithm can be a k-anonymity algorithm based on generalization levels, as shown in the following formula: .

[0029] In step S102, the foundation layer of this application also incorporates a configurable data quality rule engine within its data quality monitoring and measurement system. This engine performs quality checks on the multi-source heterogeneous data after compliance processing. Specifically, the quality checks include data integrity (whether there are missing or lost frames), accuracy (whether it is truly valid), consistency (whether the multi-source data is self-consistent and whether the timestamps are synchronized), and timeliness (whether it is updated promptly). The data quality rule engine automatically evaluates and quantifies the multi-source heterogeneous data after compliance processing, and determines the data defects present based on the automated evaluation and quantification scores. It also provides data cleaning and repair tools and a quality score report, forming a data quality "health check center." This application also pre-sets evaluation dimensions corresponding to the quality checks, including quality evaluation dimensions, value evaluation dimensions, and contribution evaluation dimensions. Therefore, the data quality rule engine performs multi-dimensional evaluations of the target multi-source data to obtain evaluation data containing scores for each dimension and a comprehensive score. The foundation layer inputs the complete target multi-source data and evaluation data as an indivisible data package into the sharing layer.

[0030] This application can also visualize data assets, specifically using visualization dashboards based on Elasticsearch + Kibana or Grafana, and supporting the display of data lineage diagrams.

[0031] In a specific implementation of step S102, one embodiment involves: performing a multi-dimensional evaluation of the target multi-source data to obtain evaluation data, including: S1021. Set quality assessment dimensions, value assessment dimensions, and contribution assessment dimensions for the target multi-source data respectively; different assessment dimensions correspond to different sets of assessment parameters. S1022. Based on the evaluation parameter set, configure and run the preset evaluation models corresponding to the quality evaluation dimension, the value evaluation dimension, and the contribution evaluation dimension, respectively, to perform multi-dimensional evaluation and generate evaluation data containing scores for each dimension and a comprehensive score.

[0032] In steps S1021-S1022, this application sets quality assessment dimensions, value assessment dimensions, and contribution assessment dimensions for the target multi-source data, respectively. Examples of specific models and parameters for each assessment dimension are as follows: Quality assessment dimensions: These aim to evaluate the basic usability of the data. Assessment parameters include completeness, accuracy, consistency, and timeliness.

[0033] The integrity coefficient reflects the completeness of data records. The calculation formula is: integrity coefficient = 1 - (number of missing fields / total number of fields), where the number of missing fields refers to the number of empty or invalid fields in the data record, and the total number of fields is the total number of fields that should be included.

[0034] The accuracy coefficient reflects the degree of agreement between the data annotation and the actual situation. The calculation formula is: Accuracy coefficient = Number of correct records / Total number of records, where the number of correct records refers to the number of data records that have been verified to be correct, and the total number of records is the total number of records in the dataset.

[0035] The consistency coefficient reflects the degree of self-consistency among multi-source data. The calculation formula is: consistency coefficient = 1 - (number of inconsistent data pairs / total number of data pairs), where inconsistent data pairs refer to contradictory information provided by different data sources in the same scenario, and the total number of data pairs refers to the total number of data pairs in the dataset that can be compared for consistency.

[0036] The timeliness coefficient reflects the timeliness of data updates. The calculation formula is: Timeliness coefficient = 1 / (1 + delay days), where the delay days refer to the time difference between the data collection time and the current time. The longer the delay, the lower the timeliness coefficient.

[0037] The constructed quality assessment model is a weighted linear model: Q(D) = w1 × Completeness Coefficient + w2 × Accuracy Coefficient + w3 × Consistency Coefficient + w4 × Timeliness Coefficient, where the weights w1, w2, w3, and w4 satisfy w1 + w2 + w3 + w4 = 1. These weights can be dynamically configured according to different application scenarios. For example, for perception algorithm training, more emphasis may be placed on accuracy (higher w2), while for simulation testing, more emphasis may be placed on timeliness (higher w4). The system can initially be set with a set of default values, such as w1 = 0.3, w2 = 0.3, w3 = 0.2, and w4 = 0.2, and platform administrators are allowed to adjust them based on operational data. The specific methods for determining the weights include, but are not limited to: preset by the platform administrator based on industry expert experience, dynamic optimization based on historical transaction data using linear regression methods, or adaptive configuration according to different data application scenarios.

[0038] Value assessment dimensions: These aim to evaluate the commercial and technological scarcity of the data. Assessment parameters include scenario scarcity coefficient, application value coefficient, and timeliness coefficient.

[0039] The scarcity coefficient reflects the rarity of the scenarios covered by the data. The formula is: Scarcity Coefficient = 1 / (1 + Total Amount of Similar Data), where the total amount of similar data refers to the number of datasets of the same scenario type already existing on the shared platform. A larger total amount of similar data results in a lower scarcity coefficient, indicating less scarcity; conversely, a smaller total amount of similar data results in a higher scarcity coefficient, indicating stronger scarcity.

[0040] The application value coefficient reflects the data's fit to the algorithm model and its positive gain effect. The calculation formula is: Application Value Coefficient = (Number of Annotated Boxes / Scene Duration) × Scene Complexity Coefficient × Annotation Quality Coefficient × Scene Diversity Coefficient, where: Number of Annotated Boxes refers to the number of manually annotated target boxes in the dataset, including boxes for various targets such as vehicles, pedestrians, traffic signs, obstacles, and lane lines; Scene Duration refers to the total data acquisition time (unit: seconds), which can be converted to equivalent duration based on the acquisition frame rate for image data; Scene Complexity Coefficient is a preset weight value, determined based on the data acquisition... The scene type is determined, with a value range of [1, 5]. For example, a simple scene takes a value of 1, a moderately complex scene takes a value of 3, and a highly complex scene takes a value of 5. The annotation quality coefficient is determined based on the level of detail in the data annotation, with a value range of [1, 3]. For example, 2D bounding box annotations take a value of 1, 3D bounding box annotations take a value of 2, and semantic segmentation annotations take a value of 3. The scene diversity coefficient is determined based on the number of different scene types in the dataset, with a value range of [1, 2]. Scene types include road types, weather conditions, lighting conditions, traffic density, etc. The more diverse the scene types, the larger the coefficient value. This formula comprehensively reflects the positive impact of annotation density, scene complexity, annotation quality, and scene diversity on data value through a product model.

[0041] The timeliness coefficient reflects the degree of decay of the time difference between the data collection time and the current time. The calculation formula is: Timeliness coefficient = e^(-λ × data age), where λ is a preset decay factor (such as 0.01), and data age refers to the number of days between the data collection date and the current date. The older the data, the more exponentially the timeliness coefficient decays, reflecting the characteristic of the reduced value of old data.

[0042] The constructed value assessment model is a product model: V(D) = scarcity coefficient × application value coefficient × timeliness coefficient. This model reflects the combined impact of these factors on data value; a low value for any one of these coefficients will significantly lower the overall value score.

[0043] Contribution assessment dimensions: These dimensions aim to trace the source ownership and processing contributions of data, providing a basis for points allocation. Assessment parameters include popularity, usage, and temporal contribution and ecosystem development.

[0044] Popularity reflects the degree of attention data receives on the sharing platform. The calculation formula is: H1(D) = log(historical search count + 1), where the historical search count refers to the total number of times the data has been retrieved by users through keyword searches, category filtering, etc., since its listing. By taking the logarithm of the search count and adding 1 for smoothing, the increasing relationship of data attention is reflected while avoiding excessive inflation of popularity values ​​due to extreme values.

[0045] Popularity reflects the degree to which data is actually used on the sharing platform. The calculation formula is: H2(D) = log(historical transaction count + 1), where the historical transaction count refers to the total number of times the data has been purchased, downloaded, or otherwise obtained and used by users since it was listed. A logarithmic function is also used for smoothing, so that the popularity value increases with the number of uses, but the growth rate gradually slows down, to reflect the diminishing marginal utility of high-popularity data.

[0046] Time-series contribution reflects the cumulative value of a data provider's continuous contributions, while a decay coefficient incentivizes recent contributions. The calculation formula is as follows: Where T is the number of consecutive months of contribution, and λ is the decay coefficient (e.g., 0.1), used to reduce the weight of historical contributions and highlight the value of recent contributions. Additional rewards can be obtained for ecological construction (such as providing toolchains, participating in standard setting, introducing new nodes, etc.).

[0047] This model comprehensively considers both the short-term popularity (attention and usage) and long-term contribution (time-series contribution) of the data, and encourages recently active contributors through a decay coefficient λ. The constructed contribution evaluation model is: CV(D)=(H1(D)+H2(D))×(1+time-series contribution), where the time-series contribution acts as a multiplier factor, which can amplify the comprehensive score of data providers with high sustained contributions.

[0048] After the evaluation is completed, the system will perform a notarization and solidification operation on the evaluation data obtained: calculate the SHA-256 hash value of the data combination to generate a unique data fingerprint; use the fingerprint as a leaf node to access the Merkle tree (a binary tree data structure used for efficient verification of data integrity), calculate the hash of the parent node layer by layer, and write the final Merkle root hash into the consortium blockchain smart contract; finally, submit the target multi-source data metadata (including asset ID, on-chain notarization address, and evaluation data summary) that has been notarized on the chain to the data asset catalog of the shared layer for registration.

[0049] In step S103, this application pre-sets a sharing platform in the sharing layer. Based on this sharing platform, users with different identities can trade target multi-source data, thereby realizing data circulation. On the sharing platform, the sharing layer designs a hierarchical sharing mechanism and a points-based incentive mechanism. The hierarchical sharing mechanism is used to process data with different sensitivities and commercial value, such as... Figure 3As shown, the hierarchical sharing mechanism divides autonomous driving scenario data into three sharing levels, each with different circulation paths and rules. Based on this hierarchical sharing mechanism, the target multi-source data and the evaluation data are processed to dynamically determine the target transaction points for the target multi-source data within the sharing platform built into the sharing layer. This application uses a points incentive mechanism with "Data Tokens" (DT) as the carrier, allowing users with different identities to obtain corresponding transaction points by performing different data processing operations while obtaining the target multi-source data. Transaction points can also be obtained through RMB top-ups, with one unit of points constantly redeemable for equivalent RMB in computing resources, storage resources, or data processing services. Table 1 shows the attribute descriptions of the transaction points. Any user who has registered properly can obtain transaction points by performing data-related operations or topping up.

[0050] Table 1. Attribute Description of Transaction Points

[0051] In a specific implementation of step S103, one embodiment is as follows: determining whether the transaction points held by the user account associated with the application layer, obtained based on the points incentive mechanism, meet the transaction conditions corresponding to the target transaction points, including: S1031. Identify the data type of the target multi-source data, wherein the data type includes at least the points market pool data type or the bilateral negotiation pool data type; S1032. Based on the identified data type, call the corresponding method from the preset multiple transaction point determination methods; S1033. Execute the invoked transaction score determination method to determine the target transaction score corresponding to the target multi-source data.

[0052] In steps S1031-S1033, after receiving the target multi-source data and the corresponding evaluation data, the sharing layer of this application identifies the data type of the target multi-source data. The data type includes points market pool data, bilateral negotiation pool data, and open sharing pool data, which is determined based on the scarcity coefficient of the target multi-source data. Based on the data type, the corresponding transaction points determination method is determined. Different data types correspond to different transaction points determination methods. The called transaction points determination method is then executed to determine the target transaction points corresponding to the target multi-source data.

[0053] For data in the open sharing pool that is highly common and has low commercial sensitivity, such as anonymized routine traffic scenario data, weather and environmental data, as well as benchmark data and open-source datasets used for academic research, the open sharing pool adopts an open sharing model. Data providers upload such data to the platform's open sharing pool and receive basic points rewards according to the data points system. Any certified user of the platform can use a small number of points or obtain this data for free, thereby lowering the threshold for basic R&D in the industry, promoting technology popularization and ecosystem prosperity. The sharing platform performs standardized processing and quality monitoring to ensure data availability.

[0054] In the specific implementation of step S1033, one embodiment is as follows: the data type includes at least the points market pool data type; The invoked transaction score determination method is executed to determine the target transaction score corresponding to the target multi-source data, including: A1. Identify the data provider of the target multi-source data and retrieve the historical transaction data of the data provider from the sharing platform; A2. Based on the historical transaction data, the evaluation data, and the preset expert opinion rules, the initial transaction score of the target multi-source data is dynamically adjusted to obtain the target transaction score.

[0055] In steps A1-A2, the points market pool data of this application covers data with high commercial and training value, such as finely annotated specific scenario datasets, high-quality twin scenarios, synthetic data, and some non-sensitive extreme scenario data. The points market pool data adopts a "points pricing, market transaction" model. The data provider for the target multi-source data is determined, and historical transaction data (such as prices, transaction rates, and user reviews of previously listed data) is retrieved from the sharing platform from this data provider. Based on the historical transaction data, multi-dimensional evaluation data (such as quality and value scores), and preset expert opinion rules (such as price guidance ranges set by industry experts), the initial transaction points set by the data provider are adjusted using a points pricing dynamic adjustment model to obtain the final target transaction points. The points pricing dynamic adjustment model is as follows: ; Where Pcurrent is the current suggested price; P base Base price (anchored to resource value); D total : Current total demand integral; S total : Current total supply integral; k: Market sensitivity coefficient. This model achieves automatic adjustment of integral value with resource supply and demand within the ecosystem.

[0056] In a specific implementation of step S1033, one embodiment is as follows: the data type includes at least a bilateral negotiation pool data type; The invoked transaction score determination method is executed to determine the target transaction score corresponding to the target multi-source data, including: B1. By performing privacy computing technology through the shared platform, the needs of data requesters are matched with the data resources of data providers in encrypted form to identify potential users of the target multi-source data; B2. Provide a trusted online negotiation room based on a trusted execution environment for both parties who are successfully matched to conduct multi-dimensional negotiations; B3. Based on the negotiation results generated in the online trusted negotiation room and the guarantee mechanism of the sharing platform based on smart contracts, determine the target transaction points.

[0057] In steps B1-B3, the bilateral negotiation pool data in this application consists of high-value, sensitive data, involving extremely scarce data related to a company's core competitiveness, such as real accident data, extreme test scenarios involving trade secrets, high-precision map ground truth, and in-depth data that, although anonymized, can still be used to infer business strategies after aggregation. A customized "bilateral negotiation-platform guarantee" model is adopted, whereby data providers only provide a data catalog but do not publicly list it. Data users can publish specific high-value, sensitive data requests on the platform. After obtaining authorization from both parties, the sharing platform uses privacy computing technology to perform "ciphertext matching" between requests and data resources, or pushes request intentions to potential providers after anonymization, without exposing specific requester information. Upon successful matching, the sharing platform provides both parties with an online trusted negotiation room based on a trusted execution environment. This trusted execution environment is a CPU hardware-level secure isolation environment that ensures data is not stolen or tampered with during computation. Both parties negotiate on the scope, duration, exclusivity, and points-based pricing of the data in the online trusted negotiation room. Based on the negotiation results generated in the online trusted negotiation room, and the guarantee mechanism of the sharing platform based on smart contracts (such as a dispute mediation fund), the final target transaction points are determined, and a standardized "High-Value Data Sharing Protocol" is formed and stored on the blockchain.

[0058] In step S104, the application layer of this application has a data usage requirement for autonomous driving scenarios, and needs to complete the transaction process of target multi-source data based on the points incentive mechanism built by the sharing layer. Specifically, the data user first uses the visualization dashboard built by the sharing platform based on Elasticsearch+Kibana or Grafana to retrieve the target multi-source data that meets its own R&D needs in the data asset catalog, and views the target multi-source data, evaluation data, and target transaction points dynamically determined by the sharing layer. The data user needs to confirm whether the transaction conditions corresponding to the target transaction points of the target multi-source data are met, that is, the user's transaction points are greater than or equal to the target transaction points, and the user has completed the consortium blockchain identity authentication and signed the data use compliance agreement (the agreement terms are solidified through smart contracts, which clearly define the scope, duration, rights and obligations of data use).

[0059] In step S105, if the user meets the transaction conditions corresponding to the target transaction points, the sharing platform automatically triggers the transaction process based on the smart contract of the sharing layer, deducting the corresponding amount of transaction points from the user. Simultaneously, based on blockchain notarization technology, access permissions to the target multi-source data are unlocked, and the compliant and notarized target multi-source data and corresponding evaluation data are pushed to the application layer. After the transaction is completed, the smart contract of the sharing layer automatically records the entire transaction process, including the identities of both parties, the amount of transaction points, the scope of data access permissions, the transaction timestamp, etc., generating an immutable audit record and synchronously updating the data usage records and data lineage diagram in the data asset catalog, achieving full-chain traceability of the transaction. Furthermore, the transaction points deducted during the transaction will be distributed to relevant participants such as data providers and processors according to the contribution evaluation dimensions preset by the basic layer, further improving the closed loop of the points incentive mechanism, ultimately achieving safe, compliant, and efficient sharing of autonomous driving scenario data and promoting collaborative industrial development.

[0060] In a specific implementation of step S105, one embodiment involves granting the application layer access permission to the target multi-source data, including: S10511. Upload the target multi-source data to the shared platform and verify the target multi-source data from multiple dimensions to obtain verification results; S10512. If the verification result is successful, an uploading event is generated based on the verification result, and the target multi-source data is uploaded. The uploading event is also pushed to application layer users who have subscribed to the relevant data types through the event notification mechanism of the sharing platform.

[0061] In steps S10511-S10512, the sharing layer of this application initiates the data listing process for the target multi-source data through the sharing platform, and conducts multi-dimensional verification of the target multi-source data to ensure the compliance, uniqueness, and reasonable pricing of the listed data. Specifically: First, uniqueness verification: the sharing platform searches the data asset catalog and compares the SHA-256 data fingerprint of the target multi-source data with the fingerprint information of the data already stored in the catalog to check whether the target multi-source data already exists, avoiding duplicate listing; Second, reasonable pricing verification: combining the multi-dimensional evaluation data (quality, value, and contribution dimension scores) output by the basic layer, and referring to the shared platform's preset dynamic adjustment model for points pricing, it verifies whether the target transaction points are within a reasonable range; Third, secondary compliance verification: the compliance processing records and on-chain evidence validity of the target multi-source data are verified again to ensure that the listed target multi-source data complies with relevant data security laws and regulations and platform rules. Based on the above multi-dimensional verification process, the sharing platform generates a clear verification result. When all the above multi-dimensional verification processes are passed, the verification result is "verification passed". The sharing platform automatically generates a standardized listing event and starts a subscription notification mechanism to push the listing event to all eligible subscription data application parties. The notification content includes the retrieval path of the target multi-source data, the evaluation overview, the target transaction points and the listing validity period, making it easy to be retrieved and viewed.

[0062] In a specific implementation of step S105, another embodiment is as follows: after granting the application layer access permission to the target multi-source data, the following steps are included: S10521. Configure corresponding points acquisition sub-mechanisms according to the different roles of users in the data flow process, wherein the roles include data providers, data processors and data users; S10522. In response to the user performing a preset behavior corresponding to their role, the corresponding points acquisition sub-mechanism is run to enable the user account to acquire corresponding transaction points, and the points acquisition record is written into the consortium blockchain corresponding to the shared platform for storage.

[0063] In steps S10521-S10522, this application pre-configures different identities and corresponding point acquisition sub-mechanisms based on the flow patterns of the target multi-source data. The flow patterns of the target multi-source data include three stages: provision, processing, and application. Three roles are set up for these three stages: data provider, data processor, and data user. The point incentive mechanism of this application works synergistically with the hierarchical sharing mechanism to ensure the provision, processing, and use of the target multi-source data. The platform operator of the sharing platform, such as an industry association, is responsible for maintaining the system's operation, and its core revenue comes from transaction fee points and system maintenance rewards. The entire system, through the circulation of points, effectively incentivizes data supply, processing, and consumption, forming a sustainable collaborative ecosystem.

[0064] The system configures differentiated point-earning sub-mechanisms based on users' different roles in data flow: Data Providers: Earn points by uploading vehicle-side, roadside, and cloud-based data, as well as accident data, especially high-value scenarios such as dangerous accidents, extreme weather, and functional degradation. The reward formula is as follows: DT earned =V base *S type *Q(D)*V(D)*CV+B bonus ; Vase is the base score (yuan / TB), such as 100 DT / TB for regular road data, 300 DT / TB for extreme weather data, and 500 DT / TB for accident scenario data; Stype: scenario type coefficient (1.0~2.0); Q(D) data quality model; V(D) data value model; CV data contribution model; B bonus Additional bonuses (e.g., exclusive access +20%); Data processors earn points by providing services such as data annotation, cleaning, and simulation. Their earnings formula is: DT procss =T base ×A accuracy ×C complexity ; Where T base Basic task reward (e.g., marked box: 0.1DT / box); A accuracy : Annotation / simulation accuracy coefficient (≥95%→1.2); C complexity Task complexity coefficient (1.0~2.0); Data users: Earn points by using data and providing valuable feedback. The reward formula is: DT feedback =F base ×R rating ; Among them, F base To provide basic rewards for feedback (e.g., 10 DT / time); R rating The rating coefficients are used to provide feedback, where a positive rating is 1.5, a neutral rating is 1.0, and a negative rating is 0.5.

[0065] Subsequently, the application layer user initiates a usage request, granting the module (or smart contract) to determine whether the user's account points are greater than or equal to the target transaction points. If so, the smart contract automatically executes: deducting the corresponding amount of points from the user's account, distributing them to relevant parties such as data providers and processors according to preset rules, and granting the application layer user's account access to the target multi-source data (such as generating a temporary decryption key or acquisition token). At this point, the entire data sharing process is complete, and all operation records generate an immutable audit log on the blockchain.

[0066] The execution process of smart contracts covers the entire lifecycle of data sharing, including: the data listing stage (data verification, pricing confirmation, event triggering), the data purchase stage (balance check, points locking, order creation), the data delivery stage (permission granting, points transfer), the dispute resolution stage (arbitration ruling, points adjustment), and the transaction completion and feedback stage (reputation update, reward distribution), ensuring that the entire transaction process is traceable and tamper-proof.

[0067] Application Examples To more clearly illustrate the technical solution of this application, a simplified application example is provided below: In a certain autonomous driving data sharing platform, an autonomous driving algorithm company (as a data user) needs to obtain extreme weather scenario data for algorithm training. The company has already obtained 5,000 transaction points by participating in the platform's ecosystem, which are stored in a user account on the consortium blockchain.

[0068] Data provider A uploaded a dataset containing 1000 rain and fog weather scenarios. After compliance processing at the basic layer (anonymization and format standardization), the dataset entered the evaluation stage. The quality assessment model calculated a quality score of 0.85, and the value assessment model calculated a value score of 0.92. After comprehensive evaluation, the system dynamically priced the dataset at 3000 transaction points. The sharing platform performed uniqueness verification (confirming no duplicate uploads), pricing reasonableness verification (confirming the price is within a reasonable range), and secondary compliance verification on the dataset before generating an upload event and pushing it to users who had subscribed to extreme weather data.

[0069] The algorithm company retrieved the dataset through the shared platform's visual dashboard. After confirming that it met their R&D needs, they initiated a transaction request. The smart contract automatically verified the company's account balance (5000 points ≥ 3000 points). Upon successful verification, the transaction was executed: 3000 points were deducted from the company's account, distributed according to preset rules (the data provider receives 80%, or 2400 points, and the platform receives 20%, or 600 points, as a transaction fee), and the company's account was granted data access permissions. A temporary decryption key was generated for downloading the data.

[0070] After the transaction is completed, all transaction records (identities of both parties, point amounts, data access permissions, timestamps, etc.) are written to the consortium blockchain to generate an immutable audit log. If the algorithm company subsequently provides valuable feedback on the data quality, it can receive additional points. This completes a closed loop of data sharing.

[0071] This application example illustrates the specific operation of the technical solution in practical applications, demonstrating its characteristics of automation, security, reliability, and incentive compatibility.

[0072] Example 2 This application also provides a data sharing device for autonomous driving scenarios, such as... Figure 4 The diagram shows a block diagram of an autonomous driving scenario data sharing device. The functions implemented by this device correspond to the steps of executing an autonomous driving scenario data sharing method on a terminal device as described above. This device can be understood as a server component including a processor. The autonomous driving scenario data sharing device described in this application is applied to a data sharing system, which includes a data layer, a base layer, a sharing layer, and an application layer. The device includes: The input module 401 is used to input multi-source heterogeneous data of the autonomous driving scenario obtained by the data layer into the base layer, and to perform compliance processing on the multi-source heterogeneous data to obtain target multi-source data. Evaluation module 402 is used to perform multi-dimensional evaluation on the target multi-source data to obtain evaluation data, and input the target multi-source data and its corresponding evaluation data into the shared layer after the evaluation is completed; The processing module 403 is used to process the target multi-source data and the evaluation data through the sharing layer to dynamically determine the target transaction points of the target multi-source data in the sharing platform built into the sharing layer; wherein, the sharing platform is configured with a points incentive mechanism based on blockchain and smart contracts; The response module 404 is used to respond to the use request for the target multi-source data initiated by the application layer and determine whether the transaction points held by the user account associated with the application layer and obtained based on the points incentive mechanism meet the transaction conditions corresponding to the target transaction points. The granting module 405 is used to, if satisfied, deduct an amount equivalent to the target transaction points from the user account through the smart contract built into the sharing layer, and grant the application layer access permission to the target multi-source data, so as to realize data sharing in the autonomous driving scenario.

[0073] In one feasible implementation, the processing module includes: Identify the data type of the target multi-source data, wherein the data type includes at least the points market pool data type or the bilateral negotiation pool data type; based on the identified data type, call the corresponding method from a variety of preset transaction points determination methods; The invoked transaction score determination method is executed to determine the target transaction score corresponding to the target multi-source data.

[0074] In one feasible implementation, the processing module further includes: Identify the data provider for the target multi-source data, and retrieve the historical transaction data of the data provider from the sharing platform; Based on the historical transaction data, the evaluation data, and the preset expert opinion rules, the initial transaction score of the target multi-source data is dynamically adjusted to obtain the target transaction score.

[0075] In one feasible implementation, the processing module also includes: The sharing platform performs privacy-preserving computation technology to perform encrypted matching between the data requester's needs and the data provider's data resources, in order to identify potential users of the target multi-source data. Provide a trusted online negotiation room based on a trusted execution environment for both parties who are successfully matched to conduct multi-dimensional negotiations; The target transaction points are determined based on the negotiation results generated in the online trusted negotiation room and the guarantee mechanism of the sharing platform based on smart contracts.

[0076] In one feasible implementation, the evaluation module includes: For the target multi-source data, quality assessment dimensions, value assessment dimensions, and contribution assessment dimensions are set respectively; different assessment dimensions correspond to different sets of assessment parameters. Based on the set of evaluation parameters, the preset evaluation models corresponding to the quality evaluation dimension, the value evaluation dimension, and the contribution evaluation dimension are configured and run to perform multi-dimensional evaluation and generate evaluation data containing scores for each dimension and a comprehensive score.

[0077] In one possible implementation, the granting module includes: The target multi-source data is uploaded to the sharing platform, and the target multi-source data is verified from multiple dimensions to obtain verification results; If the verification result is successful, an uploading event is generated based on the verification result, and the target multi-source data is uploaded. The uploading event is also pushed to application layer users who have subscribed to the relevant data types through the event notification mechanism of the sharing platform.

[0078] In one feasible implementation, the processing module further includes: Configure corresponding points acquisition sub-mechanisms based on the different roles of users in the data flow process. The roles include data providers, data processors, and data users. In response to a user performing a preset action corresponding to their role, the corresponding points acquisition sub-mechanism is run to enable the user's account to acquire corresponding transaction points, and the points acquisition record is written to the consortium blockchain corresponding to the sharing platform for storage.

[0079] Example 3 This application also provides an electronic device, such as Figure 5As shown, it includes: a processor 501, a memory 502, and a bus 503. The memory 502 stores machine-readable instructions that can be executed by the processor 501. When the electronic device is running, the processor 501 and the memory 502 communicate through the bus 503. When the machine-readable instructions are executed by the processor 501, the steps of any one of the autonomous driving scenario data sharing methods described above are executed.

[0080] Example 4 This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of any of the methods for sharing data in an autonomous driving scenario.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0082] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0084] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0085] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for sharing data in autonomous driving scenarios, characterized in that, Applied to a data sharing system, which includes a data layer, a infrastructure layer, a sharing layer, and an application layer, the method includes: The multi-source heterogeneous data of the autonomous driving scenario obtained by the data layer is input to the base layer, and the multi-source heterogeneous data is processed for compliance to obtain the target multi-source data. The target multi-source data is evaluated in multiple dimensions to obtain evaluation data, and after the evaluation is completed, the target multi-source data and its corresponding evaluation data are input into the sharing layer. The target multi-source data and the evaluation data are processed through the shared layer to dynamically determine the target transaction points of the target multi-source data in the shared platform built into the shared layer; wherein, the shared platform is configured with a points incentive mechanism based on blockchain and smart contracts; In response to the application layer's request to use the target multi-source data, it is determined whether the transaction points held by the user account associated with the application layer, obtained based on the points incentive mechanism, meet the transaction conditions corresponding to the target transaction points. If the conditions are met, the smart contract built into the sharing layer will deduct an amount equivalent to the target transaction points from the user account and grant the application layer access to the target multi-source data to achieve data sharing in the autonomous driving scenario.

2. The method according to claim 1, characterized in that, Determining whether the transaction points held by the user account associated with the application layer, obtained based on the points incentive mechanism, meet the transaction conditions corresponding to the target transaction points includes: Identify the data type of the target multi-source data, wherein the data type includes at least the points market pool data type or the bilateral negotiation pool data type; Based on the identified data type, the corresponding method is called from among the preset multiple methods for determining transaction points; The invoked transaction score determination method is executed to determine the target transaction score corresponding to the target multi-source data.

3. The method according to claim 2, characterized in that, The data type includes at least the points market pool data type; The invoked transaction score determination method is executed to determine the target transaction score corresponding to the target multi-source data, including: Identify the data provider for the target multi-source data, and retrieve the historical transaction data of the data provider from the sharing platform; Based on the historical transaction data, the evaluation data, and the preset expert opinion rules, the initial transaction score of the target multi-source data is dynamically adjusted to obtain the target transaction score.

4. The method according to claim 2, characterized in that, The data types include at least the bilateral negotiation pool data type; Executing the invoked transaction score determination method to determine the target transaction score corresponding to the target multi-source data further includes: The sharing platform performs privacy-preserving computation technology to perform encrypted matching between the data requester's needs and the data provider's data resources, in order to identify potential users of the target multi-source data; Provide a trusted online negotiation room based on a trusted execution environment for both parties who are successfully matched to conduct multi-dimensional negotiations; The target transaction points are determined based on the negotiation results generated in the online trusted negotiation room and the guarantee mechanism of the sharing platform based on smart contracts.

5. The method according to claim 1, characterized in that, The target multi-source data is evaluated in multiple dimensions to obtain evaluation data, including: For the target multi-source data, quality assessment dimensions, value assessment dimensions, and contribution assessment dimensions are set respectively; different assessment dimensions correspond to different sets of assessment parameters. Based on the set of evaluation parameters, the preset evaluation models corresponding to the quality evaluation dimension, the value evaluation dimension, and the contribution evaluation dimension are configured and run to perform multi-dimensional evaluation and generate evaluation data containing scores for each dimension and a comprehensive score.

6. The method according to claim 1, characterized in that, Granting the application layer access permission to the target multi-source data includes: The target multi-source data is uploaded to the sharing platform, and the target multi-source data is verified from multiple dimensions to obtain verification results; If the verification result is successful, an uploading event is generated based on the verification result, and the target multi-source data is uploaded. The uploading event is also pushed to application layer users who have subscribed to the relevant data types through the event notification mechanism of the sharing platform.

7. The method according to claim 1, characterized in that, After granting the application layer access permission to the target multi-source data, the process includes: Configure corresponding points acquisition sub-mechanisms based on the different roles of users in the data flow process. These roles include data providers, data processors, and data users. In response to a user performing a preset action corresponding to their role, the corresponding points acquisition sub-mechanism is run to enable the user's account to acquire corresponding transaction points, and the points acquisition record is written to the consortium blockchain corresponding to the sharing platform for storage.

8. An autonomous driving scenario data sharing device, characterized in that, Applied to a data sharing system, the data sharing system comprising a data layer, a foundation layer, a sharing layer, and an application layer, the device includes: The input module is used to input multi-source heterogeneous data of autonomous driving scenarios obtained by the data layer into the base layer, and to perform compliance processing on the multi-source heterogeneous data to obtain target multi-source data. The evaluation module is used to perform multi-dimensional evaluation on the target multi-source data, obtain evaluation data, and input the target multi-source data and its corresponding evaluation data into the shared layer after the evaluation is completed. The processing module is used to process the target multi-source data and the evaluation data through the sharing layer to dynamically determine the target transaction points of the target multi-source data in the sharing platform built into the sharing layer; wherein, the sharing platform is configured with a points incentive mechanism based on blockchain and smart contracts; The response module is used to respond to the use request for the target multi-source data initiated by the application layer, and to determine whether the transaction points held by the user account associated with the application layer and obtained based on the points incentive mechanism meet the transaction conditions corresponding to the target transaction points. The granting module is used to, if the conditions are met, deduct an amount equivalent to the target transaction points from the user account through the smart contract built into the sharing layer, and grant the application layer access permission to the target multi-source data, so as to realize data sharing in autonomous driving scenarios.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of a data sharing method for an autonomous driving scenario as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the autonomous driving scenario data sharing method as described in any one of claims 1 to 7.

Images