Drainage basin water and soil conservation engineering data sharing and management platform based on block chain

Abstract

The invention discloses a block chain-based watershed water and soil conservation engineering data sharing and management platform, and relates to the technical field of block chains, and the platform comprises a data uploading module which is used for uploading monitoring data to an alliance chain according to a preset chaining rule; the station credibility set determination module is used for carrying out station access behavior disassembly and determining a station credibility set; the access management mutual recognition group determination module is used for dividing access management mutual recognition groups and determining Q access management mutual recognition groups; the mutual identification key pair obtaining module is used for generating Q access management mutual identification key pairs; and the access management verification module is used for obtaining Q access management mutual recognition private key slice sets and randomly distributing the Q access management mutual recognition private key slice sets to Q access management mutual recognition groups for access management verification of the alliance chain. The technical problems that in the prior art, watershed water and soil conservation engineering data sharing management is low in reliability and has security holes are solved, and the technical effects of improving sharing relation security and improving access reliability are achieved.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a blockchain-based watershed soil and water conservation project data sharing and management platform. Background Art

[0002] Currently, watershed soil and water conservation projects involve the collection and monitoring of extensive environmental data from diverse sources. This data is typically processed through traditional centralized data storage and management systems. However, this traditional management approach faces numerous challenges, including inefficient data management and sharing, data credibility, security during data upload, and data traceability. Furthermore, due to the cyclical nature of monitoring data and the distributed nature of the sites, existing systems struggle to implement personalized management and effective access control for data from different sites. Summary of the Invention

[0003] This application provides a blockchain-based watershed soil and water conservation project data sharing and management platform, which is used to solve the technical problems of low reliability and security loopholes in watershed soil and water conservation project data sharing and management in the existing technology.

[0004] In view of the above problems, this application provides a blockchain-based watershed soil and water conservation project data sharing and management platform, which includes:

[0005] The site collection acquisition module is used to obtain the distributed monitoring site collection of the soil and water conservation project in the target watershed and assign a unique site identity key pair to each distributed monitoring site;

[0006] A data uploading module, configured to upload the monitoring data of the distributed monitoring site set to the consortium chain according to a preset uploading rule through a site identity key pair, wherein the distributed monitoring site set includes an upload period set;

[0007] A site credibility set determination module, configured to analyze site access behaviors of the distributed monitoring site set according to preset credibility indicators to determine a site credibility set;

[0008] an access management mutual recognition group determination module, configured to divide the distributed monitoring site set into access management mutual recognition groups based on the upload period set and the site credibility set, and determine Q access management mutual recognition groups, where Q is a positive integer;

[0009] A mutual recognition key pair obtaining module is used to generate Q access management mutual recognition key pairs for each of the Q access management mutual recognition groups based on the RSA algorithm, wherein the Q access management mutual recognition key pairs include Q access management mutual recognition public keys and Q access management mutual recognition private keys;

[0010] The access management verification module is used to sparsely split the Q access management mutual recognition private keys based on Q historical asynchronous segmentation repositories, obtain Q access management mutual recognition private key slice sets, and randomly distribute the Q access management mutual recognition private key slice sets to Q access management mutual recognition groups for access management verification of the alliance chain.

[0011] Preferably, the preset chain-up rules include: when the monitoring data of the distributed monitoring site set is data obtained through sensor monitoring, the monitoring data is recorded as a hash summary, and the hash summary is uploaded to the alliance chain; when the monitoring data of the distributed monitoring site set is data obtained through remote sensing monitoring, the storage path of the monitoring data is uploaded to the alliance chain.

[0012] Preferably, the site credibility set determination module is used to perform the following steps: obtaining the site access behavior log set of the distributed monitoring site set; extracting indicators from the site access behavior log set according to preset credibility indicators to obtain a site credibility indicator set; using an indicator analyzer to identify the site credibility indicator set to determine the site credibility set.

[0013] Preferably, the preset credibility indicators include data accuracy, site network stability and access abnormality frequency.

[0014] Preferably, the access management mutual recognition group determination module is used to perform the following steps: randomly selecting Q division reference sites from the distributed monitoring site set; dividing the distributed monitoring site set into Q division reference sites according to the upload cycle set and the site credibility set to obtain Q initial access management mutual recognition groups; using a dual-objective division verification function to verify the Q initial access management mutual recognition groups, if the verification passes, the Q initial access management mutual recognition groups are used as Q access management mutual recognition groups; if the verification fails, obtaining a re-division instruction, and re-selecting the reference sites based on the re-division instruction.

[0015] Preferably, the dual-objective partitioning verification function is:

[0016]

[0017] Among them, Sep is the discrimination output by the dual-objective partitioning verification function, n q is the total number of distributed monitoring sites in the qth initial access management mutual recognition group, t qj is the upload period of the jth distributed monitoring site in the qth initial access management mutual recognition group, is the upload period for dividing the reference site in the qth initial access management mutual recognition group, p qjis the site credibility of the jth distributed monitoring site in the qth initial access management mutual recognition group, is the site credibility of the reference site in the qth initial access management mutual recognition group, and λ is the weight for balancing the upload period and site credibility.

[0018] Preferably, the access management verification module is used to perform the following steps: randomly split the Q access management mutual recognition private keys according to the number of distributed monitoring sites in the Q access management mutual recognition groups, and obtain Q initial access management mutual recognition private key slice sets and Q slice position sets; approximately identify the Q initial access management mutual recognition private key slice sets and Q historical asynchronous slice repositories, and adjust the Q slice position sets according to the identification results to obtain Q access management mutual recognition private key slice sets.

[0019] Preferably, the access management verification module is also used to perform the following steps: respectively perform similarity identification on the Q initial access management mutual recognition private key slice sets and the Q historical asynchronous segmentation repositories to determine the similarity of the Q initial access management mutual recognition private key slices; obtain Q adjustment quantities for adjusting the Q segmentation position sets according to the size of the similarity of the Q initial access management mutual recognition private key slices; randomly adjust the Q segmentation position sets according to a preset adjustment scale according to the Q adjustment quantities to obtain Q access management mutual recognition private key slice sets.

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0021] This application uploads the monitoring data of a distributed monitoring site set to the alliance chain according to the preset chain-up rules through a site identity key pair, wherein the distributed monitoring site set includes an upload period set, and then the site access behavior of the distributed monitoring site set is disassembled according to the preset credibility index to determine the site credibility set, and then the distributed monitoring site set is divided into access management mutual recognition groups based on the upload period set and the site credibility set to determine Q access management mutual recognition groups, wherein Q is a positive integer, and then Q access management mutual recognition key pairs are generated for the Q access management mutual recognition groups based on the RSA algorithm, wherein the Q access management mutual recognition key pairs include Q access management mutual recognition public keys and Q access management mutual recognition private keys, and with Q historical asynchronous segmentation repositories as constraints, the Q access management mutual recognition private keys are sparsely segmented to obtain Q access management mutual recognition private key slice sets, and the Q access management mutual recognition private key slice sets are randomly distributed to Q access management mutual recognition groups for access management verification of the alliance chain. The technical effect of improving the reliability and security of data sharing management in the blockchain is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the structure of a watershed soil and water conservation project data sharing and management platform based on blockchain provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of the process of obtaining Q access management mutual recognition private key slice sets in the blockchain-based watershed soil and water conservation project data sharing and management platform provided in an embodiment of the present application;

[0024] Description of the reference numerals: site set obtaining module 11 , data uploading module 12 , site credibility set determining module 13 , access management mutual recognition group determining module 14 , mutual recognition key pair obtaining module 15 , access management verification module 16 . DETAILED DESCRIPTION

[0025] This application provides a blockchain-based watershed soil and water conservation project data sharing and management platform to solve the technical problems of low reliability and security loopholes in watershed soil and water conservation project data sharing and management in the existing technology.

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, platform, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, platforms, products or devices.

[0028] Examples, such as Figure 1 As shown, this application provides a watershed soil and water conservation engineering data sharing and management platform based on blockchain, wherein the platform includes:

[0029] The site set acquisition module 11 is used to obtain a distributed monitoring site set of the soil and water conservation project in the target watershed and assign a unique site identity key pair to each distributed monitoring site;

[0030] In one possible embodiment, the target watershed soil and water conservation project is a watershed soil and water conservation project in any watershed for which data sharing is required. The distributed monitoring stations are stations deployed at different locations in the target watershed for collecting watershed soil and water conservation-related data (such as water quality, water quantity, and sediment content). Optionally, the distributed monitoring stations include river channel sensor nodes, remote sensing observation stations, meteorological stations, and the like.

[0031] Preferably, to ensure independence between sites and reliable traceability of data sources, each distributed monitoring site is assigned a unique site identity key pair, typically automatically generated by a pre-set key management module, which also records basic site attribute information (such as geographic location, monitoring type, communication method, etc.). This ensures that data subsequently uploaded to the blockchain has a legitimate signature and can be traced back to a specific site, laying the foundation for trusted data upload, group division, access control, and other aspects.

[0032] Preferably, when each monitoring site is connected to the platform, the identity key generation process is triggered. First, a unique site identifier (such as UUID or site number) is generated, and then a key pair application is initiated to the preset key management module. The key management module obtains the private key and public key according to the set algorithm (for example, using ECC), where the private key (Private Key) is used for local data signing. The public key (Public Key) is used for signature verification and identity identification on the alliance chain. The key is converted into a format that can be transmitted and stored (such as PEM, DER, Hex string), and then the public key is written to the blockchain node registry as the site identity credential; the private key is saved locally at the site or in a trusted module (such as a secure storage area).

[0033] The data uploading module 12 is used to upload the monitoring data of the distributed monitoring site set to the alliance chain according to the preset chain-up rules through the site identity key pair, wherein the distributed monitoring site set includes an upload period set;

[0034] Furthermore, the preset chain-up rules include: when the monitoring data of the distributed monitoring site set is data obtained through sensor monitoring, the monitoring data is recorded as a hash summary and the hash summary is uploaded to the alliance chain; when the monitoring data of the distributed monitoring site set is data obtained through remote sensing monitoring, the storage path of the monitoring data is uploaded to the alliance chain.

[0035] In one possible embodiment, the data upload module 12 is the core component of the platform responsible for data upload. Its main function is to upload data from a collection of distributed monitoring sites to the alliance chain in a secure manner. The preset chain rules specify specific rules for how to process and upload different types of monitoring data to the alliance chain. The hash summary record is to perform cryptographic hashing on the monitoring data obtained by the sensor to generate a fixed-length summary (hash). The summary is uploaded to the chain without uploading the original data. Storage path upload is to upload the location (path) of the data storage for high-dimensional data obtained through remote sensing monitoring, rather than the data itself, to save storage resources.

[0036] Preferably, the distributed monitoring site collection obtains the collected raw data through sensors or remote sensing means, and then each site uses its unique private key to digitally sign the collected data. This ensures that the data is not tampered with during the upload process and can prove that the data does indeed come from the site. The type of data transmission and the method of uploading the data are then determined based on the data collection type of the distributed monitoring site collection (such as monitoring and collection through sensors or remote sensing monitoring and collection). After uploading the data, the nodes in the consortium chain network verify the uploaded data using the public key of the distributed monitoring site to confirm the authenticity and integrity of the data. The verified data is written to the blockchain and becomes permanent evidence. Any subsequent access can trace back to the source and history of the data.

[0037] For large-scale or complex datasets (such as remote sensing images or satellite data), directly uploading raw data may cause storage and bandwidth pressure. In this case, uploading the data storage path (for example, the address of the data stored through IPFS) ensures data storage and access while ensuring data integrity and verifiability.

[0038] The processed hash summary record or storage path is uploaded to the blockchain as an immutable transaction record. This not only ensures data credibility but also provides complete data traceability through the blockchain, allowing anyone to verify the data's origin and historical changes. This achieves the technical effect of ensuring data security, transparency, and traceability while improving the efficiency of data sharing and management.

[0039] A site credibility set determination module 13 is configured to analyze site access behaviors of the distributed monitoring site set according to preset credibility indicators to determine a site credibility set;

[0040] Furthermore, the site credibility set determination module is configured to perform the following steps:

[0041] Obtaining a site access behavior log set of the distributed monitoring site set;

[0042] Extracting indicators from the site access behavior log set according to preset credibility indicators to obtain a site credibility indicator set;

[0043] The site credibility indicator set is identified by using an indicator analyzer to determine the site credibility set.

[0044] Furthermore, the preset credibility indicators include data accuracy, site network stability and access abnormality frequency.

[0045] In one embodiment of the present application, the Site Credibility Set Determination Module 13 is the core module within the platform, responsible for calculating and determining site credibility based on historical site behavior and performance evaluation data. This module's task is to evaluate each distributed monitoring site based on pre-set credibility metrics to determine its credibility within the system. Because different sites have varying equipment performance and security, subsequent verification can utilize different combinations of distributed monitoring sites with varying levels of site credibility to ensure security during each visit.

[0046] The actual usage behavior of the site is then analyzed, including upload frequency, data integrity, network connectivity, and other aspects. The credibility of each distributed monitoring site is determined after analysis. The site access behavior log collection refers to the operation logs recorded by all sites in the system, including the site's upload records, access history, error logs, etc. Pre-set credibility indicators are used to quantify the credibility of the site, such as data accuracy (whether there are errors or data anomalies), site network stability (network quality and stability during the upload process), and access anomaly frequency (whether there are frequent inaccessibility, disconnection, and other anomalies).

[0047] First, access behavior logs for each distributed monitoring site are collected, including upload records and system interaction logs. These logs provide a data source for subsequent site credibility assessments. Based on pre-set credibility metrics, the module extracts metrics from each site's access behavior logs. These metrics include: data accuracy, such as whether the site's uploaded data contains outliers, loss, or inconsistencies; site network stability, such as the number of network disconnections and latency during site data uploads; and access anomaly frequency, such as whether the site frequently experiences upload failures or frequent disconnections.

[0048] The extracted set of site credibility indicators is analyzed by an indicator analyzer to identify the credibility of each site and obtain a site credibility set, which can be used for subsequent decisions such as access control and data sharing. Preferably, multiple sample site access behavior logs and multiple sample site credibility indicators are obtained as training data. A framework based on a feedforward neural network is supervised and trained using this training data until convergence is achieved, resulting in the trained indicator analyzer.

[0049] By analyzing the historical behavior of the site and evaluating its reliability, we can provide a basis for dynamic permission management and data sharing control, and prevent data falsification and unstable data sources from affecting system operations.

[0050] an access management mutual recognition group determination module 14, configured to divide the distributed monitoring site set into access management mutual recognition groups based on the upload period set and the site credibility set, and determine Q access management mutual recognition groups, where Q is a positive integer;

[0051] Furthermore, the access management mutual recognition group determination module is used to perform the following steps:

[0052] Randomly selecting Q partition reference sites from the distributed monitoring site set;

[0053] Dividing the distributed monitoring site set into Q divided reference sites according to the upload period set and the site credibility set, and obtaining Q initial access management mutual recognition groups;

[0054] Using a dual-objective partitioning verification function, verifying the Q initial access management mutual recognition groups, and if the verification passes, using the Q initial access management mutual recognition groups as Q access management mutual recognition groups;

[0055] If the verification fails, a re-division instruction is obtained, and the reference site is re-selected based on the re-division instruction.

[0056] Furthermore, the dual-objective partitioning verification function is:

[0057]

[0058] Among them, Sep is the discrimination output by the dual-objective partitioning verification function, n q is the total number of distributed monitoring sites in the qth initial access management mutual recognition group, t qj is the upload period of the jth distributed monitoring site in the qth initial access management mutual recognition group, is the upload period for dividing the reference site in the qth initial access management mutual recognition group, p qj is the site credibility of the jth distributed monitoring site in the qth initial access management mutual recognition group, is the site credibility of the reference site in the qth initial access management mutual recognition group, and λ is the weight for balancing the upload period and site credibility.

[0059] In one possible embodiment, the upload cycle set is the time frequency of data upload by the distributed monitoring site set, reflecting the data update cycle and the activity of the monitoring site. The site credibility set reflects the reliability of the distributed monitoring site. When dividing the access management mutual recognition group, the similarity of the upload cycle and the difference in site credibility are mainly considered. The more similar the upload cycle is, the more consistent the time interval for access management mutual recognition is, which improves management efficiency. The higher the difference in site credibility is, the more it can ensure that each access management mutual recognition group contains sites of various reliability levels, so that at least one reliable site can be authenticated during authentication.

[0060] Preferably, Q partitioning reference sites are randomly selected from the distributed monitoring site set, and the Q partitioning reference sites are used as the basis for partitioning the distributed monitoring site set. The distributed monitoring site set is traversed, and the upload cycle similarity and site credibility similarity of each distributed monitoring site with the Q partitioning reference sites are calculated using the cosine similarity calculation formula. Then, the upload cycle similarity is weighted by the difference between 1 and the site credibility similarity to obtain Q two-dimensional similarities. Each distributed monitoring site is divided into the partitioning reference site corresponding to the maximum value of the Q two-dimensional similarities, thereby obtaining the Q initial access management mutual recognition groups.

[0061] Then, the Q initial access management mutual recognition groups are input into the dual-objective partitioning verification function for analysis to determine the discrimination of the overall partitioning. The higher the upload cycle similarity and the more inconsistent the site credibility, the higher the overall discrimination. Determine whether the discrimination output by the dual-objective partitioning verification function meets the preset discrimination threshold (the minimum discrimination that meets the requirements pre-set by those skilled in the art). If so, the verification passes, and the Q initial access management mutual recognition groups are used as Q access management mutual recognition groups. If not, the verification fails, and a re-division instruction is obtained. The re-division instruction is a command to re-divide the distributed monitoring site set according to the upload cycle set and the site credibility set. Then, the reference site is re-selected based on the re-division instruction. The division result is verified according to the above verification process until the verification passes, and the Q access management mutual recognition groups are obtained.

[0062] A mutual recognition key pair obtaining module 15 is configured to generate Q access management mutual recognition key pairs for each of the Q access management mutual recognition groups based on an RSA algorithm, wherein the Q access management mutual recognition key pairs include Q access management mutual recognition public keys and Q access management mutual recognition private keys;

[0063] In one possible embodiment, a pair of keys is generated for each of the Q access management mutual recognition groups: an access management mutual recognition public key and an access management mutual recognition private key, thereby paving the way for subsequent data access verification. The RSA algorithm is used to generate the public and private key pairs. For each access management mutual recognition group (a type with Q groups), the RSA algorithm is used to generate a pair of keys, namely, an access management mutual recognition public key and an access management mutual recognition private key. The public and private keys of each access management mutual recognition group are generated independently, ensuring the uniqueness and security of the key pair for each group.

[0064] The access management verification module 16 is used to sparsely split the Q access management mutual recognition private keys based on Q historical asynchronous segmentation repositories, obtain Q access management mutual recognition private key slice sets, and randomly distribute the Q access management mutual recognition private key slice sets to Q access management mutual recognition groups for access management verification of the alliance chain.

[0065] Furthermore, the access management verification module is used to perform the following steps:

[0066] Randomly split the Q access management mutual recognition private keys according to the number of distributed monitoring sites in the Q access management mutual recognition groups to obtain Q initial access management mutual recognition private key slice sets and Q slice position sets;

[0067] Approximate identification is performed on the Q initial access management mutual recognition private key slice sets and the Q historical asynchronous slicing repositories respectively, and the Q slicing position sets are adjusted according to the identification results to obtain Q access management mutual recognition private key slice sets.

[0068] Further, such as Figure 2 As shown, the access management verification module is also used to perform the following steps:

[0069] Performing similarity identification on the Q initial access management mutual recognition private key slice sets and the Q historical asynchronous sharding repositories respectively to determine the similarity of the Q initial access management mutual recognition private key slices;

[0070] Obtaining Q adjustment quantities for adjusting the Q slice position sets according to the approximations of the Q initial access management mutual recognition private key slices;

[0071] According to the Q adjustment quantities, the Q segmentation position sets are randomly adjusted according to a preset adjustment scale to obtain Q access management mutual recognition private key slice sets.

[0072] In one embodiment of the present application, Q historical asynchronous sharding repositories are Q databases that store sharded private key fragments, and the fragments in the repository are used to subsequently verify the validity of the private key. When sharding the Q access management mutual recognition private keys, the lower the similarity between the slices and the Q historical asynchronous sharding repositories, the higher the security. The Q access management mutual recognition private key slice sets are used to be distributed to the distributed monitoring sites in the Q access management mutual recognition groups for joint joint authentication. When a distributed monitoring site needs to access data on the alliance chain, it is necessary to make an access management mutual recognition private key slice request to the distributed monitoring site in the corresponding access management mutual recognition group. If the access management mutual recognition private key slice set returned by the request is combined with the access management mutual recognition private key slice of the distributed monitoring site and the corresponding Q access management mutual recognition public keys, the access management verification is passed, and data access on the alliance chain is allowed. Due to the uneven distribution of credibility in each access management mutual recognition group, the security of at least one initial access management mutual recognition private key slice in the initial access management mutual recognition private key slice set is guaranteed, so the security of access management can be guaranteed by means of splicing verification.

[0073] Preferably, the Q access management mutual recognition private keys are randomly split based on the number of distributed monitoring sites within the Q access management mutual recognition groups. That is, the Q access management mutual recognition private keys are randomly split by the number of distributed monitoring sites within the Q access management mutual recognition groups minus 1, thereby obtaining Q initial access management mutual recognition private key slice sets and Q slice position sets. The initial access management mutual recognition private key slices are fragments formed by randomly splitting the initial access management mutual recognition private key. The slice positions are the locations where the initial access management mutual recognition private key is split.

[0074] Using cosine similarity, the similarity between the Q initial access management mutual recognition private key slice sets and the Q private key slices stored in the historical asynchronous sharding repository is calculated, and the maximum value is taken to obtain the approximation of the Q initial access management mutual recognition private key slices. The approximation of the Q initial access management mutual recognition private key slices reflects the reliability of the slices in the Q initial access management mutual recognition private key slice set. The higher the similarity, the lower the reliability.

[0075] The approximations of the Q initial access management mutual recognition private key slices are compared with the approximations of the access management mutual recognition private key slices preset by those skilled in the art, and the inverse of the ratio is multiplied by a preset adjustment amount (an adjustment amount preset by those skilled in the art) to obtain the Q adjustment amounts. According to the Q adjustment amounts, the Q segmentation position sets are randomly adjusted according to a preset adjustment scale (a single segmentation position adjustment distance preset by those skilled in the art) to obtain the Q access management mutual recognition private key slice sets. By randomly segmenting and distributing the access management mutual recognition private key slices, the security of data upload and access is effectively enhanced, ensuring that only verified legitimate sites can access platform data, thereby preventing malicious sites from tampering with data or abusing permissions.

[0076] In summary, the embodiments of the present application have at least the following technical effects:

[0077] This application uploads the monitoring data of a distributed monitoring site set to the alliance chain according to the preset chain-up rules through a site identity key pair, wherein the distributed monitoring site set includes an upload period set, and then the site access behavior of the distributed monitoring site set is disassembled according to the preset credibility index to determine the site credibility set, and then the distributed monitoring site set is divided into access management mutual recognition groups based on the upload period set and the site credibility set to determine Q access management mutual recognition groups, wherein Q is a positive integer, and then Q access management mutual recognition key pairs are generated for the Q access management mutual recognition groups based on the RSA algorithm, wherein the Q access management mutual recognition key pairs include Q access management mutual recognition public keys and Q access management mutual recognition private keys, and with Q historical asynchronous segmentation repositories as constraints, the Q access management mutual recognition private keys are sparsely segmented to obtain Q access management mutual recognition private key slice sets, and the Q access management mutual recognition private key slice sets are randomly distributed to Q access management mutual recognition groups for access management verification of the alliance chain. The technical effect of improving the reliability and security of data sharing management in the blockchain is achieved.

[0078] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0080] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. The blockchain-based watershed soil and water conservation project data sharing and management platform is characterized by: The platform includes: The site collection acquisition module is used to obtain the distributed monitoring site collection of the soil and water conservation project in the target watershed and assign a unique site identity key pair to each distributed monitoring site; A data uploading module, configured to upload the monitoring data of the distributed monitoring site set to the consortium chain according to a preset uploading rule through a site identity key pair, wherein the distributed monitoring site set includes an upload period set; A site credibility set determination module, configured to analyze site access behaviors of the distributed monitoring site set according to preset credibility indicators to determine a site credibility set; an access management mutual recognition group determination module, configured to divide the distributed monitoring site set into access management mutual recognition groups based on the upload period set and the site credibility set, and determine Q access management mutual recognition groups, where Q is a positive integer; A mutual recognition key pair obtaining module is used to generate Q access management mutual recognition key pairs for each of the Q access management mutual recognition groups based on the RSA algorithm, wherein the Q access management mutual recognition key pairs include Q access management mutual recognition public keys and Q access management mutual recognition private keys; The access management verification module is used to sparsely split the Q access management mutual recognition private keys based on Q historical asynchronous segmentation repositories, obtain Q access management mutual recognition private key slice sets, and randomly distribute the Q access management mutual recognition private key slice sets to Q access management mutual recognition groups for access management verification of the alliance chain.

2. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 1 is characterized in that: The preset chain-up rules include: when the monitoring data of the distributed monitoring site set is data obtained through sensor monitoring, the monitoring data is recorded in a hash summary and the hash summary is uploaded to the alliance chain; when the monitoring data of the distributed monitoring site set is data obtained through remote sensing monitoring, the storage path of the monitoring data is uploaded to the alliance chain.

3. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 1 is characterized in that: The site credibility set determination module is used to perform the following steps: Obtaining a site access behavior log set of the distributed monitoring site set; Extracting indicators from the site access behavior log set according to preset credibility indicators to obtain a site credibility indicator set; The site credibility indicator set is identified by using an indicator analyzer to determine the site credibility set.

4. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 3 is characterized in that: The preset credibility indicators include data accuracy, site network stability and access abnormality frequency.

5. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 1 is characterized in that: The access management mutual recognition group determination module is used to perform the following steps: Randomly selecting Q partition reference sites from the distributed monitoring site set; Dividing the distributed monitoring site set into Q divided reference sites according to the upload period set and the site credibility set, and obtaining Q initial access management mutual recognition groups; Using a dual-objective partitioning verification function, verifying the Q initial access management mutual recognition groups, and if the verification passes, using the Q initial access management mutual recognition groups as Q access management mutual recognition groups; If the verification fails, a re-division instruction is obtained, and the reference site is re-selected based on the re-division instruction.

6. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 5 is characterized in that: The dual-objective partitioning verification function is: Among them, Sep is the discrimination output by the dual-objective partitioning verification function, n q is the total number of distributed monitoring sites in the qth initial access management mutual recognition group, t qj is the upload period of the jth distributed monitoring site in the qth initial access management mutual recognition group, is the upload period for dividing the reference site in the qth initial access management mutual recognition group, p qj is the site credibility of the jth distributed monitoring site in the qth initial access management mutual recognition group, is the site credibility of the reference site in the qth initial access management mutual recognition group, and λ is the weight for balancing the upload period and site credibility.

7. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 1 is characterized in that: The access management verification module is used to perform the following steps: Randomly split the Q access management mutual recognition private keys according to the number of distributed monitoring sites in the Q access management mutual recognition groups to obtain Q initial access management mutual recognition private key slice sets and Q slice position sets; Approximate identification is performed on the Q initial access management mutual recognition private key slice sets and the Q historical asynchronous slicing repositories respectively, and the Q slicing position sets are adjusted according to the identification results to obtain Q access management mutual recognition private key slice sets.

8. The blockchain-based watershed soil and water conservation engineering data sharing and management platform according to claim 7 is characterized in that: The access management verification module is also used to perform the following steps: Performing similarity identification on the Q initial access management mutual recognition private key slice sets and the Q historical asynchronous sharding repositories respectively to determine the similarity of the Q initial access management mutual recognition private key slices; Obtaining Q adjustment quantities for adjusting the Q slice position sets according to the approximations of the Q initial access management mutual recognition private key slices; According to the Q adjustment quantities, the Q segmentation position sets are randomly adjusted according to a preset adjustment scale to obtain Q access management mutual recognition private key slice sets.

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