Block chain account book establishment method and system for smart agriculture

Abstract

The invention discloses a block chain account book establishment method and system for intelligent agriculture, and the method comprises the following steps: collecting agricultural data through a 5G-6G network and intelligent agricultural equipment, and carrying out the primary processing and transmission through edge calculation and an intelligent network slicing technology; the collected agricultural data is stored through a block chain technology, a block chain edge computing node is deployed in the Internet of Things equipment, and decentralized data processing and storage are carried out; the stored data provides information and support for each application of the smart agriculture, the data collection efficiency and processing capability of the smart agriculture are improved, the decision accuracy is improved, and the development of intelligent precision agricultural equipment is promoted; meanwhile, the problems of popularization and actual effects of intelligent agricultural application are solved, the security and privacy protection of data are improved, and the problem of data sources is solved; in addition, the system also solves the challenge of processing large-capacity data, and optimizes the operation efficiency and safety of the whole intelligent agricultural system.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture technology, and in particular to a method and system for establishing a blockchain ledger for smart agriculture. Background Technology

[0002] Blockchain ledgers ensure data security, immutability, and transparency, thereby improving data management and traceability in agricultural production. Several major problems currently exist in agriculture: First, outdated agricultural sensor technology leads to inaccurate and unstable data collection; second, the accuracy of plant and animal models and intelligent decision-making is low, with many decisions being time-series control rather than on-demand control; and finally, a lack of intelligent precision farming equipment results in poor operational quality. Furthermore, agricultural big data is characterized by its fragmentation and isolation, lacking effective data sharing and integration mechanisms, and data security and privacy protection issues remain unresolved.

[0003] Existing smart agriculture systems largely rely on traditional database technologies, resulting in insufficient data security and privacy protection. Data in traditional systems is easily tampered with and lacks transparency and traceability. Furthermore, existing technologies exhibit poor scalability when processing large volumes of data (such as photos and videos). In addition, traditional systems are often centralized, making them prone to single points of failure and hindering large-scale deployment and application. Summary of the Invention

[0004] To address the numerous existing problems, this invention provides a blockchain ledger establishment method and system for smart agriculture. This invention improves the data collection efficiency and processing capabilities of smart agriculture, enhances decision-making accuracy, and promotes the development of intelligent precision agricultural equipment. Simultaneously, it solves the problems of widespread adoption and practical effectiveness of smart agriculture applications, improves data security and privacy protection, and addresses the issue of data sourcing. Furthermore, this invention solves the challenge of processing large volumes of data and optimizes the operational efficiency and security of the entire smart agriculture system.

[0005] A method for establishing a blockchain ledger for smart agriculture includes the following steps:

[0006] Agricultural data is collected by utilizing 5G-6G networks and smart agricultural equipment, and then preliminarily processed and transmitted using edge computing and smart network slicing technologies.

[0007] The collected agricultural data is stored using blockchain technology, and blockchain edge computing nodes are deployed in IoT devices for decentralized data processing and storage.

[0008] The stored data provides information and support for various applications in smart agriculture.

[0009] Preferably, the collected agricultural data is processed in real time at the data generation location to complete edge computing, and intelligent network slicing technology is used to optimize network resources and transmit the pre-processed data.

[0010] The agricultural data includes: soil moisture, temperature, light levels, crop growth status, climate change, and crop health.

[0011] Preferably, photos and videos from agricultural data are stored in a peer-to-peer distributed file storage system, and the hash values ​​corresponding to the photos and videos are stored in a blockchain.

[0012] Preferably, the deployment of blockchain edge computing nodes in IoT devices for decentralized data processing and storage includes:

[0013] Install and configure a blockchain edge computing node in an Internet of Things (IoT) device, wherein the blockchain edge computing node includes: smart sensors and a control system;

[0014] The agricultural data collected is processed through the blockchain edge computing node, including: analyzing soil conditions and climate data;

[0015] The processed data is stored using blockchain technology inside the blockchain edge computing node.

[0016] Preferably, the stored data provides information and support for various applications of smart agriculture, including:

[0017] By utilizing environmental and crop growth data, we can monitor crop health and predict yields.

[0018] Analyze climate and crop data to predict and manage pest and disease risks;

[0019] By using data on crop growth, processing, and transportation recorded on the blockchain, a complete traceability chain for products can be established.

[0020] Analyze production and environmental data to predict market demand and optimize sales strategies;

[0021] Water resources and fertilizers should be allocated rationally based on soil and climate data.

[0022] A blockchain ledger system for smart agriculture includes:

[0023] The data acquisition and processing module collects agricultural data using 5G-6G networks and smart agricultural equipment, and performs preliminary processing and transmission through edge computing and smart network slicing technology.

[0024] An edge processing module is used to store the collected agricultural data using blockchain technology, and to deploy blockchain edge computing nodes in IoT devices for decentralized data processing and storage.

[0025] The results application module provides information and support for various applications of smart agriculture by storing the data.

[0026] Preferably, the collected agricultural data is processed in real time at the data generation location to complete edge computing, and intelligent network slicing technology is used to optimize network resources and transmit the pre-processed data.

[0027] The agricultural data includes: soil moisture, temperature, light levels, crop growth status, climate change, and crop health.

[0028] Preferably, photos and videos from agricultural data are stored in a peer-to-peer distributed file storage system, and the hash values ​​corresponding to the photos and videos are stored in a blockchain.

[0029] Preferably, the deployment of blockchain edge computing nodes in IoT devices for decentralized data processing and storage includes:

[0030] Install and configure a blockchain edge computing node in an Internet of Things (IoT) device, wherein the blockchain edge computing node includes: smart sensors and a control system;

[0031] The agricultural data collected is processed through the blockchain edge computing node, including: analyzing soil conditions and climate data;

[0032] The processed data is stored using blockchain technology inside the blockchain edge computing node.

[0033] Preferably, the stored data provides information and support for various applications of smart agriculture, including:

[0034] By utilizing environmental and crop growth data, we can monitor crop health and predict yields.

[0035] Analyze climate and crop data to predict and manage pest and disease risks;

[0036] By using data on crop growth, processing, and transportation recorded on the blockchain, a complete traceability chain for products can be established.

[0037] Analyze production and environmental data to predict market demand and optimize sales strategies;

[0038] Water resources and fertilizers should be allocated rationally based on soil and climate data.

[0039] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0040] This invention utilizes advanced 5G-6G networks and edge computing to improve the efficiency of agricultural data collection and processing capabilities;

[0041] This invention improves decision-making accuracy through integrated technology and promotes the development of intelligent precision agricultural equipment;

[0042] This invention addresses the issues of widespread adoption and effectiveness of smart agriculture applications through a decentralized and secure data storage and sharing mechanism.

[0043] This invention uses blockchain technology to solve the problem of data source and ensure data security and privacy;

[0044] This invention combines edge computing and IPFS mechanisms to address the shortcomings of blockchain in handling large volumes of data. Attached Figure Description

[0045] Figure 1 This is a schematic flowchart of the method of the present invention;

[0046] Figure 2 This is a schematic diagram of an embodiment of the present invention;

[0047] Figure 3 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0048] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0049] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0050] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0051] When using expressions such as "at least one of A, B, and C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). Similarly, when using expressions such as "at least one of A, B, or C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, or C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0052] The accompanying drawings illustrate several block diagrams and / or flowcharts. It should be understood that some blocks, or combinations thereof, in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that, when executed by the processor, these instructions can create means for implementing the functions / operations described in these block diagrams and / or flowcharts. The technology disclosed herein can be implemented in hardware and / or software (including firmware, source code, etc.). Alternatively, the technology disclosed herein can take the form of a computer program product stored on a computer-readable storage medium, which is available for use by or in conjunction with an instruction execution system.

[0053] like Figure 1 As shown, the method for establishing a blockchain ledger for smart agriculture includes the following steps:

[0054] Agricultural data is collected by utilizing 5G-6G networks and smart agricultural equipment, and then preliminarily processed and transmitted using edge computing and smart network slicing technologies.

[0055] The collected agricultural data is stored using blockchain technology, and blockchain edge computing nodes are deployed in IoT devices for decentralized data processing and storage.

[0056] The stored data provides information and support for various applications in smart agriculture.

[0057] This invention establishes a distributed ledger for smart agriculture, a technological invention that collectively maintains reliable data in a decentralized and trustless manner. It employs a network slicing edge computing blockchain algorithm to determine incentives based on supply and demand, allowing all stakeholders in smart agriculture to participate fairly in data collection. This enables seamless, real-time, and comprehensive coverage of all data within the distributed ledger, including applications such as smart agriculture information maps, smart agriculture fresh produce quality traceability, smart agriculture e-commerce analysis, smart agriculture IoT monitoring, and smart agriculture finance. The use of network slicing edge computing AI technology empowers ordinary people with professional data collection capabilities. Combined with edge computing capabilities, this transforms smart agriculture into a customized platform and collaborative production platform within a decentralized sharing economy ecosystem. By more effectively utilizing various resources, value is generated, monitored, and distributed decentralizedly.

[0058] The core technology of this invention is a novel method for deploying a fusion network of mobile transmission networks and edge data center blockchain. The 5G-6G edge data center blockchain smart network slicing deployment involves dividing the edge data center areas at the prefecture-level city level based on administrative divisions, topography, business development needs, and the distribution of existing network resources. Each prefecture-level city is divided into multiple blockchain edge data center business core areas, each blockchain edge data center business core area is further divided into multiple blockchain edge data center business aggregation areas, and finally, each blockchain edge data center business aggregation area is divided into multiple blockchain edge data center business access areas. This forms a smart agriculture distributed ledger data transmission channel for the mobile transmission network physical network smart network slice, and a logical subnet for the decentralized blockchain smart network slice. Through flexible allocation of network resources and flexible combination of capabilities, it addresses the problem of agricultural big data sources, establishing an efficient and low-cost information acquisition system. The 5G-6G edge data center blockchain smart network slicing can meet the customized needs of blockchain in different scenarios.

[0059] This invention relates to 5G-6G edge data center blockchain edge computing and network slicing, both of which can only be realized after the entire communication network has been IT-enabled. Blockchain edge computing decentralizes content and services as much as possible to the user, deploying them on the intelligent network slice access side. This allows for data analysis, cross-domain feature mining, and dynamic strategy generation, with large traffic volumes concentrated within the intelligent network slices under the edge computing's jurisdiction. Applications include blockchain-based agricultural product quality traceability systems, blockchain-based large-scale farms, and blockchain-based e-commerce platforms. This achieves business offloading and reduces the burden on the core network and transmission network.

[0060] The 5G-6G network slicing edge computing blockchain is combined with smart agriculture operations. Each blockchain edge computing node on a network slice hosts an edge data center, and these edge data centers are interconnected. All the smart network slice edge data centers and blockchain edge computing nodes together form a distributed storage table. When data is requested from this network, a mathematical calculation method based on the data's value is used to locate the resource on which blockchain edge computing node within the blockchain edge data center, establish a connection, and download the required data.

[0061] Four areas for agricultural blockchain networking:

[0062] 1. 5G-6G intelligent network slicing, blockchain edge computing, data connection, communication, and smart agriculture physical layer;

[0063] 2. 5G-6G intelligent network slicing, blockchain edge computing, data sensing, communication, and smart agriculture control service layer;

[0064] 3. 5G-6G intelligent network slicing, blockchain, edge computing, storage, communication, and smart agriculture service application layer;

[0065] 4. 5G-6G intelligent network slicing, blockchain edge computing, communication services, and smart agriculture intelligent layer.

[0066] These four "network slice communication and smart agriculture layer domains" are the edge computing objects for communication and smart agriculture distributed ledger computing. For communication services unique to each layer of the network slice, only the targeted network slice communication and smart agriculture computing capabilities need to be deployed independently at the corresponding layer. This enables interoperability between traditional communication users and communication applications in smart agriculture and the decentralized consensus mechanism of the network slice blockchain. The network slice uses blockchain technology for distributed hierarchical control of communication services and smart agriculture.

[0067] Compared with existing systems, the system designed in this invention migrates the analysis and processing of user data from the central server to nodes on the edge data center blockchain built on 5G-6G smart network slices. The needs of smart agriculture users are processed on the edge data center server, and the collected smart agriculture user data is used as input to the nodes in the blockchain. After being hashed layer by layer, it is stored in an immutable and traceable chain-like edge data center database.

[0068] This invention relates to a method for detecting and identifying the distribution of seven categories of people—farmers (individual contractors and large-scale contractors), e-commerce personnel, seed sales personnel, agricultural product sales personnel, experts, decision-makers (government personnel), and consumers (purchasers)—using MR, signaling, resource data, field data, geolocation data, and mobile terminals in mobile communication wireless networks. For related details, please refer to the inventions "A Method for Identifying and Judging Gender and Age Differences of Special Groups Based on 5G Edge Data Center Mobile Terminals," "A Method for Detecting and Identifying the Number, Behavior, and Distribution of Passenger Groups Based on 5G Edge Data Centers," "A Device and System for Detecting the Movement Trajectory of Elderly People Based on Frequency Band Coverage Differences in 5G Edge Data Centers," and "A Smart Emergency Evacuation System for Detecting and Identifying Sudden Gatherings of Passenger Groups Based on 5G Edge Data Centers."

[0069] like Figure 2 As shown, the data collection layer of the smart agriculture distributed ledger, since information from mobile phone users can basically cover the entire region and city, establishes a relationship between MR, signaling, resource data, field data, and geographic data in the mobile communication wireless network and mobile terminals through the mobile communication network. This is achieved through correlation cross-referencing, problem clustering, and causal analysis, making each mobile phone user a data source. This involves using ordinary users' mobile terminal devices as basic sensing units, forming a collective intelligent sensing network through communication, thereby realizing the distribution of sensing tasks and the collection of sensing data. This self-organizes into a local area network blockchain distributed ledger for smart agriculture information map applications, smart agriculture fresh produce quality traceability applications, smart agriculture e-commerce analysis applications, smart agriculture IoT monitoring applications, and smart agriculture financial applications, completing large-scale and complex social sensing tasks. Blockchain edge computing nodes are responsible for analyzing and processing the collected distributed ledger data and temporarily caching the results. Simultaneously, they encapsulate the data and upload it to the smart agriculture service application layer in a publishing mode. This establishes a blockchain distributed ledger data model required for automated network slicing in smart agriculture, combining "autonomous collection from mobile communication networks + collection from mobile phone users." It realizes an automated process from data discovery, scheduling, collection, processing to deployment.

[0070] Smart agriculture applications utilize a distributed ledger platform built with blockchain technology. Another advantage of this distributed ledger information is its ability to leverage social resources from seven groups: farmers (individual contractors and large-scale contractors), e-commerce personnel, sales personnel for seeds, fertilizers, and pesticides, agricultural product sales personnel, experts, decision-makers (government personnel), and consumers (purchasers). MR, signaling, resource data, field data, and geographic data from mobile communication wireless networks are linked with smart terminal devices (such as smartphones and tablets) through the mobile communication network. Through the information reporting function provided by the smart agriculture blockchain distributed ledger application, agricultural information from the field is proactively uploaded to the application's backend in the form of text, voice, images, and videos. The backend uses real-time location information obtained through network slicing edge computing and employs a specific algorithm to calculate the on-site status of the agricultural information. The smart agriculture distributed ledger application service undergoes a complete decentralization transformation, making it a core component of the entire blockchain network and freely accessible to various upper-layer applications on the internet and edge computing blockchain network.

[0071] The decentralized location service functions of the distributed ledger in smart agriculture are shown in Table 1:

[0072] Table 1

[0073]

[0074]

[0075] Through the distributed ledger of smart agriculture, dynamic and static intelligent mining is used to trace the logic and activity relationships of seven types of personnel.

[0076] The system comprises a complete set of analytical algorithms. It achieves full data on-chaining while maintaining high efficiency through decentralization. The core of the seven personnel categories initially focuses on the dynamic and static intelligent mining of the tracking logic after location and the relationships between their participation in activities. This first requires the perception of information data, employing artificial intelligence deep learning processing methods. Essentially, this transforms unstructured information data into structured data, including the characteristics of identified population types, types of smart agriculture, agricultural product types and locations, and their participation in activities and gains. This includes data collection, data compression, and data storage. The final structured data includes the preliminary identification results. Based on a distributed ledger platform built using blockchain technology for smart agriculture applications, the system stores relevant data on the blockchain each time, achieving permanent monitoring of smart agriculture user behavior information. Preliminary data analysis and processing are completed on edge data center nodes to analyze user behavior characteristics. Finally, the analysis results from each node are sent to the corresponding cloud-based 5G-6G intelligent network slicing blockchain edge computing communication services and smart agriculture intelligent layer servers for processing.

[0077] To ensure that the basic traceability logic and dynamic / static relationships of participating activities following structured data can reflect the original characteristics as much as possible, this invention establishes a novel AI-powered blockchain-based smart agriculture user prediction adaptive data mining algorithm. This algorithm is implemented in an edge data center, analyzing and processing the information provided by individual participants (farmers (individual contractors, large-scale contractors), e-commerce personnel, sales personnel for seeds, fertilizers, and pesticides, agricultural product sellers, experts, decision-makers (government personnel), and consumers (purchasers).

[0078] Artificial intelligence blockchain predictive adaptive data mining utilizes the relationship between MR, signaling, resource data, field data, and geolocation data in mobile communication wireless networks and mobile terminals through mobile communication networks. Through correlation cross-referencing, problem clustering, and causal analysis, it analyzes historical data of smart agriculture users in edge data center warehouses. Using classification judgments, it summarizes various attribute characteristics of smart agriculture users and the relationship rules between these characteristics and the predicted volume of agricultural data uploads, thus establishing a mathematical model for smart agriculture user data uploads. Then, it inputs data from unknown smart agriculture users into the model. Applying the summarized rules to classify these users, it performs database mining to obtain adaptive data on the demographic characteristics of these unknown users, the types of smart agriculture, the types and locations of agricultural products, their participation in activities, and the benefits they receive.

[0079] Step 1: Establish relationships between MR, signaling, resource data, field data, and geographic data from the mobile communication wireless network and smart terminal devices (such as smartphones, tablets, etc.) through the mobile communication network, and collect as many attributes as possible that describe customer characteristics according to the needs of smart agriculture business.

[0080] Step 2: Processing Smart Agriculture Data

[0081] Smart agriculture comprises a set of datasets that logically belong to a single system, but physically are distributed across multiple locations in 5G-6G smart network slice edge data centers. Due to its widespread distribution, if multiple edge data center databases with identical structures are distributed across multiple sites, the management and processing methods for these datasets using AI, blockchain, smart agriculture, user prediction, and adaptive data mining algorithms are defined as follows:

[0082] Step 2.1: The total number of 5G-6G smart network slice blockchain edge computing nodes is W;

[0083] Step 2.2, Quantity Range, i.e., 5G-6G smart network slice Q = blockchain edge computing node database ω ij ;

[0084] Step 2.3, G ij: Meets the quantity range (i.e., 5G-6G smart network slice Q = blockchain edge computing node database ω) ij The actual number.

[0085] Step 2.4, Probability G ij : Meets the quantity range; that is, 5G-6G smart network slice Q = blockchain edge computing node database negative ω ij And of type ω ij The number of instances. β is the total number of databases. α is the total number of values ​​for attribute i.

[0086] Step 2.5, K ij (t) The range of condition quantities. The total amount of data at time t, with an initial value of:

[0087]

[0088] Step 3: Summarize the statistical values ​​from the databases of each blockchain edge computing node. The node data server and related computing node data servers summarize the quantity range G from each database. ij K ij (t) and calculate node data:

[0089] Step 3.1, λ ij This indicates the range of feature terms representing the processing method type. The function value, based on the density of the quantity range, is calculated as follows:

[0090]

[0091] Step 3.2, x ij (t) represents the range of the processing method type feature. The function value at time t is calculated as follows:

[0092]

[0093] S represents a characteristic that has not yet appeared in the current processing method rules.

[0094] Step 3.3: The average number of nodes within a given range is determined using the following formula:

[0095]

[0096] σ represents the selection condition value for the smart agriculture user prediction adaptive data mining algorithm.

[0097] Step 4: Establish classification criteria and summarize the various attribute characteristics rules for smart agriculture users. The key is to generate type characteristic items for smart agriculture user types. Add these to the current classification criteria, and define the possibilities in the summarized rules as follows:

[0098]

[0099] Step 5: When the 5G-6G smart network slice blockchain edge computing node establishes classification and judgment and summarizes the various attribute characteristic rules of smart agriculture users, the data value of the type characteristic item of this rule can be changed, while the data values ​​of other type characteristic items that do not appear in this rule are only standardized and defined as follows.

[0100]

[0101] W represents the value of the rules that are classified, judged, and summarized.

[0102] Step 6: After AI blockchain smart agriculture user prediction adaptive data mining, multiple classification judgments are generated, and various attribute characteristic rules of smart agriculture users are summarized. From these, rules with high value are selected and added to the final classification judgment, summarizing the various attribute characteristics of smart agriculture users.

[0103]

[0104] Step 6.1: After adaptive data mining of smart agriculture user prediction through artificial intelligence blockchain, multiple classification judgments are generated. A certain attribute feature of the various attribute feature data of smart agriculture users has a large number of non-compliant feature values. However, no stratification is defined for this attribute feature, or the higher non-compliant feature values ​​can also be represented by other attribute features. Therefore, this attribute feature should be deleted.

[0105] Step 6.2: After AI blockchain smart agriculture user prediction adaptive data mining, multiple classification judgments are generated, summarizing various attribute characteristics of smart agriculture users. A certain attribute characteristic in the data has a large number of non-compliant characteristic values, but the attribute characteristic is defined in layers, and the low-value data can be replaced with higher-value compliant characteristic values.

[0106] Step 7: Since the analyzed data mining data contains a large number of inconsistent feature values, including some data that is irrelevant to the data mining algorithm and is redundant, correlation analysis of the consistent feature values ​​is performed to filter out statistically irrelevant or weakly correlated inconsistent feature values. This filters out the consistent feature values ​​most relevant to the mining task, forming a set of relevant consistent feature values. The predictive adaptive data mining algorithm is defined as follows:

[0107] Smart agriculture user prediction adaptive conformal feature set K, where the value of each conformal feature is known, assuming there are m class values, and let K contain K i C i Class C, i = 1, ..., m, is any eigenvalue that belongs to class C. i The probability is Ki / K, where K is the total number of objects in set K, and the expected value required for a given classification that matches the features is:

[0108]

[0109] It has values ​​(a1, a2, ... a) n The attribute F of K can be used to partition K into subsets (K1, K2, ... K). n ), where k i The value of F in K is a j Those values ​​that conform to the characteristic. Let K j Includes class C i K ij The expected value of this division according to F is called the value of F, which is a weighted average.

[0110] F-partitioning prediction adaptive data is defined as:

[0111] Adaptive(F) = D(K1,K2...K) m )-D(F)

[0112] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] like Figure 3 As shown, the blockchain ledger establishment system for smart agriculture includes:

[0114] The data acquisition and processing module collects agricultural data using 5G-6G networks and smart agricultural equipment, and performs preliminary processing and transmission through edge computing and smart network slicing technology.

[0115] An edge processing module is used to store the collected agricultural data using blockchain technology, and to deploy blockchain edge computing nodes in IoT devices for decentralized data processing and storage.

[0116] The results application module provides information and support for various applications of smart agriculture by storing the data.

[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0121] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0122] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0123] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0124] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for establishing a blockchain ledger for smart agriculture, characterized in that, Includes the following steps: Agricultural data is collected by utilizing 5G-6G networks and smart agricultural equipment, and then preliminarily processed and transmitted using edge computing and smart network slicing technologies. The collected agricultural data is stored using blockchain technology, and blockchain edge computing nodes are deployed in IoT devices for decentralized data processing and storage. The stored data provides information and support for various applications in smart agriculture.

2. The method for establishing a blockchain ledger for smart agriculture according to claim 1, characterized in that, At the data generation location, the collected agricultural data undergoes immediate preliminary processing to complete edge computing. Intelligent network slicing technology is used to optimize network resources and transmit the pre-processed data. The agricultural data includes: soil moisture, temperature, light levels, crop growth status, climate change, and crop health.

3. The method for establishing a blockchain ledger for smart agriculture according to claim 1, characterized in that, Photos and videos from agricultural data are stored in a peer-to-peer distributed file storage system, and the hash values ​​corresponding to the photos and videos are stored in a blockchain.

4. The method for establishing a blockchain ledger for smart agriculture according to claim 1, characterized in that, The deployment of blockchain edge computing nodes in IoT devices for decentralized data processing and storage includes: Install and configure a blockchain edge computing node in an Internet of Things (IoT) device, wherein the blockchain edge computing node includes: smart sensors and a control system; The agricultural data collected is processed through the blockchain edge computing node, including: analyzing soil conditions and climate data; The processed data is stored using blockchain technology inside the blockchain edge computing node.

5. The method for establishing a blockchain ledger for smart agriculture according to claim 1, characterized in that, The stored data provides information and support for various applications in smart agriculture, including: By utilizing environmental and crop growth data, we can monitor crop health and predict yields. Analyze climate and crop data to predict and manage pest and disease risks; By using data on crop growth, processing, and transportation recorded on the blockchain, a complete traceability chain for products can be established. Analyze production and environmental data to predict market demand and optimize sales strategies; Water resources and fertilizers should be allocated rationally based on soil and climate data.

6. A blockchain ledger establishment system for smart agriculture, characterized in that, include: The data acquisition and processing module collects agricultural data using 5G-6G networks and smart agricultural equipment, and performs preliminary processing and transmission through edge computing and smart network slicing technology. An edge processing module is used to store the collected agricultural data using blockchain technology, and to deploy blockchain edge computing nodes in IoT devices for decentralized data processing and storage. The results application module provides information and support for various applications of smart agriculture by storing the data.

7. The blockchain ledger establishment system for smart agriculture according to claim 1, characterized in that, At the data generation location, the collected agricultural data undergoes immediate preliminary processing to complete edge computing. Intelligent network slicing technology is used to optimize network resources and transmit the pre-processed data. The agricultural data includes: soil moisture, temperature, light levels, crop growth status, climate change, and crop health.

8. The blockchain ledger establishment system for smart agriculture according to claim 1, characterized in that, Photos and videos from agricultural data are stored in a peer-to-peer distributed file storage system, and the hash values ​​corresponding to the photos and videos are stored in a blockchain.

9. The blockchain ledger establishment system for smart agriculture according to claim 1, characterized in that, The deployment of blockchain edge computing nodes in IoT devices for decentralized data processing and storage includes: Install and configure a blockchain edge computing node in an Internet of Things (IoT) device, wherein the blockchain edge computing node includes: smart sensors and a control system; The agricultural data collected is processed through the blockchain edge computing node, including: analyzing soil conditions and climate data; The processed data is stored using blockchain technology inside the blockchain edge computing node.

10. The blockchain ledger establishment system for smart agriculture according to claim 1, characterized in that, The stored data provides information and support for various applications in smart agriculture, including: By utilizing environmental and crop growth data, we can monitor crop health and predict yields. Analyze climate and crop data to predict and manage pest and disease risks; By using data on crop growth, processing, and transportation recorded on the blockchain, a complete traceability chain for products can be established. Analyze production and environmental data to predict market demand and optimize sales strategies; Water resources and fertilizers should be allocated rationally based on soil and climate data.

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