Social agricultural product trading system and method based on virtual planting

Abstract

The invention relates to the cross field of smart agriculture and electronic commerce, and discloses a social agricultural product transaction system and method based on virtual planting, and the system comprises an identity verification module which is used for verifying the identity of a user and generating a unique identity label; the virtual planting and land right confirmation module is used for acquiring the geographic position and area information of the land of the user and storing the land ownership information through a block chain technology; the intelligent planting and labor cost calculation module is used for calculating labor cost according to crop types, land areas and climate factors and generating corresponding planting plans; and an agricultural product traceability and block chain management module. Efficient matching and whole-course credible traceability of agricultural resources are realized through a block chain and an intelligent algorithm, and the land utilization rate and the supply chain transparency are improved; and in combination with a socialization function and a dynamic incentive mechanism, user collaborative ecology is constructed, the platform participation degree and decision scientificity are remarkably enhanced, and agricultural digital transformation is promoted.

Description

Technical Field

[0001] The present invention relates to the intersection of smart agriculture and e-commerce, and specifically to a socialized agricultural product trading system and method based on virtual planting. Background Art

[0002] In traditional agriculture, land resource allocation and agricultural product trading have long been plagued by information asymmetry and opaque processes. The lack of effective direct communication channels between farmers and consumers has led to widespread idle land and a mismatch between demand and demand.

[0003] Existing agricultural platforms often focus on a single aspect (such as land leasing or agricultural product sales), failing to integrate planting management, traceability verification, and social collaboration to form a complete agricultural ecosystem. Furthermore, traditional land rights confirmation relies on paper certificates and manual verification, which is inefficient and prone to disputes over ownership. Data silos are a serious problem in the agricultural product supply chain, making it difficult for consumers to obtain reliable traceability information, and food safety risks are difficult to eradicate. Existing technologies lack solutions that deeply integrate blockchain, intelligent algorithms, and social networks, making it impossible to achieve efficient circulation of agricultural resources and co-creation of value. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a social agricultural product trading system and method based on virtual planting, which solves the problems of inefficient land resource utilization, opaque agricultural product supply chain, and insufficient user participation in the existing technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a socialized agricultural product trading system and method based on virtual planting, comprising:

[0006] Authentication module, used to verify user identity and generate a unique identity;

[0007] The virtual planting and land ownership confirmation module is used to obtain the geographical location and area information of the user's land and store land ownership information through blockchain technology;

[0008] Intelligent planting and labor cost calculation module, used to calculate labor costs based on crop type, land area, and climate factors, and generate corresponding planting plans;

[0009] The agricultural product traceability and blockchain management module is used to record the planting, harvesting, transportation and sales processes of agricultural products through blockchain, ensuring that the traceability information of agricultural products is transparent and cannot be tampered with;

[0010] A social land sharing and agricultural product trading module, which is used to recommend land resources and agricultural products through social networks, and supports land leasing, agricultural product purchases, and social interaction;

[0011] User interaction and incentive module, used to reward points or virtual currency based on user behavior and promote transactions through social interaction;

[0012] The backend management system is used to manage data, user information and transaction records in the system, and supports intelligent recommendations and data analysis.

[0013] Preferably, the identity verification module further comprises:

[0014] OCR recognition component, used to identify and extract ID card information uploaded by users;

[0015] Facial recognition component, used to verify user identity by comparing the real-time photo with the ID card photo to confirm the identity.

[0016] Preferably, the virtual planting and land title confirmation module obtains the location data of the land by using GPS and geo-fencing technology, and stores the land ownership information and title certification documents on the blockchain through a blockchain smart contract.

[0017] Preferably, the intelligent planting and labor cost calculation module predicts the growth cycle of crops based on the LSTM model, and calculates the required labor costs according to the prediction results.

[0018] Preferably, the agricultural product traceability and blockchain management module records the entire process of each agricultural product from planting to sales through blockchain technology, and automatically confirms each link through smart contracts.

[0019] Preferably, the social land sharing and agricultural product trading module includes:

[0020] The social graph component is used to build social relationship chains between users and recommend land resources and agricultural products based on social relationships;

[0021] A decentralized trading component for managing agricultural product transactions through smart contracts.

[0022] Preferably, the user interaction and incentive module generates points based on user behavior and rewards virtual currency through social interaction tasks, and the virtual currency can be used to purchase agricultural products or pay rental fees.

[0023] Preferably, the backend management system optimizes the recommendation of land resources and agricultural products through an intelligent recommendation algorithm, and performs data analysis based on the user's transaction records and behaviors.

[0024] A socialized agricultural product trading method based on virtual planting includes the following steps:

[0025] Step 1: Verify user identity and generate a unique ID;

[0026] Step 2: Obtain the geographical location and area information of the user's land, and confirm the land ownership through blockchain technology;

[0027] Step 3: Generate a planting plan based on crop type, land area, and climate factors, and calculate labor costs;

[0028] Step 4: Record the planting, harvesting, transportation and sales process of agricultural products through blockchain;

[0029] Step 5: Recommend land resources and agricultural products through social networks to support land leasing, agricultural product purchases, and social interaction;

[0030] Step 6: Reward points based on users’ social behaviors and promote transactions through social interactions;

[0031] Step 7: Manage data and transaction records through the backend management system to optimize recommendations and data analysis.

[0032] Preferably, in step 2, the latitude and longitude of the land are obtained by using GPS and geo-fencing technology, and the ownership information and proof of ownership of the land are stored on the blockchain through a blockchain smart contract.

[0033] The present invention provides a socialized agricultural product trading system and method based on virtual planting.

[0034] Beneficial effects:

[0035] 1. This invention utilizes virtual farming and land rights verification technology to achieve precise matching and dynamic sharing of farmland resources. Farmers can quickly access idle land on the platform, and consumers can flexibly lease it based on their needs, addressing the fragmentation and low utilization of land in traditional agriculture. The application of blockchain technology ensures the immutability of ownership information, reduces the risk of land disputes, and makes resource transfer more efficient and reliable.

[0036] 2. This blockchain-based traceability system fully records the entire agricultural product process, from planting to sales, allowing consumers to verify product origin and quality information in real time. By automatically enforcing transaction rules through smart contracts, it reduces information asymmetry between intermediaries, improves supply chain transparency and credibility, and effectively ensures food safety.

[0037] 3. The integration of this invention's social features and the points-based incentive mechanism builds a collaborative network among users. Consumers can discover high-quality land resources through social recommendations, while farmers can earn additional income by sharing their planting experiences. The dynamic reward model converts user behavior into quantifiable value returns, creating a virtuous cycle of "engagement-incentive-reengagement," significantly boosting platform activity.

[0038] 4. This invention uses LSTM models and real-time environmental data analysis to provide farmers with scientific planting plans and cost forecasts. The backend management system's data analysis capabilities enable operators to optimize resource allocation, for example, by adjusting land recommendation strategies based on historical data. This data-driven decision-making model reduces the blindness of traditional agriculture and facilitates the industry's transformation towards a more refined and intelligent approach. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Please see the attached Figure 1 The embodiment of the present invention provides a socialized agricultural product trading system and method based on virtual planting, including:

[0042] Authentication module, used to verify user identity and generate a unique identity;

[0043] During the implementation of this invention, the identity verification module is fundamental to ensuring platform security and data accuracy. By ensuring the legitimacy and authenticity of each user's identity information, the module effectively prevents fraudulent user registration, identity forgery, and malicious activity, thereby providing reliable identity support for subsequent farmland resource management, agricultural product trading, and other operations. This module not only ensures system security but also lays a solid foundation for the platform's credibility.

[0044] In this embodiment, the identity verification module is implemented using a combination of WeChat authorization, optical character recognition (OCR) technology, and facial recognition. Specifically, when a user first uses the platform, the system will require the user to authenticate through the WeChat mini-program. The system will then retrieve the user's basic information through the WeChat authorization interface and generate a unique ID based on the phone number provided by WeChat. The user will also be required to provide photos of the front and back of their ID card. The system will then use OCR technology to perform text recognition on the ID card, automatically extracting the information on the ID card for subsequent verification.

[0045] To further enhance the security of identity authentication, the system also incorporates facial recognition. After users upload their ID information, the platform uses a facial recognition algorithm to compare it with their selfie to ensure the uploaded identity information is consistent with their identity. Specifically, the system collects a real-time photo of the user, compares it with the facial image features on the ID card, and calculates the similarity between the two images.

[0046] In this embodiment, OCR recognition and facial recognition are performed in parallel, and the extraction and verification of identity information will not be delayed due to different recognition speeds. This dual verification method effectively improves the accuracy of identity verification and reduces the risk of identity forgery and identity theft.

[0047] As an option, this module also supports obtaining the user's mobile phone number through the WeChat authorization interface and generating a unique ID by binding the mobile phone number to further ensure the uniqueness of the identity. In addition, to cope with the situation where the user forgets the password or fails to pass the verification, the platform also supports secondary verification and account recovery operations through facial recognition or SMS verification code.

[0048] Specifically, by matching facial recognition technology with ID cards, the system effectively prevents fraudulent registration using someone else's ID card. When security requirements are high, after users upload their ID card information, the system will perform multiple checks in the background, including checks on the validity and legality of the ID number, further enhancing the platform's identity verification security.

[0049] In one possible implementation, the identity authentication module can also combine facial recognition and image quality assessment technology. When the user's shooting angle or lighting is not suitable, the system can automatically prompt the user to retake the photo to ensure that the image is clear and the feature points are complete.

[0050] Generally speaking, through this identity verification module, the platform can ensure the authenticity and validity of all users' identity information, thus providing compliance assurance for subsequent operations such as farmland management, land transactions, and agricultural product purchases. At the same time, platform users will be able to enjoy a more convenient service experience without having to worry about identity information being tampered with or misused.

[0051] In one possible implementation, the system can further assess the user's identity security based on information such as their social behavior and credit history, automatically adjusting the level of verification. For users with higher trust, the system can provide a simplified verification process, thereby improving the platform's user experience.

[0052] Through the above implementation of the identity authentication module, the present invention can effectively improve the security of the platform, ensure the uniqueness and reliability of user identity, and provide a solid identity guarantee for subsequent farmland leasing, agricultural product trading, social interaction and other functions.

[0053] The virtual planting and land ownership confirmation module is used to obtain the geographical location, area and other information of the user's land, and store land ownership information through blockchain technology;

[0054] In the implementation of this invention, the virtual planting and land rights confirmation module is the core link connecting user identity verification with subsequent planting transactions. By digitizing land information and ensuring the immutability of ownership, this module provides reliable basic data support for land leasing, planting planning, and agricultural product transactions between farmers and consumers. This module not only addresses the difficulties of land rights confirmation and information opacity in traditional agriculture, but also provides technical support for the intelligent management of virtual planting.

[0055] In this embodiment, the implementation method of the virtual planting and land rights confirmation module combines geo-positioning technology and blockchain technology. Specifically, after the user completes identity authentication through the identity authentication module, the system allows the user to upload land information. The user needs to obtain the real-time latitude and longitude coordinates of the land through the GPS function of the mobile device, and upload data such as the land area and ownership certificate. The system verifies the land location through geo-fence technology to ensure that the land location uploaded by the user is consistent with the actual geographical location. After the land information is verified, the land ownership information is bound to the user identity through the blockchain smart contract and stored in the blockchain network. The data structure of the land information can be expressed as:

[0056] L=(id,lat,lon,area,proof,owner);

[0057] Among them, id is the unique identifier of the land, lat and lon are the latitude and longitude of the land respectively, area is the land area, proof is the hash value of the ownership certificate document, and owner is the identity identifier of the land owner.

[0058] As an option, the system also supports obtaining land boundary information through drone or satellite remote sensing technology, and automatically calculates land area using image recognition algorithms. For example, after a user uploads an aerial image of land boundaries, the system uses an edge detection algorithm to extract the land outline and calculate the actual area based on the scale. This process reduces manual measurement errors and improves data accuracy.

[0059] In one possible implementation, blockchain technology uses the Hyperledger Fabric framework, storing and verifying land information through chaincode. Specifically, when a user submits land information, the system invokes a smart contract to verify ownership documents. Once verified, the land data is packaged into blocks and uploaded to the blockchain. The blockchain's immutability ensures the long-term reliability of land information and supports cross-platform data sharing.

[0060] Typically, the land ownership confirmation module is closely linked to the identity verification module. For example, the land owner's identity identifier, "owner," is directly derived from the unique ID generated by the identity verification module, ensuring a one-to-one correspondence between land ownership and user identity. Furthermore, the hash value of the ownership proof document stored on the blockchain is generated using an encryption algorithm (such as SHA-256) to prevent tampering.

[0061] Specifically, geofencing technology relies on mobile device location services. When a user uploads a plot of land, the system requires the user to enable GPS on-site and acquire multiple consecutive location points in real time. If the latitude and longitude fluctuation range of a location point is less than a preset threshold (e.g., 5 meters), the location data is considered valid. This mechanism prevents users from falsifying their location and improves the authenticity of land information.

[0062] In one possible implementation, the system also supports dynamic updates of land information. For example, when land ownership changes due to leasing or transfer, a user can initiate an ownership transfer request through a smart contract. The new owner's identity is verified by an authentication module, and the original owner confirms the change with a digital signature. This change is recorded on the blockchain, forming a complete history of ownership changes.

[0063] Through the above technical solutions, the virtual planting and land rights confirmation module can provide the platform with highly reliable land data, laying the foundation for subsequent intelligent planting planning, labor fee calculation, and agricultural product traceability. Furthermore, the application of blockchain technology ensures the transparency and traceability of land information, effectively reducing the risk of land disputes.

[0064] Intelligent planting and labor cost calculation module, used to calculate labor costs based on factors such as crop type, land area, climate, etc., and generate corresponding planting plans;

[0065] In the implementation of this invention, the intelligent planting and labor cost calculation module is a key link between land title confirmation and subsequent agricultural product transactions. By combining land data, crop characteristics, and environmental factors, this module can provide farmers with accurate planting plans and dynamically calculate labor costs, thereby optimizing resource allocation and improving agricultural production efficiency. The introduction of this module effectively addresses the problems of extensive planting planning and opaque labor cost estimation in traditional agriculture, providing a scientific basis for transactions between farmers and consumers.

[0066] In this embodiment, the implementation of the intelligent planting and labor fee calculation module is based on the LSTM (Long Short-Term Memory Network) model and dynamic adjustment algorithm. Specifically, after the user completes the upload of land information through the land rights confirmation module, the system will generate a planting plan based on the land area A, crop type T and historical planting data. Labor fee C labor The calculation formula is as follows:

[0067] C labor =α T ×A×β;

[0068] Among them, α T represents the labor time coefficient per unit area of ​​crop type T, A is the land area, and β is the seasonal and climate adjustment coefficient. For example, α for rice planting T It may be 0.5 hours / square meter, while the α of growing vegetables T The climate adjustment factor β changes dynamically based on real-time meteorological data (such as rainfall and temperature), and may increase to 1.2 in the dry season and decrease to 0.8 in the rainy season.

[0069] Alternatively, the LSTM model's input parameters include historical planting records, soil moisture sensor data, and 15-day weather forecasts. By analyzing the relationship between the crop growth cycle and the external environment, the model predicts the labor hours required for each growth phase (sowing, fertilizing, and harvesting). For example, for tomato cultivation, the model might predict that the sowing phase requires two man-days per mu (approximately 2 acres) and the harvest phase requires five man-days per mu (approximately 5 acres).

[0070] In one possible implementation, the system also supports farmers to manually adjust the planting plan. If farmers choose to customize planting parameters (such as planting density), the system will recalculate the labor cost. Specifically, the planting density coefficient γ can be introduced into the formula, and the revised labor cost is:

[0071] C′ labor =α T ×A×β×γ;

[0072] The value range of γ is 0.8 to 1.5, and the default value is 1.0. This design enables the calculation model to flexibly adapt to the needs of different planting strategies.

[0073] Typically, the generation of the climate adjustment coefficient β relies on a third-party meteorological data interface. For example, the system accesses the China Meteorological Administration's API to obtain real-time data such as temperature, humidity, and wind speed, and converts it into a β value through normalization. If the maximum temperature on a given day exceeds 35°C, the system automatically increases the labor coefficient by 10% to reflect the decrease in labor efficiency in high-temperature environments.

[0074] Specifically, the LSTM model's training data includes planting records, meteorological data, and actual labor costs for the same region over the past five years. The model architecture comprises three hidden layers, each with 128 neurons, using the Reluctant Unit (ReLU) activation function. During training, the input data is segmented into 30-day time windows, and the output is the estimated labor hours for the next phase. The model is optimized using the mean squared error (MSE) loss function, achieving a final prediction error within ±5%.

[0075] In one possible implementation, the system also integrates soil sensor data. For example, when soil nitrogen content falls below a threshold, the model automatically increases the predicted labor hours for fertilization. Farmers can also use their mobile devices to view correlation analysis between soil data and model predictions. For example, the interface might display, "Current soil pH is 5.3 (low), and neutralization treatment is expected to require an additional two man-days of work."

[0076] Through the above technical solutions, the intelligent planting and labor cost calculation module can provide farmers with data-driven decision support while ensuring the transparency and rationality of labor cost calculations. The module's output is directly linked to the agricultural product traceability module. For example, the blockchain records that "a batch of tomatoes required 25 man-days to plant, with a labor cost of 2,000 yuan," providing a basis for subsequent agricultural product pricing.

[0077] The agricultural product traceability and blockchain management module is used to record the planting, harvesting, transportation and sales processes of agricultural products through blockchain, ensuring that the traceability information of agricultural products is transparent and cannot be tampered with;

[0078] In the implementation of this invention, the agricultural product traceability and blockchain management module is a core component ensuring transparency and data trust throughout the entire agricultural product process. By storing data from planting, harvesting, logistics, and sales on-chain, this module provides consumers with verifiable traceability information while establishing an unalterable quality credit system for farmers and the platform. This module closely integrates with the aforementioned smart planting module and land rights confirmation module, forming a complete data chain from production to transaction.

[0079] In this embodiment, the agricultural product traceability and blockchain management module is implemented using the Hyperledger Fabric blockchain framework and smart contract technology. Specifically, when the smart planting module generates labor cost calculation results, the system automatically triggers a blockchain smart contract to write key planting data (such as crop type, planting time, and labor hours) to the blockchain.

[0080] Alternatively, the land ID in the planting data can be directly linked to the data defined in the land title confirmation module, ensuring a strong link between agricultural products and land ownership. For example, if the land ID for a batch of tomatoes is "FARM-00321," consumers can query the historical planting records and ownership certificates of that land through the blockchain.

[0081] In one possible implementation, harvest data quality inspection reports are stored using IPFS (InterPlanetary File System) as raw files, with only the file hash value written to the blockchain. Specifically, farmers upload the quality inspection report PDF to the IPFS network, obtain a CID (content identifier), and use this CID as the quality inspection report hash value, which is then stored on the blockchain. This reduces on-chain storage pressure while ensuring the immutability of the report content.

[0082] Typically, logistics temperature control data is uploaded to the blockchain in real time via IoT devices. For example, sensors installed in refrigerated trucks collect temperature data every five minutes, and edge computing devices generate a data summary. This summary, along with the original data hash, is written to the blockchain. Consumers can view the complete temperature control curve by scanning the product's QR code.

[0083] Specifically, smart contract execution involves a multi-stage verification process. For example, in the logistics sector, when a transport vehicle arrives at a transfer station, the system calls a contract to verify whether the current location deviates from the planned route by more than a threshold (e.g., 5 kilometers). If a deviation occurs, the contract automatically sends an alert to the platform administrator and the consumer, and records the abnormal event in the exception log.

[0084] In one possible implementation, blockchains employ a hierarchical storage structure. Key metadata (such as timestamps, participant IDs, and data hashes) is stored on the main chain, while large-volume data (such as high-definition photos of growing crops) is stored on side chains. The main chain and side chains mutually verify data through a cross-chain protocol, for example, using a Merkle tree root hash for consistency verification.

[0085] As an extension, consumers can cross-verify data at any stage through the mobile app. For example, after scanning a product QR code, the app automatically retrieves the original quality inspection report from IPFS, calculates its hash value, and compares it with the hash value stored on the blockchain. If they match, a "report not tampered" indicator is displayed.

[0086] Through the implementation of this module, the platform can build a credible traceability system covering all aspects of production, circulation and consumption, effectively improve the transparency of agricultural product quality, reduce food safety risks, and provide technical support for building trust between farmers and consumers.

[0087] A social land sharing and agricultural product trading module, which is used to recommend land resources and agricultural products through social networks, and supports land leasing, agricultural product purchases, and social interaction;

[0088] In the implementation of this invention, the social land sharing and agricultural product trading module is the core functional unit that connects user interaction and resource flow. By integrating social relationship chains with decentralized trading mechanisms, this module achieves dynamic matching of land resources and precise circulation of agricultural products, while also providing a trust-based collaborative environment for platform users. This module works closely with the identity verification, land title confirmation, and traceability modules to establish a complete closed loop from social interaction to transaction fulfillment.

[0089] In this embodiment, the implementation of the social land sharing and agricultural product trading module is based on social graph analysis and smart contract technology. Specifically, when a user completes registration through the identity authentication module, the system automatically constructs their social relationship graph G = (V, E), where V represents the set of user nodes and E represents the social relationship edges between users (such as friend relationships, transaction history, and interest similarity).

[0090] User interaction and incentive module, used to reward points or virtual currency based on user behavior and promote transactions through social interaction;

[0091] In the implementation of this invention, the user interaction and incentive module is a key design for enhancing platform activity and user engagement. By combining social behavior with a virtual reward mechanism, this module effectively promotes collaboration and resource sharing among users, while providing sustainable engagement incentives for platform operations. This module is deeply integrated with the social transaction module and the identity verification module, establishing a complete chain from behavioral incentives to value conversion.

[0092] The user interaction and incentive modules effectively drive a positive cycle within the platform ecosystem. For example, User D earns Caibi (Caibi) through daily check-ins. After accumulating these coins and converting them into land lease discounts, their actual lease costs decrease by 15%, leading to increased participation in land sharing. Furthermore, transactions generated by their invited friends bring them additional rewards, forming a closed incentive loop.

[0093] Through the implementation of this module, the platform can build a multi-dimensional user incentive system, effectively transform social value, data value and transaction value, and ultimately promote the sustainable development of the agricultural sharing economy.

[0094] Backend management system, used to manage system data, user information and transaction records, and support intelligent recommendation and data analysis;

[0095] During the implementation of this invention, the backend management system serves as the central unit supporting the coordinated operation of the platform's various functional modules. By integrating data storage, analysis, and intelligent decision-making capabilities, this system provides comprehensive management support for platform operations while ensuring efficient data flow between modules. This module is deeply integrated with modules such as user interaction, land title confirmation, and land traceability, forming a closed-loop management system from data collection to strategy optimization.

[0096] In this embodiment, the backend management system is implemented using a distributed microservices architecture and big data analysis technology. Specifically, the system receives data streams generated by each module in real time through a Kafka message queue, including user behavior logs, land transaction records, and agricultural product traceability information. After cleaning, the data is stored in the Hadoop distributed file system and analyzed offline using the Spark engine. For example, a land lease heat map is automatically generated at 2:00 AM each day, noting land utilization rates and price fluctuation trends in each region.

[0097] As an option, the system adopts a multi-dimensional permission control mechanism. Administrator roles are divided into three levels: Super Administrators can access all data and system configurations; Operations Administrators can only view user behavior analysis reports; Audit Administrators are responsible for handling abnormal transaction complaints. Permission allocation is implemented through the RBAC (Role-Based Access Control) model. The authorization of each operation must meet the following requirements:

[0098] Permission = role ∩ data scope ∩ time constraint;

[0099] For example, an audit administrator can handle appeal tickets for his or her partition from 9:00 AM to 6:00 PM on weekdays, but cannot export sensitive user information.

[0100] In one possible implementation, the intelligent recommendation subsystem uses a hybrid model of collaborative filtering and content recommendation. When a user visits a land lease page, the system calculates the recommendation score in real time:

[0101] Recommendation score = 0.6 × collaborative filtering similarity + 0.4 × content matching;

[0102] Collaborative filtering similarity is calculated based on a user's historical behavior matrix, while content matching is determined by the cosine similarity between land feature vectors (area, crop type, and geographic location) and user preferences. Recommendation results are updated every 30 minutes and are cached in Redis for faster response times.

[0103] Specifically, the risk control module includes real-time monitoring and early warning functions. When an abnormal transaction pattern is detected (such as the same land being leased more than three times in a single day), the system automatically triggers the following processing flow:

[0104] Freeze relevant account operation permissions

[0105] Send warning notifications to risk control specialists

[0106] All operation traces generated by recording event logs and initiating data snapshot processing are written into the audit log to ensure traceability.

[0107] In one possible implementation, the data visualization subsystem supports custom report generation. Administrators can build analytical views by dragging and dropping fields. For example, linking the "Land Lease Volume" dimension with the "Seasonal Change" dimension to generate an interactive time-series trend chart. The system automatically optimizes query statements and enables pre-aggregation acceleration strategies for queries exceeding 100 million records.

[0108] Through the above technical solutions, the backend management system enables comprehensive control over platform operations. For example, when a natural disaster strikes a region, administrators can quickly identify affected land through the system, send batch alerts to connected users, and suspend leasing transactions for the relevant land. Simultaneously, the system automatically adjusts the weight of its recommendation algorithm to reduce the exposure of land in the affected area.

[0109] As an extension, the system integrates automated operations and maintenance. By monitoring the server cluster's CPU, memory, and network metrics, it automatically triggers horizontal scaling when resource utilization exceeds 85% for five consecutive minutes, adding new container instances to share the load. Scaling strategies are dynamically adjusted based on historical load forecasting models to ensure resource utilization remains within the optimized range of 70%-80%.

[0110] Through the implementation of this module, platform operators can obtain data-driven decision support, grasp business dynamics in real time, and quickly respond to abnormal events. At the same time, they can reduce operation and maintenance costs through automated tools and improve the stability and scalability of the overall system.

[0111] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A socialized agricultural product trading system and method based on virtual planting, characterized in that: include: Authentication module, used to verify user identity and generate a unique identity; The virtual planting and land ownership confirmation module is used to obtain the geographical location and area information of the user's land and store land ownership information through blockchain technology; Intelligent planting and labor cost calculation module, used to calculate labor costs based on crop type, land area, and climate factors, and generate corresponding planting plans; The agricultural product traceability and blockchain management module is used to record the planting, harvesting, transportation and sales processes of agricultural products through blockchain, ensuring that the traceability information of agricultural products is transparent and cannot be tampered with; A social land sharing and agricultural product trading module, which is used to recommend land resources and agricultural products through social networks, and supports land leasing, agricultural product purchases, and social interaction; User interaction and incentive module, used to reward points or virtual currency based on user behavior and promote transactions through social interaction; The backend management system is used to manage data, user information and transaction records in the system, and supports intelligent recommendations and data analysis.

2. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The identity verification module further comprises: OCR recognition component, used to identify and extract ID card information uploaded by users; Facial recognition component, used to verify user identity by comparing the real-time photo with the ID card photo to confirm the identity.

3. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The virtual planting and land title confirmation module obtains the location data of the land by using GPS and geo-fencing technology, and stores the land ownership information and title proof documents on the blockchain through blockchain smart contracts.

4. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The intelligent planting and labor cost calculation module predicts the growth cycle of crops based on the LSTM model and calculates the required labor costs based on the prediction results.

5. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The agricultural product traceability and blockchain management module uses blockchain technology to record the entire process of each agricultural product from planting to sales, and automatically confirms each link through smart contracts.

6. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The social land sharing and agricultural product trading module includes: The social graph component is used to build social relationship chains between users and recommend land resources and agricultural products based on social relationships; A decentralized trading component for managing agricultural product transactions through smart contracts.

7. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The user interaction and incentive module generates points based on user behavior and rewards virtual currency through social interaction tasks. The virtual currency can be used to purchase agricultural products or pay rental fees.

8. The socialized agricultural product trading system based on virtual planting according to claim 1 is characterized in that: The backend management system optimizes the recommendation of land resources and agricultural products through an intelligent recommendation algorithm, and performs data analysis based on the user's transaction records and behaviors.

9. A socialized agricultural product trading method based on virtual planting, used in the socialized agricultural product trading system based on virtual planting according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Verify user identity and generate a unique ID; Step 2: Obtain the geographical location and area information of the user's land, and confirm the land ownership through blockchain technology; Step 3: Generate a planting plan based on crop type, land area, and climate factors, and calculate labor costs; Step 4: Record the planting, harvesting, transportation and sales process of agricultural products through blockchain; Step 5: Recommend land resources and agricultural products through social networks to support land leasing, agricultural product purchases, and social interaction; Step 6: Reward points based on users’ social behaviors and promote transactions through social interactions; Step 7: Manage data and transaction records through the backend management system to optimize recommendations and data analysis.

10. The socialized agricultural product trading system based on virtual planting according to claim 9 is characterized in that: In step 2, the latitude and longitude of the land are obtained by using GPS and geo-fencing technology, and the ownership information and proof of ownership of the land are stored on the blockchain through a blockchain smart contract.

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