A Decentralized Trust Allocation Method and System Based on Blockchain

Abstract

This invention discloses a decentralized trust allocation method and system based on blockchain, relating to the field of trust allocation technology. The method includes: within a blockchain, acquiring the data contribution volume and activity level of multiple target entities over a historical period; wherein the blockchain is constructed based on data uploaded by multiple target entities; analyzing the upload accuracy of multiple target entities based on the data within the blockchain; randomly configuring multiple basic allocation weights for multiple target entities; calculating the allocation trust degree of the multiple basic allocation weights based on the data contribution volume, upload accuracy, and activity level of the multiple target entities; iteratively optimizing the multiple basic allocation weights based on the allocation trust degree to obtain multiple optimized allocation weights, and then allocating permissions accordingly. This invention solves the technical problem of unreasonable trust allocation weights in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of trust allocation technology, specifically to a blockchain-based decentralized trust allocation method and system. Background Technology

[0002] With the rapid development of the digital age, the generation, transmission, and storage of various types of data are on the rise. At the same time, data security and trustworthiness have become key issues restricting the development of the digital economy. However, traditional trust allocation methods typically rely on centralized institutions or systems, which not only easily lead to single points of failure but also may pose risks to data privacy and security. Summary of the Invention

[0003] This application provides a blockchain-based decentralized trust allocation method and system, which solves the technical problem of unreasonable trust allocation weights in the prior art.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: Firstly, this application provides a blockchain-based decentralized trust allocation method for data, the method comprising: Within the blockchain, the data contribution of multiple target entities over a historical period is obtained, as well as the activity level of multiple target entities over a historical period. The blockchain is constructed based on data uploaded by multiple target entities. Based on the data within the blockchain, perform upload accuracy analysis for multiple target entities; Randomly allocate multiple base weights to multiple target entities; Based on the data contribution, upload accuracy, and activity of multiple target entities, the allocation trust level of multiple basic allocation weights is calculated. Based on the allocation trust level, the multiple basic allocation weights are iteratively optimized to obtain multiple optimized allocation weights, and permissions are allocated accordingly.

[0005] Secondly, this application provides a blockchain-based decentralized trust distribution system for data, including: The information collection module is used to obtain the data contribution of multiple target entities within a historical period and the activity level of multiple target entities within a historical period within the blockchain, wherein the blockchain is constructed based on the data uploaded by multiple target entities; The data analysis module is used to analyze the upload accuracy of multiple target entities based on the data within the blockchain. The weight allocation module is used to randomly configure multiple basic weights for multiple target entities; The weight optimization module is used to calculate the allocation trust level of multiple basic allocation weights based on the data contribution, upload accuracy, and activity of multiple target subjects. Based on the allocation trust level, the multiple basic allocation weights are iteratively optimized to obtain multiple optimized allocation weights for permission allocation.

[0006] This application provides one or more technical solutions, which have at least the following technical effects or advantages: This application provides a blockchain-based decentralized trust allocation method and system. First, it acquires the data contribution volume and activity level of multiple target entities within the blockchain over a historical period, analyzing their upload accuracy to ensure subsequent analysis is based on complete and orderly data. Second, it randomly assigns multiple basic allocation weights to multiple target entities to prevent entities uploading invalid content from being incorrectly assigned high weights. Finally, based on the data contribution volume, upload accuracy, and activity level of multiple target entities, it calculates the allocation trust level of the multiple basic allocation weights. Based on this allocation trust level, iteratively optimizes the multiple basic allocation weights to obtain multiple optimized allocation weights for permission allocation. This method and system, through continuous iterative optimization of allocation weights, can more accurately reflect the actual contribution and credibility of each target entity, improving data security and credibility, and providing strong protection for secure data storage and efficient utilization.

[0007] Through the above technical solutions, this application effectively avoids single points of failure and improves the reliability and stability of the system by utilizing the decentralized characteristics of blockchain. Iterative optimization of trust allocation weights enables more reasonable permission allocation. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a blockchain-based decentralized trust allocation method provided in an embodiment of this application. Figure 2 This is a schematic diagram of a blockchain-based decentralized trust distribution system provided in an embodiment of this application.

[0010] The components represented by each number in the attached diagram are explained below: Information acquisition module 11, data analysis module 12, weight allocation module 13, weight optimization module 14. Detailed Implementation

[0011] This application provides a blockchain-based decentralized trust allocation method and system to address the technical problem of unreasonable trust allocation weights in the prior art.

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0015] Example 1, as Figure 1 As shown, this embodiment of the invention provides a blockchain-based decentralized trust allocation method, including: S10: Within the blockchain, obtain the data contribution of multiple target entities over a historical period, and obtain the activity level of multiple target entities over a historical period, wherein the blockchain is constructed based on the data uploaded by multiple target entities; In this embodiment, the data contribution and activity levels of multiple target entities over a historical period are first collected using blockchain. Blockchain is a distributed database technology that stores data in blocks and uses cryptographic methods to ensure data security and integrity. Each block contains a certain number of transaction information and is cryptographically linked to the previous block, forming a continuously growing chain. It possesses characteristics such as immutability and traceability. Multiple entities upload and download data within the blockchain. To ensure that each entity's data usage rights are more aligned with their respective contributions, trust is allocated among the entities. Based on the data uploaded by multiple targets, a blockchain is constructed to guarantee the accuracy of the database.

[0016] Data contribution is primarily reflected in the amount of data uploaded, which directly reflects the amount of data a target entity has provided to the blockchain over a historical period. Activity level, on the other hand, is determined by comparing the data upload frequency of multiple target entities with the average data upload frequency of multiple target entities within the blockchain, thus obtaining the activity level of multiple target entities over a historical period.

[0017] In summary, compared with existing technologies, this application constructs a blockchain by collecting data uploaded by target entities, thereby obtaining the data contribution and activity levels of multiple target entities over historical periods and historical events, ensuring that the blockchain construction and basic weight allocation can be based on complete and orderly data.

[0018] Specifically, step S10 in the method provided in the application embodiment includes: Obtain multiple target entities for trust assignment; Obtain the data contribution volume of multiple target entities over a historical period, where the data contribution volume includes the data upload volume; Based on the data upload frequency of multiple target entities, the ratio of the data upload frequency to the average data upload frequency of multiple target entities within the blockchain is calculated to obtain the activity level of multiple target entities.

[0019] In this embodiment, the scope requiring trust allocation is first determined, and multiple target entities that have not yet undergone trust allocation are identified. Data from these multiple target entities is uploaded to construct a blockchain. The data upload volume of each target entity over a historical period is then retrieved from the blockchain as a measure of data contribution. The data upload volume directly reflects the scale of data provided by the target entity to the blockchain database.

[0020] Activity level is determined by calculating the ratio of data upload frequency to the average data upload frequency of multiple target entities within the blockchain. This clearly shows how the activity level of each target entity in data uploads compares to the overall average. For example, if there are three target entities in the blockchain, A, B, and C, the number of data uploads within one day is as follows: Subject A uploaded 8 times, Subject B uploaded 4 times, and Subject C uploaded 6 times. At this point, A's frequency is 8 times / day, B's frequency is 4 times / day, and C's frequency is 6 times / day. Average data upload frequency = (A's frequency + B's frequency + C's frequency) ÷ number of subjects = 18 ÷ 3 = 6 times / day. Calculating activity level using "Activity = Individual subject frequency ÷ Average frequency", A's activity level: 8 ÷ 6 ≈ 1.33, B's activity level: 4 ÷ 6 ≈ 0.67, and C's activity level: 6 ÷ 6 = 1.

[0021] The obtained data activity is organized and analyzed, and the existing data is classified and statistically analyzed. The activity is graded according to the value, for example, the activity is divided into three levels: high activity, medium activity, and low activity. When the activity is 1, it means that the data upload frequency is consistent with the average data upload frequency of multiple target entities in the blockchain.

[0022] For example, if the activity level of subject A is greater than 1, it means that its upload frequency is higher than the overall average data upload frequency level, and it is relatively more active; if the activity level of subject B is less than 1, it means that its upload frequency is lower than the overall average data upload frequency level, and it is relatively inactive; if the activity level of subject C is 1, it means that its upload frequency is consistent with the overall average data upload frequency level.

[0023] In summary, compared to existing technologies, this approach first identifies the target entities to be trusted for allocation, then collects the amount of data contribution and the data upload frequency over a historical period. By calculating the ratio of the data upload frequency to the average data upload frequency, the activity level of the target entity can be obtained. By comparing the activity level values, the level of data interaction activity of different entities in the blockchain can be directly compared.

[0024] S20: Based on the data within the blockchain, perform an upload accuracy analysis for multiple target entities; In this embodiment, the upload accuracy of multiple target entities is analyzed based on the size, format, and content of the data uploaded within the blockchain to prevent entities uploading invalid content from being incorrectly assigned high weights. For example, the data size should be defined within a reasonable range according to the business scenario, the data format should conform to unified regulations, and the data content should meet relevance requirements.

[0025] Specifically, step S20 in the method provided in the application embodiment includes: Extract data features from uploaded data of multiple target entities; The data features are input into the data credibility verifier, and the data credibility is output. Uploaded data with a credibility level greater than or equal to the credibility threshold is marked as accurate data, and the accuracy rate of uploaded data from multiple target subjects is calculated.

[0026] In this embodiment of the application, data features of the uploaded data of multiple target subjects are first extracted. The data features include the size, format, and content of the data. Data features are an important basis for judging whether the data is trustworthy.

[0027] The extracted data features are input into a data credibility verifier. The verifier is based on a machine learning model trained on the data. After evaluating the input data features according to preset rules or learning patterns, the credibility verifier outputs the data credibility score. The credibility threshold is a pre-set standard used to judge the accuracy of the data.

[0028] If the reliability of the output data is greater than or equal to the reliability threshold, the corresponding uploaded data is marked as accurate data; if the reliability of the output data is less than the reliability threshold, the corresponding uploaded data is marked as inaccurate data. By counting the number of data points marked as accurate for each target subject and the total number of data points uploaded by that subject, the ratio of the number of data points marked as accurate for each target subject to the total number of data points uploaded by that subject is calculated to obtain the accuracy rate of uploaded data from multiple target subjects.

[0029] For example, if target entity D uploads 100 data entries, and after evaluation by the data trust verifier, 60 of these entries have a trustworthiness greater than the trustworthiness threshold, and 20 entries have a trustworthiness equal to the trustworthiness threshold, then target entity D's upload accuracy is 80%. Analyzing the accuracy of data uploaded by multiple target entities allows for a deeper understanding of the quality of data uploaded by each entity, providing more accurate data support for subsequent trust allocation.

[0030] Furthermore, the trusted verifier is trained using the following steps: Based on historical data within the blockchain, a set of characteristic parameters for sample uploaded data is collected. For each uploaded data feature, a credibility percentage is labeled to obtain a sample credibility parameter set. The sample credibility percentage set includes the percentage of credible data under each sample's uploaded data feature in all data. Based on machine learning, a trusted verifier is constructed. The trusted verifier is trained until convergence using a set of feature parameters of uploaded sample data and a set of sample trustworthiness parameters.

[0031] In this embodiment, training the trusted verifier requires first collecting a set of uploaded data feature parameters from historical data within the blockchain. Then, the uploaded data features are labeled with credibility. The set of uploaded data feature parameters covers aspects such as data size, format, and content. For each uploaded data feature, a credibility percentage is labeled, resulting in a sample credibility parameter set. This set reflects the proportion of trusted data among all data for each sample uploaded data feature.

[0032] Using machine learning algorithms, based on convolutional neural networks, multiple sets of sample data feature parameters and sample confidence parameter sets are input for training, constructing a confidence verifier that can be progressively trained to convergence. Based on the data feature parameter set, the sample confidence parameter set is trained, and iterative training yields a converged confidence verifier. For example, the iterative training of the confidence verifier can be achieved through the following technical path: 1. Data preparation: The data feature parameter set is used as input, and the sample confidence parameter set is used as output. 2. Model construction: A preliminary training framework is constructed based on machine learning algorithms. For example, based on a convolutional neural network, the number of nodes in the input layer equals the dimension of the input features. For example, if the sample uploaded data feature parameter set and the sample confidence parameter set together have 10 features, then the input layer contains 10 nodes; 1-3 hidden layers are set, and the number of nodes in each layer is adjusted experimentally, such as 64, 32, etc., with ReLU as the activation function; the number of nodes in the output layer equals the number of output data confidence values, and the output layer does not use an activation function, directly outputting continuous values. 3. Model training: The data feature parameter set samples are used as the model input features, and the corresponding output samples in the training set are used as supervision labels. The Adam optimizer and mean squared error (MSE) loss function are used to calculate the loss. The batch size is set to 32 and the total number of training rounds is 100. An early stopping mechanism (patience=5) is introduced. When the loss on the validation set does not decrease for 5 consecutive rounds, the training process is automatically terminated, resulting in a trained and reliable validator. This effectively avoids model overfitting while ensuring that the model reaches a convergent state. The pre-trained reliable validator can accurately output the data reliability.

[0033] During training, the trusted validator continuously learns the relationship between data features and confidence levels, adjusting its parameters and model structure until training converges. When training converges, the trusted validator can output the confidence level of the data relatively accurately based on the input data features.

[0034] In summary, compared to existing technologies, analyzing upload accuracy based on trusted validators is a crucial step in this decentralized trust allocation method. It effectively prevents entities uploading large amounts of low-quality, invalid data from gaining excessively high trust weights, ensuring the fairness and rationality of trust allocation. Simultaneously, it encourages all stakeholders to pay greater attention to the quality of uploaded data, improving the reliability and availability of the entire blockchain system.

[0035] S30: Randomly allocate multiple basic weights for multiple target subjects; In this embodiment, multiple basic allocation weights for multiple target subjects are randomly configured. This random configuration serves as an initial setting and is subsequently adjusted based on the actual performance of each subject. For example, a random number generation algorithm is used to assign an initial basic allocation weight to each target subject. The randomly configured basic allocation weights provide the foundation for subsequent optimization.

[0036] Specifically, step S30 in the method provided in the application embodiment includes: Multiple base allocation weights are randomly configured for multiple target entities, wherein the sum of the base allocation weights of the multiple target entities is 1.

[0037] In this embodiment of the application, a random number generation algorithm is used to randomly assign basic weights to multiple target entities. For example, there are 5 target entities in the blockchain (denoted as entity A to entity E). The basic weight of entity A is 0.22, the basic weight of entity B is 0.18, the basic weight of entity C is 0.25, the basic weight of entity D is 0.15, the basic weight of entity E is 0.2, and the sum of the basic weights of multiple target entities is 1.

[0038] S40: Based on the data contribution, upload accuracy, and activity of multiple target entities, calculate the allocation trust level of multiple basic allocation weights, and iteratively optimize the multiple basic allocation weights according to the allocation trust level to obtain multiple optimized allocation weights, and then allocate permissions.

[0039] In this embodiment, based on the data contribution, upload accuracy, and activity level of multiple target entities, a trust level for the allocation of multiple basic allocation weights is calculated. Data contribution, upload accuracy, activity level, and allocation weights are all positively correlated. A weighted calculation is used to optimize the weight allocation coefficients; for example, the weight ratio of data contribution is set to 50%, the weight ratio of upload accuracy to 30%, and the weight ratio of activity level to 20%. Based on the allocation trust level, the multiple basic allocation weights are iteratively optimized to obtain multiple optimized allocation weights for permission allocation. The similarity between the basic allocation weights and the weight allocation coefficients is calculated; the greater the similarity, the more similar the entities.

[0040] Specifically, step S40 in the method provided in the application embodiment includes: The data contribution is normalized to obtain the data contribution coefficient; The data contribution coefficients, upload accuracy, and activity of multiple target entities are weighted and calculated to obtain multiple weight allocation coefficients; Based on multiple weight allocation coefficients, the weight allocation is optimized to obtain multiple optimized weight allocations.

[0041] In this embodiment, the data contribution is normalized by converting the data contribution into a percentage coefficient, such as the ratio of the contribution to the average contribution of other data. The larger the ratio, the greater the contribution. For example, in a blockchain with three target entities, A, B, and C, entity A contributes 150 data, entity B contributes 200, and entity C contributes 100, with a total average contribution of 450. The contribution coefficient is calculated using the formula: "Contribution coefficient = Individual entity contribution ÷ Average contribution". The contribution coefficients for A are: 150 ÷ ​​450 ≈ 0.333, B is: 200 ÷ 450 ≈ 0.444, and C is: 100 ÷ 450 ≈ 0.222. The sum of the coefficients is: 0.333 + 0.444 + 0.222 ≈ 1 (due to slight rounding errors, the actual precise value is 1), which meets the normalization requirements.

[0042] Based on the data contribution, upload accuracy, and activity level of multiple target entities, a weighted calculation is performed to obtain multiple weight allocation coefficients. The weight ratio of data contribution, upload accuracy, and activity level is 5:3:2.

[0043] For example, the weighting of data contribution is 50%, upload accuracy is 30%, and activity is 20%. The blockchain has three target entities, A, B, and C. Entity A has a data contribution coefficient of 0.333, upload accuracy of 0.8, and activity of 1.33; entity B has a data contribution coefficient of 0.444, upload accuracy of 0.7, and activity of 0.67; entity C has a data contribution coefficient of 0.222, upload accuracy of 0.9, and activity of 1. Using the formula: Comprehensive Score = (Data Contribution Coefficient × 0.5) + (Upload Accuracy × 0.3) + (Activity × 0.2), the comprehensive score for entity A is 0.6725, for entity B it is 0.566, and for entity C it is 0.581.

[0044] The overall score is normalized to obtain the weight allocation coefficient. The weight allocation coefficient is calculated as: Weight Allocation Coefficient = Overall Score of a Specific Subject ÷ Sum of Overall Scores of All Subjects. The total overall score is calculated to be 1.8195. The allocation coefficient for Subject A is 0.37, for Subject B it is 0.311, and for Subject C it is 0.319.

[0045] Based on the obtained multiple weight allocation coefficients, the allocation trust degree of the basic allocation weights is calculated. Then, based on the trusted verifier, the multiple basic allocation weights are iteratively optimized according to the allocation trust degree to obtain multiple optimized allocation weights.

[0046] Furthermore, based on multiple weight allocation coefficients, the weight allocation is optimized to obtain multiple optimized weight allocations, including: Based on the weight allocation coefficient, the allocation confidence level of the basic allocation weights is calculated to obtain multiple allocation confidence levels for multiple basic allocation weights. Obtain the weight adjustment step size, take maximizing the sum of allocation trust as the optimization goal, iteratively optimize multiple basic allocation weights according to allocation trust, and obtain multiple optimized allocation weights; Until the preset maximum number of iterations is reached, select the multiple optimization weights with the highest trust level and allocate permissions accordingly.

[0047] In this embodiment, the allocation trust of multiple basic allocation weights is calculated based on the weight allocation coefficients. The similarity between the basic allocation weights and the weight allocation coefficients is calculated to obtain the allocation trust corresponding to each basic allocation weight. The greater the similarity, the more similar the entities. The similarity is calculated using the Euclidean distance algorithm. For example, in a blockchain with three target entities, A, B, and C, the basic allocation weights (initially set weight vectors) are [0.5, 0.3, 0.2], and the weight allocation coefficients (actual weight vectors after weighted calculation) are [0.37, 0.311, 0.319]. Using the Euclidean distance formula, the Euclidean distance is calculated to be 0.031182. The smaller the value, the smaller the numerical difference between the two entities, indicating a higher similarity.

[0048] The step size for weight adjustment is determined, which dictates the magnitude of the adjustment to the basic allocation weights in each iteration. The size of the step size affects the efficiency and accuracy of the iteration; too large a step size may skip the optimal solution, while too small a step size will lead to excessive iterations and reduced efficiency. The optimization objective is to maximize the sum of allocation confidence levels, iteratively optimizing multiple basic allocation weights based on their allocation confidence levels. During each iteration, the basic allocation weights are adjusted according to the magnitude of the allocation confidence levels, continuously increasing the sum of allocation confidence levels.

[0049] During the iterative optimization process, the system continuously monitors whether the preset maximum number of iterations has been reached. When the maximum number of iterations is reached, the system selects the multiple optimization weights with the highest trust levels. The more similar the optimization weights and weight allocation coefficients are, the more reasonable the weight allocation is (consistent with their respective contributions). Then, permissions are allocated. For example, when a target subject with high optimization permissions accesses the system simultaneously with a target subject with low permissions, it may have priority, or the target subject with high permissions may have access to all data, while the target subject with permissions below the threshold may only have access to a portion of the data, and permissions may be restricted accordingly.

[0050] Through the steps described above, compared to existing technologies, the basic allocation weights are continuously iterated and optimized to better reflect the actual contributions and performance of each target entity. During the iteration process, the similarity between the current allocation weights and the weight allocation coefficients is continuously compared. If the similarity does not reach an ideal state, the basic allocation weights are adjusted again, and optimization continues until the optimal allocation weights with the highest trust level are achieved. This incentivizes each target entity to actively upload high-quality data, increasing their data contribution, upload accuracy, and activity level, thereby gaining higher trust allocations and more permissions.

[0051] In summary, the embodiments of this application have at least the following technical effects: This application provides a blockchain-based decentralized trust allocation method and system. First, it acquires the data contribution volume and activity level of multiple target entities within the blockchain over a historical period, analyzing their upload accuracy to ensure subsequent analysis is based on complete and orderly data. Second, it randomly configures multiple basic allocation weights for multiple target entities to prevent entities uploading invalid content from being incorrectly assigned high weights. Finally, based on the data contribution volume, upload accuracy, and activity level of multiple target entities, it calculates the allocation trust level of the multiple basic allocation weights. Based on this allocation trust level, iteratively optimizes the multiple basic allocation weights to obtain multiple optimized allocation weights for permission allocation. This method and system, through continuous iterative optimization of allocation weights, can more accurately reflect the actual contribution and credibility of each target entity, improving data security and credibility, and providing strong protection for secure data storage and efficient utilization. Through the above technical solution, this application utilizes the decentralized characteristics of blockchain to improve system reliability and fairness, and iteratively optimizes trust allocation weights to achieve a more reasonable permission allocation.

[0052] Example 2, as Figure 2 As shown, based on the same inventive concept as the blockchain-based decentralized trust allocation method provided in Embodiment 1, this embodiment of the invention also provides a blockchain-based decentralized trust allocation system, including: The information collection module 11 is used to obtain the data contribution of multiple target entities within a historical period and the activity of multiple target entities within a historical period within the blockchain, wherein the blockchain is constructed based on the data uploaded by multiple target entities; The data analysis module 12 is used to analyze the upload accuracy of multiple target entities based on the data in the blockchain. The weight allocation module 13 is used to randomly configure multiple basic allocation weights for multiple target subjects; The weight optimization module 14 is used to calculate the allocation trust of multiple basic allocation weights based on the data contribution, upload accuracy and activity of multiple target subjects, and to iteratively optimize the multiple basic allocation weights based on the allocation trust to obtain multiple optimized allocation weights for permission allocation.

[0053] In one embodiment, the information acquisition module 11 is specifically used for: Obtain multiple target entities for trust assignment; Obtain the data contribution volume of multiple target entities over a historical period, where the data contribution volume includes the data upload volume; Based on the data upload frequency of multiple target entities, the ratio of the data upload frequency to the average data upload frequency of multiple target entities within the blockchain is calculated to obtain the activity level of multiple target entities.

[0054] In one embodiment, the data analysis module 12 is specifically used for: Extract data features from uploaded data of multiple target entities; The data features are input into the data credibility verifier, and the data credibility is output. Uploaded data with a credibility level greater than or equal to the credibility threshold is marked as accurate data, and the accuracy rate of uploaded data from multiple target subjects is calculated.

[0055] Furthermore, in one embodiment of the application, the trusted verifier is trained using the following steps: Based on historical data within the blockchain, a set of characteristic parameters for sample uploaded data is collected. For each uploaded data feature, a credibility percentage is labeled to obtain a sample credibility parameter set. The sample credibility percentage set includes the percentage of credible data under each sample's uploaded data feature in all data. Based on machine learning, a trusted verifier is constructed. The trusted verifier is trained until convergence using a set of feature parameters of uploaded sample data and a set of sample trustworthiness parameters.

[0056] In one embodiment, the weight allocation module 13 is specifically used for: Multiple base allocation weights are randomly configured for multiple target entities, wherein the sum of the base allocation weights of the multiple target entities is 1.

[0057] In one embodiment, the weight optimization module 14 is specifically used for: The data contribution is normalized to obtain the data contribution coefficient; The data contribution coefficients, upload accuracy, and activity of multiple target entities are weighted and calculated to obtain multiple weight allocation coefficients; Based on multiple weight allocation coefficients, the weight allocation is optimized to obtain multiple optimized weight allocations.

[0058] Furthermore, in one embodiment of the application, the step of optimizing the allocation weights based on multiple weight allocation coefficients to obtain multiple optimized allocation weights includes: Based on the weight allocation coefficient, the allocation confidence level of the basic allocation weights is calculated to obtain multiple allocation confidence levels for multiple basic allocation weights. Obtain the weight adjustment step size, take maximizing the sum of allocation trust as the optimization goal, iteratively optimize multiple basic allocation weights according to allocation trust, and obtain multiple optimized allocation weights; Until the preset maximum number of iterations is reached, select the multiple optimization weights with the highest trust level and allocate permissions accordingly.

[0059] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0060] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0061] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A blockchain-based decentralized trust allocation method for data, characterized in that, The method includes: Within the blockchain, the data contribution of multiple target entities over a historical period is obtained, as well as the activity level of multiple target entities over a historical period. The blockchain is constructed based on data uploaded by multiple target entities. Based on the data within the blockchain, perform upload accuracy analysis for multiple target entities; Randomly allocate multiple base weights to multiple target entities; Based on the data contribution, upload accuracy, and activity of multiple target entities, the allocation trust level of multiple basic allocation weights is calculated. Based on the allocation trust level, the multiple basic allocation weights are iteratively optimized to obtain multiple optimized allocation weights, and permissions are allocated accordingly. Within the blockchain, the data contribution volume of multiple target entities over a historical period is obtained, as well as the activity level of these entities over the same period, including: Obtain multiple target entities for trust assignment; Obtain the data contribution volume of multiple target entities over a historical period, where the data contribution volume includes the data upload volume; Based on the data upload frequency of multiple target entities, the ratio of the data upload frequency to the average data upload frequency of multiple target entities within the blockchain is calculated to obtain the activity level of multiple target entities; Based on the data within the blockchain, an accuracy analysis of uploads from multiple target entities is performed, including: Extract data features from uploaded data of multiple target entities; The data features are input into the data credibility verifier, and the data credibility is output. Uploaded data with a credibility level greater than or equal to the credibility threshold is marked as accurate data, and the accuracy rate of uploaded data from multiple target subjects is calculated.

2. The blockchain-based decentralized trust allocation method according to claim 1, characterized in that, The trusted verifier is trained using the following steps: Based on historical data within the blockchain, a set of characteristic parameters for sample uploaded data is collected. For each uploaded data feature, a credibility percentage is labeled to obtain a sample credibility parameter set. The sample credibility percentage set includes the percentage of credible data under each sample's uploaded data feature in all data. Based on machine learning, a trusted verifier is constructed. The trusted verifier is trained until convergence using a set of feature parameters of uploaded sample data and a set of sample trustworthiness parameters.

3. The blockchain-based decentralized trust allocation method according to claim 1, characterized in that, Randomly configure multiple base allocation weights for multiple target subjects, including: Multiple base allocation weights are randomly configured for multiple target entities, wherein the sum of the base allocation weights of the multiple target entities is 1.

4. The blockchain-based decentralized trust allocation method according to claim 1, characterized in that, Based on the data contribution, upload accuracy, and activity level of multiple target entities, the assignment trust level of multiple basic allocation weights is calculated, including: The data contribution is normalized to obtain the data contribution coefficient; The data contribution coefficients, upload accuracy, and activity of multiple target entities are weighted and calculated to obtain multiple weight allocation coefficients; Based on multiple weight allocation coefficients, the weight allocation is optimized to obtain multiple optimized weight allocations.

5. The blockchain-based decentralized trust allocation method according to claim 1, characterized in that, Based on multiple weight allocation coefficients, the weight allocation is optimized to obtain multiple optimized weight allocations, including: Based on the weight allocation coefficient, the allocation confidence level of the basic allocation weights is calculated to obtain multiple allocation confidence levels for multiple basic allocation weights. Obtain the weight adjustment step size, take maximizing the sum of allocation trust as the optimization goal, iteratively optimize multiple basic allocation weights according to allocation trust, and obtain multiple optimized allocation weights; Until the preset maximum number of iterations is reached, select the multiple optimization weights with the highest trust level and allocate permissions accordingly.

6. A blockchain-based decentralized trust distribution system for data, characterized in that: For performing the method according to any one of claims 1-5, comprising: The information collection module is used to obtain the data contribution of multiple target entities within a historical period and the activity level of multiple target entities within a historical period within the blockchain, wherein the blockchain is constructed based on the data uploaded by multiple target entities; The data analysis module is used to analyze the upload accuracy of multiple target entities based on the data within the blockchain. The weight allocation module is used to randomly configure multiple basic weights for multiple target entities; The weight optimization module is used to calculate the allocation trust level of multiple basic allocation weights based on the data contribution, upload accuracy, and activity of multiple target subjects. Based on the allocation trust level, the multiple basic allocation weights are iteratively optimized to obtain multiple optimized allocation weights for permission allocation.

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