Intelligent water conservancy Internet of Things data security storage method and system

Abstract

The invention discloses an intelligent water conservancy Internet of Things data security storage method and system. The method comprises the steps of S1, collecting various water conservancy data in a monitoring area in real time; s2, after water conservancy data is preprocessed, attribute division is carried out, and encryption is carried out to obtain a ciphertext I; s3, physical fragments are divided based on hydrological unit codes, and a block chain network is established in combination with attributes of the water conservancy monitoring equipment; s4, calculating a hash value of the preprocessed water conservancy data based on the block chain network, and performing encryption to obtain a ciphertext II; and S5, based on a Byzantine consensus, storing the first ciphertext in an off-chain node, uploading the water conservancy data after attribute division and the second ciphertext to an on-chain node, generating an on-chain data index, decrypting the second ciphertext, sending the decrypted second ciphertext to the off-chain node, obtaining a final ciphertext after consensus, and decrypting through a decryption algorithm to obtain the preprocessed water conservancy data. The intelligent water conservancy Internet of Things data can be efficiently processed, and the required data information can be accurately extracted and stored.

Description

Technical Field

[0001] The present invention relates to the technical field of secure storage of water conservancy Internet of Things data, and specifically to a method and system for secure storage of smart water conservancy Internet of Things data. Background Art

[0002] The existing technology has a problem: existing water conservancy data is centrally stored on servers, and existing on-chain and off-chain data storage solutions do not fully address the fault tolerance issues of off-chain data. Improper data storage can lead to data loss, corruption, or processing errors, increasing the risk of illegal data acquisition, thereby affecting the overall reliability and stability of the system.

[0003] The difficulty and significance of solving the above technical problems: Given the problems existing in existing technologies, researching secure data storage for the Smart Water IoT is extremely challenging. First, migrating massive amounts of water data from existing servers to an on-chain or adopting more advanced off-chain storage solutions requires addressing data integrity, consistency, and security issues during the data migration process. Second, developing off-chain data fault-tolerance mechanisms requires a deep understanding of the principles and implementation of technologies such as data redundancy, backup and recovery, and data verification, and optimizing them based on the characteristics of water data. Third, achieving seamless collaboration between on-chain and off-chain data requires addressing technical challenges such as data synchronization, cross-chain communication, and data privacy protection. Therefore, researching secure data storage for the Smart Water IoT, addressing the issues of centralized server storage of water data and off-chain data fault-tolerance, will help promote the development of water informationization and has far-reaching practical significance. Summary of the Invention

[0004] The present invention provides a method and system for securely storing data of a smart water conservancy Internet of Things, which solves the problems mentioned in the above background technology.

[0005] The present invention provides the following technical solution: a method for securely storing data of a smart water conservancy Internet of Things, comprising:

[0006] S1. Based on water conservancy monitoring equipment, real-time collection of various water conservancy data within the monitoring area;

[0007] S2, pre-processing the water conservancy data collected by S1 and performing attribute classification, and encrypting the pre-processed water conservancy data to obtain ciphertext 1;

[0008] S3, divide the physical shards based on the hydrological unit code and build a blockchain network based on the attributes of the water monitoring equipment used in S1;

[0009] S4, based on the blockchain network established in S3, calculates the hash value of the pre-processed water conservancy data and encrypts it to obtain ciphertext 2;

[0010] S5. Based on the Byzantine consensus, ciphertext 1 is stored in the off-chain node, the attribute-divided water conservancy data and ciphertext 2 are uploaded to the on-chain node, and the on-chain data index is generated. Ciphertext 2 is decrypted and sent to the off-chain node. After consensus is reached, the final ciphertext is obtained, and the pre-processed water conservancy data is obtained by decryption through the decryption algorithm.

[0011] This method solves the problem that data storage in the prior art lacks a fault-tolerant mechanism, resulting in data loss, damage or inconsistency.

[0012] Preferably, the water conservancy monitoring equipment includes water flow sensing equipment, water quality sensing equipment and rainfall sensing equipment. The water flow sensing equipment has a measuring range of 0-70m and is used to collect flow and water level data. The water quality sensing equipment has an accuracy level of ±0.5%R and is used to collect water temperature and turbidity data. The rainfall sensing equipment has a measuring range of 0-0.5-3m and is used to collect rainfall data.

[0013] Preferably, when attributes are divided, the attributes include location, time information, flow, water level, water temperature, turbidity and rainfall.

[0014] Preferably, the preprocessing of the water conservancy data collected by S1 includes: extracting the water conservancy data collected by S1, establishing relationships between the data, cleaning the data with established relationships, and eliminating redundant and erroneous data; dividing the cleaned data by attributes; and finally reviewing the divided data according to the confidence level.

[0015] Preferably, attribute partitioning is implemented using an attribute partitioning model, and the attribute partitioning model training process includes:

[0016] S2.1. Use the preset model to classify the attributes of the cleaned data;

[0017] S2.2. Construct a water conservancy corpus based on the attribute-divided data to perform secondary pre-training of the preset model;

[0018] S2.3. Use efficient parameter fine-tuning method to perform lightweight fine-tuning on the preset model after secondary pre-training;

[0019] S2.4. Use the lightweight and fine-tuned preset model to annotate the unannotated water conservancy IoT data. Calculate the confidence level of each annotated result and evaluate the reliability of each annotated result.

[0020] S2.5. Automatically classify the water conservancy IoT data with a confidence level higher than the set threshold, and manually review the water conservancy IoT data with a confidence level lower than the set threshold. Feedback the results to the preset model, which iteratively optimizes the preset model based on the feedback to dynamically adjust the classification and obtain an attribute partitioning model.

[0021] Preferably, when establishing a blockchain network, physical shards are divided according to the hydrological unit code, and each physical shard maintains an independent consensus group. A new node determines the physical shard to which it belongs based on the hydrological unit code, establishes a connection with the node in the physical shard network, and formally joins the physical shard network after a successful connection. The new node synchronizes the data of the current ledger state and regularly maintains its own transaction list. After the new node joins the blockchain network, it performs data verification, transaction processing, and block addition operations according to established rules and consensus mechanisms. Dividing the physical shards according to the hydrological unit code and maintaining an independent consensus group for each physical shard includes: according to the hydrological unit code rules, each code corresponds to a physical shard, and a dedicated water conservancy monitoring equipment cluster is deployed for each shard. An independent consensus group is used within the shard, and the node composition includes a leader node, a monitoring node of the water conservancy monitoring equipment, and an optional mobile node. The new node determines the physical shard to which it belongs based on the hydrological unit code and establishes a connection with the nodes in the physical shard network. This includes: the new node submits its location information and determines the shard to which it belongs based on the hydrological unit code; the new node sends a request to join the shard network, and the shard leader node verifies the legitimacy of the new node and returns the corresponding verification result; if the verification passes, the new node joins the shard network; otherwise, the verification fails and the new node is rejected from joining the shard network. After the new node joins the shard network, the leader node in the shard network sends a new node registration message to the blockchain network; the blockchain sends a confirmation message of successful registration to the leader node in the shard network; the leader node in the shard network sends an admission notification to the new node; after receiving the admission notification, the new node synchronizes the data of the current ledger state.

[0022] This application also proposes a smart water conservancy Internet of Things data security storage system, which is used to implement the above-mentioned smart water conservancy Internet of Things data security storage method, including:

[0023] Water conservancy data acquisition module: Based on water conservancy monitoring equipment, it collects various water conservancy data in the monitoring area in real time;

[0024] Attribute division module: pre-processes the collected water conservancy data and divides its attributes, and encrypts the pre-processed water conservancy data to obtain ciphertext 1;

[0025] Blockchain network building module: divides physical shards based on hydrological unit codes and builds a blockchain network based on the attributes of water conservancy monitoring equipment;

[0026] Water conservancy data processing module: Calculates the hash value of the pre-processed water conservancy data based on the blockchain network and encrypts it to obtain ciphertext 2;

[0027] Water conservancy data storage module: Based on the Byzantine consensus, ciphertext 1 is stored in the off-chain node, the attribute-divided water conservancy data and ciphertext 2 are uploaded to the on-chain node, and the on-chain data index is generated. After decrypting ciphertext 2, it is sent to the off-chain node, and the final ciphertext is obtained after consensus. The decryption algorithm is used to decrypt the pre-processed water conservancy data.

[0028] The present invention has the following beneficial effects:

[0029] 1. This method for secure storage of smart water IoT data processes and divides smart water IoT data, constructs a distributed network based on blockchain according to actual conditions, and ultimately achieves secure storage of smart water IoT data.

[0030] 2. This smart water conservancy IoT data security storage method designs an attribute partitioning method based on a large language model and implements attribute-based access control;

[0031] 3. This method for securely storing data in the smart water IoT uses asynchronous Byzantine consensus for off-chain fault tolerance, ensuring fault-tolerant storage of data in the smart water IoT.

[0032] 4. This method for secure storage of smart water conservancy IoT data realizes decentralized data management, which has far-reaching theoretical and practical significance for solving the problems of centralized storage of water conservancy data and off-chain data fault tolerance. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flowchart of the steps of the method for securely storing data of the smart water conservancy Internet of Things provided in Example 1 of the present invention.

[0034] Figure 2 It is a schematic diagram of the process of collecting various water conservancy data using the water conservancy monitoring equipment provided in Example 1 of the present invention.

[0035] Figure 3 It is a schematic diagram of the flow of preprocessing various water conservancy data provided by Example 1 of the present invention.

[0036] Figure 4 This is a schematic diagram of the process of establishing a blockchain network provided in Example 1 of the present invention.

[0037] Figure 5 This is a flow chart of secure storage of water conservancy Internet of Things data provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1-Figure 5 , a method for securely storing data of a smart water conservancy Internet of Things, comprising:

[0041] S1, based on water flow sensing equipment, water quality sensing equipment and rainfall sensing equipment, collect various water conservancy data in the monitoring area in real time, such as Figure 2 As shown in the figure, when collecting water conservancy Internet of Things data, a water flow sensing device with a measuring range of (0-70) m, a frequency range of 26 GHz, an accuracy range of 70 m, and a signal output of RS485 is selected. With the telemetry station and communication network as the core, a data interface is provided to the upper layer to collect and monitor flow, water level and other data; a water quality sensing device with an accuracy level of ±0.5% R, a nominal pressure of 1.6 MPA, and a range ratio of 10:01 is selected to collect and monitor water quality data such as water temperature and turbidity; a rainfall sensing device with a measuring range of (0-0.5-3) m and an output model of (4-20 mA, 0-5 VDC, 0-10 VDC) is selected to collect and monitor rainfall data; and the collected water conservancy data are integrated and processed.

[0042] S2, pre-process the water conservancy data collected by S1 and perform attribute division, and encrypt the pre-processed water conservancy data to obtain ciphertext 1, where

[0043] The flow data attributes collected and monitored by the water flow sensing equipment include:

[0044] Mark the basic traffic data attributes, including the current traffic value, the average value within a specified period, and the cumulative traffic value within a certain period;

[0045] Mark the source attributes of traffic data, device ID, device location information, and device calibration status;

[0046] Correlate hydrological attributes and verify data rationality by calculating water level-discharge relationships.

[0047] The water quality data attributes collected and monitored by water quality sensing equipment include:

[0048] Mark basic water quality data attributes, such as water temperature, turbidity, etc.

[0049] Mark the source attributes of water quality data, device ID, device location information, and device calibration status;

[0050] Correlate hydrological attributes, calculate 24-hour rate of change, and cross-validate data from multiple devices.

[0051] The rainfall sensing device collects and monitors rainfall data attributes including:

[0052] Mark basic rainfall data attributes, rainfall type, cumulative rainfall in a certain period, and continuous rainfall duration;

[0053] Mark the source attributes of rainfall data, device ID, device location information, and device calibration status;

[0054] Associated with hydrological attributes, when the rainfall exceeds the set threshold, an early warning is triggered.

[0055] The water conservancy data after attribute classification include:

[0056] Mark the data information of the water flow sensing device, such as device type, access control list ciphertext, data content, the data content is the flow data content, such as fragment ID, time, device ID, device location information, current flow value, current water level value;

[0057] Mark the data information of the water quality sensing device, such as device type, access control list ciphertext, data content, and water quality data content, such as shard ID, time, device ID, device location information, water temperature, and turbidity;

[0058] Mark the data information of the rainfall sensing device, such as device type, access control list ciphertext, data content, and rainfall data content, such as shard ID, time, device ID, device location information, rainfall type, and accumulated rainfall in a certain period of time.

[0059] like Figure 3 As shown, the preprocessing includes:

[0060] First, the collected data is extracted and the relationships between the data are sorted out; next, the connected data is cleaned to eliminate redundant and erroneous data; after the data cleaning is completed, the cleaned data is divided into different attributes based on the DeepSeek large language model; finally, the divided data is reviewed according to the confidence level, and the ciphertext of the pre-processed water conservancy IoT data is stored in the LevelDB database, with the key corresponding to the hash value of the data and the value corresponding to the ciphertext of the data.

[0061] Specifically, we used the DeepSeek large language model and fine-tuned it with water conservancy IoT data to build a corpus dedicated to the water conservancy field, helping the model understand the connection between water conservancy terminology and domain context.

[0062] Based on fine-tuning, secondary pre-training is performed using the latest data in the field of water conservancy IoT to learn the latest terminology in the field;

[0063] To reduce the amount of computation during secondary training and improve training efficiency, efficient parameter fine-tuning methods (such as LoRA and Adapter) are used for lightweight fine-tuning to achieve accurate modeling of water conservancy scenarios and optimization of pre-trained models.

[0064] Based on the optimized DeepSeek large language model, unannotated water conservancy IoT data will be automatically labeled;

[0065] Use a small amount of labeled water conservancy IoT data to train the model, and use the trained model to label the unlabeled water conservancy IoT data;

[0066] For each annotation result, a decision algorithm is used to calculate its confidence and evaluate the reliability of each annotation result;

[0067] Automatically classify the water conservancy IoT data that has a confidence level higher than the set threshold, which is filtered out by the model;

[0068] The water conservancy IoT data filtered out by the model with a confidence level lower than the set threshold is manually reviewed, and the results are fed back to the model. The model is iteratively optimized based on the feedback so that it can dynamically adjust the classification.

[0069] S3, based on the hydrological unit code, divides the physical shards and combines the attributes of the water monitoring equipment used in S1 to form a blockchain network, including:

[0070] like Figure 4 As shown in the figure, when establishing a blockchain network, physical shards are divided according to the hydrological unit code, and each physical shard maintains an independent consensus group; a new node determines the physical shard to which it belongs based on the hydrological unit code, establishes a connection with the node in the physical shard network, and officially joins the physical shard network after a successful connection. The new node synchronizes the data of the current ledger status and regularly maintains its own transaction list; after joining the blockchain network, the new node performs data verification, transaction processing, and block addition operations according to the established rules and consensus mechanism.

[0071] Physical shards are divided according to the hydrological unit code, and each physical shard maintains an independent consensus group, including: according to the hydrological unit code rules, each code corresponds to a physical shard, and a dedicated water conservancy monitoring equipment cluster is deployed for each shard. An independent consensus group is used within the shard, and the node composition includes leader nodes, monitoring nodes of water conservancy monitoring equipment, and optional mobile nodes. The monitoring nodes of water conservancy monitoring equipment can be various water conservancy monitoring equipment such as water flow sensing equipment, water quality sensing equipment, and rainfall sensing equipment. The mobile nodes can come from inspection drones.

[0072] The new node determines the physical shard to which it belongs based on the hydrological unit code and establishes a connection with the nodes in the physical shard network. The process includes: the new node submits its location information and determines the shard to which it belongs based on the hydrological unit code; the new node sends a request to join the shard network, and the shard leader node verifies the legitimacy of the new node and returns the corresponding verification result; if the verification passes, the new node joins the shard network; otherwise, the verification fails and the new node is rejected from joining the shard network.

[0073] After a new node joins the sharded network, the leader node in the sharded network sends a new node registration message to the blockchain network; the blockchain sends a confirmation message of successful registration to the leader node in the sharded network; the leader node in the sharded network sends an admission notification to the new node; after receiving the admission notification, the new node synchronizes the data of the current ledger status.

[0074] S4. The blockchain network established based on S3 calculates the hash value of the preprocessed water conservancy data and encrypts it to obtain ciphertext 2.

[0075] S5. Based on the Byzantine consensus, the ciphertext 1 is stored in the off-chain node, the water conservancy data after attribute division and the ciphertext 2 are uploaded to the on-chain node, the data index on the chain is generated, the ciphertext 2 is decrypted and sent to the off-chain node, and the final ciphertext is obtained after consensus, and the pre-processed water conservancy data is obtained by decryption through the decryption algorithm. Specifically: the pre-processed water conservancy Internet of Things data and the hash value of the pre-processed water conservancy Internet of Things data after hash calculation are encrypted through attribute-based access control to obtain ciphertext 1 and ciphertext 2; the attribute-divided data information, ciphertext 1, the hash value of the data and ciphertext 2 are sent to the off-chain node for Byzantine consensus; the ciphertext of the water conservancy Internet of Things data obtained after encryption is stored in the off-chain node, the attribute-divided data information and ciphertext 2 are uploaded to the on-chain node to generate the data index on the chain; the on-chain ciphertext 2 is decrypted and sent to the off-chain node, and the final ciphertext is obtained after consensus, and finally the pre-processed water conservancy Internet of Things data is decrypted through the decryption algorithm.

[0076] It should be noted that the ciphertext is stored in the LevelDB database, the key corresponds to the hash value of the data, and the value corresponds to the ciphertext of the data.

[0077] Store ciphertext 1 in the off-chain node, upload data information such as device type, access control list ciphertext, and data content; upload ciphertext 2 to the on-chain node to generate a unique on-chain data index.

[0078] Specifically, decrypt ciphertext 2 and send it to the off-chain node. The off-chain node performs Byzantine consensus on the decrypted hash value. After consensus, the hash value key of the data is used to access the corresponding database to obtain the ciphertext value of the data, and finally decrypt it through the corresponding decryption algorithm to obtain the pre-processed water conservancy Internet of Things data, that is, for the water flow sensing device, the shard ID, time, device ID, device location information, current flow value, current water level value and other information are obtained; for the water quality sensing device, the shard ID, time, device ID, device location information, water temperature, turbidity and other information are obtained; for the rainfall sensing device, the shard ID, time, device ID, device location information, rainfall type, accumulated rainfall in a certain period of time and other information are obtained.

[0079] In summary, the present invention provides an attribute partitioning method based on a large language model, innovatively implements attribute-based access control, and strictly protects the security of water conservancy Internet of Things data. The present invention adopts the Byzantine consensus mechanism for off-chain fault tolerance, optimizes the storage and management of water conservancy Internet of Things data, significantly improves the data reading and writing speed and access efficiency, and effectively solves the off-chain fault tolerance problem of water conservancy Internet of Things data. The present invention provides a method for secure storage of smart water conservancy Internet of Things data, which can achieve decentralized storage and management of data, greatly improving the security and credibility of data.

[0080] Example 2

[0081] A smart water conservancy Internet of Things data security storage system, used to implement the smart water conservancy Internet of Things data security storage method of Example 1, comprising:

[0082] Water conservancy data acquisition module: Based on water conservancy monitoring equipment, it collects various water conservancy data in the monitoring area in real time;

[0083] Attribute division module: pre-processes the collected water conservancy data and divides its attributes, and encrypts the pre-processed water conservancy data to obtain ciphertext 1;

[0084] Blockchain network building module: divides physical shards based on hydrological unit codes and builds a blockchain network based on the attributes of water conservancy monitoring equipment;

[0085] Water conservancy data processing module: Calculates the hash value of the pre-processed water conservancy data based on the blockchain network and encrypts it to obtain ciphertext 2;

[0086] Water conservancy data storage module: Based on the Byzantine consensus, ciphertext 1 is stored in the off-chain node, the attribute-divided water conservancy data and ciphertext 2 are uploaded to the on-chain node, and the on-chain data index is generated. After decrypting ciphertext 2, it is sent to the off-chain node, and the final ciphertext is obtained after consensus. The decryption algorithm is used to decrypt the pre-processed water conservancy data.

[0087] It should be noted that although the detailed description above mentions several modules or units for voice collection and processing, voiceprint recognition, and voice content recognition of the device used for action execution, this division is not mandatory. In fact, the features and functions of two or more modules or units described above can be embodied in a single module or unit. Conversely, the features and functions of a single module or unit described above can be further divided and embodied by multiple modules or units.

[0088] Those skilled in the art will appreciate that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation that combines hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system." Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in this disclosure. The description and embodiments are to be regarded as exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0089] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings and that various modifications and variations can be made without departing from the scope thereof, which is limited only by the appended claims.

[0090] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for securely storing data of a smart water conservancy Internet of Things, characterized in that: include: S1. Based on water conservancy monitoring equipment, real-time collection of various water conservancy data within the monitoring area; S2, pre-processing the water conservancy data collected by S1 and performing attribute classification, and encrypting the pre-processed water conservancy data to obtain ciphertext 1; S3, divide the physical shards based on the hydrological unit code and build a blockchain network based on the attributes of the water monitoring equipment used in S1; S4, based on the blockchain network established in S3, calculates the hash value of the pre-processed water conservancy data and encrypts it to obtain ciphertext 2; S5. Based on the Byzantine consensus, ciphertext 1 is stored in the off-chain node, the attribute-divided water conservancy data and ciphertext 2 are uploaded to the on-chain node, and the on-chain data index is generated. Ciphertext 2 is decrypted and sent to the off-chain node. After consensus is reached, the final ciphertext is obtained, and the pre-processed water conservancy data is obtained by decryption through the decryption algorithm.

2. The method for securely storing data in the smart water conservancy internet of things according to claim 1 is characterized in that: Water conservancy monitoring equipment includes water flow sensing equipment, water quality sensing equipment and rainfall sensing equipment. The water flow sensing equipment has a measuring range of 0-70m and is used to collect flow and water level data. The water quality sensing equipment has an accuracy level of ±0.5%R and is used to collect water temperature and turbidity data. The rainfall sensing equipment has a measuring range of 0-0.5-3m and is used to collect rainfall data.

3. The method for securely storing data of the smart water conservancy internet of things according to claim 1 or 2, characterized in that: When attributes are divided, the attributes include location, time information, flow, water level, water temperature, turbidity and rainfall.

4. The method for securely storing data of the smart water conservancy internet of things according to claim 1 or 2, characterized in that: The preprocessing of the water conservancy data collected by S1 includes: extracting the water conservancy data collected by S1, establishing relationships between the data, cleaning the data with established relationships, and eliminating redundant and erroneous data; dividing the cleaned data into attributes; and finally reviewing the divided data according to the confidence level.

5. The method for securely storing data of the smart water conservancy Internet of Things according to claim 1 is characterized in that: Attribute partitioning is implemented using the attribute partitioning model. The attribute partitioning model training process includes: S2.

1. Use the preset model to classify the attributes of the cleaned data; S2.

2. Construct a water conservancy corpus based on the attribute-divided data to perform secondary pre-training of the preset model; S2.

3. Use efficient parameter fine-tuning method to perform lightweight fine-tuning on the preset model after secondary pre-training; S2.

4. Use the lightweight and fine-tuned preset model to annotate the unannotated water conservancy IoT data. Calculate the confidence level of each annotated result and evaluate the reliability of each annotated result. S2.

5. Automatically classify the water conservancy IoT data with a confidence level higher than the set threshold, and manually review the water conservancy IoT data with a confidence level lower than the set threshold. Feedback the results to the preset model, which iteratively optimizes the preset model based on the feedback to dynamically adjust the classification and obtain an attribute partitioning model.

6. The method for securely storing data of the smart water conservancy Internet of Things according to claim 1 is characterized in that: When establishing a blockchain network, physical shards are divided according to the hydrological unit code, and each physical shard maintains an independent consensus group. New nodes determine the physical shard to which they belong based on the hydrological unit code, establish a connection with the nodes in the physical shard network, and officially join the physical shard network after a successful connection. The new node synchronizes the data of the current ledger status and regularly maintains its own transaction list. After joining the blockchain network, the new node performs data verification, transaction processing, and block addition operations according to the established rules and consensus mechanism.

7. The method for securely storing data of the smart water conservancy Internet of Things according to claim 6 is characterized in that: Physical shards are divided according to the hydrological unit code, and each physical shard maintains an independent consensus group. According to the hydrological unit code rules, each code corresponds to a physical shard, and a dedicated water conservancy monitoring equipment cluster is deployed for each shard. An independent consensus group is used within the shard, and the node composition includes the leader node, the monitoring node of the water conservancy monitoring equipment, and the optional mobile node.

8. The method for securely storing data in the smart water conservancy internet of things according to claim 6 is characterized in that: The new node determines the physical shard to which it belongs based on the hydrological unit code and establishes a connection with the nodes in the physical shard network. The process includes: the new node submits its location information and determines the shard to which it belongs based on the hydrological unit code; the new node sends a request to join the shard network, and the shard leader node verifies the legitimacy of the new node and returns the corresponding verification result; if the verification passes, the new node joins the shard network; otherwise, the verification fails and the new node is rejected from joining the shard network.

9. The method for securely storing data of the smart water conservancy Internet of Things according to claim 6 is characterized in that: After a new node joins the sharded network, the leader node in the sharded network sends a new node registration message to the blockchain network; the blockchain sends a confirmation message of successful registration to the leader node in the sharded network; the leader node in the sharded network sends an admission notification to the new node; after receiving the admission notification, the new node synchronizes the data of the current ledger status.

10. A smart water conservancy Internet of Things data security storage system, used to implement the smart water conservancy Internet of Things data security storage method according to any one of claims 1 to 9, characterized in that: include: Water conservancy data acquisition module: Based on water conservancy monitoring equipment, it collects various water conservancy data in the monitoring area in real time; Attribute division module: pre-processes the collected water conservancy data and divides its attributes, and encrypts the pre-processed water conservancy data to obtain ciphertext 1; Blockchain network building module: divides physical shards based on hydrological unit codes and builds a blockchain network based on the attributes of water conservancy monitoring equipment; Water conservancy data processing module: Calculates the hash value of the pre-processed water conservancy data based on the blockchain network and encrypts it to obtain ciphertext 2; Water conservancy data storage module: Based on the Byzantine consensus, ciphertext 1 is stored in the off-chain node, the attribute-divided water conservancy data and ciphertext 2 are uploaded to the on-chain node, and the on-chain data index is generated. After decrypting ciphertext 2, it is sent to the off-chain node, and the final ciphertext is obtained after consensus. The decryption algorithm is used to decrypt the pre-processed water conservancy data.

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