Distributed forest and grass Internet of Things data sharing method and system based on block chain

Abstract

The invention discloses a block chain-based distributed forest and grass Internet of Things data sharing method and system, and relates to the technical field of data storage, and the method comprises the steps: collecting multiple types of data in a forest and grass ecological environment, extracting feature parameters to construct a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a preset data value evaluation model, and outputting a value score. And dividing the data into a first level, a second level and a third level according to the score threshold, and adopting a corresponding block chain storage strategy. And further dividing the first-level data into flowable data and non-flowable data. And for the first-level flowable data, the second-level data and the third-level data, dynamically adjusting and updating the data level and the block chain storage mode at regular intervals. According to the method, hierarchical management of forest and grass Internet of Things data can be realized, a one-step uplink strategy is avoided, and waste of storage resources on a chain and response delay are reduced. Meanwhile, the uplink strategy can be adjusted in time according to the dynamic change of the data content, the information leakage risk is reduced, and the data utilization efficiency and the storage safety are improved.

Description

Technical Field

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

[0002] In a forest and grassland Internet of Things system, a large number of terminal devices (such as forestry sensors, drone patrol devices, weather stations, etc.) continuously collect data on forest land status, grassland growth, disaster monitoring, etc. These data are diverse in type, frequent in update, and scattered in source. To ensure the authenticity, credibility, and sharing security of the data, the system introduces blockchain technology as the underlying support and constructs 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, and through the immutability and traceability capabilities of blockchain, improves the credibility of the data and realizes secure sharing under multi-party collaboration. At the same time, combined with an off-chain database, hierarchical storage and rapid retrieval of large-scale data are realized, improving the overall data processing efficiency.

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

[0004] The object 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 implementation of the present invention, a method for sharing distributed forest and grassland Internet of Things data based on blockchain is first proposed. The method includes: Collect various data in the forest and grassland ecological environment through a front-end device, extract feature parameters from the collected data to construct a multi-dimensional feature vector, and input the multi-dimensional feature vector into a preset data value evaluation model to output the value scores of various types of data. Divide various types of data into different grades according to the value score threshold of the data, and adopt different blockchain storage management methods for data of different grades; the data grades are divided into the first grade, the second grade, and the third grade. Analyze the data of the first grade and divide the data of the first grade into flowable data and non-flowable data according to preset rule conditions. For the flowable data, second-level data, and third-level data of the first-level data, perform periodic dynamic adjustment analysis, calculate the level increase coefficient of each data, re-divide the data levels, and perform corresponding blockchain storage on the divided data to achieve data sharing.

[0006] Optionally, the steps of dividing various types of data into different levels according to the value scoring threshold of the data and adopting different blockchain storage management methods for data at different levels are as follows: Compare the value scores of various types of data with the first preset value scoring threshold and the second threshold. If the value score is less than the first preset value scoring threshold, divide the data into the third level; the first preset value scoring threshold is less than the second threshold; If the value score is not less than the sum of the first preset value scoring threshold and the second threshold, divide the data into the second level; If the value score is not less than the first preset value scoring threshold, divide the data into the third level; When the data level is the first level, perform encryption protection and calculate its hash value, and write the data body together with its metadata and hash value into the blockchain completely; When the data level is the second level, adopt IPFS distributed off-chain storage, and only upload the hash digest generated from its content identifier and data meta-information to the blockchain for reference; When the data level is the third level, cold-store or locally archive the data, and only record its summary information on the blockchain.

[0007] Optionally, the steps of dividing the first-level data into flowable data and non-flowable data according to the preset rule conditions are as follows: For various types of data corresponding to the first level, judge whether the level can be changed according to the preset rule conditions. If not, record the corresponding data as non-flowable data, and the storage method of non-flowable data cannot be changed; maintain its existing encryption and complete storage strategy on the blockchain, and do not adjust the level and storage method; If it can, record the corresponding data as flowable data, and the storage method of flowable data can be changed.

[0008] Optionally, the calculation steps of the level increase coefficient are as follows: For the flowable data, second-level data, and third-level data of the first-level data, obtain the access decay index and the aging entropy change index of all types of data within the preset period, normalize the access decay index and the aging entropy change index, map them to the numerical range of 0-1, and assign the same weight to the normalized access decay index and aging entropy change index, and calculate the level increase coefficient.

[0009] Optionally, the calculation steps of the access attenuation index are as follows: For the flowable data, second-level data, and third-level data of the first-level data, obtain the number of times each type of data is accessed per day within a preset period, and construct an access sequence ; For the access sequence Sort the elements in it and map them to rank values to obtain an ordered sequence ; Construct the rank of the standard time series ; Use the Spearman method to measure the monotonic correlation between the ordered sequence and , and the calculation formula is: ; In the formula, represents the value corresponding to the total number of days in the preset period, and and are the and th data values of the ordered sequence respectively; Calculate the access attenuation index, and the calculation formula is: , in the formula, is the access attenuation index.

[0010] Optionally, the calculation steps of the aging entropy change index are as follows: For the flowable data, second-level data, and third-level data of the first-level data, obtain the content snapshots of the data objects collected at regular intervals within a preset period; Each snapshot is converted into a vector representation through feature encoding , and calculate the similarity change between the current snapshot and the previous snapshot : Map each similarity change to information entropy : ; Obtain an entropy sequence that changes with time; Calculate the mean value of the similarity changes in the entropy sequence as the aging entropy change index.

[0011] Optionally, the steps of calculating the level increase coefficient of each data, re-dividing the levels of the data, and performing corresponding blockchain storage on the divided data: Compare the level increase coefficient of each data with the first threshold of the preset level increase coefficient. If the level increase coefficient is less than the first threshold of the preset level increase coefficient, then lower the level of the data by one level on the original level; and use the lowered level as the new level of the data, and store the data 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 second threshold of the preset level increase coefficient, the level of the data is increased by one level, and the increased level is used as the new level of the data, and the data is stored on the blockchain according to the new level.

[0012] In the second aspect of the implementation of the present invention, a sharing system for distributed forestry and grassland Internet of Things data based on blockchain is proposed. The system includes: Value scoring module: Collect various data in the forestry and grassland ecological environment through front-end devices, extract characteristic parameters from the collected data to construct a multi-dimensional feature vector, input the multi-dimensional feature vector into a preset data value evaluation model, and output the value scores of various types of data; Level judgment module: Divide various types of data into different levels according to the value score threshold of the data, and adopt different blockchain storage management methods for data at different levels; The data levels are divided into the first level, the second level, and the third level; Data division module: Analyze the first-level data, and divide the first-level data into flowable data and non-flowable data according to preset rule conditions; Data sharing module: For the flowable data of the first-level data, the second-level data, and the third-level data, perform periodic dynamic adjustment analysis, calculate the level increase coefficient of each data, re-divide the data levels, and perform corresponding blockchain storage on the divided data to achieve data sharing.

[0013] Advantages of the present invention: The present invention proposes a sharing method and system for distributed forestry and grassland Internet of Things data based on blockchain. By collecting various types of data in the forestry and grassland ecological environment through front-end devices, extracting characteristic parameters to construct a multi-dimensional feature vector, and inputting it into a preset data value evaluation model, value scores are output; The data is divided into the first, second, and third levels according to the score threshold, and different blockchain storage management methods are adopted. The first-level data is further divided into flowable and non-flowable data according to preset rules. Regular dynamic adjustment analysis is performed on the flowable data of the first level, the second level, and the third level data, the level increase coefficient is calculated, the data levels are re-divided, and the blockchain storage strategy is updated accordingly to achieve data sharing; In this way, the forestry and grassland Internet of Things data can be divided into different levels, and different storage methods are adopted for data at different levels, avoiding the "one-size-fits-all" approach to going on the chain, reducing the waste of chain resources and response delay when the blockchain stores data; In addition, it can perceive the dynamic changes of the data in a timely manner and dynamically adjust its on-chain strategy, reducing the risk of information leakage and unnecessary storage waste. Brief Description of the Drawings

[0014] The present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 It is a flowchart of a method for sharing distributed forestry and grassland Internet of Things data based on blockchain; Figure 2 It is a framework diagram of a system for sharing distributed forestry and grassland Internet of Things data based on blockchain. Detailed Embodiment

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] The embodiment of the present invention provides a method for sharing distributed forestry and grassland Internet of Things data based on blockchain. Refer to Figure 1 , Figure 1 It is a flowchart of a method for sharing distributed forestry and grassland Internet of Things data based on blockchain provided by the embodiment of the present invention. The method includes the following steps: Collect various data in the forestry and grassland ecological environment through a front-end device, extract feature parameters from the collected data to construct a multi-dimensional feature vector, input the multi-dimensional feature vector into a preset data value evaluation model, and output the value scores of various types of data; Divide various types of data into different levels according to the value score threshold of the data, and adopt different blockchain storage management methods for data at different levels; the data levels are divided into the first level, the second level, and the third level; Analyze the data at the first level, and divide the data at the first level into flowable data and non-flowable data according to the preset rule conditions; For the flowable data, the second-level data, and the third-level data of the first-level data, perform periodic dynamic adjustment analysis, calculate the level rise coefficient of each data, re-divide the data levels, and perform corresponding blockchain storage on the divided data to achieve data sharing.

[0018] Based on the method for sharing distributed forestry and grassland Internet of Things data based on blockchain provided by the embodiments of the present invention, through the above method, the forestry and grassland Internet of Things data can be divided into different levels, and different storage methods can be adopted for data of different levels, avoiding the "one-size-fits-all" approach to uploading to the blockchain, reducing the waste of chain resources and response latency when the blockchain stores data; in addition, it can timely sense the dynamic changes of data and dynamically adjust its uploading strategy, reducing the risk of information leakage and unnecessary storage waste.

[0019] In one embodiment, a variety of data in the forestry and grassland ecological environment are collected by a front-end device, and feature parameters are extracted from the collected data to construct a multi-dimensional feature vector, and the multi-dimensional feature vector is input into a preset data value evaluation model to output the value scores of various types of data; In the forestry and grassland ecological Internet of Things, front-end devices deployed in the wild environment (including environmental sensors, image acquisition devices, meteorological monitoring stations, soil monitoring nodes, drone carrying devices, etc.) can collect multi-source heterogeneous ecological data such as temperature, humidity, light, CO2 concentration, wind speed, soil pH value, terrain slope, pest images, and fire videos in real time. First, preprocessing operations are performed on these raw data, including denoising, standardization, time alignment, redundancy cleaning, etc., and then key feature parameters of various types of data are extracted, such as the fluctuation range of temperature and humidity indicators, image clarity score, data acquisition frequency, historical call popularity, the business module to which it belongs, the level of the acquisition source node, the security sensitivity score, etc., so as to construct a multi-dimensional feature vector with descriptiveness and discriminability. The constructed multi-dimensional vector is uniformly input into a preset data value evaluation model, which can comprehensively analyze the feature performance of data in dimensions such as value, security, sensitivity, timeliness, and application breadth based on weighted scoring method, support vector machine, neural network or rule-based comprehensive scoring, and output a numerical value score. This score serves as an important basis for subsequent data grading and storage strategy configuration, can intelligently judge the importance of data, and thus realize more scientific grading management and resource allocation. Through this method, efficient value recognition of multi-type and multi-granularity ecological data can be achieved in a complex forestry and grassland Internet of Things environment, providing a solid foundation for subsequent data hierarchical storage, access control, and sharing decision-making.

[0020] For example, in a certain forest area, multiple environmental monitoring nodes and high-definition cameras are deployed to collect data such as temperature, humidity, wind speed, light, soil moisture, and pest images and videos in real time. Suppose in a certain acquisition task, device A uploaded a set of data, including: a temperature change trend curve, a record of soil humidity within 24 hours, a video clip of suspicious smoke in the forest, and an infrared thermal imaging picture. After preprocessing this batch of data, the following features are extracted respectively: The time volatility of the temperature data is medium, and the data continuity is high; there are abnormal mutation points in the soil moisture records, and the acquisition frequency is high; the duration of the video clip is 15 seconds, the picture clarity is high, and there are potential fire characteristics in the image; the thermal imaging image has high contrast and high clarity, and the marked area contains high-temperature points. These features are integrated to form a set of multi-dimensional feature vectors, which are input into the "Data Value Evaluation Model". According to the set evaluation rules or deep learning algorithms, the model scores from multiple dimensions such as the importance of the data (such as whether it involves a fire), application value (whether it can be used for emergency response), sensitivity (whether it involves the core area of forest protection), and timeliness (whether it needs to be processed in real time). The model outputs the following results: the value score of the fire video is 92 (Level I high-value data); the thermal imaging map is 87 (Level I high-value data); the soil moisture data is 62 (Level II medium-value data); the temperature curve data is 45 (Level III low-value data). According to this score, the data is automatically divided into different levels, laying a foundation for subsequent selection of appropriate storage strategies (such as whether to go on-chain, whether to store only off-chain, whether to only upload a hash digest on-chain) and access permission management. This process demonstrates the ability to achieve intelligent hierarchical management of data through feature extraction and value evaluation, ensuring the security, controllability, and traceability of key data, while effectively reducing the burden on the blockchain and improving resource utilization efficiency.

[0021] In one embodiment, various types of data are divided into different levels according to the value score threshold of the data, and different blockchain storage management methods are adopted for data at different levels; the data levels are divided into the first level, the second level, and the third level; the specific steps are as follows: Compare the value scores of various types of data with the first preset value score threshold and the second threshold. If the value score is less than the first preset value score threshold, the data is divided into the third level; the first preset value score threshold is less than the second threshold; If the value score is not less than the first preset value score threshold and the second threshold, the data is divided into the second level; If the value score is not less than the first preset value score threshold, the data is divided into the third level; When the data level is the first level, perform encryption protection and calculate its hash value (such as using encryption hash algorithms such as SHA-256), and write the data body together with its metadata and hash value into the blockchain completely; When the data level is the second level, use IPFS distributed off-chain storage, and only upload the hash digest of its content identifier and data meta-information to the blockchain for reference; When the data level is the third level, cold-store the data or archive it locally (such as storing it in an independent cold backup server, local hard disk of an edge node, etc.), and only record its summary information (such as encrypted hash, file identifier) on the blockchain.

[0022] It should be noted that according to the value scoring threshold of data, various types of data collected in the forestry and grassland Internet of Things are divided into different levels, so as to adopt differentiated storage and management strategies to achieve resource optimization and data security protection. Each type of data passes through a preset data value evaluation model to obtain a numerical value score. This threshold score is compared with two preset thresholds, and the set first threshold is lower than the second threshold. If the value score of the data is lower than the first threshold, it is determined to be the third level. This type of data generally has a low value and a low usage frequency, such as the temperature and humidity data of ordinary environmental monitoring sensors, images in non-critical periods, etc. This type of data is stored in a cold storage or local archiving method with a low cost, and only the summary information of the data, such as hash values and metadata, is saved on the chain for data integrity verification and indexing; if the value score of the data is between the first threshold and the second threshold, it is classified as the second level, representing data with medium value and risk. For example, the regular ecological survey data or biodiversity statistics data of a certain forest area. These data are stored off-chain using the IPFS distributed storage technology, and only the content identifier (CID) and meta-hash value of the data are recorded on the blockchain to ensure data verifiability and save on-chain storage space; if the data value score is higher than the second threshold, it is classified as the first level, representing extremely critical and sensitive data, such as fire warning monitoring videos, real-time monitoring data of key protected species in the forest area, etc. This type of data must be strongly encrypted and protected, and the hash value is calculated through an algorithm such as SHA-256, and the original data and metadata are written into the blockchain ledger of the consortium chain together to ensure the immutability and full-process traceability of the data.

[0023] In one implementation method, through the above method, for the forestry and grassland Internet of Things data with different values, different methods are adopted to store them on the blockchain, avoiding the "one-size-fits-all" approach to going on the chain, and reducing the waste of chain resources and response delay when the blockchain stores data.

[0024] In one embodiment, the first-level data is analyzed, and the first-level data is divided into flowable data and non-flowable data according to the preset rule conditions; For each type of data corresponding to the first level, it is judged whether the level can be changed according to the preset rule conditions. If not, the corresponding data is recorded as non-flowable data, and the storage method of the non-flowable data cannot be changed; maintain its existing encrypted and complete storage strategy on the blockchain, and do not adjust the level and storage method; If it can, the corresponding data is recorded as flowable data, and the storage method of the flowable data can be changed.

[0025] It should be noted that a detailed analysis will be further carried out to determine whether the data is allowed to have its level changed based on the rule conditions preset by professionals (such as the confidentiality level of the data, the degree of dependence on real-time performance, the coupling strength with the business, regulatory compliance requirements, etc.) and its scoring results in the data value assessment model. If the data is determined according to the rules to be a strongly regulated object, involve core secrets, or its change will affect security and legal compliance, etc., the data will be marked as immobile data. The storage method of such data will remain the current blockchain encrypted and fully uploaded storage strategy, and no downgrading or off-chain transfer operations are allowed to ensure its long-term security and auditability. If the data rule judgment result allows adjustment, such as the data is important but its sensitivity decreases after the end of the phased task, or its content has a high degree of repetition and can be reconstructed from the original source, etc., it can be marked as mobile data, and it is allowed to adjust the level and change the storage strategy according to dynamic indicators such as access frequency and call situation in subsequent analysis (such as changing from on-chain full storage to IPFS off-chain storage, etc.).

[0026] For example, an image data of "identifying illegal logging behavior in forest and grassland remote sensing satellite images" was initially evaluated as first-level data and was marked as immobile data by manual rules because it directly involved illegal clues and needed to be stored for a long time for evidence collection and law enforcement and could not be downgraded; while a high-resolution image of "forest edge wetland ecological change map", although initially also judged as first-level, after the experts of the ecological research department confirmed that the phased research task had ended and the image had strong reusability, it could be converted into mobile data and could be adjusted to the second level according to the access situation in the future, and the storage strategy was changed to off-chain IPFS archiving, effectively releasing the pressure on on-chain resources. Through this mechanism, the dynamic security management and sustainable sharing of key ecological data are realized.

[0027] It should be noted that the reason for further refining the first-level data into "mobile data" and "immobile data" only, while defaulting that the second-level and third-level data can be adjusted in storage mode according to the actual situation, is mainly that the first-level data itself has a high degree of sensitivity, confidentiality or business criticality, and any form of storage mode adjustment may bring major risks in terms of security, compliance or business continuity. Therefore, it is necessary to further subdivide it internally to ensure that some absolutely sensitive core data is always in the highest-level protection state during its life cycle. In contrast, the second-level and third-level data have significantly lower sensitivity and importance, usually do not involve secrets, enterprise core strategies or strong regulatory requirements, have greater mobility and adjustability, and are suitable for realizing level floating and storage optimization through periodic dynamic evaluation, so as to improve the flexibility of data management and the utilization efficiency of storage resources while ensuring basic security.

[0028] In one implementation, the advantage of this strategy is that it can achieve a balance between security and efficiency. For first-level data, by restricting the existence of immobile data, it can provide strong guaranteed security for truly important and irreplaceable data, preventing it from being wrongly downgraded or leaked due to technical adjustments; while leaving room for flexible adjustment of the mobile part, enabling it to have a certain self-adaptive ability. For the default adjustability of second- and third-level data, it can effectively release the on-chain resources of the blockchain, reduce chain data redundancy, improve the overall data transfer and sharing efficiency, contribute to promoting the wide sharing and reuse of non-sensitive data in the forestry and grassland Internet of Things, and ultimately build a distributed data management ecosystem with highly secure core data and flexible flow of general data, realizing data sharing.

[0029] In one embodiment, for the mobile data of first-level data, second-level data, and third-level data, perform periodic dynamic adjustment analysis, calculate the level increase coefficient of each data, re-divide the levels of the data, and perform corresponding storage on the divided data; Specifically, the calculation steps of the level increase coefficient are as follows: For the mobile data of first-level data, second-level data, and third-level data, obtain the access decay index and the time effect entropy change index of all types of data within a preset period, and perform normalization processing on the access decay index and the time effect entropy change index, map them to the numerical range of 0-1, and assign the same weight to the normalized access decay index and time effect entropy change index, and calculate the level increase coefficient. The calculation formula is: , where are the time effect entropy change index and the access decay index after normalization respectively; is the level increase coefficient.

[0030] It should be noted that generally, the sum of the weights assigned to the access decay index and the time effect entropy change index is 1.

[0031] In one embodiment, the calculation steps of the access decay index are as follows: For the mobile data of first-level data, second-level data, and third-level data, obtain the number of times each type of data is accessed per day within a preset period, and construct an access sequence ; where represents the value corresponding to the total number of days in the preset period. Sort the elements in the access sequence and map them into rank values to obtain an ordered sequence : ; ; represents the number of times the data on the th day is accessed; Construct the rank of the standard time series (i.e., the original order), ; Use the Spearman method to measure the monotonic correlation and of the ordered sequences, and the calculation formula is: ; and are respectively the and th data values of the ordered sequences; Calculate the access decay index, and the calculation formula is: , where is the access decay index.

[0032] It should be noted that the data acquisition method involved in the above access decay index calculation process is as follows: within a set time period (such as the most recent 7 days or 30 days), the access frequency records of various data objects are automatically collected through logs or access control modules every day, 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, ensuring the authenticity and timeliness of data access behaviors, and providing accurate input for subsequent ordered sequence construction and Spearman correlation analysis.

[0033] It should be noted that the access decay index (AccessDecayIndex, ADI) refers to the degree of decline in the access frequency of a certain data object within a period of time, reflecting its trend of being "forgotten" or "ignored". It is based on Spearman rank correlation analysis. By comparing the monotonic relationship between the time series and the access frequency series, it judges the trend change of data access activity. When the access decay index is smaller (i.e., the rank correlation value is closer to positive correlation or there is no obvious negative trend), it indicates that the access frequency of this data has not decreased significantly during the entire preset period, and may even remain stable or increase, meaning that users still maintain a high degree of attention to this data. Therefore, the smaller the access decay index, the less the data has been forgotten, and its existing value still has sustainable business or ecological significance; especially in the distributed environment of forestry and grassland Internet of Things, some data such as real-time environmental monitoring, pest monitoring, or fire warning information, etc., require continuous high-frequency access to support front-end intelligent decision-making. The undiminished activity of such data indicates that it still has strong use value in the near future and even in the future. According to the strategy, such data is more likely to be judged that the value level should be raised, thereby triggering the migration of data from off-chain IPFS reference or cold archive status to on-chain full encryption storage to ensure the originality, integrity, and security of the data; In one implementation, the benefits of analyzing the access decay index to determine the value of data and thus change the storage method on the blockchain are as follows: By quantifying the change trend of data access activity within a preset period, it is possible to accurately identify which data is continuously delivering value and which is being marginalized, thereby achieving automated optimization of the data storage strategy. For data with a small access decay index and high activity, its storage method can be actively changed from off-chain lightweight reference to on-chain complete storage to enhance its credibility, verifiability, and traceability. At the same time, reduce the on-chain resource occupancy of cooled data, lower the storage cost, and improve the liquidity of on-chain data and the efficiency and intelligence level of the storage structure.

[0034] In one embodiment, the calculation steps of the aging entropy change index are as follows: For the flowable data of the first-level data, the second-level data, and the third-level data, obtain content snapshots of data objects collected at regular intervals within a preset period, such as text, images, videos, or structured records, etc. Each snapshot is converted into a vector representation through feature encoding , for example, text can use embedding, and images can use perceptual hash hash; calculate the similarity change between the current snapshot and the previous snapshot : ; where the content snapshot at time The similarity change, is the content snapshot vector at time ; is a similarity function, such as cosine similarity; Map each similarity change to information entropy : ; obtain an entropy sequence that changes over time; Calculate the mean of the similarity changes in the entropy sequence as the aging entropy change index.

[0035] It should be noted that the data acquisition method involved in the above calculation process of the aging entropy change index is mainly based on a regular collection mechanism, that is, within a preset period, content snapshots of each level of data (including flowable data, structured records, text, images, or videos, etc.) are extracted at fixed time intervals (such as every hour, every day). Each snapshot represents the state of the data at a certain moment. Through feature encoding means (such as word vector embedding of text, video frame hash of images, field vectorization of structured data, etc.), the snapshot is converted into a standardized vector representation, which is convenient for subsequent similarity calculation and entropy change analysis. This method ensures that the evolution process of data content can be continuously tracked in the time dimension, thereby providing a reliable basis for accurately evaluating its timeliness and change sensitivity.

[0036] 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 content volatility of data by calculating the similarity differences of data snapshots between consecutive time points and mapping these differences to information entropy. When the TEV value is larger, it indicates that the changes in data content between different time nodes are more significant, indicating that the data has stronger dynamics and timeliness, that is, the data may be continuously affected by the external environment or updated internally, 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 ecological monitoring devices with large areas and multiple nodes (such as cameras, remote sensing terminals, environmental sensors, etc.). The data collected by these devices has a high degree of timeliness sensitivity, such as pest and disease images, fire videos, vegetation change indicators, etc. If a certain type of data changes frequently in a short period of time, that is, the TEV index is relatively high, it indicates that this type of data may be in the "event evolution" or "emergency situation", and its value is much higher than that of stable data. Therefore, when performing data hierarchical storage on the blockchain, such high-TEV value data should be preferentially included in the first-level data for encrypted upload, full-node storage, and rapid response processing to ensure the 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 improves the intelligence and agility of forest and grassland Internet of Things data processing.

[0037] In one implementation method, the advantages of analyzing the Temporal Entropy Variation Index for judging the value of forest and grassland Internet of Things data and thus changing its storage method on the [blockchain] are as follows: The Temporal Entropy Variation Index has significant advantages in judging the value of forest and grassland Internet of Things data and dynamically adjusting its storage method on the [blockchain]. On the one hand, the Temporal Entropy Variation Index can effectively identify the data that has changed significantly in a short period of time, such as signs of pest and disease outbreaks, fire warning signals, or ecological anomalies, and can timely classify it as high-value and high-priority data and upload it to the core storage layer of the [blockchain] in a more secure and highly available manner, thereby ensuring the authenticity and immutability of key ecological information. On the other hand, for data with little change and stable status, its storage level can be reduced to relieve the storage pressure and redundant consumption of the [blockchain]. This value judgment mechanism based on the Temporal Entropy Variation Index makes the management of forest and grassland Internet of Things data more intelligent, accurate, and efficient, and at the same time improves the adaptability and resource allocation ability in the ecological protection scenario.

[0038] In one embodiment, the steps of calculating the level increase coefficient of each data, re-dividing the data levels, and performing corresponding blockchain storage on the divided data are as follows: Compare the level increase coefficient of each data with the first threshold of the preset level increase coefficient. If the level increase coefficient is less than the first threshold of the preset level increase coefficient, then lower the level of the data by one level on the original level; and use the lowered level as the new level of the data, and store the data 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, then the level of the data remains unchanged, and continue to store the data on the blockchain based on the original level. If the level increase coefficient is not less than the second threshold of the preset level increase coefficient, then raise the level of the data by one level, and use the raised level as the new level of the data, and store the data on the blockchain according to the new level.

[0039] It should be noted that the core of the above reclassification and storage strategy of data levels lies in dynamically evaluating the current importance and potential value change trend of each piece of data through the quantitative index of "level increase coefficient", and combining two preset thresholds of level increase coefficients to flexibly adjust its storage level and method in the blockchain. Specifically, when the level increase coefficient of a certain piece of data is less than the first threshold (such as 0.3), it indicates that its content is stable, the access frequency is low, and the timeliness is weak, showing a trend of being "forgotten" or value decline. Therefore, its current level is lowered by one level, such as from the second level to the third level, and uploaded to the blockchain according to the storage standard of the third-level data (such as lower redundancy and fewer nodes) to save storage resources; if the level increase coefficient is between the first and second thresholds (such as 0.3 to 0.7), it is determined that its value remains stable and the data level is not adjusted, maintaining the original storage state; when the level increase coefficient is greater than the second threshold (such as 0.7), it indicates that the data has been frequently accessed, changed drastically recently, or has attracted attention after content update, with high timeliness and sensitivity. At this time, its level should be raised by one level (such as from the third level to the second level, or from the second level to the first level), and a higher reliability and higher security storage strategy should be adopted according to the new level, such as multi-node redundancy, multi-chain storage, or enhanced on-chain verification. For example, for the image data collected by a camera in a forest area, if it has not been accessed for several consecutive days and there is no obvious change in the image content, its level increase coefficient is 0.2, which is lower than the first threshold, then it is adjusted from the second level to the third level, and only the off-chain summary information is retained and synchronized regularly; if the temperature and humidity detected by a sensor in a certain area change suddenly, accompanied by drastic changes in the image content, triggering multiple platform accesses and analysis calls, and its level increase coefficient reaches 0.85, it will be promoted to the first level to ensure its full storage and high-priority access in the blockchain, supporting ecological monitoring and emergency response. This mechanism not only realizes the dynamic matching of data value and storage resources, but also improves the identification and response ability of the forest and grassland Internet of Things to key data.

[0040] In one implementation manner, the above method can timely sense the dynamic changes of data and dynamically adjust the on-chain strategy, reducing the risk of information leakage and unnecessary storage waste.

[0041] It should be noted that by reclassifying the data and storing the classified data in the corresponding blockchain, data sharing can be achieved; by reclassifying the data and storing the classified data in the corresponding blockchain, the orderly sharing and precise scheduling of forest and grassland IoT data can be realized; by transmitting the data to the blockchain, the blockchain automatically selects the appropriate storage method and sharing permission according to the level and value of each type of data, so as to ensure that key data (such as pest outbreak images, wildfire warning information, etc.) can be quickly obtained and trusted verified by the supervision platform, scientific research institutions and forest patrol terminals first, while the low-level data is retained for future reference at a lower cost to avoid redundant occupation. With the characteristics of decentralization, anti-tampering and traceability of the blockchain, the authenticity and timeliness of forest and grassland ecological monitoring data during the sharing process are ensured, the traditional "information island" problem is broken, the data collaboration efficiency across departments and regions is improved, and the overall improvement of resource planning, scientific decision-making and ecological governance capabilities is promoted.

[0042] Based on the same inventive concept, the embodiment of the present invention also provides a sharing system for distributed forest and grassland IoT data based on the blockchain. See Figure 2 , Figure 2 is the framework diagram of the sharing system for distributed forest and grassland IoT data based on the blockchain provided by the embodiment of the present invention. The system includes: Value scoring module: Collect various data in the forest and grassland ecological environment through the front-end device, extract characteristic parameters from the collected data to construct a multi-dimensional feature vector, input the multi-dimensional feature vector into a preset data value evaluation model, and output the value scores of various types of data; Level judgment module: Classify various types of data into different levels according to the value score threshold of the data, and adopt different blockchain storage management methods for data at different levels; the data levels are divided into the first level, the second level and the third level; Data division module: Analyze the first-level data, and divide the first-level data into flowable data and non-flowable data according to the preset rule conditions; Data sharing module: For the flowable data of the first-level data, the second-level data and the third-level data, perform periodic dynamic adjustment analysis, calculate the level rise coefficient of each data, reclassify the data, and perform corresponding blockchain storage on the classified data to achieve data sharing.

[0043] Based on the blockchain-based distributed sharing system for forestry and grassland Internet of Things data provided by the embodiments of the present invention, through the above method, the forestry and grassland Internet of Things data can be divided into different levels, and different storage methods can be adopted for data of different levels, avoiding the "one-size-fits-all" approach to going on-chain, reducing the waste of chain resources and response latency when the blockchain stores data; in addition, it can timely sense the dynamic changes of the data and dynamically adjust its on-chain strategy, reducing the risk of information leakage and unnecessary storage waste.

[0044] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to form equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for sharing distributed forestry and grassland Internet of Things data based on blockchain, characterized in that, It includes the following steps: Collect various data in the forest and grass ecological environment through front-end devices, extract characteristic parameters from the collected data to construct a multi-dimensional feature vector, input the multi-dimensional feature vector into a preset data value evaluation model, and output the value scores of various types of data; Divide various types of data into different levels according to the value score threshold of the data, and adopt different blockchain storage management methods for data at different levels; the data levels are divided into the first level, the second level, and the third level; Analyze the data at the first level, and divide the data at the first level into flowable data and non-flowable data according to preset rule conditions; For the flowable data, the second-level data, and the third-level data of the first-level data, conduct periodic dynamic adjustment analysis, calculate the level increase coefficient of each data, re-divide the data levels, and perform corresponding blockchain storage on the divided data to achieve data sharing.

2. The sharing method of distributed forestry and grassland Internet of Things data based on blockchain according to claim 1, wherein The step of dividing various types of data into different levels according to the value score threshold of the data and adopting different blockchain storage management methods for data at different levels is as follows: Compare the value scores of various types of data with the first preset value score threshold and the second threshold. If the value score is less than the first preset value score threshold, the data is divided into the third level; the first preset value score threshold is less than the second threshold; If the value score is not less than the first preset value score threshold and the second threshold, the data is divided into the second level; If the value score is not less than the first preset value score threshold, the data is divided into the third level; When the data level is the first level, perform encryption protection and calculate its hash value, and write the data body together with its metadata and hash value into the blockchain completely; When the data level is the second level, adopt IPFS distributed off-chain storage, and only upload the hash digest generated by its content identifier and data meta-information to the blockchain for reference; When the data level is the third level, cold-store or locally archive the data, and only record its summary information on the blockchain.

3. The sharing method of distributed forestry and grassland Internet of Things data based on blockchain according to claim 1, characterized in that, The step of dividing the data at the first level into flowable data and non-flowable data according to preset rule conditions is as follows: For each type of data corresponding to the first level, judge whether the level can be changed according to preset rule conditions. If not, mark the corresponding data as non-flowable data, and the storage method of the non-flowable data cannot be changed; maintain its existing encrypted complete storage strategy on the blockchain, and do not adjust the level and storage method; If it can, mark the corresponding data as flowable data, and the storage method of the flowable data can be changed.

4. The sharing method of distributed forestry and grassland Internet of Things data based on blockchain according to claim 1, wherein, The calculation steps of the level increase coefficient are as follows: For the flowable data of the first-level data, the second-level data, and the third-level data, obtain the access decay index and the time effect entropy change index of all types of data within a preset period, normalize the access decay index and the time effect entropy change index, map them to the numerical range of 0-1, and assign the same weight to the normalized access decay index and the time effect entropy change index, and calculate the level increase coefficient.

5. The sharing method of distributed forestry and grassland Internet of Things data based on blockchain according to claim 4, wherein, The calculation steps of the access decay index are as follows: For the flowable data, second-level data, and third-level data of the first-level data, obtain the number of times each type of data is accessed every day within a preset period, and construct an access sequence ; For the access sequence sort the elements in it and map them into rank values to obtain an ordered sequence ; Construct the rank of the standard time series ; Measure the order sequence using the Spearman method and monotonic correlation , and the calculation formula is: ; where represents the value corresponding to the total number of days in the preset cycle, and are respectively the and th data values of the order sequences Calculate the access attenuation index, and the calculation formula is: , where is the access attenuation index.

6. The sharing method of distributed forestry and grassland Internet of Things data based on blockchain according to claim 4, characterized in that The calculation steps of the time effect entropy change index are as follows: For the flowable data, second-level data, and third-level data of the first-level data, obtain the content snapshots of the data objects collected regularly within a preset period for all types of data; Each snapshot is converted into a vector representation through feature encoding , calculate the similarity change between the current snapshot and the previous snapshot : Map each similarity change to information entropy : ; obtain an entropy sequence that changes over time; Calculate the mean of the similarity changes in the entropy sequence as the aging entropy change index.

7. The sharing method of distributed forestry and grassland Internet of Things data based on blockchain according to claim 1, characterized in that Steps for calculating the level increase coefficient of each data, re-dividing the data levels, and performing corresponding blockchain storage on the divided data: Compare the level increase coefficient of each data with the first threshold of the preset level increase coefficient. If the level increase coefficient is less than the first threshold of the preset level increase coefficient, then lower the level of the data by one level on the original level; and use the lowered level as the new level of the data, and store the data 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 continue to store the data on the blockchain based on the original level; If the level increase coefficient is not less than the second threshold of the preset level increase coefficient, then raise the level of the data by one level, and use the raised level as the new level of the data, and store the data on the blockchain according to the new level.

8. A blockchain-based distributed sharing system for forestry and grassland IoT data, which is used to implement the blockchain-based distributed sharing method for forestry and grassland IoT data according to any one of claims 1-7, characterized in that, The system includes: Value scoring module: Collect various data in the forest and grass ecological environment through front-end devices, extract feature parameters from the collected data to construct multi-dimensional feature vectors, input the multi-dimensional feature vectors into a preset data value evaluation model, and output the value scores of various types of data; Level judgment module: Divide various types of data into different levels according to the value score threshold of the data, and adopt different blockchain storage management methods for data at different levels; the data levels are divided into the first level, the second level, and the third level; Data division module: Analyze the first-level data, and divide the first-level data into flowable data and non-flowable data according to preset rule conditions; Data sharing module: For the flowable data, second-level data, and third-level data of the first-level data, perform periodic dynamic adjustment analysis, calculate the level increase coefficient of each data, re-divide the data levels, and perform corresponding blockchain storage on the divided data to achieve data sharing.

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