Intelligent agricultural data acquisition intelligent control system based on block chain

Abstract

The invention discloses an intelligent control system for intelligent agricultural data acquisition based on a block chain. The system comprises a data acquisition sensing module, a soil moisture content meteorological coupling calculation module, a crop chlorophyll inversion analysis module, a star-ring agricultural big data TDH storage processing module, an intelligent contract execution control module and a block chain data chaining verification module. The system obtains farmland multi-dimensional data through the data acquisition sensing module, and after the farmland multi-dimensional data is stored and processed by the star-ring agricultural big data TDH storage processing module, a soil moisture content change value and a crop chlorophyll content value are calculated by the soil moisture content meteorological coupling calculation module and the crop chlorophyll inversion analysis module respectively; the intelligent contract execution control module generates an equipment control instruction in combination with the result; and the block chain data uplink verification module verifies the data of each link and then writes the data into the block chain. According to the system, full-process coordination of data acquisition, analysis, storage, control and verification is realized, and data reliability and accurate control are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture data acquisition technology, and in particular to a blockchain-based smart agriculture data acquisition intelligent control system. Background Technology

[0002] In current smart agriculture activities, the synergy between farmland data collection, analysis, and equipment control has become crucial for improving production efficiency. As agricultural production demands increasing precision, it requires real-time acquisition of multi-dimensional data such as soil moisture, temperature, air humidity, light intensity, and crop canopy reflectivity. Specific algorithms are used to calculate soil moisture changes and crop chlorophyll content, data is stored and processed through a big data platform, and equipment control commands are generated based on the analysis results. However, traditional systems lack a unified data integration and verification mechanism, leading to potential data gaps in the data flow and difficulties in ensuring data authenticity and immutability. Therefore, there is an urgent need to construct a comprehensive intelligent system covering data collection, calculation, storage, control, and verification using a system architecture coupled with blockchain technology and multiple algorithms. This system aims to meet the agricultural production demands for data accuracy, process controllability, and information security.

[0003] Existing technologies have two significant drawbacks: First, data processing and storage lack efficient collaboration. Most systems fail to achieve deep integration of collected data, calculation results, and historical data, and cannot optimize data retrieval efficiency through distributed sharding and multi-level indexing, resulting in data call delays. Furthermore, the lack of a dedicated on-chain verification mechanism makes data susceptible to tampering during transmission and storage, making it difficult to guarantee the reliability of subsequent analysis and control command generation. Second, smart contract execution lacks close linkage with the results of multiple algorithms. Control command generation does not fully integrate the coupling analysis of soil moisture prediction values ​​and crop chlorophyll content values. Moreover, the lack of command verification and feedback loops during contract execution makes it impossible to adjust commands in a timely manner according to the equipment's execution status, resulting in insufficient equipment control precision and difficulty in adapting to the complex and ever-changing needs of farmland environments. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a smart agricultural data acquisition and intelligent control system based on blockchain.

[0005] The technical solution adopted in this invention is a blockchain-based smart agriculture data acquisition and intelligent control system, comprising: a data acquisition and sensing module, a soil moisture and meteorological coupled calculation module, a crop chlorophyll inversion analysis module, a StarRing Agriculture Big Data TDH storage and processing module, a smart contract execution control module, and a blockchain data on-chain verification module; the data acquisition and sensing module collects soil moisture, soil temperature, air temperature, air humidity, light intensity, and crop canopy reflectance data in real time through sensors deployed in different areas of farmland and transmits them to the StarRing Agriculture Big Data TDH storage and processing module; the soil moisture and meteorological coupled calculation module calls the collected data from the StarRing Agriculture Big Data TDH storage and processing module to run the soil moisture and meteorological coupled prediction algorithm to calculate the soil moisture change value within a preset time period and feeds the result back to the StarRing Agriculture Big Data TDH storage and processing module; the crop chlorophyll inversion analysis module... The chlorophyll inversion analysis module obtains crop canopy reflectance data from the Xinghuan Agricultural Big Data TDH storage and processing module, runs the crop chlorophyll content inversion model to calculate the crop chlorophyll content value, and stores it in the Xinghuan Agricultural Big Data TDH storage and processing module. The Xinghuan Agricultural Big Data TDH storage and processing module performs distributed storage, fragmentation processing, and index construction on data transmitted from different modules and provides data call interfaces to other modules. The smart contract execution control module obtains soil moisture change values ​​and crop chlorophyll content values ​​from the Xinghuan Agricultural Big Data TDH storage and processing module, runs the smart agricultural contract automatic execution algorithm to generate equipment control commands, and sends them to the execution equipment. The blockchain data on-chain verification module extracts the running data and control commands from different modules from the Xinghuan Agricultural Big Data TDH storage and processing module, completes data signature verification through the blockchain node consensus mechanism, and writes them into the blockchain ledger.

[0006] Furthermore, the expression for the soil moisture-meteorological coupled prediction algorithm run by the soil moisture-meteorological coupled calculation module is as follows: Real-time soil moisture value, Let t be the soil moisture value at time t. This is the soil moisture attenuation coefficient. For air temperature, For soil temperature, The temperature difference response coefficient, For air humidity, The light influence coefficient is... Light intensity, To predict the time step, The soil temperature and humidity coupling coefficient is defined as follows: the StarRing Agriculture Big Data TDH storage and processing module stores all the above parameters and provides parameter call services to the soil moisture and meteorological coupling calculation module; the smart contract execution control module obtains... It was later incorporated into the parameter system of the automatic execution algorithm for smart agriculture contracts.

[0007] Furthermore, the crop chlorophyll content inversion model expression run by the crop chlorophyll inversion analysis module is as follows: ;in, The chlorophyll content of crops, The crop canopy reflectance at a wavelength of 750 nm. The crop canopy reflectance at a wavelength of 670 nm. The crop canopy reflectance at a wavelength of 850 nm. The reflectance of the crop canopy at a wavelength of 550 nm. This is the reflectivity ratio coefficient. The reflectance ratio index, This is the reflectivity difference coefficient. The reflectivity difference index, The multi-wavelength reflectivity coupling coefficient; the StarRing Agriculture Big Data TDH storage and processing module classifies and stores reflectivity data of different wavelengths; the smart contract execution control module will... Crop growth status is used as a parameter for determining the automatic execution algorithm of smart agriculture contracts.

[0008] Furthermore, the expression for the automatic execution algorithm of the smart agricultural contract run by the smart contract execution control module is as follows: ;in, Output values ​​for smart contract control instructions. This is the soil moisture deviation weighting coefficient. This represents the soil moisture threshold. This is the weighting coefficient for the deviation in chlorophyll content. The threshold for chlorophyll content, These are the weighting coefficients for coupling meteorological parameters. The air temperature threshold. This represents the weighting coefficient for the coupling of light intensity and chlorophyll content. The maximum chlorophyll content of crops; the StarRing Agriculture Big Data TDH storage and processing module stores different threshold parameters, and the smart contract execution control module determines the maximum chlorophyll content of crops based on... The numerical range generates corresponding control commands for irrigation, ventilation, or supplemental lighting equipment.

[0009] Furthermore, the data processing model expression of the StarRing Agriculture Big Data TDH Storage and Processing Module is as follows: ;in, For the processed data, The raw data transmitted by the data acquisition and sensing module. These are the weighting coefficients for the original data. For data perturbation coefficients, A random number in the range of 0-1. The weighting coefficients are for historical data. Historical data stored in the StarRing Agriculture Big Data TDH Storage and Processing Module This represents the attenuation coefficient for historical data. For data time intervals, To calculate the data weighting coefficients, Output data for the soil moisture and meteorological coupled calculation module. The model outputs data to the crop chlorophyll inversion analysis module; the StarRing Agricultural Big Data TDH storage and processing module provides data support to other modules after completing the data fusion processing.

[0010] Furthermore, the data verification model expression of the blockchain data on-chain verification module is as follows: ;in, For data validation results, The hash weight coefficient for module data. For hash functions, Data for different functional modules to run. This is the weighting factor for the digital signature. For private key based Digital signature function, Control instructions generated by the smart contract execution control module. To verify the coupling weight coefficients, For XOR operation, The hash value of the data in the StarRing Agriculture Big Data TDH Storage and Processing Module; the blockchain data on-chain verification module based on The numerical value is used to determine whether the data meets the conditions for being added to the chain.

[0011] Furthermore, the smart contract execution control module includes an instruction generation unit, an instruction verification unit, an instruction issuance unit, and an execution feedback unit. The instruction generation unit retrieves soil moisture change values, crop chlorophyll content values, and preset crop growth parameter thresholds from the StarRing Agricultural Big Data TDH storage and processing module. It calculates control parameters for different execution devices according to the logical rules of the smart agricultural contract automatic execution algorithm and converts the control parameters into a standardized device control instruction format. The instruction verification unit extracts the device identifier, control parameters, and execution time information from the instruction and compares it with the device permission data and parameter range data stored in the StarRing Agricultural Big Data TDH storage and processing module to confirm that the instruction conforms to the device operation specifications and does not exceed parameter limits. The instruction issuance unit transmits the verified control instruction to the corresponding field execution device through an encrypted communication channel, simultaneously recording the instruction issuance time, device address, and instruction content, and sending it to the StarRing Agricultural Big Data TDH storage and processing module. The execution feedback unit receives instruction execution status data returned by the execution device, parses the execution results, device operating parameters, and abnormal information in the data, associates and stores the parsed data with the corresponding control instructions, and transmits it to the blockchain data on-chain verification module.

[0012] Furthermore, the StarRing Agriculture Big Data TDH Storage and Processing Module includes a data receiving unit, a data sharding unit, a data indexing unit, and a data retrieval unit. The data receiving unit establishes a real-time data transmission link with the data acquisition and sensing module, the soil moisture and meteorological coupled calculation module, and the crop chlorophyll inversion analysis module. It receives data packets sent by different modules, performs integrity checks on the data packets, removes data packets with missing data or incorrect formats, and marks the verified data packets with timestamps and module identifiers. The data sharding unit classifies the received data according to data type, acquisition time, and farmland area, and uses a distributed sharding algorithm to shard different types of data. The system divides data into blocks of a preset size, assigns a unique shard identifier to each data block, and records the correspondence between the shard identifier and the original data. The data indexing unit constructs a multi-level index structure based on the shard identifier, timestamp, module identifier, and data content characteristics of the data blocks, establishes a mapping relationship between the index and the storage address of the data blocks, and optimizes the index query algorithm to improve data retrieval speed. The data retrieval unit receives data retrieval requests sent by other modules, parses the data type, time range, and query conditions in the request, locates the corresponding data source through the index structure, extracts the required data, encapsulates it according to the request format, and feeds it back to the retrieval module, while recording the data retrieval log.

[0013] Furthermore, the blockchain data on-chain verification module includes a data extraction unit, a signature generation unit, a consensus participation unit, and a ledger writing unit. The data extraction unit extracts operational data, control commands, and equipment feedback data from the Xinghuan Agricultural Big Data TDH storage and processing module at preset time intervals. It filters the extracted data, retaining core data directly related to agricultural production control and removing redundant format information and duplicate data. The signature generation unit obtains the private key of the blockchain node, performs hash calculation on the filtered core data to obtain a data hash value, and uses the private key to digitally sign the data hash value, generating a signature data packet including the data hash value, signature information, and node identifier. The consensus participation unit broadcasts the signature data packet to other nodes in the blockchain network, receives signature data packets sent by other nodes, verifies the validity and integrity of the digital signature in the data packet, participates in the consensus algorithm calculation, and votes to confirm the data in the network according to the consensus rules. After reaching a consensus, the ledger writing unit organizes the verified core data, signature information, and consensus results according to the data format of the blockchain ledger, generates a new block, connects the new blockchain to the end of the existing blockchain ledger, and simultaneously updates the local ledger and synchronizes it to other nodes.

[0014] The blockchain-based smart agriculture data acquisition and intelligent control system operates through the following steps: First, sensors deployed in different areas of the farmland use a data acquisition and sensing module to collect real-time data on soil moisture, soil temperature, air temperature, air humidity, light intensity, and crop canopy reflectivity at a preset frequency. The collected raw data is then transmitted to the Xinghuan Agriculture Big Data TDH storage and processing module via an encrypted transmission protocol. Second, the Xinghuan Agriculture Big Data TDH storage and processing module verifies the integrity of the received raw data. After verification, the data is categorized and stored according to data type and collection area. A distributed sharding algorithm is used to process the data and construct a multi-level index structure. Third, the soil moisture and meteorological coupled calculation module retrieves the categorized soil moisture, soil temperature, air temperature, air humidity, and light intensity data from the Xinghuan Agriculture Big Data TDH storage and processing module. It then runs a soil moisture and meteorological coupled prediction algorithm to calculate soil moisture changes over a preset time period and feeds the results back to the system. The process involves six steps: First, the Xinghuan Agriculture Big Data TDH Storage and Processing Module. Second, the Crop Chlorophyll Inversion Analysis Module retrieves crop canopy reflectance data from the Xinghuan Agriculture Big Data TDH Storage and Processing Module, runs the crop chlorophyll content inversion model to calculate crop chlorophyll content, and stores the results in the Xinghuan Agriculture Big Data TDH Storage and Processing Module. Third, the Smart Contract Execution Control Module extracts soil moisture change values ​​and crop chlorophyll content values ​​from the Xinghuan Agriculture Big Data TDH Storage and Processing Module, combines these with preset crop growth parameter thresholds, runs the smart agriculture contract automatic execution algorithm to generate corresponding irrigation, ventilation, or supplemental lighting equipment control commands, verifies the control commands, and then sends them to the field execution equipment. Fourth, the Blockchain Data On-Chain Verification Module extracts different module operation data, control commands, and equipment feedback data from the Xinghuan Agriculture Big Data TDH Storage and Processing Module, filters and hashes the data, generates digital signatures using blockchain node private keys, participates in the blockchain network consensus process, and writes the data that passes consensus verification into the blockchain ledger.

[0015] Beneficial Effects: This invention proposes a blockchain-based intelligent control system for smart agriculture data acquisition. Through a data acquisition and sensing module, it acquires multi-dimensional farmland data in real time. Combined with the distributed sharding, multi-level indexing, and data fusion processing capabilities of the StarRing Agriculture Big Data TDH storage and processing module, it achieves deep integration of acquired data, calculation results, and historical data, significantly improving data retrieval efficiency and solving the problems of insufficient data processing and storage coordination and call delays in traditional systems. Simultaneously, the blockchain data on-chain verification module verifies and writes data to the ledger at each stage through hash calculation, digital signatures, and node consensus mechanisms, ensuring data authenticity and immutability, thus overcoming the shortcomings of traditional systems that lack data verification mechanisms and are easily tampered with. The smart contract execution control module closely integrates the algorithm results of the soil moisture and meteorological coupled calculation module and the crop chlorophyll inversion analysis module. It generates control commands using soil moisture change values ​​and crop chlorophyll content as core parameters, and constructs a closed-loop process through command verification, issuance, and feedback units. The commands can be dynamically adjusted according to the equipment execution status, improving the equipment control accuracy. At the same time, the StarRing Agriculture Big Data TDH storage and processing module provides comprehensive parameter support for contract execution, effectively solving the problems of insufficient linkage between smart contracts and multiple algorithms, low control accuracy, and inability to adapt to complex farmland environments in traditional systems. It fully meets the needs of agricultural production for data accuracy, process controllability, and information security. Attached Figure Description

[0016] Figure 1 This is a diagram showing the system module composition of the present invention;

[0017] Figure 2 This is a flowchart of the system operation steps of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, the smart agriculture data acquisition and intelligent control system based on blockchain includes: a data acquisition and sensing module, a soil moisture and meteorological coupled calculation module, a crop chlorophyll inversion analysis module, a StarRing Agriculture Big Data TDH storage and processing module, a smart contract execution control module, and a blockchain data on-chain verification module.

[0020] The data acquisition and sensing module collects data on soil moisture, soil temperature, air temperature, air humidity, light intensity, and crop canopy reflectivity in real time through sensors deployed in different areas of the farmland and transmits the data to the Xinghuan Agriculture Big Data TDH storage and processing module.

[0021] Specifically, the data acquisition and sensing module deploys sensors at a density of one monitoring node per 500 square meters in the farmland. Each node integrates a soil moisture sensor, a soil temperature sensor, an air temperature and humidity sensor, a light intensity sensor, and a crop canopy reflectivity sensor. The soil moisture sensor measures 0-100% soil volumetric water content with an accuracy controlled within ±2%; the soil temperature sensor measures -40℃-85℃ with an accuracy of ±0.5℃; the air temperature and humidity sensors measure -40℃-85℃ and 0-100%RH respectively, with accuracies of ±0.3℃ and ±3%RH; and the light intensity sensor measures 0-2000... With an accuracy of ±5%, the crop canopy reflectivity sensor collects data at wavelengths covering 550nm, 670nm, 750nm, and 850nm, with a spectral resolution ≤10nm. The module adopts the LoRaWAN wireless communication protocol and is set to collect data every 30 minutes. After the sensor collects the corresponding data in real time, it is aggregated through the local gateway and sent to the StarRing Agriculture Big Data TDH storage and processing module through an encrypted transmission channel. This provides continuous and accurate raw data support for subsequent algorithm calculations, ensuring the reliability of soil moisture prediction and crop chlorophyll analysis results. During implementation, the sensor needs to be calibrated regularly, with a calibration cycle set at once a month, to maintain the accuracy of parameter measurement.

[0022] The soil moisture and meteorological coupled calculation module calls the collected data from the Xinghuan Agricultural Big Data TDH storage and processing module, runs the soil moisture and meteorological coupled prediction algorithm to calculate the soil moisture change value within a preset time period, and feeds back the result to the Xinghuan Agricultural Big Data TDH storage and processing module.

[0023] Specifically, the soil moisture and meteorological coupled calculation module retrieves historical and real-time data on soil moisture, soil temperature, air temperature, air humidity, and light intensity from the Xinghuan Agriculture Big Data TDH storage and processing module for the past 72 hours. It sets the next 24 hours as the prediction period with a time step of 1 hour. When running the soil moisture and meteorological coupled prediction algorithm, it first performs time series alignment processing on the retrieved data to ensure that the timestamp deviation of each parameter does not exceed 5 minutes. Then, through the parameter mapping relationship built into the algorithm, it maps the difference between air temperature and soil temperature, the exponential change in air humidity, and the light intensity... Negative exponential changes are incorporated into the calculation to obtain hourly soil moisture changes. After the calculation is completed, the module encapsulates the results in the format of "farmland area - prediction time - soil moisture value" and feeds them back to the "soil moisture prediction results" dataset of the Xinghuan Agriculture Big Data TDH storage and processing module through the API interface. This module predicts the trend of soil moisture changes in advance, providing a lead time basis for irrigation equipment control. During implementation, it is necessary to ensure the integrity of the data called by the algorithm. When a certain parameter is missing data for more than 1 hour, the data completion mechanism is triggered, and linear interpolation of adjacent time period data is used to supplement it to ensure the continuity of calculation.

[0024] The crop chlorophyll inversion analysis module obtains crop canopy reflectance data from the Xinghuan Agricultural Big Data TDH storage and processing module, runs the crop chlorophyll content inversion model to calculate the crop chlorophyll content value, and stores it in the Xinghuan Agricultural Big Data TDH storage and processing module.

[0025] Specifically, the crop chlorophyll inversion analysis module obtains crop canopy reflectance data at wavelengths of 550nm, 670nm, 750nm, and 850nm from the "Canopy Reflectance Data" dataset of the Xinghuan Agriculture Big Data TDH storage and processing module within the past 24 hours. The data sampling interval is consistent with the acquisition module at 30 minutes. The module first performs outlier removal processing on the reflectance data, defining data with reflectance values ​​exceeding the 0-1 range or single changes exceeding 15% as outliers. After removal, the moving average method is used to smooth the data, with the window size set to 3 sampling points. Then, the crop chlorophyll content inversion analysis is performed. The model calculates the chlorophyll content of crops at corresponding time periods through ratio, difference, and coupling operations of multi-wavelength reflectance. The accuracy of the calculation results is controlled within ±5%, and the data is stored in the "Chlorophyll Analysis Results" dataset of the Xinghuan Agriculture Big Data TDH storage and processing module according to the structure of "collection time-crop area-chlorophyll content value". This allows for real-time monitoring of crop photosynthetic capacity and growth status, providing data support for determining whether crops need nutrient supplementation. During implementation, the reflectance coefficient adjustment value needs to be preset in the model according to the differences in crop varieties to ensure the accuracy of chlorophyll content calculation for different crops.

[0026] The Xinghuan Agricultural Big Data TDH Storage and Processing Module performs distributed storage, fragmentation processing, and index construction on data transmitted from different modules, and provides data call interfaces to other modules.

[0027] Specifically, the StarRing Agriculture Big Data TDH storage and processing module adopts a distributed cluster architecture, consisting of 8 data nodes. Each node is configured with a 16-core CPU, 128GB of memory, and 4TB of SSD storage. It supports data sharding storage, sharding the received raw data and calculation results data according to the dimension of "data type-collection date-farmland area," with each shard set to 64MB in size. A multi-level index is also constructed: the first-level index is divided by data type, the second-level index by collection date, and the third-level index by farmland area. The index update frequency is set to once every 15 minutes. The module first performs integrity verification on the received data. After successful verification, it performs format standardization processing, converting unstructured data from different sensors into structured data in JSON format, and then compresses the data using the Snappy compression algorithm, with a compression ratio controlled between 30% and 50%. The module provides RESTful APIs to other modules. The API data call interface has a response time requirement of ≤100ms and supports data backup. The backup strategy is to perform a full backup at 2:00 AM every day and an incremental backup every 6 hours. The backup data is stored on a remote node. This achieves efficient storage, fast retrieval, and secure backup of agricultural data, providing stable support for data interaction between various functional modules. During implementation, the operating status of cluster nodes needs to be monitored regularly. When the CPU utilization of a node exceeds 80% or the memory utilization exceeds 90%, the load balancing mechanism is triggered to distribute data processing tasks to nodes with lower loads.

[0028] The smart contract execution control module obtains soil moisture change values ​​and crop chlorophyll content values ​​from the StarRing Agriculture Big Data TDH storage and processing module, runs the smart agriculture contract automatic execution algorithm to generate equipment control commands and sends them to the execution equipment;

[0029] Specifically, the smart contract execution control module retrieves soil moisture change values ​​and crop chlorophyll content values ​​from the StarRing Agriculture Big Data TDH storage and processing module. Simultaneously, it retrieves preset crop growth parameter thresholds. The soil moisture thresholds are set according to the crop growth stage: 60%-70% for seedlings, 70%-80% for the growing stage, and 50%-60% for maturity. The chlorophyll content thresholds are set to 35-45 SPAD for seedlings, 50-60 SPAD for the growing stage, and 40-50 SPAD for maturity. When the module runs the smart agriculture contract automatic execution algorithm, it first compares the deviations of the actual soil moisture change values ​​and the corresponding thresholds, and the deviations of the actual chlorophyll content values ​​and the corresponding thresholds. Then, it combines air temperature and humidity to correct the deviations and generate equipment control commands. These commands include equipment identification, control parameters, and execution parameters. The control parameters for irrigation equipment are water flow rate (range 10-50 L / min), ventilation equipment wind speed (range 1-3 m / s), and supplemental lighting equipment light intensity (range 5000-20000 lux), with an execution duration of 15-120 minutes. The module sends control commands to the corresponding field devices via the MQTT protocol and simultaneously receives execution status data from the devices, including command reception status, parameter execution status, and equipment malfunction information. The commands and execution status data are then associated and stored in the Xinghuan Agricultural Big Data TDH storage and processing module. This achieves automated and precise control of agricultural equipment, reducing manual intervention costs. During implementation, the issued control commands need to be pre-verified to ensure that the command parameters are within the equipment's operating range and to avoid equipment damage.

[0030] The blockchain data on-chain verification module extracts different module operation data and control instructions from the StarRing Agriculture Big Data TDH storage and processing module, and writes them into the blockchain ledger after completing data signature verification through the blockchain node consensus mechanism.

[0031] Specifically, the blockchain data on-chain verification module adopts a consortium blockchain architecture, consisting of 5 consensus nodes distributed across different institutions. The consensus algorithm uses a practical Byzantine fault-tolerant algorithm, and the consensus period is strictly set to 10 seconds. During the data on-chain process, the signature generation unit first performs SHA-256 hash calculation on the selected core data (including the operating data of each module, control commands, and device feedback data) to generate a unique 256-bit transaction hash value. Then, it digitally signs the hash value using the blockchain node's private key, forming a signature data packet containing the data hash value, signature information, node identifier, and timestamp. After the consensus participation unit broadcasts the signature data packet to the blockchain network, each node receives and verifies the validity of the digital signature and its consistency with the data hash value. Voting is completed within the 10-second consensus period, and consensus is reached when more than 2 / 3 of the nodes support it. After consensus is reached, the ledger writing unit organizes the data according to the ledger structure of "block header, transaction data, and block tail". The block header contains core information such as the hash value of the previous block, the hash value of the current block, the timestamp, and the consensus node signature. The hash value of the current block is generated by a secondary hash of all transaction hash values ​​within the block. The block tail records a summary of the consensus result. Both the transaction hash value and the block header information are stored in the blockchain ledger, and each consortium blockchain node synchronously backs up the complete ledger. Due to the uniqueness and irreversibility of hash values, the block header information contains chain-linked hash verification logic. Third parties can use the query interface opened by the consortium blockchain to input the transaction hash value or the block height to retrieve the corresponding block header information and verify whether the data is truly on the chain. At the same time, by tracing the hash relationships between blocks, it can be verified whether the data has been verified by the consensus mechanism and has not been tampered with, fully meeting the requirements for public verification.

[0032] Preferably, the expression for the soil moisture-meteorological coupled prediction algorithm run by the soil moisture-meteorological coupled calculation module is as follows: Real-time soil moisture value, Let t be the soil moisture value at time t. This is the soil moisture attenuation coefficient. For air temperature, For soil temperature, The temperature difference response coefficient, For air humidity, The light influence coefficient is... Light intensity, To predict the time step, The soil temperature and humidity coupling coefficient is defined as follows: the StarRing Agriculture Big Data TDH storage and processing module stores all the above parameters and provides parameter call services to the soil moisture and meteorological coupling calculation module; the smart contract execution control module obtains... It was later incorporated into the parameter system of the automatic execution algorithm for smart agriculture contracts.

[0033] Specifically, in the implementation of the soil moisture-meteorological coupling calculation module, the soil moisture attenuation coefficient is adjusted according to soil type: 0.85-0.92 for sandy soil, 0.93-0.98 for clay soil, and 0.90-0.95 for loam soil; the temperature difference response coefficient is set according to season: 0.02-0.04 for spring, 0.05-0.07 for summer, 0.03-0.05 for autumn, and 0.01-0.03 for winter; the light influence coefficient is adjusted according to crop light requirements: 0.08-0.12 for light-loving crops and 0.04-0.07 for shade-tolerant crops; and the soil temperature-humidity coupling coefficient is fixed at 0.005-0.012. During implementation, the module first retrieves real-time soil moisture, air temperature, soil temperature, air humidity, and light intensity data every 30 minutes from the Xinghuan Agriculture Big Data TDH storage and processing module over the past 72 hours. The prediction time step is set to 1 hour, calculating the hourly soil moisture change over the next 24 hours. During the calculation, the accuracy of the air temperature and soil temperature difference calculation must be controlled within ±0.2℃, the air humidity index result must be retained to three decimal places, and the light intensity negative exponent calculation result must be retained to four decimal places. Through refined parameter adjustments, the accuracy of soil moisture prediction under different soil types, seasons, and crop varieties is improved, providing precise data support for irrigation control. After implementation, the calculation results must be compared with the actual measured soil moisture values, and the algorithm parameters must be calibrated monthly to ensure that the prediction error does not exceed ±3%.

[0034] Preferably, the crop chlorophyll content inversion model expression run by the crop chlorophyll inversion analysis module is as follows: ;in, The chlorophyll content of crops, The crop canopy reflectance at a wavelength of 750 nm. The crop canopy reflectance at a wavelength of 670 nm. The crop canopy reflectance at a wavelength of 850 nm. The reflectance of the crop canopy at a wavelength of 550 nm. This is the reflectivity ratio coefficient. The reflectance ratio index, This is the reflectivity difference coefficient. The reflectivity difference index, The multi-wavelength reflectivity coupling coefficient; the StarRing Agriculture Big Data TDH storage and processing module classifies and stores reflectivity data of different wavelengths; the smart contract execution control module will... Crop growth status is used as a parameter for determining the automatic execution algorithm of smart agriculture contracts.

[0035] Specifically, in the model application of the crop chlorophyll inversion analysis module, the parameter settings are as follows: the reflectance ratio coefficient is set according to the crop variety, with wheat set at 12-15, rice at 10-13, and corn at 14-17; the reflectance ratio index is fixed at 1.8-2.2; the reflectance difference coefficient is set at 8-11 for wheat, 7-9 for rice, and 9-12 for corn; the reflectance difference index is set at 1.2-1.5; and the multi-wavelength reflectance coupling coefficient is uniformly set at 0.03-0.06. In the implementation process, the module obtains reflectance data for 550nm, 670nm, 750nm, and 850nm wavelengths every 30 minutes from the "Canopy Reflectance Data" dataset of the Xinghuan Agriculture Big Data TDH storage and processing module within the past 24 hours. Abnormal data with values ​​exceeding the 0-1 range or single-time changes exceeding 15% are first removed, and then the data is smoothed using a moving average method based on three sampling points. The calculation first calculates the ratio of reflectance at 750nm to 670nm wavelengths and the difference between reflectance at 850nm and 550nm wavelengths. Then, the results of these two calculations are combined with their corresponding coefficients and exponents for further calculation. Finally, a multi-wavelength reflectance coupling term is added to obtain the chlorophyll content value. Differentiated parameters are applied to different crops to improve the accuracy of chlorophyll content calculation. The calculation results are compared with actual values ​​measured by a SPAD instrument. The model parameters are calibrated every two weeks to ensure that the calculation error is controlled within ±2 SPAD, providing a reliable basis for crop nutrient supplementation decisions.

[0036] Preferably, the expression for the automatic execution algorithm of the smart agricultural contract run by the smart contract execution control module is as follows: ;in, Output values ​​for smart contract control instructions. This is the soil moisture deviation weighting coefficient. This represents the soil moisture threshold. This is the weighting coefficient for the deviation in chlorophyll content. The threshold for chlorophyll content, These are the weighting coefficients for coupling meteorological parameters. The air temperature threshold. This represents the weighting coefficient for the coupling of light intensity and chlorophyll content. The maximum chlorophyll content of crops; the StarRing Agriculture Big Data TDH storage and processing module stores different threshold parameters, and the smart contract execution control module determines the maximum chlorophyll content of crops based on... The numerical range generates corresponding control commands for irrigation, ventilation, or supplemental lighting equipment.

[0037] Specifically, in the algorithm operation of the smart contract execution control module, the parameter settings include: soil moisture deviation weighting coefficient set to 0.4-0.6, chlorophyll content deviation weighting coefficient set to 0.3-0.5, meteorological parameter coupling weighting coefficient set to 0.05-0.15, and light and chlorophyll coupling weighting coefficient set to 0.05-0.1. Crop growth parameter thresholds are divided according to growth stages: soil moisture thresholds are 60%-70% for seedling stage, 70%-80% for growing stage, and 50%-60% for maturity stage; chlorophyll content thresholds are 35-45 SPAD for seedling stage, 50-60 SPAD for growing stage, and 40-50 SPAD for maturity stage; and air temperature thresholds are set to 15-30℃. During implementation, the module extracts hourly data on soil moisture changes, chlorophyll content, air temperature, and light intensity from the StarRing Agriculture Big Data TDH storage and processing module. It first calculates the deviations between the actual soil moisture value and the corresponding threshold, and the deviations between the actual chlorophyll content value and the corresponding threshold. Then, it combines these deviations with weighting coefficients, adding a coupling term for air temperature and humidity, and an exponential term for the ratio of light intensity to chlorophyll content, ultimately generating control command output values. Based on the output value range: 0-20 corresponds to the supplemental lighting equipment being turned on, with a light intensity set to 5000-10000 lux; 21-40 corresponds to the ventilation equipment being turned on, with a wind speed set to 1-2 m / s; 41-60 corresponds to the irrigation equipment being turned on, with a water flow rate set to 10-30 L / min; 61-80 corresponds to both irrigation and ventilation equipment being turned on simultaneously; and 81-100 corresponds to irrigation, ventilation, and supplemental lighting equipment being turned on simultaneously. To achieve automated and precise equipment control and reduce manual intervention, the execution effect of each control command should be recorded after implementation, and the weighting coefficients should be optimized monthly to ensure that the equipment operation meets the needs of crop growth.

[0038] Preferably, the data processing model expression of the StarRing Agricultural Big Data TDH Storage and Processing Module is as follows: ;in, For the processed data, The raw data transmitted by the data acquisition and sensing module. These are the weighting coefficients for the original data. For data perturbation coefficients, A random number in the range of 0-1. The weighting coefficients are for historical data. Historical data stored in the StarRing Agriculture Big Data TDH Storage and Processing Module This represents the attenuation coefficient for historical data. For data time intervals, To calculate the data weighting coefficients, Output data for the soil moisture and meteorological coupled calculation module. The model outputs data to the crop chlorophyll inversion analysis module; the StarRing Agricultural Big Data TDH storage and processing module provides data support to other modules after completing the data fusion processing.

[0039] Specifically, the data processing flow of the Xinghuan Agriculture Big Data TDH storage and processing module includes the following parameters: the weighting coefficient for raw data is set to 0.6-0.8, the weighting coefficient for historical data is set to 0.1-0.2, and the weighting coefficient for calculated data is set to 0.1-0.2; the data perturbation coefficient is fixed at 0.01-0.03; the historical data attenuation coefficient is set to 0.002-0.005; and the data time interval is set to 30 minutes, consistent with the acquisition frequency. During implementation, the module first receives raw data transmitted from the data acquisition and sensing module, calculated data from the soil moisture and meteorological coupled calculation module, and calculated data from the crop chlorophyll inversion analysis module. A perturbation term is added to the raw data by multiplying a random number in the 0-1 interval with the data perturbation coefficient. The weights for historical data are calculated using an exponential decay formula. The sum of the two types of calculated data is then combined with the logarithmic term. During processing, it is necessary to ensure that the raw data integrity verification pass rate is not less than 98%, the historical data retrieval delay does not exceed 50ms, and the calculated data format conversion error is 0. The processed data is stored according to "data type - processing time - farmland area," with each 64MB segment divided into a data partition. A three-level index structure is constructed: the first-level index is divided by data type (raw data, calculated data), the second-level index by processing date, and the third-level index by farmland area. The index update frequency is set to once every 15 minutes. This achieves efficient fusion and storage of multi-source agricultural data, improves data retrieval speed, and requires monitoring of data processing time after implementation to ensure that a single processing time does not exceed 10 seconds. Redundant data is cleaned up monthly to maintain the efficient operation of the storage system.

[0040] Preferably, the data verification model expression of the blockchain data on-chain verification module is as follows: ;in, For data validation results, The hash weight coefficient for module data. For hash functions, Data for different functional modules to run. This is the weighting factor for the digital signature. For private key based Digital signature function, Control instructions generated by the smart contract execution control module. To verify the coupling weight coefficients, For XOR operation, The hash value of the data in the StarRing Agriculture Big Data TDH Storage and Processing Module; the blockchain data on-chain verification module based on The numerical value is used to determine whether the data meets the conditions for being added to the chain.

[0041] Specifically, the verification mechanism of the blockchain data on-chain verification module has the following parameter settings: the module data hash weight coefficient is set to 0.5-0.7, the digital signature weight coefficient is set to 0.2-0.3, and the coupling verification weight coefficient is set to 0.1-0.2. In the implementation process, the module extracts operational data from the Xinghuan Agriculture Big Data TDH storage and processing module every 5 minutes, filters out core fields (collection time and value of raw data, result value and calculation time of calculated data, device identifier and parameters of control commands, and execution status of feedback data), and removes redundant format information (such as data transmission protocol identifiers and redundant field descriptions). Then, the filtered data undergoes SHA-256 hash calculation to generate a 256-bit hash value. The hash value is digitally signed using the blockchain node's private key, generating a data packet including signature information, node identifier, and timestamp. After broadcasting the data packet to a consortium blockchain network consisting of 5 nodes, other nodes verify whether the digital signature matches the node's public key and whether the data hash value is consistent. Upon successful verification, they participate in the consensus voting using the Byzantine Fault Tolerance algorithm. The consensus period is set to 10 seconds, and consensus is achieved when more than 3 nodes support it. Once consensus is reached, data packets are written to the blockchain ledger in chronological order. Each block includes the hash value of the previous block, forming a chain-like storage structure. To ensure the security and reliability of data on the blockchain and prevent data tampering, the blockchain ledger integrity must be checked quarterly to ensure 100% data storage accuracy, and node private keys must be updated every three months to enhance network security.

[0042] Preferably, the smart contract execution control module includes an instruction generation unit, an instruction verification unit, an instruction issuing unit, and an execution feedback unit. The instruction generation unit retrieves soil moisture change values, crop chlorophyll content values, and preset crop growth parameter thresholds from the StarRing Agricultural Big Data TDH storage and processing module. It calculates control parameters for different execution devices according to the logical rules of the smart agricultural contract automatic execution algorithm and converts the control parameters into a standardized device control instruction format. The instruction verification unit extracts the device identifier, control parameters, and execution time information from the instruction and compares it with the device permission data and parameter range data stored in the StarRing Agricultural Big Data TDH storage and processing module to confirm that the instruction conforms to the device operation specifications and does not exceed parameter limits. The instruction issuing unit transmits the verified control instruction to the corresponding field execution device through an encrypted communication channel, simultaneously recording the instruction issuance time, device address, and instruction content, and sending it to the StarRing Agricultural Big Data TDH storage and processing module. The execution feedback unit receives instruction execution status data returned by the execution device, parses the execution results, device operating parameters, and abnormal information in the data, associates and stores the parsed data with the corresponding control instructions, and transmits it to the blockchain data on-chain verification module.

[0043] Specifically, the smart contract execution control module is implemented through four units: instruction generation, instruction verification, instruction issuance, and execution feedback. The instruction generation unit retrieves hourly soil moisture change values ​​and crop chlorophyll content values ​​from the StarRing Agriculture Big Data TDH storage and processing module. Simultaneously, it retrieves preset crop growth parameter thresholds (soil moisture: seedling stage 60%-70%, growing stage 70%-80%, maturity stage 50%-60%; chlorophyll content: seedling stage 35-45 SPAD, growing stage 50-60 SPAD, maturity stage 40-50 SPAD). Following the smart agriculture contract's automatic execution algorithm logic, it calculates the control parameters for irrigation, ventilation, and supplemental lighting equipment, converting these parameters into a standardized instruction format. The instruction includes the device ID, control parameter value, and execution duration (15-120 minutes). The instruction verification unit extracts the device identifier and control parameters from the instruction and compares them with the device permission list (which specifies the range of parameters that each device can execute, such as the water flow rate of irrigation equipment being 10-50L / min) and parameter limitation data stored in the StarRing Agriculture Big Data TDH storage and processing module. Verification passes when the parameters are within the device's allowed range and the device is operating normally, with verification time controlled within 100ms. The instruction issuing unit transmits the verified instruction to the field execution device via the MQTT protocol (transmission rate ≥1Mbps, latency ≤500ms), simultaneously recording the issuing time, device address, instruction content, and sending it to the "Instruction Issuance Log" dataset in the StarRing Agriculture Big Data TDH storage and processing module. The execution feedback unit receives execution status data returned by the device every 30 seconds (including execution results, device operating parameters such as current water flow rate, and abnormal information codes), parses it, associates it with the corresponding control instruction, and stores it in the "Execution Feedback Log" dataset. When abnormal information is detected, the instruction pause process is immediately triggered and feedback is sent to the smart contract execution control module. Establish a closed-loop management system for the entire command process to ensure precise and traceable equipment control. After implementation, the daily command verification pass rate (≥99.5%) and the timeliness rate of equipment anomaly feedback processing (≥98%) must be statistically analyzed.

[0044] Preferably, the Xinghuan Agricultural Big Data TDH Storage and Processing Module includes a data receiving unit, a data sharding unit, a data indexing unit, and a data retrieval unit. The data receiving unit establishes a real-time data transmission link with the data acquisition and sensing module, the soil moisture and meteorological coupled calculation module, and the crop chlorophyll inversion analysis module. It receives data packets sent by different modules, performs integrity checks on the data packets, removes data packets with missing data or incorrect formats, and marks the verified data packets with a timestamp and module identifier. The data sharding unit classifies the received data according to data type, acquisition time, and farmland area, and uses a distributed sharding algorithm to shard different types of data. The data is divided into blocks of a preset size, and each data block is assigned a unique shard identifier. The correspondence between the shard identifier and the original data is recorded. The data indexing unit constructs a multi-level index structure based on the shard identifier, timestamp, module identifier, and data content characteristics of the data blocks, establishes a mapping relationship between the index and the storage address of the data blocks, and optimizes the index query algorithm to improve data retrieval speed. The data calling unit receives data calling requests sent by other modules, parses the data type, time range, and query conditions in the request, locates the corresponding data source through the index structure, extracts the required data, encapsulates it according to the request format, and feeds it back to the calling module, while recording the data calling log.

[0045] Specifically, the Xinghuan Agriculture Big Data TDH Storage and Processing Module is implemented in four units. The data receiving unit establishes a real-time transmission link with each front-end module (using TCP / IP protocol, bandwidth ≥100Mbps). It receives raw data from the data acquisition and sensing module every 30 minutes, calculation results from the soil moisture and meteorological coupled calculation module every hour, and analysis results from the crop chlorophyll inversion analysis module every hour. The received data packets are subjected to CRC32 integrity verification. Data packets that fail the verification need to trigger a retransmission mechanism. The number of retransmissions shall not exceed 3 times to ensure that the raw data integrity verification pass rate is ≥98%. After the verification is passed, the data packets are marked with a timestamp (accurate to the millisecond level) and module identifier (such as "acquisition module - soil moisture" or "calculation module - soil moisture prediction"). The data sharding unit categorizes data by data type (raw data, calculated result data), collection time (divided by day), and farmland area (divided into 100-mu (approximately 6.7 hectares) units. A consistent hashing algorithm is used to divide the data into 64MB shards, assigning each shard a unique 128-bit identifier. The mapping relationship between the shard identifier and the raw data is recorded and stored in the "shard index table." The data indexing unit constructs a three-level index based on the shard identifier, timestamp, module identifier, and data content characteristics (such as the numerical range of soil moisture data). The first-level index is divided by data type, the second-level index by collection date, and the third-level index by farmland area. The index update frequency is set to once every 15 minutes, ensuring that the index query response time is ≤100ms. The data retrieval unit receives RESTful API call requests from other modules (request format is JSON, including data type, time range, and area identifier). After parsing the request, it locates the data source through the index, extracts the data, and encapsulates the feedback according to the request format. The integrity of the feedback data must reach 100%, and the time for a single call must be ≤500ms. To achieve efficient storage and rapid retrieval of agricultural data and support the data interaction needs of various modules, the CPU utilization (≤80%) and memory utilization (≤90%) of cluster nodes need to be monitored in real time during implementation. When the threshold is exceeded, the load balancing mechanism is triggered to distribute the data processing tasks to nodes with lower load (load difference controlled within 10%).

[0046] Preferably, the blockchain data on-chain verification module includes a data extraction unit, a signature generation unit, a consensus participation unit, and a ledger writing unit. The data extraction unit extracts operational data, control commands, and equipment feedback data from the Xinghuan Agricultural Big Data TDH storage and processing module at preset time intervals. It filters the extracted data, retaining core data directly related to agricultural production control and removing redundant format information and duplicate data. The signature generation unit obtains the private key of the blockchain node, performs hash calculation on the filtered core data to obtain a data hash value, and uses the private key to digitally sign the data hash value, generating a signature data packet including the data hash value, signature information, and node identifier. The consensus participation unit broadcasts the signature data packet to other nodes in the blockchain network, receives signature data packets sent by other nodes, verifies the validity and integrity of the digital signature in the data packet, participates in the consensus algorithm calculation, and votes to confirm the data in the network according to the consensus rules. After reaching a consensus, the ledger writing unit organizes the verified core data, signature information, and consensus results according to the data format of the blockchain ledger, generates a new block, connects the new blockchain to the end of the existing blockchain ledger, and simultaneously updates the local ledger and synchronizes it to other nodes.

[0047] Specifically, the blockchain data on-chain verification module is implemented in four units. The data extraction unit extracts data from the Xinghuan Agriculture Big Data TDH storage and processing module every 5 minutes. The extraction scope includes the raw data (collection time, value, sensor ID) from the data acquisition and sensing module, soil moisture and chlorophyll calculation results (result value, calculation time, module identifier), smart contract control instructions (device ID, parameters, execution duration), and device feedback data (execution status, exception code). During the screening process, core fields are retained, and unnecessary information such as transmission protocol identifiers and redundant field descriptions are removed. The amount of data after screening needs to be compressed to 60%-70% of the original extracted data. The signature generation unit obtains the RSA2048-bit private key of the blockchain node, performs SHA-256 hash calculation on the screened data (generating a 256-bit hash value), and uses the private key to digitally sign the hash value (signature length ≥ 256 bytes), generating a signature data packet including the data hash value, signature information, node identifier (composed of 8 alphanumeric characters), and timestamp (accuracy to the second). The signature generation time needs to be ≤ 200ms. The consensus participation unit broadcasts the signature data packet to a consortium blockchain network consisting of 5 nodes (nodes are distributed in different physical locations, with a network latency of ≤1s). After receiving data packets from other nodes, it uses the public key of the corresponding node to verify the validity of the digital signature (verification passes when the signature matches the public key and the hash value is consistent). After successful verification, it participates in the consensus voting using the practical Byzantine fault-tolerant algorithm. The voting period is set to 10 seconds, and each node must complete its vote within 5 seconds. Consensus is achieved when more than 3 nodes support it, and the consensus success rate is required to be ≥99%. After reaching consensus, the ledger writing unit organizes the data according to the blockchain ledger format (including block header, transaction data, block tail, and block size ≤1MB), generates a new block, writes the hash value of the new block to the tail of the previous block to achieve chained storage, and simultaneously updates its local ledger and synchronizes it to other nodes (synchronization latency ≤3s), ensuring that the consistency of ledger data across nodes is ≥99.9%. To ensure the security and immutability of data uploaded to the blockchain, the integrity of the blockchain ledger must be checked quarterly (no data loss is required) and the node private key must be updated every 3 months to prevent data security risks caused by private key leakage.

[0048] The soil moisture-meteorological coupled prediction algorithm is used to calculate the changes in soil moisture over future periods. It achieves accurate prediction by integrating multi-dimensional soil and meteorological data. In implementation, the algorithm retrieves soil moisture (i.e., real-time soil moisture value), soil temperature, air temperature, air humidity, and light intensity data every 30 minutes over the past 72 hours from the StarRing Agriculture Big Data TDH storage and processing module. The prediction period is preset to the next 24 hours, with a time step of 1 hour. Simultaneously, based on soil type (sandy soil, clay soil, loam), season (spring, summer, autumn, winter), and crop light requirements (light-loving, shade-tolerant), the algorithm adjusts the soil moisture attenuation coefficient (0.85-0.98), temperature difference response coefficient (0.01-0.07), light influence coefficient (0.04-0.12), and soil temperature and humidity coupling coefficient (0.005-0.012). By calculating the difference between air temperature and soil temperature, the exponential change in air humidity, and the negative exponential change in light intensity, combined with the real-time soil moisture value, the algorithm obtains the hourly soil moisture change value and feeds the results back to the StarRing Agriculture Big Data TDH storage and processing module. This algorithm predicts soil moisture trends in advance, providing data for irrigation equipment control and preventing soil from being too dry or too wet, which could affect crop growth. It breaks through the limitations of traditional methods that rely solely on single soil data for prediction, and improves the accuracy of soil moisture prediction in different scenarios through multi-parameter coupling (with errors controlled within ±3%), laying a data foundation for precision irrigation in smart agriculture.

[0049] The crop chlorophyll content inversion model is a model that calculates chlorophyll content using crop canopy reflectance data, and is used to monitor crop growth status in real time. In the implementation process, the model obtains crop canopy reflectance data at wavelengths of 550nm, 670nm, 750nm, and 850nm every 30 minutes from the StarRing Agriculture Big Data TDH storage and processing module within the past 24 hours. First, abnormal data with values ​​exceeding the range of 0-1 or single changes exceeding 15% are removed. Then, the data is smoothed using a moving average method with 3 sampling points. Subsequently, the reflectance ratio coefficient (10-17), reflectance ratio index (1.8-2.2), reflectance difference coefficient (7-12), reflectance difference index (1.2-1.5), and multi-wavelength reflectance coupling coefficient (0.03-0.06) are adjusted according to crop varieties (wheat, rice, corn). By calculating the ratio of 750nm to 670nm reflectance and the difference between 850nm and 550nm reflectance, combined with the multi-wavelength reflectance coupling term, the crop chlorophyll content value is calculated (accuracy controlled within ±5%), and the result is stored in the StarRing Agriculture Big Data TDH storage and processing module. This model indirectly obtains chlorophyll content through reflectance data, avoiding the drawbacks of traditional destructive sampling measurements; it reflects the crop's photosynthetic capacity and nutrient status in real time, providing a reliable basis for determining whether nutrient supplementation is needed; at the same time, it adapts to different crops through differentiated parameters, ensuring that the calculation error is controlled within ±2SPAD, supporting smart agriculture's dynamic monitoring and precise management of crop growth status.

[0050] The intelligent agriculture contract automatic execution algorithm is an algorithm that generates control commands for agricultural equipment, achieving precise matching between control commands and crop growth needs. In implementation, the algorithm extracts hourly soil moisture changes and crop chlorophyll content values ​​from the StarRing Agriculture Big Data TDH storage and processing module. Simultaneously, it calls preset crop growth parameter thresholds (soil moisture: seedling stage 60%-70%, growing stage 70%-80%, maturity stage 50%-60%; chlorophyll content: seedling stage 35-45 SPAD, growing stage 50-60 SPAD, maturity stage 40-50 SPAD; air temperature: 15-30℃), adjusting the soil moisture deviation weighting coefficient (0.4-0.6), the chlorophyll content deviation weighting coefficient (0.3-0.5), and the meteorological parameter coupling weighting... The algorithm employs a weighting coefficient (0.05-0.15) and a coupling weighting coefficient (0.05-0.1) between light intensity and chlorophyll content. By calculating the deviation between actual soil moisture and chlorophyll content values ​​and corresponding thresholds, and combining the coupling terms of air temperature and humidity with the exponential term of the ratio of light intensity to chlorophyll content, it generates control command output values. Based on the output value range (0-20 for supplemental lighting, 21-40 for ventilation, 41-60 for irrigation, etc.), it generates equipment control commands including equipment identification, control parameters (water flow rate 10-50 L / min, wind speed 1-3 m / s, etc.), and execution duration (15-120 minutes). This algorithm enables automated control of agricultural equipment, reducing manual intervention; it constructs a closed loop of "data-analysis-control," ensuring that control commands meet the real-time growth needs of crops, improving equipment control accuracy, reducing labor costs in agricultural production, and promoting the upgrade of smart agriculture from "passive response" to "proactive prediction."

[0051] The Transwarp Agriculture Big Data TDH Platform is the core platform supporting data storage, processing, and interaction across the entire system. It adopts a distributed architecture to achieve efficient data management. TDH stands for Transwarp Data Hub, and it is adapted to the integration needs of various data types in agricultural scenarios, such as soil moisture, crop reflectivity, and equipment commands. In terms of implementation, the platform consists of 8 data nodes (each with a 16-core CPU, 128GB of memory, and 4TB of SSD storage), working collaboratively through four units: the data receiving unit receives data from each module using the TCP / IP protocol (bandwidth ≥100Mbps), and marks it with a timestamp (millisecond level) and module identifier after CRC32 verification (pass rate ≥98%); the data sharding unit categorizes data by data type, collection time (by day), and farmland area (per 100 mu), and divides it into 64MB shards using a consistent hashing algorithm, assigning each shard a 128-bit identifier; the data indexing unit builds a three-level index (by type, date, and region), updating it every 15 minutes to ensure query response ≤100ms; and the data retrieval unit receives requests via a RESTful API, parses them, extracts data, and provides feedback (single request time ≤500ms), while also supporting daily full backups and incremental backups every 6 hours. The platform provides data storage and retrieval support for data collection, algorithm calculation, and instruction generation, solving the problem of integrating multi-source agricultural data; it breaks down data silos, improves data retrieval efficiency (response time ≤100ms) through distributed storage and multi-level indexing, and ensures data security (off-site backup), providing a stable data foundation for the collaborative operation of all modules in the system. It is a key infrastructure for realizing data-driven smart agriculture.

[0052] like Figure 2As shown, the smart agriculture data acquisition and intelligent control system based on blockchain operates in the following steps: First, sensors deployed in different areas of the farmland through the data acquisition and sensing module collect data on soil moisture, soil temperature, air temperature, air humidity, light intensity, and crop canopy reflectivity in real time according to a preset acquisition frequency. The collected raw data is then sent to the Xinghuan Agriculture Big Data TDH storage and processing module via an encrypted transmission protocol. Second, the Xinghuan Agriculture Big Data TDH storage and processing module performs integrity verification on the received raw data. After verification, the data is classified and stored according to data type and acquisition area. Simultaneously, a distributed sharding algorithm is used to process the data and construct a multi-level index structure. Third, the soil moisture and meteorological coupled calculation module retrieves the classified and stored soil moisture, soil temperature, air temperature, air humidity, and light intensity data from the Xinghuan Agriculture Big Data TDH storage and processing module, runs the soil moisture and meteorological coupled prediction algorithm to calculate the soil moisture change value within a preset future time period, and feeds back the calculation results. The process involves six steps: First, the crop canopy reflectance data is retrieved from the Xinghuan Agricultural Big Data TDH storage and processing module. Second, the crop chlorophyll inversion analysis module obtains the crop canopy reflectance data, runs the crop chlorophyll content inversion model to calculate the crop chlorophyll content value, and stores the calculation results in the Xinghuan Agricultural Big Data TDH storage and processing module. Third, the smart contract execution control module extracts soil moisture change values ​​and crop chlorophyll content values ​​from the Xinghuan Agricultural Big Data TDH storage and processing module. Combined with preset crop growth parameter thresholds, it runs the smart agricultural contract automatic execution algorithm to generate corresponding irrigation, ventilation, or supplemental lighting equipment control commands. After verifying the control commands, they are sent to the field execution equipment. Fourth, the blockchain data on-chain verification module extracts different module operation data, control commands, and equipment feedback data from the Xinghuan Agricultural Big Data TDH storage and processing module. It filters and hashes the data, generates digital signatures using the blockchain node private key, participates in the blockchain network consensus process, and writes the data that has passed consensus verification into the blockchain ledger.

[0053] The blockchain-based smart agriculture data acquisition and intelligent control system comprehensively captures multi-dimensional farmland data through a data acquisition and sensing module. Combined with the distributed sharding, multi-level indexing, and data fusion functions of the StarRing Agriculture Big Data TDH storage and processing module, it achieves deep integration of raw collected data, algorithm calculation results, and historical data, significantly improving data retrieval and retrieval efficiency. This solves the problems of insufficient data processing and storage coordination and retrieval delays in traditional systems. Simultaneously, the blockchain data on-chain verification module, through hash calculation, digital signatures, and node consensus mechanisms, performs full-process verification and ledger writing of operational data, control commands, and equipment feedback data from each module. This ensures that the data is tamper-proof and traceable throughout the entire process, overcoming the shortcomings of traditional systems that lack dedicated data verification mechanisms and are easily tampered with, providing a reliable data foundation for subsequent analysis and control.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based intelligent control system for smart agriculture data acquisition, characterized in that: include: The system comprises a data acquisition and sensing module, a soil moisture and meteorological coupled calculation module, a crop chlorophyll inversion analysis module, a Xinghuan Agricultural Big Data TDH storage and processing module, a smart contract execution control module, and a blockchain data on-chain verification module. The data acquisition and sensing module collects real-time data on soil moisture, soil temperature, air temperature, air humidity, light intensity, and crop canopy reflectivity from sensors deployed in different areas of the farmland and transmits this data to the Xinghuan Agricultural Big Data TDH storage and processing module. The soil moisture and meteorological coupled calculation module retrieves the collected data from the Xinghuan Agricultural Big Data TDH storage and processing module, runs a soil moisture and meteorological coupled prediction algorithm to calculate the soil moisture change value within a preset future time period, and feeds the result back to the Xinghuan Agricultural Big Data TDH storage and processing module. The crop chlorophyll inversion analysis module retrieves data from the Xinghuan Agricultural Big Data TDH storage and processing module. The storage and processing module acquires crop canopy reflectance data, runs a crop chlorophyll content inversion model to calculate crop chlorophyll content values, and stores them in the Xinghuan Agricultural Big Data TDH storage and processing module. The Xinghuan Agricultural Big Data TDH storage and processing module performs distributed storage, sharding, and indexing of data transmitted from different modules and provides data call interfaces to other modules. The smart contract execution control module acquires soil moisture change values ​​and crop chlorophyll content values ​​from the Xinghuan Agricultural Big Data TDH storage and processing module, runs a smart agricultural contract automatic execution algorithm to generate equipment control commands, and sends them to the execution equipment. The blockchain data on-chain verification module extracts the operating data and control commands from different modules from the Xinghuan Agricultural Big Data TDH storage and processing module, completes data signature verification through the blockchain node consensus mechanism, and writes them into the blockchain ledger.

2. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 1, characterized in that, The expression for the soil moisture-meteorological coupled prediction algorithm run by the soil moisture-meteorological coupled calculation module is as follows: Real-time soil moisture value, Let t be the soil moisture value at time t. This represents the soil moisture attenuation coefficient. For air temperature, For soil temperature, The temperature difference response coefficient, For air humidity, The light influence coefficient is... Light intensity, To predict the time step, The soil temperature and humidity coupling coefficient is defined as follows: the StarRing Agriculture Big Data TDH storage and processing module stores all the above parameters and provides parameter call services to the soil moisture and meteorological coupling calculation module; the smart contract execution control module obtains... It was later incorporated into the parameter system of the automatic execution algorithm for smart agriculture contracts.

3. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 2, characterized in that, The crop chlorophyll content inversion model expression run by the crop chlorophyll inversion analysis module is as follows: ;in, The chlorophyll content of crops, The crop canopy reflectance at a wavelength of 750 nm. The crop canopy reflectance at a wavelength of 670 nm. The crop canopy reflectance at a wavelength of 850 nm. The reflectance of the crop canopy at a wavelength of 550 nm. This is the reflectivity ratio coefficient. The reflectance ratio index, This is the reflectivity difference coefficient. The reflectivity difference index, The multi-wavelength reflectivity coupling coefficient; the StarRing Agriculture Big Data TDH storage and processing module classifies and stores reflectivity data of different wavelengths; the smart contract execution control module will... Crop growth status is used as a parameter for determining the automatic execution algorithm of smart agriculture contracts.

4. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 3, characterized in that, The expression for the automatic execution algorithm of the smart agricultural contract run by the smart contract execution control module is as follows: ;in, Output values ​​for smart contract control instructions. This is the soil moisture deviation weighting coefficient. This represents the soil moisture threshold. This is the weighting coefficient for the deviation in chlorophyll content. The threshold for chlorophyll content, These are the weighting coefficients for the coupling of meteorological parameters. The air temperature threshold. This is the weighting coefficient for the coupling of light intensity and chlorophyll content. The maximum chlorophyll content of crops; the StarRing Agriculture Big Data TDH storage and processing module stores different threshold parameters, and the smart contract execution control module according to... The numerical range generates corresponding control commands for irrigation, ventilation, or supplemental lighting equipment.

5. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 4, characterized in that, The data processing model expression for the StarRing Agriculture Big Data TDH Storage and Processing Module is as follows: ;in, For the processed data, The raw data transmitted by the data acquisition and sensing module. These are the weighting coefficients for the original data. For data perturbation coefficients, A random number in the range of 0-1. The weighting coefficients are for historical data. Historical data stored in the StarRing Agriculture Big Data TDH Storage and Processing Module This represents the attenuation coefficient for historical data. For data time intervals, To calculate the data weighting coefficients, Output data for the soil moisture and meteorological coupled calculation module. The model outputs data to the crop chlorophyll inversion analysis module; the StarRing Agricultural Big Data TDH storage and processing module provides data support to other modules after completing the data fusion processing.

6. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 5, characterized in that, The data verification model expression of the blockchain data on-chain verification module is as follows: ;in, For data validation results, The hash weight coefficient for module data. For hash functions, Data for different functional modules to run. This is the weighting factor for the digital signature. For private key based Digital signature function, Control instructions generated by the smart contract execution control module. To verify the coupling weight coefficients, For XOR operation, The hash value of the data in the StarRing Agriculture Big Data TDH Storage and Processing Module; the blockchain data on-chain verification module based on The numerical value is used to determine whether the data meets the conditions for being added to the chain.

7. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 6, characterized in that, The smart contract execution control module includes an instruction generation unit, an instruction verification unit, an instruction issuance unit, and an execution feedback unit. The instruction generation unit retrieves soil moisture change values, crop chlorophyll content values, and preset crop growth parameter thresholds from the StarRing Agricultural Big Data TDH storage and processing module. It calculates control parameters for different execution devices according to the logical rules of the smart agricultural contract automatic execution algorithm and converts these parameters into a standardized device control instruction format. The instruction verification unit extracts the device identifier, control parameters, and execution time information from the instruction and compares it with the device permission data and parameter range data stored in the StarRing Agricultural Big Data TDH storage and processing module to confirm that the instruction conforms to the device operation specifications and does not exceed parameter limits. The instruction issuance unit transmits the verified control instruction to the corresponding field execution device through an encrypted communication channel, simultaneously recording the instruction issuance time, device address, and instruction content, and sending this information to the StarRing Agricultural Big Data TDH storage and processing module. The execution feedback unit receives instruction execution status data returned by the execution device, parses the execution results, device operating parameters, and abnormal information in the data, associates and stores the parsed data with the corresponding control instructions, and transmits it to the blockchain data on-chain verification module.

8. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 7, characterized in that, The Xinghuan Agricultural Big Data TDH Storage and Processing Module includes a data receiving unit, a data sharding unit, a data indexing unit, and a data retrieval unit. The data receiving unit establishes a real-time data transmission link with the data acquisition and sensing module, the soil moisture and meteorological coupling calculation module, and the crop chlorophyll inversion analysis module. It receives data packets from different modules, performs integrity checks on the data packets, removes packets with missing data or incorrect formats, and marks the verified data packets with timestamps and module identifiers. The data sharding unit classifies the received data according to data type, acquisition time, and farmland area, and uses a distributed sharding algorithm to segment different types of data. The system uses pre-defined data blocks, assigns a unique shard identifier to each data block, and records the correspondence between the shard identifier and the original data. The data indexing unit constructs a multi-level index structure based on the data block's shard identifier, timestamp, module identifier, and data content characteristics, establishes a mapping relationship between the index and the data block's storage address, and optimizes the index query algorithm to improve data retrieval speed. The data retrieval unit receives data retrieval requests from other modules, parses the data type, time range, and query conditions in the request, locates the corresponding data source through the index structure, extracts the required data, encapsulates it according to the request format, and feeds it back to the retrieval module, while simultaneously recording the data retrieval log.

9. The intelligent control system for smart agriculture data acquisition based on blockchain according to claim 8, characterized in that, The blockchain data on-chain verification module includes a data extraction unit, a signature generation unit, a consensus participation unit, and a ledger writing unit. The data extraction unit extracts operating data, control commands, and equipment feedback data from the Xinghuan Agricultural Big Data TDH storage and processing module at preset time intervals. The extracted data is filtered to retain core data directly related to agricultural production control and to remove redundant format information and duplicate data. The signature generation unit obtains the private key of the blockchain node, performs hash calculation on the filtered core data to obtain a data hash value, and uses the private key to digitally sign the data hash value to generate a signature data packet including the data hash value, signature information, and node identifier. The consensus participation unit broadcasts the signature data packet to other nodes in the blockchain network, receives signature data packets sent by other nodes, verifies the validity and integrity of the digital signature in the data packet, participates in the consensus algorithm operation, and votes to confirm the data in the network according to the consensus rules. After reaching a consensus, the ledger writing unit organizes the verified core data, signature information and consensus results according to the data format of the blockchain ledger, generates a new block, connects the new blockchain to the end of the existing blockchain ledger, and updates the local ledger and synchronizes it to other nodes.

10. The blockchain-based intelligent control system for smart agriculture data acquisition according to any one of claims 1-9, characterized in that, The system operates through the following steps: First, sensors deployed in different areas of the farmland via the data acquisition and sensing module collect data on soil moisture, soil temperature, air temperature, air humidity, light intensity, and crop canopy reflectivity in real time at a preset acquisition frequency. The collected raw data is then sent to the Xinghuan Agricultural Big Data TDH storage and processing module via an encrypted transmission protocol. Second, the Xinghuan Agricultural Big Data TDH storage and processing module performs integrity verification on the received raw data. After verification, the data is categorized and stored according to data type and acquisition area. A distributed sharding algorithm is used to process the data and construct a multi-level index structure. Third, the soil moisture and meteorological coupled calculation module retrieves the categorized soil moisture, soil temperature, air temperature, air humidity, and light intensity data from the Xinghuan Agricultural Big Data TDH storage and processing module. It then runs the soil moisture and meteorological coupled prediction algorithm to calculate the soil moisture change value within a preset future time period and feeds the calculation results back to the Xinghuan Agricultural Big Data TDH storage and processing module. The process involves six steps: First, the crop canopy reflectance data is obtained from the Xinghuan Agricultural Big Data TDH storage and processing module. Second, the crop chlorophyll inversion analysis module retrieves this data, runs the crop chlorophyll content inversion model to calculate the chlorophyll content, and stores the results in the Xinghuan Agricultural Big Data TDH storage and processing module. Third, the smart contract execution control module extracts soil moisture change values ​​and crop chlorophyll content values ​​from the Xinghuan Agricultural Big Data TDH storage and processing module. It then combines these with preset crop growth parameter thresholds to run the smart agricultural contract automatic execution algorithm, generating corresponding control commands for irrigation, ventilation, or supplemental lighting equipment. After verifying the control commands, the commands are sent to the field execution equipment. Fourth, the blockchain data on-chain verification module extracts different module operation data, control commands, and equipment feedback data from the Xinghuan Agricultural Big Data TDH storage and processing module. It filters and hashes the data, generates digital signatures using the blockchain node's private key, participates in the blockchain network consensus process, and writes the data that passes consensus verification into the blockchain ledger.

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