Sharing method and system of distributed forest and grassland Internet of Things data based on blockchain

Abstract

The present invention discloses a method and system for sharing distributed forest and grassland Internet of Things data based on blockchain, which relates to the field of data storage technology. It collects multiple types of data from forest and grassland ecological environments, extracts characteristic parameters to construct multi-dimensional feature vectors, and inputs them into a preset data value assessment model to output a value score. The data is divided into first, second, and third levels according to the scoring threshold, and a corresponding blockchain storage strategy is adopted. The first-level data is further divided into mobile and non-mobile data. For the first-level mobile data, second-level and third-level data, the data level and blockchain storage method are regularly and dynamically adjusted and updated. This method can realize the hierarchical management of forest and grassland Internet of Things data, avoid a "one-size-fits-all" chain-up strategy, and reduce the waste of on-chain storage resources and response delays. At the same time, the chain-up strategy can be adjusted in time according to the dynamic changes in the data content, reduce the risk of information leakage, and improve data utilization efficiency and storage security.

Description

Technical Field

[0001] The present invention relates to the field of data storage technology, and in particular to a method and system for sharing distributed forest and grassland Internet of Things data based on blockchain. Background Art

[0002] In the forest and grassland IoT system, a large number of terminal devices (such as forestry sensors, drone inspections, and weather stations) continuously collect data on forestland status, grassland growth, and disaster monitoring. This data is diverse, frequently updated, and sourced from diverse sources. To ensure the authenticity and security of this data, the system incorporates blockchain technology as an underlying support, establishing a distributed forest and grassland data sharing mechanism based on blockchain. This mechanism stores forest and grassland data from different regions and institutions in a distributed manner. Leveraging the immutability and traceability of blockchain, this data enhances trustworthiness and enables secure sharing through multi-party collaboration. Furthermore, incorporating an off-chain database, this system enables tiered storage and rapid retrieval of large-scale data, improving overall data processing efficiency.

[0003] However, in practical applications, there are still some key issues that need to be addressed urgently: First, there are differences in the levels of forest and grassland IoT data. For example, the security of temperature and humidity data is far lower than that of highly sensitive data such as wildfire warning information and pest and disease images. If they are uploaded to the chain in a "one-size-fits-all" manner, it will cause waste of blockchain resources and response delays; second, the importance of data has the characteristics of dynamic change. The status of some data may rise or fall, but the existing sharing mechanism is difficult to perceive in time and dynamically adjust its chain-up strategy, which may lead to the risk of information leakage and unnecessary storage waste. Summary of the Invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and provide a method and system for sharing distributed forest and grassland Internet of Things data based on blockchain.

[0005] In the first aspect of the present invention, a method for sharing distributed forest and grassland IoT data based on blockchain is first proposed, and the method includes:

[0006] The front-end equipment collects a variety of data from the forest and grassland ecological environment, extracts characteristic parameters from the collected data to construct a multi-dimensional feature vector, inputs the multi-dimensional feature vector into the preset data value assessment model, and outputs the value score of each type of data;

[0007] Divide various types of data into different levels based on the data value scoring threshold, and adopt different blockchain storage management methods for different levels of data; data levels are divided into first level, second level and third level;

[0008] Analyze the first-level data and divide it into mobile data and non-mobile data according to preset rules and conditions;

[0009] For the first-level data, the second-level data and the third-level data, periodic dynamic adjustment analysis is carried out, the level increase coefficient of each data is calculated, the data is re-classified, and the divided data is stored in the corresponding blockchain to realize data sharing.

[0010] Optionally, the steps of dividing multiple types of data into different levels according to the value scoring threshold of the data and adopting different blockchain storage management methods for data of different levels are as follows:

[0011] Comparing the value scores of each type of data with the first and second preset value score thresholds, and classifying the data into the third level if the value score is less than the first preset value score threshold; the first preset value score threshold is less than the second threshold;

[0012] If the value score is not less than the first threshold value and the second threshold value, the data is classified into the second level;

[0013] If the value score is not less than the preset first value score threshold, the data is classified into the third level;

[0014] When the data level is the first level, it is encrypted and protected, and its hash value is calculated. The data body, along with its metadata and hash value, is completely written into the blockchain.

[0015] When the data level is the second level, it is stored in the IPFS distributed off-chain, and only the hash summary generated by its content identifier and data metadata is uploaded to the blockchain for reference;

[0016] When the data level is the third level, the data is cold stored or archived locally, and only its summary information is recorded on the blockchain.

[0017] Optionally, the step of dividing the first-level data into mobile data and non-mobile data according to preset rule conditions is:

[0018] For all types of data corresponding to the first level, whether the level can be changed is determined based on the preset rules and conditions. If not, the corresponding data will be recorded as non-mobile data, and the storage method of non-mobile data cannot be changed; its existing encrypted and complete storage strategy on the blockchain will be maintained, and the level and storage method will not be adjusted;

[0019] If possible, the corresponding data will be recorded as flowable data, and the storage method of the flow data can be changed.

[0020] Optionally, the calculation steps of the level increase coefficient are:

[0021] For the flowable data of the first level data, the second level data and the third level data, the access decay index and the time entropy change index of all types of data within the preset period are obtained, and the access decay index and the time entropy change index are normalized and mapped to the numerical range of 0-1. The same weight is assigned to the normalized access decay index and the time entropy change index to calculate the level increase coefficient.

[0022] Optionally, the steps for calculating the access decay index are:

[0023] For the mobile data of the first level data, the second level data and the third level data, obtain the number of times all types of data are accessed every day within the preset period and build the access sequence ; For access sequence Sort the elements in and map them into rank values ​​to get the order column ;

[0024] Constructing the rank of a standard time series ;

[0025] Use Spearman's method to measure the order of and Monotonic correlation of , the calculation formula is: Where, Indicates the value corresponding to the total number of days in the preset cycle. and Order columns and No. data values;

[0026] Calculate the access decay index using the following formula: , where is the access decay exponent.

[0027] Optionally, the calculation steps of the aging entropy change index are:

[0028] For the mobile data of the first level data, the second level data and the third level data, obtain all types of data within the preset period and regularly collect content snapshots of data objects;

[0029] Each snapshot is converted into a vector representation through feature encoding , calculate the similarity change between the current snapshot and the previous snapshot :

[0030] Change each similarity Mapped to information entropy : ; Get the entropy sequence that changes with time;

[0031] The mean of the similarity change in the entropy sequence is calculated as the time-dependent entropy change index.

[0032] Optionally, the following steps are performed: calculating the level increase coefficient of each data, reclassifying the data into levels, and storing the reclassified data in the corresponding blockchain:

[0033] Compare the level increase coefficient of each data with the preset level increase coefficient first threshold. If the level increase coefficient is less than the preset level increase coefficient first threshold, the data will be downgraded by one level from the original level; the downgraded level will be used as the new level of the data, and the data will be stored on the blockchain according to the new level;

[0034] If the level increase coefficient is not less than the first threshold of the preset level increase coefficient but less than the second threshold of the preset level increase coefficient, the level of the data remains unchanged and the data continues to be stored on the blockchain based on the original level;

[0035] If the level increase coefficient is not less than the preset level increase coefficient second threshold, the level of the data will be increased by one level, and the increased level will be used as the new level of the data, and the data will be stored on the blockchain according to the new level.

[0036] In a second aspect of the present invention, a distributed forest and grassland IoT data sharing system based on blockchain is proposed, the system comprising:

[0037] Value scoring module: This module collects various data from forest and grassland ecological environments through front-end devices, extracts characteristic parameters from the collected data, constructs multi-dimensional feature vectors, inputs the multi-dimensional feature vectors into a preset data value assessment model, and outputs value scores for various types of data.

[0038] Level judgment module: divides various types of data into different levels according to the data value scoring threshold, and adopts different blockchain storage and management methods for data of different levels; data levels are divided into first level, second level and third level;

[0039] Data division module: Analyzes the first-level data and divides the first-level data into mobile data and non-mobile data according to preset rules and conditions;

[0040] Data sharing module: For the first-level data, the second-level data and the third-level data, periodic dynamic adjustment analysis is carried out, the level increase coefficient of each data is calculated, the data is re-classified, and the divided data is stored in the corresponding blockchain to realize data sharing.

[0041] Beneficial effects of the present invention:

[0042] This invention proposes a distributed forest and grassland IoT data sharing method and system based on blockchain. This method uses front-end devices to collect multiple types of data from forest and grassland ecosystems, extracts characteristic parameters to construct multidimensional feature vectors, and inputs them into a preset data value assessment model to output a value score. Data is then divided into first, second, and third levels based on scoring thresholds, and different blockchain storage management methods are used. First-level data is further divided into mobile and non-mobile data based on preset rules. Regular dynamic adjustment and analysis are performed on mobile, second, and third-level data, calculating the level increase coefficient, reclassifying the data, and updating the blockchain storage strategy accordingly to achieve data sharing. This allows forest and grassland IoT data to be divided into different levels and stored in different ways, avoiding a "one-size-fits-all" on-chain approach and reducing chain resource waste and response delays when storing data on the blockchain. Furthermore, the system can promptly detect and dynamically adjust the on-chain strategy based on dynamic changes in data, minimizing the risk of information leakage and unnecessary storage waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1 A flowchart of a method for sharing distributed forest and grassland IoT data based on blockchain;

[0045] Figure 2 This is a framework diagram of the distributed forest and grassland IoT data sharing system based on blockchain. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] The embodiment of the present invention provides a method for sharing distributed forest and grassland Internet of Things data based on blockchain. Figure 1 , Figure 1 Flowchart of a method for sharing distributed forest and grassland IoT data based on blockchain provided by an embodiment of the present invention. The method includes the following steps:

[0048] The front-end equipment collects a variety of data from the forest and grassland ecological environment, extracts characteristic parameters from the collected data to construct a multi-dimensional feature vector, inputs the multi-dimensional feature vector into the preset data value assessment model, and outputs the value score of each type of data;

[0049] Divide various types of data into different levels based on the data value scoring threshold, and adopt different blockchain storage management methods for different levels of data; data levels are divided into first level, second level and third level;

[0050] Analyze the first-level data and divide it into mobile data and non-mobile data according to preset rules and conditions;

[0051] For the first-level data, the second-level data and the third-level data, periodic dynamic adjustment analysis is carried out, the level increase coefficient of each data is calculated, the data is re-classified, and the divided data is stored in the corresponding blockchain to realize data sharing.

[0052] Based on the distributed forest and grassland Internet of Things data sharing method based on blockchain provided by the embodiment of the present invention, the forest and grassland Internet of Things data can be divided into different levels through the above method, and different storage methods can be adopted for data of different levels, avoiding the "one-size-fits-all" chain-up, reducing the waste of chain resources and response delay when the blockchain stores data; in addition, it can timely perceive the dynamic changes of data and dynamically adjust its chain-up strategy, reduce the risk of information leakage, and reduce unnecessary storage waste.

[0053] In one embodiment, a variety of data from forest and grassland ecological environments are collected through front-end equipment, and feature parameters are extracted from the collected data to construct a multi-dimensional feature vector. The multi-dimensional feature vector is input into a preset data value assessment model to output the value score of each type of data.

[0054] In the forest and grassland ecological IoT, front-end equipment deployed in the field (including environmental sensors, image acquisition equipment, meteorological monitoring stations, soil monitoring nodes, and drone-mounted devices) collects real-time, multi-source, heterogeneous ecological data such as temperature, humidity, light, CO2 concentration, wind speed, soil pH, terrain slope, pest and disease images, and fire videos from target areas. This raw data undergoes preprocessing, including denoising, standardization, time alignment, and redundancy removal. Key characteristic parameters are then extracted from each data type, such as the fluctuation range of temperature and humidity indicators, image clarity score, data acquisition frequency, historical call volume, business module, source node level, and security sensitivity score. This constructs a multidimensional feature vector with both descriptive and discriminative properties. This vector is then fed into a pre-defined data value assessment model. This model, which can use weighted scoring, support vector machines, neural networks, or rule-based comprehensive scoring, comprehensively analyzes the data's characteristics in terms of value, security, sensitivity, timeliness, and applicability, and outputs a numerical value score. This scoring serves as an important basis for subsequent data tiering and storage strategy configuration, intelligently determining data importance and enabling more scientific tiered management and resource allocation. This approach enables efficient value identification of multi-type and multi-granular ecological data in complex forest and grassland IoT environments, providing a solid foundation for subsequent data tiered storage, access control, and sharing decisions.

[0055] For example, in a forest area, multiple environmental monitoring nodes and high-definition cameras are deployed to collect real-time data on temperature, humidity, wind speed, light intensity, soil moisture, and images and videos of pests and diseases. Suppose that during a data collection mission, device A uploads a set of data, including a temperature trend curve, a 24-hour soil moisture record, a video clip of suspicious smoke in the forest, and an infrared thermal image. After preprocessing this data, the following features are extracted:

[0056] Temperature data exhibits moderate temporal volatility and high data continuity; soil moisture records contain unusual mutation points and are collected frequently; the video clip is 15 seconds long, with high definition and potential fire characteristics; the thermal imagery exhibits high contrast and clarity, with high-temperature areas marked in the marked areas. These features are integrated into a multidimensional feature vector, which is then fed into the "Data Value Assessment Model." Based on predefined evaluation rules or deep learning algorithms, the model scores the data based on multiple dimensions, including importance (e.g., whether it involves fire activity), application value (e.g., whether it can be used for emergency response), sensitivity (e.g., whether it involves core forest protection areas), and timeliness (e.g., whether real-time processing is required). The model outputs the following results: the fire video's value score is 92 (Level I High Value Data); the thermal image is 87 (Level I High Value Data); the soil moisture data is 62 (Level II Medium Value Data); and the temperature curve data is 45 (Level III Low Value Data). Based on this score, the data is automatically categorized into different levels, laying the foundation for selecting appropriate storage strategies (e.g., whether to store data on-chain, off-chain only, or only as hash summaries) and access rights management. This process demonstrates the ability to achieve intelligent hierarchical management of data through feature extraction and value assessment, which not only ensures the security, controllability and traceability of key data, but also effectively reduces the burden on the blockchain and improves resource utilization efficiency.

[0057] In one embodiment, multiple types of data are divided into different levels according to the data value scoring threshold, and different blockchain storage management methods are adopted for data of different levels; the data levels are divided into the first level, the second level and the third level; the specific steps are:

[0058] Compare the value scores of each type of data with the first and second thresholds of the preset value scores. If the value score is less than the first threshold of the preset value score, the data is classified into the third level; if the first threshold of the preset value score is less than the second threshold;

[0059] If the value score is not less than the first threshold value and the second threshold value, the data is classified into the second level;

[0060] If the value score is not less than the preset first value score threshold, the data is classified into the third level;

[0061] When the data level is level 1, it is encrypted and protected, and its hash value is calculated (e.g., using a cryptographic hash algorithm such as SHA-256), and the data itself, along with its metadata and hash value, is completely written into the blockchain.

[0062] When the data level is the second level, it is stored in the IPFS distributed off-chain, and only the hash summary generated by its content identifier and data metadata is uploaded to the blockchain for reference;

[0063] When the data level is the third level, the data is cold stored or archived locally (such as stored in an independent cold backup server, local hard disk of the edge node, etc.), and only its summary information (such as encrypted hash, file identifier) ​​is recorded on the blockchain.

[0064] It should be noted that the various types of data collected in the forest and grassland IoT are divided into different levels based on the data value scoring threshold, thereby adopting differentiated storage and management strategies to achieve resource optimization and data security. Each type of data is subjected to a preset data value assessment model to obtain a numerical value score. This threshold score is compared with two preset thresholds, and the first threshold is set lower than the second threshold. If the data's value score is below the first threshold, it is classified as Level 3. This type of data is generally of low value and rarely used, such as temperature and humidity data from ordinary environmental monitoring sensors and images from non-critical periods. This type of data is stored in low-cost cold storage or local archiving. Only summary information of the data, such as hash values ​​and metadata, is stored on-chain for data integrity verification and indexing. If the data's value score is between the first and second thresholds, it is classified as Level 2, representing data of medium value and risk. For example, regular ecological survey data or biodiversity statistics from a certain forest area are stored off-chain using IPFS distributed storage technology. Only the data's content identifier (CID) and meta-hash value are recorded on the blockchain to ensure data verifiability and conserve on-chain storage space. If the data's value score is above the second threshold, it is classified as Level 1, representing extremely critical and sensitive data, such as fire warning surveillance videos and real-time monitoring data of key protected species in forest areas. This type of data must be strongly encrypted and hashed using algorithms such as SHA-256. The data, along with the original data and metadata, is fully written into the consortium chain's blockchain ledger to ensure data immutability and full traceability.

[0065] In one implementation method, through the above method, different methods are adopted to store forest and grassland IoT data of different values ​​on the blockchain, avoiding the "one-size-fits-all" chain-up and reducing the waste of chain resources and response delays when the blockchain stores data.

[0066] In one embodiment, the first level data is analyzed and divided into flowable data and non-flowable data according to preset rule conditions;

[0067] For all types of data corresponding to the first level, whether the level can be changed is determined based on the preset rules and conditions. If not, the corresponding data will be recorded as non-mobile data, and the storage method of non-mobile data cannot be changed; its existing encrypted and complete storage strategy on the blockchain will be maintained, and the level and storage method will not be adjusted;

[0068] If possible, the corresponding data will be recorded as flowable data, and the storage method of the flow data can be changed.

[0069] It should be noted that further detailed analysis will be conducted to determine whether data can be reclassified based on pre-defined rules and conditions (such as the confidentiality level of the data, its reliance on real-time performance, its coupling with the business, and regulatory compliance requirements), as well as its score in the data value assessment model. If the rules determine that the data is subject to strict regulation, contains core confidential information, or its change would affect security and legal compliance, the data will be marked as non-mobile data. The storage method for such data will remain the current blockchain-encrypted, full on-chain storage strategy, with no downgrade or off-chain transfer allowed to ensure its long-term security and auditability. If the data rules determine that the data allows for adjustment, such as if the sensitivity of the data decreases after the completion of the phased task, or if the content is highly repetitive and can be reconstructed from the original source, the data will be marked as mobile data. In subsequent analysis, the data can be reclassified and its storage strategy can be changed (for example, from full on-chain storage to IPFS off-chain storage) based on dynamic indicators such as access frequency and call usage.

[0070] For example, a piece of image data showing "illegal logging identified in remote sensing satellite imagery of forests and grasslands" was initially assessed as Level 1 data. Because it directly implicates clues to illegal activities, it was manually marked as non-mobile data, requiring long-term preservation for evidence collection and law enforcement, and could not be downgraded. A high-resolution image of "ecological changes in wetlands at the edge of forests," while also initially assessed as Level 1, was later converted to Mobile Data after experts from the Ecological Research Department confirmed the completion of its research phase and its high reusability. This data could then be adjusted to Level 2 based on accessibility, with the storage strategy shifting to off-chain IPFS archiving, effectively alleviating on-chain resource pressure. This mechanism enables dynamic, secure management and sustainable sharing of critical ecological data.

[0071] It should be noted that the reason why only the first-level data is further divided into "mobile data" and "immobile data", while the second-level and third-level data are allowed to adjust their storage methods according to actual circumstances, is mainly because the first-level data itself is highly sensitive, confidential or business-critical. Any form of storage method adjustment may bring significant risks to security, compliance or business continuity. Therefore, it is necessary to further subdivide it to ensure that some absolutely sensitive core data is always protected at the highest level throughout its life cycle. In comparison, the second and third-level data are significantly less sensitive and important, usually not involving secrets, core corporate strategies or strong regulatory requirements. They have greater mobility and adjustability, and are suitable for level floating and storage optimization through periodic dynamic assessment, thereby improving the flexibility of data management and the efficiency of storage resource utilization while ensuring basic security.

[0072] In one implementation method, the benefit of this strategy is that it can strike a balance between security and efficiency. For the first-level data, by limiting the existence of non-flowing data, a strong bottom-line security guarantee can be provided for truly important and irreplaceable data to prevent it from being mistakenly downgraded or leaked due to technical adjustments; while flexible adjustment space is retained for the flowable part to enable it to have a certain degree of adaptability. The default adjustability of the second and third-level data can effectively release blockchain resources, reduce chain data redundancy, improve the overall data flow and sharing efficiency, and help promote the widespread sharing and reuse of non-sensitive data in the forest and grassland Internet of Things, and ultimately build a distributed data management ecosystem with "high protection of core data and flexible flow of general data" to achieve data sharing.

[0073] In one embodiment, a periodic dynamic adjustment analysis is performed on the flowable data of the first level data, the second level data, and the third level data, the level increase coefficient of each data is calculated, the data is reclassified into levels, and the divided data is stored accordingly;

[0074] Specifically, the calculation steps for the level increase coefficient are as follows:

[0075] For the mobile data of the first level data, the second level data and the third level data, the access decay index and the time entropy change index of all types of data within the preset period are obtained, and the access decay index and the time entropy change index are normalized and mapped to the value range of 0-1. The normalized access decay index and the time entropy change index are assigned the same weight to calculate the level increase coefficient. The calculation formula is: , where are the normalized time-dependent entropy change index and access decay index respectively; is the level increase coefficient.

[0076] It should be noted that, in general, the sum of the weights assigned to the access decay index and the time entropy change index is 1.

[0077] In one embodiment, the steps for calculating the access decay index are:

[0078] For the mobile data of the first level data, the second level data and the third level data, obtain the number of times all types of data are accessed every day within the preset period and build the access sequence Where, Indicates the value corresponding to the total number of days in the preset cycle, for access sequence Sort the elements in and map them into rank values ​​to get the order column : ; ; Indicates the Number of times the data of the day was accessed;

[0079] Constructing the rank of a standard time series (i.e. original order), ;

[0080] Use Spearman's method to measure the order of and Monotonic correlation of , the calculation formula is: ; and Order columns and No. data values;

[0081] Calculate the access decay index using the following formula: , where is the access decay exponent.

[0082] It should be noted that the data acquisition method involved in the above-mentioned access decay index calculation process is: within a set time period (such as the last 7 days or 30 days), the daily access frequency records of various data objects are automatically collected through the log or access control module, and these access times are constructed into an access sequence in chronological order; this access sequence is derived from trusted access sources such as on-chain call logs, database query records, and API gateway logs to ensure the authenticity and timeliness of data access behavior, and provide accurate input for subsequent order sequence construction and Spearman correlation analysis.

[0083] It's important to note that the Access Decay Index (ADI) measures the decrease in access frequency of a data object over a period of time, reflecting its tendency to be forgotten or neglected. It's based on Spearman rank correlation analysis, comparing the monotonic relationship between time series and access frequency series to determine trends in data access activity. A smaller ADI (i.e., a rank correlation value closer to a positive correlation or lacking a clear negative trend) indicates that the data's access frequency has not significantly decreased over the entire preset period, and may even remain stable or increase, indicating that users may still maintain a high level of interest in the data. Therefore, a smaller ADI indicates that the data has not been forgotten and its value still holds sustainable business or ecological significance. This is particularly true in distributed forest and grassland IoT environments, where certain data, such as real-time environmental monitoring, pest and disease monitoring, or fire warning information, requires continuous and frequent access to support front-end intelligent decision-making. The continued activity of such data indicates its continued value in the near and future. According to the strategy, this type of data is more likely to be judged as having a higher value rating, which will trigger the migration of the data from off-chain IPFS reference or cold archive status to on-chain fully encrypted storage to ensure the originality, integrity and security of the data;

[0084] In one implementation, analyzing the access decay index to determine data value and thus change its storage method on the blockchain offers the following benefits: by quantifying the changing trends in data access activity over a preset period, it is possible to accurately identify which data continues to deliver value and which is being marginalized, thereby enabling automated optimization of data storage strategies. For data with a low access decay index and high activity, its storage method can be proactively switched from off-chain lightweight references to full on-chain storage, enhancing its credibility, verifiability, and traceability. This also reduces on-chain resource usage for depleted data, lowering storage costs and improving the efficiency and intelligence of on-chain data liquidity and storage structures.

[0085] In one embodiment, the calculation steps of the aging entropy change index are:

[0086] For Level 1 data, including mobile data, Level 2 data, and Level 3 data, obtain snapshots of the content of all data objects, such as text, images, videos, or structured records, within a preset period.

[0087] Each snapshot is converted into a vector representation through feature encoding , such as text can be embedded, images can be hashed using perceptual hashing; calculate the similarity change between the current snapshot and the previous snapshot : ; where the content snapshot is at time The similarity change of For time The content snapshot vector of is a similarity function, such as cosine similarity;

[0088] Change each similarity Mapped to information entropy : ; Get the entropy sequence that changes with time;

[0089] The mean of the similarity change in the entropy sequence is calculated as the time-dependent entropy change index.

[0090] It should be noted that the data acquisition method involved in the calculation of the time-sensitive entropy change index described above is primarily based on a timed acquisition mechanism. This involves extracting content snapshots of various levels of data (including mobile data, structured records, text, images, or videos) at fixed intervals (e.g., hourly or daily) within a preset period. Each snapshot represents the state of the data at a specific moment. Through feature encoding techniques (such as word embedding for text, video frame hashing for images, and field vectorization for structured data), the snapshot is converted into a standardized vector representation, facilitating subsequent similarity calculations and entropy change analysis. This approach ensures the continuous tracking of the evolution of data content over time, providing a reliable foundation for accurately assessing its timeliness and sensitivity to change.

[0091] It should be noted that the Temporal Entropy Variation Index (TEV) is an important indicator for measuring the degree of drastic changes in data content in the time dimension. It quantifies the volatility of data content by calculating the differences in similarity between data snapshots at consecutive time points and mapping these differences into information entropy. The larger the TEV value, the more significant the changes in data content between different time nodes, indicating that the data has stronger dynamics and timeliness, that is, the data may be continuously affected by the external environment or internal state updates, and has higher monitoring value and decision-making significance in practical applications. In the distributed environment of forest and grassland Internet of Things, data sources often involve large-scale, multi-node ecological monitoring equipment (such as cameras, remote sensing terminals, environmental sensors, etc.). The data collected by these devices are highly time-sensitive, such as images of pests and diseases, fire videos, vegetation change indicators, etc. If a certain type of data changes frequently within a short period of time, meaning it has a high TEV index, it indicates that this data may be in an "event evolution" or "emergency situation," and its value is far higher than stable data. Therefore, when performing hierarchical data storage on the blockchain, this high-TEV value data should be prioritized as first-tier data for encrypted upload, full-node storage, and rapid response processing to ensure real-time protection, management, and emergency response of forest and grassland resources. This mechanism not only optimizes the use of blockchain storage resources but also enhances the intelligence and agility of forest and grassland IoT data processing.

[0092] In one implementation, analyzing the Time-Aging Entropy Change Index (TAE) for determining the value of forest and grassland IoT data and thereby adjusting its storage method offers the following benefits: The TEE has significant advantages for determining the value of forest and grassland IoT data and dynamically adjusting its storage method accordingly. On the one hand, the TEE can effectively identify data that changes significantly over a short period of time, such as signs of pest and disease outbreaks, fire warning signals, or ecological anomalies. This data can be promptly classified as high-value, high-priority data and uploaded to the core storage layer with greater security and availability, thereby ensuring the authenticity and immutability of critical ecological information. On the other hand, for data that changes little and remains stable, its storage level can be lowered, alleviating storage pressure and redundant consumption. This value judgment mechanism based on the TEE makes forest and grassland IoT data management more intelligent, accurate, and efficient, while also improving adaptability and resource allocation capabilities in ecological protection scenarios.

[0093] In one embodiment, the steps of calculating the level increase coefficient of each data, reclassifying the data into levels, and storing the divided data in the corresponding blockchain are as follows:

[0094] Compare the level increase coefficient of each data with the preset level increase coefficient first threshold. If the level increase coefficient is less than the preset level increase coefficient first threshold, the data will be downgraded by one level from the original level; the downgraded level will be used as the new level of the data, and the data will be stored on the blockchain according to the new level;

[0095] If the level increase coefficient is not less than the first threshold of the preset level increase coefficient but less than the second threshold of the preset level increase coefficient, the level of the data remains unchanged and the data continues to be stored on the blockchain based on the original level;

[0096] If the level increase coefficient is not less than the preset level increase coefficient second threshold, the level of the data will be increased by one level, and the increased level will be used as the new level of the data, and the data will be stored on the blockchain according to the new level.

[0097] It should be noted that the core of the above-mentioned data level redivision and storage strategy is to dynamically evaluate the current importance and potential value change trend of each data through the quantitative indicator of "level increase coefficient", and flexibly adjust its storage level and method in the blockchain by combining two preset level increase coefficient thresholds. Specifically, when the level-up coefficient of a piece of data is less than the first threshold (such as 0.3), it means that its content is stable, the access frequency is low, and the timeliness is weak, showing a trend of being "forgotten" or decreasing in value. Therefore, its current level is downgraded by one level, such as from the second level to the third level, and uploaded to the blockchain with the storage standard of the third-level data (such as lower redundancy, fewer nodes) to save storage resources; if the level-up coefficient is between the first and second thresholds (such as 0.3 to 0.7), it is judged that its value remains stable, the data level is not adjusted, and the original storage status is maintained; and when the level-up coefficient is greater than the second threshold (such as 0.7), it means that the data has been frequently accessed recently, has changed dramatically, or has attracted attention after the content is updated, and has high timeliness and sensitivity. At this time, its level should be upgraded by one level (such as from the third level to the second level, or from the second level to the first level), and a storage strategy with higher reliability and higher security should be adopted according to the new level, such as multi-node redundancy, multi-chain storage or enhanced chain verification. For example, if image data collected by a camera in a forest area has not been accessed for several consecutive days and the image content has not changed significantly, its level increase coefficient is 0.2, which is lower than the first threshold. In this case, it will be adjusted from the second level to the third level, and only the off-chain summary information will be retained and synchronized regularly. If a sensor in a certain area detects a sudden change in temperature and humidity, accompanied by drastic changes in image content, triggering multiple platform access and analysis calls, its level increase coefficient reaches 0.85, and it will be upgraded to the first level, ensuring its full storage and high-quality access in the blockchain to support ecological monitoring and emergency response. This mechanism not only achieves a dynamic matching of data value and storage resources, but also improves the forest and grassland IoT's ability to identify and respond to key data.

[0098] In one implementation, the above method can timely perceive the dynamic changes of data and dynamically adjust its chain-up strategy, reduce the risk of information leakage, and reduce unnecessary storage waste.

[0099] It should be noted that data sharing is achieved by reclassifying data into different levels and storing them on the blockchain. This allows for the orderly sharing and precise scheduling of forest and grassland IoT data. By transferring data to the blockchain, the blockchain automatically selects the appropriate storage method and sharing permissions based on the level and value of each data type, ensuring that critical data (such as pest and disease outbreak images and wildfire warning information) is quickly accessed and trusted by regulatory platforms, scientific research institutions, and forest patrol terminals, while lower-level data is retained for future reference at a lower cost, avoiding redundant use. Leveraging the decentralized, tamper-proof, and traceable nature of blockchain, the authenticity and timeliness of forest and grassland ecological monitoring data can be ensured during sharing, breaking down traditional "information silos," improving cross-departmental and cross-regional data collaboration, and promoting comprehensive improvements in resource coordination, scientific decision-making, and ecological governance capabilities.

[0100] Based on the same inventive concept, the present invention also provides a distributed forest and grassland IoT data sharing system based on blockchain. Figure 2 , Figure 2 A framework diagram of a distributed forest and grassland IoT data sharing system based on blockchain provided in an embodiment of the present invention, the system comprising:

[0101] Value scoring module: This module collects various data from forest and grassland ecological environments through front-end devices, extracts characteristic parameters from the collected data, constructs multi-dimensional feature vectors, inputs the multi-dimensional feature vectors into a preset data value assessment model, and outputs value scores for various types of data.

[0102] Level judgment module: divides various types of data into different levels according to the data value scoring threshold, and adopts different blockchain storage and management methods for data of different levels; data levels are divided into first level, second level and third level;

[0103] Data division module: Analyzes the first-level data and divides the first-level data into mobile data and non-mobile data according to preset rules and conditions;

[0104] Data sharing module: For the first-level data, the second-level data and the third-level data, periodic dynamic adjustment analysis is carried out, the level increase coefficient of each data is calculated, the data is re-classified, and the divided data is stored in the corresponding blockchain to realize data sharing.

[0105] Based on the distributed forest and grassland Internet of Things data sharing system based on blockchain provided by the embodiment of the present invention, the forest and grassland Internet of Things data can be divided into different levels through the above-mentioned method, and different storage methods can be adopted for data of different levels, avoiding the "one-size-fits-all" chain-up, reducing the waste of chain resources and response delay when the blockchain stores data; in addition, it can timely perceive the dynamic changes of data and dynamically adjust its chain-up strategy, reduce the risk of information leakage, and reduce unnecessary storage waste.

[0106] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for sharing distributed forest and grassland IoT data based on blockchain, characterized in that: The following steps are involved: The front-end equipment collects a variety of data from the forest and grassland ecological environment, extracts characteristic parameters from the collected data to construct a multi-dimensional feature vector, inputs the multi-dimensional feature vector into the preset data value assessment model, and outputs the value score of each type of data; Divide various types of data into different levels based on the data value scoring threshold, and adopt different blockchain storage management methods for different levels of data; data levels are divided into first level, second level and third level; Analyze the first-level data and divide it into mobile data and non-mobile data according to preset rules and conditions; For the first-level data, the mobile data, the second-level data, and the third-level data, periodic dynamic adjustment analysis is carried out to calculate the level increase coefficient of each data, reclassify the data into levels, and store the divided data in the corresponding blockchain to achieve data sharing; The steps to classify various types of data into different levels based on the data value scoring threshold and adopt different blockchain storage management methods for different levels of data are as follows: Comparing the value scores of each type of data with the first and second preset value score thresholds, and classifying the data into the third level if the value score is less than the first preset value score threshold; the first preset value score threshold is less than the second threshold; If the value score is not less than the preset first value score threshold but not higher than the second threshold, the data is classified into the second level; If the value score is higher than the preset value score second threshold, the data is classified into the first level; When the data level is the first level, it is encrypted and protected, and its hash value is calculated. The data body, along with its metadata and hash value, is completely written into the blockchain. When the data level is the second level, it is stored in the IPFS distributed off-chain, and only the hash summary generated by its content identifier and data metadata is uploaded to the blockchain for reference; When the data level is the third level, the data is cold stored or archived locally, and only its summary information is recorded on the blockchain.

2. The method for sharing distributed forest and grassland Internet of Things data based on blockchain according to claim 1 is characterized in that: The steps for dividing the first-level data into flowable data and non-flowable data according to the preset rule conditions are as follows: For all types of data corresponding to the first level, whether the level can be changed is determined based on the preset rules and conditions. If not, the corresponding data will be recorded as non-mobile data, and the storage method of non-mobile data cannot be changed; its existing encrypted and complete storage strategy on the blockchain will be maintained, and the level and storage method will not be adjusted; If possible, the corresponding data will be recorded as flowable data, and the storage method of the flow data can be changed.

3. The method for sharing distributed forest and grassland Internet of Things data based on blockchain according to claim 1 is characterized in that: The calculation steps of the grade increase coefficient are as follows: For the flowable data of the first level data, the second level data and the third level data, the access decay index and the time entropy change index of all types of data within the preset period are obtained, and the access decay index and the time entropy change index are normalized and mapped to the numerical range of 0-1. The same weight is assigned to the normalized access decay index and the time entropy change index to calculate the level increase coefficient.

4. The method for sharing distributed forest and grassland Internet of Things data based on blockchain according to claim 3 is characterized in that: The calculation steps of the access decay index are: For the mobile data of the first level data, the second level data and the third level data, obtain the number of times all types of data are accessed every day within the preset period and build the access sequence ; For access sequence Sort the elements in and map them into rank values ​​to get the order column ; Constructing the rank of a standard time series ; Use Spearman's method to measure the order of and Monotonic correlation of , the calculation formula is: Where, Indicates the value corresponding to the total number of days in the preset cycle. and Order columns and No. data values; Calculate the access decay index using the following formula: , where is the access decay exponent.

5. The method for sharing distributed forest and grassland Internet of Things data based on blockchain according to claim 3 is characterized in that: The calculation steps of the aging entropy change index are: For the mobile data of the first level data, the second level data and the third level data, obtain all types of data within the preset period and regularly collect content snapshots of data objects; Each snapshot is converted into a vector representation through feature encoding , calculate the similarity change between the current snapshot and the previous snapshot : Change each similarity Mapped to information entropy : ; Get the entropy sequence that changes with time; The mean of the similarity change in the entropy sequence is calculated as the time-dependent entropy change index.

6. The method for sharing distributed forest and grassland Internet of Things data based on blockchain according to claim 1 is characterized in that: Calculate the level increase coefficient of each data, reclassify the data into levels, and store the divided data in the corresponding blockchain: Compare the level increase coefficient of each data with the preset level increase coefficient first threshold. If the level increase coefficient is less than the preset level increase coefficient first threshold, the data will be downgraded by one level from the original level; the downgraded level will be used as the new level of the data, and the data will be stored on the blockchain according to the new level; If the level increase coefficient is not less than the first threshold of the preset level increase coefficient but less than the second threshold of the preset level increase coefficient, the level of the data remains unchanged and the data continues to be stored on the blockchain based on the original level; If the level increase coefficient is not less than the preset level increase coefficient second threshold, the level of the data will be increased by one level, and the increased level will be used as the new level of the data, and the data will be stored on the blockchain according to the new level.

7. A distributed forest and grassland Internet of Things data sharing system based on blockchain, used to implement the distributed forest and grassland Internet of Things data sharing method based on blockchain as described in any one of claims 1 to 6, characterized in that: The system comprises: Value scoring module: This module collects various data from forest and grassland ecological environments through front-end equipment, extracts characteristic parameters from the collected data, constructs multi-dimensional feature vectors, inputs the multi-dimensional feature vectors into a preset data value assessment model, and outputs value scores for various types of data. Level judgment module: divides various types of data into different levels according to the data value scoring threshold, and adopts different blockchain storage and management methods for data of different levels; data levels are divided into first level, second level and third level; Data division module: Analyzes the first-level data and divides the first-level data into mobile data and non-mobile data according to preset rules and conditions; Data sharing module: For the first-level data, the second-level data and the third-level data, periodic dynamic adjustment analysis is carried out, the level increase coefficient of each data is calculated, the data is re-classified, and the divided data is stored in the corresponding blockchain to realize data sharing.

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