A rural information platform data right and anti-counterfeiting system based on blockchain and trusted authentication

Abstract

The application relates to a rural information platform data right and anti-fake system based on a blockchain and trusted authentication, belonging to the technical field of computer information security, which comprises a dynamic risk perception unit, which is used for acquiring information publishing rates in a platform, acquiring keyword sentiment tendencies, acquiring network connection quality data of nodes, and fusing the information publishing rates, the keyword sentiment tendencies and the network connection quality data of the nodes to generate a system comprehensive risk index; an adaptive authentication strategy generation unit, which is used for receiving the system comprehensive risk index and generating an authentication depth value in combination with an estimated value of to-be-published information; and a multi-level authentication execution unit, which is used for receiving the authentication depth value, matching preset authentication levels, and attaching a digital signature to original information passing the authentication to generate a signed data packet. The application balances the overall security of the platform and the information publishing efficiency, and significantly enhances the resilience and environmental adaptability of the system in a crisis state.

Description

Technical Field

[0001] This invention relates to the field of computer information security, specifically to a data ownership confirmation and anti-counterfeiting system for rural information platforms based on blockchain and trusted authentication. Background Technology

[0002] Existing blockchain-based information platforms face inherent technical contradictions when applied to rural life service scenarios involving high concurrency and low-value information interaction. Blockchain technology, requiring immutability to ensure data ownership and prevent counterfeiting, typically relies on deep and complex verification processes; while the efficiency and inclusiveness pursued by rural information platforms demand a concise and rapid information dissemination process. This contradiction is amplified dramatically in the unique rural environment characterized by low digital literacy, low network quality, low value density, high timeliness requirements, high trust needs, and high rates of sudden gatherings. Traditional technical solutions typically adhere to a static, one-time authentication model, treating on-chain trust and off-chain authentication as fixed entry barriers. This design bias ignores the unique behavioral patterns and environmental constraints of rural users. Especially when facing information overload and trust crises caused by sudden rural collective events, the system often fails due to its inability to dynamically adjust the balance between security strategies and processing efficiency. Existing technologies fail to provide a solution that can dynamically perceive rural environmental risks, adaptively adjust authentication strategies, and balance operational efficiency and economy.

[0003] To address the shortcomings of the aforementioned background technologies, this invention aims to provide a rural information platform data ownership and anti-counterfeiting system capable of dynamically sensing rural environmental risks and adaptively adjusting data ownership and authentication strategies, thereby maximizing information dissemination efficiency and system resilience while ensuring data credibility. This system aims to overcome the technical challenge of traditional static authentication models easily failing in special rural scenarios, especially under the impact of crisis events.

[0004] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a data ownership confirmation and anti-counterfeiting system for rural information platforms based on blockchain and trusted authentication, so as to solve the problems mentioned in the background art.

[0006] The technical solution of the present invention includes:

[0007] The dynamic risk perception unit is used to obtain the information release rate within the platform, obtain keyword sentiment, obtain network connection quality data of nodes, and integrate the information release rate, keyword sentiment, and network connection quality data of nodes to generate a comprehensive system risk index.

[0008] The adaptive authentication strategy generation unit receives the system's comprehensive risk index and, in conjunction with the estimated value of the information to be published, generates an authentication depth value.

[0009] The multi-level authentication execution unit is used to receive the authentication depth value, match the preset authentication level, and attach a digital signature to the original information that has passed authentication, thereby generating a signed data packet.

[0010] The lightweight consensus and on-chain anchoring unit is used to receive signed data packets, execute the rural reputation consensus mechanism and batch anchoring strategy, and confirm and store data ownership.

[0011] Preferably, the process for generating the system's comprehensive risk index is as follows:

[0012] Obtain the amount of information published within the current unit of time and calculate the information flow risk factor;

[0013] The weighted frequency of panic-inducing words in the current information stream is extracted using a pre-defined natural language processing model, and the panic risk factor is calculated.

[0014] Get the proportion of the number of nodes whose network status exceeds the threshold among the current active users to the total number of active nodes, and set the proportion as the weak network environment risk factor;

[0015] By combining information flow risk factors, panic risk factors, and weak network environment risk factors, a weighted summation is performed to generate a comprehensive system risk index.

[0016] Preferably, the calculation process for the information flow risk factor is as follows:

[0017] Obtain the standard baseline traffic calculated by moving average of historical traffic data from the platform;

[0018] By combining the current information release volume per unit time with the regular baseline traffic, a logarithmic transformation is performed to obtain the information traffic risk factor.

[0019] Preferably, the process for generating the authentication depth value is as follows:

[0020] Obtain the system's overall risk index and calculate the risk adjustment item;

[0021] Obtain the estimated value of the information to be published and calculate the value adjustment item;

[0022] The authentication depth value is generated by combining the preset basic authentication depth, risk adjustment items, and value adjustment items.

[0023] Preferably, the process by which the multi-level authentication execution unit matches the preset authentication level is as follows:

[0024] The received authentication depth value is compared and analyzed with the preset authentication depth threshold.

[0025] When the authentication depth value is less than or equal to the first preset threshold, a static authentication is performed.

[0026] When the authentication depth value is greater than the first preset threshold and less than or equal to the second preset threshold, static authentication and SMS verification code authentication are performed.

[0027] When the authentication depth value is greater than the second preset threshold and less than or equal to the third preset threshold, silent authentication, SMS verification code authentication and social graph guarantee authentication are performed.

[0028] When the authentication depth value is greater than the third preset threshold, static default authentication, SMS verification code authentication, social graph guarantee authentication, and biometric verification are performed.

[0029] Preferably, the process of the rural credit consensus mechanism is as follows:

[0030] Obtain the overall reputation score of the candidate nodes;

[0031] Based on the comprehensive reputation score and the preset selection pressure coefficient, the election probability of each candidate node is calculated.

[0032] Based on the election probability, block producers are elected from the candidate nodes.

[0033] Preferably, the update process for the comprehensive credit score is as follows:

[0034] Obtain the node's reputation score from the previous moment;

[0035] Retrieve the reputation reward / penalty value generated by the node's recent behavior;

[0036] The exponential moving average model is used, and the credit score at the current time is calculated based on the credit score, credit reward / penalty value, and preset learning rate of the previous time step.

[0037] Preferably, the batch anchoring strategy process is as follows:

[0038] Collect all signed data packets generated within a certain period of time;

[0039] Construct a Merkle tree based on the aggregated data packets;

[0040] Record the root hash of the Merkle tree as a transaction on the blockchain;

[0041] The signed data packets are stored in a distributed file system.

[0042] This invention provides an improved rural information platform data ownership confirmation and anti-counterfeiting system based on blockchain and trusted authentication, which has the following improvements and advantages compared with the prior art:

[0043] 1. This invention constructs a closed-loop technology system encompassing dynamic risk perception, adaptive policy generation, multi-level authentication execution, and lightweight on-chain evidence storage. This system can accurately respond to dynamic changes in rural information platforms during emergencies, such as information surges, the spread of panic, or network instability, and adaptively adjust the strength of security policies. This overcomes the technical shortcomings of traditional static authentication models, which are prone to failure due to policy rigidity in complex environments. By applying differentiated authentication depths to information of different risks and values, it achieves a balance between overall platform security and information dissemination efficiency, significantly enhancing the system's resilience and environmental adaptability in crisis situations.

[0044] 2. The dynamic risk perception unit designed in this invention decomposes key variables affecting system stability into information flow risk factors, panic risk factors, and weak network environment risk factors, and then performs weighted fusion to generate a comprehensive system risk index. This multi-dimensional and quantitative risk perception mechanism, compared with the general risk assessment of traditional technologies, can more accurately and sensitively identify the source and degree of risk, providing a solid data foundation for the subsequent fine-tuning of adaptive authentication strategies, thereby significantly improving the system's response speed and accuracy to environmental changes.

[0045] 3. This invention establishes an authentication strength matching mechanism that is linked to both risk and value through the collaborative work of an adaptive authentication strategy generation unit and a multi-level authentication execution unit. This mechanism can dynamically generate an authentication depth value based on the system's comprehensive risk index and estimated information value, and match it to a multi-level authentication system covering silent default authentication, SMS verification code authentication, social graph guarantee authentication, and even biometric verification. This refined matching avoids the inefficiency caused by over-authenticating all information under low-risk conditions, and also avoids the security risks caused by insufficient authentication of high-value information under high-risk conditions. While ensuring the authenticity of key information, it also greatly improves the user experience and inclusiveness of the system in normal scenarios.

[0046] 4. The lightweight consensus and on-chain anchoring unit adopted in this invention effectively solves the efficiency and cost bottlenecks when blockchain technology is directly applied to high-concurrency, low-value information scenarios. Firstly, the rural reputation consensus mechanism narrows the consensus scope to a group of high-reputation nodes. Through probabilistic election based on comprehensive reputation scores, it significantly reduces the communication overhead and computing resource consumption for reaching consensus, and significantly improves the efficiency of data packaging and on-chaining. Secondly, the batch anchoring strategy combines the immutability of on-chain storage with the low-cost characteristics of off-chain distributed storage. It only stores the Merkle root hash of the aggregated massive data packets on the chain, making it economically possible to perform low-cost, high-efficiency rights confirmation and anti-counterfeiting for each piece of rural information, thus ensuring the economic feasibility and scalability of the system. Attached Figure Description

[0047] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0048] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0050] Example 1

[0051] Please see Figure 1 This invention provides a data ownership confirmation and anti-counterfeiting system for rural information platforms based on blockchain and trusted authentication, comprising:

[0052] The dynamic risk perception unit is used to obtain the information release rate within the platform, obtain keyword sentiment, obtain network connection quality data of nodes, and integrate the information release rate, keyword sentiment, and network connection quality data of nodes to generate a comprehensive system risk index.

[0053] The adaptive authentication strategy generation unit receives the system's comprehensive risk index and, in conjunction with the estimated value of the information to be published, generates an authentication depth value.

[0054] The multi-level authentication execution unit is used to receive the authentication depth value, match the preset authentication level, and attach a digital signature to the original information that has passed authentication, thereby generating a signed data packet.

[0055] Lightweight consensus and on-chain anchoring unit is used to receive signed data packets, execute the rural reputation consensus mechanism and batch anchoring strategy, and confirm and store data ownership.

[0056] This embodiment provides a data ownership confirmation and anti-counterfeiting system for a rural information platform based on blockchain and trusted authentication. The system aims to maximize information dissemination efficiency and system resilience while ensuring data credibility by dynamically sensing rural environmental risks and adaptively adjusting data ownership confirmation and authentication strategies. This system aims to overcome the technical challenge of traditional static authentication models easily failing in special rural scenarios, especially under the impact of crisis events. In this embodiment, the system is deployed in the server cluster of a county-level rural information platform, providing services to registered users of the platform. The system includes four core units, and their collaborative workflow is as follows:

[0057] The dynamic risk perception unit aims to monitor key indicators of the platform's information ecosystem and external environment in real time and integrate them into a quantitative comprehensive system risk index. In this embodiment, the unit periodically acquires information release rates within the platform, keyword sentiment analysis obtained using natural language processing technology, and network connection quality data of user nodes collected through network probes at a frequency of seconds through a preset monitoring interface. These multi-dimensional data are fused and calculated to generate a comprehensive system risk index that accurately reflects the current risk level faced by the system.

[0058] The adaptive authentication strategy generation unit aims to dynamically generate the necessary authentication depth value for each piece of information to be published based on real-time risk and information value, forming the core decision-making function of the system. In this embodiment, the unit receives the system's comprehensive risk index transmitted in real time by the dynamic risk perception unit, and combines it with the estimated value of the information to be published, which is declared by the information publisher or estimated by the content analysis model. Based on these two inputs, the unit calculates a quantified authentication depth value through a non-linear multi-dimensional decision model, which directly determines the strictness of the subsequent authentication process.

[0059] The core of this nonlinear, multidimensional decision-making model is the authentication depth value generation formula in Example 3; its multidimensionality is reflected in the simultaneous consideration of two input variables: system risk and information value; its nonlinearity is reflected in the model's handling of these two variables: the system comprehensive risk index... Exponential function was used This amplification causes certification requirements to increase disproportionately and dramatically with the risk; simultaneously, it affects the estimated value of information. Logarithmic function was used ( The processing involves making the authentication requirements increase with the value, but at a gradually slowing rate; therefore, this model does not refer to a separate, unpublished set of complex algorithms, but specifically to this core computational paradigm.

[0060] The multi-level authentication execution unit aims to execute the authentication strategy and is the direct interactor in the user authentication process. In this embodiment, the unit has a pre-set authentication library containing multiple authentication levels, each level corresponding to a specific set of authentication operations. The unit receives the authentication depth value calculated by the adaptive authentication strategy generation unit and compares it with the preset authentication depth threshold to match the most suitable authentication level under the current risk and value. The unit guides the information publishing user to complete all authentication operations required by the level. After successful authentication, the unit uses the platform's private key to attach a digital signature containing authentication level, timestamp, user identifier, and other information to the original information, generating a signed data packet.

[0061] The lightweight consensus and on-chain anchoring unit aims to perform final ownership confirmation and anti-counterfeiting notarization of authenticated data in an efficient and low-cost manner. In this embodiment, the unit receives signed data packets generated by a multi-level authentication execution unit. To improve efficiency and reduce costs, the unit first executes a rural reputation consensus mechanism to probabilistically elect block producers from a group of high-reputation candidate nodes based on their reputation scores, thereby quickly completing block packaging. Secondly, the unit adopts a batch anchoring strategy to construct a Merkle tree off-chain for all signed data packets generated within a certain period. Only the root hash of the tree is recorded as a transaction on the blockchain, while the original data packets are stored in a lower-cost distributed file system, thus completing data ownership confirmation and notarization.

[0062] This embodiment establishes a closed-loop technology system, encompassing risk perception, strategy generation, authentication execution, and on-chain evidence storage, through the collaborative work of the four units described above. It can adaptively adjust the strength of security strategies based on dynamic environmental changes faced by the rural information platform, such as information overload or the spread of panic caused by unforeseen events. This not only enhances the system's resilience and environmental adaptability in crisis situations, avoiding systemic failures due to rigid authentication strategies, but also achieves an optimal balance between overall platform security and information dissemination efficiency by applying differentiated authentication depths to different types of information. This improves the reliability and cost-effectiveness of data ownership confirmation and anti-counterfeiting for the rural information platform.

[0063] Example 2

[0064] The process of generating the system's comprehensive risk index is as follows:

[0065] Obtain the amount of information published within the current unit of time and calculate the information flow risk factor;

[0066] The weighted frequency of panic-inducing words in the current information stream is extracted using a pre-defined natural language processing model, and the panic risk factor is calculated.

[0067] Get the proportion of the number of nodes whose network status exceeds the threshold among the current active users to the total number of active nodes, and set the proportion as the weak network environment risk factor;

[0068] By combining information flow risk factors, fear-emotion risk factors, and weak network environment risk factors, a weighted summation calculation is performed to generate a comprehensive system risk index;

[0069] The calculation process for the information flow risk factor is as follows:

[0070] Obtain the standard baseline traffic calculated by moving average of historical traffic data from the platform;

[0071] By combining the current information release volume per unit time with the regular baseline traffic, a logarithmic transformation is performed to obtain the information traffic risk factor;

[0072] Based on Example 1, this embodiment describes the process of generating the comprehensive risk index of the system. The purpose is to accurately quantify the key external variables that lead to system failure, such as information surge, panic, and weak network environment, so as to serve as the sole and clear input basis for subsequent self-adaptive strategy adjustments.

[0073] In this embodiment, the system's comprehensive risk index is generated by weighted summation of three core risk factors; this index is defined as:

[0074]

[0075] In this expression, It is the system comprehensive risk index, which physically represents the comprehensive risk level currently faced by the system. It is a dimensionless non-negative real number, and its value is positively correlated with the risk level. These are risk contribution weights, which physically represent the contribution of each risk component to the total risk. They are all positive real numbers and their sum is 1. They are derived from the preset initial values ​​based on regression analysis of historical rural crisis data and can be fine-tuned and optimized during system operation through machine learning algorithms such as gradient descent.

[0076] As a preferred implementation, the initial value can be set to Because information overload and panic are typically more relevant and influential during crisis events; the objective function for fine-tuning optimization can be defined as minimizing; a comprehensive cost function that includes authentication failure rate and average user waiting time; in this way, the system can automatically learn which type of risk should be given more attention at different stages;

[0077] This is an information flow risk factor, the source of which is calculated using the formula below; It is a risk factor for panic, the source of which is calculated using the method described below; It is a risk factor for weak network environments, and its source is calculated using the method described below;

[0078] The formula for calculating the panic risk factor S is: ,in The weighted frequencies of the currently calculated panic-inducing words are calculated. The standard baseline frequency is calculated from historical data;

[0079] The process of obtaining the amount of information released within the current unit of time and calculating the information traffic risk factor aims to quantify the impact of a surge in information release volume. In this embodiment, the calculation process of the information traffic risk factor is further specified. The underlying logic is to obtain the conventional baseline traffic calculated by moving average of the platform's historical traffic data. The conventional baseline traffic refers to the expected amount of information released by the platform under normal conditions. Its function is to provide a dynamic benchmark for judging whether the current traffic is abnormal. It is derived from the moving average calculation of traffic data of all non-holiday periods within the past natural month of the platform. Based on this baseline, the information release volume within the current unit of time and the conventional baseline traffic are combined and logarithmically transformed to obtain the information traffic risk factor. The calculation method is as follows:

[0080]

[0081] In this expression, It is an information flow risk factor; It represents the current amount of information released. Physically, it represents the total number of messages received by the platform within a preset time window, such as one minute. It is a dimensionless count value, and its source is the real-time statistics from the platform's information receiving module. This is the regular baseline flow, whose physical meaning and source are as defined above;

[0082] To prevent division by zero errors, when When the calculation result is 0, the system sets it to an extremely small positive constant, or directly sets the information flow risk factor at that moment. Set to the preset maximum value;

[0083] The technical motivation for using logarithmic transformation is to focus on reflecting the magnitude of the impact rather than the absolute multiple, thereby smoothing the impact of extreme events and enhancing the robustness of the model. The natural logarithm function is described in the document as a logarithmic transformation process.

[0084] The process of extracting the weighted frequency of panic-inducing words in the current information stream using a pre-defined natural language processing model and calculating the panic risk factor aims to quantify the risk of a trust crisis caused by the spread of negative emotions. The pre-defined natural language processing model refers to a text sentiment analysis model built on a recurrent neural network, which is used to identify and quantify panic emotions in text content. This model has been pre-trained using a corpus of rural scene language covering local dialects and specific expressions to ensure the accuracy of identification.

[0085] In this embodiment, the model specifically adopts an architecture containing a two-layer long short-term memory network, with each layer containing 128 neurons; the rural scene corpus contains at least 100,000 text data collected from county-level media convergence centers, local forums, and past emergency event reports, and manually annotated; the goal of model training is to identify panic-inducing words in the text related to topics such as natural disasters, public health, and production safety; the weighted frequency of panic-inducing words... The calculation method is as follows:

[0086]

[0087] in, It is the number of words in the preset panic vocabulary list. It is the first The panic weight of each word, for example, earthquake has a higher weight than wind, with a value range of 1-10. It refers to the number of times the word appears in the information stream within a specific evaluation time window. This refers to the total number of words in the information flow within that time window; the panic vocabulary and its weights are predefined by rural governance experts and psychologists. : Summation index, representing the i-th word in the preset panic vocabulary list;

[0088] This model processes all information text within the current unit of time and outputs the weighted frequency of panic-inducing words; panic risk factor. The calculation method is similar to that of the information flow risk factor, which also uses logarithmic transformation and takes the ratio of the current weighted frequency of panic words to its historical baseline frequency as input.

[0089] The process of obtaining the proportion of currently active nodes with network conditions exceeding a threshold out of the total number of active nodes, and setting this proportion as a risk factor for weak network environments, aims to quantify the risk of unreliable data transmission caused by weak network infrastructure. A network condition exceeding a threshold refers to a node's network latency or packet loss rate exceeding a preset threshold. This threshold is determined by statistically analyzing historical network data and selecting a quantile that covers 95% of normal network fluctuations as the threshold. For example, it could be set as a latency greater than 200 milliseconds or a packet loss rate greater than 5%, ensuring that only statistically significant fluctuations are included in the risk assessment. (Weak network environment risk factor) It is directly defined as this proportional value, and its range is naturally within... The interval is derived from the periodic detection and statistics of all currently active nodes by the platform's network monitoring module;

[0090] Through the aforementioned specific risk index generation method, this embodiment decomposes the abstract system risk into three precisely measurable indicators: information flow, panic, and weak network environment. By utilizing logarithmic transformation and normalization, the calculation of the risk index is ensured to be stable and robust. Compared to general risk assessment, this multi-dimensional and quantitative risk perception mechanism enables the system to more accurately and sensitively identify the source and degree of risk, providing a solid data foundation for the subsequent refined and differentiated adjustment of adaptive authentication strategies, thereby significantly improving the system's response speed and accuracy to environmental changes.

[0091] The process of generating the authentication depth value is as follows:

[0092] Obtain the system's overall risk index and calculate the risk adjustment item;

[0093] Obtain the estimated value of the information to be published and calculate the value adjustment item;

[0094] The authentication depth value is generated by combining the preset basic authentication depth, risk adjustment items, and value adjustment items.

[0095] Based on Example 1, this embodiment elaborates on the generation process of authentication depth value, with the aim of creating a non-linear, multi-dimensional authentication strategy decision model, so that authentication strength can be matched in a refined and differentiated manner according to real-time risk and information value.

[0096] In this embodiment, the authentication depth value is generated by merging calculation models; these models are constructed as follows:

[0097]

[0098] In this expression, It is the authentication depth value, which physically represents the quantification of the authentication rigor required for a single piece of information. It is a dimensionless numerical value. It is the basic authentication depth, which physically means the system is in a completely normal state. The minimum authentication level required for all information is a preset system constant. It is the comprehensive system risk index calculated from the preceding steps; It is the value of information estimation, which in physical terms is the economic or social value contained in a single piece of information, and its source is provided by the information publisher or estimated through content analysis models; Risk sensitivity coefficient, used to adjust the extent to which system risk affects the depth of certification; Risk response nonlinear exponent is a constant greater than 1, which causes the certification depth to increase exponentially with the increase of risk. Value sensitivity coefficient, used to adjust the impact of the information's intrinsic value on authentication depth; : Natural logarithm function; The baseline value constant, whose function is to ensure that the parameters of the logarithmic function are dimensionless;

[0099] In this embodiment, the content analysis model is a keyword-matching-based weighted scoring model. The system has a pre-set value keyword library containing keywords of different categories, and each category is assigned a basic value. The model calculates the number and category of keywords matched in the information to be published, and then performs a weighted sum based on factors such as information length and whether it contains images or videos, thereby quickly estimating the information value. ;

[0100] In a preferred embodiment, the model can also incorporate phrase recognition and semantic association analysis to more accurately assess the nonlinear value effect produced by the combination of keywords;

[0101] It is a benchmark constant, and its function is to ensure that the parameters of the logarithmic function are dimensionless; It is a risk sensitivity coefficient, used to adjust the extent to which system risk affects the certification depth; It is a nonlinear risk response exponent, a constant greater than 1. Its function is to make the certification depth increase exponentially with the increase of risk, so as to quickly improve the security level when the risk surges. This is the value sensitivity coefficient, used to adjust the impact of the information's intrinsic value on the authentication depth; the above parameters The source is the establishment of a simulation platform to simulate various rural failure scenarios, with the optimization goal of minimizing the probability of system failure within an acceptable average release delay, and the result is obtained through reinforcement learning algorithm training.

[0102] To facilitate understanding and implementation, a set of empirical parameters obtained through preliminary simulation can be provided as a reference: Basic Certification Depth Risk sensitivity coefficient Risk response nonlinear index Value sensitivity coefficient Under these parameters, when the system is risk-free, And the information value is zero. At that time, the certification depth is the lowest, 10; when the risk index... When it reaches 0.5, the contribution of the risk item is When the risk index When it reaches 2, the contribution of the risk item surges to This demonstrates the rapid response characteristic of exponential growth.

[0103] The process of obtaining the system's overall risk index and calculating the risk adjustment term is mathematically represented by the formula... Partial; the calculation of this risk adjustment item will take the input system comprehensive risk index. Through nonlinear exponents and sensitivity coefficient This amplification resulted in a disproportionately sharp increase in certification requirements as the risk index rose.

[0104] The process of obtaining the estimated value of information to be published and calculating the value adjustment item is mathematically represented by the formula... Partial; the calculation of this value adjustment term involves estimating the value of information through a logarithmic function. This achieves the effect that authentication requirements increase with the value of information, but the growth rate gradually slows down. This is in line with the general understanding that the higher the value, the higher the security guarantee required, while also taking into account cost-effectiveness.

[0105] The process of combining the preset basic certification depth, risk adjustment items, and value adjustment items to generate a certification depth value is essentially the process of combining the above three parts. Add them together to get the final authentication depth value. This value is then output to the multi-level authentication execution unit.

[0106] By using this authentication depth value generation model, this embodiment breaks away from the static and singular authentication threshold mode in traditional technologies. It can tailor the authentication strength setting for each piece of information based on two dimensions: system risk and information value. This refined matching mechanism avoids the inefficiency caused by over-authenticating all information under low risk conditions, and also avoids the security risks caused by insufficient authentication of high-value information under high risk conditions. Thus, it optimizes the resource allocation of the entire platform on a macro level and achieves the best balance between security and user experience.

[0107] The process of matching the preset authentication level with the multi-level authentication execution unit is as follows:

[0108] The received authentication depth value is compared and analyzed with the preset authentication depth threshold.

[0109] When the authentication depth value is less than or equal to the first preset threshold, a static authentication is performed.

[0110] When the authentication depth value is greater than the first preset threshold and less than or equal to the second preset threshold, static authentication and SMS verification code authentication are performed.

[0111] When the authentication depth value is greater than the second preset threshold and less than or equal to the third preset threshold, silent authentication, SMS verification code authentication and social graph guarantee authentication are performed.

[0112] When the authentication depth value is greater than the third preset threshold, static default authentication, SMS verification code authentication, social graph guarantee authentication and biometric verification are performed.

[0113] This embodiment describes the process of matching a preset authentication level with a multi-level authentication execution unit, based on embodiment 1. The purpose is to discretize the continuous authentication depth values ​​into specific and executable authentication operation levels.

[0114] The process of comparing and analyzing the received authentication depth value with the preset authentication depth threshold is the basis for authentication level matching decisions; the preset authentication depth threshold refers to a set of pre-defined numerical sequences. These factors collectively constitute the boundary for the authentication level division. To ensure the scientific validity of this threshold setting, the method is as follows: ensuring that each level upgrade brings significant security gains, while the increased user operation time is within the generally acceptable range for rural users; the source is dynamic fine-tuning and optimization by the platform based on operational data such as average user authentication success time and abandonment rate; in this embodiment, it is assumed that three threshold levels are preset, namely the first preset threshold... The second preset threshold and the third preset threshold ;

[0115] In this embodiment, in conjunction with the foregoing... The calculation parameters can be set to the first preset threshold. The second preset threshold The third preset threshold This means that when the calculated authentication depth value... When the value is less than or equal to 20, only silent authentication is performed without any awareness; when the value is in the range of (20, 60), SMS verification is initiated; when the value is in the range of (60, 120), social guarantee is required; and when the value exceeds 120, the highest level of biometric verification must be performed. The setting of these thresholds provides a clear and operable quantitative basis for upgrading the authentication level.

[0116] Based on the results of this comparative analysis, the corresponding certification level will be applied:

[0117] When the authentication depth value is less than or equal to the first preset threshold, a silent authentication is performed. Silent authentication refers to an authentication method that does not require active user operation. Its working principle is based on automatic background verification of the user's historical credit record and the current device fingerprint. This is suitable for routine information release scenarios with extremely low risk and low value.

[0118] When the authentication depth value is greater than the first preset threshold and less than or equal to the second preset threshold, static authentication and SMS verification code authentication are performed. SMS verification code authentication is an authentication method that requires users to enter a dynamic verification code received through their registered mobile phone number to verify the consistency of the user's identity.

[0119] When the authentication depth value is greater than the second preset threshold and less than or equal to the third preset threshold, silent authentication, SMS verification code authentication and social graph guarantee authentication are executed. Social graph guarantee authentication refers to an authentication method that uses the rural social network of acquaintances for trust endorsement. Its working principle is that the system requires the information publisher to select 1 to 2 high-credit users from its preset social relationship list, such as neighbors and village cadres, and the system sends a confirmation request to the guarantor. Only after the guarantor confirms online can the authentication be passed.

[0120] The establishment of the pre-set social relationship list follows a strict verification process: User A, wishing to add User B as a social relationship contact, must initiate an application, and User B must confirm and agree online. To prevent fake relationships, the online relationship requires at least one authentication node, such as an administrator designated by the village committee, to conduct offline verification or cross-verification before it can be activated for guarantee. High-credit users refer to those with high overall credit scores. The top 20% of nodes in this system, or nodes that act as platform-certified organizations such as village committees or cooperatives. This ensures the reliability of the guarantee itself;

[0121] When the authentication depth value is greater than the third preset threshold, the following methods are executed: silent authentication, SMS verification code authentication, social graph guarantee authentication, and biometric verification. Biometric verification refers to a method of identity verification that uses the inherent biometric features of the human body. In this embodiment, the user is required to complete a lightweight face recognition or liveness detection through the front-end camera of the mobile device to provide the highest level of identity verification.

[0122] This embodiment designs a multi-level authentication system, from silent authentication and SMS verification to social guarantees and biometric verification, and associates these levels with dynamically calculated authentication depth values, achieving adaptive authentication operations. This design greatly enhances the system's inclusiveness and accessibility. For users in rural areas with lower digital literacy, in most common scenarios, they can complete the operation simply through seamless silent authentication. When system risks increase or information of significant value is involved, the system can seamlessly upgrade to a stronger authentication method to ensure the authenticity of critical information. This not only improves the user experience but also ensures the platform can operate effectively in various complex environments.

[0123] Example

[0124] The process of the rural credit consensus mechanism is as follows:

[0125] Obtain the overall reputation score of the candidate nodes;

[0126] Based on the comprehensive reputation score and the preset selection pressure coefficient, the election probability of each candidate node is calculated.

[0127] Based on the election probability, block producers are elected from the candidate nodes;

[0128] The update process for the overall credit score is as follows:

[0129] Obtain the node's reputation score from the previous moment;

[0130] Retrieve the reputation reward / penalty value generated by the node's recent behavior;

[0131] The exponential moving average model is used, and the credit score at the current time is calculated based on the credit score, credit reward and penalty value at the previous time step and the preset learning rate.

[0132] Based on Example 1, this embodiment elaborates on the rural credit consensus mechanism and its core comprehensive credit score update process, aiming to solve the efficiency bottleneck problem of blockchain consensus in high-concurrency rural scenarios while ensuring security.

[0133] The core of the rural reputation consensus mechanism lies in electing block producers based on the comprehensive reputation score of nodes. To implement this mechanism, the comprehensive reputation score of candidate nodes is obtained. Candidate nodes refer to a group of entities that are pre-certified by the platform and have high prestige in the real world, such as village committees, cooperatives, or highly reputable individual users. The comprehensive reputation score is a quantitative indicator used to measure the reliability and contribution of a node's long-term behavior.

[0134] Based on the comprehensive reputation score and the preset selection pressure coefficient, the election probability of each candidate node is calculated. The preset selection pressure coefficient is a constant greater than or equal to 1, which is used to adjust the influence of the reputation score on the election probability. The larger the value, the higher the probability that a high-reputation node will be selected.

[0135] In this embodiment, a pressure coefficient is selected. The empirical value can be taken as 2; when At that time, the probability of election is directly proportional to the credit score; when At that time, the election probability is proportional to the square of the reputation score, which can significantly increase the probability of high-reputation nodes being selected without excessively sacrificing randomness, thus achieving a balance between efficiency and decentralization.

[0136] The coefficient is derived from a set of empirical values ​​that achieve the optimal balance between network stability and transaction confirmation time, selected through simulation tests. The election probability of each candidate node is calculated as follows:

[0137]

[0138] In this expression, It is the node election probability, and in physical terms, it refers to the candidate nodes. The probability of being selected as a block producer in this round of elections; It is a node The overall credit score is calculated using the formula below; It is about selecting the pressure coefficient; It is the set of all candidate nodes;

[0139] When the combined reputation score of all candidate nodes When all values ​​are 0, the system will use random uniform sampling to elect a producer from the candidate nodes, meaning that each node has an equal probability of being elected.

[0140] : Summation index, representing a node in the candidate node set;

[0141] Based on the election probability, a block producer is elected from the candidate nodes. In this embodiment, a roulette wheel selection algorithm is used to randomly select a node as the block producer for this round based on the probability distribution calculated above. The node is responsible for packaging transactions and generating new blocks.

[0142] To ensure the dynamism and fairness of the reputation score, specific regulations are made regarding the update process of the comprehensive reputation score. This process is implemented through an exponential moving average model. The process includes obtaining the node's reputation score at the previous moment and obtaining the reputation reward and penalty value generated by the node's recent behavior. The reputation reward and penalty value is a quantitative evaluation of the node's behavior within a period. For example, a positive value is awarded for successfully producing a block, while a negative value is penalized for being elected but failing to produce a block on time.

[0143] To ensure a clear reward and punishment mechanism, the system pre-sets a reputation reward and punishment rule table; for example: within an evaluation cycle, such as 24 hours, if a node is successfully elected and produces blocks on time, Add 10 points; elected but failed to produce blocks on time, 20 points will be deducted; acting as a guarantor for information that was later proven to be false. Deduct 50 points; Maintain stable online presence and provide network service. Add 1 point; the total reward / penalty value within a cycle is the sum of the scores corresponding to all behaviors; this rule table is public to all users to ensure fairness and transparency;

[0144] An exponential moving average model is used, and the current credit score is calculated based on the previous credit score, credit reward / penalty value, and a preset learning rate. The preset learning rate is a parameter between 0 and 1, which determines the rate of decay of historical credit and the influence weight of new behavior.

[0145] In this embodiment, the learning rate The possible value is 0.1; this means that each time the credit score is updated, the previous credit score accounts for 90% of the weight, while the reward or penalty value for recent behavior accounts for 10%. This setting allows the credit score to remain relatively stable, avoiding drastic fluctuations due to a single accidental mistake, while also ensuring that long-term performance is gradually reflected in the credit score.

[0146] The current credit score is calculated as follows:

[0147]

[0148] In this expression, It is the current credit score; It is the credit score from the previous moment, which is derived from the calculation results of the node in the previous cycle; It is the learning rate; It is a recent reputation reward / penalty value, derived from periodic assessments of node behavior;

[0149] By introducing a reputation-based consensus mechanism, this embodiment narrows the scope of consensus participation to a small number of high-reputation nodes, greatly reducing the communication overhead and computing resources required to reach consensus, significantly improving transaction processing speed, and meeting the high timeliness requirements of the rural information platform. At the same time, the dynamic reputation score update mechanism constitutes an effective incentive and penalty system, encouraging nodes to participate in consensus honestly and stably, thereby ensuring efficiency while maintaining the long-term security and stability of the entire system.

[0150] Example 4

[0151] The process of batch anchoring strategy is as follows:

[0152] Collect all signed data packets generated within a certain period of time;

[0153] Construct a Merkle tree based on the aggregated data packets;

[0154] Record the root hash of the Merkle tree as a transaction on the blockchain;

[0155] The signed data packets are stored in a distributed file system;

[0156] This embodiment, based on embodiment 1, elaborates on the process of batch anchoring strategy, aiming to solve the problem of huge storage costs caused by confirming the rights of massive, low-value rural information item by item on the blockchain.

[0157] The core idea of ​​the batch anchoring strategy is to combine on-chain and off-chain storage to achieve a balance between cost and security; the execution process is as follows:

[0158] The system aggregates all signed data packets generated within a certain period of time. A time window, such as 1 minute, or a data packet quantity threshold, such as 500, is set to aggregate all signed data packets generated by the multi-level authentication execution unit.

[0159] A Merkle tree is constructed based on the aggregated data packets; a Merkle tree is a hash tree in which each leaf node is the hash value of a single data packet, and each non-leaf node is the combined hash of the hash values ​​of all its child nodes; the advantage of this data structure is that it can summarize and verify the integrity of the entire dataset using a single root hash value.

[0160] The root hash of the Merkle tree is recorded as a transaction on the blockchain. The system submits the final calculated Merkle tree root hash, along with the timestamp and other metadata, as the payload of a transaction to the lightweight consensus and on-chain anchoring unit for consensus and recording on the blockchain. Since the blockchain only stores a very small hash value instead of all the original data, the on-chain storage cost is greatly reduced.

[0161] The signed data packets are stored in a distributed file system; distributed file systems, such as IPFS and InterPlanetary File System, are decentralized storage solutions with a cost far lower than on-chain storage in blockchain; all original, signed data packets are uploaded to this system; users can verify that the data has been confirmed and has not been tampered with at a specific point in time by comparing the data packets stored in the distributed file system and their paths in the Merkle tree with the root hash recorded on the blockchain;

[0162] By adopting a batch anchoring strategy, this embodiment cleverly utilizes the immutability of blockchain to ensure the ultimate credibility of data storage, while leveraging the low-cost characteristics of off-chain distributed storage to accommodate massive amounts of raw data. This collaborative on-chain and off-chain storage method makes it economically feasible to confirm ownership and prevent counterfeiting for every piece of information in the rural information platform. It successfully solves the cost bottleneck problem when blockchain technology is directly applied to high-concurrency, low-value information scenarios, ensuring the economic feasibility and scalability of the system.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data ownership confirmation and anti-counterfeiting system for a rural information platform based on blockchain and trusted authentication, characterized in that, include: The dynamic risk perception unit is used to obtain the amount of information published within the platform in the current unit of time and calculate the information flow risk factor. The weighted frequency of panic-inducing words in the current information stream is extracted using a pre-defined natural language processing model, and the panic risk factor is calculated. Get the proportion of the number of nodes whose network status exceeds the threshold among the current active users to the total number of active nodes, and set the proportion as the weak network environment risk factor; By combining information flow risk factors, panic risk factors, and weak network environment risk factors, a weighted summation is performed to generate a comprehensive system risk index. The calculation process of the information flow risk factor is as follows: obtain the conventional baseline traffic by performing a moving average calculation on the platform's historical traffic data; combine the current information release volume per unit time with the conventional baseline traffic, and perform a logarithmic transformation to obtain the information flow risk factor. The formula for calculating the panic risk factor is as follows: ； In the formula, S represents the risk factor for panic. The weighted frequency of panic-inducing words in the current information stream. The standard baseline frequency is calculated from historical data; The formula for calculating the system's comprehensive risk index is as follows: ； In the formula, The system's comprehensive risk index is defined as follows: I represents the information flow risk factor, S represents the panic risk factor, and N represents the weak network environment risk factor. These are the risk contribution weights corresponding to each risk factor; An adaptive authentication strategy generation unit is used to receive the system's comprehensive risk index and, in conjunction with the estimated value of the information to be published, generate an authentication depth value. The generation process of the authentication depth value is as follows: The system's comprehensive risk index is obtained, and a risk adjustment item is calculated; the estimated value of the information to be published is obtained, and a value adjustment item is calculated; the preset basic authentication depth, risk adjustment item, and value adjustment item are combined and calculated to generate the authentication depth value; the calculation formula for the authentication depth value is: ； In the formula, For authentication depth value, Basic certification depth, Here, V represents the system's comprehensive risk index, α represents the estimated information value, β represents the risk response nonlinearity index, γ represents the value sensitivity coefficient, and ln represents the natural logarithm function. As a benchmark value constant; A multi-level authentication execution unit is used to receive the authentication depth value, match a preset authentication level, and attach a digital signature to the original information that has passed authentication to generate a signed data packet. The process of the multi-level authentication execution unit matching the preset authentication level is as follows: the received authentication depth value is compared and analyzed with a preset authentication depth threshold; when the authentication depth value is less than or equal to the first preset threshold, a silent authentication is performed. When the authentication depth value is greater than the first preset threshold and less than or equal to the second preset threshold, static authentication and SMS verification code authentication are performed. When the authentication depth value is greater than the second preset threshold and less than or equal to the third preset threshold, silent authentication, SMS verification code authentication and social graph guarantee authentication are performed. When the authentication depth value is greater than the third preset threshold, silent authentication, SMS verification code authentication, social graph guarantee authentication and biometric verification are performed. This unit guides the information publishing user to complete all authentication operations required for this level. After all authentication operations for the corresponding authentication level are passed, a digital signature is attached to the original information. A lightweight consensus and on-chain anchoring unit is used to receive signed data packets, execute the rural reputation consensus mechanism and batch anchoring strategy, and confirm and store data ownership. The rural reputation consensus mechanism process is as follows: obtain the comprehensive reputation score of candidate nodes; calculate the election probability of each candidate node based on the comprehensive reputation score and a preset selection pressure coefficient; elect block producers from the candidate nodes according to the election probability. The batch anchoring strategy process is as follows: collect all signed data packets generated within a certain period of time; construct a Merkle tree based on the collected data packets; record the root hash of the Merkle tree as a transaction on the blockchain; and store the signed data packets in a distributed file system.

2. The rural information platform data ownership confirmation and anti-counterfeiting system based on blockchain and trusted authentication as described in claim 1, characterized in that, The update process for the comprehensive credit score is as follows: Obtain the node's reputation score from the previous moment; Retrieve the reputation reward / penalty value generated by the node's recent behavior; The exponential moving average model is used, and the credit score at the current time is calculated based on the credit score, credit reward / penalty value and preset learning rate of the previous time step.

Images